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A Life-strategy Classification of Grassland Soil Prokaryotesand Its Applications in Interpreting Alpine MeadowResponses to Environmental Changes
Author
Che, Rongxiao
Published
2018-03
Thesis Type
Thesis (PhD Doctorate)
School
School of Environment and Sc
DOI
https://doi.org/10.25904/1912/374
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from
http://hdl.handle.net/10072/378096
Griffith Research Online
https://research-repository.griffith.edu.au
A Life-strategy Classification of Grassland Soil Prokaryotes
and Its Applications in Interpreting Alpine Meadow
Responses to Environmental Changes
Mr. Rongxiao Che
B. Sc
Environmental Futures Research Institute
School of Environment and Science
Griffith University
Submitted in fulfillment of the requirements for the degree of
Doctor of Philosophy
March 2018
I
Abstract 1
The extensive applications of high-throughput sequencing have remarkably improved 2
our abilities to analyze soil microbial community profiles. However, due to the paucity 3
of knowledge on characterizing most of the microbial lineages, the predominant 4
challenge in investigating soil microbes has shifted from community composition 5
descriptions to the interpretations for their ecological implications. Currently, assessing 6
the relative abundances of microbial lineages with different life strategies (i.e., 7
copiotrophs and oligotrophs, analogous to r- and K- specialists, respectively) is the most 8
widely used approach to explore the ecological implications of microbial community 9
profiles. Moreover, the relative abundance of copiotrophs and oligotrophs is usually 10
closely correlated with soil microbial respiration rates. Collectively, identifying the life-11
strategies of soil microbial lineages is not only essential to interpreting the ecological 12
implications of microbial community profiles, but also crucial to understanding the 13
links between microbial communities and their ecological functions. 14
15
Nonetheless, with almost all of the life-strategy classification efforts being made at the 16
phylum level, the life strategies of microbial lineages at finer taxonomy levels remain 17
largely unknown. Although the majority (> 90%) of soil microbes are dormant and 18
contribute little to the ecosystem functioning, the life-strategy classifications are 19
seldom conducted targeting the active microbial populations. Furthermore, grasslands 20
cover around one-third of the global terrestrial surface, providing essential services for 21
maintaining our planetary health. However, the life-strategies of grassland soil 22
II
microbial lineages were far less identified than those of forests and farmlands. 23
24
Therefore, my thesis aimed to determine the life strategies of total and active 25
prokaryotic lineages in grassland soils, and then tried to apply them to interpret the 26
responses of alpine meadow soil prokaryotes to environmental changes. Briefly, in 27
Chapter 2, I assessed the possibilities of using the methods based on 16S rRNA to 28
identify the prokaryotic life strategies. Subsequently, in Chapter 3, I classified grassland 29
soil prokaryotic lineages (from kingdom to genus) into copiotroph-oligotroph 30
categories, using methods based on 16S rDNA and rRNA. Finally, in Chapters 3 and 4, 31
I tried to interpret the responses of prokaryotic communities to litter amendments, 32
phosphorus fertilization, livestock grazing, and experimental warming based on the 33
proportional changes of copiotrophic and oligotrophic microbial lineages. 34
35
In Chapter 2, soil samples collected from a Tibetan alpine meadow were amended with 36
different amounts of glutamate. The 16S rDNA and rRNA copies, as well as community 37
structures based on 16S rDNA and rRNA were analyzed using real-time PCR and 38
terminal-restriction fragment length polymorphism, respectively. Except for 16S rRNA 39
copies and rRNA-rDNA ratios, all of the indices based on rDNA and rRNA were 40
significantly correlated with the soil microbial respiration rates. However, the 16S 41
rRNA-based bacterial community structure could explain 72.7% of the soil microbial 42
respiration variations, which far outperformed the other indices. These findings indicate 43
that the 16S rRNA-based community structure is a sensitive indicator for soil microbial 44
III
respiration activity, and also highlight its potential for identifying microbial life 45
strategies (i.e., copiotrophs and oligotrophs). This study provides the basis for the 16S 46
rRNA-based life strategy classifications of prokaryotic lineages in Chapter 3. 47
48
In Chapter 3, I systematically classified the soil prokaryotic lineages into copiotrophic 49
and oligotrophic categories through examining their responses to glucose amendment, 50
and tested the relationships between their relative abundances and microbial respiration 51
rates. Soils were collected from 32 natural grasslands on the Inner Mongolia and the 52
Tibetan plateaus. The prokaryotic community structures were analyzed by MiSeq 53
sequencing based on 16S rDNA and rRNA. Copies of 16S rDNA and rRNA were 54
determined using real-time PCR. The prokaryotic lineages with significantly increased 55
and decreased proportions under the glucose amendment were classified as copiotrophs 56
and oligotrophs, respectively. The results showed that prokaryotic lineages based on 57
16S rDNA and rRNA showed similar responses to the glucose amendment. Their 58
relationships with microbial respiration rates were also similar. Proteobacteria, 59
Bacteroidetes, Firmicutes, and most of the lineages under these phyla were copiotrophs, 60
while Archaea, Acidobacteria, Chloroflexi, Planctomycetes, Gemmatimonadetes, 61
Tectomicrobia, Nitrospirae, Armatimonadetes, Verrucomicrobia, and most of their finer 62
lineages were oligotrophs. Although the cross-site life-strategy diversifications of the 63
phyla were observed, at the operational taxonomy unit (OTU) level, the glucose 64
amendment shifted the prokaryotic community profiles towards a similar direction. 65
This suggests that it is possible to rank the prokaryotic lineages at the finer taxonomy 66
IV
level according to their life-strategies. The proportions of most copiotrophic and 67
oligotrophic lineages showed positive and negative correlations with microbial 68
respiration rates, respectively. However, the proportions of Acidobacteria, a widely 69
recognized oligotrophic phylum, as well as its finer lineages, showed positive 70
correlations with the microbial respiration rates. Collectively, these findings provide a 71
systematic identification of the total and active prokaryotic lineage life-strategies in 72
grassland soils, and lay bases for the ranking of prokaryotic lineages according to their 73
life strategies. They also highlight the potential risks in using the proportions of the 74
copiotrophic and oligotrophic lineages to assess the heterotrophic respiration rates 75
across different sites. 76
77
In Chapter 4, a microcosm experiment was conducted to investigate the responses of 78
total and active soil microbes to phosphorus and grass litter amendments. Soil samples 79
were collected from a degraded Tibetan alpine meadow. Microbial abundance and 80
rDNA transcriptional activity were determined through real-time PCR. Total and active 81
microbial community profiles were analyzed using MiSeq sequencing based on DNA 82
and RNA, respectively. The results showed that soil microbial activity, rDNA 83
transcription, and fungal abundance significantly increased under the litter amendment. 84
In addition, the litter amendment significantly decreased microbial α-diversity (i.e., 85
richness, evenness, Shannon index, Chao 1 index), while it significantly increased the 86
microbial community dispersion. Microbial community compositions were also 87
significantly altered by the litter amendment. Specifically, the relative abundances of 88
V
copiotrophs and oligotrophs significantly increased and decreased under the litter 89
amendment, respectively. Interestingly, the changes in the proportions of microbial 90
lineages were related to their rDNA-rRNA ratios. The relative abundances of those with 91
high and low rDNA-rRNA ratios increased and decreased under the litter amendment, 92
respectively. Nevertheless, neither phosphorus amendment nor phosphorus-litter 93
interaction exerted significant effects on soil microbes. Collectively, these findings 94
suggest that increasing litter input to the degraded grassland soils can result in more 95
copiotrophic microbial communities with higher activity, lower α-diversity, and more 96
divergent community compositions. Additionally, this study provide bases for future 97
studies of identifying fungal lineage life-strategies using the methods based on ITS 98
RNA. 99
100
In Chapter 5, a six-year field experiment was conducted to investigate the effects of 101
asymmetric warming and moderate grazing on total and active soil microbes in a 102
Tibetan Kobresia alpine meadow. Soil bacterial abundance and 16S rDNA 103
transcriptional activity were determined using real-time PCR. Total and active soil 104
prokaryotic community structures were analyzed through MiSeq sequencing based on 105
16S rDNA and rRNA, respectively. The results showed that the soil prokaryotic 106
community was more sensitive to warming than grazing. The warming significantly 107
decreased soil microbial respiration rates, 16S rDNA transcription activity, and 108
dispersion of total prokaryotic community structures, but significantly increased the α 109
diversity of active procaryotes. Warming also significantly increased the relative 110
VI
abundance of oligotrophic microbes, whereas it decreased the copiotrophic lineage 111
proportions. The functional profiles predicted from the total prokaryotic community 112
structures remained unaffected by warming. However, the rRNA-based predictions 113
suggested that DNA replication, gene expression, signal transduction, and protein 114
degradation were significantly suppressed under the warming. The grazing only 115
significantly decreased the 16S rDNA transcription and total prokaryotic richness. 116
Overall, these findings suggest that warming can shift soil prokaryotic communities to 117
more oligotrophic and less active status, highlighting the importance of investigating 118
active microbes to improve our understanding of ecosystem feedback to climate 119
changes and human activities. 120
121
In summary, the findings in the thesis notably enhance our abilities to interpret the 122
changes in prokaryotic community profiles in an ecological meaningful manner, 123
placing a foundation for future life-strategy identifications. In addition, they provide 124
bases for incorporating microbial indices to model ecosystem carbon dynamics, and 125
improve our understanding of the alpine meadow responses to degradation restoration, 126
climate change, and human activities. 127
VII
128
Declaration of Originality 129
This work has not previously been submitted for a degree or diploma in any university. 130
To the best of my knowledge and belief, the dissertation contains no material previously 131
published or written by any other people except where due reference is made in the 132
dissertation. 133
134
135
Rongxiao Che 136
21 March 2018 137
138
139
140
141
VIII
142
IX
143
Acknowledgement 144
My thesis would not have been possible without the support, inspiration, guidance, and 145
encouragement from my supervisors, laboratory mates, friends, families, and everyone 146
around me during the past three and half years. My boundless thanks and appreciation 147
to all of them, thanks for being part of this wonderful journey. 148
149
Particularly, I would like to express my greatest and heartfelt appreciation to my co-150
principal supervisors, Prof. Zhihong Xu and Weijin Wang. I sincerely appreciate Prof. 151
Xu for offering me the opportunity to participate the joint Ph.D. program between 152
Griffith University and University of Chinese Academy of Sciences (UCAS). I would 153
also acknowledge Prof. Xu for the enormous efforts he made on every stage of my Ph.D. 154
project. During the past three and half years, my knowledge and perspective in this field 155
have been substantially improved owing to his generosity of sharing knowledge, 156
experience, and research ideas. Each time I had a meeting with Prof. Xu, I was deeply 157
touched by his incredible passion for scientific research, which would impress and 158
impact me for a life-long period. I would like to express my gratitude to Prof. Wang for 159
his incredible investment in the design, execution, and writing of my Ph.D. project. 160
Especially, I would acknowledge Prof. Wang for his constructive suggestions and 161
patient proof-reading for each of my journal paper. Definately, none of my papers could 162
be published without the incredible efforts made by Prof. Wang. 163
164
I gratefully appreciate my external supervisor Prof. Xiaoyong Cui, who is also the 165
X
principal survivor of my Ph.D. project in UCAS. Most of the experiments included in 166
the project were conducted in Prof. Cui’s laboratory. Without his overly generous 167
support, my Ph.D. project would not have been completed. I would like to acknowledge 168
Prof. Yanfen Wang, Prof. Yanbin Hao, A/Prof. Haishan Niu, and A/Prof. Kai Xue for 169
their constructive suggestions and comments for my Ph.D. project in every group 170
meeting. 171
172
Thanks to all the wonderful colleagues from Ecosystem Ecology Group at UCAS for 173
their valuable contribution to my project. My cordial thanks to A/Prof. Yongcui Deng, 174
Fang Wang, Shutong Zhou, and Jinling Qin for their great help in the experiment 175
conduction, data analysis, and thesis revision. Thanks to Biao Zhang for providing the 176
Climate information of the sampling sites. Thanks to Linfeng Li and Biao Zhang for 177
providing valuable comments and suggestions to improve the quality of the thesis. I 178
sincerely appreciate the sampling and experiment assistance from Prof. Shiping Wang, 179
Prof. Xiangzhen Li, A/Prof. Lili Jiang, A/Prof. Minjie Yao, Jing Zhang, Bao Jiang, 180
Wenjun Liu, Hui Zhang, Linfeng Li, Zhe Pang, Wenyu Fan, Shutong Zhou, Anquan 181
Xia, Di Wang, Hanke Liu, Chaoting Zhou, Yuan Zhang, Qingzhou Zhao, Xing Zhao, 182
and Haibei Research Station stuff. 183
184
I would also like to appreciate my colleagues and friends at Griffith University: 185
Geoffrey Lambert, Yan Zhao, Prof. Xiaoqi Zhou, Jian Wang, Wenyuan Zhang, Qi Jiang, 186
Li Fu, Iman Tahmasbian, Thi Thu Nhan Nguyen, Tian Hu, Tengjiao Liu, Qiushi Ning, 187
XI
Dianjie Wang, Mone Nouansyvong, and Yaling Zhang. I would like to address the 188
special thanks to my friends Juan Tao, Longzhou Zhang, Li Tang, Man Xiao, and 189
Rebecca Wei for bringing me so many happinesses, which make the life colorful and 190
charming. 191
192
I would like to deeply appriciate Julie Stevenson, Peter Biddulph, Annette Veness, 193
Louise Fryer, Gerry Milne, and Lindsay Norris for their linguistic assistances during 194
the preparation of the thesis. 195
196
I cordially appreciate the financial support from the Griffith University, Strategic 197
Priority Research Program (B) of the Chinese Academy of Sciences, National Key 198
Research and Development Program of China, Strategic Priority Research Program (A) 199
of the Chinese Academy of Sciences, and National Natural Science Foundation of 200
China. 201
202
I would like to express my deepest appreciation to my parents and in-laws for their 203
selfless, heartfelt, and boundless loves through my life. Your encouragements and 204
supports are the vital sources of motivations that pushing me to pursue the dream to 205
become a scientist. 206
207
Lastly, yet most importantly, my earnest gratitude goes to my wife Dong Liu. Thanks 208
for listening to my problems and complaints; thanks for providing perspectives and 209
XII
comforts; thanks for tolerating, supporting, and marrying me without expecting 210
anything in return; and thanks for being the solidest backing for all the successes 211
throughout my life. 212
XIII
213
Papers Published or in Preparation during the Ph.D. 214
Candidature (First Author) 215
1. Che RX, Deng YC, Wang F, Wang WJ, Xu ZH, Hao YB, Xue K, Zhang B, Tang L, 216
Zhou HK, Cui XY. (2018): Autotrophic and symbiotic diazotrophs dominate 217
nitrogen-fixing communities in Tibetan grassland soils. Science of the Total 218
Environment 639, 997–1006. 219
2. Che RX, Qin JL, Tahmasbian I, Wang F, Zhou ST, Xu ZH, Cui XY. (2018): Litter 220
amendment rather than phosphorus can dramatically change inorganic nitrogen 221
pools in a degraded grassland soil by affecting nitrogen-cycling microbes. Soil 222
Biology and Biochemistry 120, 145–152. 223
3. Che RX, Deng YC, Wang WJ, Rui YC, Zhang J, Tahmasbian I, Tang L, Wang SP, 224
Wang YF, Xu ZH, Cui XY. (2018): Long-term warming rather than grazing 225
significantly changed total and active soil procaryotic community structures. 226
Geoderma 316, 1–10. 227
4. Che RX, Wang F, Wang WJ, Zhang J, Zhao X, Rui YC, Xu ZH, Wang YF, Hao YB, 228
Cui XY. (2017): Increase in ammonia-oxidizing microbe abundance during 229
degradation of alpine meadows may lead to greater soil nitrogen loss. 230
Biogeochemistry 136, 341–352. 231
5. Che RX, Deng YC, Wu YB, Zhang J, Wang F, Tang L, Li LF, Ma S, Liu HK, Zhao 232
X, Wang YF, Hao YB, Cui XY. (2017): Relationships between biological nitrogen 233
fixation and available nitrogen at scales from molecular to community level. 234
XIV
Chinese Journal of Ecology 36, 224–232. 235
6. Che RX, Wang WJ, Zhang J, Nguyen NTT, Tao J, Wang F, Wang YF, Xu ZH, Cui 236
XY. (2016): Assessing soil microbial respiration capacity using rDNA- and 237
rRNA-based indices: A review. Journal of Soils and Sediments 16, 2698–2708. 238
7. Che RX, Wang F, Wang YF, Deng YC, Zhang J, Ma S, Cui XY. (2016): A review on 239
the methods for measuring total microbial activity in soils. Acta Ecologica Sinica 240
36, 2103–2112. 241
8. Che RX, Deng YC, Wang F, Wang WJ, Xu ZH, Wang YF, Cui XY. (2015): 16S 242
rRNA-based bacterial community structure is a sensitive indicator of soil 243
respiration activity. Journal of Soils and Sediments 15, 1987–1990. 244
9. Che RX, Wang F, Wang WJ, Xu ZH, Tao J, Li LF, Tahmasbian I, Cui XY. Total and 245
active soil microbial responses highlight the risks of increasing litter input to 246
recover degraded alpine meadows. Land Degradation and Development. (Under 247
Review) 248
10. Wang F*, Che RX*, Xu ZH, Wang YF, Cui XY. Assessing soil extracellular DNA 249
decomposition dynamics through plasmid amendment coupled with real-time PCR. 250
Journal of Soils and Sediments. (Under Review; *Contributed equally to this work) 251
11. Che RX, Xu ZH, Wang WJ, Wang YF, Cui XY. A copiotroph-oligotroph 252
classification of grassland soil microbes. (Will be submitted to Nature Ecology 253
and Evolution) 254
255
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Papers Published during the Ph.D. Candidature (Coauthor) 256
1. Tahmasbian I, Xu ZH, Boyd S, Zhou J, Esmaeilani R, Che RX, Bai SH. (2018) 257
Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen 258
and their isotopic compositions. Geoderma. (In press) 259
2. Nguyen TTN, Wallace HM, Xu CY, Zwieten LV, Weng Z, Xu ZH, Che RX, 260
Tahmasbian I, Hu HW, Bai SH. (2018): The effects of short term, long term and 261
reapplication of biochar on soil bacteria. Science of the Total Environment. 636, 262
142–151. 263
3. Tao J, He DK, Kennard MJ, Ding CZ, Bunn SE, Liu CL, Jia YT, Che RX, Chen YF. 264
(2018): Strong evidence for changing fish reproductive phenology under climate 265
warming on the Tibetan Plateau. Global Changes Biology 24, 2093–2104. 266
4. Zhang YL, Zhang MY, Tang L, Che RX, Chen H, Blumfield T, Boyd S, 267
Nouansyvong M, Xu ZH. (2018): Long-term harvest residue retention could 268
decrease soil bacterial diversities probably due to favouring oligotrophic lineages. 269
Microbial Ecology. (In press) 270
5. Liu XC, Fan PL, Che RX, Li H, Yi LN, Zhao N, Garber PA, Li Fang, Jiang ZG. 271
(2018): Fecal bacterial diversity of wild Sichuan snub-nosed monkeys 272
(Rhinopithecus roxellana). American Journal of Primatology 80, e22753. 273
6. Filibeck G, Cancellieri L, Sperandii MG, Belonovskaya E, Sobolev N, Tsarevskaya 274
N, Becker T, Berastegi A, Bückle C, Che RX, Conti F, Dembicz I, Fantinato E, 275
Frank D, Frattaroli AR, Garcia-Mijangos I, Guglielmino A, Janišová M, Maestri 276
S, Magnes M, Rosati L, Vynokurov D, Dengler J, Biurrun I. (2018): Biodiversity 277
XVI
patterns of dry grasslands in the Central Apennines (Italy) along a precipitation 278
gradient: experiences and first results from the 10th EDGG Field Workshop. 279
Bulletin of the Eurasian Dry Grassland Group 36, 25–41. 280
7. Tahmasbian I, Sinegani AAS, Nguyen TTN, Che RX, Phan TD, Bai SH. (2017): 281
Application of manures to mitigate the harmful effects of electrokinetic 282
remediation of heavy metals on soil microbial properties in polluted soils. 283
Environmental Science and Pollution Research, 24 (34), 26485–26496. 284
8. Nguyen NTT, Xu CY, Tahmasbian I, Che RX, Xu ZH, Zhou, XH, Wallace HM, Bai 285
SH. (2017): Effects of biochar on soil available inorganic nitrogen: A review and 286
meta-analysis. Geoderma 288, 79–96. 287
9. Zhang J, Wang F, Che RX, Wang P, Liu HK, Ji BM, Cui XY. (2016): Precipitation 288
shapes communities of arbuscular mycorrhizal fungi in Tibetan alpine steppe. 289
Scientific Reports 6, 23488. 290
10. Tao J, Che RX, He DK, Yan Y, Sui X, Chen YF. (2015): Trends and potential 291
cautions in food web research from a bibliometric analysis. Scientometrics 105, 292
435–447. 293
11. Ma S, Zhu XX, Zhang J, Zhang LR, Che RX, Wang F, Liu HK, Niu HS, Wang SP, 294
Cui XY. (2015): Warming decreased and grazing increased plant uptake of amino 295
acids in an alpine meadow. Ecology and Evolution 5, 3995–4005. 296
12. Deng YC, Che RX, Wu YB, Wang YF, Cui XY. (2015): A review of the 297
physiological and ecological characteristics of methanotrophs and methanotrophic 298
community diversity in the natural wetlands. Acta Ecologica Sinica 35, 4579–299
XVII
4591. 300
13. Wu YB, Che RX, Ma S, Deng YC, Zhu MJ, Cui XY. (2014): Estimation of root 301
production and turnover in an alpine meadow: comparison of three measurement 302
methods. Acta Ecologica Sinica 34, 3529–3537. 303
304
305
306
307
308
309
310
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311
XIX
ALL PAPERS INCLUDED ARE CO-AUTHORED 312
313
Acknowledgement of Papers Included in this Thesis 314
315
Included in this thesis are papers in Chapters 1, 2, 3, 4, and 5 which are co-authored with other 316
researchers. My contribution to each co-authored paper is outlined at the front of the relevant 317
chapter. The bibliographic details or status for these papers including all authors, are: 318
Chapter 1: Che RX, Wang WJ, Zhang J, Nguyen NTT, Tao J, Wang F, Wang YF, Xu ZH, Cui 319
XY. (2016): Assessing soil microbial respiration capacity using rDNA- and rRNA-based 320
indices: A review. Journal of Soils and Sediments 16, 2698–2708. DOI: 10.1007/s11368-016-321
1563-6 322
Chapter 2: Che RX, Deng YC, Wang F, Wang WJ, Xu ZH, Wang YF, Cui XY. (2015): 16S 323
rRNA-based bacterial community structure is a sensitive indicator of soil respiration activity. 324
Journal of Soils and Sediments 15, 1987–1990. DOI: 10.1007/s11368-015-1152-0 325
Chapter 3: Che RX, Xu ZH, Wang WJ, Wang YF, Cui XY. A copiotroph-oligotroph 326
classification of grassland soil microbes. (In Preparation) 327
Chapter 4: Che RX, Qin JL, Tahmasbian I, Wang F, Zhou ST, Xu ZH, Cui XY. (2018): Litter 328
amendment rather than phosphorus can dramatically change inorganic nitrogen pools in a 329
degraded grassland soil by affecting nitrogen-cycling microbes. Soil Biology and Biochemistry 330
120, 145–152. DOI: 10.1016/j.soilbio.2018.02.006 331
Che RX, Wang F, Wang WJ, Xu ZH, Tao J, Li LF, Tahmasbian I, Cui XY. Total and active 332
soil microbial responses highlight the risks of increasing litter input to recover degraded alpine 333
meadows Land Degradation and Development. (Under Review) 334
Chapter 5: Che RX, Deng YC, Wang WJ, Rui YC, Zhang J, Tahmasbian I, Tang L, Wang SP, 335
Wang YF, Xu ZH, Cui XY. (2018): Long-term warming rather than grazing significantly 336
changed total and active soil procaryotic community structures. Geoderma 316, 1–10. DOI: 337
10.1016/j.geoderma.2017.12.005 338
339
Appropriate acknowledgements of those who contributed to the research but did not qualify as 340
authors are included in each paper. 341
342
(Signed) ________ ________ (Date)______________ 343
Rongxiao CHE 344
345
(Countersigned) __ ______ (Date)______________ 346
Supervisor: Zhihong Xu 347
348
XX
349
350
XXI
351
Table of Contents 352
Abstract .......................................................................................................................... I 353
Declaration of Originality .......................................................................................... VII 354
Acknowledgement ....................................................................................................... IX 355
Papers Published or in Preparation during the Ph.D. Candidature (First Author) .... XIII 356
Papers Published during the Ph.D. Candidature (Coauthor)...................................... XV 357
Acknowledgement of Papers Included in this Thesis ............................................... XIX 358
Table of Contents ...................................................................................................... XXI 359
List of Figures ......................................................................................................... XXV 360
List of Tables .......................................................................................................... XXXI 361
List of Abbreviations........................................................................................... XXXIII 362
Chapter 1. General Introduction .................................................................................... 1 363
1.1. From r- and K-selections to copiotroph and oligotroph strategies .................. 3 364
1.2. Life-strategy classification of soil prokaryotes based on the manipulation of 365
organic carbon availability ...................................................................................... 6 366
1.2.1. Methods of the review ........................................................................... 8 367
1.2.2. An overview of the studies on soil microbial responses to manipulations 368
of organic matter input ..................................................................................... 8 369
1.2.3. A summary of prokaryotic life-strategy classification based on organic 370
carbon manipulation....................................................................................... 10 371
1.3. Prokaryotic life-strategy classification based on the correlations with microbial 372
respiration rates ..................................................................................................... 15 373
1.3.1. Methods of the review ......................................................................... 17 374
1.3.2. An overview of the included studies .................................................... 18 375
1.3.3. Soil microbial respiration and prokaryotic abundance ........................ 21 376
1.3.4. Soil microbial respiration and prokaryotic community structures ....... 23 377
1.4. The applications of prokaryotic life-strategy classifications in interpreting the 378
ecosystem responses to environmental changes ................................................... 29 379
1.5. Knowledge gaps in the soil prokaryotic lineage life-strategy classifications and 380
applications ........................................................................................................... 31 381
1.6. Aims and the framework of my Ph.D. project ............................................... 32 382
1.7. References ...................................................................................................... 36 383
Chapter 2. Relationships between Soil Microbial Respiration and rDNA- or rRNA-384
based Indices under a Glutamate Amendment Gradient .............................................. 53 385
2.1. Abstract .......................................................................................................... 55 386
2.2. Introduction .................................................................................................... 55 387
2.3. Materials and methods ................................................................................... 57 388
2.3.1. Study site .............................................................................................. 57 389
2.3.2. Soil collection, incubation, and microbial respiration measurements . 57 390
2.3.3. Soil nucleic acid extraction and cDNA synthesis ................................ 58 391
2.3.4. Real-time PCR ..................................................................................... 59 392
2.3.5. Terminal restriction fragment polymorphism (T-RFLP) ...................... 60 393
XXII
2.3.6. Statistical analysis ................................................................................ 61 394
2.4. Results ............................................................................................................ 62 395
2.5. Discussion ...................................................................................................... 63 396
2.6. Conclusions .................................................................................................... 66 397
2.7. References ...................................................................................................... 68 398
Chapter 3. A Copiotroph-Oligotroph Classification of Grassland Soil Prokaryotic 399
Lineages ....................................................................................................................... 73 400
3.1. Abstract .......................................................................................................... 75 401
3.2. Introduction .................................................................................................... 76 402
3.3. Material and methods ..................................................................................... 80 403
3.3.1. Study sites and soil collections ................................................................... 80 404
3.3.2. Experimental design, soil incubations, and microbial respiration 405
determination ................................................................................................. 82 406
3.3.3. Measurements of soil physicochemical properties .............................. 83 407
3.3.4. Soil nucleic acid extraction and the synthesis of cDNA ...................... 84 408
3.3.5. Real-time PCR ..................................................................................... 84 409
3.3.6. MiSeq Sequencing and bioinformatic analysis .................................... 85 410
3.3.7. Statistical analysis ................................................................................ 87 411
3.4. Results ............................................................................................................ 88 412
3.4.1. Soil physicochemical properties .......................................................... 88 413
3.4.2. Soil microbial biomass, abundance and activity .................................. 89 414
3.4.3. Soil prokaryotic diversity ..................................................................... 90 415
3.4.4. Soil prokaryotic community composition ............................................ 92 416
3.5. Discussion .................................................................................................... 101 417
3.6. Conclusions .................................................................................................. 108 418
3.7 References ..................................................................................................... 110 419
Chapter 4. The Application of Microbial Life-strategy Classification in Explaining the 420
Soil Microbial Responses to Litter and Phosphorus Amendments ............................ 121 421
4.1. Abstract ........................................................................................................ 124 422
4.2. Introduction .................................................................................................. 125 423
4.3. Materials and methods ................................................................................. 128 424
4.3.1. Study sites, soil sampling, and litter collection.................................. 128 425
4.3.2. Experiment design and soil incubation .............................................. 130 426
4.3.3. Soil bio-physicochemical analysis ..................................................... 132 427
4.3.4. Soil nucleic acid extraction, RNA reverse transcription, and quantitative 428
PCR .............................................................................................................. 133 429
4.3.5. MiSeq sequencing and bioinformatics analysis ................................. 134 430
4.3.6. Statistics ............................................................................................. 135 431
4.4. Results .......................................................................................................... 136 432
4.4.1. Responses of soil properties to the litter and P amendments ............. 136 433
4.4.2. The effects of the litter and P amendments on soil microbial biomass, 434
activity, abundance, and diversity ................................................................ 137 435
4.4.3. Soil microbial community compositions ........................................... 140 436
4.4.4. The effects of the litter and P amendments on soil microbial community 437
XXIII
compositions ................................................................................................ 141 438
4.4.5. The relationships between soil properties and microbes ................... 145 439
4.5. Discussion .................................................................................................... 146 440
4.6. Conclusions .................................................................................................. 153 441
4.7. References .................................................................................................... 154 442
Chapter 5. The Application of Microbial Life-strategy Classification in Explaining the 443
Responses of Soil Microbes to Warming and Grazing .............................................. 169 444
5.1. Abstract ........................................................................................................ 171 445
5.2. Introduction .................................................................................................. 172 446
5.3. Materials and methods ................................................................................. 175 447
5.3.1. Study site ............................................................................................ 175 448
5.3.2. Experimental design........................................................................... 176 449
5.3.3 Soil sampling and the measurements of soil properties and belowground 450
biomass ........................................................................................................ 178 451
5.3.4. Nucleic acid extraction and cDNA synthesis ..................................... 179 452
5.3.5. Real-time PCR ................................................................................... 180 453
5.3.6. MiSeq sequencing and bioinformatics ............................................... 180 454
5.3.7. Statistical analysis .............................................................................. 183 455
5.4. Results .......................................................................................................... 184 456
5.4.1. Soil and plant properties .................................................................... 184 457
5.4.2. The soil bacterial abundance, 16S rDNA transcriptional activity, and 458
microbial respiration rates............................................................................ 186 459
5.4.3. The total and active prokaryotic α diversities .................................... 188 460
5.4.4. The total and active soil prokaryotic community structures .............. 188 461
5.4.5. The putative function profiles based on PICRUSt analysis ............... 196 462
5.5. Discussion .................................................................................................... 198 463
5.6 Conclusions ................................................................................................... 203 464
5.7. References .................................................................................................... 205 465
Chapter 6. General Conclusions and Perspectives ..................................................... 217 466
6.1. General conclusions ..................................................................................... 218 467
6.2. Perspectives for future studies ..................................................................... 225 468
Supplementary Materials ........................................................................................... 227 469
470
471
472
XXIV
473
XXV
List of Figures 474
Figure 1.1. The relationships among rDNA, rRNA and ribosome. Only the classical 475
arrangements were shown, and the arrangements may vary in some microbial 476
species (Che et al., 2016). ...................................................................................... 7 477
Figure 1.2. An overview of the studies concerning on the response of soil prokaryotes 478
to the manipulation of organic carbon availability. HTS: high-throughput 479
sequencing; DGGE: denaturing gradient gel electrophoresis; T-RFLP: terminal-480
restriction fragment length polymorphism. ............................................................. 9 481
Figure 1.3. The responses of soil prokaryotic phylum proportions to organic carbon 482
amendments. Response ratios are present in log10 (phylum proportions in amended 483
soils/phylum proportions in unamended soils). Experiment duration is presented as 484
log10 (the number of days). ................................................................................... 12 485
Figure 1.4. The geographical distributions of the studies that simultaneously measured 486
soil microbial respiration and rDNA- or rRNA-based indices. ............................ 19 487
Figure 1.5. An overview of the studies simultaneously measuring prokaryotic indices 488
and microbial respiration rates. HTS: high-throughput sequencing; DGGE: 489
denaturing gradient gel electrophoresis; T-RFLP: terminal-restriction fragment 490
length polymorphism. ........................................................................................... 20 491
Figure 1.6. Correlations between soil microbial respiration and bacterial (a) or archaeal 492
(b) rDNA copies. All the data were extracted from the published literature and were 493
normalized using the min-max methods. ***: P < 0.001. .................................... 21 494
Figure 1.7. Correlations between soil microbial respiration and the relative abundance 495
of Proteobacteria (a), Actinobacteria (b), or Firmicutes (c). All the data were 496
extracted from the published literature using 454 pyrosequencing and were 497
normalized using the min-max methods. *: P < 0.05; **: P < 0.01. .................... 26 498
Figure 1.8. The conceptual model and experimental flowchart of my thesis. SMR: soil 499
microbial respiration. ............................................................................................ 35 500
Figure 2.1. NMDS ordinations of 16S rRNA- and rDNA-based bacterial community 501
structures. Squares: 16S rDNA-based bacterial community structures; circles: 16S 502
rRNA-based bacterial community structures; black squares and circles: bacterial 503
community structures of four replicates of soil that were frozen before the 504
incubation. “Low” to “High”: the lowest to the highest soil microbial respiration. 505
The NMDS was based on Bray-Curtis dissimilarity matrix; Stress = 0.078, assuring 506
the reliability of ordinations. The respective aggregation of black squares and 507
circles lend ratification to the results of T-RFLP. ................................................. 62 508
Figure 2.2. The relationships between soil microbial respiration and the 16S rDNA-509
based (a) or 16S rRNA-based (b) community structure. SMR: soil microbial 510
respiration; CS: community structure; ***: P < 0.001. Soil microbial respiration 511
dissimilarity was calculated by the Euclidean distance; community structure 512
dissimilarity was determined using the Bray-Curtis method. The relationships were 513
tested via the Mantel test....................................................................................... 63 514
Figure 3.1. Distribution of the sampling sites. ............................................................ 81 515
Figure 3.2. The effects of glucose amendments on soil properties. The response ratios 516
XXVI
are presented as log2 (glucose amendment/control). DOC: soil dissolved organic 517
carbon content; TC: soil total carbon content; TN: soil total nitrogen content. The 518
results of paired t-tests are also shown in each chart. .......................................... 89 519
Figure 3.3. The responses of soil microbial biomass, abundance, and activity to glucose 520
amendments. The response ratios are presented as log2 (glucose 521
amendment/control). The results of paired t-tests are also shown in each chart. 90 522
Figure 3.4. Responses of soil prokaryotic α-diversity indices to glucose amendment. 523
The response ratios are presented as log2 (glucose amendment/control). The results 524
of paired t-tests are also embedded in each chart. ............................................... 91 525
Figure 3.5. NMDS ordinations of the prokaryotic community structures (a and b) and 526
responses of their dispersions to glucose amendment (c and d). The NMDS 527
ordinations are based on Bray-Curtis dissimilarity matrix; the effects of glucose 528
amendment on soil prokaryotic community structures are determined using 529
PERMANOVA based on Bray-Curtis dissimilarity matrix. The community 530
dispersions are calculated using betadisper functions in vegan package, and 531
presented as the distance to centroid based on PCoA ordinations. ...................... 92 532
Figure 3.6. The relative abundances of prokaryotic lineages across study sites and 533
under different treatments. ................................................................................... 93 534
Figure 3.7. The responses of 16S rDNA relative abundances of prokaryotic phyla to 535
glucose amendments. The response ratios are presented as log2 (glucose 536
amendment/control). The results of paired t-tests are also embedded in each chart.537
.............................................................................................................................. 94 538
Figure 3.8. The responses of 16S rRNA relative abundances of prokaryotic phyla to 539
glucose amendments. The response ratios are presented as log2 (glucose 540
amendment/control). The results of paired t-tests are also embedded in each chart.541
.............................................................................................................................. 95 542
Figure 3. 9. The differences between 16S rRNA and rDNA relative abundances of 543
prokaryotic phyla. The rRNA-rDNA ratios are presented as log2 (rRNA 544
copies/rDNA copies). The columns above 0 represent microbial lineages that were 545
more abundant in the active prokaryotic communities than in the total prokaryotic 546
communities; while those below 0 represent microbial lineages that were less 547
abundant in the active prokaryotic communities than in the total prokaryotic 548
communities. The results of paired t-tests are also embedded in each chart. ...... 95 549
Figure 3.10. The correlations between 16S rDNA relative abundances of prokaryotic 550
phyla and microbial respiration rates. The correlations are based on the unamended 551
soils. The microbial respiration rate is represented as μg CO2 g-1 soil day-1, and all 552
the data are presented as 1og10 (1+X). The correlation coefficient is embedded in 553
each chart; *: P < 0.05; **: P < 0.01; **: P < 0.001. .......................................... 96 554
Figure 3.11. The correlations between 16S rRNA relative abundances of prokaryotic 555
phyla and microbial respiration rates. The correlations are based on the unamended 556
soils. The microbial respiration rate is represented as μg CO2 g-1 soil day-1, and all 557
the data are presented as 1og10 (1+X). The correlation coefficient is embedded in 558
each chart; *: P < 0.05; **: P < 0.01; **: P < 0.001. .......................................... 97 559
Figure 3.12. The responses of prokaryotic lineages to glucose amendment across all 560
XXVII
the study sites. ...................................................................................................... 98 561
Figure 3.13. The responses of prokaryotic OTU proportions to the glucose amendment. 562
The t values are calculated based on paired t-tests, and only t values with P < 0.05 563
are shown. The numbers within or above each box represented the numbers of 564
OTUs within Archaea each bacterial phylum. ..................................................... 99 565
Figure 3.14. The correlations between prokaryotic OTU proportions and microbial 566
respiration rates. The r values are calculated based on Pearson correlations, and 567
only r values with P < 0.05 are shown. The numbers within or above each box 568
represent the numbers of OTUs within Archaea and each bacterial phylum. .... 100 569
Figure 4.1. The photographs of the study sites for soil sampling (a) and litter collection 570
(b). ....................................................................................................................... 129 571
Figure 4.2. The effects of litter and P amendments on soil properties. DOC: dissolved 572
organic carbon; AP: available P; DN: dissolved N; IN: inorganic N; DON: 573
dissolved organic N; SMR: soil microbial respiration rates; FDA: fluorescein 574
diacetate. CK: control; P: P amendment; L: litter amendment; LP: litter and P 575
amendments. Relative activity of FDA hydrolase was presented in the ratios of the 576
fluorescence value of each sample to the average fluorescence value of the four CK 577
soils. L**: the effect of litter amendment was significant with P < 0.01; L***: the 578
effect of litter amendment was significant with P < 0.001; P•: the effect of P 579
amendment was marginal with P < 0.1; P***: the effect of P amendment was 580
significant with P < 0.001. All the data were presented in mean ± SE, n = 4. Bars 581
with different letters represent significant differences at P < 0.05. .................... 137 582
Figure 4.3. Effects of litter and P amendments on soil microbial rDNA and rRNA copies. 583
All the data were presented in mean ± SE, n = 4. CK: control; P: P amendments; L: 584
litter amendments; LP: litter and P amendments; “***”: P < 0.001. Bars with 585
different letters represent significant differences at P < 0.05. ............................ 138 586
Figure 4.4. Effects of litter and P amendments on soil microbial α-diversity. All the 587
data were presented in mean ± SE, n = 4. CK: control; P: P amendments; L: litter 588
amendments; LP: litter and P amendments; “***”: P < 0.001. Bars with different 589
letters represent significant differences at P < 0.05. ........................................... 139 590
Figure 4.5. The NMDS ordinations of the rDNA and rRNA based community structures 591
at the OTU level. L: effect of litter amendments; “***”: P < 0.001. .................. 139 592
Figure 4.6. Effects of litter and P amendments on soil microbial community dispersion. 593
All the data were presented in mean ± SE, n = 4. CK: control; P: P amendments; L: 594
litter amendments; LP: litter and P amendments; “***”: P < 0.001. Bars with 595
different letters represent significant differences at P < 0.05. ............................ 140 596
Figure 4.7. The relative abundance of main prokaryotic (a) and fungal (b) lineages 597
under different treatments. All the data were presented in mean - SE, n = 4. CK: 598
control; P: P amendments; L: litter amendments; LP: litter and P amendments. 140 599
Figure 4.8. The responses of prokaryotic lineage proportions to the litter amendments. 600
The effects of litter amendments were determined using the LEfSe analysis with a 601
threshold on the logarithmic LDA score of 3.5. The lineages in green were enriched 602
in the soils without litter amendments, while the lineages in red were more 603
abundant in the litter-amended soils. .................................................................. 142 604
XXVIII
Figure 4.9. The responses of fungal lineage proportions to the litter amendments. The 605
effects of litter amendments were determined using the LEfSe analysis with a 606
threshold on the logarithmic LDA score of 3.0. The lineages in green were enriched 607
in the soils without litter amendments, while the lineages in red were more 608
abundant in the litter-amended soils. .................................................................. 143 609
Figure 4.10. The relationships between soil properties and microbes. •: P < 0.1; *: P < 610
0.05; **: P < 0.01; ***: P < 0.001. FDA: fluorescein diacetate. The numbers in the 611
out circle represented the correlation coefficients. The correlations between soil 612
properties and the copies of rDNA and rRNA were tested using Pearson correlation; 613
the correlations between soil properties and microbial community structures were 614
tested using envfit based on NMDS.................................................................... 145 615
Figure 5.1. Soil and plant properties under different treatments. NWNG: no-warming 616
with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; 617
WG: warming with grazing; W: effect of warming; G: effect of grazing; W×G: 618
interaction effect of warming and grazing. All the data were presented in mean ± 619
SE, n = 4. Bars with different letters indicate significant differences. ............... 185 620
Figure 5.2. The soil 16S rDNA copies (a), 16S rRNA copies (b), 16S rRNA-rDNA 621
ratios (c), and microbial respiration rates under different treatments. NWNG: no 622
warming with no grazing; NWG: no warming with grazing; WNG: warming with 623
no grazing; WG: warming with grazing; W: effect of warming; G: effect of grazing. 624
All the data were presented in mean ± SE, n = 4. Bars with different letters indicate 625
significant differences at P < 0.05. ..................................................................... 187 626
Figure 5.3. The total and active soil prokaryotic diversity indices under different 627
treatments. The prokaryotic community dispersion was represented as the distance 628
to centroid based on PCoA ordination. NWNG: no-warming with no grazing; 629
NWG: no warming with grazing; WNG: warming with no grazing; WG: warming 630
with grazing; W: effect of warming; G: effect of grazing; W×G: interaction effect 631
of warming and grazing. All the data were presented in mean ± SE, n = 4. Bars with 632
different letters indicate significant differences. ................................................. 188 633
Figure 5.4. The total and active soil prokaryotic community compositions under 634
different treatments. NWNG: no warming with no grazing; NWG: no warming 635
with grazing; WNG: warming with no grazing; WG: warming with grazing. Others: 636
the sum of phylum occupying less than 0.5% of the total population. All the data 637
were presented in mean - SE, n = 4. ................................................................... 189 638
Figure 5.5. The total and active soil prokaryotic community compositions at family 639
level. NWNG: no warming with no grazing; NWG: no warming with grazing; 640
WNG: warming with no grazing; WG: warming with grazing. Others, the sum of 641
families occupying less than 1.0% of the total population. ................................ 190 642
Figure 5.6. The total and active soil prokaryotic community compositions at genus 643
level. NWNG: no warming with no grazing; NWG: no warming with grazing; 644
WNG: warming with no grazing; WG: warming with grazing. Others, the sum of 645
genera occupying less than 0.5% of the total population.................................... 191 646
Figure 5.7. The NMDS ordinations of the total and active soil prokaryotic community 647
structures based on OTUs (a and b) and putative functions (c and b). NWNG: no 648
XXIX
warming with no grazing; NWG: no warming with grazing; WNG: warming with 649
no grazing; WG: warming with grazing. The putative functions were determined 650
through PICRUSt analysis with KEGG pathway assignment at level three. ...... 191 651
Figure 5.8. The responses of the proportions of total (a) and active (b) soil prokaryotic 652
lineages to warming. The effects of warming were determined using the LEfSe 653
analysis, and the threshold on the absolute logarithmic LDA score was 3.0. ..... 192 654
Figure 5. 9. The relative abundance of copiotrophic and oligotrophic lineages under 655
different treatments. NWNG: no-warming with no grazing; NWG: no warming 656
with grazing; WNG: warming with no grazing; WG: warming with grazing; W: 657
effect of warming. All the data were presented in mean ± SE, n = 4. Bars with 658
different letters indicate significant differences. The copiotrophic lineages included 659
Bacteroidetes, Betaproteobacteria, and Gammaproteobacteria. The oligotrophic 660
lineages included Archaea, Acidobacteria, Actinobacteria, Planctomycetes, 661
Verrucomicrobia, and Chloroflexi. The life-strategy of these lineages was classified 662
based on their responses to organic matter amendments in the published 663
investigations. ..................................................................................................... 193 664
Figure 5.10. The relationships between total (a and b) and active (c and d) soil 665
prokaryotic lineages and microbial respiration rates. NWNG: no-warming with no 666
grazing; NWG: no warming with grazing; WNG: warming with no grazing; WG: 667
warming with grazing. The copiotrophic lineages included Bacteroidetes, 668
Betaproteobacteria, and Gammaproteobacteria. The oligotrophic lineages included 669
Archaea, Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and 670
Chloroflexi. The life-strategy of these lineages was classified based on their 671
responses to organic matter amendments in the published investigations. ......... 194 672
Figure 5.11. The relationships between total (a and b) and active (c and d) soil 673
prokaryotic lineages and belowground biomass. NWNG: no-warming with no 674
grazing; NWG: no warming with grazing; WNG: warming with no grazing; WG: 675
warming with grazing. The copiotrophic lineages included Bacteroidetes, 676
Betaproteobacteria, and Gammaproteobacteria. The oligotrophic lineages included 677
Archaea, Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and 678
Chloroflexi. The life-strategy of these lineages was classified based on their 679
responses to organic matter amendments in the published investigations. ......... 194 680
Figure 5.12. The relationships between environmental factors and the community 681
structures based on 16S rDNA (a) and rRNA (b). NWNG: no-warming with no 682
grazing; NWG: no warming with grazing; WNG: warming with no grazing; WG: 683
warming with grazing. The vectors represent the soil properties that were 684
significantly correlated with the corresponding NCG community structures (P < 685
0.05); the directions of the vectors represent the increase gradient of each soil 686
properties; longer vector represents stronger correlations. All the vectors were 687
drawn using the envfit function in vegan package. ............................................. 196 688
Figure 5.13. The relationships between total (a) and active (b) soil prokaryotic 689
community structures and the environmental factors, as determined using the 690
multivariate regression tree analysis. SM: soil moisture (%); ST: soil temperature 691
(°C). The bar plots showed the average relative abundances of OTUs in each split 692
XXX
groups, and the numbers (n) under the bars represented the sample number within 693
each group. .......................................................................................................... 196 694
Figure 5.14. Responses of the proportions of putative functions to warming. The 695
functional profiles were predicted based on 16S rRNA amplicon sequencing, using 696
PICRUSt analysis with KEGG pathway assignment at level three. The effects of 697
warming were determined using the LEfSe analysis, and only the putative 698
functions with an absolute logarithmic LDA score > 2.0 were shown. .............. 197 699
700
701
702
XXXI
List of Tables 703
Table 1.1. Comparisons between r- and K-selections (adapted from Pianka 1970). .... 3 704
Table 1.2. Potential traits of copiotroph and oligotroph microbes (adapted from Fierer 705
2007). ...................................................................................................................... 4 706
Table 1.3. A summary of the studies on the life-strategy classification based on organic 707
carbon amendments (modified based on Ho et al. 2017)...................................... 11 708
Table 1.4. Correlations between bacterial 16S rDNA-based lineages and soil microbial 709
respiration. ............................................................................................................ 24 710
Table 2.1. The correlations* between soil microbial respiration and other variables. 61 711
Table 3.1. Some background information of the study sites. ...................................... 81 712
Table 4.1. Some properties of the soils used for incubation. ..................................... 130 713
Table 5.1. Methods of grazing treatments. ................................................................ 177 714
Table 5.2. The effects of warming, grazing, and their interactions on soil and plant 715
properties............................................................................................................. 186 716
Table 5.3. The effects of warming, grazing, and their interactions on the soil microbial 717
communities. ....................................................................................................... 187 718
Table 5.4. The relationships between environmental factors and the community 719
structures based on 16S rDNA and rRNA. ......................................................... 195 720
Table S3.1. A summary of the responses of soil prokaryotic lineage proportions to the 721
glucose amendment and their correlations with microbial respiration rates. Mean: 722
the average relative abundances of the prokaryotic lineages across all the soil 723
samples; trDNA: the t values generated from the paired t-tests of the 16S rDNA 724
relative abundances of prokaryotic lineages between the soils with glucose 725
amendment and the controls; trRNA: the t values generated from the paired t-tests of 726
the 16S rRNA relative abundances of prokaryotic lineages between the soils with 727
glucose amendment and the controls; RrDNA: the correlation coefficients of the 728
correlations between soil microbial respiration rates and 16S rDNA relative 729
abundances of prokaryotic lineages; RrRNA: the correlation coefficients of the 730
correlations between soil microbial respiration rates and 16S rRNA relative 731
abundances of prokaryotic lineages; tRD: the t values generated from the paired t-732
tests between 16S rDNA and rRNA relative abundances of prokaryotic lineages. 733
The negative number represent “negative correlations”, “more abundant in the 734
glucose-amended soils”, or “the 16S rRNA relative abundances are higher than 735
those of 16S rDNA”. The bold values represent significant differences or 736
correlations with P < 0.05. .................................................................................. 228 737
738
739
XXXII
740
741
742
XXXIII
List of Abbreviations 743
744
Abbreviations Explanations
Aci Acidobacteria
Act Actinobacteria
ANOVA Analysis of Variance
Bac Bacteroidetes
C Carbon
cDNA Complementary DNA
CO2 Carbon Dioxide
