PHOSPHORUS UPTAKE BY STREAM BENTHIC BIOFILMS …

The Pennsylvania State University

The Graduate School

Department of Ecosystem Science and Management

PHOSPHORUS UPTAKE BY STREAM BENTHIC BIOFILMS: EMPIRICAL AND

EXPERIMENTAL APPROACHES TO EXPLAINING VARIATION

A Dissertation in

Wildlife and Fisheries Science

by

Keith J. Price

2012 Keith J. Price

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2012

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The dissertation of Keith J. Price was reviewed and approved* by the following:

Hunter J. Carrick

Professor of Aquatic Ecosystems Ecology

Dissertation Advisor

Chair of Committee

John E. Carlson

Professor of Molecular Genetics

Director, Schatz Center for Tree Molecular Genetics

Jonathan Lynch

Professor of Plant Nutrition

John M. Regan

Associate Professor of Environmental Engineering

Tyler Wagner

Adjunct Associate Professor of Fisheries Ecology

Assistant Unit Leader, PA Cooperative Fish and Wildlife Research Unit

Michael G. Messina

Professor of Forest Resources

Head, Department of Ecosystem Science and Management

*Signatures are on file in the Graduate School

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ABSTRACT

Elevated phosphorus (P) concentrations in streams are frequently linked with

eutrophication and diminished water quality. Stream biofilms appear to play important

roles in P assimilation thus representing a valuable transformation of nutrients in aquatic

ecosystems. However, little work has identified parameters explaining variation in

uptake rates, evaluated the effect of common disturbance techniques, addressed how

increasing P-loads affect assimilative abilities, or estimated the influence of initial

assimilatory processes on biofilm P dynamics. Therefore, there were four central

approaches (chapters) to this dissertation: 1) perform an assessment of peer-reviewed

literature reporting aquatic microbial P-uptake rates, 2) evaluate the effect of physical

disturbance techniques commonly used in benthic biofilm metabolic studies, 3) measure

P-uptake rates for benthic biofilms along an experimental and natural nutrient gradient,

and 4) evaluate spatio-temporal P fluxes in biofilms. Regarding the first research

approach, several ecological/experimental parameters were found significant in

describing and explaining observed variation in published aquatic P-uptake rates:

microbial group (benthic, planktonic), source (culture, wild), and sample time (long,

short). This underscored the varied nature of microbial assimilatory kinetics and

provided a quantitative synthesis of uptake rates thereby advancing nutrient dynamic

models. The second chapter showed that common biofilm sampling techniques (physical

disturbance) caused no differential effects on kinetic parameter estimates (t= 0.69, p=

0.492, df= 33), lending credence to numerous metabolic studies on benthic microbes

post-abrasion and highlighting the potential for microbial uptake following scouring

events. The third chapter identified the occurrence of P saturation in some stream

biofilms and quantified its effect on the uptake of new P additions, and further concluded

that nitrogen was a synergistic nutrient for resident benthic biofilms, particularly in

streams of higher productivity (P legacy effect). Lastly, the fourth chapter demonstrated

rapid P exchange processes occurring at early time periods (i.e., ≤ 5 minutes), the

magnitude of which seems to diminish over longer periods (i.e., 15 - 30 minutes), further

suggesting that experimental time periods scaled to hours or longer obscure such

fundamental short-term responses. Overall, the studies conducted here employ both

empirical and experimental techniques and help to explain ecological and biological

variation in biofilm P-uptake rates.

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TABLE OF CONTENTS

LIST OF FIGURES .................................................................................................... viii

LIST OF TABLES ....................................................................................................... xii

ACKNOWLEDGEMENTS ............................................................................................ xv

BACKGROUND ............................................................................................................ 1

LITERATURE CITED .............................................................................................. 5

CHAPTER 1. META-ANALYTICAL APPROACH TO EXPLAIN VARIATION IN

MICROBIAL PHOSPHORUS UPTAKE RATES IN AQUATIC ECOSYSTEMS ............... 14

1.1 ABSTRACT ................................................................................................... 15

1.2 INTRODUCTION ............................................................................................ 16

1.3 METHODS .................................................................................................... 18

1.4 RESULTS ...................................................................................................... 23

1.5 DISCUSSION ................................................................................................. 26

1.6 ACKNOWLEDGEMENTS ................................................................................. 34

1.7 LITERATURE CITED ...................................................................................... 34

CHAPTER 2. EFFECTS OF PHYSICAL DISTURBANCE ON PHOSPHORUS UPTAKE IN

TEMPERATE STREAM BIOFILMS ......................................................................... 58

2.1 ABSTRACT ................................................................................................... 59

2.2 INTRODUCTION ............................................................................................ 60

2.3 METHODS .................................................................................................... 62

• P-UPTAKE RATES OF INTACT VS. SCRAPED BIOFILMS (SINGLE TIME POINT) . 62

• M-M PARAMETERS ALONG DISTINCT TIME-COURSES AND SHORT-TERM

FLUX ESTIMATIONS ..................................................................................... 64

• ABIOTIC SORPTION, P STORAGE, AND CELL VIABILITY ................................ 64

• CLEAN TECHNIQUES .................................................................................... 66

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• STATISTICAL ANALYSES ............................................................................. 68

2.4 RESULTS ...................................................................................................... 69

2.5 DISCUSSION ................................................................................................. 71

2.6 ACKNOWLEDGEMENTS ................................................................................. 75

2.7 LITERATURE CITED ...................................................................................... 75

CHAPTER 3. EFFECTS OF NUTRIENT LOADING ON PHOSPHORUS UPTAKE BY

BIOFILMS SITUATED ALONG A STREAM PRODUCTIVITY GRADIENT ................... 88

3.1 ABSTRACT ................................................................................................... 89

3.2 INTRODUCTION ............................................................................................ 90

3.3 METHODS .................................................................................................... 93

• STUDY SITES ............................................................................................... 93

• DESIGN OF FIELD EXPERIMENTS- IN SITU ENRICHMENT SYSTEM (ISES) ...... 94

• ANALYTICAL MEASUREMENTS- BIOMASS AND AREAL NUTRIENT

CONCENTRATIONS ...................................................................................... 95

• ANALYTICAL MEASUREMENTS- P-UPTAKE ................................................. 96

• STATISTICAL ANALYSES- PARAMETRIC STATISTICS .................................... 98

• STATISTICAL ANALYSES- MULTILEVEL (HIERARCHICAL) MODEL ............. 100

3.4 RESULTS .................................................................................................... 101

• PHYSICOCHEMICAL CONDITIONS ............................................................... 101

• EFFECTS OF INCREASING P-LOADINGS ON BIOFILM P-UPTAKE .................. 102

• EFFECTS OF N WITH P-LOADINGS ON BIOFILM P-UPTAKE ......................... 104

• EFFECTS OF STREAM PRODUCTIVITY AND CELLULAR STOICHIOMETRY ON

BIOFILM P-UPTAKE ................................................................................... 105

3.5 DISCUSSION ............................................................................................... 107

• EVIDENCE FOR SATURATION OF BIOFILM P-UPTAKE.................................. 107

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• INTERACTIVE EFFECTS OF N AND P-LOADINGS ON BIOFILM P-UPTAKE ..... 112

3.6 ACKNOWLEDGEMENTS ............................................................................... 117

3.7 LITERATURE CITED .................................................................................... 117

CHAPTER 4. QUALITATIVE EVALUATION OF SPATIO-TEMPORAL PHOSPHORUS

FLUXES IN STREAM BIOFILMS.......................................................................... 144

4.1 ABSTRACT ................................................................................................. 145

4.2 INTRODUCTION .......................................................................................... 146

4.3 METHODS .................................................................................................. 148

• STATISTICAL ANALYSES ........................................................................... 150

4.4 RESULTS .................................................................................................... 151

4.5 DISCUSSION ............................................................................................... 154

• INITIAL ASSIMILATORY PROCESSES ........................................................... 154

• NUTRIENT LEGACIES ................................................................................. 160

• SPATIAL-TEMPORAL EFFECTS ................................................................... 163

4.6 ACKNOWLEDGEMENTS ............................................................................... 165

4.7 LITERATURE CITED .................................................................................... 165

CONCLUSION ......................................................................................................... 191

LITERATURE CITED .......................................................................................... 194

APPENDICES ........................................................................................................... 197

A. SUMMARY OF EXPERIMENTS TESTING EFFECTS OF PHYSICAL DISTURBANCE

ON P-UPTAKE (CHAPTER 2) .............................................................................. 197

B. DESCRIPTIVE STATISTICS FOR INTACT VS. SCRAPED STREAM BIOFILM P-

UPTAKE (CHAPTER 2) ....................................................................................... 198

C. GENERA LIST (100X) FROM INCREASING LEVELS OF DISTURBANCE

EXPERIMENT (CHAPTER 2) ............................................................................... 199

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D. GENERA LIST (400X) FROM INCREASING LEVELS OF DISTURBANCE

EXPERIMENT (CHAPTER 2) ............................................................................... 200

E. FRACTION OF BIOFILM GROWTH FORMS RECOVERED FROM DISTURBANCE

TREATMENTS (CHAPTER 2) .............................................................................. 201

F. MICHAELIS-MENTEN STATISTICS FOR SCRAPED BIOFILM ASSEMBLAGES (60

MIN) (CHAPTER 2) ............................................................................................ 202

G. MICHAELIS-MENTEN STATISTICS FOR INTACT BIOFILM ASSEMBLAGES (60

MIN) (CHAPTER 2) ............................................................................................ 203

H. MICHAELIS-MENTEN STATISTICS FOR SCRAPED BIOFILM ASSEMBLAGES (5 -

12 MIN) (CHAPTER 2) ....................................................................................... 204

I. MICHAELIS-MENTEN STATISTICS FOR INTACT BIOFILM ASSEMBLAGES (5 - 12

MIN) (CHAPTER 2) ............................................................................................ 205

J. MICHAELIS-MENTEN STATISTICS FOR SCRAPED BIOFILM ASSEMBLAGES (30 -

60 MIN) (CHAPTER 2) ....................................................................................... 206

K. MICHAELIS-MENTEN STATISTICS FOR INTACT BIOFILM ASSEMBLAGES (30 -

60 MIN) (CHAPTER 2) ....................................................................................... 207

L. PENNSYLVANIA MAP DEPICTING LOCATIONS OF 47 STREAMS FROM WHICH

EIGHT WERE SELECTED TO DEPLOY ISES (CHAPTER 3) .................................... 208

M. SUMMARY TABLE OF ISES EXPERIMENTS (CHAPTER 3) ............................. 209

N. SCATTER PLOT OF AREAL CARBON VS. AREAL CHLOROPHYLL-A FROM ISES

(CHAPTER 3) .................................................................................................... 210

O. TOTAL ALKALINITY MEASURED FROM EIGHT PENNSYLVANIA STREAMS

(CHAPTER 4) .................................................................................................... 211

P. SUMMARY TABLE OF SPATIO-TEMPORAL EXPERIMENTS (CHAPTER 4).......... 213

Q. SCATTER PLOT OF P-EFFLUX VS. POLY-P (CHAPTER 4) ................................ 214

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LIST OF FIGURES

Figure 1.1. Boxplot of log10-transformed P-uptake rates for the best predictor

parameters determined using mixed modeling: sample time, source, and

microbial group ................................................................................................. 57

