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Title: Symbiont infection affects whitefly dynamics in the field
Author: Peter Asiimwe Suzanne E. Kelly Martha S. Hunter
PII: S1439-1791(14)00093-0DOI: http://dx.doi.org/doi:10.1016/j.baae.2014.08.005Reference: BAAE 50811
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Received date: 9-5-2014Revised date: 12-8-2014Accepted date: 16-8-2014
Please cite this article as: Asiimwe, P., Kelly, S. E., and Hunter, M. S.,Symbiontinfection affects whitefly dynamics in the field, Basic and Applied Ecology (2014),http://dx.doi.org/10.1016/j.baae.2014.08.005
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Symbiont infection affects whitefly dynamics in the field 1
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For submission to Basic and Applied Ecology 4
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Peter Asiimwe a, b
, Suzanne E. Kelly a and Martha S. Hunter
a* 6
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a Department of Entomology, 410 Forbes Building, The University of Arizona, Tucson, 8
AZ 85719, USA. [email protected] 9
b Present address: Monsanto Company, 800 N. Lindbergh Blvd, St. Louis, MO. 63167, 10
USA. [email protected] 11
*Corresponding author. Tel.: +15206219350; fax: +15206211150. 12
Email address: [email protected]. 13
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Abstract 16
Inherited intracellular insect endosymbionts may manipulate host reproduction or provide 17
fitness benefits to their hosts in ways that result in their rapid spread throughout a host 18
population. Fitness benefits in particular can result in the increased pest potential of 19
agriculturally important insects. While benefits due to endosymbiont infection have been 20
well studied in laboratory assays, very little is known about how these benefits translate 21
to insect performance in the field. Laboratory experiments have shown that the 22
maternally inherited bacterial endosymbiont, Rickettsia, increases the fitness of its 23
whitefly host, Bemisia tabaci, through improved fecundity, faster development times and 24
female-biased sex ratios. We conducted field population cage studies to determine 25
whether the benefits conferred by Rickettsia to whiteflies in the laboratory were evident 26
on one of its major hosts, cotton, under field conditions in Arizona, USA. In cages with 27
either Rickettsia-infected or uninfected whiteflies, we observed up to ten-fold higher 28
whitefly egg and nymph densities when whiteflies were Rickettsia-infected compared 29
with uninfected whiteflies throughout the season. We also observed a steep initial 30
increase in Rickettsia frequency in population cages started with either 25% or 50% 31
Rickettsia-infected whiteflies, with the 50% cages approaching fixation within three 32
generations. Using growth rates obtained in the density cages, we calculated and 33
compared an expected trajectory of the frequencies of Rickettsia infection with the 34
observed frequencies. Results showed similar observed and expected frequencies of 35
Rickettsia in the first two generations, followed by a significantly lower than expected 36
frequency in three of four treatment/sample combinations at the end of the season. Taken 37
together, these results confirm the patterns of fecundity and population growth observed 38
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in laboratory assays, under field conditions, as well as provides preliminary empirical 39
support for a Rickettsia equilibrium frequency of less than 100%. 40
Zusammenfassung 41
42
Vererbte intrazelluläre Endosymbionten von Insekten können die Fortpflanzung des 43
Wirtes manipulieren oder ihren Wirten in einer Weise Fitnessvorteile bieten, die in ihrer 44
schnellen Ausbreitung in der Wirtspopulation resultiert. Insbesondere Fitnessvorteile 45
können zur Folge haben, dass das Schadenspotential von landwirtschaftlich wichtigen 46
Insekten zunimmt. Während solche durch Endosymbionten vermittelten Vorteile unter 47
Laborbedingungen gut untersucht sind, ist wenig darüber bekannt, wie sich diese Vorteile 48
auf die Performanz von Insekten im Freiland auswirken. Laborversuche haben gezeigt, 49
dass der maternal vererbte bakterielle Endosymbiont, Rickettsia, die Fitness seines Wirts, 50
der Mottenschildlaus Bemisia tabaci, erhöht. Dies geschieht durch gesteigerte Fekundität, 51
höhere Entwicklungsgeschwindigkeit und ein zu den Weibchen hin verschobenes 52
Geschlechterverhältnis. Wir führten Käfigversuche durch, um zu ermitteln, ob die von 53
Rickettsia im Labor vermittelten Vorteile auch im Freiland (Arizona, USA) auf 54
Baumwolle, einem der wichtigsten Wirte von B. tabaci, zutage traten. In Käfigen mit 55
entweder mit Rickettsia infizierten oder nicht infizierten Mottenschildläusen 56
beobachteten wir über die Saison bis zu zehnmal höhere Eier- und Nymphendichten bei 57
infizierten B. tabaci als bei nicht infizierten. Wir beobachteten auch einen steilen 58