CS Community Structure
DGGE Denaturing Gradient Gel Electrophoresis
DN Dissolved Nitrogen
DNA Deoxyribonucleic Acid
DOC Dissolved Organic Carbon
FATE Free-air Temperature Enhancement
FDA Fluorescein Diacetate
Fir Firmicutes
G Grazing
HTS High Throughput Sequencing
IPCC Intergovernmental Panel on Climate Change
ITS Internal Transcribed Spacer
LEfSe Linear Discriminant Analysis Effect Size
MAP Mean Annual Precipitation
MAT Mean Annual Temperature
N Nitrogen
NCBI National Center for Biotechnology Information
NMDS Non-metric Multidimensional Scaling
O2 Oxygen
OTU Operational Taxonomic Unit
P Phosphorus
PCoA Principal Coordinates Analysis
PCR Polymerase Chain Reaction
PERMANOVA Permutation Multivariate Analysis of Variance
PICRUSt
Phylogenetic Investigation of Communities by Reconstruction
of Unobserved States
Pro Proteobacteria
Qiime Quantitative Insights Into Microbial Ecology
rDNA Ribosome DNA
RNA Ribonucleic Acid
rRNA Ribosome RNA
SM Soil Moisture
SMR Soil Microbial Respiration
XXXIV
List of Abbreviations 745
Abbreviations Explanations
ST Soil Temperature
TC Soil Total Carbon
TN Soil Total Nitrogen
T-RFLP Terminal-Restriction Fragment Length Polymorphism
Ver Verrucomicrobia
W Warming
WHC Water-holding capacity
746
747
1
748
Chapter 1. General Introduction* 749
750
751
752
*This chapter forms the basis of the following journal paper: 753
Che RX, Wang WJ, Zhang J, Nguyen NTT, Tao J, Wang F, Wang YF, Xu ZH, Cui XY. 754
(2016): Assessing soil microbial respiration capacity using rDNA- and rRNA-755
based indices: A review. Journal of Soils and Sediments 16: 2698–2708. DOI: 756
10.1007/s11368-016-1563-6 757
758
759
2
760
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 761
This chapter includes a co-authored paper. The bibliographic details of the co-authored 762
paper, including all authors, are: 763
Che RX, Wang WJ, Zhang J, Nguyen NTT, Tao J, Wang F, Wang YF, Xu ZH, Cui XY. (2016): 764
Assessing soil microbial respiration capacity using rDNA- and rRNA-based indices: A review. 765
Journal of Soils and Sediments 16: 2698–2708. DOI: 10.1007/s11368-016-1563-6 766
My contribution to the paper involved: 767
Publication collection; reading the publications; the extraction of the data; statistical 768
analysis; categorisation of the data into a usable format; and writing the paper. 769
770
The copyright of the paper has been transformed to the Publisher, but I reserve the 771
right to include the paper as a chapter in the thesis. 772
773
774
(Signed) _________ _______ (Date)______________ 775
Rongxiao Che 776
777
(Countersigned) _____ ___ (Date)______________ 778
Corresponding author of paper: Xiaoyong Cui 779
780
(Countersigned) ______ ___ (Date)______________ 781
Supervisor: Zhihong Xu 782
783
784
785
786
3
1.1. From r- and K-selections to copiotroph and oligotroph strategies 787
As one of the most fundamental concepts in ecology, the terms r- and K-selection were 788
first coined by MacArthur and Wilson (1967). Then, Pianka (1970) elaborated the 789
features of r-selection and K-selection, and applied them to understand the evolutionary 790
histories of all the organisms (Table 1.1). In these terms, “r” refers to the maximal 791
intrinsic growth rate, while “K” is the carrying capacity. Accordingly, the r category is 792
used to describe those organisms with high growth-rate, small body size, short longevity, 793
and low growth efficiency (Pianka, 1970). In contrast, the K categories are usually 794
characterized by their low growth-rate, large body size, long longevity, and high growth 795
efficiency (Pianka, 1970). Generally, the r categories prefer to live in the environments 796
with abundant resources, whereas the K categories are more competitive when the 797
resources are limited and the population size approaches carrying capacity (Pianka, 798
1970). 799
Table 1.1. Comparisons between r- and K-selections (adapted from Pianka 1970). 800
r-selection K-selection
Climate Variable and (or) unpredictable Constant and (or) predictable
Mortality Catastrophic, undirected, density-dependent Directed, density-dependent
Survivorship Type III Deevey Type I or II Deevey
Population size Variable in time, nonequilibrium Constant, equilibrium
competition Variable, often lax Usually keen
Relative abundance Often does not fit broken stick model Usually fits broken stick model
Favored by selection 1) rapid development;
2) high μmax;
3) early reproduction;
4) small body size;
5) semelparity
1) slow development, greater competitive ability;
2) lower resource thresholds;
3) delayed reproduction;
4) larger body size;
5) iteroparity
Longevity Short Long
Leads to Productivity Efficiency
801
4
Although the r-K theory has been revised and replaced by some more advanced models 802
(e.g., Reznick et al., 2002), it is still one of the most widely-used concepts to describe 803
the life-history evolution and life strategies of plants and animals. Compared to plants 804
and animals, microbes are evidently more related to r categories. However, as proposed 805
by Pianka (1970), no organisms can be classified as complete r or K categories, but all 806
must reach some compromise between them. Thus, the microbes can be further divided 807
into relative r or K categories. Instead of the r- and K- selection, microbiologists prefer 808
to use copiotroph and oligotroph (analogous to the r- and K-selection categories) to 809
describe microbial life strategies (Table 1.2). 810
Table 1.2. Potential traits of copiotroph and oligotroph microbes (adapted from Fierer 2007). 811
Copiotrophs Oligotrophs
Growth rates High μmax Low μmax
Growth efficiency Low High
Basal metabolism High Low
Substrate affinity Low KS, poor competitors when
substrates are limited
High KS, competitive when substrates
are limited
Responses to substrate additions Short lag time before growth on the
fresh substrate, large proportion of
enzymes are produced constitutively
Long lag time before growth on the
fresh substrate, most enzymes are
induced, not constitutive
Population size Variable in time Constant
Cultivability High Low
rRNA operon copy number Usually more than five Usually less than two
Tolerance to environmental
stress and disturbance
Highly sensitive to stress, formatting
spore when exposed to suboptimal
environmental conditions
Individuals can maintain viability
under environmental stress and
disturbance
Relationships with the activity
of microbial communities
Their relative abundance usually
positively correlated with microbial
activity
Their relative abundance usually
negatively correlated with microbial
activity
812
Actually, the idea of dividing microbes into copiotroph and oligotroph categories was 813
proposed even earlier than the r- and K-selection (Winogradsky, 1924). Subsequently, 814
5
concepts of these terminologies have been modified many times (Andrews, 1984; 815
Andrews and Harris, 1986; Fierer et al., 2007; Gottschal, 1985; Hirsch et al., 1979; 816
Koch, 2001; Meyer, 1994; Padmanabhan et al., 2003). In this thesis, I employed the 817
concept defined by Fierer et al. (2007), as it is the most widely-used one in describing 818
microbial life strategies. Specifically, copiotrophs are those microbes with high 819
maximum growth rate, low growth efficiency, and high requirement for nutrients (Table 820
1.2). They are usually more abundant in an environment with higher organic carbon 821
content, and their relative abundances are often positively correlated with the activity 822
of microbial communities. In contrast, oligotrophs refer to those microbes with low 823
maximum growth rate, high growth efficiency, and low requirement for nutrients (Table 824
1.2). Oligotrophs are usually more competitive in an environment with low availability 825
of organic carbon, and their proportions show negative correlations with the activity of 826
microbial communities. 827
828
On the basis of the aforementioned features of the copiotrophs and oligotrophs (Table 829
1.2), there are a number of ways to identify microbial life strategies. First, the most 830
widely-used manner to distinguish copiotrophs and oligotrophs is examining the 831
relative responses of microbial lineages to the variations in organic carbon availability 832
(Fierer et al., 2007; Ho et al., 2017). Second, the relationships between the microbial 833
lineage proportions and microbial activity (usually respiration rates) have also been 834
employed to determine the life strategies of microbial lineages in a couple of studies 835
(Che et al., 2016b; Fierer et al., 2007). In addition, the life strategies of microbial 836
6
lineages have been characterized based on genomic sequencing (Haggerty and Dinsdale, 837
2017; Lauro et al., 2009), growth kinetics (Chen et al., 2016), and substrate affinity 838
(Chen et al., 2016; Kits et al., 2017). In soils, due to the difficulties in determining the 839
microbial growth kinetics, substrate affinity, and genomes, most of the life-strategy 840
classification efforts have been made based on the first two methods. Therefore, in the 841
following sections, only the research progress in life-strategy classifications based on 842
the manipulation of organic carbon availability and relationships with microbial 843
respiration rates, were synthesized. As my thesis only focused on the life-strategy 844
classification of soil prokaryotes, only the studies concerning the soil prokaryotes were 845
included. 846
847
1.2. Life-strategy classification of soil prokaryotes based on the manipulation of 848
organic carbon availability 849
As mentioned above, copiotrophs are usually more competitive in the soils with 850
abundant resources, while oligotrophs prefer the soils with limited nutrient availability 851
(Table 1.2). Accordingly, the relative abundance of copiotrophic lineages would 852
increase when the soils receive more organic substrates (Cleveland et al., 2007; Fierer 853
et al., 2007). In contrast, oligotrophic lineage proportions in soils would decrease with 854
higher organic matter input. It is not difficult to manipulate the organic matter 855
availability in soils, and the community profiles of soil microbes can be easily 856
determined with the culture-independent methods (e.g., high throughput sequencing). 857
Therefore, the manipulation of organic carbon availability is the most widely used 858
7
method to identify the life-strategies of soil prokaryotes (Ho et al., 2017). 859
860 Figure 1.1. The relationships among rDNA, rRNA and ribosome. Only the classical arrangements were 861
shown, and the arrangements may vary in some microbial species (Che et al., 2016). 862
863
Currently, rDNA and rRNA (Fig. 1.1) have been proven to be the most powerful 864
biomolecule for identifying microbial communities by virtue of their microbial ubiquity 865
(Woese et al., 1990), phylogenetic consistency (Hugenholtz et al., 1998; Woese and Fox, 866
1977; Woese et al., 1990), and metabolic associativity (Blagodatskaya and Kuzyakov, 867
2013; Blazewicz et al., 2013; Che et al., 2015). Accordingly, technologies based on 868
rDNA and rRNA are the most widely-used methods to analyze soil prokaryotic 869
community compositions. Moreover, the resolution and accuracy of these methods far 870
outperform other analyzing technologies. Therefore, here, I only summarized the 871
studies using the methods based on rDNA and rRNA. 872
873
8
1.2.1. Methods of the review 874
The publications concerning the responses of soil prokaryotic community profiles to 875
the manipulation of soil organic matter availability were obtained by searching the ISI 876
Core Collection online database (http://apps.webofknowledge.com). Firstly, I searched 877
the database using a series of searching terms, and then manually screened the output 878
publications. Only the studies which met the following criterions were included: (1) the 879
study was based on experiment; (2) the study determined soil prokaryotic community 880
profiles using the methods based on rDNA and rRNA; (3) soil was amended with high 881
C/N organic matter (e.g., plant residues, cellulose, and glucose). As a result, 38 papers 882
were included in the following review. 883
884
I recorded the following information in the 38 papers: (1) types of experiments and 885
ecosystems; (2) treatments and duration in each experiment; (3) molecular biological 886
techniques; (4) response of prokaryotic indices. In addition, I extracted the relative 887
abundance of prokaryotic phyla (based on 454 pyrosequencing or MiSeq sequencing) 888
to further determine the responses of prokaryotic phyla to organic matter availability 889
manipulation. 890
891
1.2.2. An overview of the studies on soil microbial responses to manipulations of 892
organic matter input 893
Approximately 70% of the related investigations were published after 2010 (Fig. 1.2), 894
which indicates that increased attention has been paid to soil microbial responses to 895
9
organic matter input manipulation. However, most of these studies were conducted 896
using soils from farmlands and forests (Fig. 1.2), with only three investigations 897
collecting soils from grasslands (Che et al., 2015; Eilers et al., 2010; Hossain and 898
Sugiyama, 2011). As organic input is easier to manipulate in the microcosm 899
experiments, more related studies were conducted using laboratory incubations than 900
field manipulations (Fig. 1.2). 901
902
Figure 1.2. An overview of the studies concerning on the response of soil prokaryotes to the manipulation 903
of organic carbon availability. HTS: high-throughput sequencing; DGGE: denaturing gradient gel 904
electrophoresis; T-RFLP: terminal-restriction fragment length polymorphism. 905
906
Regarding molecular biology technologies, in most of the studies, prokaryotic 907
community structures were analyzed using high-throughput sequencing and denaturing 908
gradient gel electrophoresis (DGGE). After 2010, high-throughput sequencing was 909
increasingly employed to analyze the microbial community structures (e.g., Guo et al., 910
2017; Su et al., 2017; Sun et al., 2017). In addition, terminal-restriction fragment length 911
polymorphism (T-RFLP), clone library, and automated ribosomal intergenic spacer 912
analysis (ARISA) were also employed to reveal the microbial community profiles in 913
several studies (Fig. 1.2). However, most of the microbial community structure analysis 914
10
was based on DNA, while the RNA-based community structures were only analyzed in 915
three studies (Che et al., 2015; Li et al., 2017; Pennanen et al., 2004). In addition, most 916
studies were conducted with soils collected from a single study site, while the 917
investigations across multiple sites were scant. 918
919
1.2.3. A summary of prokaryotic life-strategy classification based on organic 920
carbon manipulation 921
Most of the 38 studies only roughly examined the overall responses of soil prokaryotic 922
community structures to the changes in organic matter input (Fang et al., 2007; Hossain 923
and Sugiyama, 2011; Luo et al., 2008). The responses of prokaryotic lineage relative 924
abundances were only examined in less than 15 studies (Bernard et al., 2007; Cleveland 925
et al., 2007; Fierer et al., 2007). In this section, I mainly summarized the research 926
progress on the changes in prokaryotic lineage proportions to organic carbon 927
amendments, and tried to make a rough life-strategy classification of soil prokaryotic 928
phyla. 929
930
Although the responses of prokaryotic community structures to organic amendment 931
were investigated in multitude earlier studies (Castaldini et al., 2005; Cookson et al., 932
2005; Padmanabhan et al., 2003; Pennanen et al., 2004; Schutter and Dick, 2001), the 933
first systematic classification of soil prokaryotes into copiotroph-oligotroph categories 934
by manipulating the organic carbon availability was conducted by Fierer et al. (2007). 935
936
937
11
Table 1.3. A summary of the studies on the life-strategy classification based on organic carbon 938
amendments (modified based on Ho et al. 2017). 939
Treatments Molecular
biotechnologies
Copiotrophic
phyla
Oligotrophic
phyla References
13C-glucose amendment Clone library
β-Proteobacteria
γ-Proteobacteria
Bacteroidetes
(Padmanabhan et al., 2003)
Sucrose amendments Real-time PCR
(phylum specific)
Bacteroidetes
β-Proteobacteria Acidobacteria (Fierer et al., 2007)
Organic matter (leached
from plant litter)
amendments
Clone library γ-Proteobacteria Acidobacteria
Actinobacteria (Cleveland et al., 2007)
13C-Wheat residue
amendments Clone library
β-Proteobacteria
γ-Proteobacteria
Actinobacteria
Cyanobacteria
Gemmatimonadetes
Planctomycetes
(Bernard et al., 2007)
Glucose, glycine, and
citric acid amendments Pyrosequencing β-Proteobacteria (Eilers et al., 2010)
Tree leaf litter
amendment and removal Pyrosequencing α-Proteobacteria Acidobacteria (Nemergut et al., 2010)
Cotton straw amendments Clone library γ-Proteobacteria Sphingobacteria
Verrucomicrobia (Huang et al., 2012)
Tree leaf litter
manipulations Pyrosequencing α-Proteobacteria Acidobacteria (Leff et al., 2012)
Olive residue amendments Pyrosequencing Proteobacteria
Acidobacteria
Actinobacteria
Gemmatimonadetes
(Siles et al., 2014)
13C-glucose amendments MiSeq and
real-time PCR
β-Proteobacteria
γ-Proteobacteria
Firmicutes
Actinobacteria
Acidobacteria (Hungate et al., 2015)
(Morrissey et al., 2016)
13C-xylose and 13C-
cellulose amendments Pyrosequencing
Firmicutes
Bacteroidetes
Actinobacteria
Verrucomicrobia
Planctomycetes
Chloroflexi
(Pepe-Ranney et al., 2016)
Leaf litter amendments MiSeq Proteobacteria Acidobacteria
Firmicutes (Sun et al., 2017)
940
In that study, Fierer et al. (2007) amended soils with different amounts of sucrose, and 941
found that the relative abundance of Bacteroidetes and β-Proteobacteria showed 942
significant positive correlations with the sucrose amendments. Conversely, the 943
12
proportions of Acidobacteria were negatively correlated with the sucrose amendments. 944
These findings were further confirmed by a meta-analysis based on the difference in 945
prokaryotic phylum proportions between bulk and rhizosphere soils. Therefore, Fierer 946
and his colleagues proposed that it is feasible to classify specific bacterial phyla into 947
the ecological categories of copiotroph (i.e., Bacteroidetes and β-Proteobacteria) and 948
oligotroph (i.e., Acidobacteria). Following the study, a number of investigations were 949
conducted to identify the prokaryotic life strategies through amending soils with 950
organic carbon (Table 1.3). 951
952
Figure 1.3. The responses of soil prokaryotic phylum proportions to organic carbon amendments. 953
Response ratios are present in log10 (phylum proportions in amended soils/phylum proportions in 954
unamended soils). Experiment duration is presented as log10 (the number of days). 955
956
As synthesized in Table 1.3, in most of the studies, Proteobacteria and Acidobacteria 957
have been classified as copiotrophs and oligotrophs, respectively. In addition, in several 958
studies, Bacteroidetes were identified as copiotrophs (Fierer et al., 2007; Padmanabhan 959
et al., 2003; Pepe-Ranney et al., 2016), while Gemmatimonadetes, Chloroflexi, 960
Planctomycetes, and Verrucomicrobia were identified as oligotrophic lineages (Table. 961
1.3). These findings further supported the idea that it is possible to classify prokaryotic 962
13
lineages into copiotroph-oligotroph categories. 963
964
Nevertheless, among the studies, I also observed inconsistent classifications of life 965
strategy at the phylum level. For example, Actinobacteria were identified as oligotrophs 966
in a few studies (Hungate et al., 2015; Morrissey et al., 2016; Pepe-Ranney et al., 2016). 967
However, some other investigations found they were oligotrophic lineages (Bernard et 968
al., 2007; Cleveland et al., 2007; Siles et al., 2014). This is also supported by my meta-969
analysis which showed that the responses of phylum proportions to the organic carbon 970
amendments were highly variable across different investigations (Fig. 1.3). 971
972
These inconsistencies could be largely attributed to the life-strategy diversifications 973
within each prokaryotic phylum (Yuste et al., 2014). Indeed, a phylum is a rather low-974
resolution taxonomic unit. Each phylum could encompass a vast number of species, 975
possessing a wide range of physiological capabilities. Thus, it is persuasive to assert 976
that determining the life strategy at finer taxonomic levels (e.g., family and genus) 977
could obtain more consistent classifications (Ho et al., 2017). 978
979
Moreover, most of the life-strategy classification efforts have been made using DNA 980
based methods (Fig. 1.2), targeting the total microbes in soils. Nevertheless, the 981
majority of soil microbes are dormant (Lennon and Jones, 2011), which indicates that 982
only a small fraction of the microbial population in soils are active and responsible to 983
the organic carbon amendments. Accordingly, organic matter amendments may only 984
14
elicit very minor changes in total microbial community compositions, which are 985
difficult to assess. Thus, compared to the technologies targeting active microbial 986
populations, the DNA-based methods are more likely to bring in misguided information, 987
contributing to the inconsistencies in the life-strategy classification. Indeed, as observed 988
in several studies, the response of RNA-based prokaryotic community profiles showed 989
more sensitive and consistent responses to the gradient organic carbon amendment (Che 990
et al., 2015; Pennanen et al., 2004). Furthermore, RNA-based methods are extensively 991
employed to determine active microbial community compositions (Blagodatskaya and 992
Kuzyakov, 2013; Che et al., 2016b). Therefore, classifying microbial life-strategies 993
using RNA-based methods should be, at least, as important as that via DNA-based 994
methods, and may improve the consistency in the life-strategy classification of soil 995
microbes. 996
997
In addition, the biases introduced by the different molecular biotechnologies employed 998
in various studies (Fig. 1.2; Table 1.3) can also lead to the contradictory life-strategy 999
classifications. First, the low-resolution technologies (e.g., DGGE and clone library) 1000
could not accurately describe soil prokaryotic community compositions. Second, 1001
although the resolution of high-throughput sequencing is much higher, the differences 1002
in the selections of PCR primer sets and bioinformatics analysis pipelines can also cause 1003
unconformities in the life-strategy classification of soil prokaryotes. Thus, it is difficult 1004
to compare the results of life-strategy classifications from different studies. 1005
1006
15
The microbial life-strategies have been recognized as a continuum, and thus the life-1007
strategy of a specific microbial lineage could change with different community 1008
compositions. However, most of the investigations were only focused on soils collected 1009
from single or few study sites with similar prokaryotic community profiles. Thus, the 1010
differences in prokaryotic community compositions across the studies may be the most 1011
responsible factor causing the inconsistencies in the life-strategy classifications. As 1012
mentioned above, it is challenging to compare the results from different studies directly 1013
due to the various methodologies used. Therefore, conducting systematic studies with 1014
soils collected from various ecosystems can dramatically improve our understanding of 1015
the life-strategy of microbial lineages. 1016
1017
Collectively, it should be feasible to classify prokaryotic lineages into copiotroph-1018
oligotroph categories through organic carbon amendments. However, the existing life-1019
strategy classification of soil prokaryotes may be unreliable due to the drawbacks in 1020
methodologies. As a result, cross-site studies using RNA-based methods with high 1021
resolution are extremely desired for microbial life-strategy classifications. 1022
1023
1.3. Prokaryotic life-strategy classification based on the correlations with 1024
microbial respiration rates 1025
Microbial respiration is the aerobic or anaerobic biochemical process in which energy-1026
containing compounds are oxidized to yield energy (Pell et al. 2005). In most situations, 1027
soil microbial respiration is aerobic, and thus ecologists usually employ O2 1028
16
consumption or CO2 production rates to determine the respiration intensity (Pell et al. 1029
2005). Hence, in my thesis, soil microbial respiration refers to the microbial process of 1030
O2 uptake and CO2 release with energy production, and was determined based on CO2 1031
emission rates. 1032
1033
Evidently, soil microbial respiration rates generally rise with increasing availability of 1034
organic substrates, when temperature, moisture, and aeration are at same level. The 1035
microbial respiration rate, hence, can be seen as an index that reflects the organic 1036
resource status in soils. As mentioned above, the copiotrophic microbes are more 1037
competitive than their oligotrophic counterparts when the resource is abundant, and 1038
would occupy higher proportions, and vice versa. Therefore, the relative abundances of 1039
copiotrophic prokaryotes should be positively correlated with soil microbial respiration 1040
rates, while the correlations between oligotroph proportions and soil microbial 1041
respiration rates should be negative. 1042
1043
In recent decades, soil microbial respiration is an extensively examined soil process by 1044
virtue of its inseparable relationship with the global carbon cycle (Bond-Lamberty et 1045
al., 2004; Schimel and Schaeffer, 2012; Schlesinger and Andrews, 2000), climate 1046
changes (Stocker, 2014; Wang et al., 2014), soil quality (Lima et al., 2013; Paz-Ferreiro 1047
and Fu, 2016), and microbial activity (Blagodatskaya and Kuzyakov, 2013; Che et al., 1048
2016b). Therefore, classifying microbial life strategies based on the relationships 1049
between microbial lineage proportions and microbial respiration rates is not only crucial 1050
17
to interpreting microbial community dynamics from the ecological perspectives, but 1051
also fundamental to soil respiration modelling, prediction, and regulation through 1052
management (Bier et al., 2015; Graham et al., 2014; Graharni et al., 2016; Lindemann 1053
et al., 2016; Widder et al., 2016). As mentioned above, the rDNA and rRNA based 1054
technologies are the most widely-used and precise methods to analyze soil prokaryotic 1055
community compositions at present. Therefore, in the following sections, I synthesized 1056
the studies concerning the relationships between soil microbial respiration and 1057
prokaryotic lineage proportions, and only the studies based on rDNA and rRNA were 1058
included. 1059
1060
1.3.1. Methods of the review 1061
To identify the publications related to both soil microbial respiration and microbes, I 1062
searched the ISI Core Collection online database (http://apps.webofknowledge.com) 1063
for papers published since 2000. From the output, I analyzed the abstract and found 1064
there were 402 high-quality papers involving rDNA- or rRNA-based indices. 1065
Subsequently, I examined the full texts of the 402 papers according to the following 1066
criteria: (1) the studies was based on experiment; (2) the studies simultaneously 1067
measured soil microbial background respiration and indices based on rDNA or rRNA; 1068
and (3) the studies investigated soil prokaryotes. As a result, 69 papers were included 1069
in the following review. 1070
1071
For each of the 69 papers I recorded the following information: (1) soil locations and 1072
18
ecotypes; (2) experiment types, treatments, and duration; (3) molecular biological 1073
techniques; (4) response of soil microbial respiration and prokaryotic indices; and (5) 1074
relationships between soil microbial respiration and prokaryotes. 1075
1076
I also extracted the prokaryotic abundance, the proportion of prokaryotic phyla (based 1077
on high-throughput sequencing), and the corresponding microbial respiration data from 1078
these publications to further test the links between soil microbial respiration and rDNA- 1079
or rRNA-based indices. All of these data were normalized using the min-max methods 1080
(Xnor = (X - Xmin)/(Xmax - Xmin)) to eliminate the operational impacts in different studies. 1081
The correlations between soil microbial respiration and rDNA-based microbial indices 1082
were tested using R (R Development Core Team, 2016) with the Pearson methods. 1083
1084
1.3.2. An overview of the included studies 1085
In total, 191 soil sampling locations were identified from the 66 papers (3 papers 1086
without site information). I analyzed the 191 sampling sites, and found that the studies 1087
were unevenly distributed around the world. As shown in the map (Fig. 1.4), most 1088
studies were conducted in the USA and Europe, while other parts of the world remain 1089
rather poorly investigated. In addition, most of these sites were identified as forest 1090
(45.2%), whereas grassland (25.0%), and cropland (18.9%) were less investigated. 1091
19
1092 Figure 1.4. The geographical distributions of the studies that simultaneously measured soil microbial 1093
respiration and rDNA- or rRNA-based indices. 1094
1095
As for molecular biological techniques, these studies were usually conducted using 1096
DNA-based PCR-DGGE, high throughput sequencing, qPCR, T-RFLP, clone library, 1097
and PhyloChip (e.g., Imparato et al., 2016; Nazaries et al., 2015; Placella et al., 2012; 1098
Stark et al., 2015), while only 7 studies used the RNA-based methods (e.g., Barnard et 1099
al., 2015; Che et al., 2015; Pennanen et al., 2004). Compared to the studies published 1100
before 2010, the studies published after 2010 used more advanced techniques such as 1101
high throughput sequencing and qPCR, whereas the traditional techniques, such as 1102
PCR-DGGE, were much less employed (Fig. 1.5). 1103
20
1104
Figure 1.5. An overview of the studies simultaneously measuring prokaryotic indices and microbial 1105
respiration rates. HTS: high-throughput sequencing; DGGE: denaturing gradient gel electrophoresis; T-1106
RFLP: terminal-restriction fragment length polymorphism. 1107
1108
These studies covered a wide range of soil conditions and managements including soil 1109
moisture (Barnard et al., 2015; Evans et al., 2014), temperature (Peltoniemi et al., 2015), 1110
organic substrate (Che et al., 2015; Padmanabhan et al., 2003; Pennanen et al., 2004), 1111
fertilization (Andert and Mumme, 2015; Ramirez et al., 2012; Ros et al., 2006), 1112
contamination (Kaplan et al., 2014; Maila et al., 2005; Wakelin et al., 2010), land 1113
management patterns (Nogueira et al., 2006; Ramirez-Villanueva et al., 2015; Xue et 1114
al., 2006), and site conditions (Fierer et al., 2007; Tardy et al., 2015). As it is usually 1115
difficult to exclude the influence of plant root respiration on soil microbial respiration 1116
measurement in field studies, laboratory incubation is the most commonly used method 1117
(Barnard et al., 2015; Che et al., 2015), and in some studies, laboratory incubation was 1118
combined with field manipulations (Nazaries et al., 2015; Stark et al., 2015) or cross-1119
site surveys (Fierer et al., 2007; Ramirez et al., 2012) to obtain deeper insights (Fig. 1120
1.5). 1121
21
1122
1.3.3. Soil microbial respiration and prokaryotic abundance 1123
At the domain level, prokaryotes can be divided into bacteria and archaea. In this 1124
section, I summarized the studies simultaneously measured soil microbial respiration 1125
and prokaryotic abundance to obtain some insights into their general life strategies. Soil 1126
microbial respiration should be linked to all the microbes in soils, and thus it seems 1127
fundamentally inappropriate to separately link soil microbial respiration to the 1128
abundance of a single microbial domain. However, from another point of view, these 1129
correlations may reflect the survival capacities of bacteria and archaea in the soils with 1130
different microbial respiration rates, which contributes to understanding their life 1131
strategies. Among the 69 publications, thirteen studies measured bacterial abundance, 1132
but only two studies measured the archaeal abundance. 1133
1134
1135
Figure 1.6. Correlations between soil microbial respiration and bacterial (a) or archaeal (b) rDNA copies. 1136
All the data were extracted from the published literature and were normalized using the min-max methods. 1137
***: P < 0.001. 1138
1139
Generally, the relationships between soil microbial respiration and bacterial abundance 1140
22
are highly dependent on the types of treatments or environmental conditions. Some 1141
studies detected positive correlations between bacterial abundance and microbial 1142
respiration across different sites (Liu et al., 2012; Placella et al., 2012) or in response 1143
to plant-derived substrate additions (de Graaff et al., 2010). Conversely, my study found 1144
that 16S rDNA copies were negatively correlated with microbial respiration in the soils 1145
amended with different amounts of glutamate (Che et al., 2015). Moreover, in many 1146
other studies, bacterial 16S rDNA copies remained stable even though soil microbial 1147
respiration showed dramatic variations in response to rewetting (Barnard et al., 2015; 1148
Blazewicz et al., 2014; Placella et al., 2012), hydrochar amendments (Andert and 1149
Mumme, 2015), and temperature changes (Stark et al., 2015). Consistently, the meta-1150
analysis suggested no significant correlations between soil microbial respiration and 1151
bacterial 16S rDNA copies (Fig. 1.6a, P = 0.842). Collectively, these findings indicate 1152
that at the domain level, it almost impossible to assign bacteria into a copiotroph-1153
oligotroph category, and that bacterial abundance is not a proper index to assess soil 1154
microbial respiration. 1155
1156
The inconsistent correlations between the soil bacterial abundance and microbial 1157
respiration could be largely caused by the composition discrepancies of different soil 1158
bacterial communities. The directions of the bacterial abundance-respiration 1159
correlations may be determined by the proportions of the bacteria that prefer the soils 1160
supporting high or low microbial respiration (Bernard et al., 2007; Cleveland et al., 1161
2007; Fierer et al., 2007; Yuste et al., 2014). In addition, the variability in genome 16S 1162
23
rDNA copy numbers (Vetrovsky and Baldrian, 2013) of the soil bacteria should also 1163
contribute to the correlation inconsistencies. Another persuasive explanation is the 1164
variation in bacterial metabolic states. In most situation, the majority of soil microbes 1165
are dormant (Lennon and Jones, 2011). However, some dramatic environmental 1166
changes (e.g., rewetting) can significantly stimulate the metabolic activity of microbes. 1167
As the time is not enough for microbial DNA replication, the correlations between the 1168
soil bacterial abundance and microbial respiration are strongly dependent on the 1169
microbial metabolic states, and are thus also dramatically influenced by the 1170
measurement period. 1171
1172
Compared to bacteria, the archaeal abundances showed more consistent correlations 1173
with soil microbial respiration. In both studies, archaeal abundance showed negative 1174
correlations with soil microbial respiration rates (Andert and Mumme, 2015; Barnard 1175
et al., 2015). This is also supported by the meta-analysis (Fig. 1.6b, P < 0.001), and 1176
suggest that archaea prefer the soils with low microbial respiration capacity. However, 1177
as only few related studies have been conducted to date, this relationship still needs to 1178
be verified in future investigations. 1179
1180
1.3.4. Soil microbial respiration and prokaryotic community structures 1181
Among the 69 publications, there are 63 studies measuring bacterial community 1182
structure, but only eight studies for archaea. The overall relationships between soil 1183
microbial respiration and prokaryotic community structures can be tested using 1184
24
multivariable statistics such as the Mantel analysis (e.g., Che et al., 2015). The 1185
relationships between relative abundances of the specific prokaryotic lineages and 1186
respiration are usually determined through the Pearson or Spearman correlation tests 1187
(e.g., Fierer et al., 2007). 1188
1189
Table 1.4. Correlations between bacterial 16S rDNA-based lineages and soil microbial respiration. 1190
Positive Correlation Negative Correlation Treatments Sequencing Methods References
Bac Aci Cross sites Sanger (Fierer et al. 2007)
β-Proteobacteria-Pro Cross sites Sanger (Fierer et al. 2007)
Phycicoccus-Act Kutzneria-Act Moisture, Season Pyrosequencing (Yuste et al. 2014)
Arthrobacter-Act Actinomadura-Act Moisture, Season Pyrosequencing (Yuste et al. 2014)
Microbacterium-Act Corallococcus-Pro Moisture, Season Pyrosequencing (Yuste et al. 2014)
Spirosoma-Bac Roseateles-Pro Moisture, Season Pyrosequencing (Yuste et al. 2014)
Flavitalea-Bac Bryobacter-Aci Moisture, Season Pyrosequencing (Yuste et al. 2014)
Phenylobacterium-Pro Gp17-Aci Moisture, Season Pyrosequencing (Yuste et al. 2014)
Rhodanobacter-Pro Gp11-Aci Moisture, Season Pyrosequencing (Yuste et al. 2014)
Dokdonella-Pro Gp10-Aci Moisture, Season Pyrosequencing (Yuste et al. 2014)
Lysobacter-Pro Subdivision3_gis -Ver Moisture, Season Pyrosequencing (Yuste et al. 2014)
Act Aci Land management, Year Pyrosequencing (Orr et al. 2015)
Fir Land management, Year Pyrosequencing (Orr et al. 2015)
WS3 Land management, Year Pyrosequencing (Orr et al. 2015)
Pro: Proteobacteria; Act: Actinobacteria; Bac: Bacteroidetes, Aci: Acidobacteria; Ver: Verrucomicrobia; 1191
Fir: Firmicutes. 1192
1193
Similar to the bacterial abundance, the relationships between bacterial community 1194
structures and soil microbial respiration are not consistent across the studies. On the 1195
one hand, in most studies, the treatments eliciting microbial respiration changes could 1196
also lead to significant bacterial 16S rDNA-based community structures discrepancies 1197
(Bier et al., 2015). Some studies have even detected significant correlations between 1198
soil respiration and the bacterial community structures across different sites (Fierer et 1199
al., 2007), glutamate additions (Che et al., 2015), moisture contents (Yuste et al., 2014), 1200
land management practices (Orr et al., 2015), litter quality (Cleveland et al., 2014), fire 1201
25
burning (Goberna et al., 2012), and seasonal or annual variations (Orr et al., 2015; Yuste 1202
et al., 2014). On the other hand, in some short-term studies, bacterial 16S rDNA-based 1203
community structures seemed unrelated to soil microbial respiration variations 1204
(Barnard et al., 2015; Leff et al., 2012; Placella et al., 2012), which may be attributed 1205
to the retarded response of soil bacteria and extracellular DNA degradation (Levy-1206
Booth et al., 2007; Morrissey et al., 2015). 1207
1208
Most of the studies have only focused on the overall relationships between soil 1209
microbial respiration and the bacterial rDNA-based community structures, whereas the 1210
relationships between microbial respiration and specific bacterial lineages were only 1211
tested in three studies (Table 1.4). In a cross-site investigation, Fierer et al. (2007) found 1212
that the relative abundances of Bacteroidetes and β-Proteobacteria were positively 1213
correlated with soil microbial respiration, while the Acidobacteria proportion was 1214
negatively correlated with soil microbial respiration. In a study on farmland 1215
management, Orr et al. (2015) found that the relative abundances of Firmicutes, 1216
Acidobacteria, and WS3 were negatively correlated with soil microbial respiration rates, 1217
while Actinobacterial proportion was positively correlated with soil microbial 1218
respiration. My meta-analysis showed that the relative abundances of Proteobacteria (r 1219
= 0.202, P = 0.035), Actinobacteria (r = -0.252, P = 0.008), and Firmicutes (r = -0.275, 1220
P = 0.008) were significantly but weakly correlated with the soil microbial respiration 1221
(Fig. 1.7), while the relative abundances of other phyla showed no significant 1222
correlations with soil microbial respiration. The inconsistent correlations between the 1223
relative abundances of bacterial phyla or classes and soil microbial respiration would 1224
26
be a consequence of life-strategy diversifications within lineages. As reported by Yuste 1225
et al. (2014), even the genus under the same phylum showed functional diversifications 1226
(Table 1.4), which also suggested that the specific linkages between soil bacterial 1227
lineages and soil microbial respiration should be analyzed at finer taxonomic levels. 1228
1229
Figure 1.7. Correlations between soil microbial respiration and the relative abundance of Proteobacteria 1230
(a), Actinobacteria (b), or Firmicutes (c). All the data were extracted from the published literature using 1231
454 pyrosequencing and were normalized using the min-max methods. *: P < 0.05; **: P < 0.01. 1232
1233
As only few studies analyzed the archaeal community structures, it is really difficult to 1234
draw any conclusions. However, these studies at least suggested that the relationships 1235
between archaeal community structures and soil microbial respiration were not 1236
consistent across treatments and sites (Goberna et al., 2012; Li et al., 2015; Nazaries et 1237
al., 2015; Wakelin et al., 2010). In addition, unlike bacterial community structure, there 1238
are no studies testing the relationships between fungal or archaeal lineages and soil 1239
microbial respiration rates. 1240
1241
The rRNA-based indices, reflecting the community properties of active prokaryotes, 1242
should be more closely related to the microbial respiration. However, there were only 1243
27
seven studies simultaneously determining the soil microbial respiration and rRNA-1244
based indices. Although both rRNA copies and microbial respiration are widely used as 1245
proxies of total microbial activity, only three studies simultaneously measured them 1246
(Barnard et al., 2015; Che et al., 2015; Placella et al., 2012) and one on archaeal rRNA 1247
copies (Barnard et al., 2015). However, none of these studies observed significant 1248
correlations between total soil microbial rRNA copies and soil microbial respiration, 1249
which indicates that total soil microbial rRNA copies may be not good indicators of soil 1250
microbial respiration. In these studies, the decoupling between soil microbial 1251
respiration and rRNA copies can be caused by a number of reasons. First, the 1252
relationships between microbial activity and cellular rRNA concentration may vary 1253
with different species (Blazewicz et al., 2013), and thus the microbial community 1254
discrepancies may contribute to the decoupling (Che et al., 2015; Placella et al., 2012). 1255
Second, when the soil cannot provide all the nutrients that are required for the rRNA 1256
synthesis, microbes may tend to increase the rRNA turnover rates rather than 1257
concentration to meet the higher demand for protein production (Che et al., 2015). Third, 1258
technical issues, such as the variations of soil rRNA extracting efficiency, may also 1259
distort the relationships between soil microbial respiration and rRNA copies (Blazewicz 1260
et al., 2013). 1261
1262
The rRNA-based community profiling methods are widely employed to identify the 1263
active population of soil prokaryotes. However, the relationships between soil microbial 1264
respiration and the rRNA-based community structures are still far from well-understood. 1265
28
Currently, only seven studies simultaneously measured them, with seven on bacteria, 1266
but nil on archaea. As the relationships between soil microbial respiration and rRNA-1267
based community structures in three studies (i.e., Bernard et al., 2007; Castaldini et al., 1268
2005; Cesarz et al., 2013) were not clearly described, here, I only report the results of 1269
the other four studies. 1270
1271
These four studies showed that the bacterial 16S rRNA-based community structures 1272
were correlated well with the short-term response of soil microbial respiration to 1273
rewetting and labile substrate addition, and clearly outperformed other rDNA- or 1274
rRNA-based indices (Barnard et al., 2015; Che et al., 2015; Pennanen et al., 2004; 1275
Placella et al., 2012). These findings suggested that the bacterial 16S rRNA-based 1276
community structures are potentially robust indices to indicate soil microbial 1277
respiration, and to identify life-strategies of soil prokaryotes. Although four studies are 1278
still far from enough to assess the possibilities of linking soil microbial respiration and 1279
the rRNA-based community structures, these studies do provide potentially promising 1280
directions to establish the soil respiration-microbe links, and to classify prokaryotic life-1281
strategies. 1282
1283
To sum up, the relationships between soil microbial respiration and rDNA- or rRNA-1284
based indices are far from well-understood, and some significant knowledge gaps need 1285
to be addressed in future studies. First, although all of the 69 publications 1286
simultaneously measured both soil microbial respiration and rDNA- or rRNA-based 1287
29
indices, only 11 publications statistically tested the respiration-microbe relationships. 1288
Second, most studies have only focused on the overall relations between soil microbial 1289
respiration and rDNA- or rRNA-based community structures, whereas the relationships 1290
between specific lineages and soil microbial respiration remain largely unknown. Third, 1291
rRNA-based indices, especially the rRNA-based microbial community structures, seem 1292
promising to underpin the soil microbe-respiration relationships; however, it is still 1293
difficult to draw any conclusions from the few existing studies. Fourth, in these 69 1294
studies, the microbial rDNA- or rRNA-based indices were analyzed using various 1295
techniques. It has been well known that using different molecular biological techniques, 1296
and even same techniques with different primer sets or bioinformatics analysis methods 1297
(Bru et al., 2008; Engelbrektson et al., 2010), can dramatically influence the 1298
measurements of microbial indices, and thus the respiration-microbe relationships. 1299
Therefore, these 69 studies may only provide limited information on the respiration-1300
microbe relationships, and there is an urgent need to conduct more systematic studies 1301
using identical molecular biological techniques (e.g., MiSeq with same primer set). 1302
1303
1.4. The applications of prokaryotic life-strategy classifications in interpreting the 1304
ecosystem responses to environmental changes 1305
The applications of molecular biotechnologies, especially the high-throughput 1306
sequencing, have been rapidly increasing our knowledge of understanding the 1307
responses of soil microbial communities to environmental changes. However, most of 1308
the related publications only reported the overall changes in microbial abundances, 1309
30
community compositions, and diversities (e.g., Potard et al., 2017; Shi et al., 2017; Tin 1310
et al., 2018), while only a few studies explained these variations from an ecological 1311
perspective (e.g., Zhou et al., 2018). Among the studies attempting to reveal the 1312
ecological implications of microbial community dynamics, determining the changes in 1313
the proportions of copiotroph-oligotroph categories is the most widely-used method 1314
(e.g., Ling et al., 2017; Mickan et al., 2018; Zeng et al., 2017). 1315
1316
In the past decade, hundreds of studies have been published, applying the prokaryotic 1317
life-strategy classifications to understand the variations in microbial community 1318
compositions. These investigations covered a wide range of environmental changes and 1319
human disturbances, such as land degradation, fertilization, precipitation manipulation, 1320
grass mowing, and fencing, and in most of them, soil microbial community changes 1321
were analyzed through high-throughput sequencing (Buelow et al., 2016; Kotas et al., 1322
2017; Li et al., 2016; Schlatter et al., 2017; Zhou et al., 2018). Although high-1323
throughput sequencing can deeply reveal the microbial community composition at 1324
genus and even species levels, most of the studies only explained the changes in 1325
copiotroph-oligotroph proportions at phylum or class levels (Schlatter et al., 2017; 1326
Wang et al., 2017; Wang et al., 2016b; Xun et al., 2016). Specifically, the copiotrophic 1327
lineages mainly included Proteobacteria and Bacteroidetes, while Acidobacteria and 1328
Gemmatimonadetes were generally identified as oligotrophs (Wang et al., 2016a; Wang 1329
et al., 2016b; Zhou et al., 2018). However, controversies exist in the applications. For 1330
instance, Actinobacteria were classified as copiotrophs in some studies (Mickan et al., 1331
31
2018; Wang et al., 2016b), while they were classified into oligotrophic categories in 1332
some other studies (Zhou et al., 2018). In addition, as mentioned above, rRNA-based 1333
prokaryotic profiles are more connected with ecosystem functioning, and have been 1334
analyzed in hundreds of investigations (Blazewicz et al., 2013; Che et al., 2016a; Che 1335
et al., 2016b). However, none of these studies have explained the changes in rRNA-1336
based soil prokaryotic community profiles from the copiotroph-oligotroph perspective. 1337
1338
1.5. Knowledge gaps in the soil prokaryotic lineage life-strategy classifications and 1339
applications 1340
Based on the literature reviews, I found that the life-strategies of prokaryotic lineages 1341
are still far from well-understood, and their applications are limited. The main 1342
knowledge gaps in the soil prokaryotic life-strategy classifications and applications are 1343
as follows. 1344
1345
(1) Most life-strategy classifications were conducted at the (sub)phylum level, while 1346
the life strategies of prokaryotic lineages at a finer level (e.g., family and genus level) 1347
remain largely unknown. 1348
1349
(2) Most of the life-strategy classifications were conducted using 16S rDNA-based 1350
methods. The 16S rRNA-based indices are more connected with microbial respiration, 1351
and are more sensitive to organic carbon amendments. However, no effort has been 1352
made to classify the prokaryotic life strategies using 16S rRNA-based methods. 1353
32
1354
(3) Almost all the investigations concerning the prokaryotic life-strategy classifications 1355
were based on the soil collected from a single study site. However, the classifications 1356
based on cross-site organic carbon amendments are still scant. 1357
1358
(4) The applications of prokaryotic life-strategy classifications in interpreting changes 1359
in microbial communities were mainly at the (sub)phylum level. Limited attention has 1360
been paid to the explaining of rRNA-based microbial community changes. 1361
1362
1.6. Aims and the framework of my Ph.D. project 1363
The overall aims of my Ph.D. project were to identify the life strategies of grassland 1364
soil prokaryotic lineages using 16S rDNA and rRNA based methods, and to apply these 1365
findings to improve our understanding of alpine meadow responses to environmental 1366
changes. To achieve these aims, in Chapter 2, I compared the sensitivity of prokaryotic 1367
16S rDNA and rRNA based indices in response to gradient glutamate amendments, and 1368
examined their relationships with microbial respiration rates. In Chapter 3, I combined 1369
large-scale soil sampling, organic carbon amendments under laboratory incubations, 1370
microbial respiration measurements, and MiSeq sequencing of 16S rDNA and rRNA to 1371
systematically classify the prokaryotic lineages into copiotroph-oligotroph categories. 1372
Then, in Chapters 4 and 5, I applied the prokaryotic copiotroph-oligotroph 1373
classification to understand the responses of Tibetan alpine meadows to experimental 1374
warming and grazing, as well as to the amendments of litter and phosphorus. Finally, 1375
33
in Chapter 6, I summarized the findings in the four experimental chapters and proposed 1376
directions for further investigations. The aims of the experimental chapters were 1377
detailed as follows. The conceptual model and experimental flowchart of the thesis 1378
were shown in Fig. 1.8. 1379
1380
Chapter 2. Relationships between Soil Microbial Respiration and rDNA- or rRNA-1381
Based Indices under a Glutamate Amendment Gradient 1382
1383
Aim: This chapter aimed to assess whether the rRNA-based microbial indices are more 1384
sensitive to organic matter amendments, and show stronger correlation with microbial 1385
respiration rates than the rDNA-based indices. 1386
1387
Chapter 3. A Copiotroph-oligotroph Classification of Grassland Soil Prokaryotic 1388
Lineages 1389
1390
Aim: This chapter aimed to classify soil prokaryotic lineages into copiotrophic and 1391
oligotrophic categories, using methods based on 16S rDNA and rRNA, with soils 1392
collected from 32 grasslands on the Inner Mongolian and the Tibetan plateaus. 1393
1394
Chapter 4. The Application of Microbial Life-strategy Classification in Explaining 1395
the Soil Microbial Responses to Litter and Phosphorus Amendments 1396
1397
34
Aim: This chapter aimed to investigate the responses of total and active microbes to 1398
the amendments of phosphorus and litter to degraded alpine meadow soils, and also 1399
tried to apply the microbial life-strategy classifications to explain the responses. 1400
1401
Chapter 5. The Application of Microbial Life-strategy Classification in Explaining 1402
the Responses of Soil Microbes to Warming and Grazing 1403
1404
Aim: This chapter aimed to investigate the main and interactive effects of warming 1405
and grazing on total and active soil prokaryotes in a Kobresia alpine meadow on the 1406
Tibetan Plateau, and to understand the effects on soil prokaryotes from the perspective 1407
of life-strategy classification. 1408
1409
35
1410
1411
Figure 1.8. The conceptual model and experimental flowchart of my thesis. SMR: soil microbial respiration. 1412
36
1.7. References 1413
Andert, J., Mumme, J., 2015. Impact of pyrolysis and hydrothermal biochar on gas-1414
emitting activity of soil microorganisms and bacterial and archaeal community 1415
composition. Applied Soil Ecology 96, 225–239. 1416
Andrews, J., 1984. Relevance of r-and K-theory to the ecology of plant pathogens. In: 1417
M. Klug, C. Reddy (Eds.), Current Perspectives in Microbial Ecology. 1418
American Society of Microbiology, Washington, D.C., USA, pp. 1–7. 1419
Andrews, J.H., Harris, R.F., 1986. r- and K-selection and microbial ecology. In: K.C. 1420
Marshall (Eds.), Advances in Microbial Ecology. Springer US, Boston, MA, pp. 1421
99–147. 1422
Barnard, R.L., Osborne, C.A., Firestone, M.K., 2015. Changing precipitation pattern 1423
alters soil microbial community response to wet-up under a Mediterranean-type 1424
climate. ISME Journal. 9(4), 946–957. 1425
Bernard, L., Mougel, C., Maron, P.A., Nowak, V., Leveque, J., Henault, C., Haichar, 1426
F.E.Z., Berge, O., Marol, C., Balesdent, J., Gibiat, F., Lemanceau, P., Ranjard, 1427
L., 2007. Dynamics and identification of soil microbial populations actively 1428
assimilating carbon from 13C-labelled wheat residue as estimated by DNA- and 1429
RNA-SIP techniques. Environmental Microbiology 9(3), 752–764. 1430
Bier, R.L., Bernhardt, E.S., Boot, C.M., Graham, E.B., Hall, E.K., Lennon, J.T., 1431
Nemergut, D.R., Osborne, B.B., Ruiz-González, C., Schimel, J.P., 2015. 1432
Linking microbial community structure and microbial processes: an empirical 1433
and conceptual overview. FEMS Microbiology Ecology 91(10), fiv113. 1434
37
Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: Critical review 1435
of estimation criteria and approaches. Soil Biology and Biochemistry 67, 192–1436
211. 1437
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as 1438
an indicator of microbial activity in environmental communities: limitations and 1439
uses. ISME Journal 7(11), 2061–2068. 1440
Blazewicz, S.J., Schwartz, E., Firestone, M.K., 2014. Growth and death of bacteria and 1441
fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. 1442
Ecology 95(5), 1162–1172. 1443
Bond-Lamberty, B., Wang, C.K., Gower, S.T., 2004. A global relationship between the 1444
heterotrophic and autotrophic components of soil respiration? Global Change 1445
Biology 10(10), 1756–1766. 1446
Bru, D., Martin-Laurent, F., Philippot, L., 2008. Quantification of the detrimental effect 1447
of a single primer-template mismatch by real-time PCR using the 16S rRNA 1448
gene as an example. Applied and Environmental Microbiology 74(5), 1660–1449
1663. 1450
Buelow, H.N., Winter, A.S., Van Horn, D.J., Barrett, J.E., Gooseff, M.N., Schwartz, E., 1451
Takacs-Vesbach, C.D., 2016. Microbial community responses to increased 1452
water and organic matter in the arid soils of the McMurdo Dry Valleys, 1453
Antarctica. Frontiers in Microbiology 7, 1040. 1454
Castaldini, M., Turrini, A., Sbrana, C., Benedetti, A., Marchionni, M., Mocali, S., 1455
Fabiani, A., Landi, S., Santomassimo, F., Pietrangeli, B., Nuti, M.P., Miclaus, 1456
38
N., Giovannetti, M., 2005. Impact of Bt corn on rhizospheric and on beneficial 1457
mycorrhizal symbiosis and soil eubacterial communities iosis in experimental 1458
microcosms. Applied and Environmental Microbiology 71(11), 6719–6729. 1459
Cesarz, S., Fender, A.C., Beyer, F., Valtanen, K., Pfeiffer, B., Gansert, D., Hertel, D., 1460
Polle, A., Daniel, R., Leuschner, C., Scheu, S., 2013. Roots from beech (Fagus 1461
sylvatica L.) and ash (Fraxinus excelsior L.) differentially affect soil 1462
microorganisms and carbon dynamics. Soil Biolology and Biochemistry 61, 23–1463
32. 1464
Che, R.X., Deng, Y.C., Wang, F., Wang, W.J., Xu, Z.H., Wang, Y.F., Cui, X.Y., 2015. 1465
16S rRNA-based bacterial community structure is a sensitive indicator of soil 1466
respiration activity. Journal of Soils and Sediments 15(9), 1987–1990. 1467
Che, R.X., Wang, F., Wang, Y.F., Deng, Y.C., Zhang, J., Ma, S., Cui, X.Y., 2016a. A 1468
review on the methods for measuring total microbial activity in soils. Acta 1469
Ecologica Sinica 36(08), 2103–2112. 1470
Che, R.X., Wang, W.J., Zhang, J., Nguyen, T.T.N., Tao, J., Wang, F., Wang, Y.F., Xu, 1471
Z.H., Cui, X.Y., 2016b. Assessing soil microbial respiration capacity using 1472
rDNA- or rRNA-based indices: a review. Journal of Soils and Sediments 16(12), 1473
2698–2708. 1474
Chen, Y.P., Chen, G.S., Robinson, D., Yang, Z.J., Guo, J.F., Xie, J.S., Fu, S.L., Zhou, 1475
L.X., Yang, Y.S., 2016. Large amounts of easily decomposable carbon stored in 1476
subtropical forest subsoil are associated with r-strategy-dominated soil 1477
microbes. Soil Biology and Biochemistry 95, 233–242. 1478
39
Cleveland, C.C., Nemergut, D.R., Schmidt, S.K., Townsend, A.R., 2007. Increases in 1479
soil respiration following labile carbon additions linked to rapid shifts in soil 1480
microbial community composition. Biogeochemistry 82(3), 229–240. 1481
Cleveland, C.C., Reed, S.C., Keller, A.B., Nemergut, D.R., O'Neill, S.P., Ostertag, R., 1482
Vitousek, P.M., 2014. Litter quality versus soil microbial community controls 1483
over decomposition: a quantitative analysis. Oecologia 174(1), 283–294. 1484
Cookson, W.R., Abaye, D.A., Marschner, P., Murphy, D.V., Stockdale, E.A., Goulding, 1485
K.W.T., 2005. The contribution of soil organic matter fractions to carbon and 1486
nitrogen mineralization and microbial community size and structure. Soil 1487
Biology and Biochemistry 37(9), 1726–1737. 1488
de Graaff, M.A., Classen, A.T., Castro, H.F., Schadt, C.W., 2010. Labile soil carbon 1489
inputs mediate the soil microbial community composition and plant residue 1490
decomposition rates. New Phytologist 188(4), 1055–1064. 1491
Eilers, K.G., Lauber, C.L., Knight, R., Fierer, N., 2010. Shifts in bacterial community 1492
structure associated with inputs of low molecular weight carbon compounds to 1493
soil. Soil Biology and Biochemistry 42(6), 896–903. 1494
Engelbrektson, A., Kunin, V., Wrighton, K.C., Zvenigorodsky, N., Chen, F., Ochman, 1495
H., Hugenholtz, P., 2010. Experimental factors affecting PCR-based estimates 1496
of microbial species richness and evenness. ISME Journal 4(5), 642–647. 1497
Evans, S.E., Wallenstein, M.D., Burke, I.C., 2014. Is bacterial moisture niche a good 1498
predictor of shifts in community composition under long-term drought? 1499
Ecology 95(1), 110–122. 1500
40
Fang, M., Motavalli, P.P., Kremer, R.J., Nelson, K.A., 2007. Assessing changes in soil 1501