Figure 2.1. Stacked column chart of benthic poly-P (mgP/mgChl) vs. disruption

treatment according to growth form ................................................................. 86

Figure 2.2. Thellier plots used to estimate Cmin for scraped (left) and intact (right)

biofilm assemblages .......................................................................................... 87

Figure 3.1. Mid-Atlantic region (left) and Pennsylvania statewide map (right)

showing the location of the eight ISES experiments in both of the major

physiographic provinces in Pennsylvania (Appalachian Plateau and

Piedmont) ........................................................................................................ 140

Figure 3.2. Scatter plot of Chl-specific P-uptake (log10(nmolP/μgChl/day)) versus

P-loading (log10(µg[PO4]/day)) for each stream tested with ISES (n= 10 per

stream) fit with linear regression and 95% confidence intervals .................... 141

Figure 3.3. Regression slopes (left) and intercepts (right) modeled as linear

functions of percent agriculture across eight streams sampled using ISES .... 142

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Figure 3.4. Scatter plot of log P-uptake (log10(nmolP/μgChl/day)) vs. log10 N:P

(top) and log10 C:P (bottom) ratios fit with linear regression at subgroups (N-

load) for streams in each province .................................................................. 143

Figure 4.1. Scatter plot of P flux (log10(µgP/µgChl)) vs. time (minute) conducted

on intact biofilm assemblages from eight Pennsylvania streams of varying

productivity in Summer 2010 ......................................................................... 183



productivity in Spring 2010 ............................................................................ 184



productivity in Winter 2010 ............................................................................ 185



productivity in Fall 2009 ................................................................................ 186

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Figure 4.5. Scatter plot with linear regression (solid-line) and LOESS (dashed-

line) of (log10) P-uptake (µgP/µgChl/min) (top) and (log10) P-efflux

(µgP/µgChl/min) (bottom) vs. (log10) part-P (mgP/m2) (left) and (log10) Chl

accumulation (mgChl/m2/d) (right) for intact biofilm assemblages established

on artificial substrata (tiles) from eight Pennsylvania streams of varying

productivity over four seasons 2009 - 2010 ................................................... 187

Figure 4.6. Scatter plot with linear regression (solid-line) and LOESS (dashed-

line) of (log10) P-uptake (µgP/µgChl/min) (top) and (log10) P-efflux

(µgP/µgChl/min) (bottom) vs. (log10) part-P (mgP/m2) (left) and (log10) Chl

(mgChl/m2) (right) for biofilm assemblages established on natural substrata

(rocks) from eight Pennsylvania streams of varying productivity over four

seasons 2009 - 2010 ........................................................................................ 188

Figure 4.7. Box plot of (log10) P-uptake (top) and (log10) P-efflux (bottom)

(µgP/µgChl/min) across eight streams situated in two geological provinces in

Pennsylvania over four seasons (n= 64) ......................................................... 189

Figure 4.8. Scatter plot with linear regression of (log10) assimilatory (stable) P-

uptake (30 minutes) vs. (log10) initial P-uptake (≤5 minutes) (µgP/µgChl/min)

for intact biofilm assemblages established on artificial substrata (tiles) from

eight Pennsylvania streams of varying productivity over four seasons 2009 -

2010 (n= 64) ................................................................................................... 190

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Figure C.1. Schematic of my four chapter dissertation research illustrating the

refinement and concentration of hypotheses through the successive

applications of preceding research findings ................................................... 196

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LIST OF TABLES

Table 1.1. Phosphorus uptake rates (μg P μg Chl a–1

d–1

) measured for two

microbial functional groups and five other experimental variables analyzed in

the meta-analysis with associated reference ..................................................... 51

Table 1.2. Model selection results for predicting the model probability ................ 54

Table 1.3. ANOVA results examining differences among predictors of log10-

transformed P-uptake rate (μg P μg Chl a–1

d–1

): microbial group, source, and

sample time ....................................................................................................... 55

Table 1.4. Descriptive statistics for all experimental parameters tested in the

meta-analysis of P-uptake rates (μg P μg Chl a–1

d–1

) ...................................... 56

Table 2.1. Three-way mixed-model ANOVA testing the main fixed effects of

treatment (n= 2) and experiment (n= 3) and random effect of site (n= 2) on P-

uptake rates (μgP/μgChl/d) for intact vs. scraped stream biofilms ................... 85

Table 3.1. A summary of geographic and biogeochemical characteristics for

streams where ISES experiments were conducted in 2008 ............................ 136

Table 3.2. Regression statistics for biofilm Chl-specific P-uptake

(log10(nmolP/μgChl/day)) versus P-loading (log10(µg[PO4]/day)) determined

from ISES experiments carried out in streams of varying nutrient content in

both Plateau and Piedmont provinces ............................................................. 137

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Table 3.3. Likelihood ratio test results from iterative model building in

determining important parameters in explaining P-uptake against P-loading 138

Table 3.4. Regression statistics for biofilm Chl-specific P-uptake

(log10(nmolP/μgChl/day)) versus P-loading (log10(µg[PO4]/day)) without (n=

5) or with (n= 5) simultaneous N-loading ...................................................... 139

Table 4.1. P-uptake and P-efflux estimates from LOESS plots of P flux

(log10(µgP/µgChl)) vs. time (minute) and breakpoint estimates from the

‘segmented’ package in R for experiments conducted on intact biofilm

assemblages from eight Pennsylvania streams of varying productivity over

four seasons 2009 - 2010 ................................................................................ 177

Table 4.2. Linear regression statistics for (log10) P-uptake (µgP/µgChl/min) and

(log10) P-efflux (µgP/µgChl/min) vs. (log10) part-P (mgP/m2) and (log10) Chl

accumulation (mgChl/m2/d) for biofilm assemblages established on tiles, and

(log10) part-P (mgP/m2) and (log10) Chl (mgChl/m

2) for biofilm assemblages

established on natural substrata (rocks) from eight Pennsylvania streams of

varying productivity over four seasons 2009 - 2010 ...................................... 178

Table 4.3. Two-way ANOVA results for P-uptake (log10(µgP/µgChl/min)) and P-

efflux (log10(µgP/µgChl/min)) by season and province for replicate

measurements on intact biofilms across eight Pennsylvania streams of

varying productivity over four seasons 2009 - 2010 ...................................... 179

xiv

Table 4.4. Homogenous subsets based on Tukey's HSD post-hoc test for (log10)

P-uptake by season ......................................................................................... 180

Table 4.5. Homogenous subsets based on Tukey's HSD post-hoc test for (log10)

P-efflux by season ........................................................................................... 180

Table 4.6. Descriptive statistics for P-uptake and P-efflux (µgP/µgChl/min) for

biofilms on duplicate tiles in eight streams situated across two geologic

provinces over four seasons (n= 64) ............................................................... 181

Table 4.7. Pearson correlation matrix of all LOESS derived uptake estimations

and biochemical parameters tested in eight Pennsylvania streams over four

seasons (n= 32) ............................................................................................... 182

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ACKNOWLEDGEMENTS

First, I wish to thank my advisor Dr. Hunter Carrick for affording me this

tremendous opportunity and providing me profound insights on the methods and

principles of academic research. I am greatly appreciative to my committee members

Drs. John Carlson, Jonathan Lynch, John Regan, and Tyler Wagner for their generosity

with their time and expertise. They have been supportive in many aspects of this work

and I have learned much from each of them through enjoyable and valuable discussions.

I offer my gratitude to the Department of Ecosystem Science and Management for

providing financial support through teaching assistantships. To my FRB compatriots:

Andrew, Aubrey, Becca, Erin, and Melissa, I give mad props for filling in my blanks. I

received satisfying technical, moral, and (especially) corporeal support from these and

other sexy colleagues for which I am quite obliged. Additionally, I am eternally grateful

to Curious George for providing me towels, blood plasma, and pool floats during an

inadvertent stay inside a Peruvian prison.

Finally, and most importantly, I want to express my gratitude to my parents,

Joseph and Margaret Price, the keystone of my education, who taught me in high school

that “the tie makes the man”. I attribute this achievement to their humor, wisdom, and

generosity which provided motivation for me to complete this work. Quite fittingly, I

dedicate this dissertation to them.

Peace.

1

BACKGROUND

Photosynthesis, an essential physiological process that involves the capture of

light energy from the sun and its conversion into stored chemical energy, is understood to

be the process by which Earth’s atmosphere has been oxygenated and biological diversity

has proliferated (Raymond et al. 2002, Bekker et al. 2004). Plants capable of

photosynthesis require adequate light, water, and nutrients from the soil/sediment for

growth. Plant growth and reproduction thus depends on the availability and accessibility

of essential nutrients (Knecht and Göransson 2004, Zhou and Hosomi 2008). According

to the single limiting nutrient theorem (i.e., Liebig's Law of the Minimum), only one

nutrient is typically in shortest supply and thus controls production. Plant growth,

however, can be maximized through allocation of biomass to resource (nutrient)-

acquiring areas (Tilman 1988). Nutrients have been found to not only serve an important

role in growth and respiration but also in modulating gene expression and generating

hormonal response (Takei et al. 2002).