anfänglichen Anstieg der Rickettsia-Infektionen bei Käfigpopulationen, die mit 25%- 59
oder 50% igem Befall gestartet waren, wobei die 50%-Populationen sich innerhalb von 60
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drei Generationen der Fixation annäherten. Mit den Wachstumsraten aus den 61
Käfigversuchen berechneten wir eine Kurve der zu erwartenden Infektionsraten und 62
verglichen diese mit den beobachteten Infektionsraten. Die Ergebnisse zeigten ähnliche 63
Beobachtungs- und Erwartungswerte für die beiden ersten Generationen, gefolgt von 64
signifikant geringeren Beobachtungswerten in drei von vier Behandlung/Probetermin-65
Kombinationen am Ende der Saison. Zusammengenommen bestätigen diese Ergebnisse 66
aus dem Freiland die Muster von Fekundität und Populationswachstum, die im Labor 67
beobachtet wurden, und sie stellen eine vorläufige empirische Unterstützung für eine 68
Gleichgewichtsfrequenz von unter 100% bei Rickettsia-Infektionen dar. 69
70
Keywords: endosymbionts; Rickettsia; competition; Bemisia tabaci; whiteflies; cotton; 71
Wolbachia; insect pest; invasive species. 72
73
Introduction 74
Maternally-inherited, bacterial endosymbionts are prevalent and influential in 75
arthropod biology. Primary bacterial symbionts are obligate mutualists and play a 76
nutritional role by providing essential nutrients lacking in the diet (Baumann, 2005; 77
Douglas, 1998). Secondary symbionts are facultative, and may contribute directly to host 78
fitness, or manipulate host reproduction in ways that increase the proportion or fitness of 79
(infected) daughters (Bull, 1983; O'Neill, Hoffmann & Werren, 1997; Werren, Baldo & 80
Clark, 2008). The benefits to hosts include ecological mediation such as thermal 81
tolerance (Henry, Peccoud, Simon, Hadfield, Maiden et al., 2013; Russell & Moran, 82
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2006), host range expansion (Ferrari, Scarborough & Godfray, 2007; Henry et al., 2013; 83
Tsuchida, Koga & Fukatsu, 2004; Tsuchida, Koga, Matsumoto & Fukatsu, 2011), 84
insecticide resistance (Berticat, Rousset, Raymond, Berthomieu & Weill, 2002) and 85
resistance to parasitoids (Oliver, Degnan, Hunter & Moran, 2009; Oliver, Russell, Moran 86
& Hunter, 2003) and pathogens (Hedges, Brownlie, O'Neill & Johnson, 2008; 87
Scarborough, Ferrari & Godfray, 2005). In spite of concerted efforts to understand the 88
effects of secondary symbionts on hosts in laboratory and growth chamber studies, very 89
little is known about how these effects translate to the field. Given the marked 90
differences in laboratory and field conditions, and the sometimes-unexpected dynamics 91
of symbionts in host populations (Oliver, Campos, Moran & Hunter, 2008; Xi, Khoo & 92
Dobson, 2005), it is important to understand how symbionts influence the population and 93
field ecology of their hosts. 94
Dramatic spread of insect endosymbionts in host populations has been 95
documented in a handful of cases (Duplouy, Hurst, O'Neill & Charlat, 2010; Himler, 96
Adachi-Hagimori, Bergen, Kozuch, Kelly et al., 2011; Hoshizaki & Shimada, 1995; 97
Jaenike, Unckless, Cockburn, Boelio & Perlman, 2010; Kriesner, Hoffmann, Lee, Turelli 98
& Weeks, 2013; Turelli & Hoffman, 1991). The mechanisms behind spread have 99
typically been assessed in laboratory assays where the infection status of hosts can be 100
controlled and directly compared (Himler et al., 2011; Hoffmann, Montgomery, 101
Popovici, Iturbe-Ormaetxe, Johnson et al., 2011; Hoffmann & Turelli, 1988; Jaenike et 102
al., 2010). Between these studies and field studies, however, are a wealth of abiotic 103
differences, including temperature extremes and fluctuations, precipitation and wind, and 104
expected biotic differences in plant quality, intraspecific density-dependent responses and 105
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competitive interactions of the insect hosts, alternative host plants, spatial complexity, 106
and the host’s natural enemies. In general, the effects of symbiont infection in the 107
laboratory are unlikely to be equivalent to those in the field. For example Xi et al. (2005) 108
observed differences in fitness costs of Wolbachia infection in mosquitoes between 109
population cage and individual assays, results that underlined the importance of 110
population level assessments of symbiont effects on the host. Understanding the 111
population consequences of symbiont infection for host biology in the field is of even 112
more critical importance as the applied use of symbionts for pest or vector control moves 113
from the laboratory to the field (Hoffmann et al., 2011; Walker, Johnson, Moreira, Iturbe-114
Ormaetxe, Frentiu et al., 2011). 115
Bacterial endosymbionts are common in one of the worst global agricultural pests, 116
the complex of species collectively known as the sweetpotato whitefly Bemisia tabaci 117
(Hemiptera: Aleyrodidae). Within these is the especially invasive species provisionally 118