microbial communities and carbon mineralization in Bt and non-Bt corn 1502
residue-amended soils. Applied Soil Ecology 37(1-2), 150–160. 1503
Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward an ecological classification of 1504
soil bacteria. Ecology 88(6), 1354–1364. 1505
Goberna, M., Garcia, C., Insam, H., Hernandez, M., Verdu, M., 2012. Burning fire-1506
prone mediterranean shrublands: Immediate changes in soil microbial 1507
community structure and ecosystem functions. Microbial Ecology 64(1), 242–1508
255. 1509
Gottschal, J.C., 1985. Some reflections on microbial competitiveness among 1510
heterotrophic bacteria. Antonie van Leeuwenhoek 51(5-6), 473–494. 1511
Graham, E.B., Wieder, W.R., Leff, J.W., Weintraub, S.R., Townsend, A.R., Cleveland, 1512
C.C., Philippot, L., Nemergut, D.R., 2014. Do we need to understand microbial 1513
communities to predict ecosystem function? A comparison of statistical models 1514
of nitrogen cycling processes. Soil Biology and Biochemistry 68, 279–282. 1515
Graharni, E.B., Knelman, J.E., Schindlbacher, A., Siciliano, S., Breulmann, M., 1516
Yannarell, A., Bemans, J.M., Abell, G., Philippot, L., Prosser, J., Foulquier, A., 1517
Yuste, J.C., Glanville, H.C., Jones, D.L., Angel, F., Salminen, J., Newton, R.J., 1518
Buergmann, H., Ingram, L.J., Hamer, U., Siljanen, H.M.P., Peltoniemi, K., 1519
Potthast, K., Baneras, L., Hartmann, M., Banerjee, S., Yu, R.-Q., Nogaro, G., 1520
Richter, A., Koranda, M., Castle, S.C., Goberna, M., Song, B., Chatterjee, A., 1521
Nunes, O.C., Lopes, A.R., Cao, Y., Kaisermann, A., Hallin, S., Strickland, M.S., 1522
41
Garcia-Pausas, J., Barba, J., Kang, H., Isobe, K., Papaspyrou, S., Pastorelli, R., 1523
Lagomarsino, A., Lindstrom, E.S., Basiliko, N., Nemergut, D.R., 2016. 1524
Microbes as engines of ecosystem function: When does community structure 1525
enhance predictions of ecosystem processes? Frontiers in Microbiology 7, 214. 1526
Guo, J., Liu, W., Zhu, C., Luo, G., Kong, Y., Ling, N., Wang, M., Dai, J., Shen, Q., Guo, 1527
S., 2017. Bacterial rather than fungal community composition is associated with 1528
microbial activities and nutrient-use efficiencies in a paddy soil with short-term 1529
organic amendments. Plant and Soil, 1–15. 1530
Haggerty, J.M., Dinsdale, E.A., 2017. Distinct biogeographical patterns of marine 1531
bacterial taxonomy and functional genes. Global Ecology and Biogeography 1532
26(2), 177–190. 1533
Hirsch, P., Bernhard, M., Cohen, S., Ensign, J., Jannasch, H., Koch, A., Marshall, K., 1534
Poindexter, J., Rittenberg, S., Smith, D., 1979. Life under conditions of low 1535
nutrient concentrations. In: M. Shilo (Eds.), Strategies of Microbial Life in 1536
Extreme Environments. Weinheim, Germany, Verlag Chemie, pp. 357–372. 1537
Ho, A., Di Lonardo, D.P., Bodelier, P.L.E., 2017. Revisiting life strategy concepts in 1538
environmental microbial ecology. FEMS Microbiology Ecology 93(3), fix006. 1539
Hossain, M.Z., Sugiyama, S., 2011. Influences of plant litter diversity on decomposition, 1540
nutrient mineralization and soil microbial community structure. Grassland 1541
Science 57(2), 72–80. 1542
Huang, W.R., Bai, Z.H., Hoefel, D., Hu, Q., Lv, X., Zhuang, G.Q., Xu, S.J., Qi, H.Y., 1543
Zhang, H.X., 2012. Effects of cotton straw amendment on soil fertility and 1544
42
microbial communities. Frontiers of Environmental Science and Engineering 1545
6(3), 336–349. 1546
Hugenholtz, P., Goebel, B.M., Pace, N.R., 1998. Impact of culture-independent studies 1547
on the emerging phylogenetic view of bacterial diversity. Journal of 1548
Bacteriology 180(18), 4765–4774. 1549
Hungate, B.A., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 1550
Koch, B.J., Liu, C.M., McHugh, T.A., Marks, J.C., Morrissey, E.M., Price, L.B., 1551
2015. Quantitative microbial ecology through stable isotope probing. Applied 1552
and Environmental Microbiology 81(21), 7570–7581. 1553
Imparato, V., Santos, S.S., Johansen, A., Geisen, S., Winding, A., 2016. Stimulation of 1554
bacteria and protists in rhizosphere of glyphosate-treated barley. Applied Soil 1555
Ecology 98, 47–55. 1556
Kaplan, H., Ratering, S., Hanauer, T., Felix-Henningsen, P., Schnell, S., 2014. Impact 1557
of trace metal contamination and in situ remediation on microbial diversity and 1558
respiratory activity of heavily polluted Kastanozems. Biology and Fertility of 1559
Soils 50(5), 735–744. 1560
Kits, K.D., Sedlacek, C.J., Lebedeva, E.V., Han, P., Bulaev, A., Pjevac, P., Daebeler, A., 1561
Romano, S., Albertsen, M., Stein, L.Y., 2017. Kinetic analysis of a complete 1562
nitrifier reveals an oligotrophic lifestyle. Nature 549(7671), 269. 1563
Koch, A.L., 2001. Oligotrophs versus copiotrophs. Bioessays 23(7), 657–661. 1564
Kotas, P., Choma, M., Santruckova, H., Leps, J., Triska, J., Kastovska, E., 2017. 1565
Linking above- and belowground responses to 16 Years of fertilization, mowing, 1566
43
and removal of the dominant species in a temperate grassland. Ecosystems 20(2), 1567
354–367. 1568
Lauro, F.M., McDougald, D., Thomas, T., Williams, T.J., Egan, S., Rice, S., DeMaere, 1569
M.Z., Ting, L., Ertan, H., Johnson, J., 2009. The genomic basis of trophic 1570
strategy in marine bacteria. Proceedings of the National Academy of Sciences 1571
of the United States of America 106(37), 15527–15533. 1572
Leff, J.W., Nemergut, D.R., Grandy, A.S., O'Neill, S.P., Wickings, K., Townsend, A.R., 1573
Cleveland, C.C., 2012. The effects of soil bacterial community structure on 1574
decomposition in a tropical rain forest. Ecosystems 15(2), 284–298. 1575
Lennon, J.T., Jones, S.E., 2011. Microbial seed banks: the ecological and evolutionary 1576
implications of dormancy. Nature Reviews Microbiology 9(2), 119–130. 1577
Levy-Booth, D.J., Campbell, R.G., Gulden, R.H., Hart, M.M., Powell, J.R., Klironomos, 1578
J.N., Pauls, K.P., Swanton, C.J., Trevors, J.T., Dunfield, K.E., 2007. Cycling of 1579
extracellular DNA in the soil environment. Soil Biology and Biochemistry 1580
39(12), 2977–2991. 1581
Li, H., Xu, Z.W., Yang, S., Li, X.B., Top, E.M., Wang, R.Z., Zhang, Y.G., Cai, J.P., Yao, 1582
F., Han, X.G., Jiang, Y., 2016. Responses of soil bacterial communities to 1583
nitrogen deposition and precipitation increment are closely linked with 1584
aboveground community variation. Microbial Ecology 71(4), 974–989. 1585
Li, L.N., Qu, Z., Wang, B.L., Qu, D., 2017. Dynamics of the abundance and structure 1586
of metabolically active Clostridium community in response to glucose additions 1587
in flooded paddy soils: closely correlated with hydrogen production and Fe(III) 1588
44
reduction. Journal of Soils and Sediments 17(6), 1727–1740. 1589
Li, X.F., You, F., Bond, P.L., Huang, L.B., 2015. Establishing microbial diversity and 1590
functions in weathered and neutral Cu-Pb-Zn tailings with native soil addition. 1591
Geoderma 247, 108–116. 1592
Lima, A.C.R., Brussaard, L., Totola, M.R., Hoogmoed, W.B., de Goede, R.G.M., 2013. 1593
A functional evaluation of three indicator sets for assessing soil quality. Applied 1594
Soil Ecology 64, 194–200. 1595
Lindemann, S.R., Bernstein, H.C., Song, H.-S., Fredrickson, J.K., Fields, M.W., Shou, 1596
W., Johnson, D.R., Beliaev, A.S., 2016. Engineering microbial consortia for 1597
controllable outputs. ISME Journal 10(9), 2077. 1598
Ling, N., Chen, D.M., Guo, H., Wei, J.X., Bai, Y.F., Shen, Q.R., Hu, S.J., 2017. 1599
Differential responses of soil bacterial communities to long-term N and P inputs 1600
in a semi-arid steppe. Geoderma 292, 25–33. 1601
Liu, Y.Z., Zhou, T., Crowley, D., Li, L.Q., Liu, D.W., Zheng, J.W., Yu, X.Y., Pan, G.X., 1602
Hussain, Q., Zhang, X.H., Zheng, J.F., 2012. Decline in topsoil microbial 1603
quotient, fungal abundance and C utilization efficiency of rice paddies under 1604
heavy metal pollution across south China. PLOS One 7(6), e38858. 1605
Luo, W., D'Angelo, E.M., Coyne, M.S., 2008. Organic carbon effects on aerobic 1606
polychlorinated biphenyl removal and bacterial community composition in soils 1607
and sediments. Chemosphere 70(3), 364–373. 1608
MacArthur, R.H., Wilson, E.O., 1967. Theory of Island Biogeography. Princeton 1609
University Press, Princeton, New Jersey, USA. 1610
45
Maila, M.P., Randima, P., Surridge, K., Dronen, K., Cloete, T.E., 2005. Evaluation of 1611
microbial diversity of different soil layers at a contaminated diesel site. 1612
International Biodeterioration and Biodegradation 55(1), 39–44. 1613
Meyer, O., 1994. Functional groups of microorganisms. In: E. Schulze, H. Mooney 1614
(Eds.), Biodiversity and Ecosystem Function. Springer-Verlag, New York, USA, 1615
pp. 67–96. 1616
Mickan, B.S., Abbott, L.K., Fan, J.W., Hart, M.M., Siddique, K.H.M., Solaiman, Z.M., 1617
Jenkins, S.N., 2018. Application of compost and clay under water-stressed 1618
conditions influences functional diversity of rhizosphere bacteria. Biology and 1619
Fertility of Soils 54(1), 55–70. 1620
Morrissey, E.M., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 1621
Koch, B.J., Liu, C.M., Hayer, M., McHugh, T.A., Marks, J.C., Price, L.B., 1622
Hungate, B.A., 2016. Phylogenetic organization of bacterial activity. ISME 1623
Journal 10(9), 2336–2340. 1624
Morrissey, E.M., McHugh, T.A., Preteska, L., Hayer, M., Dijkstra, P., Hungate, B.A., 1625
Schwartz, E., 2015. Dynamics of extracellular DNA decomposition and 1626
bacterial community composition in soil. Soil Biology and Biochemistry 86, 1627
42–49. 1628
Nazaries, L., Tottey, W., Robinson, L., Khachane, A., Abu Al-Soud, W., Sorensen, S., 1629
Singh, B.K., 2015. Shifts in the microbial community structure explain the 1630
response of soil respiration to land-use change but not to climate warming. Soil 1631
Biology and Biochemistry 89, 123–134. 1632
46
Nemergut, D.R., Cleveland, C.C., Wieder, W.R., Washenberger, C.L., Townsend, A.R., 1633
2010. Plot-scale manipulations of organic matter inputs to soils correlate with 1634
shifts in microbial community composition in a lowland tropical rain forest. Soil 1635
Biology and Biochemistry 42(12), 2153–2160. 1636
Nogueira, M.A., Albino, U.B., Brandao-Junior, O., Braun, G., Cruz, M.F., Dias, B.A., 1637
Duarte, R.T.D., Gioppo, N.M.R., Menna, P., Orlandi, J.M., Raimam, M.P., 1638
Rampazo, L.G.L., Santos, M.A., Silva, M.E.Z., Vieira, F.P., Torezan, J.M.D., 1639
Hungria, M., Andrade, G., 2006. Promising indicators for assessment of 1640
agroecosystems alteration among natural, reforested and agricultural land use 1641
in southern Brazil. Agriculture Ecosystems and Environment 115(1–4), 237–1642
247. 1643
Orr, C.H., Stewart, C.J., Leifert, C., Cooper, J.M., Cummings, S.P., 2015. Effect of crop 1644
management and sample year on abundance of soil bacterial communities in 1645
organic and conventional cropping systems. Journal of Applied Microbiology 1646
119(1), 208–214. 1647
Padmanabhan, P., Padmanabhan, S., DeRito, C., Gray, A., Gannon, D., Snape, J.R., Tsai, 1648
C.S., Park, W., Jeon, C., Madsen, E.L., 2003. Respiration of 13C-labeled 1649
substrates added to soil in the field and subsequent 16S rRNA gene analysis of 1650
13C-labeled soil DNA. Applied and Environmental Microbiology 69(3), 1614–1651
1622. 1652
Paz-Ferreiro, J., Fu, S., 2016. Biological Indices for Soil Quality Evaluation: 1653
Perspectives and Limitations. Land Degradation and Development 27(1), 14–1654
47
25. 1655
Peltoniemi, K., Laiho, R., Juottonen, H., Kiikkila, O., Makiranta, P., Minkkinen, K., 1656
Pennanen, T., Penttila, T., Sarjala, T., Tuittila, E.S., Tuomivirta, T., Fritze, H., 1657
2015. Microbial ecology in a future climate: effects of temperature and moisture 1658
on microbial communities of two boreal fens. FEMS Microbiology Ecology 1659
91(7), 14. 1660
Pennanen, T., Caul, S., Daniell, T.J., Griffiths, B.S., Ritz, K., Wheatley, R.E., 2004. 1661
Community-level responses of metabolically-active soil microorganisms to the 1662
quantity and quality of substrate inputs. Soil Biology and Biochemistry 36(5), 1663
841–848. 1664
Pepe-Ranney, C., Campbell, A.N., Koechli, C.N., Berthrong, S., Buckley, D.H., 2016. 1665
Unearthing the ecology of soil microorganisms using a high resolution DNA-1666
SIP approach to explore cellulose and xylose metabolism in soil. Frontiers in 1667
Microbiology 7, 703. 1668
Pianka, E.R., 1970. On r-and K-selection. The American Naturalist 104(940), 592–597. 1669
Placella, S.A., Brodie, E.L., Firestone, M.K., 2012. Rainfall-induced carbon dioxide 1670
pulses result from sequential resuscitation of phylogenetically clustered 1671
microbial groups. Proceedings of the National Academy of Sciences of the 1672
United States of America 109(27), 10931–10936. 1673
Potard, K., Monard, C., Le Garrec, J.L., Caudal, J.P., Le Bris, N., Binet, F., 2017. 1674
Organic amendment practices as possible drivers of biogenic volatile organic 1675
compounds emitted by soils in agrosystems. Agriculture Ecosystems and 1676
48
Environment 250, 25–36. 1677
R Development Core Team (2016) R: a language and environment for statistical 1678
computing. R Foundation for Statistical Computing, Vienna, Austria. 1679
http://www.R-project.org/. 1680
Ramirez-Villanueva, D.A., Bello-Lopez, J.M., Navarro-Noya, Y.E., Luna-Guido, M., 1681
Verhulst, N., Govaerts, B., Dendooven, L., 2015. Bacterial community structure 1682
in maize residue amended soil with contrasting management practices. Applied 1683
Soil Ecology 90, 49–59. 1684
Ramirez, K.S., Craine, J.M., Fierer, N., 2012. Consistent effects of nitrogen 1685
amendments on soil microbial communities and processes across biomes. 1686
Global Change Biology 18(6), 1918–1927. 1687
Reznick, D., Bryant, M.J., Bashey, F., 2002. r- and K-selection revisited: the role of 1688
population regulation in life-history evolution. Ecology 83(6), 1509–1520. 1689
Ros, M., Pascual, J.A., Garcia, C., Hernandez, M.T., Insam, H., 2006. Hydrolase 1690
activities, microbial biomass and bacterial community in a soil after long-term 1691
amendment with different composts. Soil Biology and Biochemistry 38(12), 1692
3443–3452. 1693
Schimel, J., Schaeffer, S.M., 2012. Microbial control over carbon cycling in soil. 1694
Frontiers in Microbiology 3, 348. 1695
Schlatter, D.C., Yin, C.T., Hulbert, S., Burke, I., Paulitz, T., 2017. Impacts of repeated 1696
glyphosate use on wheat-associated bacteria are small and depend on glyphosate 1697
use history. Applied and Environmental Microbiology 83(22), e01354-17. 1698
49
Schlesinger, W.H., Andrews, J.A., 2000. Soil respiration and the global carbon cycle. 1699
Biogeochemistry 48(1), 7–20. 1700
Schutter, M., Dick, R., 2001. Shifts in substrate utilization potential and structure of 1701
soil microbial communities in response to carbon substrates. Soil Biology and 1702
Biochemistry 33(11), 1481–1491. 1703
Shi, P.L., Zhang, Y.X., Hu, Z.Q., Ma, K., Wang, H., Chai, T.Y., 2017. The response of 1704
soil bacterial communities to mining subsidence in the west China aeolian sand 1705
area. Applied Soil Ecology 121, 1–10. 1706
Siles, J.A., Rachid, C., Sampedro, I., Garcia-Romera, I., Tiedje, J.M., 2014. Microbial 1707
diversity of a mediterranean soil and its changes after biotransformed dry olive 1708
residue amendment. PLOS One 9(7), e103035. 1709
Stark, S., Mannisto, M.K., Ganzert, L., Tiirola, M., Haggblom, M.M., 2015. Grazing 1710
intensity in subarctic tundra affects the temperature adaptation of soil microbial 1711
communities. Soil Biology and Biochemistry 84, 147–157. 1712
Stocker, T.F., 2014. Climate change 2013: the physical science basis: Working Group I 1713
contribution to the Fifth assessment report of the Intergovernmental Panel on 1714
Climate Change. Cambridge University Press. 1715
Su, P., Lou, J., Brookes, P.C., Luo, Y., He, Y., Xu, J.M., 2017. Taxon-specific responses 1716
of soil microbial communities to different soil priming effects induced by 1717
addition of plant residues and their biochars. Journal of Soils and Sediments 1718
17(3), 674–684. 1719
Sun, H., Wang, Q.X., Liu, N., Li, L., Zhang, C.G., Liu, Z.B., Zhang, Y.Y., 2017. Effects 1720
50
of different leaf litters on the physicochemical properties and bacterial 1721
communities in Panax ginseng-growing soil. Applied Soil Ecology 111, 17–24. 1722
Tardy, V., Spor, A., Mathieu, O., Leveque, J., Terrat, S., Plassart, P., Regnier, T., 1723
Bardgett, R.D., van der Putten, W.H., Roggero, P.P., Seddaiu, G., Bagella, S., 1724
Lemanceau, P., Ranjard, L., Maron, P.A., 2015. Shifts in microbial diversity 1725
through land use intensity as drivers of carbon mineralization in soil. Soil 1726
Biology and Biochemistry 90, 204–213. 1727
Tin, H.S., Palaniveloo, K., Anilik, J., Vickneswaran, M., Tashiro, Y., Vairappan, C.S., 1728
Sakai, K., 2018. Impact of land-use change on vertical soil bacterial 1729
communities in Sabah. Microbial Ecology 75(2), 459–467. 1730
Vetrovsky, T., Baldrian, P., 2013. The variability of the 16S rRNA gene in bacterial 1731
genomes and its consequences for bacterial community analyses. PLOS One 1732
8(2), e57923. 1733
Wakelin, S.A., Chu, G.X., Broos, K., Clarke, K.R., Liang, Y.C., McLaughlin, M.J., 1734
2010. Structural and functional response of soil microbiota to addition of plant 1735
substrate are moderated by soil Cu levels. Biology and Fertility of Soils 46(4), 1736
333–342. 1737
Wang, J.C., Song, Y., Ma, T.F., Raza, W., Li, J., Howland, J.G., Huang, Q.W., Shen, 1738
Q.R., 2017. Impacts of inorganic and organic fertilization treatments on 1739
bacterial and fungal communities in a paddy soil. Applied Soil Ecology 112, 1740
42–50. 1741
Wang, J.C., Xue, C., Song, Y., Wang, L., Huang, Q.W., Shen, Q.R., 2016a. Wheat and 1742
51
rice growth stages and fertilization regimes alter soil bacterial community 1743
structure, but not diversity. Frontiers in Microbiology 7, 1207. 1744
Wang, Y., Hao, Y., Cui, X.Y., Zhao, H., Xu, C., Zhou, X., Xu, Z., 2014. Responses of 1745
soil respiration and its components to drought stress. Journal of Soils and 1746
Sediments 14(1), 99–109. 1747
Wang, Y., Ji, H.F., Gao, C.Q., 2016b. Differential responses of soil bacterial taxa to 1748
long-term P, N, and organic manure application. Journal of Soils and Sediments 1749
16(3), 1046–1058. 1750
Widder, S., Allen, R.J., Pfeiffer, T., Curtis, T.P., Wiuf, C., Sloan, W.T., Cordero, O.X., 1751
Brown, S.P., Momeni, B., Shou, W., Kettle, H., Flint, H.J., Haas, A.F., Laroche, 1752
B., Kreft, J.-U., Rainey, P.B., Freilich, S., Schuster, S., Milferstedt, K., van der 1753
Meer, J.R., Grokopf, T., Huisman, J., Free, A., Picioreanu, C., Quince, C., 1754
Klapper, I., Labarthe, S., Smets, B.F., Wang, H., Isaac Newton Institute, F., 1755
Soyer, O.S., 2016. Challenges in microbial ecology: building predictive 1756
understanding of community function and dynamics. ISME Journal 10(11), 1757
2557. 1758
Winogradsky, S., 1924. Sur la microflora autochtone de la terre arable, 178. Comptes 1759
Rendus Hebdomadaire des Se´ ances de l’Academie des Sciences, Paris. 1760
Woese, C.R., Fox, G.E., 1977. Phylogenetic structure of the prokaryotic domain: the 1761
primary kingdoms. Proceedings of the National Academy of Sciences of the 1762
United States of America 74(11), 5088–5090. 1763
Woese, C.R., Kandler, O., Wheelis, M.L., 1990. Towards a natural system of organisms: 1764
52
proposal for the domains archaea, bacteria, and eucarya. Proceedings of the 1765
National Academy of Sciences of the United States of America 87(12), 4576–1766
4579. 1767
Xue, D., Yao, H.Y., Huang, C.Y., 2006. Microbial biomass, N mineralization and 1768
nitrification, enzyme activities, and microbial community diversity in tea 1769
orchard soils. Plant and Soil 288(1–2), 319–331. 1770
Xun, W.B., Zhao, J., Xue, C., Zhang, G.S., Ran, W., Wang, B.R., Shen, Q.R., Zhang, 1771
R.F., 2016. Significant alteration of soil bacterial communities and organic 1772
carbon decomposition by different long-term fertilization management 1773
conditions of extremely low-productivity arable soil in South China. 1774
Environmental Microbiology 18(6), 1907–1917. 1775
Yuste, J.C., Fernandez-Gonzalez, A.J., Fernandez-Lopez, M., Ogaya, R., Penuelas, J., 1776
Lloret, F., 2014. Functional diversification within bacterial lineages promotes 1777
wide functional overlapping between taxonomic groups in a Mediterranean 1778
forest soil. FEMS Microbiology Ecology 90(1), 54–67. 1779
Zeng, Q.C., An, S.S., Liu, Y., 2017. Soil bacterial community response to vegetation 1780
succession after fencing in the grassland of China. Science of the Total 1781
Environment 609, 2–10. 1782
Zhou, Z., Wang, C., Luo, Y., 2018. Effects of forest degradation on microbial 1783
communities and soil carbon cycling: A global meta-analysis. Global Ecology 1784
and Biogeography 27(1), 110–124. 1785
1786
53
1787
Chapter 2. Relationships between Soil Microbial Respiration 1788
and rDNA- or rRNA-based Indices under a Glutamate 1789
Amendment Gradient* 1790
1791
1792
*This chapter forms the basis of the following journal manuscript: 1793
Che RX, Deng YC, Wang F, Wang WJ, Xu ZH, Wang YF, Cui XY. (2015): 16S rRNA-1794
based bacterial community structure is a sensitive indicator of soil respiration 1795
activity. Journal of Soils and Sediments 15, 1987–1990. DOI: 10.1007/s11368-1796
015-1152-0 1797
1798
1799
54
1800
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 1801
This chapter includes a co-authored paper. The bibliographic details of the co-authored 1802
paper, including all authors, are: 1803
Che RX, Deng YC, Wang F, Wang WJ, Xu ZH, Wang YF, Cui XY. (2015): 16S rRNA-based 1804
bacterial community structure is a sensitive indicator of soil respiration activity. Journal of 1805
Soils and Sediments 15, 1987–1990. DOI: 10.1007/s11368-015-1152-0 1806
My contribution to the paper involved: 1807
Experimental design and conduction; statistical analysis; categorisation of the data into 1808
a usable format; and writing the paper. 1809
1810
The copyright of the paper has been transformed to the Publisher, but I reserve the 1811
right to include the paper as a chapter in the thesis. 1812
1813
1814
(Signed) _________________________________ (Date)______________ 1815
Rongxiao Che 1816
1817
(Countersigned) ___________________________ (Date)______________ 1818
Corresponding author of paper: Xiaoyong Cui 1819
1820
(Countersigned) ___ __ (Date)______________ 1821
Supervisor: Zhihong Xu 1822
1823
1824
1825
1826
1827
55
2.1. Abstract 1828
Understanding the relationships between soil microbial respiration and community 1829
compositions is not only pivotal for soil respiration prediction, modeling, and 1830
management, but also essential to enhancing the understanding of microbial life 1831
strategies. This study aimed to assess the relationships between soil microbial 1832
respiration and bacterial rDNA- or rRNA-based indices under a glutamate amendment 1833
gradient. Soil samples collected from a Tibetan alpine meadow were amended with 1834
different amounts of glutamate to establish a microbial respiration gradient. Bacterial 1835
16S rDNA copies, rRNA copies, rRNA-rDNA ratios, as well as rDNA- and rRNA-1836
based community structure were analyzed using real-time PCR and terminal-restriction 1837
fragment length polymorphism. Except for 16S rRNA copies and rRNA-rDNA ratios, 1838
all of the rDNA- or rRNA-based indices significantly correlated with the soil microbial 1839
respiration rates. However, the 16S rRNA-based bacterial community structure 1840
explained 72.7 % of the variations in soil microbial respiration rates, which 1841
outperformed all other rDNA- or rRNA-based indices. These findings imply that the 1842
16S rRNA-based community structure is a sensitive indicator for soil microbial 1843
respiration activity, and also highlight its potentials for identifying microbial life 1844
strategies (i.e., copiotrophs and oligotrophs). 1845
1846
2.2. Introduction 1847
Soil respiration, as a key biological process of the global carbon cycle, has received 1848
increasing attention, especially in the alpine meadow ecosystems with high soil organic 1849
56
carbon stocks (Chen et al., 2013; Moriyama et al., 2013; Wang et al., 2002). 1850
Understanding the relationships between soil respiration activity and microbial 1851
community is pivotal for soil respiration prediction and regulation through management. 1852
Moreover, their relationships are widely employed to identify the life strategies of 1853
different microbial lineages (Che et al., 2016; Fierer et al., 2007). Nevertheless, it is 1854
still a challenge to establish these relationships partly due to the lack of robust microbial 1855
indices. 1856
1857
With technical innovations, the bacterial 16S rDNA- or rRNA-based indices (i.e., 16S 1858
rDNA copies, rRNA copies, rRNA-rDNA ratios, and rDNA- or rRNA-based community 1859
structure) have been increasingly used to characterize the bacterial populations. 1860
Generally, the 16S rDNA copies, rRNA copies, and the rRNA-rDNA ratios were used 1861
to determine the bacterial abundance, total activity and average activity, respectively 1862
(Campbell and Kirchman, 2013), while the 16S rDNA- and rRNA-based community 1863
structures were employed to determine the total and potentially active bacterial 1864
community structures, respectively (Barnard et al., 2015). As bacterial abundance, 1865
activity and community structure may affect soil microbial respiration (Barnard et al., 1866
2015; Fierer et al., 2007; Wang et al., 2003), I hypothesized that the bacterial 16S 1867
rDNA- or rRNA-based indices may lend a series of useful tools for exploring the 1868
relationships between soil microbial respiration and bacterial communities. 1869
57
In this study, the correlations between soil microbial respiration rates and bacterial 1870
rDNA- or rRNA-based indices were tested to 1) assess the relationships between soil 1871
microbial respiration and bacterial rDNA- or rRNA-based indices; 2) examine the 1872
potentials of bacterial rDNA- or rRNA-based indices in the life-strategy classification 1873
of bacterial lineages. 1874
1875
2.3. Materials and methods 1876
2.3.1. Study site 1877
This research was, conducted at the Haibei Alpine Meadow Ecosystem Research 1878
Station (37° 37′ N, 101° 12′ E), situated in the northeast of the Tibetan Plateau. With an 1879
average elevation of 3 200 m, the research station experiences a typical plateau 1880
continental climate. The mean annual temperature and precipitation are -1.7 °C and 570 1881
mm, respectively (Zhao et al., 2006). The soil is classified as Gelic Cambisols (WRB 1882
1998). The vegetation of the study site is dominated by species such as, Potentilla nivea, 1883
Kobresia humilis, and Elymus nutans (Wang et al., 2012); the average coverage of 1884
graminoids, forbs, and legumes is about 86%, 86%, and 28%, respectively (Wang et al., 1885
2012). 1886
1887
2.3.2. Soil collection, incubation, and microbial respiration measurements 1888
A bulk soil sample (0–5 cm depth) was collected from multiple points at the Haibei 1889
Research Station. After being sieved to 2 mm, the soil sample was stored at 4 °C for six 1890
months to eliminate the plant root respiration. Then, an aliquot of the field moist 1891
58
(34.6 %, w / w) soil containing 10 g of dry mass was placed into each of fifteen 250-ml 1892
amber jars. The 15 jars were pre-incubated in darkness, at 25 °C for 14 days to stabilize 1893
the soil conditions. During the incubation, the jars were covered with sealing films with 1894
small apertures to reduce evaporation, and water was replenished every two days to 1895
keep soil moisture close to the field moisture. Subsequently, sodium glutamate solution 1896
(0.75 ml) was sprayed onto the soils using an injector (1 ml), on the 15th and 19th day 1897
of the incubation, to replenish water and establish the soil microbial respiration gradient. 1898
The amounts of sodium glutamate added to the soils ranged from 0 to 30 mg/g dry soil, 1899
with 2.1 mg/g dry soil increment. The range of the substrate addition rates was chosen 1900
based on our preliminary experiments, according to Lipson et al. (1999). In this 1901
substrate range, the microbial respiration rates linearly increased with the increase in 1902
substrate input. The soil microbial respiration rates were measured on the 16th, 18th, 1903
20th, and 21st day of the incubation, using the GFS-3000 Gas Exchange System (Heinz 1904
Walz, Effeltrich, Germany). Soil microbial respiration rates were calculated based on 1905
the CO2 production rates, and expressed on dry soil mass basis. At the end of the 1906
incubation, soil samples were firstly flash-frozen in liquid nitrogen, and then stored at 1907
-80 °C until the nucleic acids were extracted. 1908
1909
2.3.3. Soil nucleic acid extraction and cDNA synthesis 1910
I used 0.25 and 1.0 g soil for DNA and RNA extraction, respectively. The extraction 1911
was conducted using the PowerSoil™ DNA Isolation Kit and PowerSoil® Total RNA 1912
Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA), as described by suppliers. 1913
59
After the removal of DNA residuals using DNase I (MO BIO Laboratories, Carlsbad, 1914
CA, USA), RNA extracts were reverse-transcribed into cDNA using the 1915
PrimeScript™II 1st Strand cDNA Synthesis Kit with random hexamers (Takara Bio 1916
Inc., Shiga, Japan). Then, DNA and cDNA solutions were diluted 5 and 50 times, 1917
respectively, for subsequent operations. 1918
1919
2.3.4. Real-time PCR 1920
Copies of 16S rDNA and its transcripts were determined using a 7500 Real-Time PCR 1921
System (Applied Biosystems, Foster City, CA, USA) with universal bacterial 16S 1922
rRNA gene primers 338F (ACT CCT ACG GGA GGC AG, Lane, et al. 1991), and 518R 1923
(ATT ACC GCG GCT GCT GG, Muyzer et al. 1993). The 20 μL qPCR reaction system 1924
contained 1 μL template DNA (DNA, cDNA, or 10-fold serially diluted standard), 10 1925
μL Maxima™ SYBR Green or ROX (2 ×, Fermentas, Waltham, MA, USA), 0.5 μL 1926
forward primer (20 μmol L-1), 0.5 μL reverse primer (20 μmol.L-1), 0.25 μL bovine 1927
serum albumin (25 mg mL−1; Promega, Madison, WI, USA), and 7.75 μL nuclease-free 1928
water. Standard curves were constructed using plasmids harboring the 16S rRNA gene 1929
fragment. All DNA, cDNA, and standards were analysed in triplicate, and three no-1930
template controls were used to check the contaminations of reagents. PCR runs started 1931
with an initial denaturation and enzyme activation step for 10 minutes at 95 °C, 1932
followed by 40 cycles of 15 s at 95 °C, 30 s at 56 °C, 30 s at 72 °C, and 15 s at 80 °C. 1933
I recorded the fluorescence signal at 80 °C to attenuate the influence of primer dimers. 1934
The specificity of PCR products was verified by melting curve analysis. The qPCR 1935
60
efficiencies were around 95%, and the Standard curve R2 ≥ 0.999. The absence of PCR 1936
inhibitors in DNA and cDNA solutions was checked via qPCR with dilution series of 1937
corresponding solutions (Hargreaves et al., 2013). 1938
1939
2.3.5. Terminal restriction fragment polymorphism (T-RFLP) 1940
The T-RFLP analysis was conducted with universal bacterial 16S rRNA gene primers 1941
27F (AGA GTT TGA TCM TGG CTC AG, Giovannoni et al. 1991), and 519R (GWA 1942
TTA CCG CCG CKG CTG, Lane et al. 1985). The forward primer was labelled with 1943
the fluorescent dye FAM (6-carboxyfluorescein) at the 5’ end. The 50 μL PCR reaction 1944
system contained 2.5 μL template DNA (or cDNA) solution, 25 μL Premix Taq™ Hot 1945
Start Version (Takara Bio Inc., Shiga, Japan), 1 μL forward primer (20 μmol L-1), 1 μL 1946
reverse primer (20 μmol L-1), 0.5 μL bovine serum albumin (25 mg mL−1; Promega, 1947
Madison, WI, USA), and 20 μL nuclease-free water. PCR programs were as follows: 1948
initial denaturation and enzyme activation at 95, °C for 10 minutes, followed by 30 1949
cycles of 45 s at 95 °C, 40 s at 56 °C, 55 s at 72 °C, ending with a final extension at 1950
72 °C for 10 minutes. All the samples were run in duplicate, and the duplicate PCR 1951
products were pooled for the following operations. After being purified using the 1952
Agarose Gel DNA Purification Kit (Axgen Biotechnology Corporation, Hangzhou, 1953
China), the purified PCR products were completely digested by restriction 1954
endonuclease ALU Ⅰ (New England Biolabs Inc. Beverly, MA, USA). Terminal 1955
restriction fragments were size-separated by the Beijing Tsingke BioTeck Co. Ltd 1956
(Beijing, China) using the 3730 system (Applied Biosystems, Foster City, CA, USA). 1957
61
Then, the electropherograms were retrieved using PeakScanner 1.0 (Applied 1958
Biosystems, Foster City, CA, USA) and the threshold for peak assignment was + or - 1959
0.5 bp. After uniformization, unqualified peaks with size < 30 bp, > 540 bp or peak 1960
height < 20 were removed. Proportions of peaks were calculated according to their peak 1961
areas. Then, the proportions were arcsine transformed for the following data analysis. 1962
1963
2.3.6. Statistical analysis 1964
Table 2.1. The correlations* between soil microbial respiration and other variables. 1965
1966
*The correlations between soil respiration activity and BCS were tested by the Mantel test based on the 1967
Pearson method: soil microbial respiration dissimilarity was calculated by the Euclidean distance; 16S 1968
rDNA- and rRNA-based BCS dissimilarities were determined using the Bray-Curtis method. Other 1969
correlations were tested by the simple Pearson correlation. Some data were transformed to meet the 1970
normality requirement of Pearson correlation analysis. 1971
**rDNA/rRNA ratio of 16S rRNA copies to 16S rDNA copies, BCS bacterial community structure 1972
The average of the soil microbial respiration rate measured on the 16th, 18th, 20th, and 1973
21st days of the incubation was used to represent the microbial respiration rate at each 1974
glutamate application rate. Non-metric Multidimensional Scaling (NMDS) was used as 1975
an ordination method to visualize the 16S rRNA- or rDNA-based bacterial community 1976
structure discrepancies; the distance between points represents their Bray-Curtis 1977
dissimilarities (Bray and Curtis, 1957). The relationships between rDNA- or rRNA-1978
based indices and the microbial respiration rates were tested using the Pearson 1979
Variables Transformation r r2 P
Glutamate amounts No 0.973 0.947 < 0.001
16S rDNA copies X0.5 −0.701 0.491 0.004
16S rRNA copies X0.5 −0.159 0.025 0.569
rDNA/rRNA** No 0.386 0.148 0.154
16S rRNA-based BCS** X0.5 0.472 0.223 <0.001
16S rDNA-based BCS** X0.5 0.853 0.727 <0.001
62
correlation analysis or Mantel test. Some data were square root transformed to meet the 1980
normality requirement of analysis (Table 2.1). All statistical tests were performed using 1981
R (R Development Core Team 2015) with the vegan package (Oksanen et al. 2015). 1982
2.4. Results 1983
The different amounts of sodium glutamate addition resulted in an evident soil 1984
microbial respiration gradient in different soil samples (r = 0.973, P < 0.001, Table 2.1). 1985
There was no significant correlation between soil microbial respiration and the bacterial 1986
16S rRNA copies (P = 0.569) or the ratio of 16S rRNA copies to 16S rDNA copies (P 1987
= 0.154). In contrast, a significant negative relationship was observed between soil 1988
microbial respiration and the 16S rDNA copies (r = -0.701, P = 0.004). However, the 1989
16S rDNA copies could only explain less than 50 % of the soil microbial respiration 1990
variation (Table 2.1). 1991
1992 Figure 2.1. NMDS ordinations of 16S rRNA- and rDNA-based bacterial community structures. Squares: 1993
16S rDNA-based bacterial community structures; circles: 16S rRNA-based bacterial community 1994
structures; black squares and circles: bacterial community structures of four replicates of soil that were 1995
frozen before the incubation. “Low” to “High”: the lowest to the highest soil microbial respiration. The 1996
NMDS was based on Bray-Curtis dissimilarity matrix; Stress = 0.078, assuring the reliability of 1997
ordinations. The respective aggregation of black squares and circles lend ratification to the results of T-1998
RFLP. 1999
63
As shown in the NMDS plot, 16S rRNA-based bacterial community structures aligned 2000
almost linearly in the direction from low to high soil microbial respiration, while the 2001
16S rDNA-based bacterial community structures showed higher and irregular 2002
variations (Fig. 2.1). Consistently, the Mantel test suggested that the soil microbial 2003
respiration was more closely correlated with 16S rRNA-based bacterial community 2004
structure (r = 0.853, P < 0.001, Fig. 2.2b) than the 16S rDNA-based bacterial 2005
community structure (r = 0.472, P < 0.001, Fig. 2.2a). About 73% and 22% of the soil 2006
microbial respiration variations could be interpreted by the 16S rRNA- and 16S rDNA-2007
based bacterial community structures, respectively (Table 2.1). 2008
2009
Figure 2.2. The relationships between soil microbial respiration and the 16S rDNA-based (a) or 16S 2010
rRNA-based (b) community structure. SMR: soil microbial respiration; CS: community structure; ***: 2011
P < 0.001. Soil microbial respiration dissimilarity was calculated by the Euclidean distance; community 2012
structure dissimilarity was determined using the Bray-Curtis method. The relationships were tested via 2013
the Mantel test. 2014
2015
2.5. Discussion 2016
As suggested by Blazewicz et al. (2013), the relationships between cellular rRNA 2017
concentration and microbial activity may vary in different microbial species. Thus, the 2018
poor correlation between 16S rRNA copies and soil microbial respiration rates might 2019
64
be attributed to the shifts of soil bacterial community structures at different glutamate 2020
addition rates. In many studies, 16S rRNA copies were used as the surrogate of bacterial 2021
activity (Blazewicz et al., 2013). However, in line with some existing studies (Kerkhof 2022
and Kemp, 1999; Placella et al., 2012), the result suggested that the biological meanings 2023
of 16S rRNA copies must be interpreted with caution. 2024
2025
A possible explanation for the negative correlation between soil microbial respiration 2026
rates and 16S rDNA copies might be the competition among soil bacteria. In this study, 2027
glutamate supplied C and N to bacteria, but other resources might be still limited. In 2028
this situation, bacteria that had higher competitive capacity might destroy other bacteria 2029
to acquire more of the “limited resources” (Goberna et al., 2014; Goldfarb et al., 2011). 2030
Meanwhile, the more active bacteria might consume more of the limited resources than 2031
the less active microbes. Consequently, when the amount of limited resources remained 2032
the same, higher microbial activity results in a decrease in the microbial abundance 2033
(16S rDNA copies). Although 16S rDNA copies were significantly correlated with soil 2034
microbial respiration rates, it could only explain less than 50% of the soil microbial 2035
respiration variation, indicating that soil microbial respiration is not very sensitive to 2036
the changes of this index. Moreover, 16S rDNA copies cannot provide any taxonomy 2037
information. Therefore, 16S rDNA copy number is not a satisfactory index in relation 2038
to the respirational activity of the soil microbial communities. 2039
2040
65
In consistency with previous studies (Eilers et al., 2010; Fierer et al., 2007), the 2041
correlation between soil microbial respiration and 16S rDNA-based bacterial 2042
community structure was significant but not very strong (r = 0.472, Table 2.1). Indeed, 2043
the carbon mineralization ability and the substrate use efficiency can be different for 2044
different bacteria species (Fierer et al., 2007), with some bacteria less active or even 2045
dormant and some only metabolizing substrates without biomass accumulation 2046
(Devevre and Horwath, 2000). Thus, the 16S rDNA-based community structure, 2047
representing the total bacterial community structure, was only weakly correlated with 2048
soil microbial respiration rates (Table 2.1). Interestingly, this study found that the 16S 2049
rRNA-based bacterial community structure had a much stronger correlation with soil 2050
microbial respiration, interpreting 72.7 % of the soil microbial respiration variation (Fig. 2051
2.2; Table 2.1). As the catalytic core of protein (including enzyme) synthesis, rRNA 2052
concentration has been shown to be intimately correlated to the metabolic activity in a 2053
number of bacterial species, but these correlations can be either positive or negative 2054
(Blazewicz et al., 2013). Thus, the strong correlation can be attributed to the close 2055
relationship between rRNA and metabolism. Overall, these results suggest that the 16S 2056
rRNA-based bacterial community structure is a sensitive indicator of substrate-induced 2057
soil microbial respiration. 2058
2059
In this study, glutamate was used to establish the soil microbial respiration gradient for 2060
three reasons. First, simple carbon substrates including amino acid (important 2061
components of plant root exudates) usually play a key role in nourishing microorganism 2062
66
communities and are important substrates of soil respiration (Hartmann et al., 2009; Liu 2063
et al., 2006; van Hees et al., 2005). Second, soil microbial communities are more 2064
sensitive to simple substrates than complex compounds (Goldfarb et al., 2011). Finally, 2065
glutamate has been shown to be the favorite substrate of soil microbes (Lipson et al., 2066
1999). As only one type of soil and treatment was involved in the current research, I 2067
cannot extrapolate the conclusion of this study to more general situations. Nevertheless, 2068
a recent study reported that changes in 16S rRNA-based soil bacterial community 2069
structure significantly correlated with soil CO2 release after soil rewetting (Barnard et 2070
al., 2015). Therefore, 16S rRNA-based bacterial community structure may be a 2071
sensitive indicator of soil microbial respiration in a wide range of situations and offers 2072
a promising molecular index to relate soil microbial community to their activities. 2073
However, further studies using different soils, substrates, and more precise taxonomy 2074
techniques should be conducted to confirm the results of this preliminary research. 2075
2076
2.6. Conclusions 2077
This preliminary study found that although both the community compositions based on 2078
16S rDNA and rRNA showed significant correlations with soil microbial respiration 2079
rates, the 16S rRNA based community structures clearly outperformed the 16S rDNA 2080
based community structures. These findings suggest that 16S rRNA-based community 2081
structure is a sensitive indicator of soil microbial respiration activity. In addition, the 2082
microbial respiration gradient was essentially a glutamate gradient. Therefore, this 2083
67
study also highlights the potentials of using rRNA-based techniques to classify the life 2084
strategies (i.e., copiotrophs and oligotrophs) of microbial lineages. 2085
2086
2087
68
2.7. References 2088
Barnard, R.L., Osborne, C.A., Firestone, M.K., 2015. Changing precipitation pattern 2089
alters soil microbial community response to wet-up under a mediterranean-type 2090
climate. ISME Journal 9(4), 946–957. 2091
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as 2092
an indicator of microbial activity in environmental communities: limitations and 2093
uses. ISME Journal 7(11), 2061–2068. 2094
Bray, J.R., Curtis, J.T., 1957. An ordination of upland forest communities of southern 2095
Wisconsin. Ecological Monographs 27(4), 326–349. 2096
Campbell, B.J., Kirchman, D.L., 2013. Bacterial diversity, community structure and 2097
potential growth rates along an estuarine salinity gradient. ISME Journal. 7(1), 2098
210–220. 2099
Che, R.X., Wang, W.J., Zhang, J., Nguyen, T.T.N., Tao, J., Wang, F., Wang, Y.F., Xu, 2100
Z.H., Cui, X.Y., 2016. Assessing soil microbial respiration capacity using 2101
rDNA- or rRNA-based indices: a review. Journal of Soils and Sediments 16(12), 2102
2698–2708. 2103
Chen, H., Zhu, Q., Peng, C., Wu, N., Wang, Y., Fang, X., Gao, Y., Zhu, D., Yang, G., 2104
Tian, J., Kang, X., Piao, S., Ouyang, H., Xiang, W., Luo, Z., Jiang, H., Song, X., 2105
Zhang, Y., Yu, G., Zhao, X., Gong, P., Yao, T., Wu, J., 2013. The impacts of 2106
climate change and human activities on biogeochemical cycles on the Qinghai-2107
Tibetan Plateau. Global Change Biology 19(10), 2940–2955. 2108
Devevre, O.C., Horwath, W.R., 2000. Decomposition of rice straw and microbial 2109
69
carbon use efficiency under different soil temperatures and moistures. Soil 2110
Biology and Biochemistry 32(11–12), 1773–1785. 2111
Eilers, K.G., Lauber, C.L., Knight, R., Fierer, N., 2010. Shifts in bacterial community 2112
structure associated with inputs of low molecular weight carbon compounds to 2113
soil. Soil Biology and Biochemistry 42(6), 896–903. 2114
Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward an ecological classification of 2115
soil bacteria. Ecology 88(6), 1354–1364. 2116
Goberna, M., Navarro-Cano, J.A., Valiente-Banuet, A., Garcia, C., Verdu, M., 2014. 2117
Abiotic stress tolerance and competition-related traits underlie phylogenetic 2118
clustering in soil bacterial communities. Ecology Letters 17(10), 1191–1201. 2119
Goldfarb, K.C., Karaoz, U., Hanson, C.A., Santee, C.A., Bradford, M.A., Treseder, 2120
K.K., Wallenstein, M.D., Brodie, E.L., 2011. Differential growth responses of 2121
soil bacterial taxa to carbon substrates of varying chemical recalcitrance. 2122
Frontiers in Microbiology 2, 94. 2123
Hargreaves, S.K., Roberto, A.A., Hofmockel, K.S., 2013. Reaction- and sample-2124
specific inhibition affect standardization of qPCR assays of soil bacterial 2125
communities. Soil Biology and Biochemistry 59(0), 89–97. 2126
Hartmann, A., Schmid, M., van Tuinen, D., Berg, G., 2009. Plant-driven selection of 2127
microbes. Plant and Soil 321(1–2), 235–257. 2128
Kerkhof, L., Kemp, P., 1999. Small ribosomal RNA content in marine Proteobacteria 2129
during non-steady-state growth. FEMS Microbiology Ecology 30(3), 253–260. 2130
Lipson, D.A., Schmidt, S.K., Monson, R.K., 1999. Links between microbial population 2131
70
dynamics and nitrogen availability in an alpine ecosystem. Ecology 80(5), 2132
1623–1631. 2133
Liu, H.S., Li, L.H., Han, X.G., Huang, J.H., Sun, J.X., Wang, H.Y., 2006. Respiratory 2134
substrate availability plays a crucial role in the response of soil respiration to 2135
environmental factors. Applied Soil Ecology 32(3), 284–292. 2136
Moriyama, A., Yonemura, S., Kawashima, S., Du, M., Tang, Y., 2013. Environmental 2137
indicators for estimating the potential soil respiration rate in alpine zone. 2138
Ecological Indicators 32, 245–252. 2139
Oksanen J., Blanchet F.G., Kindt R., Legendre P., Minchin P.R., O’Hara R.B., Simpson 2140
G.L., Solymos P., Stevens M.H.H., Wagner H., 2015. Vegan: community 2141
ecology package. http://CRAN.R-project.org/package=vegan. 2142
Placella, S.A., Brodie, E.L., Firestone, M.K., 2012. Rainfall-induced carbon dioxide 2143
pulses result from sequential resuscitation of phylogenetically clustered 2144
microbial groups. Proceedings of the National Academy of Sciences of the 2145
United States of America 109(27), 10931–10936. 2146
R Development Core Team, 2015. R: a language and environment for statistical 2147
computing. R Foundation for Statistical Computing, Vienna, Austria. 2148
http://www.R-project.org/. 2149
van Hees, P.A.W., Jones, D.L., Finlay, R., Godbold, D.L., Lundstomd, U.S., 2005. The 2150
carbon we do not see - the impact of low molecular weight compounds on 2151
carbon dynamics and respiration in forest soils: a review. Soil Biology and 2152
Biochemistry 37(1), 1–13. 2153
71
Wang, G.X., Qian, J., Cheng, G.D., Lai, Y.M., 2002. Soil organic carbon pool of 2154
grassland soils on the Qinghai-Tibetan Plateau and its global implication. 2155
Science of the Total Environment 291(1–3), 207–217. 2156
Wang, S., Duan, J., Xu, G., Wang, Y., Zhang, Z., Rui, Y., Luo, C., Xu, B., Zhu, X., 2157
Chang, X., Cui, X., Niu, H., Zhao, X., Wang, W., 2012. Effects of warming and 2158
grazing on soil N availability, species composition, and ANPP in an alpine 2159
meadow. Ecology 93(11), 2365–2376. 2160
Wang, W.J., Dalal, R.C., Moody, P.W., Smith, C.J., 2003. Relationships of soil 2161
respiration to microbial biomass, substrate availability and clay content. Soil 2162
Biology and Biochemistry 35(2), 273–284. 2163
WRB, 1998. World reference base for soil resources. FAO/ISRIC/ISSS, Italy. 2164
Zhao, L., Li, Y.N., Xu, S.X., Zhou, H.K., Gu, S., Yu, G.R., Zhao, X.Q., 2006. Diurnal, 2165
seasonal and annual variation in net ecosystem CO2 exchange of an alpine 2166
shrubland on Qinghai-Tibetan plateau. Global Change Biology 12(10), 1940–2167
1953. 2168
2169
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2170
73
2171
2172
Chapter 3. A Copiotroph-Oligotroph Classification of 2173
Grassland Soil Prokaryotic Lineages* 2174
2175
2176
2177
*This chapter forms a part of the following journal manuscript: 2178
Che RX, Xu ZH, Wang WJ, Wang YF, Cui XY. A copiotroph-oligotroph classification 2179
of grassland soil microbes. (Plan to submit to Nature Ecology and Evolution). 2180
2181
2182
2183
74
2184
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 2185
This chapter includes a co-authored paper. The status of the co-authored paper, 2186
including all authors, are: 2187
Che RX, Xu ZH, Wang WJ, Wang YF, Cui XY. A copiotroph-oligotroph classification of 2188
grassland soil microbes. (In preparation) 2189
My contribution to the paper involved: 2190
Experimental design and conduction; statistical analysis; categorisation of the data into 2191
a usable format; and writing the paper. 2192
2193
2194
(Signed) _________________________________ (Date)______________ 2195
Rongxiao Che 2196
2197
(Countersigned) ___________________________ (Date)______________ 2198
Corresponding author of paper: Xiaoyong Cui 2199
2200
(Countersigned) ___________________________ (Date)______________ 2201
Supervisor: Zhihong Xu 2202
2203
2204
2205
2206
2207
75
3.1. Abstract 2208
Identifying the life strategies of microbial lineages is crucial to interpreting the 2209
ecological implications of microbial community compositions, as well as establishing 2210
the links between microbial communities and their ecological functions. However, there 2211
is still a paucity of knowledge on the life-strategy classification of microbial lineages 2212
at finer taxonomy levels, and the classification targeting the active populations has 2213
seldom been conducted. Here, I systematically classify the soil prokaryotic lineages as 2214
copiotrophs and oligotrophs, by examining their responses to glucose amendment, and 2215
their correlations with soil microbial respiration rates. Soils were collected from 32 2216
natural grasslands on the Inner Mongolia and the Tibetan plateaus. Copies of 16S rDNA 2217
and rRNA were measured by real-time PCR. The total and active prokaryotic 2218
community structures were analyzed through MiSeq sequencing based on 16S rDNA 2219
and rRNA, respectively. The results showed that the total and active prokaryotic 2220
lineages responded to the glucose amendments with consistency across all the study 2221
sites. Their relationships with the soil microbial respiration rates were also similar. 2222
Proteobacteria, Bacteroidetes, Firmicutes, and most of the finer lineages under these 2223
phyla were copiotrophs, while Archaea, Acidobacteria, Chloroflexi, 2224
Gemmatimonadetes, Planctomycetes, Tectomicrobia, Nitrospirae, Armatimonadetes, 2225
Verrucomicrobia, FBP, and their lineages were oligotrophs. Although different life-2226
strategies existed among some lineages under the same phyla, the overall responses of 2227
prokaryotic community profiles at the operational taxonomy unit level to the glucose 2228
amendment shared similar ordinations. The relative abundances of most copiotrophic 2229
76
and oligotrophic lineages showed positive and negative correlations with soil microbial 2230
respiration rates, respectively. However, the relative abundance of Acidobacteria, a 2231
widely-recognized oligotrophic lineage, as well as its finer lineages, showed positive 2232
correlations with the soil microbial respiration rates. Collectively, these findings 2233
provide a systematic understanding of the life-strategies of the total and active 2234
prokaryotic lineages in the grassland soils, and also highlight the possibility and some 2235
issues of using the relative abundances of copiotrophic and oligotrophic lineages to 2236
assess the heterotrophic respiration capacity across different study sites. 2237
2238
3.2. Introduction 2239
As an essential component of ecosystems, soil microbes and their key roles in 2240
ecosystem functioning have been recognized and investigated for more than a century 2241
(Fierer, 2017). The technological advances in the past decades, especially the 2242
applications of high-throughput sequencing, have substantially improved the ability to 2243
examine the abundance, diversity, and community compositions of soil microbes (Che 2244
et al., 2016b). Thus, the main challenge in soil microbial ecology studies has shifted 2245
from describing microbial communities to interpreting their ecological implications 2246
(Fierer, 2017). Assessing the relative abundances of copiotrophs and oligotrophs 2247
(analogous to r- and K-specialist) is the most widely-used approach to understand the 2248
ecological implications of microbial community profiles (Ho et al., 2017). Moreover, 2249
the relative abundances of copiotrophs and oligotrophs are positively and negatively 2250
correlated with soil microbial respiration, respectively (Che et al., 2016b; Fierer et al., 2251
77
2007). Therefore, identifying the life strategies of soil microbial lineages not only 2252
contributes to understanding the ecological implications of microbial indices, but also 2253
provides crucial bases for understanding the links between microbial communities and 2254
their ecological functions. For instance, in this way, we can recognize whether a 2255
microbial community is more K- or r-specialist dominated, and can also qualitatively 2256
identify the overall activity of the microbial community. 2257
2258
The concepts of copiotrophs and oligotrophs have been proposed and developed since 2259
approximately a century ago (Andrews, 1984; Andrews and Harris, 1986; Gottschal, 2260
1985; Hirsch et al., 1979; Koch, 2001; Meyer, 1994; Padmanabhan et al., 2003; 2261
Winogradsky, 1924). However, the first attempt at their classifications was made just 2262
one decade ago (Fierer et al., 2007). In that study, Fierer et al. (2007) defined 2263
copiotrophs as the microbial linages which are more competitive in environments with 2264
higher availability of organic carbon, while oligotrophs are usually abundant in more 2265
barren environments. The relative abundances of copiotrophs and oligotrophs are 2266
usually positively and negatively correlated with heterotrophic respiration rates, 2267
respectively. Combining cross-site survey, organic carbon amendment, and meta-2268
analysis, Fierer et al. (2007) classified Acidobacteria into the category of oligotrophs, 2269
and identified Bacteroidetes and Betaproteobacteria as copiotrophs. Following the 2270
study, a number of further investigations have been conducted to identify copiotrophic 2271
and oligotrophic microbial lineages (Ho et al., 2017). However, with most 2272
investigations targeting the microbial lineages at phylum or class level (Cleveland et 2273
78
al., 2007; Eilers et al., 2010; Ho et al., 2017), the microbial lineages at finer taxonomy 2274
level remains poorly examined. Furthermore, despite the fact that more than 90% of 2275
soil microbes are dormant (Blagodatskaya and Kuzyakov, 2013; Fierer, 2017; Lennon 2276
and Jones, 2011), most of the classifications were based on the responses of total 2277
microbes (Cleveland et al., 2007; Eilers et al., 2010; Fierer et al., 2007). Although the 2278
active microbial populations are more sensitive to environmental changes and more 2279
relevant to ecosystem functioning, our knowledge of the life-strategy classification of 2280
active microbial lineages is still scant. 2281
2282
Currently, 16S rRNA-based molecular biology methods are widely used to investigate 2283
the active soil microbes ( Che et al., 2015; Che et al., 2016a; Che et al., 2016b; Che et 2284
al., 2018). First, the coding gene of 16S rRNA (16S rDNA) is the most widely-used 2285
molecular marker to investigate the prokaryotes (Che et al., 2016b), owing to the 2286
phylogeny consistency of its sequence variations. Second, as an essential component of 2287
ribosomes, rRNA contents reflect the demand of protein synthesis, and are thus 2288
intimately connected with microbial vitality. Indeed, a few studies have showed that 2289
rRNA is abundant in the highly active microbial cells, while it degrades rapidly along 2290
with the death or dormancy of microbes (Kerkhof and Kemp, 1999; Lahtinen et al., 2291
2008; Muttray and Mohn, 1999; Perez-Osorio et al., 2010; Segev et al., 2012). 2292
Moreover, a recent report highlights the promising prospect of 16S rRNA-based 2293
methods in generating full-length 16S rDNA sequences, overcoming the primer bias in 2294
high-throughput sequencing (Karst et al., 2018). Collectively, a combination of the 2295
79
methods based on 16S rDNA and rRNA can markedly improve our understanding of 2296
the life-strategies of both total and active prokaryotic lineages. 2297
2298
Grasslands cover around 30% of the terrestrial surface of the earth, and provide 2299
multitude essential services such as supporting graziery, tourism, and CO2 fixation 2300
(Bloor and Pottier, 2014). In the past decades, microbes in grassland soils have been 2301
extensively studied using both rDNA and rRNA based methods (Che et al., 2015; Che 2302
et al., 2018; Wang et al. 2018). However, microbial life strategy classification has 2303
seldom been conducted for grassland soils. Moreover, classification using rRNA-based 2304
methods or using soils from multiple sites has never been investigated so far. 2305
2306
In this study, I aimed to systematically classify soil prokaryotic lineages into 2307
copiotrophic and oligotrophic categories using the soils collected from 32 grasslands 2308
on the Inner Mongolian and the Tibetan plateaus. Total and active soil prokaryotic 2309
community profiles were determined through MiSeq sequencing of 16S rDNA and 2310
rRNA, respectively. The classifications were based on 1) differences in prokaryotic 2311
lineage relative abundances between the soils amended with water and those receiving 2312
glucose solutions; and 2) the correlations between the prokaryotic lineage proportions 2313
and soil microbial respiration rates. The prokaryotic community profiles were analyzed 2314
using the MiSeq sequencing of 16S rDNA and rRNA. Based on previous microbial life 2315
strategy classification studies, I hypothesized that: 1) compared to the 16S rDNA 2316
relative abudnaces of prokaryotic lineages, the 16S rRNA relative abudnaces of the 2317
80
lineages are more sensitive to the glucose amendments, and show stronger correlations 2318
with soil microbial respiration rates; 2) despite the high variability in the prokaryotic 2319
community structures across study sites, their responses to the glucose amendments 2320
should be consistent; and 3) the prokaryotic lineages with increased relative abudances 2321
under the glucose amendments also show positive correlations with soil microbial 2322
respiration, and are more abundant in the active prokaryotic communities than in the 2323
total prokaryotic communities, and vice versa. 2324
2325
3.3. Material and methods 2326
3.3.1. Study sites and soil collections 2327
Soil samples were collected from 32 natural grasslands evenly distributed on the Inner 2328
Mongolia and the Tibetan plateaus (Fig. 3.1). With a maximum site-to-site distance of 2329
approximately 4 000 km, the study sites covered a wide range of elevations, soil 2330
properties, as well as the types of climate and vegetation (Table 3.1). Subsequently, 2331
soils (0–5 cm depth) were sampled with a 7-cm auger from multiple points at each site, 2332
and then thoroughly homogenized, and sieved to 2 mm. Each of the soil samples was 2333
divided into two subsamples, with one subsample air-dried and the other kept at -20 °C. 2334
The elevations and geographical coordinates were recorded using a GPS, and the mean 2335
annual temperature and precipitation were obtained from China Meteorological Data 2336
Sharing Service System (http://data.cma.cn/site/index.html). 2337
81
2338 Figure 3.1. Distribution of the sampling sites. 2339
2340
MAP: mean annual precipitation; MAT: mean annual temperature; TC: soil total C; TN: soil total N. 2341
Table 3.1. Some background information of the study sites.