Similarly, algal production is both dependent on and stimulated by nutrient

availability (Schindler et al. 2001), and nutrient ratios of terrestrial plants are similar to

ratios found in plankton (Knecht and Göransson 2004). However, nutrient enrichment of

streams and lakes due to human activities can to lead to excessive algal biomass

(Carpenter et al. 1998, Smith et al. 1999) which can alter community structure (Miltner

and Rankin 1998) and in extreme cases, deplete dissolved oxygen (Dodds and Welch

2000). This can reduce suitable habitat conditions for fish and invertebrate survival

(Welch 1992). Much focus, therefore, has been aimed at reducing nutrient loadings to

freshwaters in an effort to reduce the primary production that drives eutrophication

2

(Bowes et al. 2007), the most widespread water quality problem in the United States

(Carpenter et al. 1998). Increased nutrient loadings may also affect in-stream nutrient

uptake capacity (Young and Huryn 1999). This is of particular concern because of

streams’ terminal export to downstream waters (e.g., lakes and oceans) (Carpenter et al.

1998, Correll 1998, Dodds and Welch 2000). Mulholland et al. (2008) suggested that

management of nutrient loading to streams is imperative to maintaining their nutrient

removal functions.

Phosphorus (P) is a major plant macronutrient, composing approximately 0.2% of

plant dry weight (Schachtman et al. 1998). Phosphorus is likewise an essential element

for algal growth, involved in structural and metabolic processes (Scinto and Reedy 2003,

Steinman and Mulholland 2006). It is required in energy molecules (e.g., ATP),

synthesis of macromolecules (e.g., DNA), photosynthesis and respiration (Raghothama

1999), and phosphorylation of sugars (Steinman and Mulholland 2006). Increased

anthropogenic inputs of P are of concern because large quantities (soluble inorganic P) in

lake, reservoir, and stream systems are the most common cause of eutrophication (Likens

1972, Moss et al. 1986, Correll 1998). Numerous studies have closely tied excessive

concentrations of P to eutrophication of freshwater stream systems (Van Nieuwenhuyse

and Jones 1996, Mainstone and Parr 2002, Withers and Hodgkinson 2009). Nitrogen (N)

is also important in regulating algal productivity (Nydick et al. 2004); it is required for

synthesis of amino acids and protein production (Colla et al. 2007). There is abundant

data indicating that supplies of N and/or P limit primary production in freshwater (Elser

et al. 1990). Furthermore, studies have found that combined enrichment with both N and

P can produce greater algal biomass compared to additions of N or P alone, suggesting

3

co-limitation may be typical in lotic systems (Francoeur 2001, Liess and Hillebrand

2006). In fact, N and P have been found to be the most pervasive stressors of streams in

the US (USEPA 2006). However, despite the importance of N in limiting biomass, N-

fixation by some aquatic microbes and N2 exchange between the atmosphere and water

suggests that a stronger focus on P transport management is most important to limiting

freshwater nutrient loading (Sharpley and Rekolainen 1997) and preventing freshwater

eutrophication (McDowell 2003, Withers and Jarvie 2008). Nevertheless, simply

reducing P-loads may not directly translate to improved water quality. It is necessary to

first understand how P can limit microbial metabolism and how in-stream processes can

modify P before the effects of nutrient loading can be realized (Withers and Jarvie 2008,

Marcarelli et al. 2009).

The effects of nutrient loads on a stream depend on the amount of nutrients that

are extracted from the water by biota (taking up inorganic nutrients) (Nijboer and

Verdonschot 2004). Stream biofilms (benthic bacteria, fungi, and algae associated with

submerged substrata, e.g., rocks, sand, plants (Lock et al. 1984)) play an important role in

P-uptake, assimilation, and retention in aquatic ecosystems (Pringle et al. 1988, Lind et

al. 1992, Lampert and Sommer 1997, McCormick et al. 1997, Reddy et al. 1999, Dodds

2003) and function as the primary link between dissolved inorganic nutrients in the water

and higher trophic levels (Hynes 1970). P-uptake, where P is transported from the water

column into the benthos (Dodds 2003), is thought to be the principle pathway by which P

is accumulated by benthic biofilms (Lean 1973). Biofilms have a high affinity for P and

have been linked to increased uptake efficiency and rapid nutrient recycling in P-limited

systems (Schindler 1977, Sand-Jensen 1983; Wetzel 1996, Havens et al. 1999). House

4

and Casey (1989) suggest benthic (biofilms) and/or phytoplankton communities may

account for 10 - 15% of riverine P flux and McColl (1974) found that as much as 97% of

added PO43-

was taken up by stream-bed associated biota (i.e., biofilms) under low flow

conditions in a semi-natural small stream system. Biofilms can intercept nutrients

leached from underlying substrata and/or take-up nutrients in overlying water (Riber et

al. 1983, Carlton and Wetzel 1988) and thereby strip the water column of nutrients and

reduce transported loads (Kim et al. 1990, McCormick and Scinto 1999, Noe et al. 2002,

Dodds 2003). P-loads can be controlled by biofilms as they influence rates of P retention

through uptake (Pringle et al. 1988, Dodds 2003) and storage (Costerton et al. 1987,

Freeman et al. 1995). Retention can buffer the impacts of high P-loadings on

downstream communities (Svendsen et al. 1995) and thus, the integrity and efficiency of

this process is crucial (Aldridge et al. 2009). Biofilms therefore have a significant role in

stream P dynamics and fundamental biogeochemical processes (Ryder and Miller 2005)

and can be a significant stream buffer (biotic sink) against the effects of increasing

nutrient loads and eutrophication (Reddy et al. 1999, Stevenson 2001, Dodds 2003,

Steinman and Mulholland 2006). Since nutrient uptake describes the rate of an important

stream process, it provides a measure of the performance of a stream system (Bunn et al.

1999). Determining nutrient uptake rates for stream biofilms can thus provide useful

information in assessing the effects of nutrient loads (Meyer et al. 2005, Steinman and

Mulholland 2006, Marcarelli et al. 2009).

Given the seriousness of nutrient loading to streams, particularly the importance

of P and N in generating potentially harmful effects to aquatic systems and the role of

benthic biofilms in mitigating such effects, my research here will focus on empirical and

5

experimental approaches that help to understand ecological and biological variation of

biofilm P-uptake rates. This dissertation is divided into four chapters. Chapter 1 applies

a meta-analytical approach to explain variation in microbial P-uptake rates. This study

was conducted to highlight the various ecological and experimental parameters that can

influence biotic assimilation of P. Chapter 2 focuses on the effect of procedural

techniques (physical disturbance) on P-uptake rates and cell viability. Chapter 3 explores

the effects of nutrient loading on P-uptake by biofilms situated along a stream

productivity gradient. Specifically this research shows how point phosphorus and

nitrogen loadings can moderate biofilm response to new P and the role of legacy effects

in generating different physiological uptake responses. Lastly, Chapter 4 examines

biofilm response to new P across spatial and temporal gradients; emphasis is given to

initial assimilatory kinetics and integration of the singular and interactive effects of space

and time on P fluxes.

LITERATURE CITED

Aldridge, K.T., Brookes, J.D., and Ganf, G.G. 2010. Changes in abiotic and biotic

phosphorus uptake across a gradient of stream condition. River Research and

Applications 26:636-649.

Bekker, A., Holland, H.D., Wang, P.L., Rumble, D. 3rd, Stein, H.J., Hannah, J.L.,

Coetzee, L.L., and Beukes, N.J. 2004. Dating the rise of atmospheric oxygen.

Nature 427:117-120.

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Bowes, M.J., Smith, J.T., Hilton, J., Sturt, M.M., and Armitage, P.D. 2007. Periphyton

biomass response to changing phosphorus concentrations in a nutrient-impacted

river: A new methodology for phosphorus target setting. Canadian Journal of

Fisheries and Aquatic Sciences 64:227-238.

Bunn, S.E., Davies, P.M., and Mosisch, T.D. 1999. Ecosystem measures of river health

and their response to riparian and catchment degradation. Freshwater Biology

41:333-345.

Carlton, R.G. and Wetzel, R.G. 1988. Phosphorus flux from lake sediments: Effect of

epipelic algal oxygen production. Limnology and Oceanography 33:562-570.

Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N., and Smith,

V.H. 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen.

Ecological Applications 8:559-568.

Colla, L.M., Reinehr, C.O., Reichert, C., and Costa, J.A.V. 2007. Production of biomass

and nutraceutical compounds by Spirulina platensis under different temperature

and nitrogen regimes. Bioresource Technology 98:1489-1493.

Correll, D.L. 1998. The role of phosphorus in the eutrophication of receiving waters: A

review. Journal of Environmental Quality 27:261-266.

Costerton, J.W., Cheng, K.J., Geesey, G.G., Ladd, T.I., Nickel, J.C., Dasgupta, M., and

Marrie, T. 1987. Bacterial biofilms in nature and disease. Annual Review of

Microbiology 41:435-464.

Dodds, W.K. 2003. The role of periphyton in phosphorus retention in shallow freshwater

aquatic systems. Journal of Phycology 39:840-849.

7

Dodds, W.K. and Welch, E. 2000. Establishing nutrient criteria in streams. Journal of the

North American Benthological Society 19:186-196.

Elser, J.J., Marzolf, E.R., and Goldman, C.R. 1990. Phosphorus and nitrogen limitation of

phytoplankton growth in the freshwaters of North America: A review and critique

of experimental enrichments. Canadian Journal of Fisheries and Aquatic Sciences

47:1468-1477.

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14

CHAPTER 1

META-ANALYTICAL APPROACH TO EXPLAIN VARIATION IN MICROBIAL PHOSPHORUS

UPTAKE RATES IN AQUATIC ECOSYSTEMS1

1 Price KJ, Carrick HJ (2011) Meta-analytical approach to explain variation in microbial phosphorus

uptake rates in aquatic ecosystems. Aquatic Microbial Ecology 65:89-102.