called “B” species or Middle East-Asia Minor 1 (MEAM 1) species (Dinsdale, Cook, 119
Riginos, Buckley & De Barro, 2010; Liu, Colvin & De Barro, 2012). Heavy whitefly 120
infestations cause plant stunting and chlorosis. Perhaps even more significantly, B.tabaci 121
is the vector for dozens of plant viruses that affect many different crops around the world 122
(Oliveira, Henneberry & Anderson, 2001). In addition to the primary nutritional symbiont 123
Portiera aleyrodidarum (Sloan & Moran, 2012; Thao & Baumann, 2004), secondary 124
symbionts found among members of the Bemisia complex include Wolbachia, 125
Hamiltonella, Arsenophonous, Fritschea, Cardinium, Hemipteriphilus and Rickettsia 126
(Bing, Yang, Zchori-Fein, Wang & Liu, 2013; Gottlieb, Ghanim, Chiel, Gerling, Portnoy 127
et al., 2006; Moran, Russell, Koga & Fukatsu, 2005). Although their roles are generally 128
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unknown, some of these symbionts appear to increase the whitefly’s pest status. For 129
example, Hamiltonella may improve transmission of plant viruses (Gottlieb, Zchori-Fein, 130
Mozes-Daube, Kontsedalov, Skaljac et al., 2010). A survey of B.tabaci [MEAM 1] in 131
Arizona, USA, indicated that Rickettsia sp. nr. bellii spread rapidly within the B. tabaci 132
host population in just 6 years, with infection rates rising from 1% in 2000 to 97% by 133
2006 (Himler et al., 2011). In laboratory comparisons, Rickettsia-infected B. tabaci were 134
associated with increased fecundity, reduced development time, increased survival and a 135
female-biased sex ratio (Himler et al., 2011). These characteristics, individually or 136
collectively, could have contributed to the rapid spread of Rickettsia in B. tabaci 137
populations in Arizona. 138
The performance of symbiont-infected whiteflies in the field is especially difficult 139
to observe directly against the backdrop of the intensive management of the agricultural 140
crops in the Southwestern USA, especially in cole crops, cucurbits and cotton, on which 141
these whiteflies depend. Changes in planting dates, regional suppression of other pests in 142
the system, e.g. Lygus hesperus and Pectinophora gossypiella, as well as improvements 143
in conservation of natural enemies by selective insecticides all occurred during and after 144
the spread of Rickettsia in whiteflies (Ellsworth & Martinez-Carrillo, 2001; Naranjo, 145
2005; Naranjo & Ellsworth, 2009a; Naranjo, Ellsworth & Hagler, 2004) and have 146
resulted in effective management of this pest (Naranjo et al., 2009a; Naranjo & 147
Ellsworth, 2009b). In fact, despite the clear benefits of Rickettsia infection in laboratory 148
experiments, whitefly pest problems in desert grown cotton have not generally increased 149
since the spread of Rickettsia (Naranjo et al., 2009a; Naranjo et al., 2009b). This study 150
was conducted to determine the cause of this apparent contradiction. Are whiteflies no 151
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worse pests because the effects of one change (in performance of whiteflies) are negated 152
by a series of other management changes that tipped the scales towards whitefly 153
suppression? Or is the performance of whiteflies infected with Rickettsia no better than 154
that of uninfected whiteflies under field conditions? Here we examined the effects of 155
Rickettsia sp. nr. bellii infection on population performance of B. tabaci (B species) in 156
the field on cotton in Arizona, USA. 157
Materials and methods 158
Insect cultures 159
The whiteflies used for this study were obtained from a laboratory culture in which the 160
nuclear background of Rickettsia-infected (R+) and uninfected (R
–) lines were 161
homogenized by introgression. Whiteflies were collected and established from Maricopa, 162
AZ in 2006 and the introgression series occurred in 2009 (Himler et al., 2011). In the 163
spring of 2012 prior to the summer field season, a second introgression series was 164
conducted between R+ females and R
– males for four generations to homogenize any 165
genetic differences that may have arisen in these cultures via genetic drift, and to reduce 166
any culture-specific environmental differences. These lines differed by cytotype but were 167
>98% similar in nuclear genes. Laboratory cultures were maintained on cowpea (Vigna 168
unguiculata). Prior to introduction of whiteflies in the field cages, separate cultures of 169
both R+ and R
– lines were established on cotton (Gossypiun hirsutum L) in growth 170
chambers and maintained for four generations. 171
Field experiments 172
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Population growth field experiment 173
Both field experiments were conducted at the University of Arizona Campus Agricultural 174
Center, Tucson, AZ, USA. Cotton plants of the variety DP164B2RF were planted in a 175
half-acre plot on 24th
April 2012 and managed according to standard agronomic practices 176