Sites Elevation (m) MAP (mm) MAT (°C) Grassland types Moisture (%) TC (%) TN (%)
S01 3457 612.07 4.79 Alpine meadow 33.68 9.48 0.75
S02 3218 413.46 -2.18 Alpine steppe 5.70 4.87 0.43
S03 3168 339.60 -3.57 Alpine desert 4.33 2.46 0.06
S04 3407 246.91 1.94 Alpine steppe 9.79 3.25 0.19
S05 3522 61.94 4.83 Alpine desert 3.76 2.20 0.03
S06 4315 468.73 -3.74 Alpine meadow 17.57 3.37 0.25
S07 4332 538.08 -1.51 Alpine meadow 41.59 9.34 0.73
S08 2992 353.33 -0.23 Alpine steppe 13.05 2.44 0.16
S09 4048 360.04 -0.41 Alpine steppe 5.52 0.43 0.05
S10 4451 296.35 -1.13 Alpine meadow 11.19 0.58 0.07
S11 4830 257.63 -2.89 Alpine steppe 10.34 0.76 0.08
S12 4620 228.17 -3.43 Alpine steppe 6.65 0.33 0.04
S13 4981 181.93 -3.08 Alpine steppe 9.48 1.32 0.12
S14 4567 116.23 -2.95 Alpine desert 1.32 0.33 0.03
S15 4639 118.70 -2.75 Alpine steppe 5.35 1.25 0.08
S16 4475 196.39 -3.24 Alpine steppe 8.23 2.54 0.08
S17 450 527.21 -0.13 River flood beach 41.43 5.47 0.46
S18 500 391.57 -0.53 Temperate steppe 15.97 3.20 0.27
S19 550 353.16 -2.19 Temperate steppe 9.11 4.15 0.36
S20 683 298.34 0.36 Temperate steppe 5.54 2.07 0.20
S21 580 270.07 0.90 Temperate steppe 6.54 1.19 0.13
S22 720 419.67 0.54 Temperate meadow steppe 41.54 6.81 0.55
S23 820 344.74 1.46 Temperate steppe 12.19 1.42 0.14
S24 740 280.35 1.74 Temperate steppe 11.09 1.75 0.17
S25 830 260.82 0.57 Temperate steppe 7.46 1.06 0.10
S26 1167 264.79 1.33 Temperate steppe 7.62 0.99 0.11
S27 1020 192.34 2.11 Temperate desert steppe 4.19 0.53 0.08
S28 942 146.37 3.64 Temperate desert steppe 2.48 0.18 0.03
S29 1073 153.13 5.51 Temperate desert steppe 4.87 0.48 0.07
S30 1158 171.09 6.38 Temperate desert steppe 4.77 0.37 0.06
S31 1533 174.89 6.63 Temperate desert steppe 5.23 0.67 0.08
S32 1430 267.95 8.27 Temperate desert steppe 5.61 0.73 0.06
82
3.3.2. Experimental design, soil incubations, and microbial respiration 2342
determination 2343
This study followed a paired design without replication. The fresh soils collected from 2344
each of the 32 sites were placed into two flasks (130 mL), each containing 20 g of dry 2345
mass. The 64 flasks were then pre-incubated in darkness for 21 days to stabilize soil 2346
conditions. The soils were incubated at 20 °C which was approximately the average air 2347
temperature when the soils were collected from the grasslands. During the pre-2348
incubation, water was replenished every three days to keep the soil moisture close to 2349
the field conditions. On the day 22 of the incubation, the moisture content of all the 2350
soils was adjusted to 55% water-holding capacity (WHC), and the two soils from each 2351
site was eithor amended with glucose solution (2 mg C. g soil-1) or received equal 2352
amount of water. Then, all the soils were continuously incubated for three weeks. 2353
During the incubation, water was replenished every three days, and the soils with 2354
glucose amendments received other two repetitive additions of glucose (2 mg C. g soil-2355
1) on the 29th and 35th day of the incubation. In this study, glucose was chosen as it is 2356
a simple carbon substrate that does not contain nitrogen, and also the most widely used 2357
substrate for identifying the microbial lineage life-strategies. 2358
2359
The soil microbial respiration rates were measured on day 40 of the incubation, which 2360
is in the middle of the last amendment period, and thus, to some extent, represent the 2361
overall microbial respiration during the incubation. After being flushed with fresh air 2362
for 5 min, all the flasks were sealed with rubber stoppers. Then, the headspace air (10 2363
83
mL) in each flask was sampled four times using a syringe (30 mL), after the flasks were 2364
sealed for 0.5, 4.5, 8.5, and 24.5 hours. The CO2 concentrations in the gas samples were 2365
determined using a gas chromatograph equipped with a flame ionization detector 2366
(Agilent 7890A GC System, Palo Alto, CA, USA), and the soil microbial respiration 2367
rates were calculated based on the increase of CO2 concentrations with time. At the end 2368
of the incubation, the soils were flash-frozen in liquid nitrogen, and then stored at -2369
80 °C. 2370
2371
3.3.3. Measurements of soil physicochemical properties 2372
Soil moisture was measured by drying at 105 °C for 48 hours. Soil WHC was 2373
determined using sieved soils as described by Wang et al. (2003). Soil pH values were 2374
determined (soil to water ratio of 1:5) using a pH meter (STARTER 3100, Ohaus 2375
Instruments Co., Ltd., Shanghai, China). Microbial biomass carbon and nitrogen 2376
contents were measured using the chloroform fumigation-incubation method (Brookes 2377
et al., 1985; Vance et al., 1987). Contents of soil NH4+-N, NO3
--N, dissolved nitrogen, 2378
and dissolved organic carbon were determined using 0.5 M K2SO4 solution extraction 2379
(0.5 M; soil mass to extractant ratio of 1:5). Then, the NO3--N concentrations were 2380
measured using the method described by Doane and Horwáth (2003) with a SpectraMax 2381
Paradigm Multi-Mode Microplate Reader (Molecular Devices, Sunnyvale, CA, USA). 2382
The NH4+-N contents were determined using the microplate reader as described by 2383
Weatherburn (1967). The soil dissolved organic carbon and nitrogen contents were 2384
analyzed using a TOC Analyzer (Liqui TOC II; Elementar Analysensysteme GmbH, 2385
84
Hanau, Germany). The soil total carbon and nitrogen contents were determined, using 2386
an auto elemental analyzer. 2387
2388
3.3.4. Soil nucleic acid extraction and the synthesis of cDNA 2389
Soil RNA and DNA were extracted from 1.00 g and 0.25 g of fresh soils, respectively. 2390
The RNA and DNA extractions were separately conducted using a PowerSoil® Total 2391
RNA Isolation Kit and a PowerSoil™ DNA Isolation Kit (MO BIO Laboratories, 2392
Carlsbad, CA, USA), as detailed by the manufacturers. At the end of the RNA extraction, 2393
the genome DNA residuals were digested with DNase I (MO BIO Laboratories, 2394
Carlsbad, CA, USA) which was then removed with the DNase removal resin. 2395
Subsequently, the cDNA was synthesized using a PrimeScript™II 1st Strand cDNA 2396
Synthesis Kit (Takara Bio Inc., Shiga, Japan) with the RNA extracts as templates. 2397
2398
3.3.5. Real-time PCR 2399
The copies of 16S rDNA and its transcripts were quantified in triplicate with universal 2400
prokaryotic primer set (515F, 5’-GTG CCA GCM GCC GCG GT AA-3’, Caporaso et 2401
al., 2011; 909R, 5’- CCC CGY CAA TTC MTT TRA GT -3’, Wang and Qian, 2009) 2402
using a QuantStudio 7 flex Real-Time PCR System (Applied Biosystems, Foster City, 2403
CA, USA) with 384-well plates. The 10-μL reaction systems contained: 5.2 μL SYBR 2404
Green and ROX mixture (2 ×, Takara Bio Inc., Shiga, Japan), 0.25 μL each primer (10 2405
μmol L-1), 0.5 μL template DNA or cDNA, and 3.8 μL nuclease-free water. The 2406
plasmids harboring the corresponding target gene fragments were used to construct the 2407
85
standard curves. In brief, the PCR product of each gene was generated with the 2408
aforementioned degenerate primer set, and was purified and ligated to pMD®18-T 2409
vectors (Takara Bio Inc., Shiga, Japan), following the manufacturer’s instructions. 2410
Subsequently, the vectors were transformed into Escherichia coli DH5α competent 2411
cells, and were cultured and screened to obtain the monoclones. Then, plasmids were 2412
extracted from the liquid culture of the monoclones, and sequenced. Finally, the 2413
plasmids with corrected inserted fragments were quantified and used as standards for 2414
the real-time PCR. All the DNA and cDNA samples were analyzed in triplicate. PCR 2415
runs started with an initial denaturation at 95 °C for 30 s, followed by 40 cycles of 5 s 2416
at 95 °C, 30 s at 56 °C, 40 s at 72 °C, and 30 s at 80 °C. The fluorescence signals were 2417
recorded at 80 °C to attenuate the influences of primer dimers. 2418
2419
3.3.6. MiSeq Sequencing and bioinformatic analysis 2420
The MiSeq sequencing of the 16S rDNA and rRNA was conducted as described in our 2421
previous publication (Che et al., 2018). Briefly, the PCR products were generated using 2422
the 515F-909R primer set with barcode sequences (12 bp) being added to the 5’-end of 2423
515F. The PCR reaction system (50 μL) consisted of 25 μL Premix Taq™ Hot Start 2424
Version (Takara Bio Inc., Shiga, Japan), 2 μL primer mixture (5 μmol L-1 for each 2425
primer), 22 μL PCR grade water, and 1μL template. The PCR amplification started with 2426
a 10 min denaturation at 95 °C, followed by 30 cycles of 20 s at 95 °C, 30 s at 56 °C, 2427
and 45 s at 72 °C, ending with a 10 min final extension. For each sample, two separate 2428
PCR amplifications were conducted, after which the PCR products from the two 2429
86
amplifications were pooled. Subsequently, all the PCR products were purified using a 2430
GeneJET Gel Extraction Kit (Thermo Scientific Fermentas) after being separated by 2431
agarose gel electrophoresis. Finally, all the purified PCR products were quantified using 2432
Nanodrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA), and pooled together 2433
with an equal molar from each sample. 2434
2435
The MiSeq sequencing was conducted at the analytical center of the Chengdu Institute 2436
of Biology, Chinese Academy of Sciences. The sequencing samples were prepared 2437
using a TruSeq DNA kit following the manufacturer’s instructions. The purified 2438
products were diluted, denatured, re-diluted, and mixed with PhiX (equal to 30% of 2439
final DNA amount) as described in the Illumina library preparation protocols. All the 2440
samples were finally applied to an Illumina MiSeq system for sequencing using a 2441
Reagent Kit v3 2 × 300 bp. 2442
2443
The raw MiSeq sequences were first spliced and assigned to each sample according to 2444
the barcodes using “Quantitative Insights Into Microbial Ecology” (Qiime; v1.9.1; 2445
Caporaso et al., 2010). Then, an operational taxonomic unit (OTU) table and the OTU 2446
centroid sequences were generated following the default UPARSE OTU analysis 2447
pipeline (Edgar, 2013) using Usearch (v8.0.1623; Edgar, 2010). In brief, the low-quality 2448
contigs (expected errs > 1.00, barcode mismatch, primer mismatch, or shorter than 374) 2449
and chimers were removed using the default UPASE pipeline. In addition, the chimers 2450
were further checked and removed using uchime with silva.gold.align as references. 2451
87
Subsequently, I clustered OTUs and picked out OTU centroid sequences with an 2452
identity cut-off of 97%. The OTU centroid sequences were then annotated using Mothur 2453
(v1.27; Schloss et al., 2009) against the Silva database (v128), using the method of 2454
Wang et al. (2007), with pseudo-bootstrap confidence score = 80%. Finally, the 2455
sequence number of each sample was rarefied to 10 000 to calculate α diversity, β 2456
diversity, and the composition of each sample using R (R Development Core Team 2018) 2457
with vegan packages (Oksanen et al., 2018). In this study, the community dispersion 2458
was calculated using betadisper functions in vegan package, and presented as the 2459
distance to centroid based on PCoA ordination. 2460
2461
3.3.7. Statistical analysis 2462
The effects of the glucose amendment on the prokaryotic 16S rDNA copies, 16S rRNA 2463
copies, lineage proportions, and soil properties were assessed using paired t-test. The 2464
responses of prokaryotic lineage relative abundances to the glucose amendments were 2465
further analyzed using the linear discriminant analysis effect size (LEfSe) method 2466
(Segata et al., 2011). The overall responses of microbial community compositions to 2467
the glucose amendment were visualized with non-metric multidimensional scaling 2468
(NMDS) and determined via permutation multivariate analysis of variance 2469
(PERMANOVA). The relationships between soil microbial respiration rates and 2470
prokaryotic indices were revealed using Pearson correlation. The community 2471
composition distance was calculated based on Bray-Curtis dissimilarities. The LEfSe 2472
analysis was conducted using the online Huttenhower Galaxy server 2473
88
(huttenhower.sph.harvard.edu/galaxy), and the other statistics were conducted in R (R 2474
Development Core Team, 2017) with the vegan package. 2475
2476
3.4. Results 2477
3.4.1. Soil physicochemical properties 2478
Although all the soil properties examined in this study differed greatly across the study 2479
sites, some of them showed consistent responses to the glucose amendment at the end 2480
of the incubation (Fig. 3.2). Specifically, the glucose amendment significantly increased 2481
the contents of soil total and dissolved organic carbon, and significantly decreased the 2482
soil pH and nitrate nitrogen content (Fig. 3.2). In contrast, the other soil properties 2483
showed highly inconsistent (e.g., the total nitrogen contents and the ammonium 2484
nitrogen contents) responses to the glucose amendment (Fig. 3.2). 2485
89
2486 Figure 3.2. The effects of glucose amendments on soil properties. The response ratios are presented as 2487
log2 (glucose amendment/control). DOC: soil dissolved organic carbon content; TC: soil total carbon 2488
content; TN: soil total nitrogen content. The results of paired t-tests are also shown in each chart. 2489
2490
3.4.2. Soil microbial biomass, abundance and activity 2491
As revealed by the paired t-test, the glucose amendment significantly increased the soil 2492
microbial biomass carbon content at most sites (Fig. 3.3a), but it actually decreased the 2493
microbial biomass carbon content in almost 30% of the sampling sites. In contrast, the 2494
glucose amendment exerted no significant effect on soil microbial biomass nitrogen 2495
content (Fig. 3.3b) and soil prokaryotic abundance (Fig. 3.3c). However, the 2496
transcriptional activity of the prokaryotic 16S rDNA was significantly and consistently 2497
enhanced by the glucose amendment (Figs. 3.3d and 3.3e). In addition, the glucose 2498
amendment dramatically increased the microbial respiration across all the soils, with 2499
90
more than a tenfold increase in most soils (Fig. 3.3f). 2500
2501
2502
Figure 3.3. The responses of soil microbial biomass, abundance, and activity to glucose amendments. 2503
The response ratios are presented as log2 (glucose amendment/control). The results of paired t-tests are 2504
also shown in each chart. 2505
3.4.3. Soil prokaryotic diversity 2506
The responses of soil prokaryotic diversity indices to the glucose amendment were 2507
consistent across the study sites (Figs. 3.4 and 3.5). Specifically, prokaryotic richness, 2508
Chao1, Shannon index, and evenness based on 16S rDNA and rRNA significantly 2509
decreased under the glucose amendment (Fig. 3.4). Interestingly, the 16S rRNA-based 2510
diversity indices generally decreased more than the 16S rDNA-based indices under the 2511
glucose amendment (Fig. 3.4). However, the dispersions of prokaryotic communities 2512
tended to increase under the glucose amendment (Fig. 3.5). In particular, the dispersions 2513
91
of 16S rRNA-based prokaryotic community compositions significantly increased 2514
following the glucose amendment (Figs. 3.5b and 3.5d). 2515
2516
Figure 3.4. Responses of soil prokaryotic α-diversity indices to glucose amendment. The response ratios 2517
are presented as log2 (glucose amendment/control). The results of paired t-tests are also embedded in 2518
each chart. 2519
92
2520
Figure 3.5. NMDS ordinations of the prokaryotic community structures (a and b) and responses of their 2521
dispersions to glucose amendment (c and d). The NMDS ordinations are based on Bray-Curtis 2522
dissimilarity matrix; the effects of glucose amendment on soil prokaryotic community structures are 2523
determined using PERMANOVA based on Bray-Curtis dissimilarity matrix. The community dispersions 2524
are calculated using betadisper functions in vegan package, and presented as the distance to centroid 2525
based on PCoA ordinations. 2526
2527
3.4.4. Soil prokaryotic community composition 2528
Although the soil prokaryotic community compositions based on 16S rDNA and rRNA 2529
were highly variable among the soils, most of them were dominated by Proteobacteria, 2530
Actinobacteria, Acidobacteria, and Bacteroidetes (Fig. 3.6). On average, they 2531
accounted for 37.8%, 15.7%, 14.5%, and 9.6% of the prokaryotic communities, 2532
respectively. The Proteobacteria mainly consisted of Alphaproteobacteria (17.8%), 2533
Betaproteobacteria (9.0%), Gammaproteobacteria (4.7%), and Deltaproteobacteria 2534
(6.4%). In addition, Chloroflexi (4.2%), Planctomycetes (4.0%), Gemmatimonadetes 2535
(3.9%), Firmicutes (2.2%), Tectomicrobia (1.3%), Nitrospirae (0.9%), 2536
Armatimonadetes (0.8%), Cyanobacteria (0.5%), Verrucomicrobia (0.3%), and FBP 2537
(0.2%) were found in the soils (Fig. 3.6). Archaea accounted for 3.0% of soil 2538
prokaryotic community (Fig. 3.6), which were mainly classified as Thaumarchaeota 2539
(2.8%). 2540
93
2541
Figure 3.6. The relative abundances of prokaryotic lineages across study sites and under different 2542
treatments. 2543
2544
The NMDS and PERMANOVA tests suggested that the glucose amendment 2545
significantly altered the total and active prokaryotic community structures (P < 0.001; 2546
Fig. 3.5). Accordingly, the relative abundances of prokaryotic phyla also showed 2547
significant responses to the glucose amendment (Figs. 3.7 and 3.8). For most phyla, the 2548
responses of their proportions based on 16S rDNA and rRNA were consistent. The 2549
glucose amendment significantly increased the relative abundances of Proteobacteria 2550
and Firmicutes, but decreased the proportions of Acidobacteria, Chloroflexi, 2551
Planctomycetes, Gemmatimonadetes, Tectomicrobia, Nitrospirae, Armatimonadetes, 2552
Verrucomicrobia, Archaea, and FBP (Figs. 3.7 and 3.8). However, the changes in 16S 2553
94
rRNA relative abundances of Actinobacteria and Bacteriodetes in response to the 2554
glucose amendment were more sensitive than their 16S rDNA relative abundances. For 2555
instance, the 16S rRNA relative abundances of Actinobacteria and Bacteriodetes 2556
significantly decreased and increased under the glucose amendment, respectively (Fig. 2557
3.8b and 3.8d). Nevertheless, the 16S rDNA relative abundances of Actinobacteria and 2558
Bacteriodetes showed highly inconsistent responses to the glucose amendment in 2559
different soils (Fig. 3.7b and 3.7d). 2560
2561
2562
Figure 3.7. The responses of 16S rDNA relative abundances of prokaryotic phyla to glucose amendments. 2563
The response ratios are presented as log2 (glucose amendment/control). The results of paired t-tests are 2564
also embedded in each chart. 2565
2566
2567
2568
95
2569
Figure 3.8. The responses of 16S rRNA relative abundances of prokaryotic phyla to glucose amendments. 2570
The response ratios are presented as log2 (glucose amendment/control). The results of paired t-tests are 2571
also embedded in each chart. 2572
2573
2574
Figure 3. 9. The differences between 16S rRNA and rDNA relative abundances of prokaryotic phyla. 2575
The rRNA-rDNA ratios are presented as log2 (rRNA copies/rDNA copies). The columns above 0 2576
represent microbial lineages that were more abundant in the active prokaryotic communities than in the 2577
total prokaryotic communities; while those below 0 represent microbial lineages that were less abundant 2578
in the active prokaryotic communities than in the total prokaryotic communities. The results of paired t-2579
tests are also embedded in each chart. 2580
96
Interestingly, the 16S rRNA-rDNA ratios of the major prokaryotic phylum relative 2581
abundances were consistent across the different soils (Fig. 3.9). Compared to the 16S 2582
rDNA relative abundances, the 16S rRNA relative abundances of Proteobacteria (Fig. 2583
3.9), Actinobacteria, Tectomicrobia, and Cyanobacteria were significantly higher. 2584
Conversely, the 16S rRNA relative abundances of Acidobacteria, Bacteriodetes, 2585
Chloroflexi, Planctomycetes, Gemmatimonadetes, Firmicutes, Nitrospirae, FBP, and 2586
Archaea were significantly lower than their rDNA relative abundances (Fig. 3.9). 2587
2588
Figure 3.10. The correlations between 16S rDNA relative abundances of prokaryotic phyla and microbial 2589
respiration rates. The correlations are based on the unamended soils. The microbial respiration rate is 2590
represented as μg CO2 g-1 soil day-1, and all the data are presented as 1og10 (1+X). The correlation 2591
coefficient is embedded in each chart; *: P < 0.05; **: P < 0.01; **: P < 0.001. 2592
97
2593 Figure 3.11. The correlations between 16S rRNA relative abundances of prokaryotic phyla and microbial 2594
respiration rates. The correlations are based on the unamended soils. The microbial respiration rate is 2595
represented as μg CO2 g-1 soil day-1, and all the data are presented as 1og10 (1+X). The correlation 2596
coefficient is embedded in each chart; *: P < 0.05; **: P < 0.01; **: P < 0.001. 2597
2598
As for the 32 unamended soils, the 16S rDNA relative abundances of most prokaryotic 2599
phyla showed significant correlations with the soil microbial respiration rates (Fig. 2600
3.10). The proportions of Proteobacteria, Acidobacteria, and Bacteriodetes were 2601
positively correlated with soil microbial respiration (Fig. 3.10). On the contrary, the 2602
relative abundances of Actinobacteria, Chloroflexi, Planctomycetes, 2603
Gemmatimonadetes, Firmicutes, Tectomicrobia, Armatimonadetes, and FBP showed 2604
significant negative correlation with the microbial respiration rates (Fig. 3.10). In 2605
addition, the relative abundances of bacterial and archaeal rDNA were positively and 2606
98
negatively correlated with the soil microbial respiration rates, respectively (Fig. 3.10o 2607
and 3.10p). Compared to the 16S rDNA relative abundances, the relative abundances 2608
of 16S rRNA showed similar but weaker correlations with the soil microbial respiration 2609
rates (Fig. 3.11). Specifically, the soil microbial respiration rate was significantly 2610
positively correlated with the relative abundances of bacteria, Proteobacteria, and 2611
Acidobacteria (Fig. 3.11). However, it was significantly negatively correlated with the 2612
relative abundances of Archaea, Actinobacteria, and Planctomycetes (Fig. 3.11). 2613
2614 Figure 3.12. The responses of prokaryotic lineages to glucose amendment across all the study sites. 2615
99
2616
Figure 3.13. The responses of prokaryotic OTU proportions to the glucose amendment. The t values are 2617
calculated based on paired t-tests, and only t values with P < 0.05 are shown. The numbers within or 2618
above each box represented the numbers of OTUs within Archaea each bacterial phylum. 2619
2620
In addition to the prokaryotic phyla, I examined the responses of prokaryotic lineage 2621
proportions to the glucose amendment at finer taxonomy levels (from class to OTU 2622
levels). As revealed by the LEfSe and paired t-tests, different sublineages of most phyla 2623
responded similarly to the glucose amendment (Fig. 3.12, Table S3.1). For example, the 2624
relative abundance of multitude sublineages under Acidobacteria, Thaumarchaeota, 2625
Chloroflexi, Planctomycetes, Gemmatimonadetes, and Nitrospirae also significantly 2626
decreased under the glucose amendment (Fig. 3.12, Table S3.1). Similarly, the 2627
proportions of a few sublineages under Firmicutes and Bacteroidetes significantly 2628
increased under the glucose amendment (Fig. 3.12). However, there were also some 2629
sublineages of several phyla responding differently to the glucose amendment (Fig. 2630
100
3.12, Table S3.1). In particular, under the glucose amendment, although the relative 2631
abundances of Proteobacteria significantly increased, the proportions of 2632
Deltaproteobacteria and its sublineages significantly decreased (Fig. 3.12). In addition, 2633
the sublineages of Actinobacteria showed diverse responses to the glucose amendment 2634
(Fig. 3.12, Table S3.1). At the OTU level, the OTU proportions under most phyla 2635
showed consistent responses to the glucose amendment. However, the responses of the 2636
OTUs under Proteobacteria, Actinobacteria, and Bacteroidetes to the glucose 2637
amendment were highly inconsistent. 2638
2639
2640
Figure 3.14. The correlations between prokaryotic OTU proportions and microbial respiration rates. The 2641
r values are calculated based on Pearson correlations, and only r values with P < 0.05 are shown. The 2642
numbers within or above each box represent the numbers of OTUs within Archaea and each bacterial 2643
phylum. 2644
2645
2646
101
The relative abundances of the finer taxonomy lineages also differed between the 16S 2647
rDNA- and rRNA-based measurements (Table S3.1). The paired t-test suggested that 2648
most sublineages under Euryarchaeota, Acidimicrobiia, Actinobacteria (class), 2649
Thermoleophilia, Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, 2650
Armatimonadetes, KD4 (a class of Chloroflexi), Cyanobacteria, Fibrobacteres, 2651
Clostridia, and Tectomicrobia had higher relative abundances of 16S rRNA compared 2652
to their relative abundance of 16S rDNA (Table S3.1). However, most sublineages 2653
under other phyla or classes (e.g., Thaumarchaeota, Acidobacteria, Planctomycetacia, 2654
and Gammaproteobacteria) accounted for higher proportions in the prokaryotic 16S 2655
rDNA than in the 16S rRNA (Table S3.1). 2656
2657
The correlations between prokaryotic lineage relative abundance and soil microbial 2658
respiration rates were highly variable among the sublineages under Archaea, 2659
Actinobacteria, Chloroflexi, Gemmatimonadetes, and Armatimonadetes (Table S3.1; 2660
Fig. 3.14). However, the sublineages under Proteobacteria, Acidobacteria, and 2661
Bacteriodetes showed consistent correlations with soil microbial respiration rates 2662
(Table S3.1; Fig. 3.14). 2663
2664
3.5. Discussion 2665
This study found that the active prokaryotic community compositions showed more 2666
sensitive responses to the glucose amendment than the total prokaryotic community 2667
compositions (Figs. 3.5, 3.7, and 3.8). These findings are in line with my first 2668
102
hypothesis as well as the results in Chapter 2 (Che et al., 2015), and suggest that 16S 2669
rRNA-based methods are promising for identifying the life strategies of prokaryotic 2670
lineages. Indeed, the high sensitivity of 16S rRNA-based microbial indices to organic 2671
matter amendment has been documented in a number of previous studies (Li et al., 2017; 2672
Pennanen et al., 2004). Some other investigations also showed that the active microbial 2673
community compositions were usually sensitive to the organic matter amendments, 2674
using methods based on stable isotope probes (Bernard et al., 2007; Cebron et al., 2011; 2675
Lee et al., 2017; Padmanabhan et al., 2003; Ye et al., 2015). As mentioned in Chapter 2676
1, the active microbial community profiles have been analyzed using 16S rRNA-based 2677
methods in hundreds of studies (Barnard et al., 2015; Blagodatskaya and Kuzyakov, 2678
2013; Blazewicz et al., 2013; Che et al., 2016a; Che et al., 2016b). Nonetheless, their 2679
ecological implications have seldom been interpreted, owing to the lack of microbial 2680
lineage life-strategy identifications based on 16S rRNA. The findings in this study 2681
suggest that most of the identified life-strategies of the prokaryotic lineages based on 2682
16S rRNA and rDNA were similar (Figs. 3.7, 3.8, and 3.12; Table S3.1). Thus, the 2683
ecological implications of prokaryotic community profiles based on 16S rRNA can be 2684
interpreted in similar ways as those based on 16S rDNA. 2685
2686
Although there was great variability of prokaryotic community compositions across the 2687
study sites, the glucose amendment significantly and consistently increased the 2688
proportions of several prokaryotic phyla (e.g., Proteobacteria and Firmicutes; Figs. 3.7 2689
and 3.8). These findings support my second hypothesis, and suggest that compared to 2690
103
other phyla, Proteobacteria and Firmicutes can be classified as copiotrophs, which is 2691
also supported by a number of existing investigations (Hungate et al., 2015; Morrissey 2692
et al., 2016; Pepe-Ranney et al., 2016; Siles et al., 2014). Similarly, Acidobacteria, 2693
Chloroflexi, Planctomycetes, Gemmatimonadetes, Tectomicrobia, Nitrospirae, 2694
Armatimonadetes, Verrucomicrobia, Thaumarchaeota, and FBP can be classified as 2695
oligotrophs based on their responses to the glucose amendment in this study (Figs. 3.7 2696
and 3.8). Again, this is consistent with a few previous studies (Bernard et al., 2007; 2697
Cleveland et al., 2007; Fierer et al., 2007; Huang et al., 2012; Morrissey et al., 2016; 2698
Pepe-Ranney et al., 2016; Siles et al., 2014). Collectively, these results indicate that 2699
even at the phylum level, we can still obtain some consistent life strategy classifications 2700
of microbial lineages. The life-strategy classification at finer taxonomy levels can be 2701
found in Table S3.1. 2702
2703
As proposed by Blazewicz et al. (2013), microbes maintaining higher ribosome levels 2704
are likely to be better competitors when the conditions become favorable, and are thus 2705
potential copiotrophs. In this study, I found that most copiotrophic microbial lineages 2706
had higher relative abundances of 16S rRNA compared to their relative abundances of 2707
16S rDNA, and most oligotrophs were more enriched in the 16S rDNA based 2708
communities (Fig. 3.9 and Table S3.1). However, there were also some exceptions. 2709
Therefore, the last part of my second hypothesis still needs to be examined in future 2710
studies. 2711
2712
104
As mentioned above, the proportions of copiotrophs and oligotrophs could positively 2713
and negatively correlate with soil microbial respiration rates, respectively (Che et al., 2714
2016b; Fierer et al., 2007). Accordingly, in this study, within each site, the glucose 2715
amendment significantly stimulated soil microbial respiration, increased the copiotroph 2716
proportions, and decreased the relative abundance of oligotrophs (Figs. 3.3f, 3.7, and 2717
3.8). Moreover, across the study sites, the relative abundances of most copiotrophs and 2718
oligotrophs were positively and negatively correlated with soil microbial respiration 2719
rates, respectively (Figs. 3.10 and 3.11). However, there were also some prokaryotic 2720
lineages controverting this rule. In particular, the relative abundance of Acidobacteria 2721
were significantly decreased by the glucose amendment (Figs. 3.7c and 3.8c), while it 2722
showed significant positive correlations with the soil microbial respiration rates (Figs. 2723
3.10c and 3.11c). These findings challenged our traditional view that the Acidobacteria 2724
proportions should negatively correlate with soil microbial respiration rates across 2725
different study sites (Fierer et al., 2007). Similarly, Firmicutes were identified as 2726
copiotrophs, but exhibited a negative correlation with soil microbial respiration rates, 2727
which is also supported by the meta-analysis in Chapter 1 (Che et al., 2016b). A 2728
persuasive explanation for these controversies is the outperformance of other 2729
environmental factors in determining the microbial community profiles. For instance, 2730
the determinative role of pH value in shaping soil microbial community compositions 2731
has been recognized in a few studies (Feng et al., 2014; Shen et al., 2013; Siciliano et 2732
al., 2014; Xiong et al., 2012). In summary, these findings suggest that the proportions 2733
of copiotrophic and oligotrophic phyla are robust indicators to assess the microbial 2734
105
respiration activity variations within each site. However, their applications to assess the 2735
differences in cross-site microbial respiration must be conducted with caution. 2736
2737
Controversial life-strategy classifications of same microbial lineages have been 2738
observed in many studies (Ho et al., 2017). For instance, Actinobacteria and Firmicutes 2739
were classified into different categories in various studies (Morrissey et al., 2016; Pepe-2740
Ranney et al., 2016; Siles et al., 2014; Sun et al., 2017). Accordingly, in this study, 2741
several microbial lineages (e.g., Actinobacteria, Bacteroidetes, and Firmicutes) showed 2742
highly inconsistent responses to the glucose amendment across different study sites 2743
(Figs. 3.7 and 3.8). As proposed in the previous publications, these controversies could 2744
be mainly attributed to the life-strategy diversifications in microbial lineages at the finer 2745
taxonomy level (Fierer, 2017; Ho et al., 2017; Yuste et al., 2014). Indeed, in this study, 2746
I observed obvious diversifications in the responses of the finer-level prokaryotic 2747
lineages to the glucose amendment and their correlations with soil microbial respiration 2748
rates (Fig. 3.12, 3.13, and 3.14; Table S3.1). In particular, the diversifications even 2749
existed for the phyla that showed consistent responses to the glucose amendment. For 2750
example, although Proteobacteria were classified as copiotrophic phylum in the soils 2751
from most study sites, more than half of their OTUs actually exhibited negative 2752
responses to the glucose amendment (Fig. 3.12). Thus, the life strategy of a phylum is 2753
actually determined by life-strategies of the dominant finer-level lineages. Interestingly, 2754
in this study, I found that the OTUs under the same family or genus showed consistent 2755
responses to the glucose amendment. Moreover, as revealed by the NMDS, the glucose 2756
106
amendment shifted the prokaryotic community profiles towards a similar direction. 2757
Collectively, these findings highlight the possibility of ranking the prokaryotic families 2758
and genus from most copiotrophic to most oligotrophic, which deserves further 2759
exploration. 2760
2761
Whether rRNA copies can be used to indicate microbial vitality or not has been 2762
questioned and debated for decades (Blagodatskaya and Kuzyakov, 2013; Blazewicz et 2763
al., 2013; Che et al., 2016a; Kerkhof and Kemp, 1999). In Chapter 2, the study 2764
suggested that 16S rRNA copies in an alpine meadow soil showed a weak response to 2765
the glutamate amendment (Che et al., 2015). However, in this study, the paired t-test 2766
showed that the glucose amendment significantly increased the 16S rRNA copies, and 2767
the effects were consistent across most study sites (Fig. 3.3d). Consequently, the weak 2768
response of 16S rRNA copies to organic carbon addition in Chapter 2 could be just a 2769
special case, and the rRNA copies are actually an excellent proxy of microbial activity 2770
in most cases. In contrast, this study also highlights the poor performance of 16S rDNA 2771
copies to indicate soil microbial vitality, which is supported by the meta-analysis in 2772
Chapter 1 (Che et al., 2016b). 2773
2774
Establishing the links between microbial communities and their ecological functions is 2775
a long-term goal for microbial ecology research (Bier et al., 2015; Graham et al., 2014; 2776
Graharni et al., 2016). Additionally, incorporating microbial indices into ecosystem 2777
process modeling and assessment would considerably improve the prediction and 2778
107
assessment accuracy (Treseder et al., 2012). Nevertheless, which microbial indices 2779
should be employed to establish the links and incorporated into the corresponding 2780
models remains an open question. The findings in this study suggest that the 16S rRNA 2781
copies and relative abundance of Proteobacteria are potentially promising indices to 2782
establish the links between heterotrophic respiration and microbial community, and 2783
may also contribute to modeling soil heterotrophic respiration. 2784
2785
In line with several investigations which observed significant decreases in microbial α-2786
diversities under organic matter amendments (Cesarano et al., 2017; Sun et al., 2015), 2787
in this study, the glucose amendment significantly and consistently decreased the 2788
microbial α-diversity (Fig. 3.4). This could be mainly elicited by the intensified 2789
competition among microbial lineages under the glucose amendment. The glucose 2790
amendment increased the availability of organic carbon. However, other resources such 2791
as phosphorus and nitrogen may become limiting factors. In particular, the contents of 2792
available nitrogen significantly decreased under the glucose amendment (Fig. 3.2). 2793
Thus, the copiotrophs could outcompete and destroy their oligotrophic counterparts to 2794
obtain more limited resources (Eilers et al., 2010; Fierer et al., 2007; Koch, 2001), and 2795
thus decreased the microbial α-diversity. Furthermore, in this study, the number of 2796
copiotrophic lineages is far less than that of oligotrophic lineages (Table S3.1), which 2797
could further decrease the microbial α-diversity. 2798
2799
108
3.6. Conclusions 2800
This study showed that although the soil prokaryotic communities dramatically differed 2801
across different grasslands, most of their responses to the glucose amendment are 2802
consistent. The soil microbial 16S rRNA copies, 16S rRNA-rDNA ratios, community 2803
composition dispersion, biomass, and respiration rates, as well as the relative 2804
abundances of Bacteria, Proteobacteria, Bacteroidetes, and Firmicutes significantly 2805
increased under the glucose amendment. The glucose amendment also significantly 2806
decreased the prokaryotic α-diversity, as well as the proportions of Archaea, 2807
Acidobacteria, Chloroflexi, Planctomycetes, Gemmatimonadetes, Tectomicrobia, 2808
Nitrospirae, Armatimonadetes, Verrucomicrobia, and FBP. In particular, the overall 2809
responses of the community profiles showed a similar ordination pattern across the 2810
studies sites, which suggests that it is possible to rank finer-level prokaryotic lineages 2811
according to their life-strategies. Most of the prokaryotic lineages that increased and 2812
decreased under the glucose amendment also showed positive and negative correlations 2813
with the soil microbial respiration rates across the grasslands, respectively. 2814
Nevertheless, some lineages, in particular, Acidobacteria, did not exhibit this pattern, 2815
which might be attributed to the outperformance of other environmental factors over 2816
organic carbon availability in determining the soil microbial community structures. In 2817
addition, there were evident life-strategy diversifications under each prokaryotic 2818
lineage, especially for the Proteobacteria. Collectively, this study suggests that despite 2819
the existence of life-strategy diversification, it is still possible to recognize several 2820
general copiotrophic and oligotrophic prokaryotic lineages across different grasslands. 2821
109
Nevertheless, using the relative abundances of copiotrophs and oligotrophs to assess 2822
the cross-sites microbial respiration capacities must be conducted with care. 2823
110
3.7 References 2824
Andrews, J., 1984. Relevance of r-and K-theory to the ecology of plant pathogens. In: 2825
M. Klug, C. Reddy (Eds.), Current Perspectives in Microbial Ecology. 2826
American Society of Microbiology, Washington, D.C., USA, pp. 1–7. 2827
Andrews, J.H., Harris, R.F., 1986. r- and K-selection and microbial ecology. In: K.C. 2828
Marshall (Eds.), Advances in Microbial Ecology. Springer US, Boston, MA, pp. 2829
99–147. 2830
Barnard, R.L., Osborne, C.A., Firestone, M.K., 2015. Changing precipitation pattern 2831
alters soil microbial community response to wet-up under a Mediterranean-type 2832
climate. ISME Journal. 9(4), 946–957. 2833
Bernard, L., Mougel, C., Maron, P.A., Nowak, V., Leveque, J., Henault, C., Haichar, 2834
F.E.Z., Berge, O., Marol, C., Balesdent, J., Gibiat, F., Lemanceau, P., Ranjard, 2835
L., 2007. Dynamics and identification of soil microbial populations actively 2836
assimilating carbon from 13C-labelled wheat residue as estimated by DNA- and 2837
RNA-SIP techniques. Environmental Microbiology 9(3), 752–764. 2838
Bier, R.L., Bernhardt, E.S., Boot, C.M., Graham, E.B., Hall, E.K., Lennon, J.T., 2839
Nemergut, D.R., Osborne, B.B., Ruiz-González, C., Schimel, J.P., 2015. 2840
Linking microbial community structure and microbial processes: an empirical 2841
and conceptual overview. FEMS Microbiology Ecology 91(10), fiv113. 2842
Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: Critical review 2843
of estimation criteria and approaches. Soil Biology and Biochemistry 67, 192–2844
211. 2845
111
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as 2846
an indicator of microbial activity in environmental communities: limitations and 2847
uses. ISME Journal 7(11), 2061–2068. 2848
Bloor, J., Pottier, J., 2014. Grazing and spatial heterogeneity: implications for grassland 2849
structure and function. Gransslands Biodiversity and Conservation in a 2850
Changing World. 2014, 135–162. 2851
Brookes, P., Landman, A., Pruden, G., Jenkinson, D., 1985. Chloroform fumigation and 2852
the release of soil nitrogen: a rapid direct extraction method to measure 2853
microbial biomass nitrogen in soil. Soil Biology and Biochemistry 17(6), 837–2854
842. 2855
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, 2856
E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, 2857
S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., 2858
Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, 2859
W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME 2860
allows analysis of high-throughput community sequencing data. Nature 2861
Methods 7(5), 335–336. 2862
Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., 2863
Turnbaugh, P.J., Fierer, N., Knight, R., 2011. Global patterns of 16S rRNA 2864
diversity at a depth of millions of sequences per sample. Proceedings of the 2865
National Academy of Sciences of the United States of America 108, 4516–4522. 2866
Cebron, A., Louvel, B., Faure, P., France-Lanord, C., Chen, Y., Murrell, J.C., Leyval, 2867
112
C., 2011. Root exudates modify bacterial diversity of phenanthrene degraders 2868
in PAH-polluted soil but not phenanthrene degradation rates. Environmental 2869
Microbiology 13(3), 722–736. 2870
Cesarano, G., De Filippis, F., La Storia, A., Scala, F., Bonanomi, G., 2017. Organic 2871
amendment type and application frequency affect crop yields, soil fertility and 2872
microbiome composition. Applied Soil Ecology 120, 254–264. 2873
Che, R., Deng, Y., Wang, W., Rui, Y., Zhang, J., Tahmasbian, I., Tang, L., Wang, S., 2874
Wang, Y., Xu, Z., Cui, X., 2018. Long-term warming rather than grazing 2875
significantly changed total and active soil procaryotic community structures. 2876
Geoderma 316, 1–10. 2877
Che, R.X., Deng, Y.C., Wang, F., Wang, W.J., Xu, Z.H., Wang, Y.F., Cui, X.Y., 2015. 2878
16S rRNA-based bacterial community structure is a sensitive indicator of soil 2879
respiration activity. Journal of Soils and Sediments 15(9), 1987–1990. 2880
Che, R.X., Wang, F., Wang, Y.F., Deng, Y.C., Zhang, J., Ma, S., Cui, X.Y., 2016a. A 2881
review on the methods for measuring total microbial activity in soils. Acta 2882
Ecologica Sinica 36(08), 2103–2112. 2883
Che, R.X., Wang, W.J., Zhang, J., Nguyen, T.T.N., Tao, J., Wang, F., Wang, Y.F., Xu, 2884
Z.H., Cui, X.Y., 2016b. Assessing soil microbial respiration capacity using 2885
rDNA- or rRNA-based indices: a review. Journal of Soils and Sediments 16(12), 2886
2698–2708. 2887
Cleveland, C.C., Nemergut, D.R., Schmidt, S.K., Townsend, A.R., 2007. Increases in 2888
soil respiration following labile carbon additions linked to rapid shifts in soil 2889
113
microbial community composition. Biogeochemistry 82(3), 229–240. 2890
Doane, T.A., Horwáth, W.R., 2003. Spectrophotometric determination of nitrate with a 2891
single reagent. Analytical Letters 36(12), 2713–2722. 2892
Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. 2893
Bioinformatics 26(19), 2460–2461. 2894
Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon 2895
reads. Nature Methods 10(10), 996–998. 2896
Eilers, K.G., Lauber, C.L., Knight, R., Fierer, N., 2010. Shifts in bacterial community 2897
structure associated with inputs of low molecular weight carbon compounds to 2898
soil. Soil Biology and Biochemistry 42(6), 896–903. 2899
Feng, Y., Grogan, P., Caporaso, J.G., Zhang, H., Lin, X., Knight, R., Chu, H., 2014. pH 2900
is a good predictor of the distribution of anoxygenic purple phototrophic 2901
bacteria in Arctic soils. Soil Biology and Biochemistry 74, 193–200. 2902
Fierer, N., 2017. Embracing the unknown: disentangling the complexities of the soil 2903
microbiome. Nature Reviews Microbiology 15(10), 579–590. 2904
Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward an ecological classification of 2905
soil bacteria. Ecology 88(6), 1354–1364. 2906
Gottschal, J.C., 1985. Some reflections on microbial competitiveness among 2907
heterotrophic bacteria. Antonie van Leeuwenhoek 51(5–6), 473–494. 2908
Graham, E.B., Wieder, W.R., Leff, J.W., Weintraub, S.R., Townsend, A.R., Cleveland, 2909
C.C., Philippot, L., Nemergut, D.R., 2014. Do we need to understand microbial 2910
communities to predict ecosystem function? A comparison of statistical models 2911
114
of nitrogen cycling processes. Soil Biology and Biochemistry 68, 279–282. 2912
Graharni, E.B., Knelman, J.E., Schindlbacher, A., Siciliano, S., Breulmann, M., 2913
Yannarell, A., Bemans, J.M., Abell, G., Philippot, L., Prosser, J., Foulquier, A., 2914
Yuste, J.C., Glanville, H.C., Jones, D.L., Angel, F., Salminen, J., Newton, R.J., 2915
Buergmann, H., Ingram, L.J., Hamer, U., Siljanen, H.M.P., Peltoniemi, K., 2916
Potthast, K., Baneras, L., Hartmann, M., Banerjee, S., Yu, R.-Q., Nogaro, G., 2917
Richter, A., Koranda, M., Castle, S.C., Goberna, M., Song, B., Chatterjee, A., 2918
Nunes, O.C., Lopes, A.R., Cao, Y., Kaisermann, A., Hallin, S., Strickland, M.S., 2919
Garcia-Pausas, J., Barba, J., Kang, H., Isobe, K., Papaspyrou, S., Pastorelli, R., 2920
Lagomarsino, A., Lindstrom, E.S., Basiliko, N., Nemergut, D.R., 2016. 2921
Microbes as engines of ecosystem function: When does community structure 2922
enhance predictions of ecosystem processes? Frontiers in Microbiology 7, 214. 2923
Hirsch, P., Bernhard, M., Cohen, S., Ensign, J., Jannasch, H., Koch, A., Marshall, K., 2924
Poindexter, J., Rittenberg, S., Smith, D., 1979. Life under conditions of low 2925
nutrient concentrations. In: M. Shilo (Eds.), Strategies of Microbial Life in 2926
Extreme Environments. Weinheim, Germany, Verlag Chemie, pp. 357–372. 2927
Ho, A., Di Lonardo, D.P., Bodelier, P.L.E., 2017. Revisiting life strategy concepts in 2928
environmental microbial ecology. FEMS Microbiology Ecology 93(3), fix006. 2929
Huang, W.R., Bai, Z.H., Hoefel, D., Hu, Q., Lv, X., Zhuang, G.Q., Xu, S.J., Qi, H.Y., 2930
Zhang, H.X., 2012. Effects of cotton straw amendment on soil fertility and 2931
microbial communities. Frontiers of Environmental Science and Engineering 2932
6(3), 336–349. 2933
115
Hungate, B.A., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 2934
Koch, B.J., Liu, C.M., McHugh, T.A., Marks, J.C., Morrissey, E.M., Price, L.B., 2935
2015. Quantitative microbial ecology through stable isotope probing. Applied 2936
and Environmental Microbiology 81(21), 7570–7581. 2937
Karst, S.M., Dueholm, M.S., McIlroy, S.J., Kirkegaard, R.H., Nielsen, P.H., Albertsen, 2938
M., 2018. Retrieval of a million high-quality, full-length microbial 16S and 18S 2939
rRNA gene sequences without primer bias. Nature Biotechnology. 2940
Kerkhof, L., Kemp, P., 1999. Small ribosomal RNA content in marine Proteobacteria 2941
during non-steady-state growth. FEMS Microbiology Ecology 30(3), 253-260. 2942
Koch, A.L., 2001. Oligotrophs versus copiotrophs. Bioessays 23(7), 657–661. 2943
Lahtinen, S.J., Ahokoski, H., Reinikainen, J.P., Gueimonde, M., Nurmi, J., Ouwehand, 2944
A.C., Salminen, S.J., 2008. Degradation of 16S rRNA and attributes of viability 2945
of viable but nonculturable probiotic bacteria. Letters in Applied Microbiology 2946
46(6), 693–698. 2947
Lee, C.G., Watanabe, T., Asakawa, S., 2017. Bacterial community incorporating carbon 2948
derived from plant residue in an anoxic non-rhizosphere soil estimated by DNA-2949
SIP analysis. Journal of Soils and Sediments 17(4), 1084–1091. 2950
Lennon, J.T., Jones, S.E., 2011. Microbial seed banks: the ecological and evolutionary 2951
implications of dormancy. Nature Reviews Microbiology 9(2), 119–130. 2952
Li, L.N., Qu, Z., Wang, B.L., Qu, D., 2017. Dynamics of the abundance and structure 2953
of metabolically active Clostridium community in response to glucose additions 2954
in flooded paddy soils: closely correlated with hydrogen production and Fe(III) 2955
116
reduction. Journal of Soils and Sediments 17(6), 1727–1740. 2956
Meyer, O., 1994. Functional groups of microorganisms. In: E. Schulze, H. Mooney 2957
(Eds.), Biodiversity and Ecosystem Function. Springer-Verlag, New York, USA, 2958
pp. 67–96. 2959
Morrissey, E.M., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 2960
Koch, B.J., Liu, C.M., Hayer, M., McHugh, T.A., Marks, J.C., Price, L.B., 2961
Hungate, B.A., 2016. Phylogenetic organization of bacterial activity. ISME 2962
Journal 10(9), 2336–2340. 2963
Muttray, A.F., Mohn, W.W., 1999. Quantitation of the population size and metabolic 2964
activity of a resin acid degrading bacterium in activated sludge using slot-blot 2965
hybridization to measure the rRNA : rDNA ratio. Microbial Ecology 38(4), 2966
348–357. 2967
Oksanen J., Blanchet F.G., Kindt R., Legendre P., Minchin P.R., O’Hara R.B., Simpson 2968
G.L., Solymos P., Stevens M.H.H., Wagner H., 2018. Vegan: community 2969
ecology package. http://CRAN.R-project.org/package=vegan. 2970
Padmanabhan, P., Padmanabhan, S., DeRito, C., Gray, A., Gannon, D., Snape, J.R., Tsai, 2971
C.S., Park, W., Jeon, C., Madsen, E.L., 2003. Respiration of 13C-labeled 2972
substrates added to soil in the field and subsequent 16S rRNA gene analysis of 2973
13C-labeled soil DNA. Applied and Environmental Microbiology 69(3), 1614–2974
1622. 2975
Pennanen, T., Caul, S., Daniell, T.J., Griffiths, B.S., Ritz, K., Wheatley, R.E., 2004. 2976
Community-level responses of metabolically-active soil microorganisms to the 2977
117
quantity and quality of substrate inputs. Soil Biology and Biochemistry 36(5), 2978
841–848. 2979
Pepe-Ranney, C., Campbell, A.N., Koechli, C.N., Berthrong, S., Buckley, D.H., 2016. 2980
Unearthing the ecology of soil microorganisms using a high resolution DNA-2981
SIP approach to explore cellulose and xylose metabolism in Soil. Frontiers in 2982
Microbiology 7, 703. 2983
Perez-Osorio, A.C., Williamson, K.S., Franklin, M.J., 2010. Heterogeneous rpoS and 2984
rhlR mRNA levels and 16S rRNA/rDNA (rRNA gene) ratios within 2985
Pseudomonas aeruginosa biofilms, sampled by laser capture microdissection. 2986
Journal of Bacteriology 192(12), 2991–3000. 2987
R Development Core Team, 2018. R: a language and environment for statistical 2988
computing. R Foundation for Statistical Computing, Vienna, Austria. 2989
http://www.R-project.org/. 2990
Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., 2991
Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., Sahl, J.W., Stres, 2992
B., Thallinger, G.G., Van Horn, D.J., Weber, C.F., 2009. Introducing mothur: 2993
open-source, platform-independent, community-supported software for 2994
describing and comparing microbial communities. Applied and Environmental 2995
Microbiology 75(23), 7537–7541. 2996
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., 2997
Huttenhower, C., 2011. Metagenomic biomarker discovery and explanation. 2998
Genome Biology 12(6), 18. 2999
118
Segev, E., Smith, Y., Ben-Yehuda, S., 2012. RNA dynamics in aging bacterial spores. 3000
Cell 148(1–2), 139–149. 3001
Shen, C., Xiong, J., Zhang, H., Feng, Y., Lin, X., Li, X., Liang, W., Chu, H., 2013. Soil 3002
pH drives the spatial distribution of bacterial communities along elevation on 3003