15

1.1 ABSTRACT

Despite the fact that microbial uptake represents an important transformation of

nutrients in aquatic ecosystems, few comprehensive studies have identified key

parameters and evaluated their relative importance in explaining variation in uptake rates.

Therefore, I performed an assessment of peer-reviewed literature that reported aquatic

microbial phosphorus (P) uptake rates. The search yielded 36 different papers which

presented results of 102 uptake estimates. I then constructed a meta-analysis to examine

the effects of key parameters on uptake. Microbial group (benthic, planktonic), source

(culture, wild), and sample time (long, short) were significant parameters in explaining

observed variation in published P-uptake rates. Planktonic microbes had higher P-uptake

rates (65 μg P μg Chl a–1

d−1), compared with benthic (9 μg P μg Chl a

–1 d−1

). Lower

affinity for P by benthic microbes could be attributed to adnate growth forms, which can

create boundary layers separating cells from ambient P and promoting internal P cycling.

Cultured microbes exhibited higher P-uptake rates compared with wild samples, although

this trend was not significant (F= 2.63, p= 0.108), suggesting that cultured microbes in

these studies represented reasonable analogues. Shorter sampling times yielded over 3-

fold higher P-uptake rates (58 μg P μg Chl a–1

d−1

) and appear to represent more accurate

estimates of gross uptake. Microbes subject to longer time regimes may be

physiologically altered by experimental conditions; estimates might therefore not reflect

instantaneous uptake. My results highlight the influence of ecological variation on P

assimilation and provide criteria for developing a general model to predict observed

variation in microbial P-uptake rates.

16

1.2 INTRODUCTION

Despite the fact that microbial uptake represents an important transformation of

nutrients in aquatic ecosystems, few comprehensive studies exist that identify key

parameters and evaluate their relative importance in explaining variation in uptake rates.

Microbial uptake is a key component contributing to phosphorus (P) cycling models in

aquatic environments (Webster et al. 2009). Microbial uptake and other in-stream

processes resulted in annual removal of 33% of the soluble reactive phosphorus (SRP)

entering a first-order forested stream (Mulholland 2004). Additionally, periphyton on

submerged artificial substrata eliminated 0.83 mg P m-2

d-1

from eutrophic lakes (Jöbgen

et al. 2004). While benthic microbes have a substantial ability to alter P fluxes (Dodds

2003), their uptake velocities have been found to be lower than those of planktonic algae

and/or bacteria owing perhaps to boundary-layer constraints (Riber and Wetzel 1987,

Hwang et al. 1998). Conversely, benthic biofilms are typically surrounded by a

mucilaginous matrix of extracellular polymeric substances (e.g., polysaccharides;

Hoagland et al. 1993) which can aid in sequestration of nutrients from the environment.

For instance, polymeric secretions can act as a ‘sorptive sponge’ binding and

concentrating ions in proximity to cells (Decho and Herndl 1995, Decho 2000),

potentially leading to elevated uptake. These varying ideas in the literature suggest that

synthesizing kinetic ecological data on these separate but interrelated microbial groups

could shed light on P dynamics in aquatic systems.

While a number of studies have measured P-uptake rates for different microbial

groups, little has been done to integrate and evaluate uptake rates within and among

groups. Bacteria are particularly efficient in removing P at ambient concentrations owing

17

to their small size and high surface area to volume ratios and have proven superior

competitors in terms of uptake kinetics especially at low orthophosphate concentrations

(Currie and Kalff 1984a,b, Rosenberg and Ramus 1984). However, phytoplankton

display significant P-uptake kinetics as well (Tarapchak and Moll 1990) and can have

higher maximum uptake velocities (Vmax) compared with bacteria (e.g., Thingstad et al.

1993). That said, a synthesis of uptake rate values of these microbes could quantify a

breadth of rate estimations to determine general differences and lead to a better

understanding of variation in reported P-uptake rates. In addition to aforementioned

biological differences, previous uptake studies have found that variations in rates may

arise due to the effects of experimental and environmental variables (Reynolds and

Kristensen 2008). For example, microbial source (i.e., cultures vs. wild populations) or

duration of experimental manipulation can lead to variation which may influence the

reported results (Osenberg et al. 1999). This is not ideal because accurate uptake rates

are needed for the development of nutrient dynamics (retention) models for aquatic

systems (e.g., Marcé and Armengol 2009). Identifying potential sources of variation in

microbial P-uptake studies will promote a more comprehensive understanding and could

advance standardization of experimental techniques. Moreover, analysis on the

interactive effects of key ecological and experimental variables on P-uptake rates could

provide further insight into the interrelatedness of factors affecting uptake measurements,

which, to my knowledge, has not been previously attempted.

Meta-analysis is a method for quantitative synthesis and analysis of results and

has been used widely in ecology (Hays et al. 2005). Meta-analyses have been effective in

providing a quantitative and statistically valid method of comparing findings and

18

exploring variation among multiple ecological studies (Hedges and Olkin 1985, Arnqvist

and Wooster 1995, Gurevitch and Hedges 1999, Myers et al. 1999, Francoeur 2001,

Stewart 2010). I used a meta-analysis here because it allowed us to summarize and

statistically analyze a collection of independent data across a range of research studies

(e.g., Hedges and Olkin 1985, Gurevitch and Hedges 1999, Osenberg et al. 1999); this

approach further allowed us to examine key factors in explaining variation in P-uptake

rates. Specifically, I evaluated variation in P-uptake across a series of ecological and

experimental parameters (i.e., microbial group, source, experimental sample time,

system, region, and non-biological sorption) to explore natural and methodological

influences on reported uptake rates.

1.3 METHODS

I performed a comprehensive assessment of the published, peer-reviewed

literature to identify experimentally-derived estimates of P-uptake by aquatic microbes. I

used mainstream search engines and electronic databases to retrieve data published in the

primary scientific literature (i.e., Google Scholar, Biological Abstracts, Web of Science,

BioOne, and JSTOR). Additionally, a few journals (e.g., Journal of Phycology) were

manually searched from 1970 to 2009. I included in my analysis studies that met the

following criteria: (1) uptake was measured for one or both of the specific microbial

groups; (2) uptake was derived using P-loss from water or direct microbial P-uptake

experiments; and (3) uptake values were able to be normalized to common biomass units.

While a larger number of uptake measurements have been published, many were

excluded due to the absence of chlorophyll a (Chl a) data for biomass normalizations

19

(e.g., Rigler 1956). Adherence to these a priori conditions insured that statistical

comparisons were made on a common set of standard uptake quantities.

All uptake data were normalized for biomass and expressed in common units, μg

P μg Chl a–1

d−1

. Some studies that published biomass-normalized uptake estimates were

excluded due to expression in incomparable units (e.g., g P cell−1

h−1

) owing to scale

differences (Nan and Dong 2004). P-uptake rate (effect size) extracted from each study

was the response variable subjected to further analysis. Chl a values were converted

from dry weight (DW) using a literature derived average carbon (C):Chl a ratio of 49.38

for planktonic algae (Bothwell 1985, Riemann et al. 1989, Weisse et al. 1990, Cloern et

al. 1995, Coveney and Wetzel 1995) and 68.22 for benthic algae (de Jonge 1980,

Bothwell 1985, Gould and Gallagher 1990, Romaní and Sabater 2000) and assuming

carbon content of 41.94% for both microbial groups (Strickland 1965, Mayzaud and

Martin 1975, Härdstedt-Roméo 1982, Andersen and Hassen 1991, Jørgensen et al. 1991,

Klumpp et al. 1992, Anderson 1995, Qin et al. 2007). Chl a values were converted from

ash-free dry mass (AFDM) using an AFDM:Chl a ratio of 150, which has been used

before for periphyton (Warwick 2000); 4 values were converted from these units, all of

which were for benthic algae.

I selected key ecological/experimental parameters to test in my analyses, based on

primary research. ‘Microbial group’ was selected to test my a priori hypotheses

concerning distinct physiologies between these assemblages (see 1.2 Introduction).

‘Sample time’ was added to the model analysis due to its reported influence on uptake

kinetics (e.g., Harrison et al. 1989), and ‘source’ was added to test whether or not

cultured microbes adequately represent natural kinetics (Portielje and Lijklema 1994,

20

Hwang et al. 1998). Factors of ‘region’ and ‘system’ were also added to the analysis to

test for the influence of geographical and aquatic origin on predicting P-uptake,

respectively. Finally, testing for ‘abiotic sorption’ was factored into the analysis because

of the varied literature on abiotic processes. For instance, Klotz (1985) found that the

contribution of biotic processes to P cycling was minimal in comparison to abiotic

processes. However, Khoshmanesh et al. (1999) and Scinto and Reddy (2003) found

biotic processes accounted for a much larger proportion of P-uptake (45% and 83%,

respectively).

Data were then classified into microbial group (benthic and planktonic), source

(cultured and wild), sample time (long and short), region (North America and other),

system (lentic or lotic), and control for abiotic sorption (yes and no) according to

parameters reported. The source parameter was divided into wild samples and culture,

which included single species isolates. Sample time was divided into short (0 - 10

minutes) and long (> 10 minutes) because of a clear break point in the data set and

evidence for the importance of early sampling on the order of minutes or less in kinetic

studies (e.g., Goldman et al. 1981, Goldman and Glibert 1982). Finer resolution among

parameters was avoided to prevent rank deficiency in statistical tests. All data were

obtained from results reported in the publication text or extracted from published tables

and figures. For multiple uptake values reported in a single publication, I used the range

for each experiment as stand-alone data entries for the meta-analysis.

Meta-analysis was used to compare the variety of reported uptake rates for all

selected ecological/ experimental factors. Some have cautioned against the use of

parametric tests for meta-analyses due to variations between values from different

21

experiments and within experiments (Gurevitch and Hedges 1993, 1999, Stram 1996).