for cotton in central and southern Arizona. Because B. tabaci is common in cotton in 177
AZ, and the frequency of Rickettsia in the local population is ~95%, our study comparing 178
the performance of R+ and R
– whiteflies necessitated the use of whitefly-proof fabric 179
cages around growing cotton plants. Plants to be caged were chosen from interior rows of 180
the cotton field, at least 6 m apart. After cages were put in place, treatments were 181
randomly assigned to cages. Throughout the growing season, all plants were treated 182
identically. For the population growth experiment, cages were seeded with either R+ or 183
R– whiteflies. Thirty-two cages were placed over individual plants at the five-leaf stage 184
(16 cages per treatment). Each cage was made of two inverted U-shaped metal rods sunk 185
into the ground to which a fine polyester (voile) fabric cage (rectangular, with a fabric 186
sleeve for introducing and sampling whiteflies) was tied. The fabric was anchored at the 187
bottom by attachment to a 10 cm diameter PVC pipe sunk into the soil around the plant 188
with a plastic cable tie. This design prevented most insects from getting into the cages, 189
although some ants and flies were later found, presumably entering via the soil. Two 190
weeks after setting up cages, when the plants had approximately 10 leaves, introductions 191
of either Rickettsia infected (R+) or uninfected (R
-) whiteflies were started and continued 192
weekly for three weeks to mimic the staggered natural immigration of whiteflies into 193
cotton fields. On June 21, June 28 and July 3, 2012 we introduced 10, 10 and 24 pairs of 194
males and females, respectively, of either R+ or R
- whiteflies into each cage. Sampling 195
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was conducted two weeks following the last introduction date and every two weeks 196
thereafter, for a total of four sampling dates. At each sampling date, single main stem 197
leaves were collected from each cage at an interval of four nodes beginning with the top 198
most leaf (e.g. leaves 1, 5, 9, etc.). Because the plants at the time of first sampling had 199
approximately 50-100 leaves, the leaves removed represented a relatively small 200
proportion of the total leaf surface of the plant. The harvested leaves were placed in 201
Ziploc bags and taken to the lab where whole leaf counts of whitefly eggs and total 202
nymphs were made for each leaf. Leaf areas were measured using a Leaf Area meter (LI-203
COR, 2004) to obtain whitefly densities (numbers/cm2). 204
Competition population cage experiment 205
The population cage experiment was set up concurrently in the same cotton plot, using 206
more of the cages described above. Two treatments varied in initial frequencies of R+ 207
whiteflies (approximately 25% and 50%), each with eight cages. Treatments were set up 208
using a replacement series (De Wit, 1960) in which the relative proportions of R+
and R– 209
whiteflies varied but the density of whiteflies released remained the same across 210
treatments. For the 25% R+ treatment, three introductions of combinations of R
+ and R
– 211
whiteflies were made on the same dates as the previous experiment as follows: i) 4 pairs 212
of R+, 11 pairs of R
– ii) 3 pairs of R
+, 12 pairs of R
– iii) 6 pairs of R
+, 18 pairs of R
–. For 213
the 50% R+ treatment, the three introductions consisted of i) 8 pairs of R
+, 7 pairs of R
– 214
ii) 7 pairs of R+, 8 pairs of R
– iii) 12 pairs each of R
+ and R
– whiteflies. Sampling was 215
conducted every three weeks, an interval that approximates a generation for whiteflies in 216
the field. At each sampling date, 20-30 whitefly adults were collected from each cage. 217
Adults were collected from the interior of the cage netting and from the undersides of 218
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multiple leaves. Adult whiteflies were stored in 95% EtOH until diagnostic polymerase 219
chain reaction (PCR) was conducted to determine the relative frequency of Rickettsia 220
infection status. 221
Diagnostic Polymerase Chain Reaction (PCR) 222
To determine Rickettsia infection frequency in the population cage experiment, a chelex 223
DNA extraction protocol was used (Chiel, Zchori-Fein, Inbar, Gottlieb, Adachi-Hagimori 224
et al., 2009) and PCR amplification was conducted on single whitefly females. The PCR 225
was conducted using Rickettsia-specific 16S rDNA primers (Chiel et al., 2009) and 226
included a negative control of sterile water and a positive control of a Rickettsia infected 227
whitefly. PCR amplifications were carried out with a program used for Rickettsia 228
diagnostic PCR in whiteflies (Himler et al., 2011). Four μl of PCR product was 229
visualized on a 1% agarose gel (1X TAE) stained with 0.64 μl 20X SYBR Green 230
(Molecular Probes, Eugene, OR). For the samples that showed no Rickettsia infection, 231
DNA extraction quality was verified using whitefly mtDNA (COI) primers (Frolich, 232