Changbai Mountain. Soil Biology and Biochemistry 57, 204–211. 3004
Siciliano, S.D., Palmer, A.S., Winsley, T., Lamb, E., Bissett, A., Brown, M.V., van Dorst, 3005
J., Ji, M., Ferrari, B.C., Grogan, P., 2014. Soil fertility is associated with fungal 3006
and bacterial richness, whereas pH is associated with community composition 3007
in polar soil microbial communities. Soil Biology and Biochemistry 78, 10–20. 3008
Siles, J.A., Rachid, C., Sampedro, I., Garcia-Romera, I., Tiedje, J.M., 2014. Microbial 3009
diversity of a mediterranean soil and its changes after biotransformed dry olive 3010
residue amendment. PLOS One 9(7), e103035. 3011
Sun, H., Wang, Q.-x., Liu, N., Li, L., Zhang, C.-g., Liu, Z.-b., Zhang, Y.-y., 2017. 3012
Effects of different leaf litters on the physicochemical properties and bacterial 3013
communities in Panax ginseng-growing soil. Applied Soil Ecology 111, 17–24. 3014
Sun, R.B., Zhang, X.X., Guo, X.S., Wang, D.Z., Chu, H.Y., 2015. Bacterial diversity in 3015
soils subjected to long-term chemical fertilization can be more stably 3016
maintained with the addition of livestock manure than wheat straw. Soil Biology 3017
and Biochemistry 88, 9–18. 3018
Treseder, K.K., Balser, T.C., Bradford, M.A., Brodie, E.L., Dubinsky, E.A., Eviner, V.T., 3019
Hofmockel, K.S., Lennon, J.T., Levine, U.Y., MacGregor, B.J., Pett-Ridge, J., 3020
Waldrop, M.P., 2012. Integrating microbial ecology into ecosystem models: 3021
119
challenges and priorities. Biogeochemistry 109(1-3), 7–18. 3022
Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring 3023
soil microbial biomass C. Soil biology and Biochemistry 19(6), 703–707. 3024
Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naive Bayesian classifier for 3025
rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied 3026
and Environmental Microbiology 73(16), 5261–5267. 3027
Wang, S., Wang, X., Han, X., Deng, Y., 2018. Higher precipitation strengthens the 3028
microbial interactions in semi-arid grassland soils. Global Ecology and 3029
Biogeography. 3030
Wang, W.J., Dalal, R.C., Moody, P.W., Smith, C.J., 2003. Relationships of soil 3031
respiration to microbial biomass, substrate availability and clay content. Soil 3032
Biology and Biochemistry 35(2), 273–284. 3033
Wang, Y., Qian, P.-Y., 2009. Conservative fragments in bacterial 16S rRNA genes and 3034
primer design for 16S ribosomal DNA amplicons in metagenomic studies. 3035
PLOS One 4(10), e7401. 3036
Weatherburn, M., 1967. Phenol-hypochlorite reaction for determination of ammonia. 3037
Analytical Chemistry 39(8), 971–974. 3038
Winogradsky, S., 1924. Sur la microflora autochtone de la terre arable, 178. Comptes 3039
Rendus Hebdomadaire des Se´ ances de l’Academie des Sciences, Paris. 3040
WRB, 1998. World reference base for soil resources. FAO/ISRIC/ISSS, Italy. 3041
Xiong, J., Liu, Y., Lin, X., Zhang, H., Zeng, J., Hou, J., Yang, Y., Yao, T., Knight, R., 3042
Chu, H., 2012. Geographic distance and pH drive bacterial distribution in 3043
120
alkaline lake sediments across Tibetan Plateau. Environmental Microbiology 3044
14(9), 2457–2466. 3045
Ye, R.Z., Doane, T.A., Morris, J., Horwath, W.R., 2015. The effect of rice straw on the 3046
priming of soil organic matter and methane production in peat soils. Soil 3047
Biology and Biochemistry 81, 98–107. 3048
Yuste, J.C., Fernandez-Gonzalez, A.J., Fernandez-Lopez, M., Ogaya, R., Penuelas, J., 3049
Lloret, F., 2014. Functional diversification within bacterial lineages promotes 3050
wide functional overlapping between taxonomic groups in a Mediterranean 3051
forest soil. FEMS Microbiology Ecology 90(1), 54–67. 3052
3053
3054
3055
121
3056
Chapter 4. The Application of Microbial Life-strategy 3057
Classification in Explaining the Soil Microbial Responses to 3058
Litter and Phosphorus Amendments* 3059
3060
3061
*This chapter forms the basis of the following journal manuscript: 3062
Che RX, Qin JL, Tahmasbian I, Wang F, Zhou ST, Xu ZH, Cui XY (2018). Litter 3063
amendment rather than phosphorus can dramatically change inorganic nitrogen 3064
pools in a degraded grassland soil by affecting nitrogen-cycling microbes. Soil 3065
Biology and Biochemistry 120:145–152. DOI: 10.1016/j.soilbio.2018.02.006 3066
(Some results were not included in this Chapter). 3067
3068
Che RX, Wang F, Wang WJ, Xu ZH, Tao J, Li LF, Tahmasbian I, Cui XY. Total and 3069
active soil microbial responses highlight the risks of increasing litter input to 3070
recover degraded alpine meadows. Land Degradation and Development. (Under 3071
Review) 3072
3073
122
3074
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 3075
This chapter includes a co-authored paper. The bibliographic details of the co-authored 3076
paper, including all authors, are: 3077
Che RX, Qin JL, Tahmasbian I, Wang F, Zhou ST, Xu ZH, Cui XY (2018). Litter amendment 3078
rather than phosphorus can dramatically change inorganic nitrogen pools in a degraded 3079
grassland soil by affecting nitrogen-cycling microbes. Soil Biology and Biochemistry 120:145–3080
152. 3081
My contribution to the paper involved: 3082
Experimental design and conduction; statistical analysis; categorisation of the data into 3083
a usable format; and writing the paper. 3084
3085
The copyright of the paper has been transformed to the Publisher, but I reserve the 3086
right to include the paper as a chapter in the thesis. 3087
3088
3089
(Signed) _________________________________ (Date)______________ 3090
Rongxiao Che 3091
3092
(Countersigned) ___________________________ (Date)______________ 3093
Corresponding author of paper: Xiaoyong Cui 3094
3095
(Countersigned) ___________________________ (Date)______________ 3096
Supervisor: Zhihong Xu 3097
3098
3099
3100
3101
3102
3103
123
3104
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 3105
This chapter includes a co-authored paper. The status of the co-authored paper, 3106
including all authors, are: 3107
Che RX, Wang F, Wang WJ, Xu ZH, Tao J, Li LF, Tahmasbian I, Cui XY. Total and active 3108
soil microbial responses highlight the risks of increasing litter input to recover degraded alpine 3109
meadows. Land Degradation and Development. (Under Review) 3110
My contribution to the paper involved: 3111
Experimental design and conduction; statistical analysis; categorisation of the data into 3112
a usable format; and writing the paper. 3113
3114
3115
(Signed) _________________________________ (Date)______________ 3116
Rongxiao Che 3117
3118
(Countersigned) ___________________________ (Date)______________ 3119
Corresponding author of paper: Xiaoyong Cui 3120
3121
(Countersigned) ___________________________ (Date)______________ 3122
Supervisor: Zhihong Xu 3123
3124
3125
3126
3127
3128
3129
3130
124
4.1. Abstract 3131
Phosphorus addition and increasing organic matter input are extensively applied to 3132
restore the degraded grasslands. However, there is still a paucity of knowledge on the 3133
effects of these restoration efforts on soil microbes, especially for the active populations. 3134
Here, a microcosm experiment was conducted to investigate the responses of total and 3135
active soil microbes to phosphorus and litter amendments. Soil samples were collected 3136
from a degraded Tibetan alpine meadow. Microbial abundance and rDNA 3137
transcriptional activity were determined through real-time PCR. Total and active 3138
microbial community profiles were analyzed using MiSeq sequencing based on DNA 3139
and RNA, respectively. The results showed that the litter amendment significantly 3140
increased soil microbial activity, rDNA transcriptional activity, and fungal abundance, 3141
whereas it exerted no significant effect on prokaryotic abundance. Additionally, the 3142
litter amendment significantly decreased microbial α-diversity, but increased their β-3143
diversity. Under the litter amendment, the proportions of the microbial lineages with 3144
high rRNA-rDNA ratios (e.g., γ-Proteobacteria and Coniochaetales; most of them are 3145
copiotrophs) increased, while those with low rRNA-rDNA ratios (e.g., Archaea, 3146
Acidobacteria, and Helotiales; most of them are oligotrophs) decreased. Interestingly, 3147
the number of the microbial lineages with increased relative abundances under the litter 3148
amendments was far less than those with decreased proportions. However, no 3149
significant effects of the phosphorus amendments on soil microbes were observed. 3150
Collectively, these findings suggest that increasing litter input to the degraded grassland 3151
soils can result in more copiotrophic microbial communities with higher activity, lower 3152
125
α-diversity, and more divergent community compositions. 3153
3154
4.2. Introduction 3155
Approximately half of the grasslands on our planet have been degraded, attributing to 3156
the intensified climate change and human activities (Gang et al., 2014). The grassland 3157
degradations have dramatically decreased soil nutrient availability, ecosystem 3158
biodiversity, and pasture productivity, threatening the lives of millions of herdsmen (Bai 3159
et al., 2008; Li et al., 2016; Liu et al., 2018b; Yao et al., 2016). Thus, a great quantity 3160
of measures, such as phosphorus (P) fertilisation (Reed et al., 2007; Smits et al., 2008) 3161
and increasing organic matter input (Averett et al., 2004; Harris et al., 2015; Yang et 3162
al., 2017), have been applied to recover the degraded grasslands. Soil microbes have 3163
been proven to be sensitive and responsible for the developments of grassland 3164
degradations (Che et al., 2017; Li et al., 2016). Moreover, determining microbial 3165
responses to the manipulation of organic matter input is the most widely-used approach 3166
t to identify the life-strategies of microbial lineages (Fierer et al., 2007; Ho et al., 2017). 3167
Therefore, examining the effects of litter and P amendments on microbes in degraded 3168
soils can not only provide vital guidelines for restoring the degraded grasslands, but 3169
also contribute to the identification of microbial lineage life-strategies. 3170
3171
In recent decades, soil microbial responses to organic matter amendments is extensively 3172
investigated in the field of microbial ecology (Ho et al., 2017). Soil microbial activity 3173
stimulations, community profile shifts, and diversity changes under organic matter 3174
126
amendments were frequently observed in various studies (Cesarano et al., 2017; Fierer 3175
et al., 2007; Ramirez-Villanueva et al., 2015; Sun et al., 2017; Yan et al., 2018). 3176
Similarly, significant effects of P amendments on soil microbial community 3177
compositions, activities, and diversities have also been documented in a few 3178
publications (Liu et al., 2018a; Poeplau et al., 2016; Treseder, 2004). However, with 3179
most of the studies conducted on farmlands and forests (Guo et al., 2017; Liu et al., 3180
2018a; Mitchell et al., 2016; Su et al., 2017), grasslands have only been investigated in 3181
very few studies (Eilers et al., 2010; Leff et al., 2015). Additionally, although the 3182
majority of soil microbes are dormant and only weakly connected with soil 3183
functionality (Blagodatskaya and Kuzyakov, 2013; Lennon and Jones, 2011), the 3184
responses of the active microbial populations have rarely been determined (Che et al., 3185
2016b; Fierer, 2017). Moreover, no investigations regarding the responses of active soil 3186
microbes to the grassland restorations have been conducted to date. Collectively, it is 3187
vital to determine the responses of degraded grassland soil microbes, especially the 3188
active fraction, to litter and P amendments. 3189
3190
Currently, soil prokaryotic and fungal communities are broadly explored using 3191
molecular methods based on 16S rDNA and internal transcribed spacer (ITS; e.g., real-3192
time PCR and MiSeq sequencing), respectively (Che et al., 2016b). As summarized in 3193
my previous reviews (Che et al., 2016a; Che et al., 2016b), the methods based on 3194
rRNA and ITS RNA have been widely employed to examine the active microbial 3195
populations due to the following reasons. First, rRNA is the catalytic core for peptide 3196
127
synthesis of microbes. It is usually abundant in active microbial cells, and would be 3197
degraded rapidly in the dormant or deceased cells (Kerkhof and Kemp, 1999; Lahtinen 3198
et al., 2008; Perez-Osorio et al., 2010; Segev et al., 2012). Second, ITS only exists in 3199
the transcript precursors of rDNA polycistrons, and thus it provides an even better 3200
opportunity to capture the community profiles of metabolically active microbes 3201
(Anderson and Parkin, 2007; Baldrian et al., 2012; Bastida et al., 2017). Therefore, a 3202
combination of the methods based on rDNA, 1rRNA, ITS DNA, and ITS rRNA would 3203
provide systematic insights into the total and active soil microbial communities. 3204
3205
As the highest plateau and one of the largest geographic units on the earth, the Tibetan 3206
Plateau is mainly covered by natural grasslands which are experiencing severe 3207
degradation as a consequence of climate change and livestock overgrazing (Cui and 3208
Graf, 2009; Wang et al., 2016). The increasingly severe degradation of the Tibetan 3209
grasslands has led to a dramatic decrease in the availability of P and organic carbon 3210
(Che et al., 2017; Liu et al., 2018b). In particular, P has been recognized as a limiting 3211
factor of the primary productivity of the Tibetan grasslands (Bing et al., 2016; Dong et 3212
al., 2016; Zhou et al., 2017). Thus, increasing litter and P inputs are frequently proposed 3213
and utilized to restore the degraded Tibetan grasslands (Cai et al., 2015; Che et al., 2017; 3214
Dong et al., 2016). However, as mentioned above, soil microbial responses to these 3215
restoration efforts have never been analyzed. 3216
3217
Therefore, in this study, I aimed to reveal the main and interactive effects of litter and 3218
128
P amendments on total and active soil microbes. The soil was collected from a degraded 3219
alpine meadow on the Tibetan Plateau. Microbial activity was determined via 3220
measuring soil microbial respiration rate and FDA hydrolase activity, while microbial 3221
abundance and rDNA transcription activity were determined through real-time PCR 3222
based on rDNA and rRNA, respectively. Total and active microbial community profiles 3223
were separately analyzed through MiSeq sequencing based on DNA (prokaryotic 16S 3224
rDNA and fungal ITS DNA) and RNA (prokaryotic 16S rRNA and fungal ITS RNA). 3225
I hypothesized that 1) litter amendment significantly increase microbial abundance, 3226
microbial activity, and the α-diversity of active soil microbes; 2) litter and P 3227
amendments significantly alter the microbial community compositions; 3) litter 3228
amendment significantly increase the relative abundance of microbial lineages which 3229
are more abundant in the active microbial communities, but decrease the proportions of 3230
the lineages which are more abundant in the total microbial communities; and 4) the 3231
community compositions of total and active microbes are significantly different without 3232
litter amendment, but this differences can be eliminated by the litter amendments. 3233
3234
4.3. Materials and methods 3235
4.3.1. Study sites, soil sampling, and litter collection 3236
The soil and litter were separately sampled from a degraded (Fig. 4.1a) and a grazing-3237
free (Fig. 4.1b) Tibetan alpine meadow near the Haibei Alpine Meadow Ecosystem 3238
Research Station (37° 37′ N, 101° 12′ E; 3 200 m asl) which has been described in my 3239
previous publication (Che et al., 2018a). Briefly, this region experiences a typical 3240
129
plateau continental climate, with mean annual temperature and precipitation of −1.7 °C 3241
and 570 mm, respectively (Zhao et al., 2006). The soil was identified as Gelic 3242
Cambisols (WRB, 1998). According to the criterion proposed by Lin et al. (2015), the 3243
degraded alpine meadow was at the fourth stage of degradation, but the erosion of the 3244
mattic epipedon was not serious (Fig. 4.1a). With an average coverage of 65%, the plant 3245
community at the degraded alpine meadow was dominated by Kobresia humilis and 3246
Leontopodium alpinum. The grazing-free grassland was near the degraded alpine 3247
meadow, and was dominated by Elymus nutans, Poa pratensis, Kobresia humilis, 3248
Festuca ovina, and Potentilla nivea (Fig. 4.1b). 3249
3250
3251
Figure 4.1. The photographs of the study sites for soil sampling (a) and litter collection (b). 3252
3253
130
I randomly collected soil samples (0–10 cm; A horizon; C: 6.30%; N: 0.56%; P: 0.033%; 3254
and C/N/P atomic ratio of 495:38:1) from the degraded alpine meadow, using a steel 3255
auger with a 7-cm diameter, in July 2014. Then, the soils were homogenized, sieved to 3256
≤ 2 mm, transported to the laboratory in a box with ice block, and preserved at 4 °C 3257
until the start of the incubation. The main characteristics of the soils were detailed in 3258
Table S1. The grass litter (C: 41.75%; N: 0.687%; P: 0.037%; and C/N/P atomic ratio 3259
of 2915:41:1) was collected from the grazing-free grassland, in December 2013. After 3260
the collection, the litter was dried at 65 °C to constant weight, ball milled, and 3261
homogenized for the subsequent operations (Tahmasbian et al., 2017). 3262
3263
Table 4.1. Some properties of the soils used for incubation. 3264
Background After pre-incubation
Soil
texture
0.25 mm ≤ Ф < 2.00 mm (%) 18.31 ± 0.44 NA
0.05 mm ≤ Ф < 0.25 mm (%) 25.64 ± 1.03 NA
0.02 mm ≤ Ф < 0.05 mm (%) 22.25 ± 0.85 NA
0.002 mm ≤ Ф < 0.02 mm (%) 19.25 ± 0.48 NA
Ф < 0.002 mm (%) 14.55 ± 0.47 NA
Total carbon concentrations (%) 6.30 ± 0.01 NA
Total nitrogen concentrations (%) 0.56 ± 0.01 NA
Total P concentrations (g kg-1) 0.33 ± 0.00 NA
Water holding capacity (%) 96.07 ± 10.31 NA
Gravimetric moisture contents (%) 36.25 ± 0.04 NA
pH values 8.10 ± 0.02 8.01 ± 0.03
Available P concentrations (mg kg-1) 8.12 ± 0.34 8.16 ± 0.28
Dissolved organic carbon concentrations (mg kg-1) 327.03 ± 6.28 190.29 ± 18.80
NH4+-N concentrations (mg kg-1) 22.98 ± 1.11 10.28 ± 1.16
NO3--N concentrations (mg kg-1) 29.62 ± 0.75 35.75 ± 0.64
Inorganic nitrogen concentrations (mg kg-1) 52.60 ± 1.70 46.03 ± 1.49
All the soil properties were presented as mean ± SE, n = 4. 3265
3266
4.3.2. Experiment design and soil incubation 3267
This microcosm experiment followed a two-way factorial design with four replicates 3268
131
for each treatment. The two experimental factors were P (0 or 0.025 g P kg-1 dry soil) 3269
and litter (0 or 67 g kg-1 dry soil) amendments. The P amendment was achieved by 3270
applying calcium superphosphate solutions. Thus, there were four treatments (i.e., 3271
control, P amendment, litter amendment, and litter and P amendments) and sixteen 3272
microcosms in total. The litter amendment rate was based on the peak plant biomass in 3273
the grazing-free alpine meadow, and also in line with a previous study employing 3274
carbon amendment to restore the tallgrass prairie (Averett et al., 2004). There were three 3275
reasons for choosing the P addition rate. First, the available phosphorus contents in the 3276
untreated soil were around 8 mg kg-1, and thus amending the soil with 25 mg P kg-1soil 3277
could theoretically make the soil available phosphorus reach an abundant level (more 3278
than 30 mg kg-1). Second, the P addition rate approximately equaled to the total P 3279
contents in the added litter. Thus, the amendment rate could make it comparable 3280
between chemical fertilizer and litter amendments in improving the P availability. 3281
Finally, this addition rate approximately equaled the usual P fertilization (Wang et al. 3282
2015). 3283
3284
The incubation was conducted with an aliquot of the field moist (36.25 %, w/w; 37.73% 3285
water holding capacity) soils (30 g of dry mass) which were separately placed into each 3286
of the sixteen 130-ml flasks. These flasks were then pre-incubated in darkness, at 25 °C 3287
for 14 days to stabilize the soil conditions. The calcium superphosphate solution (3 ml) 3288
and litter powder (2.0 g) were added to the corresponding soils and homogenized on 3289
the fifteenth day of the incubation. Subsequently, all the flasks were continually 3290
132
incubated in darkness, at 25 °C for 21 days. During the incubation, all the jars were 3291
uncapped, and water was replenished every three days to keep soil moisture close to 3292
the field moisture. Soil microbial respiration was measured on the last day of the 3293
incubation based on the CO2 production rates, and the CO2 concentrations were 3294
determined using an Agilent 7890A gas chromatograph. At the end of the incubation, I 3295
collected all the soil samples which were then stored at -80 °C. 3296
3297
4.3.3. Soil bio-physicochemical analysis 3298
I determined the concentrations of soil microbial biomass N and C through the 3299
chloroform fumigation-extraction method (Brookes et al., 1985; Vance et al., 1987), as 3300
detailed in my previous study (Ma et al., 2015). Soil dissolved organic C (DOC), 3301
dissolved N (DN), NH4+-N, and NO3
--N were extracted with the K2SO4 solution (0.5 3302
M) within one week after the incubation. Then, I determined soil DOC and DN 3303
concentrations through a TOC Analyzer (Liquid TOC II; Elementar Analysensysteme 3304
GmbH, Hanau, Germany), and measured soil NH4+-N and NO3
--N concentrations using 3305
an autoflow analyzer (AutoAnalyzer 3 System; SEAL Analytical GmbH, Norderstedt, 3306
Germany). In addition, soil pH values were determined using a pH meter (STARTER 3307
3100, Ohaus Instruments Co., Ltd., Shanghai, China) in a 1:5 soil-water suspension, 3308
and soil available P concentrations were analyzed using the molybdate-ascorbic acid 3309
methods as described by Olsen (1954). I also measured soil fluorescein diacetate (FDA) 3310
hydrolase activity following the description of Jiang et al. (2011) to reflect the overall 3311
active soil hydrolase. 3312
133
3313
4.3.4. Soil nucleic acid extraction, RNA reverse transcription, and quantitative 3314
PCR 3315
Soil DNA and RNA were separately extracted using PowerSoil™ DNA Isolation Kit 3316
and a PowerSoil® Total RNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA) 3317
from 0.25 and 1.0 g fresh soil, respectively. After the removal of DNA residuals from 3318
the RNA extracts with a DNase I kit (MO BIO Laboratories, Carlsbad, CA, USA), the 3319
RNA was reverse-transcribed into cDNA using a PrimeScript™ II 1st Strand cDNA 3320
Synthesis Kit with random hexamers (Takara Bio Inc., Shiga, Japan). 3321
3322
I determined the copies of 16S rDNA, 16S rRNA, 28S rDNA, and 28S rRNA with the 3323
universal primer sets for prokaryotes (515F: 5’-GTG CCA GCM GCC GCG GT AA-3324
3’, Caporaso et al., 2011; 909R: 5’-CCC CGY CAA TTC MTT TRA GT-3’, Wang and 3325
Qian, 2009) and fungi (LR0R: 5’-ACC CGC TGA ACT TAA GC-3’; LR3: 5’-CCG 3326
TGT TTC AAG ACG GG-3’, Barnard et al., 2015). Quantitative PCR was conducted 3327
in a 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The 3328
20 μL reaction mixtures contained: 10 μL of Maxima™ SYBR Green or ROX (2 ×, 3329
Thermo Fisher Scientific, Waltham, MA, USA), 0.5 μL of each primer (20 μmol L-1), 3330
1 μL of template DNA (DNA or cDNA), and 8.0 μL of nuclease-free water. Standard 3331
curves were constructed using plasmids harboring the corresponding 16S or 28S rDNA 3332
fragments. The samples, standards, and no template controls were all analyzed in 3333
triplicate. The quantitative PCR program was as following: 30 s at 95 °C; 40 cycles of 3334
134
5 s at 95 °C, 30 s at 58 °C, 45 s at 72 °C, and 30 s at 80 °C. The fluorescence signal was 3335
recorded at 80 °C to attenuate influences of primer dimers, and the specificities of PCR 3336
products were tested by melting curve analysis. 3337
3338
4.3.5. MiSeq sequencing and bioinformatics analysis 3339
The PCR amplicons of 16S rDNA and rRNA were generated with 909R and barcoded 3340
515F, while the fungal ITS DNA and RNA were amplified with gITS7 (5’-GTG ART 3341
CAT CGA RTC TTT G-3’; Ihrmark et al., 2012) and ITS4 (5’-TCC TCC GCT TAT 3342
TGA TAT GC-3’; White et al., 1990). A 12-bp barcode was added to the 5’-end of each 3343
515F and ITS4. The 50 μL reaction mixtures contained: 25 μL Premix Taq™ Hot Start 3344
Version (Takara Bio Inc., Shiga, Japan), 1 μL of each primer (10 μmol L-1), 1 μL of 3345
template DNA (DNA or cDNA), and 22 μL of nuclease-free water. The PCR programs 3346
were as following: 10 min at 95 °C; 30 (16S rDNA and rRNA) or 35 (ITS DNA or RNA) 3347
cycles of 30 s at 95 °C, 30 s at 58 °C, and 45 s at 72 °C; and a final extension at 72 °C 3348
for 10 minutes. As for each sample, three independent amplifications were conducted, 3349
and the PCR products from the three amplifications were pooled into one tube, and the 3350
PCR products were then purified using the GeneJET Gel Extraction Kit (Thermo 3351
Scientific, Lithuania). Subsequently, all the PCR products were pooled together with 3352
an equal molar amount from each sample. 3353
3354
The MiSeq sequencing was conducted by the analytical center at the Chengdu Institute 3355
of Biology, Chinese Academy of Sciences. The samples were prepared using a TruSeq 3356
135
DNA kit following the manufacturer’s instructions. The purified products were diluted, 3357
denatured, re-diluted, and mixed with PhiX (equal to 30% of final DNA amount) as 3358
described in the Illumina library preparation protocols, and were then applied to an 3359
Illumina MiSeq system for sequencing with the Reagent Kit v2 2 × 250 bp. 3360
3361
I processed the prokaryotic and fungal sequence data using “Quantitative Insights Into 3362
Microbial Ecology” (Qiime; v1.9.1; Caporaso et al., 2010), Usearch (v8.0.1623; Edgar, 3363
2010), Mothur (v1.27; Schloss et al., 2009), and R (R Development Core Team 2018). 3364
Firstly, I spliced and assigned the paired-end sequences to each sample based on their 3365
barcodes in Qiime. Subsequently, the OTU table and the OTU centroid sequences were 3366
generated according to the default UPARSE OTU analysis pipeline (Edgar, 2013). 3367
Specifically, I clustered OTUs with an identity cutoff of 97%, and assigned that 3368
taxonomy to each OTU centroid sequences using Wang’s methods (pseudobootstrap 3369
confidence score = 80%) against the Silva (v128; prokaryotes) and UNITE (v7.2; fungi) 3370
database. Finally, the α diversity, β diversity, and the composition of each sample were 3371
calculated in R with “vegan” packages (Oksanen et al., 2018) after rarefaction. The 3372
microbial β diversity was calculated using “betadisper” function in the vegan package. 3373
3374
4.3.6. Statistics 3375
The main and interactive effects of litter and P amendments on soil microbial abundance, 3376
activity, and diversity were analyzed through two-way analysis of variance (ANOVA) 3377
and Duncan’s post hoc test. Responses of microbial community compositions to litter 3378
136
and P amendments were tested via permutation multivariate analysis of variance 3379
(PERMANOVA) and visualized by non-metric multidimensional scaling (NMDS). I 3380
performed linear discriminant analysis effect size (LEfSe; Segata et al., 2011) to reveal 3381
the response of each microbial lineage proportion to litter and P amendments. The 3382
relationships between microbial indices and environmental indices were tested through 3383
Pearson correlations and envfit based on NMDS. The LEfSe analysis was conducted 3384
using the online Huttenhower Galaxy server (huttenhower.sph.harvard.edu/galaxy), 3385
and all the other analysis was conducted in R with the vegan package. 3386
3387
4.4. Results 3388
4.4.1. Responses of soil properties to the litter and P amendments 3389
All the soil physicochemical properties examined in this study, except soil dissolved 3390
organic N concentrations, were significantly affected by the litter amendment (Fig. 4.2). 3391
Specifically, the litter amendment significantly increased the concentrations of soil 3392
DOC, available P, dissolved N, and NH4+-N, but decreased the pH values and 3393
concentrations of soil NO3--N and inorganic N (Fig. 4.2). In contrast, the P amendments 3394
only significantly decreased soil pH values, and marginally increased soil microbial 3395
biomass C (Fig. 4.2). No significant interactive effects were observed between the litter 3396
and P amendments on the soil properties in this study (Fig. 4.2). 3397
137
3398
Figure 4.2. The effects of litter and P amendments on soil properties. DOC: dissolved organic carbon; 3399
AP: available P; TDN: total dissolved N; IN: inorganic N; DON: dissolved organic N; FDA: fluorescein 3400
diacetate. CK: control; P: P amendment; L: litter amendment; LP: litter and P amendments. Relative 3401
activity of FDA hydrolase was presented in the ratios of the fluorescence value of each sample to the 3402
average fluorescence value of the four CK soils. L**: the effect of litter amendment was significant with 3403
P < 0.01; L***: the effect of litter amendment was significant with P < 0.001; P•: the effect of P 3404
amendment was marginal with P < 0.1; P***: the effect of P amendment was significant with P < 0.001. 3405
All the data were presented in mean ± SE, n = 4. Bars with different letters represent significant 3406
differences at P < 0.05. 3407
3408
4.4.2. The effects of the litter and P amendments on soil microbial biomass, activity, 3409
abundance, and diversity 3410
The litter amendment significantly increased the soil microbial biomass C and N (Figs. 3411
4.2i and 4.2j). The FDA hydrolase activity and soil microbial respiration rates also 3412
dramatically increased under the litter amendment (Figs. 4.2k and 4.2l). In addition, the 3413
litter amendment significantly increased the microbial rDNA transcription and fungal 3414
138
abundance, whereas it exerted no significant effects on the prokaryotic abundance (Fig. 3415
4.3). However, no significant effects of the P amendment and litter-P interaction on the 3416
soil microbial biomass, abundance, and activity were observed in this study (Figs. 4.2 3417
and 4.3). 3418
3419
Figure 4.3. Effects of litter and P amendments on soil microbial rDNA and rRNA copies. All the data 3420
were presented in mean ± SE, n = 4. CK: control; P: P amendments; L: litter amendments; LP: litter and 3421
P amendments; “***”: P < 0.001. Bars with different letters represent significant differences at P < 0.05. 3422
3423
The diversity of soil prokaryotes and fungi showed high response consistency to all the 3424
treatments (Figs. 4.4, 4.5, and 4.6). Specifically, the microbial richness, Chao1 index, 3425
Shannon index, and evenness all significantly decreased under litter amendments (Fig. 3426
4.4). However, the NMDS analysis showed that the litter amendment dramatically 3427
increased the variability in microbial community compositions (Fig. 4.5). This is further 3428
supported by the significantly increased microbial β-diversity under the litter 3429
amendment (Fig. 4.6). Again, no significant effects of P amendment and litter-P 3430
interaction on the microbial diversity were observed in this study. 3431
3432
139
3433 Figure 4.4. Effects of litter and P amendments on soil microbial α-diversity. All the data were presented 3434
in mean ± SE, n = 4. CK: control; P: P amendments; L: litter amendments; LP: litter and P amendments; 3435
“***”: P < 0.001. Bars with different letters represent significant differences at P < 0.05. 3436
3437
3438 Figure 4.5. The NMDS ordinations of the soil microbial community structures at the OTU level. L: effect 3439
of litter amendments; “***”: P < 0.001. 3440
140
3441
Figure 4.6. Effects of litter and P amendments on soil microbial community dispersion. All the data were 3442
presented in mean ± SE, n = 4. CK: control; P: P amendments; L: litter amendments; LP: litter and P 3443
amendments; “***”: P < 0.001. Bars with different letters represent significant differences at P < 0.05. 3444
3445
4.4.3. Soil microbial community compositions 3446
3447 Figure 4.7. The relative abundance of main prokaryotic (a) and fungal (b) lineages under different 3448
treatments. All the data were presented in mean - SE, n = 4. CK: control; P: P amendments; L: litter 3449
amendments; LP: litter and P amendments. 3450
3451
Acidobacteria were the most abundant phylum of the total prokaryotes in soils without 3452
litter amendments (Fig. 4.7a). In the soils with litter amendment, the total prokaryotic 3453
communities were dominated by Proteobacteria, especially the α- and γ-Proteobacteria 3454
(Fig. 4.7a). However, the active prokaryotes were dominated by Proteobacteria 3455
regardless with or without litter amendment (Fig. 4.7a). In addition, Bacteroidetes, 3456
Actinobacteria, Planctomycetes, Firmicutes, Gemmatimonadetes, Chloroflexi, and 3457
141
Nitrospirae were also observed in the prokaryotic communities (Fig. 4.7a). The relative 3458
abundance of archaea was generally lower than 1%, especially in the soils with the litter 3459
amendments (Fig. 4.7a). 3460
3461
More than 90% of the soil fungi were classified as Ascomycota, and the relative 3462
abundance of Basidiomycota (4.06%) was also high in some of the soil samples. As 3463
shown in Fig. 4.7b, the most abundant fungal order was Hypocreales (55.97%) which 3464
was followed by Coniochaetales (7.30%), Helotiales (7.23%), Pleosporales (4.86%), 3465
Xylariales (4.85%), Sordariales (3.42%), Sebacinales (2.28%), and Pezizales (1.88%). 3466
The proportions of some unclassified order under Ascomycota were also high (Fig. 3467
4.7b). 3468
3469
4.4.4. The effects of the litter and P amendments on soil microbial community 3470
compositions 3471
The total and active soil microbial community structures significantly differed from 3472
each other, except for fungal communities under the litter amendment (Fig. 4.5). 3473
Nevertheless, their responses to the P and litter amendments were similar. Specifically, 3474
the litter rather than the P amendment significantly altered the soil microbial community 3475
compositions. Interestingly, under the litter amendment, the number of the microbial 3476
lineages with increased relative abundance was far less than that with decreased 3477
proportions. For instance, the litter amendment significantly decreased the proportions 3478
of more than 120 prokaryotic lineages, but only increased the relative abundances of 3479
less than 60 prokaryotic lineages (Fig. 4.8). This was even more significant for soil 3480
142
fungal communities in which only four lineages became more abundant, while the 3481
proportions of more than 50 fungal lineages significantly decreased (Fig. 4.9). 3482
3483
3484
Figure 4.8. The responses of prokaryotic lineage proportions to the litter amendments. The effects of 3485
litter amendments were determined using the LEfSe analysis with a threshold on the logarithmic LDA 3486
score of 3.5. The lineages in green were enriched in the soils without litter amendments, while the 3487
lineages in red were more abundant in the litter-amended soils. 3488
3489
143
3490 Figure 4.9. The responses of fungal lineage proportions to the litter amendments. The effects of litter 3491
amendments were determined using the LEfSe analysis with a threshold on the logarithmic LDA score 3492
of 3.0. The lineages in green were enriched in the soils without litter amendments, while the lineages in 3493
red were more abundant in the litter-amended soils. 3494
3495
As for soil prokaryotes, the lineages with increased proportions under the litter 3496
amendment mainly belonged to Proteobacteria and Actinobacteria, while the lineages 3497
with decreased proportions were mainly classified as archaea, Acidobacteria, 3498
Planctomycetes, Gemmatimonadetes, Chloroflexi, and Nitrospirae (Fig. 4.8). 3499
Additionally, the litter amendment also significantly decreased the relative abundances 3500
of δ-Proteobacteria and some lineages under β-Proteobacteria (Fig. 4.8). Regarding soil 3501
fungi, the litter amendment significantly increased the relative abundances of some 3502
lineages under Sordariomycetes, Pezizomycetes, and Agaricomycetes (Fig. 4.9). 3503
However, the relative abundances of the lineages under Tremellomycetes, 3504
144
Sordariomycetes, Pezizomycotina, Microbotryomycetes, Leotiomycetes, 3505
Geoglossomycetes, Dothideomycetes, Chytridiomycetes, and Agaricomycetes 3506
significantly decreased following the litter amendment (Fig. 4.9). 3507
3508
Interestingly, most of the lineages with increased proportions under the litter 3509
amendment were generally less abundant in total microbial communities than in active 3510
ones (e.g., γ-Proteobacteria, Actinobacteria, and Coniochaetales; Fig. 4.7). Conversely, 3511
the microbial lineages with lowered proportions under the litter amendment were 3512
generally more abundant in the total microbial communities than in the active microbial 3513
communities (e.g., Acidobacteria, Gemmatimonadetes, and Helotiales; Fig. 4.7). 3514
However, there were also some lineages that did not follow this rule. For instance, the 3515
proportion of δ-Proteobacteria in the active prokaryotic community was significantly 3516
higher than in the total prokaryotic community, but their relative abundance in both 3517
active and total communities significantly decreased under the litter amendment (Figs. 3518
4.7 and 4.8). In addition, the relative abundance of Nitrospirae was similar in the total 3519
and active prokaryotic community (Fig. 4.7). However, the litter amendment 3520
significantly decreased their proportions (Fig. 4.7). 3521
3522
145
4.4.5. The relationships between soil properties and microbes 3523
3524
Figure 4.10. The relationships between soil properties and microbes. •: P < 0.1; *: P < 0.05; **: P < 0.01; 3525
***: P < 0.001. FDA: fluorescein diacetate. The numbers in the out circle represented the correlation 3526
coefficients. The correlations between soil properties and the copies of rDNA and rRNA were tested 3527
using Pearson correlation; the correlations between soil properties and microbial community structures 3528
were tested using envfit based on NMDS. 3529
3530
The fungal abundance and the microbial rDNA transcription activity showed similar 3531
relationships with soil properties. Specifically, they were positively correlated with 3532
microbial biomass, respiration rates, FDA hydrolase activity, as well as the contents of 3533
soil DOC, NH4+-N, and available P, whereas negatively correlated with soil pH values 3534
and the contents of soil DN, NO3--N and IN (Fig. 4.10). However, none of the soil 3535
properties included in this study showed significant correlations with the prokaryotic 3536
abundance (Fig. 4.10). The total and active microbial community structures were 3537
significantly correlated with most of the soil properties included in this study (Fig. 4.10). 3538
However, the dissolved organic N (DON) contents and FDA hydrolase activity showed 3539
146
no significant correlations with soil microbial community structures, and DN contents 3540
were only significantly correlated with the community structures of active prokaryotes. 3541
In addition, the total prokaryotic community structures showed no significant 3542
correlations with soil pH values and soil microbial biomass (Fig. 4.10e). Interestingly, 3543
although the soil microbial respiration rates showed more significant correlations with 3544
fungal abundance than with that of prokaryotes, its correlation with prokaryotic 3545
community structures was much stronger than with fungal community structures (Fig. 3546
4.10). 3547
3548
4.5. Discussion 3549
The α-diversities of total and active soil microbes significantly decreased under the 3550
litter amendment (Fig. 4.4). This partially rejects my first hypothesis, but it is in line 3551
with my findings in Chapter 3 (Fig. 3.4) and several existing investigations which also 3552
documented the reductions in microbial α-diversities under organic matter amendments 3553
(Cesarano et al., 2017; Sun et al., 2015). The decrease in soil microbial α-diversity 3554
under the litter amendments could be mainly elicited by the competitions among 3555
microbial species. Under the litter amendments, copiotrophic microbes could 3556
outcompete and even destroy their oligotrophic neighbors, which could exclude some 3557
microbial lineages, and thus decreased the microbial richness (Eilers et al., 2010; Fierer 3558
et al., 2007; Koch, 2001). Interestingly, I found that the amount of the microbial 3559
lineages with higher proportions under the litter amendments were much less than that 3560
occupying decreased percentages (Figs. 4.8 and 4.9). This suggested that the litter 3561
amendments only benefited a small fraction of microbial lineages but suppressed a 3562
147
much larger proportion, which could be another reason for the decrease in soil microbial 3563
α-diversity. Biodiversity is a vital indicator of ecosystem stability (Awasthi et al., 2014; 3564
Deng, 2012; Feng et al., 2017), and has been recognized as a key driver of soil 3565
multifunctionality (Delgado-Baquerizo et al., 2017). Therefore, increasing litter input 3566
to restore the degraded alpine meadow may cause decline in the ecosystem stability and 3567
affect the soil functionality, and thus must be applied with cautions. However, 3568
temporally variable responses of soil microbial α-diversities to organic carbon 3569
amendments have also been observed in a number of studies (Chen et al., 2015; Siles 3570
et al., 2014). Thus, the findings in this study need to be confirmed in further studies. 3571
3572
Another interesting finding in this study was the increase in microbial β-diversity under 3573
the litter amendments (Fig. 4.6). This is consistent with my findings in Chapter 3 which 3574
also showed an increase in the prokaryotic community dispersion under the glucose 3575
amendments (Fig. 3.5). It suggests that the litter amendment might randomly support 3576
the copiotrophic microbial lineages, which indicates that the outcomes of increasing 3577
organic matter input to the degraded alpine meadow are difficult to be accurately 3578
predicted. 3579
3580
Organic matter amendments are widely used to identify the life strategies of soil 3581
microbial lineages. In line with the findings in Chapter 3, in this study, the rRNA-based 3582
microbial profiles showed similar but more sensitive responses to the litter amendment, 3583
which partially supports my second hypothesis. The proportions of the generally 3584
148
recognized copiotrophic lineages (e.g., Alphaproteobacteria and Gammaproteobacteria) 3585
significantly increased under the litter amendments (Bernard et al., 2007; Cleveland et 3586
al., 2007; Huang et al., 2012; Hungate et al., 2015; Leff et al., 2012; Morrissey et al., 3587
2016; Nemergut et al., 2010; Padmanabhan et al., 2003). Similarly, the relative 3588
abundances of previously identified oligotrophs such as Deltaproteobacteria, 3589
Acidobacteria, Chloroflexi, Planctomycetes, Nitrospirae, and Gemmatimonadetes 3590
significantly decreased under the litter amendment (Bernard et al., 2007; Cleveland et 3591
al., 2007; Fierer et al., 2007; Hungate et al., 2015; Leff et al., 2012; Morrissey et al., 3592
2016; Nemergut et al., 2010; Pepe-Ranney et al., 2016; Siles et al., 2014; Sun et al., 3593
2017). Collectively, these findings indicate that the DNA-based life-strategy 3594
classifications could also be applied to interpret the variations in RNA-based microbial 3595
community profiles, and further highlight the importance of life-strategy classification 3596
in explaining the microbial responses to environmental changes. 3597
3598
However, the litter amendment also significantly decreased the proportion of 3599
Betaproteobacteria which have been identified as copiotrophs (Bernard et al., 2007; 3600
Eilers et al., 2010; Fierer et al., 2007; Hungate et al., 2015; Morrissey et al., 2016; 3601
Padmanabhan et al., 2003). Similarly, although Actinobacteria have been identified as 3602
oligotrophs in Chapter 3 and several publications (Bernard et al., 2007; Cleveland et al., 3603
2007; Siles et al., 2014), their proportions tended to increase under the litter 3604
amendments. On the one hand, these inconsistencies could be elicited by the life-3605
strategy diversifications of the lineages at finer taxonomy level, which has been 3606
149
observed in Chapter 3 and some other studies (Hungate et al., 2015; Morrissey et al., 3607
2016). On the other hand, most studies that respectively classified Betaproteobacteria 3608
and Actinobacteria into copiotrophs and oligotrophs were based on labile carbon 3609
amendments (Cleveland et al., 2007; Fierer et al., 2007). The grass litter used in this 3610
study is more recalcitrant than the labile substrates (e.g., glucose and sucrose). 3611
Therefore, the increase in Actinobacterial proportions under the litter amendments 3612
could partially attribute to their outstanding abilities in decomposing recalcitrant 3613
substrates (Book et al., 2014; Hartmann et al., 2012; Khodadad et al., 2011). 3614
Accordingly, the decrease in the relative abundance of Betaproteobacteria under the 3615
litter amendments indicates that the Betaproteobacteria are less competitive than other 3616
copiotrophic microbial lineages when receiving recalcitrant substrates amendments. 3617
Consequently, these findings suggest that the copiotroph-oligotroph classifications of 3618
microbial lineages based on the amendments of labile and recalcitrant organic 3619
substrates can be inconsistent. 3620
3621
As proposed in a recent review, fungi are generally more oligotrophic than bacteria, due 3622
to their ability utilize recalcitrant organic substrates (Ho et al., 2017). However, I found 3623
that the ratios of fungi to bacteria significantly increased under the litter and glucose 3624
amendments (a complementary study based on Chapter 3). Moreover, my meta-analysis 3625
suggested that the fungal abundance was positively correlated with soil microbial 3626
respiration rates (Che et al., 2016b). Collectively, these findings suggest that, in contrast 3627
to the traditional view, soil fungi are probably more copiotrophic than bacteria, even 3628
150
though they have the ability of decomposing recalcitrant organic matter. 3629
3630
Compared to prokaryotes, the life-strategy of fungal lineages remain less studied (Ho 3631
et al., 2017; van der Wal et al., 2013). This study indicates that Sordariomycetes, 3632
Pezizomycetes, and Agaricomycetes are copiotrophic lineages. On the contrary, 3633
Tremellomycetes, Sordariomycetes, Pezizomycotina, Microbotryomycetes, 3634
Leotiomycetes, Geoglossomycetes, Dothideomycetes, Chytridiomycetes, and 3635
Agaricomycetes are more oligotrophic lineages. However, these classifications showed 3636
little consistency with previous studies (Baldrian and Valaskova, 2008; Chigineva et al., 3637
2009; Di Lonardo et al., 2013; Lunghini et al., 2013; Veraart et al., 2015), which can be 3638
caused by the variations in microbial community compositions. Therefore, determining 3639
the life-strategy of fungal lineages may be more challenging than bacteria, deserving 3640
further exploration. 3641
3642
The relationship between soil microbes and their respiration has been examined in a 3643
few studies (Bier et al., 2015; Che et al., 2015; Che et al., 2016b; Liu et al., 2018c). 3644
This study found that prokaryotic community structure is a more sensitive indicator of 3645
microbial respiration activity than other microbial indices such as 16S rDNA copies 3646
and fungal community structures (Fig. 4.10). This is in line with my findings in chapter 3647
2 (Che et al., 2015), and suggests that prokaryotic community composition can be 3648
introduced as a promising parameter to improve the accuracy of ecosystem respiration 3649
modeling. 3650
151
3651
Interestingly, I found that the litter amendment tended to increase the proportions of 3652
microbial lineages with high rRNA-rDNA ratios, but decreased those with low rRNA-3653
rDNA ratios, which supports my third hypothesis. This suggests that the copiotrophs 3654
may harbor higher cellular RNA concentrations than oligotrophs. However, in Chapter 3655
3, I found some microbial lineages violate this rule, and thus more studies are required 3656
to verify whether the high cellular RNA concentration is a common feature of the 3657
copiotrophs. Whether RNA-based methods can be used to indicate the active microbial 3658
populations has been debated for decades (Blagodatskaya and Kuzyakov, 2013; 3659
Blazewicz et al., 2013; Che et al., 2016a). In this study, I found that the litter 3660
amendments eliminated the differences between the community structures based on ITS 3661
RNA and DNA, while the differences between 16S rDNA and rRNA based community 3662
profiles still existed. Given the fact that the litter amendment could activate microbes, 3663
these findings suggest that the ITS RNA based fungal communities could reflect the 3664
active fungal populations. However, the implications of rRNA-based community 3665
profiles still need to be examined in further studies. These results partially support my 3666
fourth hypothesis. 3667
3668
The changes in soil inorganic nitrogen pools under the litter amendments have been 3669
explained and mainly attributed to the response of nitrogen-cycling microbes in my 3670
recent studies (Che et al., 2018b). Accordingly, the findings in this study are in line with 3671
the recent publication. For instance, using the functional gene-based real-time PCR, I 3672
152
have found that the litter amendments benefited nitrogen-fixers but depressed nitrifies 3673
(Che et al., 2018b). This is in accordance with the increase of Rhizobium and 3674
Burkholderiales (many members are nitrogen fixers), as well as the decrease in 3675
Nitrospirae and Thaumarchaeota (many members are nitrifiers) under the litter 3676
amendment (Fig. 4.8). Therefore, determining the life-strategies of microbial lineages 3677
can also contribute to improving our understanding of changes in nutrient pools. 3678
3679
Similar to some existing studies, no significant responses of soil microbes to the P 3680
amendments were observed in this study. Actually, in line with numerous existing 3681
investigations (Bohn et al., 2002; Sposito, 2008), I found that the addition of P fertilizer 3682
did not significantly increase soil available P concentrations. This is due to the fact that 3683
the phosphate, introduced in the form of chemical fertilizer, tends to precipitate in 3684
calcium phosphates, such as apatite and octacalcium phosphate, in the alkaline soils 3685
(Bohn et al., 2002; Sposito, 2008). Hence, the partial failure of my second hypothesis 3686
could be explained by the unchanged soil available P concentrations in the P-amended 3687
soils. Conversely, soil available P concentrations significantly increased under the litter 3688
amendment, even though the total P input of the litter and P amendments were almost 3689
equal in this study. These findings suggest that organic matter amendments could be a 3690
promising way to raise the availability of P in soils, which is also supported by a recent 3691
investigation (Mackay et al., 2017). 3692
3693
Although the soil pH was significantly decreased by both the litter and P amendments, 3694
153
in this study, the underlying mechanisms could be very different. Specifically, the P was 3695
added in the form of calcium superphosphate whose solution is acidic, and thus the 3696
decrease in soil pH under P amendment was expected. However, the decrease in soil 3697
pH after the litter amendment might be attributed to the increase in acid metabolite (e.g., 3698
citric acid and lactate) that produced by the soil microbes. 3699
3700
4.6. Conclusions 3701
This study showed that soil microbial activity, rDNA transcription, and fungal 3702