The distribution of these data was evaluated using a normal probability plot and an

Anderson-Darling normality test statistic. The plot of raw uptake data revealed a strong

skew and a heavy right tail, clearly showing that the data are inconsistent with a normal

distribution. The data were therefore log transformed (1+x) to base 10 to minimize

heteroscedasticity, attain a more normal distribution, and reduce the influence of outliers

(Zar 1974). While data transformation was implemented and successful in meeting the

homogeneity of variances assumption, the within-study variance (reference parameter)

could not be eliminated. Therefore, a mixed-model analysis was used because the

heteroscedasticity of the reference parameter violated assumptions of parametric

statistical tests (e.g., ANOVA), a common problem in meta-analyses owing to multiple

studies with dissimilar variances and sample sizes (Hedges and Olkin 1985). Gurevitch

and Hedges (1993) and Stram (1996) have proposed the use of mixed models in meta-

analysis as a way to combine the advantages of random and fixed effects models as they

incorporate a component of between-study variation into the estimates (Gurevitch and

Hedges 1999).

I constructed several mixed models in an effort to determine the best combination

of fixed and random effects in determining P-uptake rates. A null hypothesis testing

approach was used to determine the significance of fixed and random effects in models.

An additive approach was followed to the mixed-model fitting, where a series of

increasingly complex models were fit and significance of the added effects determined by

Bayesian information criterion (BIC) (Kass and Raftery 1995, Wasserman 2000). BIC is

used as criterion in model selection where a smaller BIC indicates better fit between the

22

model and the data; it can measure and compare the degree of support (evidence) in the

data for multiple competing models. The BIC approach to hypothesis testing may be an

improvement over statistical significance testing using p-values because of the ability to

test a number of alternative models (Raftery 1995). Further, BIC imposes a higher

penalty on the number of parameters (compared to AIC), and therefore leads to the

selection of less complicated models (Lin et al. 2009). I used model difference

calculations to determine statistical significance of the evidence in favor of one or the

other model hypotheses (Neuenschwander et al. 2003). When comparing alternative

models, BIC differences of 0 to 2 were interpreted as weak evidence of a given model

selection (i.e., impossible to discriminate between models), 6 to 10 showed strong

evidence, and > 10 showed very strong evidence (Raftery 1995).

The ‘base’ model was simply a null model containing only a fixed intercept used

for comparison purposes after addition of terms. Afterwards, a second model with a

random intercept (reference) was fit to test the hypothesis that there is significant

variation among references in average log(uptake rates). The ‘base’ model was nested

within the second model in that it only differed by a single random effect. In this case, a

simple (null) model was compared to a more complex (reference effect) model to see if

the added parameter should be used in later analyses. The random intercept (reference)

model was a linear mixed model fit using restricted maximum likelihood (REML). A

fixed grouping (categorical) microbial group term was then added as I proceeded fitting

progressively more complex models by adding fixed effect predictor variables. The

intent of this additive fitting was to attain the most parsimonious model (most complex,

23

yet simplest). Model assumptions were assessed, specifically normal distribution of

residuals, using quantile-quantile (Q-Q) plots of residuals and leverage plots.

Once the best model was derived, those parameters in the model were tested for

significance using parametric means (ANOVA). ANOVA (analysis of variance) has

been found to be a useful statistical test in meta-analyses (e.g., Brett and Goldman 1997).

I used ANOVA here given that my data met all assumptions for equality of variance

(among my groups) and normal, Gaussian data distribution (Gurevitch and Hedges 1999).

For instance, Bartlett’s and Levene’s tests (equal variances) confirmed homogeneity of

variance across all parameters (group, source, and sample time) examined in the meta-

analysis (p> 0.05). Mixed-model analyses were performed using R (R Development

Core Team 2006). Graphing was carried out using Minitab 16 software. Descriptive

statistics and parametric tests were performed using SPSS version 18.0. Mean values and

standard error of the mean are reported.

1.4 RESULTS

My review of the uptake literature identified 102 individual microbial P-uptake

experiments recorded in 36 different papers published over a 35 yr period (Table 1.1).

Results from the mixed-model BIC analysis showed that the microbial group model

(fixed intercept, random reference, and fixed microbial group) was favored (Δi= 0.00),

and thus had the greatest degree of support (evidence) in predicting P-uptake (Table 1.2).

The models were ranked in terms of performance in predicting P-uptake rates and further

showed that the model containing source was below the suggested maximum for

determining strong Bayesian evidence; the model containing the sample time parameter

24

was also smaller than the determination value, and therefore was analyzed further using

parametric means (see below). The system parameter was dropped from analysis due to

inherent correlation with microbial group. Region and abiotic mixed models were not

well supported by the Bayesian evidence.

Of the 102 individual P-uptake values reported and analyzed in this meta-analysis,

33 controlled for abiotic sorption, 29 of which were from wild microbial sources. While

some experiments controlled for abiotic sorption, values were typically not reported (e.g.,

Bothwell 1985, Hwang et al. 1998), limiting quantitative analyses. Background

phosphorus concentration ([P], in μg l−1

; range= <0.001 to 24.2 mg l−1

) was extracted

from 44 of the 102 experiments and subsequently regressed against corresponding P-

uptake values. P-uptake (log transformed) showed a significant linear regression against

[P] (log transformed) (r2= 0.14, F= 7.01, p= 0.011). Uptake vs. [P], split by microbial

group, showed a slight, negative relationship in benthic experiments and a slight, positive

relationship in planktonic experiments; however, neither regression was significant (p>

0.05). The biogeochemical conditions in which the studies were conducted varied greatly

and prohibited categorization for statistical analysis; experiment locations included the

Laurentian Great Lakes, limestone streams, wetlands, and marine areas (e.g., North Sea).

Of the 102 individual P-uptake estimates, 60% derived uptake from disappearance of P

from water while 40% derived from uptake of P directly into aquatic microbes. Mean P-

uptake rates were not different between these experiment methods (F= 0.14, p= 0.708).

Relatedly, 63% of the experiments used radiotracers in estimating P-uptake rates. Uptake

rates between tracer experiments (x = 56.3 μg P μg Chl a−1

d−1

) vs. non-tracer experiments

(x = 15.1 μg P μg Chl a−1

d−1

) were not significantly different (F= 1.92, p= 0.169). As

25

stated in methods, microbial group data were split into two groups to prevent rank

deficiency (interaction term with <1 observation for every combination of the factor

levels) which prevents full matrix calculations and analysis of all interactions in a three-

way ANOVA. Nonetheless, I parsed microbial group data into three groups (benthic

microbes, planktonic microbes, and bacterioplankton) to estimate the effect size of

bacteria on P-uptake. Rank deficiencies were present; I therefore analyzed these data

using a one-way ANOVA. No data were available for benthic bacteria, thus limiting my

analysis to planktonic bacterial groups. Bacterioplankton had 30 observations in the

dataset (x = 94.4 ± 25.8 μg P μg Chl a−1

d−1

). A post-hoc Tukey’s HSD test showed that

bacterial uptake was significantly different from benthic groups (p< 0.001) but not

different from planktonic groups (p= 0.112), thus warranting consolidation.

Three-way ANOVA results yielded a significant difference in mean P-uptake rate

between microbial groups (F= 21.13, p< 0.001) (Table 1-3). Interactions of microbial

group and sample time (F= 4.80, p= 0.031) and source and sample time (F= 4.22, p=

0.043) were also significant in the analysis. A three-way interaction of microbial group,

source, and sample time (F= 3.68, p= 0.058) was marginally significant. The mean P-

uptake rate for planktonic groups (65.2 ±14.4 μg P μg Chl a−1

d−1

) was 7.3 times greater

than benthic groups (8.9 ±4.1 μg P μg Chl a−1

d−1

) (Table 1-4, Figure 1.1). Experiments

using cultured microbes showed higher mean P-uptake (52.2 ±12.6 μg P μg Chl a−1

d−1

)

compared to wild microbes (31.0 ±12.2 μg P μg Chl a−1

d−1

). Experiments with shorter

sample times (0 - 10 minutes) had threefold higher mean P-uptake rates (58.0 ±14.3 μg P

μg Chl a−1

d−1) than longer experiments (17.5 ±5.4 μg P μg Chl a

−1 d−1

).

26

1.5 DISCUSSION

Bayesian information criterion supported my research hypothesis that microbial

group would be the strongest single predictor of P-uptake in the meta-analysis. This

finding was further supported in the literature as microbial uptake has been seen to

represent an important and significant biological mechanism for P removal in aquatic

systems (Confer 1972, McColl 1974, Tarapchak and Moll 1990, McCormick et al. 2006).

Planktonic microbes exhibited higher P-uptake rates than benthic microbes, which was in

accordance with my hypothesis. Since these two distinct communities occupy different

niches in aquatic systems (benthic vs. pelagic), there is no reason to believe that their

physiological ecology should be similar (Reuter et al. 1986). Additionally, the P

concentration required to saturate growth for benthic microbes is much greater compared

to planktonic (Reynolds 2006), likely due to the growth form of benthic assemblages

(Hill et al. 2009). Early P cycling research found that most orthophosphate uptake was

associated with the smallest aquatic particles (Rigler 1956). Moreover, some planktonic

groups have a relatively low P storage capacity (Vadstein et al. 1988), and competition

between different aquatic microbial groups for P is a function of both uptake and storage

capacity (Kilham 1978).

The lower uptake rates demonstrated by benthic microbes may be attributable to

boundary layer formation and internal recycling (Riber and Wetzel 1987). A diffusive

boundary layer has been shown to form around intact microbial biofilms (Jørgensen and

Revsbech 1985). The formation of these physical boundary layers atop intact benthic

films may insulate microbes and inhibit nutrient uptake from the water column into biota

within the film (Riber and Wetzel 1987, Reuter and Axler 1992, Hwang et al. 1998)

27

thereby reducing the ability of benthic microbes to compete for water column nutrients.