Torres-Jerez, Bedford, Markham & Brown, 1999) and the 16S rDNA PCR was repeated 233
to ensure these were true negatives. The proportion (frequency) of infected whiteflies was 234
calculated as the percentage of infected whiteflies to total number of female whiteflies 235
sampled in each cage. In all but 3 instances, 20 adult females were used to determine 236
Rickettsia frequency; fewer females were used (10) when the number of adults in the 237
cages was very low. 238
239
Data analysis 240
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A mixed model, repeated-measures ANOVA (Littell, Milliken, Stroup & Wolfinger, 241
1996) was used to examine the effects of Rickettsia infection on the density of immature 242
whiteflies. The replicates and their associated interactions with infection status and date 243
were entered into the model as random effects and the Kenward-Roger method was used 244
to estimate the corrected degrees of freedom. The covariance structure of the repeated 245
measures was estimated using the first order autoregressive function (AR1), because it 246
consistently maximized both Akaike’s information and Schwarz’ Bayesian criteria 247
(Littell et al., 1996). The significant main effects of infection status and sampling date 248
were examined by mean separations using the DIFF option of the LSMEANS statement. 249
All densities were log transformed (ln) and frequency data was arcsine-transformed to 250
meet normality and homogeneity of variance assumptions, although untransformed 251
means are presented. Analyses were conducted separately for whitefly eggs and nymphs 252
and all analyses were conducted in the PROC MIXED platform of SAS. 253
Expected vs. observed Rickettsia infection frequencies 254
We calculated the seasonal per capita population growth rate of immature whiteflies in 255
each of the R+ or R
– cages for each whitefly stage using the formula 256
dN/Ndt = (ln[N2] – ln[N1])/(t2 - t1), where N2 and N1 represent the total density of 257
immatures (eggs and nymphs) at t2 (final sampling date) and t1 (initial sampling date) 258
respectively. 259
The mean seasonal per capita growth rate of each type of whitefly (R+
or R–) was then 260
used to determine an expected number of R+
and R– in each of the population cage 261
treatments (with 25 or 50 initial R+
whiteflies) using the formula Nt = Nt-1+(Nt-1 x per 262
capita growth rate) where Nt is the expected density of either R+
or R– at day t and Nt-1 is 263
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the density on the previous day. We used a generational interval of 21 days for whiteflies 264
to determine the expected densities at each generation. The expected R+ frequency was 265
then determined using the formula NR+/ NR++ NR- where NR+ is the expected density of 266
R+
whiteflies and NR++ NR- is the expected total number of whiteflies. We then tested the 267
statistical significance of differences between expected and observed frequencies of 268
Rickettsia infection at each generation of the population cage experiment by calculating a 269
95% binomial confidence interval for each observed frequency and comparing it to the 270
expected frequency. Because binomial confidence intervals may be inexact especially 271
when they are close to 0 or 1 (Newcombe, 1998), five types of binomial confidence 272
intervals were generated (Asymptotic, Exact Binomial, Wilson, Agresti-Coull and 273
Jeffreys) (Sergeant, 2014 ), and the largest one chosen for each sample point. 274
275
Results 276
Population growth field experiment 277
Rickettsia-infected whitefly populations grew approximately 10 times faster than 278
uninfected populations. There were highly significant effects of Rickettsia infection, 279
sampling date and a Rickettsia-by-sampling-date interaction on whitefly egg densities 280
(F1,12.9=52.3, P<0.0001; F3,80.5=28.79, P<0.0001; and F3,80.5=18.75, P<0.0001 281
respectively). Egg densities were significantly higher in the Rickettsia-infected compared 282
to the uninfected whiteflies on all but the first sampling date with densities up to 10 times 283
higher in the R+ plots by the end of the season. (P < 0.05; Fig. 1A). Similarly, there were 284
significant effects of Rickettsia infection, sampling date and a Rickettsia-by-time 285
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interaction on whitefly nymph densities (F1,12.8=39.58, P<0.0001; F3,80.6=19.79, 286
P<0.0001; and F3,80.6=12.97, P<0.0001 respectively). Nymph densities were also 287
significantly higher in the Rickettsia infected compared to the uninfected whiteflies on all 288
but the first sampling date (P < 0.05; Fig. 1B). 289
Competition population cage experiment 290
The frequency of Rickettsia infection increased dramatically in population cages initiated 291
with either 25% or 50% Rickettsia-infected whiteflies. There were significant effects of 292
initial frequency and generation on the frequency of Rickettsia-infected whiteflies (F1,28 = 293