abundance significantly increased under the litter amendment. In addition, the litter 3703
amendment significantly decreased microbial α-diversity, but significantly increased 3704
the microbial β-diversity. Microbial community compositions also significantly altered 3705
following the litter amendment. The proportions of copiotrophs and oligotrophs 3706
significantly increased and decreased under the litter amendment, respectively. 3707
Interestingly, the changes in microbial lineages proportions could be related to their 3708
rDNA-rRNA ratios. Nevertheless, neither P amendments nor P-litter interaction exerted 3709
significant effects on soil microbes. Collectively, these findings suggest that increasing 3710
litter input to the degraded grassland soils can result in more copiotrophic microbial 3711
communities with higher activity, lower α-diversity, and more variable community 3712
compositions. 3713
3714
3715
154
4.7. References 3716
Anderson, I.C., Parkin, P.I., 2007. Detection of active soil fungi by RT-PCR 3717
amplification of precursor rRNA molecules. Journal of Microbiological 3718
Methods 68(2), 248–253. 3719
Averett, J.M., Klips, R.A., Nave, L.E., Frey, S.D., Curtis, P.S., 2004. Effects of soil 3720
carbon amendment on nitrogen availability and plant growth in an experimental 3721
tallgrass prairie restoration. Restoration Ecology 12(4), 568–574. 3722
Awasthi, A., Singh, M., Soni, S.K., Singh, R., Kalra, A., 2014. Biodiversity acts as 3723
insurance of productivity of bacterial communities under abiotic perturbations. 3724
ISME Journal 8(12), 2445–2452. 3725
Bai, Z.G., Dent, D.L., Olsson, L., Schaepman, M.E., 2008. Proxy global assessment of 3726
land degradation. Soil Use Management 24(3), 223–234. 3727
Baldrian, P., Kolarik, M., Stursova, M., Kopecky, J., Valaskova, V., Vetrovsky, T., 3728
Zifcakova, L., Snajdr, J., Ridl, J., Vlcek, C., Voriskova, J., 2012. Active and total 3729
microbial communities in forest soil are largely different and highly stratified 3730
during decomposition. ISME Journal 6(2), 248–258. 3731
Baldrian, P., Valaskova, V., 2008. Degradation of cellulose by basidiomycetous fungi. 3732
FEMS Microbiology Reviews 32(3), 501–521. 3733
Barnard, R.L., Osborne, C.A., Firestone, M.K., 2015. Changing precipitation pattern 3734
alters soil microbial community response to wet-up under a Mediterranean-type 3735
climate. ISME Journal 9(4), 946–957. 3736
Bastida, F., Torres, I.F., Andrés-Abellán, M., Baldrian, P., López-Mondéjar, R., 3737
155
Větrovský, T., Richnow, H.H., Starke, R., Ondoño, S., García, C., 2017. 3738
Differential sensitivity of total and active soil microbial communities to drought 3739
and forest management. Global Change Biology 23(10), 4185–4203. 3740
Bernard, L., Mougel, C., Maron, P.A., Nowak, V., Leveque, J., Henault, C., Haichar, 3741
F.E.Z., Berge, O., Marol, C., Balesdent, J., Gibiat, F., Lemanceau, P., Ranjard, 3742
L., 2007. Dynamics and identification of soil microbial populations actively 3743
assimilating carbon from 13C-labelled wheat residue as estimated by DNA- and 3744
RNA-SIP techniques. Environmental Microbiology 9(3), 752–764. 3745
Bier, R.L., Bernhardt, E.S., Boot, C.M., Graham, E.B., Hall, E.K., Lennon, J.T., 3746
Nemergut, D.R., Osborne, B.B., Ruiz-González, C., Schimel, J.P., 2015. 3747
Linking microbial community structure and microbial processes: an empirical 3748
and conceptual overview. FEMS microbiology ecology 91(10), fiv113. 3749
Bing, H.J., Wu, Y.H., Zhou, J., Sun, H.Y., Luo, J., Wang, J.P., Yu, D., 2016. 3750
Stoichiometric variation of carbon, nitrogen, and phosphorus in soils and its 3751
implication for nutrient limitation in alpine ecosystem of Eastern Tibetan 3752
Plateau. Journal of Soils and Sediments 16(2), 405–416. 3753
Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: Critical review 3754
of estimation criteria and approaches. Soil Biology and Biochemistry 67, 192–3755
211. 3756
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as 3757
an indicator of microbial activity in environmental communities: limitations and 3758
uses. ISME Journal 7(11), 2061–2068. 3759
156
Bohn, H.L., Myer, R.A., O'Connor, G.A., 2002. Soil chemistry. John Wiley & Sons. 3760
Book, A.J., Lewin, G.R., McDonald, B.R., Takasuka, T.E., Doering, D.T., Adams, A.S., 3761
Blodgett, J.A., Clardy, J., Raffa, K.F., Fox, B.G., 2014. Cellulolytic 3762
Streptomyces strains associated with herbivorous insects share a 3763
phylogenetically linked capacity to degrade lignocellulose. Applied and 3764
Environmental Microbiology 80(15), 4692–4701. 3765
Brookes, P., Landman, A., Pruden, G., Jenkinson, D., 1985. Chloroform fumigation and 3766
the release of soil nitrogen: a rapid direct extraction method to measure 3767
microbial biomass nitrogen in soil. Soil Biology and Biochemistry 17(6), 837–3768
842. 3769
Cai, H.Y., Yang, X.H., Xu, X.L., 2015. Human-induced grassland 3770
degradation/restoration in the central Tibetan Plateau: The effects of ecological 3771
protection and restoration projects. Ecological Engineering 83, 112–119. 3772
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, 3773
E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, 3774
S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., 3775
Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, 3776
W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME 3777
allows analysis of high-throughput community sequencing data. Nature 3778
Methods 7(5), 335–336. 3779
Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., 3780
Turnbaugh, P.J., Fierer, N., Knight, R., 2011. Global patterns of 16S rRNA 3781
157
diversity at a depth of millions of sequences per sample. Proceedings of the 3782
National Academy of Sciences of the United States of America 108, 4516–4522. 3783
Cesarano, G., De Filippis, F., La Storia, A., Scala, F., Bonanomi, G., 2017. Organic 3784
amendment type and application frequency affect crop yields, soil fertility and 3785
microbiome composition. Applied Soil Ecology 120, 254–264. 3786
Che, R.X., Deng, Y.C., Wang, W., Rui, Y.C., Zhang, J., Tahmasbian, I., Tang, L., Wang, 3787
S.P., Wang, Y.F., Xu, Z.H., Cui, X.Y., 2018a. Long-term warming rather than 3788
grazing significantly changed total and active soil procaryotic community 3789
structures. Geoderma 316, 1–10. 3790
Che, R.X., Qin, J.L., Tahmasbian, I., Wang, F., Zhou, S.T., Xu, Z.H., Cui, X.Y., 2018b. 3791
Litter amendment rather than phosphorus can dramatically change inorganic 3792
nitrogen pools in a degraded grassland soil by affecting nitrogen-cycling 3793
microbes. Soil Biology and Biochemistry 120(2018), 145–152. 3794
Che, R.X., Deng, Y.C., Wang, F., Wang, W.J., Xu, Z.H., Wang, Y.F., Cui, X.Y., 2015. 3795
16S rRNA-based bacterial community structure is a sensitive indicator of soil 3796
respiration activity. Journal of Soils and Sediments 15(9), 1987–1990. 3797
Che, R.X., Wang, F., Wang, W.J., Zhang, J., Zhao, X., Rui, Y.C., Xu, Z.H., Wang, Y.F., 3798
Hao, Y.B., Cui, X.Y., 2017. Increase in ammonia-oxidizing microbe abundance 3799
during degradation of alpine meadows may lead to greater soil nitrogen loss. 3800
Biogeochemistry 136(3), 341–352. 3801
Che, R.X., Wang, F., Wang, Y.F., Deng, Y.C., Zhang, J., Ma, S., Cui, X.Y., 2016a. A 3802
review on the methods for measuring total microbial activity in soils. Acta 3803
158
Ecologica Sinica 36(08), 2103–2112. 3804
Che, R.X., Wang, W.J., Zhang, J., Nguyen, T.T.N., Tao, J., Wang, F., Wang, Y.F., Xu, 3805
Z.H., Cui, X.Y., 2016b. Assessing soil microbial respiration capacity using 3806
rDNA- or rRNA-based indices: a review. Journal of Soils and Sediments 16(12), 3807
2698–2708. 3808
Chen, L., Zhang, J.B., Zhao, B.Z., Zhou, G.X., Ruan, L., 2015. Bacterial community 3809
structure in maize stubble-amended soils with different moisture levels 3810
estimated by bar-coded pyrosequencing. Applied Soil Ecology 86, 62–70. 3811
Chigineva, N.I., Aleksandrova, A.V., Tiunov, A.V., 2009. The addition of labile carbon 3812
alters litter fungal communities and decreases litter decomposition rates. 3813
Applied Soil Ecology 42(3), 264–270. 3814
Cleveland, C.C., Nemergut, D.R., Schmidt, S.K., Townsend, A.R., 2007. Increases in 3815
soil respiration following labile carbon additions linked to rapid shifts in soil 3816
microbial community composition. Biogeochemistry 82(3), 229–240. 3817
Cui, X.F., Graf, H.F., 2009. Recent land cover changes on the Tibetan Plateau: a review. 3818
Climate Change 94(1–2), 47–61. 3819
Delgado-Baquerizo, M., Trivedi, P., Trivedi, C., Eldridge, D.J., Reich, P.B., Jeffries, 3820
T.C., Singh, B.K., 2017. Microbial richness and composition independently 3821
drive soil multifunctionality. Functional Ecology. 3822
Deng, H., 2012. A review of diversity-stability relationship of soil microbial community: 3823
What do we not know? Journal of Environmental Sciences 24(6), 1027–1035. 3824
Di Lonardo, D.P., Pinzari, F., Lunghini, D., Maggi, O., Granito, V.M., Persiani, A.M., 3825
159
2013. Metabolic profiling reveals a functional succession of active fungi during 3826
the decay of Mediterranean plant litter. Soil Biology and Biochemistry 60, 210–3827
219. 3828
Dong, J.F., Cui, X.Y., Wang, S.P., Wang, F., Pang, Z., Xu, N., Zhao, G.Q., Wang, S.P., 3829
2016. Changes in biomass and quality of alpine steppe in response to N & P 3830
fertilization in the Tibetan Plateau. PLOS One 11(5), e0156146. 3831
Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. 3832
Bioinformatics 26(19), 2460–2461. 3833
Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon 3834
reads. Nature Methods 10(10), 996–998. 3835
Eilers, K.G., Lauber, C.L., Knight, R., Fierer, N., 2010. Shifts in bacterial community 3836
structure associated with inputs of low molecular weight carbon compounds to 3837
soil. Soil Biology and Biochemistry 42(6), 896–903. 3838
Feng, K., Zhang, Z.J., Cai, W.W., Liu, W.Z., Xu, M.Y., Yin, H.Q., Wang, A.J., He, Z.L., 3839
Deng, Y., 2017. Biodiversity and species competition regulate the resilience of 3840
microbial biofilm community. Molecular Ecology 26(21), 6170–6182. 3841
Fierer, N., 2017. Embracing the unknown: disentangling the complexities of the soil 3842
microbiome. Nature Reviews Microbiology 15(10), 579–590. 3843
Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward an ecological classification of 3844
soil bacteria. Ecology 88(6), 1354–1364. 3845
Gang, C.C., Zhou, W., Chen, Y.Z., Wang, Z.Q., Sun, Z.G., Li, J.L., Qi, J.G., Odeh, I., 3846
2014. Quantitative assessment of the contributions of climate change and 3847
160
human activities on global grassland degradation. Environmental Earth 3848
Sciences 72(11), 4273–4282. 3849
Guo, J., Liu, W., Zhu, C., Luo, G., Kong, Y., Ling, N., Wang, M., Dai, J., Shen, Q., Guo, 3850
S., 2017. Bacterial rather than fungal community composition is associated with 3851
microbial activities and nutrient-use efficiencies in a paddy soil with short-term 3852
organic amendments. Plant and Soil, 1–15. 3853
Harris, R.B., Wang, W.Y., Badinqiuying, Smith, A.T., Bedunah, D.J., 2015. Herbivory 3854
and competition of Tibetan steppe vegetation in winter pasture: effects of 3855
livestock exclosure and plateau pika reduction. PLOS One 10(7), e0132897. 3856
Hartmann, M., Howes, C.G., VanInsberghe, D., Yu, H., Bachar, D., Christen, R., 3857
Nilsson, R.H., Hallam, S.J., Mohn, W.W., 2012. Significant and persistent 3858
impact of timber harvesting on soil microbial communities in Northern 3859
coniferous forests. ISME Journal 6(12), 2199. 3860
Ho, A., Di Lonardo, D.P., Bodelier, P.L.E., 2017. Revisiting life strategy concepts in 3861
environmental microbial ecology. FEMS Microbiology Ecology 93(3), fix006. 3862
Huang, W.R., Bai, Z.H., Hoefel, D., Hu, Q., Lv, X., Zhuang, G.Q., Xu, S.J., Qi, H.Y., 3863
Zhang, H.X., 2012. Effects of cotton straw amendment on soil fertility and 3864
microbial communities. Frontiers of Environmental Science and Engineering 3865
6(3), 336–349. 3866
Hungate, B.A., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 3867
Koch, B.J., Liu, C.M., McHugh, T.A., Marks, J.C., Morrissey, E.M., Price, L.B., 3868
2015. Quantitative microbial ecology through stable isotope probing. Applied 3869
161
and Environmental Microbiology 81(21), 7570–7581. 3870
Ihrmark, K., Bödeker, I., Cruz-Martinez, K., Friberg, H., Kubartova, A., Schenck, J., 3871
Strid, Y., Stenlid, J., Brandström-Durling, M., Clemmensen, K.E., 2012. New 3872
primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of 3873
artificial and natural communities. FEMS microbiology ecology 82(3), 666–677. 3874
Jiang, B.S., Li, H.Y., Yang, X.T., 2011. Application of FDA hydrolase activity in 3875
studying soil microbial activity in grass land. Journal of Henan Agricultural 3876
Sciences 40, 130–133. 3877
Kerkhof, L., Kemp, P., 1999. Small ribosomal RNA content in marine Proteobacteria 3878
during non-steady-state growth. FEMS Microbiology Ecology 30(3), 253–260. 3879
Khodadad, C.L., Zimmerman, A.R., Green, S.J., Uthandi, S., Foster, J.S., 2011. Taxa-3880
specific changes in soil microbial community composition induced by 3881
pyrogenic carbon amendments. Soil Biology and Biochemistry 43(2), 385–392. 3882
Koch, A.L., 2001. Oligotrophs versus copiotrophs. Bioessays 23(7), 657–661. 3883
Lahtinen, S.J., Ahokoski, H., Reinikainen, J.P., Gueimonde, M., Nurmi, J., Ouwehand, 3884
A.C., Salminen, S.J., 2008. Degradation of 16S rRNA and attributes of viability 3885
of viable but nonculturable probiotic bacteria. Letters in Applied Microbiology 3886
46(6), 693–698. 3887
Leff, J.W., Jones, S.E., Prober, S.M., Barberan, A., Borer, E.T., Firn, J.L., Harpole, W.S., 3888
Hobbie, S.E., Hofmockel, K.S., Knops, J.M.H., McCulley, R.L., La Pierre, K., 3889
Risch, A.C., Seabloom, E.W., Schutz, M., Steenbock, C., Stevens, C.J., Fierer, 3890
N., 2015. Consistent responses of soil microbial communities to elevated 3891
162
nutrient inputs in grasslands across the globe. Proceedings of the National 3892
Academy of Sciences of the United States of America 112(35), 10967–10972. 3893
Leff, J.W., Nemergut, D.R., Grandy, A.S., O'Neill, S.P., Wickings, K., Townsend, A.R., 3894
Cleveland, C.C., 2012. The effects of soil bacterial community structure on 3895
decomposition in a tropical rain forest. Ecosystems 15(2), 284–298. 3896
Lennon, J.T., Jones, S.E., 2011. Microbial seed banks: the ecological and evolutionary 3897
implications of dormancy. Nature Reviews Microbiology 9(2), 119–130. 3898
Li, Y., Wang, S., Jiang, L., Zhang, L., Cui, S., Meng, F., Wang, Q., Li, X., Zhou, Y., 3899
2016. Changes of soil microbial community under different degraded gradients 3900
of alpine meadow. Agriculture, Ecosystems and Environment 222, 213–222. 3901
Lin, L., Li, Y.K., Xu, X.L., Zhang, F.W., Du, Y.G., Liu, S.L., Guo, X.W., Cao, G.M., 3902
2015. Predicting parameters of degradation succession processes of Tibetan 3903
Kobresia grasslands. Solid Earth 6(4), 1237–1246. 3904
Liu, M., Liu, J., Chen, X., Jiang, C., Wu, M., Li, Z., 2018a. Shifts in bacterial and fungal 3905
diversity in a paddy soil faced with phosphorus surplus. Biology and Fertility 3906
of Soils 54(2), 259–267. 3907
Liu, S.B., Zamanian, K., Schleuss, P.M., Zarebanadkouki, M., Kuzyakov, Y., 2018b. 3908
Degradation of Tibetan grasslands: Consequences for carbon and nutrient cycles. 3909
Agriculture, Ecosystems and Environment 252, 93–104. 3910
Liu, Y.-R., Delgado-Baquerizo, M., Wang, J.-T., Hu, H.-W., Yang, Z., He, J.-Z., 2018c. 3911
New insights into the role of microbial community composition in driving soil 3912
respiration rates. Soil Biology and Biochemistry 118, 35–41. 3913
163
Lunghini, D., Granito, V.M., Di Lonardo, D.P., Maggi, O., Persiani, A.M., 2013. Fungal 3914
diversity of saprotrophic litter fungi in a Mediterranean maquis environment. 3915
Mycologia 105(6), 1499–1515. 3916
Ma, S., Zhu, X.X., Zhang, J., Zhang, L.R., Che, R.X., Wang, F., Liu, H.K., Niu, H.S., 3917
Wang, S.P., Cui, X.Y., 2015. Warming decreased and grazing increased plant 3918
uptake of amino acids in an alpine meadow. Ecology and Evolution 5(18), 3919
3995–4005. 3920
Mackay, J.E., Macdonald, L.M., Smernik, R.J., Cavagnaro, T.R., 2017. Organic 3921
amendments as phosphorus fertilisers: Chemical analyses, biological processes 3922
and plant P uptake. Soil Biology and Biochemistry 107, 50–59. 3923
Mitchell, P.J., Simpson, A.J., Soong, R., Schurman, J.S., Thomas, S.C., Simpson, M.J., 3924
2016. Biochar amendment and phosphorus fertilization altered forest soil 3925
microbial community and native soil organic matter molecular composition. 3926
Biogeochemistry 130(3), 227–245. 3927
Morrissey, E.M., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., 3928
Koch, B.J., Liu, C.M., Hayer, M., McHugh, T.A., Marks, J.C., Price, L.B., 3929
Hungate, B.A., 2016. Phylogenetic organization of bacterial activity. ISME 3930
Journal 10(9), 2336–2340. 3931
Nemergut, D.R., Cleveland, C.C., Wieder, W.R., Washenberger, C.L., Townsend, A.R., 3932
2010. Plot-scale manipulations of organic matter inputs to soils correlate with 3933
shifts in microbial community composition in a lowland tropical rain forest. Soil 3934
Biology and Biochemistry 42(12), 2153–2160. 3935
164
Oksanen J., Blanchet F.G., Kindt R., Legendre P., Minchin P.R., O’Hara R.B., Simpson 3936
G.L., Solymos P., Stevens M.H.H., Wagner H., 2018. Vegan: community 3937
ecology package. http://CRAN.R-project.org/package=vegan.. 3938
Olsen, S.R., 1954. Estimation of available phosphorus in soils by extraction with 3939
sodium bicarbonate. Miscellaneous Paper Institute for Agricultural Research 3940
Samaru. 3941
Padmanabhan, P., Padmanabhan, S., DeRito, C., Gray, A., Gannon, D., Snape, J.R., Tsai, 3942
C.S., Park, W., Jeon, C., Madsen, E.L., 2003. Respiration of 13C-labeled 3943
substrates added to soil in the field and subsequent 16S rRNA gene analysis of 3944
13C-labeled soil DNA. Applied and Environmental Microbiology 69(3), 1614–3945
1622. 3946
Pepe-Ranney, C., Campbell, A.N., Koechli, C.N., Berthrong, S., Buckley, D.H., 2016. 3947
Unearthing the ecology of soil microorganisms using a high resolution DNA-3948
SIP approach to explore cellulose and xylose metabolism in soil. Frontiers in 3949
Microbiology 7, 703 3950
Perez-Osorio, A.C., Williamson, K.S., Franklin, M.J., 2010. Heterogeneous rpoS and 3951
rhlR mRNA Levels and 16S rRNA/rDNA (rRNA Gene) Ratios within 3952
Pseudomonas aeruginosa biofilms, sampled by laser capture microdissection. 3953
Journal of Bacteriology 192(12), 2991–3000. 3954
Poeplau, C., Herrmann, A.M., Kätterer, T., 2016. Opposing effects of nitrogen and 3955
phosphorus on soil microbial metabolism and the implications for soil carbon 3956
storage. Soil Biology and Biochemistry 100, 83–91. 3957
165
R Development Core Team, 2018. R: a language and environment for statistical 3958
computing. R Foundation for Statistical Computing, Vienna, Austria. 3959
http://www.R-project.org/. 3960
Ramirez-Villanueva, D.A., Bello-Lopez, J.M., Navarro-Noya, Y.E., Luna-Guido, M., 3961
Verhulst, N., Govaerts, B., Dendooven, L., 2015. Bacterial community structure 3962
in maize residue amended soil with contrasting management practices. Applied 3963
Soil Ecology 90, 49–59. 3964
Reed, S.C., Seastedt, T.R., Mann, C.M., Suding, K.N., Townsend, A.R., Cherwin, K.L., 3965
2007. Phosphorus fertilization stimulates nitrogen fixation and increases 3966
inorganic nitrogen concentrations in a restored prairie. Applied Soil Ecology 3967
36(2–3), 238–242. 3968
Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., 3969
Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., Sahl, J.W., Stres, 3970
B., Thallinger, G.G., Van Horn, D.J., Weber, C.F., 2009. Introducing mothur: 3971
Open-source, platform-independent, community-supported software for 3972
describing and comparing microbial communities. Applied and Environmental 3973
Microbiology 75(23), 7537–7541. 3974
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., 3975
Huttenhower, C., 2011. Metagenomic biomarker discovery and explanation. 3976
Genome Biology 12(6), 18. 3977
Segev, E., Smith, Y., Ben-Yehuda, S., 2012. RNA dynamics in aging bacterial spores. 3978
Cell 148(1–2), 139–149. 3979
166
Siles, J.A., Rachid, C., Sampedro, I., Garcia-Romera, I., Tiedje, J.M., 2014. Microbial 3980
diversity of a mediterranean soil and its changes after biotransformed dry olive 3981
residue amendment. PLOS One 9(7), e103035. 3982
Smits, N.A.C., Willems, J.H., Bobbink, R., 2008. Long-term after-effects of fertilisation 3983
on the restoration of calcareous grasslands. Applied Vegetation Science 11(2), 3984
279-U292. 3985
Sposito, G., 2008. The chemistry of soils. Oxford university press. 3986
Su, P., Lou, J., Brookes, P.C., Luo, Y., He, Y., Xu, J.M., 2017. Taxon-specific responses 3987
of soil microbial communities to different soil priming effects induced by 3988
addition of plant residues and their biochars. Journal of Soils and Sediments 3989
17(3), 674–684. 3990
Sun, H., Wang, Q.X., Liu, N., Li, L., Zhang, C.G., Liu, Z.B., Zhang, Y.Y., 2017. Effects 3991
of different leaf litters on the physicochemical properties and bacterial 3992
communities in Panax ginseng-growing soil. Applied Soil Ecology 111, 17–24. 3993
Sun, R.B., Zhang, X.X., Guo, X.S., Wang, D.Z., Chu, H.Y., 2015. Bacterial diversity in 3994
soils subjected to long-term chemical fertilization can be more stably 3995
maintained with the addition of livestock manure than wheat straw. Soil Biology 3996
and Biochemistry 88, 9–18. 3997
Tahmasbian, I., Xu, Z., Abdullah, K., Zhou, J., Esmaeilani, R., Nguyen, T.T.N., Bai, 3998
S.H., 2017. The potential of hyperspectral images and partial least square 3999
regression for predicting total carbon, total nitrogen and their isotope 4000
composition in forest litterfall samples. Journal of Soils and Sediments, 1–13. 4001
167
Treseder, K.K., 2004. A meta-analysis of mycorrhizal responses to nitrogen, 4002
phosphorus, and atmospheric CO2 in field studies. New Phytologist 164(2), 4003
347–355. 4004
van der Wal, A., Geydan, T.D., Kuyper, T.W., de Boer, W., 2013. A thready affair: 4005
linking fungal diversity and community dynamics to terrestrial decomposition 4006
processes. FEMS Microbiology Reviews 37(4), 477–494. 4007
Vance, E., Brookes, P., Jenkinson, D., 1987. An extraction method for measuring soil 4008
microbial biomass C. Soil Biology and Biochemistry 19(6), 703–707. 4009
Veraart, A.J., Steenbergh, A.K., Ho, A., Kim, S.Y., Bodelier, P.L.E., 2015. Beyond 4010
nitrogen: The importance of phosphorus for CH4 oxidation in soils and 4011
sediments. Geoderma 259, 337–346. 4012
Wang, X.L., Han, C., Zhang, J.B., Huang, Q.R., Deng, H., Deng, Y.C., Zhong, W.H., 4013
2015. Long-term fertilization effects on active ammonia oxidizers in an acidic 4014
upland soil in China. Soil Biology and Biochemistry 84, 28–37. 4015
Wang, Y., Qian, P.-Y., 2009. Conservative fagments in bcterial 16S rRNA gnes and 4016
pimer dsign for 16S rbosomal DNA aplicons in mtagenomic sudies. PLoS One 4017
4(10), e7401 4018
Wang, Z.Q., Zhang, Y.Z., Yang, Y., Zhou, W., Gang, C.C., Zhang, Y., Li, J.L., An, R., 4019
Wang, K., Odeh, I., Qi, J.G., 2016. Quantitative assess the driving forces on the 4020
grassland degradation in the Qinghai-Tibet Plateau, in China. Ecological 4021
Informatics 33, 32–44. 4022
White, T.J., Bruns, T., Lee, S., Taylor, J., 1990. Amplification and direct sequencing of 4023
168
fungal ribosomal RNA genes for phylogenetics. PCR protocols: a guide to 4024
methods and applications 18(1), 315–322. 4025
WRB, 1998. World reference base for soil resources. FAO/ISRIC/ISSS, Italy. 4026
Yan, J., Wang, L., Hu, Y., Tsang, Y.F., Zhang, Y., Wu, J., Fu, X., Sun, Y., 2018. Plant 4027
litter composition selects different soil microbial structures and in turn drives 4028
different litter decomposition pattern and soil carbon sequestration capability. 4029
Geoderma 319, 194–203. 4030
Yang, Z.P., Baoyin, T., Minggagud, H., Sun, H.P., Li, F.Y., 2017. Recovery succession 4031
drives the convergence, and grazing versus fencing drives the divergence of 4032
plant and soil N/P stoichiometry in a semiarid steppe of Inner Mongolia. Plant 4033
and Soil 420(1–2), 303–314. 4034
Yao, Z.Y., Zhao, C.Y., Yang, K.S., Liu, W.C., Li, Y., You, J.D., Xiao, J.H., 2016. Alpine 4035
grassland degradation in the Qilian Mountains, China - A case study in 4036
Damaying Grassland. Catena 137, 494–500. 4037
Zhao, L., Li, Y.N., Xu, S.X., Zhou, H.K., Gu, S., Yu, G.R., Zhao, X.Q., 2006. Diurnal, 4038
seasonal and annual variation in net ecosystem CO2 exchange of an alpine 4039
shrubland on Qinghai-Tibetan plateau. Global Change Biology 12(10), 1940–4040
1953. 4041
Zhou, X.L., Guo, Z., Zhang, P.F., Li, H.L., Chu, C.J., Li, X.L., Du, G.Z., 2017. Different 4042
categories of biodiversity explain productivity variation after fertilization in a 4043
Tibetan alpine meadow community. Ecology and Evolution 7(10), 3464–3474. 4044
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Chapter 5. The Application of Microbial Life-strategy 4048
Classification in Explaining the Responses of Soil Microbes 4049
to Warming and Grazing* 4050
4051
4052
4053
*This chapter forms the basis of the following journal manuscript: 4054
Che RX, Deng YC, Wang WJ, Rui YC, Zhang J, Tahmasbian I, Tang L, Wang SP, Wang 4055
YF, Xu ZH, Cui XY. (2018): Long-term warming but not grazing significantly 4056
changed total and active soil prokaryotic community structures. Geoderma 316:1–4057
10. DOI: 10.1016/j.geoderma.2017.12.005 4058
4059
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170
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STATEMENT OF CONTRIBUTION TO CO-AUTHORED PUBLISHED PAPER 4062
This chapter includes a co-authored paper. The bibliographic details of the co-authored 4063
paper, including all authors, are: 4064
Che RX, Deng YC, Wang WJ, Rui YC, Zhang J, Tahmasbian I, Tang L, Wang SP, Wang YF, 4065
Xu ZH, Cui XY. (2018): Long-term warming but not grazing significantly changed total and 4066
active soil prokaryotic community structures. Geoderma 316:1–10. 4067
My contribution to the paper involved: 4068
Experimental design and conduction; statistical analysis; categorisation of the data into 4069
a usable format; and writing the paper. 4070
4071
The copyright of the paper has been transformed to the Publisher, but I reserve the 4072
right to include the paper as a chapter in the thesis. 4073
4074
4075
(Signed) _________________________________ (Date)______________ 4076
Rongxiao Che 4077
4078
(Countersigned) ___________________________ (Date)______________ 4079
Corresponding author of paper: Xiaoyong Cui 4080
4081
(Countersigned) ___________________________ (Date)______________ 4082
Supervisor: Zhihong Xu 4083
4084
4085
4086
4087
4088
4089
171
5.1. Abstract 4090
There is a paucity of knowledge in understanding the effects of warming and grazing 4091
on soil microbes and their active counterparts, especially on the Tibetan Plateau which 4092
is extremely sensitive to global warming and human activities. A six-year field 4093
experiment was conducted to investigate the effects of asymmetric warming and 4094
moderate grazing on total and active soil microbes in a Tibetan Kobresia alpine meadow. 4095
Soil bacterial abundance and 16S rDNA transcriptional activity were determined using 4096
real-time PCR. Total and active soil prokaryotic community structures were analyzed 4097
through MiSeq sequencing based on 16S rDNA and rRNA, respectively. The results 4098
showed that the soil prokaryotic community was more sensitive to the warming than 4099
the grazing. The warming significantly decreased soil microbial respiration rates, 16S 4100
rDNA transcription activity, and dispersion of total prokaryotic community structures, 4101
but significantly increased the α diversity of active procaryotes. Warming also 4102
significantly increased the relative abundance of oligotrophic microbes, whereas it 4103
decreased the copiotrophic lineage proportions. The functional profiles predicted from 4104
the total prokaryotic community structures remained unaffected by warming. However, 4105
the rRNA-based predictions suggested that DNA replication, gene expression, signal 4106
transduction, and protein degradation were significantly suppressed under the warming. 4107
The grazing only significantly decreased the 16S rDNA transcription and total 4108
prokaryotic richness. Overall, these findings suggest that warming can shift soil 4109
prokaryotic community to a more oligotrophic and less active status, highlighting the 4110
importance of investigating active microbes to improve the understanding of ecosystem 4111
172
feedbacks to climate change and human activities. 4112
4113
5.2. Introduction 4114
The unequivocal global warming profoundly alters terrestrial biodiversity and 4115
ecosystem functioning, which, in turn, can influence the ongoing global climate 4116
changes (IPCC 2013). Accordingly, soil microbes have been intensively examined in 4117
temperature manipulation investigations by virtue of their vital roles in determining 4118
ecosystem feedbacks (Melillo et al., 2017; Oliverio et al., 2017; Romero-Olivares et al., 4119
2017). Furthermore, the interactive effects between warming and human activities (e.g., 4120
grazing) on soil microbes have also been observed in a number of studies (Li et al., 4121
2016; Zhang et al., 2016b). However, most of these studies only focused on the 4122
responses of total soil microbial communities (Knox et al., 2017; Zhang et al., 2017b; 4123
Zhang et al., 2016b). The active microbes have rarely been investigated due to the 4124
difficulties in soil labeling and RNA extractions (Che et al., 2016a). Nevertheless, soil 4125
microbes have a wide range of vitality, with a small active population being active, 4126
while the majority is dormant (Fierer, 2017; Lennon and Jones, 2011). Compared to 4127
total soil microbes, active soil microbes usually show higher sensitivity to 4128
environmental changes (Barnard et al., 2015; Che et al., 2015; Xue et al., 2016b), and 4129
are more connected with soil functionality (Che et al., 2016b). Therefore, examining 4130
the effects of warming and human activities on active soil microbial communities can 4131
improve our understanding of ecosystem feedbacks to the future climate changes. 4132
4133
173
In the recent decade, rRNA-based methods (e.g., rRNA sequencing) have been widely 4134
employed to identify active soil microbial communities (Barnard et al., 2015; Che et 4135
al., 2015; Che et al., 2016b; Mueller et al., 2016) for the following reasons. First, rRNA 4136
genes (rDNAs) are the most widely-used molecular markers to determine microbial 4137
abundances and community compositions (Che et al., 2016b). Second, rRNAs are 4138
indispensable for protein synthesis, and in some pure cultures, their cellular 4139
concentration correlates well with microbial growth rates (Kerkhof and Kemp, 1999; 4140
Muttray and Mohn, 1999; Perez-Osorio et al., 2010). Third, the dormancy and decease 4141
of microbes usually accompany rRNA degradation (Lahtinen et al., 2008; Segev et al., 4142
2012). Fourth, the rRNA-based methods can identify active microbes without labeling, 4143
which avoids disturbance and captures more reliable active microbial profiles. 4144
Moreover, the invention and popularization of the commercial soil RNA extraction kits 4145
have largely solved the difficulties in soil RNA extraction (e.g., Che et al., 2017). 4146
Consequently, a combination of the rDNA- and rRNA-based methods can systematicly 4147
determine the responses of both total and active soil microbes (Che et al., 2016a; Che 4148
et al., 2016b). 4149
4150
Tibetan Plateau, known as the third pole of our planet, is extremely sensitive to global 4151
warming (Chen et al., 2013; Chen et al., 2015; Liu and Chen, 2000) and human 4152
activities, particularly livestock grazing (Chen et al., 2013; Zhou et al., 2005). There 4153
are 23.2 Pg of organic carbon (2.5% of the global pool) stored in the Tibetan soils (Wang 4154
et al., 2002), and thus even a slight variation in this organic carbon pool can result in a 4155
174
considerable feedback to the global warming. Soil microbes, especially the active 4156
populations, play a key role in determining the dynamics of soil organic carbon pools 4157
(Che et al., 2016b). Moreover, they have been also recognized as a vital factor for 4158
influencing the sustainability of Tibetan alpine meadows (Che et al., 2017). Therefore, 4159
determining the individual and combined effects of warming and grazing on soil 4160
microbial community is not only important for predicting the Tibetan ecosystem 4161
feedbacks to global warming, but also crucial to providing a basis for optimizing the 4162
grazing strategies under the climate change. 4163
4164
In Tibetan alpine meadows, the previous studies suggested that warming significantly 4165
increased soil respiration, litter mass losses, phosphomonoesterase activity, soil nutrient 4166
contents, and belowground biomass, and significantly affected soil N2O emission and 4167
plant community composition (Hu et al., 2010; Lin et al., 2011; Luo et al., 2010; Luo 4168
et al., 2009; Rui et al., 2011; Rui et al., 2012; Wang et al., 2012). Compared to warming, 4169
the effects of grazing were generally weak and sometimes converse (Jiang et al., 2016; 4170
Wang et al., 2012). The most significant effect of grazing was the decrease in litter input 4171
(Luo et al., 2009). In addition, significant warming-grazing interactive effects were 4172
observed in several studies (Li et al., 2016; Rui et al., 2011; Wang et al., 2012). 4173
Responses of soil microbes to warming and grazing were analyzed in a few 4174
investigations (Li et al., 2016; Yang et al., 2013; Zhang et al., 2017a; Zhang et al., 4175
2016b). However, as mentioned above, these studies only assessed the total soil 4176
microbes, while the effects of warming and grazing on the active soil microbes have 4177
175
never been investigated in this region. In addition, although soil microbes should be 4178
sensitive or responsive to the aforementioned changes, significant effects of warming 4179
and grazing on total soil bacterial communities were seldom observed in the previous 4180
studies (Li et al., 2016; Zhang et al., 2016b). 4181
4182
Therefore, in this study, I aimed to investigate the main and interactive effects of 4183
warming and grazing on total and active soil prokaryotes in a Kobresia alpine meadow 4184
on the Tibetan Plateau. Bacterial abundance and 16S rRNA transcriptional activity were 4185
determined using 16S rDNA and rRNA copies, respectively. Total and active 4186
prokaryotic community structures were analyzed using MiSeq sequencing based on 16S 4187
rDNA and rRNA, respectively. On the basis of the aforementioned findings, I 4188
hypothesized that: 1) warming and grazing exerted more profound effects on active 4189
prokaryotes than total prokaryotes in soils; 2) warming stimulated soil microbial 4190
activity and 16S rDNA transcription; and 3) soil prokaryotes were less sensitive to 4191
grazing than warming, but grazing could partially offset the effects of warming. 4192
4193
5.3. Materials and methods 4194
5.3.1. Study site 4195
This research was conducted at the Haibei Alpine Meadow Ecosystem Research Station 4196
(37° 37′ N, 101° 12′ E), situated in the northeast of the Tibetan Plateau. With an average 4197
elevation of 3200 m, this region has a typical plateau continental climate. The mean 4198
annual temperature and precipitation were –1.7 °C and 570 mm, respectively (Zhao et 4199
176
al., 2006). The soil was classified as Gelic Cambisols (WRB, 1998). The vegetation of 4200
the study site was dominated by species such as Potentilla nivea, Kobresia humilis, and 4201
Elymus nutans (Wang et al., 2012). The average coverages of graminoids, forbs, and 4202
legumes were about 86%, 86%, and 28%, respectively (Wang et al., 2012). A detailed 4203
description of the experimental site can be found in my previous studies (Jiang et al., 4204
2016; Ma et al., 2015). 4205
4206
5.3.2. Experimental design 4207
The field treatments first started in May 2006 and continued until 2012 to reveal the 4208
effects of warming and grazing on alpine meadow ecosystems. The experimental design 4209
has been provided in my previous studies (Jiang et al., 2016; Luo et al., 2009). It was a 4210
two-way factorial design with four replicates. In total, 16 orbicular plots (3 m diameter) 4211
were established in the field with a randomized complete block design. The interval 4212
between every two adjacent plots was 3 m. 4213
4214
The experimental warming was achieved by an infrared heating system named free-air 4215
temperature enhancement (FATE) that has been detailed by Luo et al. (2009). The 4216
canopy temperature increase in the warming plots was 1.2 °C during the day and 1.7 °C 4217
at night in the growth seasons (May to September), while it was 1.5 °C in the daytime 4218
and 2.0 °C at night in the non-growth seasons (October to April). These temperature 4219
increases were in accordance with the predictions of global warming (IPCC, 2013). In 4220
each plot, type-K thermocouples (Campbell Scientific, Logan, Utah, USA) were used 4221
177
to automatically measure soil temperature at 0-5 and 5-10 cm. All temperature data 4222
were recorded and stored in CR1000 dataloggers. 4223
4224
From August 2006 to October 2010, the grazing plots were grazed by Tibetan domestic 4225
sheep (Ovis aries; Table 5.1). One or two sheep were fenced in the plots for one to two 4226
hours to remove plant to approximately half of the canopy height. The details of the 4227
sheep grazing treatments have been described in our previous study (Rui et al., 2012). 4228
In November 2011 and April 2012, instead of sheep grazing, clipping was employed to 4229
simulate winter grazing (Table 5.1). The clipping removed approximately 90% of the 4230
grass litter. Overall, these grazing treatments were approximately equivalent to local 4231
moderate grazing. 4232
Table 5.1. Methods of grazing treatments. 4233
Time Methods Intensity*
August 2006 Sheep grazing 50%
July 2007 Sheep grazing 50%
August 2007 Sheep grazing 50%
September 2007 Sheep grazing 50%
July 2008 Sheep grazing 50%
August 2008 Sheep grazing 50%
July 2009 Sheep grazing 50%
August 2009 Sheep grazing 50%
July 2010 Sheep grazing 50%
August 2010 Sheep grazing 50%
November 2011 Clipping 90%
April 2012 Clipping 90%
*The grazing intensity was indicated by the variations of canopy height. 4234
4235
178
5.3.3 Soil sampling and the measurements of soil properties and belowground 4236
biomass 4237
The soil samples (0–10 cm depth) of each plot was collected, using a steel auger, from 4238
five points, in August 2012. After obvious roots were manually removed from the soils, 4239
the soil samples for nucleic acid extraction were immediately flash-frozen and stored 4240
in liquid nitrogen. The soils were preserved at –80 °C in the laboratory. The soil samples 4241
used for soil physicochemical property analysis were first sieved to ≤ 2 mm, then 4242
transported to the laboratory in an insulated box with ice blocks, and then preserved at 4243
-20 °C. In addition, a soil core of 20-cm diameter in each plot was sampled to determine 4244
the belowground plant biomass. 4245
4246
Soil gravimetric moisture was measured by drying fresh soils at 105 °C for 24 hours. 4247
Soil dissolved organic carbon (DOC), NH4+-N, and NO3
--N contents were determined 4248
using the 2 M KCl extraction method (soil mass to extractant ratio of 1:4) with the fresh 4249
soil within one week after the collection. After the removal of carbonate by adding HCl, 4250
the extracted DOC concentrations were determined using a TOC Analyzer (Liqui TOC 4251
II; Elementar Analysensysteme GmbH, Hanau, Germany). Then, the concentrations of 4252
soil NH4+-N, NO3
--N were measured using an auto-flow analyzer (Auto Analyzer 3 4253
System; SEAL Analytical GmbH, Norderstedt, Germany). Soil total carbon and 4254
nitrogen contents were analyzed with an automatic elemental analyzer. Soil pH was 4255
determined using a pH meter in a 1:5 soil-water suspension. The soil microbial biomass 4256
was measured with the chloroform fumigation-extraction method, as detailed in our 4257
179
previous report (Ma et al., 2015). 4258
4259
Soil microbial respiration was measured as the CO2 production rates using the soils 4260
sieved to ≤ 2 mm. Field-moist soil samples, containing 10 g of dry mass, were placed 4261
into 130-ml flasks which were then pre-incubated at 20 °C, with good aeration, for 4262
seven days to stabilize the soil conditions. On the eighth day of the incubation, the 4263
flasks were sealed with rubber stoppers. After one hour, 10 mL of the headspace air of 4264
each flask was sampled using a 30-mL syringe, every two hours, for four times. The 4265
CO2 concentrations were determined using an Agilent 7890A gas chromatograph. The 4266
soil microbial respiration rates were calculated based on the increase of CO2 4267
concentrations over time. 4268
4269
5.3.4. Nucleic acid extraction and cDNA synthesis 4270
I used 0.3 and 2.0 g of soil to extract DNA and RNA, respectively. The extractions were 4271
conducted separately using a PowerSoil™ DNA Isolation Kit and a PowerSoil® Total 4272
RNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA). The RNA extracts 4273
were treated with DNase I (MO BIO Laboratories, Carlsbad, CA, U.S.A.) to remove 4274
DNA residuals. After that, the RNAs were reverse-transcribed into cDNA using a 4275
PrimeScript™II 1st Strand cDNA Synthesis Kit with random hexamers (Takara Bio 4276
Inc., Shiga, Japan). The DNA and cDNA solutions were diluted 10 and 50 times 4277
respectively for the subsequent operations. 4278
4279
180
5.3.5. Real-time PCR 4280
The copy numbers of 16S rDNA and its transcript were determined using an ABI 4281
StepOne Plus® Real-Time PCR Systems (Applied Biosystems, Foster City, CA, USA) 4282
with the universal bacterial primer set ( 338F, 5’-ACT CCT ACG GGA GGC AG-3’, 4283
Lane, 1991; 518R, 5’-ATT ACC GCG GCT GCT GG-3’, Muyzer et al., 1993). Each of 4284
the 25 μL reaction mixture contained: 1 μL template DNA (DNA, cDNA, or 10-fold 4285
serially diluted standards), 12.5 μL Maxima™ SYBR Green/ROX (2 ×, Fermentas, 4286
Waltham, MA, USA), 0.5 μL forward primer (20 μmol L-1), 0.5 μL reverse primer (20 4287
μmol L-1), 0.25 μL bovine serum albumin (25 mg mL−1; Promega, Madison, WI, USA), 4288
and 10.25 μL nuclease-free water. Standard curves were constructed using plasmids 4289
harboring 16S rDNA fragments. All the DNAs and cDNAs were analyzed in triplicate, 4290
and three no template controls were used to check the contamination of reagents. The 4291
PCR runs started with an initial denaturation and enzyme activation step for 10 minutes 4292
at 95 °C, followed by 40 cycles of 15 s at 95 °C, 30 s at 56 °C, 30 s at 72 °C, and 15 s 4293
at 80 °C. I recorded fluorescence signal at 80 °C to attenuate influence of primer dimers. 4294
The specificity of PCR products was tested by melting curve analysis. The 4295
amplification efficiencies were around 95%; the r2 values of the standard curve were > 4296
0.995. The absence of PCR inhibitors in RNA, DNA, and cDNA solutions was checked 4297
via qPCR with dilution series of corresponding solutions (Hargreaves et al., 2013). 4298
4299
5.3.6. MiSeq sequencing and bioinformatics 4300
The PCR amplicons for the MiSeq sequencing were generated with barcoded universal 4301
181
primers for prokaryotes (515F, 5’-GTG CCA GCM GCC GCG GT AA-3’; Caporaso et 4302
al., 2011; 909R, 5’- CCC CGY CAA TTC MTT TRA GT -3’; Wang and Qian, 2009). 4303
The 12 bp barcode sequences were added to the 5’-end of 515F. The 50 μL reaction 4304
system contained: 1 μL template DNA, 25 μL Premix Taq™ Hot Start Version (Takara 4305
Bio Inc., Shiga, Japan), 1 μL each primer (20 μmol L-1), and 22 μL water. The PCR 4306
programs were as follows: initial denaturation at 94 °C for 1 minute, followed by 30 4307
cycles of 20 s at 95 °C, 30 s at 56 °C, and 45 s at 72 °C, ending with a final extension 4308
at 72 °C for 10 minutes. There were two technical replicates for each sample, and the 4309
PCR products from two technical replicates were pooled into one tube. The PCR 4310
products were then purified using the GeneJET Gel Extraction Kit (Thermo Scientific, 4311
Lithuania). Subsequently, all the samples were pooled together with an equal molar 4312
amount from each sample. 4313
4314
The MiSeq sequencing was conducted by the analytical center at the Chengdu Institute 4315
of Biology, Chinese Academy of Sciences. The sequencing samples were prepared 4316
using a TruSeq DNA kit following the manufacturer’s instructions. The purified 4317
products were diluted, denatured, re-diluted, and mixed with PhiX (equal to 30% of 4318
final DNA amount) as described in the Illumina library preparation protocols, and were 4319
then applied to an Illumina Miseq system for sequencing with the Reagent Kit v3 2 × 4320
300 bp. 4321
4322
The sequence data were processed using a series of tools including “Quantitative 4323
182
Insights Into Microbial Ecology” (Qiime; v1.9.1; Caporaso et al., 2010), Mothur (v1.27; 4324
Schloss et al., 2009), Usearch (v8.0.1623; Edgar, 2010), and R (R Develoment Core 4325
Team, 2016). The paired-end sequences were first spliced and assigned to each sample 4326
based on their barcodes using Qiime. Then, the contigs were analyzed using the default 4327
UPARSE operational taxonomic unit (OTU) analysis pipeline (Edgar, 2013) to generate 4328
an OTU table and the OTU representative sequences. Specifically, the OTUs were 4329
clustered at a 97% identity threshold. The taxonomic identities of the OTU 4330
representative sequences were determined against the Silva database (v128) with 4331
pseudo-bootstrap confidence score = 80% (Wang et al., 2007). Finally, all the samples 4332
were rarefied to 15955 sequences to calculate α diversity, β diversity, and the 4333
composition of each sample using R with vegan packages (Oksanen et al., 2017). The 4334
prokaryotic community dispersion was calculated using betadisper functions in vegan 4335
package, and presented as the distance to centroid based on PCoA ordination. In 4336
addition, I calculated the relative abundance of copiotrophic and oligotrophic lineages 4337
based on their responses to organic matter amendments in the published investigations 4338
(Che et al., 2016b; Ho et al., 2017). Specifically, the copiotrophic lineages included 4339
Bacteroidetes, Betaproteobacteria, and Gammaproteobacteria. The oligotrophic 4340
lineages included Archaea, Acidobacteria, Actinobacteria, Planctomycetes, 4341
Verrucomicrobia, and Chloroflexi. All the raw sequencing data have been deposited in 4342
the NCBI Sequence Read Archive under the BioProject PRJNA417160, with accession 4343
number from SAMN07977869 to SAMN07977900. 4344
4345
183
The microbial functional profiles were predicted using the “phylogenetic investigation 4346
of communities by reconstruction of unobserved states” (PICRUSt; Langille et al., 4347
2013). PICRUSt is an approach using an extended ancestral-state reconstruction 4348
algorithm to predict microbial functional compositions on the basis of 16S rRNA gene 4349
profiles (Langille et al., 2013). In this study, the closed reference OTU table for 4350
PICRUSt was generated using QIIME following the online instructions with the 4351
UCLAST algorithm and Green Gene reference database. The PICRUSt analysis was 4352
performed using the online Galaxy version of the Langille Lab 4353
(galaxy.morganlangille.com). The function predictions were performed based on 4354
KEGG orthology at level three. 4355
4356
5.3.7. Statistical analysis 4357
The main and interactive effects of warming and grazing on the soil properties, plant 4358
biomass, bacterial abundance, 16S rDNA transcriptional activity, microbial respiration 4359
rates, and prokaryotic diversities were analyzed using the two-way analysis of variance 4360
(ANOVA) followed by the Duncan’s test. The normality and homogeneity of variance 4361
were tested by Shapiro and Bartlett test, respectively. I performed non-metric 4362
multidimensional scaling (NMDS) and permutational multivariate analysis of variance 4363
(PERMANOVA) to reveal the effects of warming and grazing on the total and active 4364
soil prokaryotic community and predictive function profiles. All the community 4365
composition distances were calculated based on Bray-Curtis dissimilarities. The 4366
responses of the relative abundance of prokaryotic lineages (from phylum to genus) and 4367
184
the predictive functions to warming were further determined using the “linear 4368
discriminant analysis effect size” (LEfSe) method (Segata et al., 2011). The Pearson 4369
correlation test, envfit based on NMDS, and multivariate regression tree analysis were 4370
applied to determine the relationships between environmental factors and soil 4371
prokaryotic communities. The LEfSe analysis was performed using the online 4372
Huttenhower Galaxy server (huttenhower.sph.harvard.edu/galaxy) with default 4373
pipelines. All the other statistical analyses were conducted using R with vegan and 4374
mvpart packages. 4375
4376
5.4. Results 4377
5.4.1. Soil and plant properties 4378
Both the warming and grazing significantly increased the soil temperature (P < 0.001, 4379
Fig. 5.1a). On average, the warming and grazing caused a 1.57 °C and 0.98 °C rise in 4380
soil temperature, respectively (Fig. 5.1a). However, no significant effects of interaction 4381
between the warming and grazing on soil temperature were observed (Table 5.2). 4382
Warming significantly reduced the soil gravimetric moisture by 11.91% (P < 0.001, Fig. 4383
5.1b), while no significant effects of grazing and warming-grazing interaction on soil 4384
gravimetric moisture were observed (Table 5.2, Fig. 5.1b). 4385
4386
In addition, the warming significantly decreased the soil NH4+-N contents (P = 0.022; 4387
Fig. 5.1d) and increased the belowground plant biomass (P = 0.042; Fig. 5.1f). The soil 4388
DOC contents tended to increase under warming, although the increase was not 4389
185
statistically significant (P = 0.069; Fig. 5.1e). The warming-grazing interactions 4390
significantly offset the individual effects of warming and grazing on the soil NH4+-N 4391
contents (P = 0.016; Fig. 5.1d) and pH values (P = 0.002; Fig. 5.1c). All the other soil 4392
properties included in this study remained unaffected under the treatments (Table 5.2). 4393
4394
4395
Figure 5.1. Soil and plant properties under different treatments. NWNG: no-warming with no grazing; 4396
NWG: no warming with grazing; WNG: warming with no grazing; WG: warming with grazing; W: effect 4397
of warming; G: effect of grazing; W×G: interaction effect of warming and grazing. All the data were 4398
presented in mean ± SE, n = 4. Bars with different letters indicate significant differences. 4399
4400
186
Table 5.2. The effects of warming, grazing, and their interactions on soil and plant properties. 4401
Warming (W) Grazing (G) W × G
F P F P F P
Soil temperature 129.442 0.000 51.356 0.000 0.009 0.927
Soil moisture contents 69.636 0.000 0.000 0.991 4.639 0.052
Soil total C contents 0.317 0.584 0.003 0.957 0.096 0.761
Soil total N contents 0.018 0.895 0.014 0.906 0.054 0.820
Soil C : N 1.081 0.319 0.032 0.862 0.015 0.905
Soil pH values 0.279 0.607 2.509 0.139 15.268 0.002
Soil NO3--N contents 1.248 0.286 0.681 0.425 0.031 0.863
Soil NH4+-N contents 6.971 0.022 0.621 0.446 7.787 0.016
Soil inorganic N contents 1.690 0.218 0.621 0.446 0.126 0.729
Soil dissolved organic C contents 4.000 0.069 1.740 0.212 0.020 0.889
Soil microbial biomass C contents 0.507 0.490 0.130 0.725 1.725 0.214
Soil microbial biomass N contents 0.009 0.927 0.228 0.642 1.285 0.279
Belowground plant biomass 5.161 0.042 0.157 0.699 2.164 0.167
4402
5.4.2. The soil bacterial abundance, 16S rDNA transcriptional activity, and 4403