Kinetic calculations have shown that internal P recycling is a more significant source of

nutrients than external sources for benthic microbes (Riber and Wetzel 1987). These

results suggest that there is an intrinsic, physical, and resultant chemical conditioning in

benthic microbes that favors internal nutrient recycling over ambient water column

uptake. Nonetheless, benthic microbes can be competitive with planktonic groups for

pelagic P supply under some circumstances (e.g., Axler and Reuter 1996). Further,

benthic microbes on organic substrates can sequester nutrients associated with the

benthos (sediment-water interface) and limit the amount that is released into the water

column (Pringle 1990, Hagerthey and Kerfoot 1998, Woodruff et al. 1999). In this

regard, benthic microbes may outcompete planktonic groups for available nutrients

(Hansson 1990). That said, the substantial range of P-uptake reported in the literature for

both benthic and planktonic microbial groups may give some insight into the highly

dynamic nature of these assemblages, slowing development of robust dissolved nutrient-

biomass models for aquatic systems (Biggs 2000). When splitting the dataset into three

microbial groups, bacterioplankton showed the greatest uptake rates. This is in accord

with past research demonstrating that prokaryotes are capable of rapid and considerable

phosphate uptake in aquatic systems, especially during periods of low P levels (Jackim et

al. 1977). While bacteria may show higher specific orthophosphate uptake rates under P-

limiting conditions, phytoplankton in oligotrophic aquatic systems can substitute

phospholipids with non-P membrane lipid molecules (Van Mooy et al. 2009), effectively

reducing required P and thus remaining competitive with bacteria. Normalization to Chl

a would bias uptake rates if bacteria were the principal constituents, but their abundance

28

would be included in carbon, AFDM, and DW estimations (Podgórska et al. 2008,

Pusceddu and Danovaro 2009), all of which were included in this meta-analysis.

The sample time parameter provided moderately strong evidence for predicting P-

uptake, suggesting it is an important source of variation in experiments (Osenberg et al.

1999). Short sample times averaged over 3 times higher uptake rates than long

incubations, a finding accurately supported by other research. For instance, time-course

experiments of phosphate uptake in plankton showed that, over a shorter interval (0 - 60

minutes) rates were nearly threefold greater than those recorded over the longer interval

(60 - 120 minutes) (Suttle et al. 1988). Furthermore, the lower P-uptake rates found in

longer sampling times might suggest a biotic acclimation to nutrients over time and/or

oversight of instantaneous/‘surge’ uptake (Goldman et al. 1981, Harrison et al. 1989);

Vmax may decrease with increasing incubation time, especially for phosphate (Harrison et

al. 1989). Uptake parameters in cells can change rapidly based on the concentration of

pulsed nutrients; therefore, longer incubation periods and sampling times might neglect

changes in uptake occurring within minutes or seconds (Conway et al. 1976, Goldman

and Glibert 1982, Parslow et al. 1985). Longer incubation times represent uptake that

approximates net rates rather than gross rates. For example, incubation times of 30 to 40

minutes may ignore biotic uptake of remineralized/ excreted P and would result in

underestimates of gross uptake (Barlow-Busch et al. 2006). In shorter term experiments,

such nutrient recycling is probably minimized and estimates are assumed to represent

total (gross) uptake (Steinman and Mulholland 1996). Phosphate can be released from

intercellular P pools, as cytoplasmic P is readily exchangeable with external P (Cembella

et al. 1984b); P exchange may even exceed net uptake (Lean and Nalewajko 1976).

29

Further, consumer (e.g., herbivores) excretion could also regenerate bioavailable P

(Fisher and Lean 1992), thereby complicating net uptake estimates. While this would

certainly be evident in longer term experiments, short term experiments (<10 minutes)

would likely not have been influenced by these artifacts to as great a degree and thus may

represent membrane transport and initial (gross) uptake rates (see Wheeler et al. 1982).

Cultured microbes showed higher, albeit non-significant, P-uptake rates compared

to wild microbes. These findings suggest that extrapolation of P-uptake values obtained

using single species isolates or cultured organisms to natural environments may be

reasonable. Though extrapolation of single species P-uptake kinetic estimates to wild

communities may be dubious (e.g., Portielje and Lijklema 1994), present study and others

have found that cultured microbes represent a reasonable analogue for wild types. For

instance, competitive properties of single isolates of marine microbes in culture have

been found to agree with natural conditions (Russell and Fielding 1974). Further, P

limitation in freshwater ecosystems has been demonstrated at several levels of

complexity, from algal cultures to whole lakes (Hecky and Kilham 1988), suggesting

some consistency in physiological and environmental responses to phosphorus.

However, extrapolation is cautioned against, particularly for benthic groups, as spatial

microbial segregation can affect physical transport processes (Portielje and Lijklema

1994). The significant interaction of group and source/sample time may have been

driven by the large values observed for short sampling periods using cultured microbes

(x = 83.0 μg P μg Chl a–1

d−1

) and/or competition among wild microbes. Competition for

phosphorus among freshwater phytoplankton has been adequately modeled with internal

storage capacity and cell size parameters (Smith and Kalff 1983). In cultured samples

30

and single species isolates, diversity and competition is irrelevant; however, in wild,

natural samples there is likely to be strong competition for resources. Competitive

responses to nutrient alterations may require some phenotypic modification by microbes

(see Demars and Edwards 2007) and if time-dependent, may explain my significant

source/sample interaction. Further, given the controlled conditions in which culture

studies are typically performed (e.g., Plato and Denovan 1974), shorter time sampling

periods may be more practicable in these situations than in wild studies. For instance,

research with marine phytoplankton culture collections grown to steady state found that

the appropriate temporal scale of NH4+ uptake may be on the order of seconds (Goldman

and Glibert 1982). However, in a mixed species community (wild) and patchy environs,

different microbes must adjust their P-uptake system to external P fluctuations

(Istvánovics and Herodek 1995); rapid/short sampling uptake estimates therefore may not

be representative of an entire mixed assemblage, thereby yielding moderate rates.

The type of uptake experiments being executed was considered in the analysis and

showed that rates were similar between PO4-3

disappearance studies and gross/net uptake

studies of radiolabeled substrate. While many more experiments derived uptake

measurements from disappearance of P from the water, the actual rates reported were not

statistically different. Similarly, Burmaster and Chisholm (1979), in their comparison of

the two methods (isotopic tracer and disappearance experiments) for measuring PO4-3

uptake, found they yield comparable results and complement each other in practicality.

These findings suggest that between these studies, P efflux and/or remineralization

processes were negligible. Further, my findings may be related to the marginally

significant three-way interaction found between microbial group, source, and sample

31

time, which indicated unsystematic changes in the data for each microbial group across

source and time and suggested that there are additional complexities in interpreting

differences between experiment types. Experiments that used tracers (regardless of

experiment type) did not show significantly different P-uptake rates from those that did

not use tracers, suggesting that both methods are equally sensitive in estimating the

magnitude of P movements.

Despite my care in conducting this meta-analysis, it likely contained biases

(Osenberg et al. 1999). There were certain limitations and inherent difficulties in this

meta-analysis, including incomplete data reporting, lack of independence among effect-

size estimates (although neutralized through mixed modeling), publication bias, and

research bias (see Gurevitch and Hedges 1999). Investigators performing the research

may have introduced bias or preference in site/species selection depending on the

hypotheses being tested (Dodds and Welch 2000). In addition, methodologies,

investigator rigor, and spatial/temporal scales varied widely. As a result, there remains

some uncertainty with reference to microbial differences. Other factors, such as nutrient

transport limitation, abiotic uptake, and biphasic uptake can further affect uptake kinetics

in both of these microbial organisms (Dodds and Biggs 2002). With regards to abiotic

uptake, as incubation duration decreases, the importance of biological processes relative

to physical processes will also decrease (Collos 1983), and passive phenomena (e.g., ion

absorption) may be more responsible for measured uptake. I examined the abiotic

component of uptake and found it was not a significant predictor of P-uptake, suggesting

that sorptive processes may make up a small fraction of total uptake (e.g., Hwang et al.

1998). This finding is in accord with previous studies showing that biological demand

32

for P (uptake) exceeds abiotic sorption, which accounts for <15% of water column P

removal (Scinto and Reddy 2003). Nonetheless, the relative importance of adsorptive

processes depends on biogeochemistry and varies across stream condition gradients as

changes in land-use affect the affinity of abiotic processes for phosphorus (Aldridge et al.

2010). Attention to time-course experiments and the interpretation of results in light of

applied experimental conditions (i.e., substrate concentration, incubation, and experiment

duration) is important when deducing kinetic rates. Meta-analyses such as these can

provide an information platform on which to build a greater understanding of complex

environmental systems.

Despite these limitations, which are intrinsic to most meta-analytical approaches,

my study revealed important differences in uptake kinetics between aquatic microbes,

sample times, and microbial sources. These results indicated that both microbial groups

act as important drivers in aquatic P dynamics, although benthic microbes may be less

expeditious at removing nutrients from water column sources. The present synthesis and

quantitative analysis of P-uptake rates across a range of ecological and experimental

parameters has never been previously attempted to my knowledge. The present work is

an integrative and important step in realizing the potential of aquatic microbes to function

in nutrient transformations and thereby to mitigate nutrient transport to downstream

ecosystems (e.g., lakes, oceans). Further, this analysis shows that microbial group,

sample time, and microbial source are highly variable; likely dependent on study

objectives. Not surprisingly, the range of uptake values spanned over 3 orders of

magnitude in accord with previous discussions of variability in P-uptake rates of

microbes (e.g., Cembella et al. 1984a). While natural physiology may explain some of

33

this tremendous variation (Auer and Canale 1982), this meta-analysis sheds light on the

potential of different ecological and experimental approaches and parameters to drive

such varied rates of uptake. This approach has left the kinetic uptake field with a range

of ‘quantitative’ data that is difficult to interpret and may lead to misleading estimates of

true uptake (cf. Strayer 1985).

Microbes represent important P storage pools in aquatic systems; accurate

estimates on the dynamics of nutrient pools can aid in better understanding nutrient

compartments in flux models. Uptake can control dissolved nutrient concentrations as

well as influence regeneration/ remineralization, fundamental processes that maintain

most primary productivity in aquatic environments (Dodds 1993, Hudson et al. 1999).