35.04; P < 0.0001 and F3,21=6.59; P = 0.002 respectively). The interaction between initial 294
frequency and generation was not significant (F3,28 = 0.5; P = 0.6821). Perhaps 295
unsurprisingly, the two treatments differed in the frequency of Rickettsia infection 296
overall, with the treatment starting at 50% maintaining a higher infection rate than the 297
treatment starting at 25% (t1,28 = -5.92; P< 0.0001). Among generations, significantly 298
higher frequencies were only observed in the F2 compared to the F1 (t1,21 = -2.63; P = 299
0.0157), where the rise in frequency is most dramatic. All other subsequent pairwise 300
comparisons between generations were not significantly different, although they were 301
consistently greater at the 50% initial frequency (P > 0.05) (Fig. 2). 302
Expected vs. observed frequencies of Rickettsia infection 303
A comparison of the expected Rickettsia-infection frequencies derived from average 304
growth rates of R+ and R
- whiteflies with the observed frequencies showed a reasonably 305
close fit in the first two generations, with 95% binomial confidence intervals for the 306
observed values including the expected frequency. In the last two generations, however, 307
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expected frequencies were significantly greater than the observed for three of the four 308
treatment/date combinations. In the third generation of the 50% treatment, the expected 309
frequency was 98% while the observed frequency was 93% (95% confidence interval 310
(CI): 88%, 97%, P<0.01). However, this difference was greater for the 25% treatment 311
where the expected frequencies approached 100%, while the observed frequencies were 312
significantly lower in both third (expected 95%, observed 84%, 95% CI: 77%, 89% 313
P<0.0001) and fourth generation (expected 99%, observed 90%, 95% CI: 84%, 95%, 314
P<0.001). 315
Discussion 316
In field cages in cotton, we found that growth rates of Rickettsia-infected 317
whiteflies were up to 10 times higher than those of uninfected whiteflies. In population 318
cages as well, the frequency of Rickettsia infection increased rapidly. Although similarly 319
beneficial effects of Rickettsia on whitefly performance have been documented in 320
laboratory studies (Himler et al 2011), this study documents that Rickettsia can also 321
increase performance in the field. In cages with only R+ whiteflies, protected from 322
natural enemies, the high densities observed by the mid to late season were associated 323
with observations of sooty mold on the leaves and sticky cotton lint. When only R- 324
whiteflies were present, whitefly densities did not reach the densities necessary to cause 325
these effects. High amounts of sooty mold growing on whitefly honeydew when the crop 326
is at peak growth interfere with the amount of photosynthate being channeled to the 327
fruiting structures, ultimately leading to smaller bolls and a reduction in yield, while 328
honeydew in the lint reduces cotton quality (Oliveira et al 2001). We note that these 329
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results pertain to a single field season in Tucson, Arizona, USA. Repetitions of this 330
experiment in different years and locations would be a valuable confirmation of the 331
generality of the results. However, high frequencies of Rickettsia across the desert 332
southwestern USA suggest that the greater growth rates of Rickettsia-infected whiteflies 333
may be widespread in this area. 334
The competitive ability of insects is driven by several life history characteristics, 335
of which fecundity, development time and survivorship are some of the most important 336
(Dean, 2006). In competition population cages, Rickettsia infection increased 337
dramatically from frequencies of either 25% or 50% Rickettsia-infected whiteflies. It is 338
possible that horizontal transmission of Rickettsia from infected to uninfected whiteflies 339
could have contributed to this result as well as differential rates of reproduction of R+ and 340
R- whiteflies. However, while horizontal transmission of Rickettsia has been shown in B. 341
tabaci in Israel (Caspi-Fluger et al. 2012), it appears to play a negligible role in this 342
interaction in the USA (Himler et al. 2011). In contrast, the population growth cage 343
experiment in the current study showed large differences in growth rates of R+ and R
- 344
whiteflies in isolation that we expect drive the increase in Rickettsia frequency when 345
these whiteflies compete. Further, the competitive superiority of the R+ whiteflies in the 346
population cages can be attributed to the presence of Rickettsia, since differences in 347
whitefly nuclear genes were largely removed by introgression. 348
The mechanism by which Rickettsia infection improves whitefly fitness is 349
unknown. While a nutritional function for this symbiont is possible, we would not 350