microbial respiration rates 4404
As shown in Fig. 5.2, warming significantly decreased the 16S rRNA copies (P = 0.029), 4405
the rRNA-rDNA ratios (P = 0.015), and the microbial respiration rates (P = 0.008). The 4406
grazing significantly decreased the 16S rRNA copies (P = 0.045) and rRNA-rDNA 4407
ratios (P = 0.041; Fig. 5.2). There were no significant interactive effects between the 4408
warming and grazing on any of these indices (Table 5.3; Fig. 5.2), and no significant 4409
responses of soil bacterial abundance to these treatments were observed (Table 5.3). 4410
The soil microbial respiration rates showed significant correlations with the 16S rRNA 4411
copies (r = 0.530, P = 0.035) and rRNA-rDNA ratios (r = 0.584, P = 0.018) rather than 4412
the 16S rDNA copies (r = –0.196, P = 0.467). 4413
187
4414
Figure 5.2. The soil 16S rDNA copies (a), 16S rRNA copies (b), 16S rRNA-rDNA ratios (c), and 4415
microbial respiration rates under different treatments. NWNG: no warming with no grazing; NWG: no 4416
warming with grazing; WNG: warming with no grazing; WG: warming with grazing; W: effect of 4417
warming; G: effect of grazing. All the data were presented in mean ± SE, n = 4. Bars with different letters 4418
indicate significant differences at P < 0.05. 4419
4420
Table 5.3. The effects of warming, grazing, and their interactions on the soil microbial communities. 4421
Warming (W) Grazing (G) W × G
F P F P F P
16S rDNA copies 0.768 0.398 0.119 0.736 2.535 0.137
16S rRNA copies 6.130 0.029 5.028 0.045 1.074 0.321
16S rRNA-rRNA ratios 8.081 0.015 5.271 0.041 0.048 0.831
Microbial respiration rates 10.208 0.008 0.571 0.465 0.019 0.892
Total procaryote Shannon diversity 0.318 0.583 4.378 0.058 0.425 0.527
Active procaryote Shannon diversity 51.748 0.000 1.239 0.287 0.000 0.988
Total procaryote richness 0.171 0.687 5.185 0.042 0.510 0.489
Active procaryote richness 23.945 0.000 0.033 0.859 0.993 0.339
Total procaryote evenness 1.003 0.336 2.774 0.122 0.252 0.625
Active procaryote evenness 64.709 0.000 3.271 0.096 0.337 0.573
Total procaryote dispersion 5.798 0.033 1.268 0.282 10.860 0.006
Active procaryote dispersion 3.981 0.069 0.611 0.450 0.024 0.880
Total copiotroph proportions 9.830 0.009 0.571 0.464 0.099 0.759
Active copiotroph proportions 10.971 0.006 0.193 0.669 0.783 0.394
Total oligotrophic proportions 6.744 0.023 0.506 0.491 0.090 0.769
Active oligotrophic proportions 7.267 0.020 0.585 0.459 0.013 0.912
Total procaryote community structures* 1.814 0.006 0.941 0.535 0.981 0.429
Active procaryote community structures* 2.111 0.002 0.891 0.661 1.004 0.385
Total procaryote putative functional profiles*# 1.845 0.124 0.346 0.905 0.039 0.700
Active procaryotes putative functional profiles *# 9.662 0.002 0.018 0.698 0.030 0.467
#The putative functional profiles were determined through PICRUSt analysis with KEGG pathway 4422
assignment at level three. *Effects on the community structures and putative functional profiles were 4423
determined by two-way PERMANOVA; effects on the other variables were tested by two-way ANOVA. 4424
188
5.4.3. The total and active prokaryotic α diversities 4425
In total, I obtained 3 609 OTUs for all the sequences. The average richness coverages 4426
for the 16S rDNA and rRNA sequencing were 71.0% and 73.9%, respectively. The total 4427
soil prokaryotic α diversities remained unaffected by the warming treatments (Table 5.3; 4428
Figs. 5.3a, 5.3b, and 5.3c), but the total soil prokaryotic richness significantly reduced 4429
under the grazing (P = 0.042; Fig. 5.3b). On the contrary, the active soil prokaryotic α 4430
diversities were highly sensitive to the warming but not grazing. Specifically, the active 4431
soil prokaryotic Shannon diversity (P < 0.001), richness (P < 0.001), and evenness (P 4432
< 0.001) were all significantly increased under the warming (Table 5.3, Figs. 5.3e, 5.3f, 4433
and 5.3g). 4434
4435
Figure 5.3. The total and active soil prokaryotic diversity indices under different treatments. The 4436
prokaryotic community dispersion was represented as the distance to centroid based on PCoA ordination. 4437
NWNG: no-warming with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; 4438
WG: warming with grazing; W: effect of warming; G: effect of grazing; W×G: interaction effect of 4439
warming and grazing. All the data were presented in mean ± SE, n = 4. Bars with different letters indicate 4440
significant differences. 4441
4442
5.4.4. The total and active soil prokaryotic community structures 4443
As shown in Fig. 5.4, the dominant phylum in both the total and active prokaryotic 4444
communities was Proteobacteria (40.78%), followed by Acidobacteria (20.43%), 4445
189
Bacteriodetes (15.23%), Planctomycetes (7.01%), Actinobacteria (4.37%), and 4446
Planctomycetes (2.85%). The relative abundance of archaea was extremely low in the 4447
total (1.33%) and active (0.75%) prokaryotic communities, and 96.15% of the archaea 4448
were classified as Thaumarchaeota which were further identified as the soil 4449
Crenarchaeotic group. 4450
4451
Figure 5.4. The total and active soil prokaryotic community compositions under different treatments. 4452
NWNG: no warming with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; 4453
WG: warming with grazing. Others: the sum of phylum occupying less than 0.5% of the total population. 4454
All the data were presented in mean - SE, n = 4. 4455
4456
The most abundant families in the total and active prokaryotic communities were 4457
Chitinophagaceae (10.03%) and Comamonadaceae (9.11%). The proportions of 4458
Planctomycetaceae, Cytophagaceae, Blastocatellaceae (Subgroup 4), 4459
Nitrosomonadaceae, and Xanthomonadales (Incertae_Sedis) were also high in both the 4460
total and active prokaryotic communities (Fig. 5.5). The Chitinophagaceae were mainly 4461
classified as Terrimonas, Ferruginibacter, and Niastella, while most of the 4462
Comamonadaceae were identified as Piscinibacter and Caenimonas. In addition, RB41, 4463
190
Haliangium, Bradyrhizobium, and Acidibacter were also identified as abundant genera 4464
in the prokaryotic communities (Fig. 5.6). The community compositions at family and 4465
genus levels were detailed in Figs. 5.5 and 5.6. 4466
4467
4468 Figure 5.5. The total and active soil prokaryotic community compositions at family level. NWNG: no 4469
warming with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; WG: 4470
warming with grazing. Others, the sum of families occupying less than 1.0% of the total population. 4471
4472
The NMDS and PERMANOVA suggested that the total and active prokaryotic 4473
communities responded similarly to the treatments, although they differed significantly 4474
from each other (P < 0.001). Specifically, the warming significantly altered both the 4475
total (P = 0.006) and active (P = 0.002) prokaryotic community structures (Table 5.3, 4476
Figs. 5.7a and 5.7b). Conversely, neither the grazing nor the warming-grazing 4477
interaction could significantly alter the prokaryotic community structures (Table 5.3, 4478
Figs. 5.7a and 5.7b). 4479
191
4480
4481
Figure 5.6. The total and active soil prokaryotic community compositions at genus level. NWNG: no 4482
warming with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; WG: 4483
warming with grazing. Others, the sum of genera occupying less than 0.5% of the total population. 4484
4485
Figure 5.7. The NMDS ordinations of the total and active soil prokaryotic community structures based 4486
on OTUs (a and b) and putative functions (c and b). NWNG: no warming with no grazing; NWG: no 4487
warming with grazing; WNG: warming with no grazing; WG: warming with grazing. The putative 4488
functions were determined through PICRUSt analysis with KEGG pathway assignment at level three. 4489
4490
In addition, the dispersion of total prokaryotic communities in the control plots were 4491
significantly higher than those in the other plots (Figs. 5.3d and 5.7a). However, the 4492
warming-grazing interaction significantly offset the individual effects of warming and 4493
192
grazing (Figs. 5.3d and 5.7a). Conversely, the dispersion of the active procaryotes 4494
remained unaffected across all the treatments (Table 5.3; Figs. 5.3h and 5.7b). 4495
4496
Figure 5.8. The responses of the proportions of total (a) and active (b) soil prokaryotic lineages to 4497
warming. The effects of warming were determined using the LEfSe analysis, and the threshold on the 4498
absolute logarithmic LDA score was 3.0. 4499
4500
As revealed by the LEfSe analysis, the relative abundance of β-Proteobacteria in the 4501
total and active prokaryotic communities significantly decreased under the warming 4502
(Figs. 5.8a and 5.8b). Conversely, the relative abundance of Actinobacteria significantly 4503
increased (Figs. 5.8a and 5.8b). In addition, as for the active prokaryotic communities, 4504
the warming significantly increased the relative abundances of Firmicutes, Chloroflexi, 4505
α-Proteobacteria, and some less abundant lineages under δ-Proteobacteria, but 4506
decreased the proportions of Verrucomicrobia, Myxococcales, and Solibacteres (Fig. 4507
5.8b). 4508
193
4509
The proportions of copiotrophic lineages in the total and active soil prokaryotic 4510
communities significantly decreased under the warming (Figs. 5.9a and 5.9c). On the 4511
contrary, the warming significantly increased the relative abundance of oligotrophic 4512
lineages (Figs. 5.9b and 5.9d). The copiotroph proportions were positively correlated 4513
with soil microbial respiration rates, whilst they were negatively correlated with the 4514
belowground plant biomass (Figs. 5.10a, 5.10c, 5.11a, and 5.11c). In contrast, the 4515
proportions of oligotrophs showed negative and positive correlations with the soil 4516
microbial respiration rates and the belowground plant biomass, respectively (Figs. 4517
5.10b, 5.10d, 5.11b, and 5.11d). 4518
4519
Figure 5. 9. The relative abundance of copiotrophic and oligotrophic lineages under different treatments. 4520
NWNG: no-warming with no grazing; NWG: no warming with grazing; WNG: warming with no grazing; 4521
WG: warming with grazing; W: effect of warming. All the data were presented in mean ± SE, n = 4. Bars 4522
with different letters indicate significant differences. The copiotrophic lineages included Bacteroidetes, 4523
Betaproteobacteria, and Gammaproteobacteria. The oligotrophic lineages included Archaea, 4524
Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and Chloroflexi. The life-strategy of 4525
these lineages was classified based on their responses to organic matter amendments in the published 4526
investigations. 4527
4528
4529
4530
4531
194
4532
4533
4534
Figure 5.10. The relationships between total (a and b) and active (c and d) soil prokaryotic lineages and 4535
microbial respiration rates. NWNG: no-warming with no grazing; NWG: no warming with grazing; 4536
WNG: warming with no grazing; WG: warming with grazing. The copiotrophic lineages included 4537
Bacteroidetes, Betaproteobacteria, and Gammaproteobacteria. The oligotrophic lineages included 4538
Archaea, Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and Chloroflexi. The life-4539
strategy of these lineages was classified based on their responses to organic matter amendments in the 4540
published investigations. 4541
4542
4543
4544
Figure 5.11. The relationships between total (a and b) and active (c and d) soil prokaryotic lineages and 4545
belowground biomass. NWNG: no-warming with no grazing; NWG: no warming with grazing; WNG: 4546
warming with no grazing; WG: warming with grazing. The copiotrophic lineages included Bacteroidetes, 4547
Betaproteobacteria, and Gammaproteobacteria. The oligotrophic lineages included Archaea, 4548
Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and Chloroflexi. The life-strategy of 4549
these lineages was classified based on their responses to organic matter amendments in the published 4550
investigations. 4551
4552
4553
4554
195
Table 5.4. The relationships between environmental factors and the community structures based on 16S 4555
rDNA and rRNA. 4556
16S rDNA 16S rRNA
r2 P r2 P
Soil temperature 0.600 0.002 0.651 0.001
Soil moisture contents 0.516 0.009 0.619 0.001
Soil total carbon contents 0.318 0.079 0.156 0.337
Soil total nitrogen contents 0.271 0.128 0.135 0.392
Soil C : N 0.047 0.734 0.013 0.921
Soil pH values 0.165 0.310 0.153 0.346
Soil NO3--N contents 0.143 0.357 0.518 0.009
Soil NH4+-N contents 0.156 0.339 0.279 0.121
Soil inorganic N contents 0.169 0.292 0.512 0.009
Soil dissolved organic carbon contents 0.183 0.273 0.107 0.484
Soil microbial biomass C contents 0.229 0.185 0.253 0.154
Soil microbial biomass N contents 0.202 0.235 0.145 0.362
Belowground plant biomass 0.346 0.065 0.401 0.032
The envfit analysis suggested that the total soil prokaryotic community structures had 4557
significant correlations with the soil temperature and moisture, while the active 4558
prokaryotic community structures were significantly correlated with the soil 4559
temperature, moisture, NO3--N content, inorganic nitrogen content, and the plant 4560
belowground biomass (Fig. 5.12; Table 5.4). In addition, the soil microbial respiration 4561
rates also showed significant correlations with the total (r2 = 0.722, P < 0.001) and 4562
active (r2 = 0.538, P = 0.007) prokaryotic community structures. The multivariate 4563
regression tree analysis showed that the total and active prokaryotic community 4564
structures were mostly affected by the soil moisture and temperature, respectively (Fig. 4565
5.13). 4566
196
4567
Figure 5.12. The relationships between environmental factors and the community structures based on 4568
16S rDNA (a) and rRNA (b). NWNG: no-warming with no grazing; NWG: no warming with grazing; 4569
WNG: warming with no grazing; WG: warming with grazing. The vectors represent the soil properties 4570
that were significantly correlated with the corresponding NCG community structures (P < 0.05); the 4571
directions of the vectors represent the increase gradient of each soil properties; longer vector represents 4572
stronger correlations. All the vectors were drawn using the envfit function in vegan package. 4573
4574
4575
Figure 5.13. The relationships between total (a) and active (b) soil prokaryotic community structures and 4576
the environmental factors, as determined using the multivariate regression tree analysis. SM: soil 4577
moisture (%); ST: soil temperature (°C). The bar plots showed the average relative abundances of OTUs 4578
in each split groups, and the numbers (n) under the bars represented the sample number within each 4579
group. 4580
4581
5.4.5. The putative function profiles based on PICRUSt analysis 4582
As shown in Figs. 5.7c and 5.7d, the community structures based on the putative 4583
functions showed much higher similarities than the OTU-based community structures 4584
among all the samples. The PERMANOVA suggested that the warming significantly 4585
altered the active prokaryotic community function structures (P = 0.002), but the total 4586
prokaryotic community function structures remained unaffected (Table 5.3). Again, the 4587
197
grazing and warming-grazing interactions exerted no significant effects on the 4588
community function structures (Table 5.3). 4589
4590
Figure 5.14. Responses of the proportions of putative functions to warming. The functional profiles were 4591
predicted based on 16S rRNA amplicon sequencing, using PICRUSt analysis with KEGG pathway 4592
assignment at level three. The effects of warming were determined using the LEfSe analysis, and only 4593
the putative functions with an absolute logarithmic LDA score > 2.0 were shown. 4594
4595
The LEfSe analysis demonstrated that the warming significantly inhibited the 4596
fundamental functions such as DNA replication, transcription, translation, protein 4597
assembly, signal transduction, cell motility, and secretion (Fig. 5.14). Some functions 4598
related to the degradation of pyrimidine and proteins were also depressed under the 4599
warming (Fig. 5.14). However, the warming also significantly enhanced membrane 4600
transportation (e.g., ABC transporters), the metabolism of some substrates (e.g., fatty 4601
acids), and the expression of some transcription factors (Fig. 5.14). 4602
4603
198
5.5. Discussion 4604
Although the previous studies suggested that warming significantly affected the plant 4605
communities and soil properties in this alpine meadow (Lin et al., 2011; Luo et al., 2009; 4606
Wang et al., 2012), the bacterial and methanotrophic community compositions were 4607
highly resistant to three years of warming (Li et al., 2016; Zheng et al., 2012). However, 4608
the current study showed that both the total and active soil prokaryotic community 4609
compositions were significantly altered after six years of warming (Table 5.3, Fig. 5.7). 4610
These findings suggested that the responses of soil microbes to warming could lag 4611
behind plant communities and soil properties. Indeed, shifts in microbial community 4612
compositions under short-term warming were extensively observed (Xiong et al., 2014; 4613
Xue et al., 2016a; Zhang et al., 2016b). Nevertheless, a number of studies showed that 4614
the responses of soil microbes to experimental warming were time-dependent (Pold et 4615
al., 2016; Romero-Olivares et al., 2017). Sometimes, it took more than one decade to 4616
exhibit the first significant response (Rinnan et al., 2009). Moreover, in line with my 4617
first hypothesis, compared to the total prokaryotic community structures, the active 4618
prokaryotic community structures showed similar but much higher sensitivities to the 4619
six-year warming (Table 5.3, Fig. 5.7). As proposed by Blazewicz et al. (2013), cellular 4620
rRNA concentration represents the growth potential of microbes. Therefore, the higher 4621
sensitivity of the active procaryotes should suggest that the responses of the soil 4622
prokaryotic community to warming could increase with time, which deserves further 4623
verification. 4624
4625
199
Enhanced soil microbial activity under warming has been frequently observed (Lin et 4626
al., 2011; Peng et al., 2015; Rinnan et al., 2009; Schindlbacher et al., 2011). However, 4627
this study suggested that soil microbes might become less active under the six-year 4628
warming compared to no warming, which denied my second hypothesis. First, the 4629
warming significantly decreased the relative abundance of β-Proteobacteria, but 4630
increased the proportion of Actinobacteria (Fig. 5.8a). The β-Proteobacteria and 4631
Actinobacteria have been respectively classified as copiotrophic and oligotrophic 4632
categories (Bernard et al., 2007; Che et al., 2016b; Fierer et al., 2007). Actually, the 4633
warming also significantly increased the relative abundance of oligotrophic categories, 4634
but decreased the copiotroph proportions (Fig. 5.9). The shifts in soil prokaryotic 4635
community composition towards oligotrophic populations indicated a decrease in 4636
microbial activity under the warming. This was in agreement with the significantly 4637
suppressed soil respiration rates and 16S rDNA transcription under the warming (Fig. 4638
5.2). Consistently, as revealed by the 16S rRNA-based PICRUSt analysis, the warming 4639
also significantly down-regulated the DNA replication, gene expression, and signal 4640
transduction (Fig. 5.14), which provided further evidence for the microbial activity 4641
decline under the warming. 4642
4643
In this study, the changes in soil prokaryotic communities could be elicited in a number 4644
of ways. As revealed by the envfit and multivariate regression tree analysis, the soil 4645
temperature and moisture showed the strongest correlations with the total and active 4646
prokaryotic community structures (Table 5.4; Figs. 5.12 and 5.13). This indicated that 4647
200
the changes in prokaryotic communities might be mainly induced by the soil 4648
temperature increase and moisture decrease under the warming (Fig. 5.1). First, the 4649
raised temperature could result in selective microbial growth (Bai et al., 2017; 4650
Supramaniam et al., 2016; Xue et al., 2016a), and thus created new warm-adaptive 4651
microbial communities which differed from the initial ones. Second, the decreased soil 4652
moisture under warming led to decrease in the availability of nutrients (Moinet et al., 4653
2016; Xue et al., 2017). This is a possible explanation for the shifts in prokaryotic 4654
communities toward oligotrophic populations under the warming (Fig. 5.9). Third, the 4655
increased plant belowground biomass under warming (Fig. 5.1f) might also elicit higher 4656
plant-microbe competition for nitrogen and other nutrients (Classen et al., 2015; 4657
Kuzyakov and Xu, 2013), altering the community composition of soil microbes. This 4658
is also supported by the significant correlations between the belowground biomass and 4659
the relative abundance of copiotrophs and oligotrophs (Fig. 5.11). In addition, as 4660
mentioned above, the responses of soil microbial communities to warming could be 4661
time-dependent (Melillo et al., 2017; Metcalfe, 2017; Romero-Olivares et al., 2017). 4662
Hence, the past changes in soil and plant properties (Lin et al., 2011; Luo et al., 2010; 4663
Luo et al., 2009; Rui et al., 2011; Rui et al., 2012; Wang et al., 2012) might have also 4664
contributed to the current alterations in soil microbial community compositions. For 4665
instance, the previously observed increases in soil respiration and litter decomposition 4666
rates (Lin et al., 2011; Luo et al., 2010) might have resulted in a rapid consumption of 4667
labile carbon, and thus decreased the relative abundance of copiotrophic microbial 4668
lineages. Collectively, in this study, the changes in prokaryotic community structures 4669
201
under warming were probably caused by the combination of both the direct and indirect 4670
effects of warming. 4671
4672
The active prokaryotic Shannon diversity index, richness, and evenness were all 4673
significantly increased under the warming, while the α-diversity of total soil 4674
procaryotes remained unaffected (Fig. 5.3). These findings suggested that more soil 4675
prokaryotic species were activated by the warming. Additionally, in the Tibetan alpine 4676
meadow, the decrease in microbial community dispersions under warming (Figs. 5.3d 4677
and 5.7a) has been observed and interpreted in the previous studies (Li et al., 2016; 4678
Zhang et al., 2016a). Nevertheless, I found that the dispersion of active prokaryotic 4679
communities remained unaffected by warming (Figs. 5.3h and 5.7b). This indicated that 4680
the decreased prokaryotic community dispersions under the warming might be mainly 4681
caused by the responses of the less active prokaryotic assemblages. The rRNA to rDNA 4682
ratios of oligotrophic lineages were generally lower than those of the copiotrophic 4683
lineages (Fig. 5.4). As the 16S rDNA copies remained unaffected by the warming (Fig. 4684
5.2a), the significantly suppressed transcription of 16S rDNA (Figs. 5.2b and 5.2c) 4685
could be mainly attributed to the decrease in copiotrophs to oligotrophs ratios under the 4686
warming. 4687
4688
In consistency with my third hypothesis, the effects of the moderate grazing on soil 4689
procaryotes were generally weak, which might suggest that the soil procaryotes had 4690
adapted to the treatment due to the long history of grazing at the study site. In addition, 4691
202
grazing mainly affects ecosystem by decreasing litter input (Luo et al., 2009). As fungi 4692
are generally copiotrophic and affected more by litter inputs than procaryotes (Che et 4693
al., 2016b; Ho et al., 2017; Yarwood et al., 2009), the grazing might have mainly affect 4694
the soil fungi rather than procaryotes. Thus, the response of soil fungi, especially the 4695
active populations, should be explored in future studies. Nevertheless, in line with my 4696
previous studies (Li et al., 2016; Luo et al., 2010; Rui et al., 2012; Wang et al., 2012; 4697
Zheng et al., 2012), I detected significant interactive effects between the warming and 4698
grazing, which all tended to offset the individual effects of warming and grazing (Figs. 4699
5.1c, 5.1d, and 5.3d). These interactions could be elicited by the contrasting effects of 4700
the warming and grazing on soil properties, plant communities, and microbial groups, 4701
as interpreted and synthesized in our previous study (Li et al., 2016). For instance, 4702
warming decreased soil moisture (Luo et al., 2009), while grazing decrease water loss 4703
by trampling (Warren et al., 1986). In addition, contrasting effects of warming and 4704
grazing on the plant functional groups have also been observed in our previous studies 4705
(Wang et al., 2012). This suggested that the moderate grazing could partially 4706
counterbalance the effects of global warming on the soil microbes and ecosystem 4707
functioning, which further supported my third hypothesis. However, I also found that 4708
the interactive effects detected in this study were much weaker than in the previous 4709
studies. For example, in contrast to the previous study (Li et al., 2016), the significant 4710
interaction effects on the microbial α diversity and community compositions were not 4711
detected in this study. This implies that the role of moderate grazing as a counterbalance 4712
to the ongoing global warming may diminish over time. 4713
203
4714
This study aimed at the overall effect of grazing which mainly affects grassland 4715
ecosystems by plant consumption, faeces deposition, and trampling (Kohler et al., 4716
2005). Plant consumption may lead to a sharp decrease in litter input (Barger et al., 4717
2004; Luo et al., 2009), leading to starvation and even decease of microbes. Faeces 4718
deposition and trampling can affect the soil microbial assemblages by altering 4719
ecosystem nutrient cycling efficiency and compacting soil, respectively. In addition, 4720
grazing can also increase soil temperature (Luo et al., 2009), which may intensify the 4721
influence of warming on soil microbial community. Indeed, investigating the overall 4722
effect of grazing cannot precisely judge which sub-effect is responsible for the 4723
ecosystem changes, and it seems easier and clearer to study the sub-effects of grazing 4724
separately. However, the sub-effects, and even the sum of all kinds of grazing sub-4725
effects, still cannot represent the real grazing effect, due to their complex interactions. 4726
Therefore, although it may be not scientifically precise, determining the overall grazing 4727
effect is a more proper and systematical way to recognize the ecosystem response to 4728
animal grazing. 4729
4730
5.6 Conclusions 4731
This study showed that the six-year warming significantly decreased the 16S rDNA 4732
transcription and the microbial respiration rates. It also significantly increased the 4733
relative abundances of oligotrophic microbes, and decreased the copiotrophic lineage 4734
proportions. The DNA replication, transcription, translation, and signal transduction 4735
204
were significantly depressed under the warming. The grazing significantly decreased 4736
soil 16S rDNA transcription and total prokaryotic richness, but exerted no significant 4737
effects on soil microbial community structures. Collectively, these findings suggest that 4738
soil prokaryotic community is more sensitive to warming than grazing, and long-term 4739
warming can shift the soil prokaryotic community to a more oligotrophic and less active 4740
status. 4741
4742
4743
205
5.7. References 4744
Bai, Z., Ma, Q., Wu, X., Zhang, Y., Yu, W., 2017. Temperature sensitivity of a PLFA-4745
distinguishable microbial community differs between varying and constant 4746
temperature regimes. Geoderma 308, 54–59. 4747
Barger, N.N., Ojima, D.S., Belnap, J., Wang, S.P., Wang, Y.F., Chen, Z.Z., 2004. 4748
Changes in plant functional groups, litter quality, and soil carbon and nitrogen 4749
mineralization with sheep grazing in an Inner Mongolian Grassland. Journal of 4750
Range Management 57, 613–619. 4751
Barnard, R.L., Osborne, C.A., Firestone, M.K., 2015. Changing precipitation pattern 4752
alters soil microbial community response to wet-up under a Mediterranean-type 4753
climate. ISME Journal. 9(4), 946–957. 4754
Bernard, L., Mougel, C., Maron, P.A., Nowak, V., Leveque, J., Henault, C., Haichar, 4755
F.E.Z., Berge, O., Marol, C., Balesdent, J., Gibiat, F., Lemanceau, P., Ranjard, 4756
L., 2007. Dynamics and identification of soil microbial populations actively 4757
assimilating carbon from 13C-labelled wheat residue as estimated by DNA- and 4758
RNA-SIP techniques. Environmental Microbiology 9(3), 752–764. 4759
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as 4760
an indicator of microbial activity in environmental communities: limitations and 4761
uses. ISME Journal. 7(11), 2061–2068. 4762
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, 4763
E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, 4764
S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., 4765
206
Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, 4766
W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME 4767
allows analysis of high-throughput community sequencing data. Nature 4768
Methods 7(5), 335–336. 4769
Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., 4770
Turnbaugh, P.J., Fierer, N., Knight, R., 2011. Global patterns of 16S rRNA 4771
diversity at a depth of millions of sequences per sample. Proceedings of the 4772
National Academy of Sciences of the United States of America 108, 4516–4522. 4773
Che, R.X., Deng, Y.C., Wang, F., Wang, W.J., Xu, Z.H., Wang, Y.F., Cui, X.Y., 2015. 4774
16S rRNA-based bacterial community structure is a sensitive indicator of soil 4775
respiration activity. Journal of Soils and Sediments 15(9), 1987–1990. 4776
Che, R.X., Wang, F., Wang, Y.F., Deng, Y.C., Zhang, J., Ma, S., Cui, X.Y., 2016a. A 4777
review on the methods for measuring total microbial activity in soils. Acta 4778
Ecologica Sinica 36(08), 2103–2112. 4779
Che, R.X., Wang, W.J., Zhang, J., Nguyen, T.T.N., Tao, J., Wang, F., Wang, Y.F., Xu, 4780
Z.H., Cui, X.Y., 2016b. Assessing soil microbial respiration capacity using 4781
rDNA- or rRNA-based indices: a review. Journal of Soils and Sediments 16(12), 4782
2698–2708. 4783
Che, R.X., Wang, F., Wang, W.J., Zhang, J., Zhao, X., Rui, Y.C., Xu, Z.H., Wang, Y.F., 4784
Hao, Y.B, Cui, X.Y., 2017. Increase in ammonia-oxidizing microbe abundance 4785
during degradation of alpine meadows may lead to greater soil nitrogen loss. 4786
Biogeochemistry 136(3), 341–352. 4787
207
Chen, H., Zhu, Q., Peng, C., Wu, N., Wang, Y., Fang, X., Gao, Y., Zhu, D., Yang, G., 4788
Tian, J., Kang, X., Piao, S., Ouyang, H., Xiang, W., Luo, Z., Jiang, H., Song, X., 4789
Zhang, Y., Yu, G., Zhao, X., Gong, P., Yao, T., Wu, J., 2013. The impacts of 4790
climate change and human activities on biogeochemical cycles on the Qinghai-4791
Tibetan Plateau. Global Change Biology 19(10), 2940–2955. 4792
Chen, J., Luo, Y., Xia, J., Jiang, L., Zhou, X., Lu, M., Liang, J., Shi, Z., Shelton, S., 4793
Cao, J., 2015. Stronger warming effects on microbial abundances in colder 4794
regions. Scientific Reports 5, 18032. 4795
Classen, A.T., Sundqvist, M.K., Henning, J.A., Newman, G.S., Moore, J.A.M., Cregger, 4796
M.A., Moorhead, L.C., Patterson, C.M., 2015. Direct and indirect effects of 4797
climate change on soil microbial and soil microbial-plant interactions: What lies 4798
ahead? Ecosphere 6(8), 1–21. 4799
Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. 4800
Bioinformatics 26(19), 2460–2461. 4801
Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon 4802
reads. Nature Methods 10(10), 996–998. 4803
Fierer, N., 2017. Embracing the unknown: disentangling the complexities of the soil 4804
microbiome. Nature Review Microbiology 15(10), 579–590. 4805
Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward an ecological classification of 4806
soil bacteria. Ecology 88(6), 1354–1364. 4807
Hargreaves, S.K., Roberto, A.A., Hofmockel, K.S., 2013. Reaction- and sample-4808
specific inhibition affect standardization of qPCR assays of soil bacterial 4809
208
communities. Soil Biology and Biochemistry 59, 89–97. 4810
Ho, A., Di Lonardo, D.P., Bodelier, P.L.E., 2017. Revisiting life strategy concepts in 4811
environmental microbial ecology. FEMS Microbiology Ecology 93(3), fix006. 4812
Hu, Y., Chang, X., Lin, X., Wang, Y., Wang, S., Duan, J., Zhang, Z., Yang, X., Luo, 4813
C., Xu, G., Zhao, X., 2010. Effects of warming and grazing on N2O fluxes in an 4814
alpine meadow ecosystem on the Tibetan plateau. Soil Biology and 4815
Biochemistry 42(6), 944–952. 4816
IPCC, 2013. Climate change 2013: the physical science basis. Cambridge University 4817
Press, Cambridge and New York. 4818
Jiang, L., Wang, S., Luo, C., Zhu, X., Kardol, P., Zhang, Z., Li, Y., Wang, C., Wang, 4819
Y., Jones, D.L., 2016. Effects of warming and grazing on dissolved organic 4820
nitrogen in a Tibetan alpine meadow ecosystem. Soil Tillage Research 158, 4821
156–164. 4822
Kerkhof, L., Kemp, P., 1999. Small ribosomal RNA content in marine Proteobacteria 4823
during non-steady-state growth. FEMS Microbiology Ecology 30(3), 253–260. 4824
Knox, M.A., Andriuzzi, W.S., Buelow, H.N., Takacs-Vesbach, C., Adams, B.J., Wall, 4825
D.H., 2017. Decoupled responses of soil bacteria and their invertebrate 4826
consumer to warming, but not freeze-thaw cycles, in the Antarctic Dry Valleys. 4827
Ecology Letter 20(10), 1242–1249. 4828
Kohler, F., Hamelin, J., Gillet, F., Gobat, J.M., Buttler, A., 2005. Soil microbial 4829
community changes in wooded mountain pastures due to simulated effects of 4830
cattle grazing. Plant and Soil 278, 327–340. 4831
209
Kuzyakov, Y., Xu, X.L., 2013. Competition between roots and microorganisms for 4832
nitrogen: mechanisms and ecological relevance. New Phytologist 198(3), 656–4833
669. 4834
Lane, D.J., 1991. 16S/23S rRNA sequencing. in: Stackebrandt, E., Goodfellow, M.D. 4835
(Eds.) Nucleic Acid Techniques in Bacterial Systematics. Wiley, New York, pp. 4836
115–175. 4837
Lahtinen, S.J., Ahokoski, H., Reinikainen, J.P., Gueimonde, M., Nurmi, J., Ouwehand, 4838
A.C., Salminen, S.J., 2008. Degradation of 16S rRNA and attributes of viability 4839
of viable but nonculturable probiotic bacteria. Letters in Applied Microbiology 4840
46(6), 693–698. 4841
Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., 4842
Clemente, J.C., Burkepile, D.E., Thurber, R.L.V., Knight, R., Beiko, R.G., 4843
Huttenhower, C., 2013. Predictive functional profiling of microbial 4844
communities using 16S rRNA marker gene sequences. Nature Biotechnology 4845
31(9), 814–821. 4846
Lennon, J.T., Jones, S.E., 2011. Microbial seed banks: the ecological and evolutionary 4847
implications of dormancy. Nature Review Microbiology 9(2), 119–130. 4848
Li, Y., Lin, Q., Wang, S., Li, X., Liu, W., Luo, C., Zhang, Z., Zhu, X., Jiang, L., Li, X., 4849
2016. Soil bacterial community responses to warming and grazing in a Tibetan 4850
alpine meadow. FEMS Microbiology Ecology 92(1), 1–10. 4851
Lin, X., Zhang, Z., Wang, S., Hu, Y., Xu, G., Luo, C., Chang, X., Duan, J., Lin, Q., Xu, 4852
B., Wang, Y., Zhao, X., Xie, Z., 2011. Response of ecosystem respiration to 4853
210
warming and grazing during the growing seasons in the alpine meadow on the 4854
Tibetan plateau. Agricultural and Forest Meteorology 151(7), 792–802. 4855
Liu, X.D., Chen, B.D., 2000. Climatic warming in the Tibetan Plateau during recent 4856
decades. International Journal of Climatology 20(14), 1729–1742. 4857
Luo, C., Xu, G., Chao, Z., Wang, S., Lin, X., Hu, Y., Zhang, Z., Duan, J., Chang, X., 4858
Su, A., Li, Y., Zhao, X., Du, M., Tang, Y., Kimball, B., 2010. Effect of warming 4859
and grazing on litter mass loss and temperature sensitivity of litter and dung 4860
mass loss on the Tibetan plateau. Global Change Biology 16(5), 1606–1617. 4861
Luo, C.Y., Xu, G.P., Wang, Y.F., Wang, S.P., Lin, X.W., Hu, Y.G., Zhang, Z.H., Chang, 4862
X.F., Duan, J.C., Su, A.L., Zhao, X.Q., 2009. Effects of grazing and 4863
experimental warming on DOC concentrations in the soil solution on the 4864
Qinghai-Tibet plateau. Soil Biology and Biochemistry 41(12), 2493–2500. 4865
Ma, S., Zhu, X., Zhang, J., Zhang, L., Che, R., Wang, F., Liu, H., Niu, H., Wang, S., 4866
Cui, X., 2015. Warming decreased and grazing increased plant uptake of amino 4867
acids in an alpine meadow. Ecology and Evolution 5(18), 3995–4005. 4868
Melillo, J.M., Frey, S.D., DeAngelis, K.M., Werner, W.J., Bernard, M.J., Bowles, F.P., 4869
Pold, G., Knorr, M.A., Grandy, A.S., 2017. Long-term pattern and magnitude 4870
of soil carbon feedback to the climate system in a warming world. Science 4871
358(6359), 101. 4872
Metcalfe, D.B., 2017. Microbial change in warming soils. Science 358(6359), 41. 4873
Moinet, G.Y.K., Cieraad, E., Hunt, J.E., Fraser, A., Turnbull, M.H., Whitehead, D., 4874
2016. Soil heterotrophic respiration is insensitive to changes in soil water 4875
211
content but related to microbial access to organic matter. Geoderma 274, 68–78. 4876
Mueller, R.C., Gallegos-Graves, L., Zak, D.R., Kuske, C.R., 2016. Assembly of active 4877
bacterial and fungal communities along a natural environmental gradient. 4878
Microbial Ecology 71(1), 57–67. 4879
Muttray, A.F., Mohn, W.W., 1999. Quantitation of the population size and metabolic 4880
activity of a resin acid degrading bacterium in activated sludge using slot-blot 4881
hybridization to measure the rRNA : rDNA ratio. Microbial Ecology 38(4), 4882
348–357. 4883
Muyzer, G., Dewaal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial 4884
populations by denaturing gradient gel electrophoresis analysis of polymerase 4885
chain reaction-amplified genes coding for 16S rRNA. Applied and 4886
Environmental Microbiology 59(3), 695–700. 4887
Oksanen J., Blanchet F.G., Kindt R., Legendre P., Minchin P.R., O’Hara R.B., Simpson 4888
G.L., Solymos P., Stevens M.H.H., Wagner H., 2017. Vegan: community 4889
ecology package. http://CRAN.R-project.org/package=vegan. 4890
Oliverio, A.M., Bradford, M.A., Fierer, N., 2017. Identifying the microbial taxa that 4891
consistently respond to soil warming across time and space. Global Change 4892
Biology 23(5), 2117–2129. 4893
Peng, F., You, Q.G., Xu, M.H., Zhou, X.H., Wang, T., Guo, J., Xue, X., 2015. Effects 4894
of experimental warming on soil respiration and its components in an alpine 4895
meadow in the permafrost region of the Qinghai-Tibet Plateau. European 4896
Journal of Soil Science 66(1), 145–154. 4897
212
Perez-Osorio, A.C., Williamson, K.S., Franklin, M.J., 2010. Heterogeneous rpoS and 4898
rhlR mRNA levels and 16S rRNA/rDNA (rRNA Gene) ratios within 4899
Pseudomonas aeruginosa biofilms, sampled by laser capture microdissection. 4900
Journal of Bacteriology 192(12), 2991–3000. 4901
Pold, G., Billings, A.F., Blanchard, J.L., Burkhardt, D.B., Frey, S.D., Melillo, J.M., 4902
Schnabel, J., van Diepen, L.T.A., DeAngelis, K.M., 2016. Long-term warming 4903
alters carbohydrate degradation potential in temperate forest soils. Applied and 4904
Environmental Microbiology 82(22): 6518–6530. 4905
R Development Core Team (2016) R: a language and environment for statistical 4906
computing. R Foundation for Statistical Computing, Vienna, Austria. 4907
http://www.R-project.org/. 4908
Rinnan, R., Stark, S., Tolvanen, A., 2009. Responses of vegetation and soil microbial 4909
communities to warming and simulated herbivory in a subarctic heath. Journal 4910
of Ecology 97(4), 788–800. 4911
Romero-Olivares, A.L., Allison, S.D., Treseder, K.K., 2017. Soil microbes and their 4912
response to experimental warming over time: A meta-analysis of field studies. 4913
Soil Biology and Biochemistry 107, 32–40. 4914
Rui, Y., Wang, S., Xu, Z., Wang, Y., Chen, C., Zhou, X., Kang, X., Lu, S., Hu, Y., Lin, 4915
Q., Luo, C., 2011. Warming and grazing affect soil labile carbon and nitrogen 4916
pools differently in an alpine meadow of the Qinghai-Tibet Plateau in China. 4917
Journal of Soils and Sediments 11(6), 903–914. 4918
Rui, Y.C., Wang, Y.F., Chen, C.R., Zhou, X.Q., Wang, S.P., Xu, Z.H., Duan, J.C., Kang, 4919
213
X.M., Lu, S.B., Luo, C.Y., 2012. Warming and grazing increase mineralization 4920
of organic P in an alpine meadow ecosystem of Qinghai-Tibet Plateau, China. 4921
Plant Soil 357(1–2), 73–87. 4922
Schindlbacher, A., Rodler, A., Kuffner, M., Kitzler, B., Sessitsch, A., Zechmeister-4923
Boltenstern, S., 2011. Experimental warming effects on the microbial 4924
community of a temperate mountain forest soil. Soil Biology and Biochemistry 4925
43(7), 1417–1425. 4926
Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., 4927
Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., Sahl, J.W., Stres, 4928
B., Thallinger, G.G., Van Horn, D.J., Weber, C.F., 2009. Introducing mothur: 4929
Open-Source, platform-independent, community-supported software for 4930
describing and comparing microbial communities. Applied and Environmental 4931
Microbiology 75(23), 7537–7541. 4932
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., 4933
Huttenhower, C., 2011. Metagenomic biomarker discovery and explanation. 4934
Genome Biology 12(6), 18. 4935
Segev, E., Smith, Y., Ben-Yehuda, S., 2012. RNA dynamics in aging bacterial spores. 4936
Cell 148(1-2), 139–149. 4937
Supramaniam, Y., Chong, C.-W., Silvaraj, S., Tan, I.K.-P., 2016. Effect of short term 4938
variation in temperature and water content on the bacterial community in a 4939
tropical soil. Applied Soil Ecology 107, 279–289. 4940
Wang, G.X., Qian, J., Cheng, G.D., Lai, Y.M., 2002. Soil organic carbon pool of 4941
214
grassland soils on the Qinghai-Tibetan Plateau and its global implication. 4942
Science of the Total Environment 291(1-3), 207–217. 4943
Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naive Bayesian classifier for 4944
rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied 4945
and Environmental Microbiology 73(16), 5261–5267. 4946
Wang, S., Duan, J., Xu, G., Wang, Y., Zhang, Z., Rui, Y., Luo, C., Xu, B., Zhu, X., 4947
Chang, X., Cui, X., Niu, H., Zhao, X., Wang, W., 2012. Effects of warming and 4948
grazing on soil N availability, species composition, and ANPP in an alpine 4949
meadow. Ecology 93(11), 2365–2376. 4950
Wang, Y., Qian, P.Y., 2009. Conservative fragments in bacterial 16S rRNA genes and 4951
primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS 4952
One 4(10): e7401. 4953
Warren, S., Thurow, T., Blackburn, W., Garza, N., 1986. The influence of livestock 4954
trampling under intensive rotation grazing on soil hydrologic characteristics. 4955
Journal of Range Management, 491–495. 4956
WRB, 1998. World reference base for soil resources. FAO/ISRIC/ISSS, Italy. 4957
Xiong, J., Sun, H., Peng, F., Zhang, H., Xue, X., Gibbons, S.M., Gilbert, J.A., Chu, H., 4958
2014. Characterizing changes in soil bacterial community structure in response 4959
to short-term warming. FEMS Microbiology Ecology 89(2), 281–292. 4960
Xue, K., M. Yuan, M., J. Shi, Z., Qin, Y., Deng, Y., Cheng, L., Wu, L., He, Z., Van 4961
Nostrand, J.D., Bracho, R., Natali, S., Schuur, E.A.G., Luo, C., Konstantinidis, 4962
K.T., Wang, Q., Cole, J.R., Tiedje, J.M., Luo, Y., Zhou, J., 2016a. Tundra soil 4963
215
carbon is vulnerable to rapid microbial decomposition under climate warming. 4964
Nature Climate Change 6(6), 595–600. 4965
Xue, K., Xie, J.P., Zhou, A.F., Liu, F.F., Li, D.J., Wu, L.Y., Deng, Y., He, Z.L., Van 4966
Nostrand, J.D., Luo, Y.Q., Zhou, J.Z., 2016b. Warming alters expressions of 4967
microbial functional genes important to ecosystem functioning. Frontiers in 4968
Microbiology 7, 668. 4969
Xue, S., Yang, X.M., Liu, G.B., Gai, L.T., Zhang, C.S., Ritsema, C.J., Geissen, V., 2017. 4970
Effects of elevated CO2 and drought on the microbial biomass and enzymatic 4971
activities in the rhizospheres of two grass species in Chinese loess soil. 4972
Geoderma 286, 25–34. 4973
Yang, Y., Wu, L., Lin, Q., Yuan, M., Xu, D., Yu, H., Hu, Y., Duan, J., Li, X., He, Z., 4974
Xue, K., van Nostrand, J., Wang, S., Zhou, J., 2013. Responses of the functional 4975
structure of soil microbial community to livestock grazing in the Tibetan alpine 4976
grassland. Global Change Biology 19(2), 637–648. 4977
Yarwood, S.A., Myrold, D.D., Hogberg, M.N., 2009. Termination of belowground C 4978
allocation by trees alters soil fungal and bacterial communities in a boreal forest. 4979
FEMS Microbiology Ecology 70(1), 151–162. 4980
Zhang, K., Shi, Y., Jing, X., He, J.-S., Sun, R., Yang, Y., Shade, A., Chu, H., 2016a. 4981
Effects of short-term warming and altered precipitation on soil microbial 4982
communities in alpine grassland of the Tibetan Plateau. Frontiers in 4983
Microbiology 7, 1032. 4984
Zhang, X.M., Johnston, E.R., Li, L.H., Konstantinidis, K.T., Han, X.G., 2017b. 4985
216
Experimental warming reveals positive feedbacks to climate change in the 4986
Eurasian Steppe. ISME Journal. 11(4), 885–895. 4987
Zhang, Y., Dong, S., Gao, Q., Liu, S., Zhou, H., Ganjurjav, H., Wang, X., 2016b. 4988
Climate change and human activities altered the diversity and composition of 4989
soil microbial community in alpine grasslands of the Qinghai-Tibetan Plateau. 4990
Science of the Total Environment 562, 353–363. 4991
Zhao, L., Li, Y.N., Xu, S.X., Zhou, H.K., Gu, S., Yu, G.R., Zhao, X.Q., 2006. Diurnal, 4992
seasonal and annual variation in net ecosystem CO2 exchange of an alpine 4993
shrubland on Qinghai-Tibetan plateau. Global Change Biology 12(10), 1940–4994
1953. 4995
Zheng, Y., Yang, W., Sun, X., Wang, S.P., Rui, Y.C., Luo, C.Y., Guo, L.D., 2012. 4996
Methanotrophic community structure and activity under warming and grazing 4997
of alpine meadow on the Tibetan Plateau. Applied Microbiology and 4998
Biotechnology 93(5), 2193–2203. 4999
Zhou, H., Zhao, X., Tang, Y., Gu, S., Zhou, L., 2005. Alpine grassland degradation and 5000
its control in the source region of the Yangtze and Yellow Rivers, China. 5001
Grassland Science 51(3), 191–203. 5002
217
5003
5004
Chapter 6. General Conclusions and Perspectives 5005
5006
5007
5008
5009
5010
218
6.1. General conclusions 5011
Recent technological innovations have largely improved our abilities to investigate soil 5012
microbial community profiles. However, with only less than 1% of microbes being 5013
cultured and characterized, the majority of soil microbial lineages are poorly described. 5014
Consequently, the predominant challenge in current microbial ecology studies has 5015
shifted from determining community compositions to interpreting them in an 5016
ecologically meaningful manner. Examining the changes in the relative abundances of 5017
copiotrophic and oligotrophic microbial lineages is the most widely used approach to 5018
reveal the ecological implications of microbial community dynamics. Furthermore, the 5019
copiotrophic and oligotrophic proportions are intimately correlated with soil microbial 5020
respiration rates. Therefore, classifying microbial lineages into copiotroph-oligotroph 5021
categories is critical to understanding the ecological implications of microbial 5022
community profiles, and establishing the microbial community-functionality linkages. 5023
5024
Nevertheless, almost all of the efforts on microbial life-strategy classifications were 5025
made at the phylum level. The life strategies of microbial lineages at finer taxonomy 5026
levels remained largely unknown. Moreover, even though more than 90% of soil 5027
microbes are dormant, and the active microbial populations are more connected with 5028
ecosystem functionality, the life-strategy classifications of active microbial 5029
communities are seldom conducted. Therefore, in my thesis, I evaluated the correlations 5030
between 16S rRNA-based community structures and microbial respiration rates of the 5031
soils amended with different amounts of glutamate (Chapter 2). Then, in Chapter 3, I 5032
219
tried to classify prokaryotic lineages (from kingdom to genus) into copiotroph-5033
oligotroph categories, by amending soils collected from 32 grasslands with glucose, 5034
using methods based on 16S rDNA and rRNA. Finally, in Chapters 4 and 5, I tried to 5035
interpret the responses of prokaryotic communities to litter amendments, phosphorus 5036
fertilization, livestock grazing, and experimental warming based on the changes in the 5037
relative abundances of copiotrophic and oligotrophic microbial lineages. 5038
5039
In Chapter 2, my study found that both the community compositions based on 16S 5040
rDNA and rRNA showed significant correlations with soil microbial respiration rates, 5041
but the 16S rDNA based community structures clearly outperformed the community 5042
structures based on 16S rRNA. These findings suggest that 16S rRNA-based 5043
community structure is a sensitive indicator of soil microbial respiration activity. In 5044
addition, the microbial respiration gradient was essentially in line with the glutamate 5045
gradient. Therefore, this study also highlights the potential of using rRNA-based 5046
methods to identify the life strategies (i.e., copiotrophs and oligotrophs) of microbial 5047
lineages. 5048
5049
The study in Chapter 3 showed that although the soil prokaryotic communities are 5050
highly variable across different grasslands, most of their responses to the glucose 5051
amendment are consistent. In addition, the 16S rRNA based indices showed similar but 5052
more sensitive responses to the glucose amendment. Specifically, the 16S rRNA copies, 5053
16S rRNA-rDNA ratios, community composition dispersion, microbial biomass and 5054
220
respiration rates, as well as the relative abundances of Bacteria, Proteobacteria, 5055
Bacteroidetes, and Firmicutes significantly increased under the glucose amendment. 5056
The glucose amendment also significantly decreased the prokaryotic α-diversity, as 5057
well as the proportions of Archaea, Acidobacteria, Chloroflexi, Planctomycetes, 5058
Gemmatimonadetes, Tectomicrobia, Nitrospirae, Armatimonadetes, Verrucomicrobia, 5059
and FBP. In particular, the overall responses of the community profiles showed similar 5060
ordination pattern across the studies sites. Most of the prokaryotic lineages increased 5061
or decreased under the glucose amendments also showed positive or negative 5062
correlations with the soil microbial respiration rates across the grasslands, respectively. 5063
Nevertheless, some lineages, in particular, Acidobacteria, did not fit this pattern. The 5064
life-strategy diversification under each prokaryotic lineage was also observed, 5065
especially for the Proteobacteria lineages. Collectively, this study suggests that despite 5066
the existence of life-strategy diversifications among the lineages within each 5067
prokaryotic phylum, it is still possible to identify several general copiotrophic and 5068
oligotrophic prokaryotic phyla across different grasslands. Nevertheless, using the 5069
relative abundances of copiotrophs and oligotrophs to assess the cross-sites microbial 5070
respiration capacities must be cautious. In addition, this study further highlights the 5071
possibility to identify the microbial lineage life-strategies using rRNA-based methods, 5072
and indicates that the ecological implications of 16S rRNA-based community profiles 5073
can be explained in similar manner to that based on 16S rDNA. 5074
5075
The study in Chapter 4 showed that soil microbial activity, rDNA transcription, and 5076
221
fungal abundance significantly increased under the litter amendment. In addition, the 5077
litter amendment significantly decreased microbial α-diversity, while it significantly 5078
increased the microbial β-diversity. Microbial community compositions were also 5079
significantly altered by the litter amendment. Specifically, the relative abundances of 5080