Additionally, P-uptake rates are useful in modeling the distribution of nuisance algal

species associated with nutrient perturbations in aquatic systems (e.g., Auer and Canale

1980). Therefore, there is a need for accurate uptake estimates for models predicting the

effects of nutrient management strategies. Differences in experimental conditions may be

driving some of the variation in uptake rates observed in this meta-analysis, and,

therefore, it may be practical in future experiments to use standardized methodologies so

as to create more useful and relatable results (Hein et al. 1995). My results presented

here should help in development of aquatic nutrient models by offering insight into some

of the ecological factors important in predicting observed variation in microbial P-uptake

rates.

34

1.6 ACKNOWLEDGMENTS

I thank T. Wagner for valuable assistance with statistical analyses, M. J.

McCarthy for constructive comments on a previous version of this manuscript, and J. T.

Scannell for assistance with data entry. Funding for this research was provided to H.J.C.

by the Pennsylvania Department of Environmental Protection (Grant No. 4100034506).

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51

Table 1.1. Phosphorus uptake rates (μg P μg Chl a–1

d–1

) measured for two microbial functional

groups and five other experimental variables analyzed in the meta-analysis with associated reference.

Original unit abbreviations: dry weight (DW), chlorophyll a (Chl a), carbon (C), ash-free dry mass

(AFDM). Chl a values were converted from DW using an average C:Chl a ratio of 49.38 for

planktonic algae and 68.22 for benthic algae and assuming a carbon content of 41.94% for both

microbial groups (see ‘Methods’ for details and references). Chl a values were converted using an

AFDM:Chl a ratio of 150.

Uptake

Values

Original

Units

Microbial

Group

Source Sample

Time

Region System Abiotic

Sorption

Reference

0.652 DW Benthic Culture Long N. America Lentic No Auer and Canale 1982

7.320 DW Benthic Culture Long N. America Lentic No Auer and Canale 1982

1.300 Chl a Planktonic Wild Short N. America Lentic No Auer and Forrer 1998

11.400 Chl a Planktonic Wild Short N. America Lentic No Auer and Forrer 1998

0.370 Chl a Planktonic Wild Short Other Lentic Yes Berman 1985

15.030 Chl a Planktonic Wild Short Other Lentic Yes Berman 1985

1.257 DW Benthic Culture Short N. America Lotic No Borchardt et al. 1994




2.640 Chl a Benthic Wild Short N. America Lotic Yes Bothwell 1985

26.400 Chl a Benthic Wild Short N. America Lotic Yes Bothwell 1985

0.576 Chl a Benthic Wild Short N. America Lotic Yes Corning et al. 1989

1.920 Chl a Benthic Wild Short N. America Lotic Yes Corning et al. 1989

0.690 DW Planktonic Wild Short N. America Lentic No Cotner and Wetzel 1992




5.645 Chl a Benthic Wild Short N. America Lotic No Davis and Minshall 1999

23.285 Chl a Benthic Wild Short N. America Lotic No Davis and Minshall 1999

0.061 DW Benthic Wild Long N. America Lotic No Davis et al. 1990

0.126 DW Benthic Wild Long N. America Lotic No Davis et al. 1990

59.462 Chl a Planktonic Culture Short Other Lentic No Falkner et al. 1984




0.149 Chl a Planktonic Wild Long N. America Lentic Yes Harrison et al. 1977

0.795 Chl a Planktonic Wild Long N. America Lentic Yes Harrison et al. 1977

64.766 DW Planktonic Culture Long N. America Lentic No Healey 1973

0.754 DW Benthic Wild Long N. America Lentic Yes Hwang et al. 1998

52




0.158 DW Planktonic Wild Short N. America Lentic Yes Hwang et al. 1998




247.572 C Planktonic Culture Short Other Lentic No Istvánovics et al. 2000







0.060 Chl a Planktonic Wild Long Other Lentic Yes Karlson 1989

0.260 Chl a Planktonic Wild Long Other Lentic Yes Karlson 1989

4.602 Chl a Planktonic Wild Short N. America Lentic No Lean and White 1983

64.800 Chl a Planktonic Wild Short N. America Lentic No Lean and White 1983

0.242 DW Benthic Culture Long N. America Lotic No Lohman and Priscu 1992

0.648 DW Benthic Culture Long N. America Lotic No Lohman and Priscu 1992

0.399 DW Benthic Culture Long Other Lentic No Nan and Dong 2004

14.400 Chl a Planktonic Wild Long N. America Lentic No Newman et al. 1994

57.600 Chl a Planktonic Wild Long N. America Lentic No Newman et al. 1994

62.172 DW Planktonic Culture Short Other Lentic No Nyholm 1977

53.694 DW Planktonic Culture Short Other Lentic No Nyholm 1977

14.978 DW Planktonic Culture Long Other Lentic No Okada et al. 1982




33.912 DW Planktonic Culture Long Other Lentic No Pauli and Kaitala 1997




2.400a Chl a Benthic Wild Short N. America Lotic Yes Perrin 1993

9.600a Chl a Benthic Wild Short N. America Lotic Yes Perrin 1993

106.510 Chl a Planktonic Culture Short N. America Lentic Yes Perry 1976




0.860 Chl a Planktonic Wild Short N. America Lentic Yes Perry 1976

1.100 Chl a Planktonic Wild Short N. America Lentic Yes Perry 1976

6.329 DW Benthic Culture Long Other Lentic No Planas et al. 1996


53




4.536b Chl a Benthic Wild Long N. America Lotic Yes Price and Carrick 2008

1.440b Chl a Benthic Wild Long N. America Lotic Yes Price and Carrick 2008

0.072 Chl a Planktonic Culture Long Other Lentic No Prieto et al. 1997

0.041 Chl a Planktonic Culture Long Other Lentic No Prieto et al. 1997

7.200 Chl a Planktonic Wild Long Other Lentic No Riegman and Mur 1986



19.647 C Benthic Culture Short N. America Lentic No Rosemarin 1982

0.953 C Benthic Culture Short N. America Lentic No Rosemarin 1982

3.023 DW Benthic Culture Short Other Lentic No Runcie et al. 2004




5.372 DW Benthic Wild Long N. America Lentic Yes Scinto and Reddy 2003



115.200 Chl a Planktonic Wild Short Other Lentic Yes Sorokin and Dallocchio 2008

151.200 Chl a Planktonic Wild Short Other Lentic Yes Sorokin and Dallocchio 2008

0.108 AFDM Benthic Wild Short N. America Lotic No Steinman and Boston 1993

0.648 AFDM Benthic Wild Short N. America Lotic No Steinman and Boston 1993

0.054 AFDM Benthic Wild Short N. America Lotic No Steinman et al. 1991

0.216 AFDM Benthic Wild Short N. America Lotic No Steinman et al. 1991

1.406 DW Benthic Wild Long N. America Lentic No Steinman et al. 1997

3.179 DW Planktonic Wild Short N. America Lentic No Steinman et al. 1997

27.554 DW Planktonic Wild Short N. America Lentic No Steinman et al. 1997

31.200 Chl a Planktonic Wild Long Other Lentic No Sweerts et al. 1986 a P-uptake rates extrapolated from Bothwell (1985) b Unpublished data

54

Table 1.2. Model selection results for predicting the model probability. K is the number of

parameters in the model including an intercept, log(L) is the log likelihood value for each model, BIC

is the Bayesian information criterion, and ∆i is the difference between the BIC value for each model

and the model with the lowest BIC (BICi - BICmin). Models were calculated using log10-transformed

P-uptake. The intercept/reference model was used as the reduced ‘base’ model, and thus not listed.

Parameter K log(L) BIC Δi

Microbial Group 4 -185.8 390.17 0.00*

Source 5 -183.9 391.02 0.84*

Sample Time 6 -183.2 394.22 4.04*

Region 7 -183.0 398.34 8.16

Abiotic Sorption 8 -182.9 402.73 12.56

* Parameters preserved for analysis with parametric statistics based on strength of evidence

55

Table 1.3. ANOVA results examining differences among predictors of log10-transformed P-uptake

rate (μg P μg Chl a–1

d–1

): microbial group, source, and sample time. Note: mean difference is

significant at α= 0.05 level.

Variable Type III Sum

of Squares

df Mean

Square

F p

Corrected Model 21.581 7 3.083 7.708 0.000

Intercept 75.866 1 75.866 189.671 0.000

Group 8.450 1 8.450 21.127 0.000

Source 1.052 1 1.052 2.631 0.108

Sample Time 1.247 1 1.247 3.117 0.081

Group * Source 1.273 1 1.273 3.182 0.078

Group * Sample Time 1.919 1 1.919 4.799 0.031

Source * Sample Time 1.690 1 1.690 4.224 0.043

Group * Source * Sample Time 1.471 1 1.471 3.679 0.058

Error 37.599 94 0.400

Total 152.324 102

Corrected Total 59.180 101

56

Table 1.4. Descriptive statistics for all experimental parameters tested in the meta-analysis of P-

uptake rates (μg P μg Chl a–1

d–1

). Missing fields are due to rank deficiency.

Microbial

Group

Source Sample

Time

Region System Abiotic

Sorption

N Mean Std.

Deviation

Benthic Culture Long N. America Lentic No 2 3.99 4.72

Lotic No 2 0.45 0.29

Other Lentic No 6 6.70 4.94

Short N. America Lentic No 2 10.30 13.22

Lotic No 4 2.07 0.78


Wild Long N. America Lentic No 1 1.41 -

Yes 7 31.48 65.28

Lotic No 2 0.09 0.05

Yes 2 2.99 2.19

Short N. America Lotic No 6 4.99 9.22

Yes 6 7.26 9.90

Planktonic Culture Long N. America Lentic No 1 64.77 -


Short N. America Lentic Yes 4 92.91 63.27


Wild Long N. America Lentic No 2 36.00 30.55

Yes 2 0.47 0.46


Yes 2 0.16 0.14

Short N. America Lentic No 10 13.03 19.90

Yes 6 110.07 246.99

Other Lentic Yes 4 70.45 74.17

57

Group

Source

Time

PlanktonicBenthic

WildCultureWildCulture

ShortLongShortLongShortLongShortLong

3.0

2.5

2.0

1.5

1.0

0.5

0.0

(lo

g)

P U

pta

ke

Ra

te

Figure 1.1. Boxplot of log10-transformed P-uptake rates for the best predictor parameters

determined using mixed modeling: sample time, source, and microbial group. Boxplots show the

median value (line) 25 and 75% quartiles (box), upper and lower limits (whiskers), and outliers (>1.5

times interquartile range; asterisks).