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ordinarily expect nutritional benefits to be conditional (see discussion below) and instead 351
consider plant manipulation to be a more likely, if still untested, possibility. 352
Whatever the mechanism, the increase in Rickettsia infection frequency in our 353
controlled caged-whitefly experiments also qualitatively echo the pattern seen at a much 354
larger geographic scale over several years. Surveys conducted in the southwestern USA 355
showed an increase in Rickettsia infection from 1% to near fixation in six years 356
(approximately 80 generations) and Rickettsia continues to persist at high frequencies 357
since then (Himler et al., 2011, Cass et al. in review). Rickettsia frequency in our 358
experiments appears to climb even more rapidly than this; the equilibrium rate of 359
infection seen across the region (within 5% of fixation) would likely have been reached 360
in the cages in less than 10 generations even with a far-lower starting frequency. Large 361
differences in spatial complexity as well as environmental and genetic heterogeneity of 362
open field populations of whiteflies are perhaps most likely to be responsible for the 363
differences in rate of increase of Rickettsia between the field cage experiments and the 364
open field. In addition, the environment whiteflies experienced in closed cage 365
experiments was in many ways intermediate between the laboratory and the open field 366
conditions. The field experiment differed markedly from laboratory conditions in climate 367
and plant physiology, both factors potentially important in the host-symbiont interaction. 368
For example, while our laboratory experiments are typically run at constant 27 °C, 369
temperatures during the period of the field experiment ranged from 20-42 °C with a mean 370
temperature of 38 °C (AZMET 2012). Also, while we have used seedling cotton in 371
laboratory experiments, the cotton plants in this study had between 10-80 large leaves. 372
On the other hand, open field conditions are subjected to greater effects of abiotic and 373
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biotic natural control, for example wind, rain, predators and parasitoids, all of which 374
could influence the rate of increase of R+ whiteflies. 375
The rapid rise in Rickettsia frequency in the first two generations of the 376
population cage experiments was well predicted by the growth rates of R+
and R- 377
whiteflies in isolation (Fig. 2). Interestingly, however, in the last two samples both the 378
25% and 50% treatment had at least one date in which the observed frequencies were 379
lower than expected. Incomplete vertical transmission rates can cause a lower equilibrium 380
frequency. While Rickettsia shows near perfect vertical transmission rates under 381
laboratory conditions (Himler et al., 2011), transmission could potentially be lower under 382
the higher temperatures experienced by whiteflies in the field. Additional abiotic and 383
biotic factors could have caused a change in the cost/benefit ratio of Rickettsia infection 384
in this study of caged whiteflies on cotton over the season, including humidity, cryptic 385
pathogens, conspecific density, the frequency of Rickettsia infection of whiteflies on a 386
plant, and plant size and physiology. More generally, however, the “slow to fixation” 387
effect we saw in this experiment appears to reflect the pattern seen at a much larger scale 388
in multiple field samples; while Rickettsia may reach very high frequencies of infection 389
(particularly in the southwestern United States), the equilibrium frequency appears to be 390
less than 100% (Himler et al., 2011, Cass et al. in review). In the open field, even more 391
factors could be important in causing the conditional benefits one might associate with 392
high but not fixed frequencies, including the effects of Rickettsia on whiteflies’ ability to 393
migrate, or on the susceptibility of whiteflies to natural enemies or pathogens. These high 394
but not fixed frequencies observed for Rickettsia differ from those seen in host population 395
surveys of endosymbionts that are nutritional mutualists or of reproductive manipulators 396
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that cause parthenogenesis (Stouthamer, 1997) where fixation is generally the rule. It also 397
differs from cytoplasmic incompatibility (CI) symbionts, in which frequencies of 398
infection that rise above an unstable equilibrium determined by CI strength, vertical 399
transmission and fitness costs also generally rise to fixation (Caspari & Watson, 1959; 400
Hoffmann & Turelli, 1997). 401
Despite the dramatic effects of Rickettsia infection on whitefly performance 402
observed in this study, these effects might not be apparent to cotton growers in the desert 403
southwestern USA. Cotton in this region is attacked by two major pests, B. tabaci and L. 404
hesperus, both of which typically cause economic losses. Over the last 15 years, an 405