copiotrophs and oligotrophs significantly increased and decreased under the litter 5081
amendment, respectively. Interestingly, the changes in microbial lineages proportions 5082
could be related to their rDNA-rRNA ratios. The relative abundances of those with high 5083
and low rDNA-rRNA ratios increased and decreased under the litter amendment, 5084
respectively. Nevertheless, neither phosphorus amendments nor phosphorus-litter 5085
interaction exerted significant effects on soil microbes. Collectively, these findings 5086
suggest that increasing litter input to the degraded grassland soils can result in more 5087
copiotrophic microbial communities with higher activity, lower α-diversity, and more 5088
variable community compositions. Additionally, this study provided bases for future 5089
studies of identifying fungal lineage life-strategies using the methods based on ITS 5090
RNA. 5091
5092
In Chapter 5, my study showed that the six-year warming significantly decreased the 5093
16S rRNA copies, rRNA-rDNA ratios, and the soil microbial respiration rates. Warming 5094
also significantly increased the relative abundances of oligotrophic microbes, while 5095
decreased the copiotrophic lineage proportions. The PICRUSt analysis suggested that 5096
the DNA replication, transcription, translation, and signal transduction were 5097
significantly depressed under warming. In addition, warming significantly increased 5098
222
the 16 rRNA-based prokaryotic α diversities and the similarity of the 16S rDNA-based 5099
community structures, while the warming-grazing interactions significantly offset the 5100
effects of warming on the dispersion of 16S rDNA-based community compositions. 5101
Grazing significantly decreased soil 16S rRNA copies, rRNA-rDNA ratios, and 16S 5102
rDNA-based procaryotic richness, but exerted no significant effects on soil microbial 5103
community structures. Although a single sampling is far from sufficient to adequately 5104
reveal the effects of warming and grazing on the alpine meadow soils, these results 5105
coupled with my previous studies conducted at the same study site can suggest: 1) the 5106
effects of warming on the soil microbes are time-dependent, and may be intensified in 5107
the future; 2) soil microbial activity is significantly depressed under the six-year 5108
warming; 3) the changes in the microbial community structures under warming can 5109
play vital roles in determining the response of microbial activity to warming; and 4) the 5110
moderate grazing may offset, at least partially, the influences of warming on soil 5111
microbes, but the offset may attenuate over time. 5112
5113
The studies included in this thesis provided a systemic classification of the life 5114
strategies of prokaryotic lineages in grassland soils. The life-strategy classification was 5115
based on the soils collected from the grasslands with a wide range of environmental 5116
conditions. Thus, the prokaryotic lineage life-strategies identified in this study can be 5117
applied to reveal the ecological implications of the prokaryotic community profile 5118
variations in a wide range of soils. In particular, for the first time, the life-strategies 5119
were classified using rRNA-based methods. Compared to the 16S rDNA-based 5120
223
community profiles, the similar but more sensitive responses of the profiles based on 5121
16S rRNA to the glucose amendment highlight the advantages of the 16S rRNA-based 5122
methods in identifying the life-strategies of prokaryotic lineages. It also provides a key 5123
basis to understand the ecological implications of 16S rRNA-based community in a 5124
similar way as that based on 16S rDNA. Overall, these findings dramatically improve 5125
our ability to reveal the ecological implications of soil prokaryotic community profiles. 5126
5127
Another key finding in my thesis is that although the prokaryotic community structures 5128
are highly variable across different grasslands, the glucose amendment shifted them 5129
towards a same direction. This suggests that it is possible to rank the fine-taxonomy-5130
level prokaryotic lineages from most copiotrophic to most oligotrophic, which lays a 5131
foundation for the future life-strategy identification of prokaryotic lineages. 5132
5133
In addition, the findings in my thesis also contribute to establishing the links between 5134
soil heterotrophic respiration and prokaryotic communities. In Chapter 2, the study 5135
suggests that 16S rRNA-based community structure can be a potentially promising 5136
indicator of the heterotrophic respiration rates. Then, in Chapter 3, my study suggests 5137
the Proteobacterial proportion showed strong positive correlation with heterotrophic 5138
respiration rates. Their correlations were consistent with heterotrophic respiration rate 5139
changes within each study site and across different study sites. Furthermore, even 5140
though no significant correlation between 16S rRNA copies and heterotrophic 5141
respiration rates was observed in Chapter 2, my studies in Chapters 3–5 indicate that in 5142
224
most cases, 16S rRNA copies are clearly a more reliable indicator of soil heterotrophic 5143
respiration than 16S rDNA copies. Collectively, these findings provide essential bases 5144
for incorporating microbial indices to modeling ecosystem carbon dynamics. 5145
5146
Finally, examining the effects of litter amendments, phosphorus fertilization, livestock 5147
grazing, and experimental warming on soil microbial communities notably improved 5148
our understanding of the responses of alpine meadows to degradation restoration, 5149
climate changes, and human activities. Specifically, investigating the effects of litter 5150
and P amendments on microbes suggest that increasing organic matter input to degraded 5151
soils may reduce the ecosystem stability, at least in the short term. This provides vital 5152
guidelines for restoring the degraded grasslands. Determining the prokaryotic responses 5153
to warming and grazing dramatically improve our understanding of ecosystem 5154
feedbacks to the future climate changes. In particular, the respective significant increase 5155
and decrease in the proportions of copiotrophs and oligotrophs highlight the 5156
contribution of prokaryotic community structure alterations to the acclimation of 5157
heterotrophic respiration under long-term warming. 5158
5159
In summary, in this thesis, I classified grassland soil prokaryotic lineages into 5160
copiotrophic and oligotrophic categories, and applied it to understand the prokaryotic 5161
responses to environmental changes. The findings in the thesis enhanced our ability to 5162
interpret the changes in prokaryotic community profiles from an ecological meaningful 5163
perspective, placing a foundation for future life-strategy identifications. They also 5164
225
provided bases for incorporating microbial indices to modeling ecosystem carbon 5165
dynamics, as well as improved our understanding of the alpine meadow responses to 5166
land degradation restoration, climate change, and human activities. 5167
5168
6.2. Perspectives for future studies 5169
On the basis of the findings in my thesis, the future studies should pay more attention 5170
to the following aspects. 5171
5172
First, the life-strategy classification of fungal lineages is even less determined than that 5173
of prokaryotes. Thus, more studies should be conducted to identify the life-strategies of 5174
fungal lineages. In particular, my study in Chapter 4 showed that the methods based on 5175
ITS RNA could be used to analyze the community profiles of active soil fungi. Thus, 5176
the ITS RNA-based methods should also be considered when conducting the 5177
classification. 5178
5179
Second, the study in Chapter 3 suggests that it is possible to rank the prokaryotic 5180
lineages from most copiotrophic to most oligotrophic. Hence, future studies should 5181
work towards this goal. If we can assign a copiotroph-oligotroph value to each 5182
microbial lineage, it may be an excellent idea to incorporate these values into analysis 5183
pipeline of the rDNA amplicon sequences. Furthermore, it is also a good idea to utilize 5184
the results of microbial life-strategy classifications to re-analyze the existing microbial 5185
sequencing data to obtain more insights into the responses of microbial communities. 5186
226
5187
Third, incorporating soil microbial indices into the modeling of the ecological process 5188
is a goal for ecologists over a long period. Based on the findings in this thesis, it would 5189
be worthy to assess whether the incorporation of Proteobacterial proportions and 16S 5190
rRNA copies can improve the prediction accuracy of modeling on ecosystem CO2 5191
dynamics. 5192
5193
Fourth, in this thesis, I used three kinds of organic matter (i.e., glutamate, glucose, and 5194
grass litter). Generally, the microbial life-strategy classifications based on different 5195
carbon sources were consistent. However, some inconsistencies for several microbial 5196
lineages were also observed. Therefore, further studies should pay more attention to the 5197
effects of carbon source types on identifying microbial life strategies. 5198
5199
Finally, microbial functional groups (e.g., diazotrophs, nitrifies, denitrifies, 5200
methanotrophs, and methanogens) are also extensively investigated in the recent 5201
decades. Therefore, identifying the overall life strategies of each functional group could 5202
also improve our ability to understanding the ecosystem processes. For instance, 5203
identifying the life-strategies of nitrogen cycling groups can not only contribute to 5204
deepening the insight into carbon and nitrogen cycling interactions, but also provide a 5205
crucial basis for soil managements. 5206
5207
5208
227
5209
5210
5211
5212
5213
5214
Supplementary Materials 5215
5216
228
Table S3.1. A summary of the responses of soil prokaryotic lineage proportions to the glucose 5217
amendment and their correlations with microbial respiration rates. Mean: the average relative abundances 5218
of the prokaryotic lineages across all the soil samples; trDNA: the t values generated from the paired t-tests 5219
of the 16S rDNA relative abundances of prokaryotic lineages between the soils with glucose amendment 5220
and the controls; trRNA: the t values generated from the paired t-tests of the 16S rRNA relative abundances 5221
of prokaryotic lineages between the soils with glucose amendment and the controls; RrDNA: the correlation 5222
coefficients of the correlations between soil microbial respiration rates and 16S rDNA relative 5223
abundances of prokaryotic lineages; RrRNA: the correlation coefficients of the correlations between soil 5224
microbial respiration rates and 16S rRNA relative abundances of prokaryotic lineages; tRD: the t values 5225
generated from the paired t-tests between 16S rDNA and rRNA relative abundances of prokaryotic 5226
lineages. The negative number represent “negative correlations”, “more abundant in the glucose-5227
amended soils”, or “the 16S rRNA relative abundances are higher than those of 16S rDNA”. The bold 5228
values represent significant differences or correlations with P < 0.05. 5229
Prokaryotic lineages Mean trDNA trRNA RrDNA RrRNA tRD
Archaea 3.04% -4.69 -6.34 -0.36 -0.48 -2.26
Archaea|Euryarchaeota 0.22% -1.36 -2.46 -0.20 -0.50 2.24
Archaea|Euryarchaeota|Thermoplasmata 0.22% -1.33 -2.37 0.05 -0.39 2.19
Archaea|Euryarchaeota|Thermoplasmata|Thermoplasmatales 0.21% -1.36 -2.47 -0.20 -0.50 2.23
Archaea|Thaumarchaeota 2.81% -4.51 -7.15 -0.32 -0.26 -4.21
Archaea|Thaumarchaeota|Soil_Crenarchaeotic_Group (SCG) 2.83% -4.26 -5.94 0.06 -0.25 -4.05
Archaea|Thaumarchaeota|SCG|SCG 2.23% -4.44 -7.50 -0.13 0.06 -4.57
Archaea|Thaumarchaeota| SCG |Unknown|Candidatus_Nitrososphaera 0.58% -3.40 -4.25 -0.63 -0.65 -0.95
Bacteria 96.96% 4.68 6.33 0.36 0.48 2.27
Bacteria|Acidobacteria 14.52% -7.58 -7.90 0.56 0.72 -21.64
Bacteria|Acidobacteria|Acidobacteria 0.41% 0.87 1.49 0.34 0.29 -2.07
Bacteria|Acidobacteria|Acidobacteria|Acidobacteriales|Acidobacteriaceae|Acido
bacterium
0.40% -0.01 0.47 -0.24 0.40 -0.23
Bacteria|Acidobacteria|Acidobacteria|Acidobacteriales|Acidobacteriaceae|Candi
datus_Koribacter
0.40% -0.31 0.37 0.45 0.65 -2.14
Bacteria|Acidobacteria|Acidobacteria|Acidobacteriales|Acidobacteriaceae|Edaph
obacter
0.40% 1.73 1.40 0.56 0.26 -1.91
Bacteria|Acidobacteria|Blastocatellia 4.49% -5.37 -2.46 0.20 0.32 -9.73
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales 4.48% -5.64 -2.79 0.17 0.40 -10.04
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|1 4.48% -5.64 -2.79 0.17 0.40 -10.04
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|Blastocatellaceae 4.48% -5.64 -2.79 0.17 0.40 -10.04
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|Blastocatellaceae|43428 0.26% -4.11 -3.25 0.43 0.66 -4.93
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|Blastocatellaceae|Blastocat
ella
0.15% -1.78 1.81 -0.38 -0.30 -5.51
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|Blastocatellaceae|RB41 3.55% -5.55 -4.07 0.13 0.36 -9.03
Bacteria|Acidobacteria|Blastocatellia|Blastocatellales|Blastocatellaceae|unculture 0.21% 0.05 2.28 0.19 0.14 -5.71
Bacteria|Acidobacteria|Holophagae 1.03% -4.33 -1.24 0.18 0.03 -7.36
Bacteria|Acidobacteria|Holophagae|Subgroup_10 0.34% -1.66 -0.45 -0.05 -0.11 -4.29
Bacteria|Acidobacteria|Holophagae|Subgroup_10|1 0.34% -1.66 -0.45 -0.05 -0.11 -4.29
Bacteria|Acidobacteria|Holophagae|Subgroup_10|ABS|19 0.31% -1.42 -0.34 -0.08 -0.13 -4.37
229
Bacteria|Acidobacteria|Holophagae|Subgroup_10|ABS|19|ABS|19_ge 0.31% -1.42 -0.34 -0.08 -0.13 -4.37
Bacteria|Acidobacteria|Holophagae|Subgroup_7 0.68% -4.43 -2.09 -0.02 0.01 -7.08
Bacteria|Acidobacteria|Solibacteres 0.79% -7.10 -6.49 -0.07 -0.17 -3.78
Bacteria|Acidobacteria|Solibacteres|Solibacterales 0.79% -7.40 -9.16 -0.12 0.50 -3.82
Bacteria|Acidobacteria|Solibacteres|Solibacterales|1 0.79% -7.40 -9.16 -0.12 0.50 -3.82
Bacteria|Acidobacteria|Solibacteres|Solibacterales|Solibacteraceae 0.79% -7.40 -9.16 -0.12 0.50 -3.82
Bacteria|Acidobacteria|Solibacteres|Solibacterales|Solibacteraceae|Bryobacter 0.60% -6.41 -7.64 -0.39 -0.06 -5.52
Bacteria|Acidobacteria|Solibacteres|Solibacterales|Solibacteraceae| Solibacter 0.13% -3.15 -3.72 0.69 0.79 1.90
Bacteria|Acidobacteria|Subgroup_17 0.15% -4.32 -6.43 0.15 -0.03 -5.83
Bacteria|Acidobacteria|Subgroup_6 7.45% -6.27 -7.10 0.00 0.12 -12.57
Bacteria|Acidobacteria|Subgroup_6|Subgroup_6 6.66% -6.65 -8.12 0.43 0.67 -13.14
Bacteria|Acidobacteria|Subgroup_6|Unknown_Order 0.69% -4.40 -5.70 0.25 0.44 0.21
Bacteria|Acidobacteria|Subgroup_6|Unknown_Order|Unknown |Vicinamibacter 0.69% -4.40 -5.70 0.25 0.44 0.21
Bacteria|Actinobacteria 15.70% 0.58 -2.80 -0.84 -0.72 12.89
Bacteria|Actinobacteria|Acidimicrobiia 1.03% -1.71 -5.21 0.12 -0.23 6.96
Bacteria|Actinobacteria|Acidimicrobiia|Acidimicrobiales 1.02% -1.90 -7.16 -0.21 -0.31 7.25
Bacteria|Actinobacteria|Acidimicrobiia|Acidimicrobiales|1 1.02% -1.90 -7.16 -0.21 -0.31 7.25
Bacteria|Actinobacteria|Acidimicrobiia|Acidimicrobiales|unclassified 0.11% -2.84 -4.11 0.08 0.45 6.55
Bacteria|Actinobacteria|Acidimicrobiia|Acidimicrobiales|uncultured 0.71% -2.30 -7.33 -0.30 -0.45 8.62
Bacteria|Actinobacteria|Actinobacteria 8.00% 2.29 -0.83 -0.33 -0.27 10.18
Bacteria|Actinobacteria|Actinobacteria|unclassified 1.35% -2.43 -4.83 -0.60 -0.41 8.60
Bacteria|Actinobacteria|Actinobacteria|unclassified|unclassified 1.34% -2.43 -4.83 -0.60 -0.41 8.60
Bacteria|Actinobacteria|Actinobacteria|Frankiales|Nakamurellaceae 1.07% 0.00 -1.15 -0.10 -0.08 3.27
Bacteria|Actinobacteria|Actinobacteria|Frankiales|Sporichthyaceae 0.66% 1.95 -2.81 0.11 0.04 9.01
Bacteria|Actinobacteria|Actinobacteria|Frankiales|Sporichthyaceae| 0.13% -0.57 -1.30 -0.03 -0.41 1.28
Bacteria|Actinobacteria|Actinobacteria|Frankiales|Sporichthyaceae|Sporichthya 0.28% -0.42 -5.18 -0.22 -0.35 5.51
Bacteria|Actinobacteria|Actinobacteria|Frankiales|uncultured|uncultured 0.11% 1.06 -1.74 -0.53 -0.51 5.10
Bacteria|Actinobacteria|Actinobacteria|Kineosporiales 0.48% 0.47 -3.77 0.19 0.03 4.60
Bacteria|Actinobacteria|Actinobacteria|Kineosporiales|Kineosporiaceae|Kineosp
oria
0.17% 0.42 -2.05 -0.23 -0.18 0.11
Bacteria|Actinobacteria|Actinobacteria|Kineosporiales|Kineosporiaceae|Quadrisp
haera
0.17% 0.01 -3.59 0.24 -0.01 4.41
Bacteria|Actinobacteria|Actinobacteria|Micrococcales|Microbacteriaceae|Microb
acterium
0.71% 3.57 1.79 -0.36 -0.17 1.03
Bacteria|Actinobacteria|Actinobacteria|Micrococcales|Micrococcaceae|Kocuria 0.25% -0.30 -1.21 -0.51 -0.31 2.32
Bacteria|Actinobacteria|Actinobacteria|Micrococcales|Micrococcaceae|Pseudarth
robacter
0.35% 2.97 0.54 -0.36 -0.18 -0.69
Bacteria|Actinobacteria|Actinobacteria|Micromonosporales|Micromonosporacea
e|Asanoa
1.49% -0.63 -3.14 0.03 0.07 3.63
Bacteria|Actinobacteria|Actinobacteria|Micromonosporales|Micromonosporacea
e|Luedemannella
1.49% -0.97 -3.64 -0.28 -0.36 3.86
Bacteria|Actinobacteria|Actinobacteria|Micromonosporales|Micromonosporacea
e|uncultured
1.49% 0.65 -2.03 0.02 -0.22 2.43
230
Bacteria|Actinobacteria|Actinobacteria|Micromonosporales|Micromonosporacea
e|Virgisporangium
1.18% -3.35 -6.64 -0.27 -0.44 6.48
Bacteria|Actinobacteria|Actinobacteria|Propionibacteriales|Nocardioidaceae|Flin
dersiella
0.89% -1.16 -1.78 -0.48 -0.36 1.23
Bacteria|Actinobacteria|Actinobacteria|Propionibacteriales|Nocardioidaceae|Krib
bella
0.89% 0.29 0.00 -0.11 -0.27 2.36
Bacteria|Actinobacteria|Actinobacteria|Propionibacteriales|Nocardioidaceae|Mar
moricola
0.87% 2.60 0.75 -0.18 -0.02 8.94
Bacteria|Actinobacteria|Actinobacteria|Propionibacteriales|Propionibacteriaceae 0.12% -0.55 -2.91 0.01 -0.19 2.73
Bacteria|Actinobacteria|Actinobacteria|Propionibacteriales|Propionibacteriaceae| 0.33% -0.27 -2.62 -0.12 -0.19 0.97
Bacteria|Actinobacteria|Actinobacteria|Pseudonocardiales|Pseudonocardiaceae|A
ctinophytocola
1.49% -1.44 -5.64 -0.40 -0.39 6.97
Bacteria|Actinobacteria|Actinobacteria|Pseudonocardiales|Pseudonocardiaceae|A
mycolatopsis
1.49% 0.28 -0.51 0.02 -0.13 2.37
Bacteria|Actinobacteria|Actinobacteria|Pseudonocardiales|Pseudonocardiaceae|C
rossiella
1.49% -3.49 -2.79 -0.32 -0.43 -0.24
Bacteria|Actinobacteria|Actinobacteria|Pseudonocardiales|Pseudonocardiaceae|L
entzea
0.18% -1.18 -3.20 -0.19 -0.16 1.55
Bacteria|Actinobacteria|Actinobacteria|Streptosporangiales 0.47% 0.90 -2.71 0.56 0.54 -0.07
Bacteria|Actinobacteria|Actinobacteria|Streptosporangiales|1 0.47% 0.90 -2.71 0.56 0.54 -0.07
Bacteria|Actinobacteria|Actinobacteria|Streptosporangiales|Streptosporangiaceae 0.11% -0.33 -0.24 0.46 0.40 0.81
Bacteria|Actinobacteria|Actinobacteria|Streptosporangiales|Streptosporangiaceae
|Microbispora
0.11% 0.73 0.63 0.12 0.40 -0.78
Bacteria|Actinobacteria|MB|A2|108 0.22% -2.78 -4.40 -0.33 -0.15 -2.60
Bacteria|Actinobacteria|MB|A2|108|MB|A2|108_or 0.22% -3.03 -5.41 -0.48 -0.37 -2.79
Bacteria|Actinobacteria|MB|A2|108|MB|A2|108_or|1 0.22% -3.03 -5.41 -0.48 -0.37 -2.79
Bacteria|Actinobacteria|MB|A2|108|MB|A2|108_or|MB|A2|108_fa 0.22% -3.03 -5.41 -0.48 -0.37 -2.79
Bacteria|Actinobacteria|MB|A2|108|MB|A2|108_or|MB|A2|108_fa|MB|A2|108 0.22% -3.03 -5.41 -0.48 -0.37 -2.79
Bacteria|Actinobacteria|Rubrobacteria 0.12% -2.63 -0.82 -0.25 -0.08 -2.28
Bacteria|Actinobacteria|Rubrobacteria|Rubrobacterales 0.12% -2.82 -0.91 -0.63 -0.43 -2.47
Bacteria|Actinobacteria|Rubrobacteria|Rubrobacterales|Rubrobacteriaceae 0.12% -2.82 -0.91 -0.63 -0.43 -2.47
Bacteria|Actinobacteria|Rubrobacteria|Rubrobacterales|Rubrobacteriaceae|Rubro
bacter
0.12% -2.82 -0.91 -0.63 -0.43 -2.47
Bacteria|Actinobacteria|Thermoleophilia 4.66% -1.55 -1.46 -0.36 -0.31 9.09
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales 1.25% -2.65 -4.19 -0.47 -0.38 2.15
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales|1 1.25% -2.65 -4.19 -0.47 -0.38 2.15
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales|Gaiellaceae 0.37% -2.18 -4.17 -0.19 -0.19 2.66
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales|Gaiellaceae|Gaiella 0.37% -2.18 -4.17 -0.19 -0.19 2.66
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales|uncultured 0.81% -2.69 -4.00 -0.51 -0.41 1.56
Bacteria|Actinobacteria|Thermoleophilia|Gaiellales|uncultured|uncultured 0.81% -2.69 -4.00 -0.51 -0.41 1.56
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales 3.38% -0.54 -1.02 -0.70 -0.61 10.86
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|0319|6M6 3.38% -0.86 -3.17 -0.64 -0.54 6.43
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|0319|6M6|0319|6M
6
0.20% -0.86 -3.17 -0.64 -0.54 6.43
231
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|1 0.20% -0.54 -1.02 -0.70 -0.61 10.86
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|288|2 0.29% -2.34 -3.06 -0.52 -0.48 3.29
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|288|2|288|2_ge 0.29% -2.34 -3.06 -0.52 -0.48 3.29
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|Elev|16S|1332 0.22% -1.12 -4.26 -0.06 0.04 -1.79
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|Elev|16S|1332|Elev
|16S|1332_ge
0.22% -1.12 -4.26 -0.06 0.04 -1.79
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|FFCH11085 0.20% -1.86 -5.58 -0.46 -0.50 8.13
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|FFCH11085|FFCH
11085_ge
0.20% -1.86 -5.58 -0.46 -0.50 8.13
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|Solirubrobacteracea
e
1.45% 1.81 0.80 -0.66 -0.59 10.59
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|Solirubrobacteracea
e|Solirubrobacter
1.45% 1.81 0.80 -0.66 -0.59 10.59
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|unclassified 0.69% -0.59 -2.05 -0.61 -0.45 8.31
Bacteria|Actinobacteria|Thermoleophilia|Solirubrobacterales|uncultured 0.10% 0.85 -1.14 -0.41 -0.69 8.21
Bacteria|Armatimonadetes 0.83% -5.45 -7.65 -0.66 -0.25 2.95
Bacteria|Armatimonadetes|Armatimonadia 0.27% -2.67 -3.03 -0.28 -0.12 -0.18
Bacteria|Armatimonadetes|Armatimonadia|Armatimonadales 0.27% -2.61 -3.58 -0.69 -0.34 0.00
Bacteria|Armatimonadetes|Armatimonadia|Armatimonadales|1 0.27% -2.61 -3.58 -0.69 -0.34 0.00
Bacteria|Armatimonadetes|Armatimonadia|Armatimonadales|Armatimonadales 0.25% -2.84 -3.29 -0.69 -0.54 -0.57
Bacteria|Armatimonadetes|Chthonomonadetes 0.11% -5.58 -3.62 0.23 0.18 1.62
Bacteria|Armatimonadetes|Chthonomonadetes|Chthonomonadales 0.10% -5.82 -3.96 0.54 0.71 1.64
Bacteria|Armatimonadetes|Chthonomonadetes|Chthonomonadales|1 0.10% -5.82 -3.96 0.54 0.71 1.64
Bacteria|Armatimonadetes|uncultured 0.40% -5.06 -5.38 -0.49 -0.54 2.77
Bacteria|Bacteroidetes 9.61% 2.12 2.40 0.35 0.22 -6.55
Bacteria|Bacteroidetes|Cytophagia|Cytophagales|Cytophagaceae| 0.36% -1.98 -2.18 -0.23 -0.32 -0.20
Bacteria|Bacteroidetes|Cytophagia|Cytophagales|Cytophagaceae|Adhaeribacter 0.14% 1.12 1.04 -0.27 -0.64 -1.71
Bacteria|Bacteroidetes|Cytophagia|Cytophagales|Cytophagaceae|Chryseolinea 0.12% -2.02 -2.23 0.31 0.04 -1.63
Bacteria|Bacteroidetes|Cytophagia|Cytophagales|Cytophagaceae|Dyadobacter 0.51% 2.57 2.48 -0.10 -0.17 0.93
Bacteria|Bacteroidetes|Cytophagia|Cytophagales|Cytophagaceae|Sporocytophaga 0.11% -1.39 -2.95 -0.05 -0.08 -1.03
Bacteria|Bacteroidetes|Flavobacteriia 1.32% 3.22 1.68 0.04 0.48 0.69
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales 1.35% 3.46 2.62 0.07 -0.12 0.71
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|1 1.35% 3.46 2.62 0.07 -0.12 0.71
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|Cryomorphaceae|Fluviico
la
0.11% 1.92 2.07 -0.04 -0.17 0.09
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|Flavobacteriaceae 1.13% 3.38 2.52 0.08 -0.15 0.33
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|Flavobacteriaceae|Flavob
acterium
1.06% 3.37 2.49 0.18 -0.14 0.51
Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|NS9_marine_group 0.10% 0.05 -1.31 0.14 0.18 2.76
Bacteria|Bacteroidetes|Sphingobacteriia 5.79% -0.63 1.16 0.27 0.25 -8.82
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales 5.76% -0.72 1.31 0.56 0.65 -9.10
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|1 5.76% -0.72 1.31 0.56 0.65 -9.10
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae 4.52% -2.15 1.33 0.55 0.58 -9.67
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae| 1.50% -3.90 0.25 0.45 0.56 -9.94
232
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|C
hitinophaga
0.16% 2.39 1.83 0.36 0.00 -2.58
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Fe
rruginibacter
0.14% 0.38 1.28 0.67 0.67 -3.47
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Fl
avisolibacter
0.50% -3.03 1.45 -0.29 0.01 -7.81
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Fl
avitalea
0.35% 0.03 2.96 0.13 0.07 -7.45
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Ni
astella
0.41% -2.03 -3.33 0.56 0.42 -3.54
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Pa
rafilimonas
0.11% 1.07 0.64 0.82 0.69 -5.06
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Se
getibacter
0.26% -3.43 -1.22 -0.18 0.00 -5.11
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|Te
rrimonas
0.36% 0.96 2.37 0.84 0.72 -4.37
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Chitinophagaceae|un
cultured
0.67% -1.12 0.34 0.74 0.77 -7.39
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|env|OPS_17 0.36% -0.27 -2.19 -0.01 0.46 6.22
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|env|OPS_17|env|OP
S_17
0.36% -0.27 -2.19 -0.01 0.46 6.22
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|Sphingobacteriaceae 0.39% 2.28 1.93 0.24 0.03 -0.32
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|uncultured 0.11% -0.62 -2.00 0.55 0.48 -2.75
Bacteria|Bacteroidetes|Sphingobacteriia|Sphingobacteriales|uncultured|unculture
d
0.11% -0.62 -2.00 0.55 0.48 -2.75
Bacteria|Chloroflexi 4.24% -3.64 -5.00 -0.77 -0.24 -8.52
Bacteria|Chloroflexi|Anaerolineae 0.38% -4.31 -1.15 -0.03 -0.14 -6.62
Bacteria|Chloroflexi|Anaerolineae|Anaerolineales 0.38% -4.49 -1.37 0.21 0.38 -6.78
Bacteria|Chloroflexi|Anaerolineae|Anaerolineales|1 0.38% -4.49 -1.37 0.21 0.38 -6.78
Bacteria|Chloroflexi|Anaerolineae|Anaerolineales|Anaerolineaceae 0.38% -4.49 -1.37 0.21 0.38 -6.78
Bacteria|Chloroflexi|Anaerolineae|Anaerolineales|Anaerolineaceae|uncultured 0.38% -4.49 -1.38 0.21 0.38 -6.78
Bacteria|Chloroflexi|Chloroflexia 1.19% -2.25 -4.56 -0.30 -0.09 -6.98
Bacteria|Chloroflexi|Chloroflexia|Chloroflexales 0.65% -3.20 -5.29 -0.19 -0.06 -6.75
Bacteria|Chloroflexi|Chloroflexia|Chloroflexales|1 0.65% -3.20 -5.29 -0.19 -0.06 -6.75
Bacteria|Chloroflexi|Chloroflexia|Chloroflexales|Roseiflexaceae 0.56% -3.49 -5.57 -0.18 -0.04 -6.11
Bacteria|Chloroflexi|Chloroflexia|Chloroflexales|Roseiflexaceae|Roseiflexus 0.56% -3.49 -5.57 -0.18 -0.04 -6.11
Bacteria|Chloroflexi|Chloroflexia|Kallotenuales 0.48% -1.46 -2.39 -0.65 -0.41 -5.25
Bacteria|Chloroflexi|Chloroflexia|Kallotenuales|1 0.48% -1.46 -2.39 -0.65 -0.41 -5.25
Bacteria|Chloroflexi|Chloroflexia|Kallotenuales|AKIW781 0.44% -1.37 -2.48 -0.67 -0.40 -5.51
Bacteria|Chloroflexi|Chloroflexia|Kallotenuales|AKIW781|AKIW781_ge 0.44% -1.37 -2.48 -0.67 -0.40 -5.51
Bacteria|Chloroflexi|Gitt|GS|136|Gitt|GS|136_or 0.16% 1.09 0.56 -0.29 -0.36 2.02
Bacteria|Chloroflexi|Gitt|GS|136|Gitt|GS|136_or|1 0.16% 1.09 0.56 -0.29 -0.36 2.02
Bacteria|Chloroflexi|Gitt|GS|136|Gitt|GS|136_or|Gitt|GS|136_fa 0.16% 1.09 0.56 -0.29 -0.36 2.02
Bacteria|Chloroflexi|Gitt|GS|136|Gitt|GS|136_or|Gitt|GS|136_fa|Gitt|GS|136_ge 0.16% 1.09 0.56 -0.29 -0.36 2.02
233
Bacteria|Chloroflexi|KD4|96 0.41% -0.35 -1.77 -0.06 0.10 5.00
Bacteria|Chloroflexi|KD4|96|KD4|96_or 0.41% -0.51 -1.96 -0.09 0.51 4.86
Bacteria|Chloroflexi|KD4|96|KD4|96_or|1 0.41% -0.51 -1.96 -0.09 0.51 4.86
Bacteria|Chloroflexi|KD4|96|KD4|96_or|KD4|96_fa 0.41% -0.51 -1.96 -0.09 0.51 4.86
Bacteria|Chloroflexi|KD4|96|KD4|96_or|KD4|96_fa|KD4|96_ge 0.41% -0.51 -1.96 -0.09 0.51 4.86
Bacteria|Chloroflexi|Ktedonobacteria 0.31% -3.94 -1.90 -0.19 -0.17 -6.86
Bacteria|Chloroflexi|Ktedonobacteria|C0119 0.30% -4.13 -2.35 -0.42 -0.41 -7.07
Bacteria|Chloroflexi|Ktedonobacteria|C0119|1 0.30% -4.13 -2.35 -0.42 -0.41 -7.07
Bacteria|Chloroflexi|Ktedonobacteria|C0119|C0119_fa 0.30% -4.13 -2.35 -0.42 -0.41 -7.07
Bacteria|Chloroflexi|Ktedonobacteria|C0119|C0119_fa|C0119_ge 0.30% -4.13 -2.35 -0.42 -0.41 -7.07
Bacteria|Chloroflexi|Thermomicrobia 0.59% -1.79 -1.96 -0.36 0.00 -7.98
Bacteria|Chloroflexi|Thermomicrobia|JG30|KF|CM45 0.49% -1.93 -1.79 -0.78 -0.38 -8.06
Bacteria|Chloroflexi|Thermomicrobia|JG30|KF|CM45|1 0.49% -1.93 -1.79 -0.78 -0.38 -8.06
Bacteria|Chloroflexi|Thermomicrobia|JG30|KF|CM45|JG30|KF|CM45_fa 0.49% -1.93 -1.79 -0.78 -0.38 -8.06
Bacteria|Chloroflexi|Thermomicrobia|JG30|KF|CM45|JG30|KF|CM45_fa|JG30|
KF|CM45_ge
0.49% -1.93 -1.79 -0.78 -0.38 -8.06
Bacteria|Chloroflexi|TK10 0.68% -3.95 -6.21 -0.17 -0.05 0.38
Bacteria|Chloroflexi|TK10|TK10_or 0.67% -4.17 -7.27 -0.40 0.10 0.26
Bacteria|Chloroflexi|TK10|TK10_or|1 0.67% -4.17 -7.27 -0.40 0.10 0.26
Bacteria|Chloroflexi|TK10|TK10_or|TK10_fa 0.67% -4.17 -7.27 -0.40 0.10 0.26
Bacteria|Chloroflexi|TK10|TK10_or|TK10_fa|TK10_ge 0.67% -4.17 -7.27 -0.40 0.10 0.26
Bacteria|Cyanobacteria 0.49% 1.44 -1.71 0.08 0.11 4.24
Bacteria|Cyanobacteria|Cyanobacteria 0.28% 1.25 -0.02 0.20 0.00 3.60
Bacteria|Cyanobacteria|Cyanobacteria|SubsectionIII|FamilyI|Leptolyngbya 0.17% -1.31 -2.50 0.22 0.10 2.42
Bacteria|FBP 0.21% -3.37 -3.58 -0.40 -0.15 -3.01
Bacteria|FBP|FBP_cl 0.21% -3.14 -2.16 0.01 -0.32 -2.89
Bacteria|FBP|FBP_cl|FBP_or 0.21% -3.37 -3.58 -0.40 -0.15 -3.01
Bacteria|FBP|FBP_cl|FBP_or|1 0.21% -3.37 -3.58 -0.40 -0.15 -3.01
Bacteria|FBP|FBP_cl|FBP_or|FBP_fa 0.21% -3.37 -3.58 -0.40 -0.15 -3.01
Bacteria|FBP|FBP_cl|FBP_or|FBP_fa|FBP_ge 0.21% -3.37 -3.58 -0.40 -0.15 -3.01
Bacteria|Fibrobacteres 0.14% -3.02 -6.71 -0.10 0.06 4.30
Bacteria|Fibrobacteres|Fibrobacteria 0.14% -2.90 -5.99 0.14 -0.20 4.23
Bacteria|Fibrobacteres|Fibrobacteria|Fibrobacterales 0.14% -3.02 -6.71 -0.10 0.06 4.30
Bacteria|Fibrobacteres|Fibrobacteria|Fibrobacterales|1 0.14% -3.02 -6.71 -0.10 0.06 4.30
Bacteria|Fibrobacteres|Fibrobacteria|Fibrobacterales|Fibrobacteraceae 0.14% -3.02 -6.71 -0.10 0.06 4.30
Bacteria|Fibrobacteres|Fibrobacteria|Fibrobacterales|Fibrobacteraceae|genus_04 0.14% -3.11 -6.73 -0.10 0.06 4.49
Bacteria|Firmicutes 2.21% 3.62 2.67 -0.43 -0.15 -4.59
Bacteria|Firmicutes|Bacilli 2.05% 3.34 2.20 -0.35 0.37 -4.68
Bacteria|Firmicutes|Bacilli|Bacillales 2.04% 3.47 2.63 -0.43 -0.12 -4.88
Bacteria|Firmicutes|Bacilli|Bacillales|1 2.04% 3.47 2.63 -0.43 -0.12 -4.88
Bacteria|Firmicutes|Bacilli|Bacillales|Alicyclobacillaceae 0.72% 2.60 1.85 -0.24 -0.17 0.53
Bacteria|Firmicutes|Bacilli|Bacillales|Alicyclobacillaceae|Tumebacillus 0.72% 2.60 1.85 -0.24 -0.17 0.53
Bacteria|Firmicutes|Bacilli|Bacillales|Bacillales_unclassified 0.56% 1.10 0.44 -0.40 -0.06 -4.44
Bacteria|Firmicutes|Bacilli|Bacillales|Bacillales_unclassified| 0.56% 1.10 0.44 -0.40 -0.06 -4.44
234
Bacteria|Firmicutes|Bacilli|Bacillales|Paenibacillaceae 0.68% 2.59 2.45 -0.39 -0.16 -2.49
Bacteria|Firmicutes|Bacilli|Bacillales|Paenibacillaceae| 0.15% 1.03 1.09 -0.12 0.05 -2.17
Bacteria|Firmicutes|Bacilli|Bacillales|Paenibacillaceae|Paenibacillus 0.49% 3.46 2.22 -0.27 -0.16 -1.55
Bacteria|Firmicutes|Clostridia 0.11% 1.37 0.65 -0.07 -0.12 3.88
Bacteria|Gemmatimonadetes 3.86% -7.85 -6.67 -0.52 -0.33 -12.20
Bacteria|Gemmatimonadetes|Gemmatimonadetes 2.91% -7.39 -5.92 -0.35 -0.12 -10.57
Bacteria|Gemmatimonadetes|Gemmatimonadetes|Gemmatimonadales|Gemmati
monadaceae|Gemmatirosa
2.89% -4.71 -3.90 -0.69 -0.41 -6.67
Bacteria|Gemmatimonadetes|Gemmatimonadetes|Gemmatimonadales|Gemmati
monadaceae|uncultured
2.89% -7.65 -6.82 -0.42 -0.13 -10.38
Bacteria|Gemmatimonadetes|Gemmatimonadetes_unclassified 2.89% -1.72 -2.29 0.08 -0.04 -1.21
Bacteria|Gemmatimonadetes|Gemmatimonadetes_unclassified|Gemmatimonadet
es_unclassified|Gemmatimonadetes_unclassified
0.32% -1.72 -2.42 -0.19 -0.23 -1.18
Bacteria|Gemmatimonadetes|Gemmatimonadetes_unclassified|Gemmatimonadet
es_unclassified|Gemmatimonadetes_unclassified|
1.71% -1.72 -2.42 -0.19 -0.23 -1.18
Bacteria|Gemmatimonadetes|Longimicrobia 0.52% -2.79 -3.04 -0.01 -0.20 -0.99
Bacteria|Gemmatimonadetes|Longimicrobia|Longimicrobiales 0.52% -2.90 -3.65 -0.27 -0.38 -0.98
Bacteria|Gemmatimonadetes|Longimicrobia|Longimicrobiales|1 0.52% -2.90 -3.65 -0.27 -0.38 -0.98
Bacteria|Gemmatimonadetes|Longimicrobia|Longimicrobiales|Longimicrobiacea
e
0.52% -2.90 -3.65 -0.27 -0.38 -0.98
Bacteria|Gemmatimonadetes|Longimicrobia|Longimicrobiales|Longimicrobiacea
e|Longimicrobiaceae_ge
0.50% -2.90 -3.76 -0.27 -0.38 -1.00
Bacteria|Gemmatimonadetes|S0134_terrestrial_group 0.25% -5.49 -4.00 -0.23 -0.35 -4.42
Bacteria|Gemmatimonadetes|S0134_terrestrial_group|S0134_terrestrial_group_o
r
0.24% -5.62 -4.40 -0.43 -0.50 -4.44
Bacteria|Gemmatimonadetes|S0134_terrestrial_group|S0134_terrestrial_group_o
r|1
0.24% -5.62 -4.40 -0.43 -0.50 -4.44
Bacteria|Gemmatimonadetes|S0134_terrestrial_group|S0134_terrestrial_group_o
r|S0134_terrestrial_group_fa
0.24% -5.62 -4.40 -0.43 -0.50 -4.44
Bacteria|Gemmatimonadetes|S0134_terrestrial_group|S0134_terrestrial_group_o
r|S0134_terrestrial_group_fa|S0134_terrestrial_group_ge
0.24% -5.62 -4.40 -0.43 -0.50 -4.44
Bacteria|Nitrospirae 0.94% -9.03 -7.59 0.25 0.26 -3.24
Bacteria|Nitrospirae|Nitrospira 0.95% -8.70 -6.21 -0.06 -0.24 -3.30
Bacteria|Nitrospirae|Nitrospira|Nitrospirales 0.94% -9.03 -7.59 0.25 0.26 -3.24
Bacteria|Nitrospirae|Nitrospira|Nitrospirales|0319|6A21 0.94% -5.01 -6.18 0.44 0.28 6.06
Bacteria|Nitrospirae|Nitrospira|Nitrospirales|0319|6A21|0319|6A21_ge 0.53% -5.01 -6.18 0.44 0.28 6.06
Bacteria|Nitrospirae|Nitrospira|Nitrospirales|1 0.53% -9.03 -7.59 0.25 0.26 -3.24
Bacteria|Nitrospirae|Nitrospira|Nitrospirales|Nitrospiraceae 0.42% -8.62 -9.90 -0.23 -0.02 -11.04
Bacteria|Nitrospirae|Nitrospira|Nitrospirales|Nitrospiraceae|Nitrospira 0.42% -8.64 -9.86 -0.22 -0.03 -11.07
Bacteria|Planctomycetes 4.04% -7.36 -9.54 -0.12 -0.45 -2.24
Bacteria|Planctomycetes|Phycisphaerae 1.67% -6.73 -6.17 0.01 -0.46 0.08
Bacteria|Planctomycetes|Phycisphaerae|Tepidisphaerales 1.54% -6.84 -8.78 -0.37 -0.46 -0.08
Bacteria|Planctomycetes|Phycisphaerae|Tepidisphaerales|1 1.54% -6.84 -8.78 -0.37 -0.46 -0.08
Bacteria|Planctomycetes|Phycisphaerae|Tepidisphaerales|Tepidisphaeraceae 1.54% -6.84 -8.78 -0.37 -0.46 -0.08
235
Bacteria|Planctomycetes|Phycisphaerae|Tepidisphaerales|Tepidisphaeraceae|Tepi
disphaeraceae_ge
1.54% -6.87 -8.78 -0.36 -0.46 -0.08
Bacteria|Planctomycetes|Planctomycetacia 2.09% -6.44 -6.43 0.18 -0.29 -5.75
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales 2.07% -6.72 -7.49 0.09 -0.36 -5.97
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|1 2.07% -6.72 -7.49 0.09 -0.36 -5.97
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae 2.07% -6.72 -7.49 0.09 -0.36 -5.97
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae 0.22% -4.70 -5.43 0.44 -0.31 -1.33
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
Gemmata
0.20% -4.06 -4.80 0.48 -0.40 -5.73
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
Pir4_lineage
0.16% -4.84 -3.09 -0.14 -0.14 -4.85
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
Pirellula
0.27% -6.33 -9.07 0.10 -0.23 -5.73
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
Planctomyces
0.33% -4.57 -3.48 -0.05 -0.10 -5.04
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
Singulisphaera
0.28% -1.96 -2.66 0.13 -0.29 -5.54
Bacteria|Planctomycetes|Planctomycetacia|Planctomycetales|Planctomycetaceae|
uncultured
0.47% -6.66 -7.47 -0.04 -0.41 2.23
Bacteria|Planctomycetes|vadinHA49 0.14% -4.48 -4.53 0.02 -0.26 0.01
Bacteria|Planctomycetes|vadinHA49|vadinHA49 0.13% -4.72 -5.28 -0.29 -0.24 -0.05
Bacteria|Planctomycetes|vadinHA49|vadinHA49|1 0.13% -4.72 -5.28 -0.29 -0.24 -0.05
Bacteria|Planctomycetes|vadinHA49|vadinHA49|vadinHA49 0.13% -4.72 -5.28 -0.29 -0.24 -0.05
Bacteria|Planctomycetes|vadinHA49|vadinHA49|vadinHA49|vadinHA49 0.13% -4.72 -5.28 -0.29 -0.24 -0.05
Bacteria|Proteobacteria 37.81% 5.81 6.39 0.69 0.66 11.95
Bacteria|Proteobacteria|Alphaproteobacteria 17.67% 7.14 6.43 0.19 0.46 4.03
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales 4.22% 3.79 4.06 0.21 0.24 3.82
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|1 4.22% 3.79 4.06 0.21 0.24 3.82
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae 4.22% 3.79 4.06 0.19 0.24 3.82
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae|A
sticcacaulis
0.11% 2.95 2.78 0.10 0.55 1.04
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae|B
revundimonas
0.90% 2.91 3.04 0.07 -0.17 0.92
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae|C
aulobacter
2.30% 2.28 2.66 0.25 0.24 2.67
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae|P
henylobacterium
0.60% 3.15 3.11 0.44 0.57 4.24
Bacteria|Proteobacteria|Alphaproteobacteria|Caulobacterales|Caulobacteraceae|u
ncultured
0.28% 3.13 3.59 -0.43 -0.23 3.91
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales 9.27% 7.73 6.28 0.71 0.38 3.05
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|1 9.27% 7.73 6.28 0.71 0.38 3.05
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Bradyrhizobiaceae 1.87% 6.19 4.38 0.75 0.62 0.43
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Bradyrhizobiaceae|Bos
ea
0.36% 4.28 4.07 0.12 -0.11 1.58
236
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Bradyrhizobiaceae|Brad
yrhizobium
1.30% 3.92 2.10 0.76 0.62 0.11
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Bradyrhizobiaceae|uncu
ltured
0.20% 1.60 2.11 0.46 0.46 0.69
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Hyphomicrobiaceae 0.88% 4.50 3.74 0.74 0.64 -1.00
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Hyphomicrobiaceae|De
vosia
0.41% 4.83 4.35 0.19 0.01 -0.60
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Hyphomicrobiaceae|Pe
domicrobium
0.13% 0.43 -2.34 0.02 0.62 0.60
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Hyphomicrobiaceae|Rh
odoplanes
0.20% -0.03 1.71 0.75 0.73 -1.29
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|JG34|KF|361 0.42% -0.61 2.17 -0.23 -0.27 1.73
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|JG34|KF|361|JG34|KF|
361_ge
0.42% -0.61 2.17 -0.23 -0.27 1.73
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Methylobacteriaceae 1.90% 2.62 2.50 -0.49 -0.40 6.78
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Methylobacteriaceae| 0.66% -0.20 1.43 -0.53 -0.37 8.35
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Methylobacteriaceae|M
icrovirga
0.93% 2.89 2.87 -0.47 -0.37 2.27
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Methylobacteriaceae|Ps
ychroglaciecola
0.17% -1.24 -0.94 -0.55 -0.15 6.49
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Phyllobacteriaceae 0.61% 6.77 4.97 0.73 0.50 1.21
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Phyllobacteriaceae| 0.10% 1.83 2.19 -0.03 -0.21 0.96
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Phyllobacteriaceae|Mes
orhizobium
0.36% 7.15 5.63 0.61 0.63 1.25
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Rhizobiaceae 1.59% 5.09 4.87 0.13 -0.17 0.38
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Rhizobiaceae|Ensifer 0.23% 2.18 2.20 -0.08 -0.18 1.06
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Rhizobiaceae|Rhizobiu
m
1.35% 4.68 5.34 0.14 -0.16 0.21
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Incertae_Sedis 0.26% 1.42 3.39 0.82 0.83 -4.40
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|unclassified 0.68% -1.72 0.81 0.56 0.20 6.89
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|unclassified| 0.68% -1.72 0.81 0.56 0.20 6.89
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Xanthobacteraceae 0.63% 0.56 0.94 0.76 0.72 -2.66
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Xanthobacteraceae|Pse
udolabrys
0.15% -0.34 -2.50 0.58 0.53 -9.15
Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Xanthobacteraceae|Vari
ibacter
0.26% -0.25 -1.92 0.78 0.57 -1.31
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodobacterales 0.39% 1.01 2.20 -0.47 -0.24 0.24
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodobacterales|1 0.39% 1.01 2.20 -0.47 -0.24 0.24
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodobacterales|Rhodobacteraceae 0.39% 1.01 2.20 -0.47 -0.24 0.24
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodobacterales|Rhodobacteraceae|
Rubellimicrobium
0.17% 0.72 2.47 -0.53 -0.30 -1.22
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales 2.86% 0.51 2.41 0.23 -0.09 4.64
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|1 2.86% 0.51 2.41 0.23 -0.09 4.64
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Acetobacteraceae 1.15% 2.16 1.49 -0.45 -0.24 6.78
237
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Acetobacteraceae| 0.20% -2.97 -0.47 -0.33 0.11 5.65
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Acetobacteraceae|
Belnapia
0.18% 2.77 0.56 -0.11 0.07 5.60
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Acetobacteraceae|
Craurococcus
0.33% -1.51 -3.49 -0.67 -0.36 5.01
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Acetobacteraceae|
Roseomonas
0.41% 2.36 2.24 -0.23 -0.13 1.54
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|DA111 0.19% -4.58 -5.01 0.22 0.44 5.27
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|DA111|DA111_ge 0.19% -4.58 -5.01 0.22 0.44 5.27
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillaceae 0.58% 1.59 4.13 0.24 0.23 -1.01
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillaceae|
Skermanella
0.27% -0.23 2.82 -0.24 0.01 -0.11
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillaceae|
uncultured
0.14% 1.46 2.15 0.48 0.38 -2.88
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillales_I
ncertae_Sedis
0.46% -1.62 2.35 0.61 0.17 -1.41
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillales_I
ncertae_Sedis|Candidatus_Alysiosphaera
0.22% -2.53 1.42 -0.30 -0.06 3.49
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|Rhodospirillales_I
ncertae_Sedis|Reyranella
0.23% -1.34 3.08 0.69 0.75 -5.35
Bacteria|Proteobacteria|Alphaproteobacteria|Rhodospirillales|unclassified 0.19% -2.77 -0.92 -0.43 -0.37 0.57
Bacteria|Proteobacteria|Alphaproteobacteria|Rickettsiales 0.16% 1.55 -0.48 -0.20 -0.34 2.47
Bacteria|Proteobacteria|Alphaproteobacteria|Rickettsiales|1 0.16% 1.55 -0.48 -0.20 -0.34 2.47
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales 0.80% 5.62 3.71 -0.08 -0.14 -2.35
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales|1 0.80% 5.62 3.71 -0.08 -0.14 -2.35
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales|Erythrobacterac
eae
0.27% 3.79 4.51 -0.21 -0.20 -3.83
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales|Erythrobacterac
eae|Altererythrobacter
0.16% 2.51 3.78 -0.21 -0.29 -4.20
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales|Sphingomonada
ceae
0.44% 5.54 3.12 0.36 -0.09 -0.27
Bacteria|Proteobacteria|Alphaproteobacteria|Sphingomonadales|Sphingomonada
ceae|Sphingomonas
0.33% 4.72 2.86 0.09 -0.17 -0.06
Bacteria|Proteobacteria|Betaproteobacteria 8.90% 1.83 1.46 0.08 0.36 4.76
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales 7.13% 2.25 2.13 0.26 0.71 4.58
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|1 7.13% 2.25 2.13 0.26 0.71 4.58
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Alcaligenaceae 0.12% -0.26 1.67 0.11 0.30 -3.09
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Alcaligenaceae|uncul
tured
0.10% -1.10 -0.39 0.11 0.31 -3.10
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Burkholderiaceae 1.89% 1.29 1.31 0.29 0.46 -0.41
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Burkholderiaceae|Bu
rkholderia|Paraburkholderia
1.75% 1.26 1.29 0.58 0.51 -0.45
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae 2.87% 2.13 -0.28 0.57 0.83 5.71
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae| 1.15% 0.14 -1.83 0.69 0.87 5.63
238
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae|Id
eonella
0.21% 1.68 -3.19 0.61 0.87 4.94
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae|R
amlibacter
0.91% 2.11 1.66 0.19 0.70 3.84
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae|R
oseateles
0.52% 1.35 0.08 0.55 0.32 3.28
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Oxalobacteraceae 2.23% 2.24 2.54 0.01 -0.05 0.16
Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Oxalobacteraceae|He
rbaspirillum
1.41% 2.30 2.35 -0.24 -0.18 0.57
Bacteria|Proteobacteria|Betaproteobacteria|Nitrosomonadales 0.88% -4.82 -4.41 0.50 0.00 2.51
Bacteria|Proteobacteria|Betaproteobacteria|Nitrosomonadales|1 0.88% -4.82 -4.41 0.50 0.00 2.51
Bacteria|Proteobacteria|Betaproteobacteria|Nitrosomonadales|Nitrosomonadacea
e
0.88% -4.82 -4.41 0.50 0.00 2.51
Bacteria|Proteobacteria|Betaproteobacteria|Nitrosomonadales|Nitrosomonadacea
e|uncultured
0.85% -4.89 -4.37 0.51 -0.01 2.53
Bacteria|Proteobacteria|Betaproteobacteria|SC|I|84 0.37% -1.47 0.78 0.83 0.70 -4.70
Bacteria|Proteobacteria|Betaproteobacteria|SC|I|84|1 0.37% -1.47 0.78 0.83 0.70 -4.70
Bacteria|Proteobacteria|Betaproteobacteria|SC|I|84|SC|I|84 0.37% -1.47 0.78 0.83 0.70 -4.70
Bacteria|Proteobacteria|Betaproteobacteria|SC|I|84|SC|I|84_fa|SC|I|84 0.37% -1.47 0.78 0.83 0.70 -4.70
Bacteria|Proteobacteria|Betaproteobacteria|TRA3|20 0.43% -6.28 -6.83 -0.10 0.66 -3.86
Bacteria|Proteobacteria|Betaproteobacteria|TRA3|20|1 0.43% -6.28 -6.83 -0.10 0.66 -3.86
Bacteria|Proteobacteria|Betaproteobacteria|TRA3|20|TRA3|20 0.43% -6.28 -6.83 -0.10 0.66 -3.86
Bacteria|Proteobacteria|Betaproteobacteria|TRA3|20|TRA3|20|TRA3|20 0.43% -6.28 -6.83 -0.10 0.66 -3.86
Bacteria|Proteobacteria|Deltaproteobacteria 6.41% -0.81 -2.07 -0.17 -0.19 12.46
Bacteria|Proteobacteria|Deltaproteobacteria|Bdellovibrionales 0.36% -2.22 -6.67 -0.16 0.07 6.71
Bacteria|Proteobacteria|Deltaproteobacteria|Bdellovibrionales|1 0.36% -2.22 -6.67 -0.16 0.07 6.71
Bacteria|Proteobacteria|Deltaproteobacteria|Bdellovibrionales|Bdellovibrionacea
e
0.34% -1.36 -6.40 -0.09 0.07 6.92
Bacteria|Proteobacteria|Deltaproteobacteria|Bdellovibrionales|Bdellovibrionacea
e|OM27_clade
0.34% -1.18 -6.40 -0.04 0.07 6.94
Bacteria|Proteobacteria|Deltaproteobacteria|Desulfurellales 0.72% -6.10 -5.74 0.18 0.50 -7.57
Bacteria|Proteobacteria|Deltaproteobacteria|Desulfurellales|1 0.72% -6.10 -5.74 0.18 0.50 -7.57
Bacteria|Proteobacteria|Deltaproteobacteria|Desulfurellales|Desulfurellaceae 0.72% -6.10 -5.74 0.18 0.50 -7.57
Bacteria|Proteobacteria|Deltaproteobacteria|Desulfurellales|Desulfurellaceae|G5
5
0.12% -5.53 -4.36 -0.40 -0.18 -5.33
Bacteria|Proteobacteria|Deltaproteobacteria|Desulfurellales|Desulfurellaceae|H1
6
0.60% -5.35 -5.17 0.27 0.55 -6.79
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales 5.02% 0.12 -1.71 -0.17 0.17 14.00
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|1 5.02% 0.12 -1.71 -0.17 0.17 14.00
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|BIrii41 0.53% -4.77 -8.25 0.22 0.42 8.14
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|BIrii41|BIrii41_ge 0.53% -4.77 -8.25 0.22 0.42 8.14
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Blfdi19 0.12% -3.36 -3.55 -0.30 -0.14 3.68
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Blfdi19|Blfdi19_ge 0.12% -3.36 -3.55 -0.30 -0.14 3.68
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Haliangiaceae 0.76% -5.41 -11.20 0.43 0.60 9.53
239
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Haliangiaceae|Halian
gium
0.76% -5.41 -11.20 0.43 0.60 9.53
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Myxococcaceae 0.29% 2.06 2.21 -0.13 -0.27 1.41
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Myxococcaceae|Myx
ococcus
0.29% 2.06 2.21 -0.13 -0.27 1.41
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|unclassified 1.65% 1.26 1.34 -0.54 -0.43 6.72
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|unclassified| 1.65% 1.26 1.34 -0.54 -0.43 6.72
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Nannocystaceae 0.20% -1.90 -2.77 -0.10 -0.01 3.05
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Nannocystaceae|Nann
ocystis
0.11% -1.28 -3.26 -0.17 -0.12 5.87
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|P3OB|42 0.10% -2.79 -4.68 0.02 -0.01 6.06
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|P3OB|42|P3OB|42 0.10% -2.79 -4.68 0.02 -0.01 6.06
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Polyangiaceae 0.51% -3.45 -4.35 0.11 0.19 7.44
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Polyangiaceae| 0.29% -3.32 -4.20 0.01 0.14 6.08
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Polyangiaceae|Sorang
ium
0.17% -2.66 -3.48 0.24 0.38 7.10
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Sandaracinaceae 0.45% -2.64 -5.34 -0.27 -0.11 8.88
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Sandaracinaceae|Sand
aracinus
0.12% 2.72 0.63 -0.03 -0.17 5.08
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|Sandaracinaceae|uncu
ltured
0.31% -3.42 -6.36 -0.26 -0.06 6.67
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|uncultured 0.15% -3.10 -4.28 -0.37 -0.23 7.47
Bacteria|Proteobacteria|Deltaproteobacteria|Myxococcales|uncultured|uncultured 0.15% -3.10 -4.28 -0.37 -0.23 7.47
Bacteria|Proteobacteria|Deltaproteobacteria|Oligoflexales 0.19% -1.52 -3.45 -0.31 -0.27 -2.38
Bacteria|Proteobacteria|Deltaproteobacteria|Oligoflexales|0319|6G20 0.19% -3.67 -4.97 -0.26 -0.02 -3.70
Bacteria|Proteobacteria|Deltaproteobacteria|Oligoflexales|0319|6G20|0319|6G20 0.11% -3.67 -4.97 -0.26 -0.02 -3.70
Bacteria|Proteobacteria|Deltaproteobacteria|Oligoflexales|053A03|B|DI|P58 0.11% -1.00 -3.23 -0.17 0.01 2.82
Bacteria|Proteobacteria|Gammaproteobacteria 4.51% 2.63 1.71 0.33 0.49 -3.66
Bacteria|Proteobacteria|Gammaproteobacteria|unclassified 0.17% -1.77 -3.08 -0.56 -0.55 2.01
Bacteria|Proteobacteria|Gammaproteobacteria|Legionellales 0.12% 0.70 2.15 0.12 -0.23 -4.51
Bacteria|Proteobacteria|Gammaproteobacteria|Legionellales|1 0.12% 0.70 2.15 0.12 -0.23 -4.51
Bacteria|Proteobacteria|Gammaproteobacteria|Legionellales|Coxiellaceae 0.10% 0.20 2.21 0.12 -0.21 -4.38
Bacteria|Proteobacteria|Gammaproteobacteria|Pseudomonadales 1.43% 2.00 2.29 0.22 0.51 1.51
Bacteria|Proteobacteria|Gammaproteobacteria|Pseudomonadales|Pseudomonadac
eae
1.43% 2.00 2.29 0.19 0.51 1.56
Bacteria|Proteobacteria|Gammaproteobacteria|Pseudomonadales|Pseudomonadac
eae|Azotobacter
1.35% 1.92 2.24 -0.05 0.02 0.41
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales 2.81% 2.88 1.36 0.76 0.74 -3.43
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|uncultured 0.13% -5.15 -6.15 0.38 0.44 -4.47
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae
1.68% 3.51 2.36 0.76 0.68 0.94
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|
0.22% 2.21 1.37 0.15 0.49 -0.81
240
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|Arenimonas
0.10% 3.62 -1.94 0.77 0.79 1.59
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|Luteibacter
0.24% 2.79 2.39 0.50 -0.12 0.86
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|Luteimonas
0.22% 3.36 -0.42 0.57 0.61 1.55
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|Lysobacter
0.41% 2.97 2.00 0.53 0.51 2.05
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonada
ceae|Pseudoxanthomonas
0.30% 1.74 1.66 0.48 0.15 0.28
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonadal
es_Incertae_Sedis
0.90% -1.86 -3.18 0.59 0.49 -6.06
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonadal
es_Incertae_Sedis|Acidibacter
0.39% -3.48 -6.33 0.73 0.78 -6.78
Bacteria|Proteobacteria|Gammaproteobacteria|Xanthomonadales|Xanthomonadal
es_Incertae_Sedis|Steroidobacter
0.48% -0.60 -1.31 0.07 0.00 -3.19
Bacteria|Tectomicrobia 1.29% -3.88 -6.46 -0.59 -0.06 7.10
Bacteria|Tectomicrobia|Tectomicrobia 1.29% -3.70 -4.77 -0.45 -0.15 6.78
Bacteria|Tectomicrobia|Tectomicrobia|Tectomicrobia 1.28% -3.92 -6.46 -0.59 -0.06 7.07
Bacteria|Tectomicrobia|Tectomicrobia|Tectomicrobia|Tectomicrobia 1.28% -3.92 -6.46 -0.59 -0.06 7.07
Bacteria|Tectomicrobia|Tectomicrobia|Tectomicrobia|Tectomicrobia|Tectomicro
bia
1.28% -3.92 -6.46 -0.59 -0.06 7.07
Bacteria|Verrucomicrobia 0.26% -4.63 -6.96 0.29 0.09 0.39
Bacteria|Verrucomicrobia|Opitutae 0.22% -4.48 -6.35 0.01 -0.32 1.64
Bacteria|Verrucomicrobia|Opitutae|Opitutales 0.22% -4.69 -7.00 0.12 0.04 1.72
Bacteria|Verrucomicrobia|Opitutae|Opitutales|Opitutaceae 0.22% -4.69 -7.00 0.12 0.04 1.72
Bacteria|Verrucomicrobia|Opitutae|Opitutales|Opitutaceae|Opitutus 0.21% -4.52 -6.87 0.14 0.05 1.93
5230
5231
5232
5233