Integrated Pest Management approach centered on the use of selective insecticides has 406
resulted in dramatic reductions in pest densities while boosting the natural control 407
(through predation and parasitism) of these pests in cotton (Naranjo et al., 2009a). 408
Additionally, more intensive management of whiteflies in spring crops that serve as a 409
source of whiteflies in early season cotton (Castle, Palumbo & Prabhaker, 2009) has 410
resulted in reduced whitefly densities in cotton. In the context of multiple management 411
changes, the potentially damaging effects of increased whitefly fitness due to Rickettsia 412
infection might be masked. It remains to be determined, however, whether Rickettsia 413
causes qualitative changes in whiteflies that may have more serious consequences. 414
Bemisia tabaci complex whiteflies vector over 100 plant viruses (Jones, 2003), and 415
symbionts may interact with plant viruses (Gottlieb et al., 2010). In this context, the rise 416
of the semi-persistent whitefly-vectored Cucurbit Yellow Stunting Disorder Virus 417
(CYSDV) (Castle et al., 2009) in melons and vegetables in the mid 2000s raises the 418
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question of whether Rickettsia infection could have a role in higher whitefly densities 419
and/or virus transmission in southwestern USA vegetable production. 420
Conclusions 421
Rickettsia sp. nr. bellii, a facultative symbiont of the invasive whitefly B. tabaci 422
species “B,” caused marked increases in population growth and Rickettsia frequency in 423
whiteflies on cotton in the field. These findings corroborate observations of rapid 424
increases in Rickettsia frequency across the southwestern USA, as one would expect for a 425
symbiont associated with fitness benefits (Himler et al., 2011), and are also consistent 426
with current observations of high frequencies of Rickettsia in whiteflies across the USA 427
(Cass et al., in review), and in some populations around the world (Bing, Ruan, Rao, 428
Wang & Liu, 2013). In field population cages, observed Rickettsia frequencies generally 429
fall short of expected frequencies, and this also resembles the pattern seen in samples 430
from cotton across the USA, where regional frequencies of Rickettsia are generally not 431
fixed, but range from 71-93% (Cass et al., in review). Lastly, while pest managers have 432
not seen a marked increase in whitefly densities in cotton since the spread of Rickettsia, 433
many changes in pest management over the same period may mask biological differences 434
in whiteflies during the same period. It also remains to be seen whether Rickettsia 435
infection plays a role in whitefly-vectored virus transmission in the alternative vegetable 436
host crops in southwestern USA. 437
438
Acknowledgements 439
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Thanks to Corinne Stouthamer and Marco Gebiola who gave constructive comments on 440
an earlier draft of the MS. This research was supported by the United States Department 441
of Agriculture AFRI grant 2010-03752 to MSH, research grant No. US-4304-10 R from 442
the United States-Israel Binational Agricultural Research and Development Fund (to 443
MSH and Einat Zchori-Fein), and a National Science Foundation grant DEB-1020460 (to 444
MSH and Anna Himler). The authors are very grateful to the following individuals for 445
help in constructing and setting up cages, culturing and counting whiteflies and 446
conducting diagnostic PCR: Cameron Baird, Anna Himler, Jimmy Conway, Saul Macias, 447
Sierra Fung and Bree Rodriguez. 448
449
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Figure captions 450
451
Fig. 1. Mean (±SE) densities of B. tabaci eggs (A) and nymphs (B) in cotton field cages 452
infested with Rickettsia infected (R+) or uninfected (R
-) whiteflies. Bars with different 453
letters indicate statistically significant differences (p<0.05) at each sampling date. 454
Fig. 2. Expected and observed proportions of B. tabaci infected with Rickettsia in cotton 455
field cages inoculated with 25% and 50% initial Rickettsia infected whiteflies. Expected 456
frequencies were calculated using the relative growth rates of R+ and R
- whiteflies 457
observed in the single whitefly type cages. Asterisks indicate where 95% binomial 458
confidence intervals around observed proportions do not include expected values 459
(**p<0.01, ***p<0.001). 460
461
462
463
464
465
466
467
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Figure 1.
0
2
4
6
8
10
12
R+ R - R+ R - R+ R - R+ R -
24-Jul 7-Aug 20-Aug 5-Sep
B. ta
ba
ci
eg
gs / c
m 2
a a
a
b
a
b
a
b
(A)
(B)
0
1
2
3
4
5
6
7
R+ R - R+ R - R+ R - R+ R -
24-Jul 7-Aug 20-Aug 5-Sep
B. ta
ba
ci
nym
ph
s / c
m 2
a a
a
b
a
b
a
b
Figure 1
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Figure 2.
0
10
20
30
40
50
60
70
80
90
100
P F1 F2 F3 F4
Pe
rc
en
t R
ic
ke
ttsia
in
fe
cte
d
25% Observed
25% Expected
50% Observed
50% Expected
**
***
***
Figure 2