58

CHAPTER 2

EFFECTS OF PHYSICAL DISTURBANCE ON PHOSPHORUS UPTAKE IN TEMPERATE

STREAM BIOFILMS

59

2.1 ABSTRACT

The evaluation of microbial biofilm nutrient uptake kinetics can provide insight

into uptake mechanisms that regulate stream productivity. While kinetic uptake

experiments have been performed on stream biofilms, there has not been an evaluation of

disturbance (removal) techniques and the effects that such abrasive methods may have on

benthic microbes. Therefore, the goal of this study was to evaluate common removal

techniques used in metabolic studies on benthic biofilms to determine effects on

phosphorus (P) uptake rate, physiological capability, and abiotic sorption. Artificial

substrata were collected from two reaches along a temperate stream; resident biofilms

were either removed via scraping or left intact. A series of short-term radiotracer

(H333

PO4) experiments were then conducted to measure P-uptake. In vivo

autofluorescence was measured as a proxy of algal physiological condition. The

experiments showed no difference in P-uptake rates (μgP/μgChl/d) between the scraped

(x = 0.77 ±0.11 (SE) μgP/μgChl/d) and intact (x = 0.91 ±0.17 (SE) μgP/μgChl/d) biofilms

(t= 0.69, p= 0.492, df= 33). Further, microbial physiology was not depressed by physical

disturbance. While killed samples yielded significantly lower uptake compared to live

biota (F= 17.51, p= 0.001), abiotic sorption still accounted for a moderate fraction of total

uptake and thus warrants estimation in metabolic studies. Overall, these findings lend

credence to the numerous experiments that investigate benthic microbial physiologic

responses post-disturbance and highlight the importance of uptake following common

physical disturbances that occur in turbulent environments.

60

2.2 INTRODUCTION

Streams are dynamic features of the landscape in part because they serve as

sediment and nutrient transport avenues (Hall et al. 2002); specifically, small streams

(width ≤10 m) represent up to 85% of total stream length in most watersheds, provide a

crucial link between terrestrial and aquatic environs, and are key elements of nutrient

transformation and downstream transport (Peterson et al. 2001, Sweeney et al. 2004).

Benthic stream biofilms are capable of assimilating and effectively retaining nutrients

that would otherwise transport downstream (Dodds 2003). Biotic uptake/assimilation

transfers inorganic nutrients into particulate form and in so doing can buffer downstream

ecosystems from more soluble reactive forms (Svendsen et al. 1995). As such, biofilms

are critical in the removal of dissolved phosphorus (P) from stream water and are major

components in stream self-purification (Sabater et al. 2002, Covich et al. 2004).

Nonetheless, owing to their complex structure, some biofilm components may be more

susceptible to physical disturbance and removal (i.e., top layer of biofilm matrix);

periodic sloughing and storm spates can act to remove portions of the biofilm and

transport them downstream (Peterson and Stevenson 1990, Biggs 1996). While physical

disturbance and removal of the biofilm is a natural occurrence and previous physiological

assays have employed physical scraping/disturbance as a biofilm extraction method (e.g.,

Tank and Webster 1998, Thompson and Sinsabaugh 2000, Miranda et al. 2007), the

effect that such disruption has on cell physiology and viability is largely unknown. That

said, researchers have inferred uptake rates from direct incorporation of radiolabel into

microbial components (e.g., Hwang et al. 1998, Scinto and Reddy 2003), while others

have used the loss of activity from the overlying water over time (e.g., Odum et al. 1958,

61

Steinman and Mulholland 2006); such differences in experimental methodologies may

produce variation in uptake rates across varying temporal and spatial scales (cf. Price and

Carrick 2011).

Nutrient retention in aquatic systems is hence a consequence of this active

biological uptake but also non-biological sorption (Haggard et al. 1999); the latter factor

can be considerable, if not the dominant process, under some conditions (Rejmánková

and Komárková 2000, Aldridge et al. 2009). However, few studies routinely test for

abiotic sorption (e.g., Steinman and Mulholland 2006) or provide measured abiotic

sorption fractions in uptake experiments, and quantitative reports on non-biological

sorption rates are varied. For example, Klotz (1985) found that the contribution to P

cycling by abiotic processes was much greater in comparison to biotic processes.

However, Mulholland et al. (1983) and Scinto and Reddy (2003) found that abiotic

process accounted for a much smaller proportion of P-uptake (10.3 and <15%,

respectively).

Michaelis-Menten (M-M) kinetics have been used to describe nutrient uptake into

biofilms (Reuter et al. 1986), facilitating estimations of the maximum uptake rate (Vmax)

and half-saturation constant (Km), although there are many instances where this model is

not supported (e.g., Tarapchak and Herche 1986). Similarly, nutrient uptake by higher

plants typically follows M-M saturation kinetics and is described by the parameters Vmax,

Km, and Cmin, the minimum nutrient concentration required for uptake to occur (Nielsen

1979, Akhtar et al. 2007). The Cmin or ‘threshold’ value has not been widely estimated

before in the aquatic phycology literature (Aubriot et al. 2000, Wagner and Falkner

2001), but may be an important factor in uptake models.

62

Therefore, the objective of this study was to examine the effect of procedural

techniques (physical disturbance) on P-uptake rate (μgP/μgChl/d); specifically I sought to

estimate: 1) differences in uptake between physically scraped and intact biofilms through

single endpoint techniques (Collos 1983) and 2) M-M kinetic parameters (Vmax, Km, and

Cmin) along varying time-courses. In addition, cell viability post-disturbance and abiotic

sorption was also investigated.

2.3 METHODS

P-uptake rates of intact vs. scraped biofilms (single time point)

A set of six unglazed ceramic tiles (surface area= 8.42 cm2) were secured to

cement blocks and established in an upstream (40.7786, -77.7696) and downstream

(40.8222, -77.8369) reach in a 3rd

order stream in a mixed land-use watershed in

Pennsylvania (USA) that eventually drains into the Chesapeake Bay (Spring Creek)

(Appendix A). Unglazed tiles have been used extensively as standardized substrate for

biofilm colonization and development to reduce sample variability (Lamberti and Resh

1985). The tiles were incubated for 30 days to allow the development of mature biofilm

assemblages (Biggs 1988), after which the tiles with intact biofilms were placed into 60

mL translucent polypropylene (Nalgene) incubation jars filled with site-specific water.

Once returned to the laboratory, three tiles from each site were scraped with a stiff

bristled brush and three tiles were left intact. For the scraped samples, 1 mL of biofilm

material with water (slurry) was removed from each replicate chamber using a sterile

repeat pipette 10 minutes after tracer (H333

PO4) injection. Samples were then injected

into a 12-place Millipore filter manifold fitted with 0.20 μm Versapor filters and 15 mL

63

glass water collection tubes. Vacuum filters and water were separately placed into

labeled 20 mL scintillation vials filled with 5 mL Ecolume scintillation cocktail (ICN

Pharmaceuticals, Costa Mesa, CA, USA) and read for activity using liquid scintillation

counting (LSC) (model LS 6000 IC; Beckman-Coulter, Fullerton, CA). For the intact

samples, 1 mL of water (overlying biofilms) was removed from each replicate chamber

using a sterile pipette, again 10 minutes after tracer injection. No obvious signs of seston

were present in the incubation jars; however, filtration was still performed prior to

removing water samples using a 5cc BD Luer-lock syringe with interchangeable filters

(Acrodisc 13 mm syringe filter with 0.2 µm Supor membrane; Pall Corp., Ann Arbor,

MI, USA). The radioactivity in the samples was determined again by LSC. Uptake rates

were calculated as ln [P0/(P0-X)] over time, where P0 is the total activity in 1mL of

radiolabelled sample and X is the radioactivity on the filter (biota), following Hwang et

al. (1998). For all samples, counts per minute (CPM) were converted into disintegrations

per minute (DPM) using an internal quench curve. These experiments differ from kinetic

experiments in that only a single time point was sampled (limiting the ability to estimate

kinetic parameters); rather the goal of these experiments was to validate single time point

estimates (e.g., Collos 1983) of P-uptake between scraped and intact benthic biofilms.

These experiments were performed in triplicate and conducted on 10/17/08, 6/3/09, and

6/11/09. Chlorophyll-a (Chl) concentrations were determined following standard

fluorometric methods using a Turner 10-AU fluorometer (Carrick et al. 1993) and

converted to areal densities (mg/m2).

64

M-M parameters along distinct time-courses and short-term flux estimations

Similar to the single-time point estimation experiments, an additional set of 20

unglazed ceramic tiles were secured to cement blocks and established in an upstream

(40.7786, -77.7696) reach as before (Spring Creek, USA). Again, tiles supporting intact

biofilms were placed into incubation jars after 30 days of residence in the stream. Once

tiles were returned to the laboratory, intact biofilms were physically removed from ten of

the collected tiles while the other ten tiles were left with intact biofilms. Next, all 20

experimental containers were amended with increasing concentrations of KH2PO4 (0 -

500 µgP/L). Duplicate experimental jars were amended with five [P]: 0 (control), 10, 20,

200, and 500 µgP/L (n= 10). For the scraped samples, 1 mL of biofilm material with

water (slurry) was removed from each replicate chamber using a sterile repeat pipette 5,

12, 18, 30, and 60 minutes after tracer (H333

PO4) injection. As before, samples were

injected into a filter manifold and subsequently placed into labeled 20 mL scintillation

vials filled with scintillation cocktail and read for activity using LSC. For the intact

samples, 1 mL of water (overlying biofilms) was removed from each replicate chamber

using a sterile pipette, again 5, 12, 18, 30, and 60 minutes after tracer injection. Seston

uptake was measured in a previous experiment and determined to be a negligible

component; however, filtration was still performed prior to removing water samples. The

radioactivity in the samples was determined again by LSC.

Abiotic sorption, P storage, and cell viability

The non-biological (abiotic) sorption of phosphorus by stream benthic biofilms

was also investigated using natural rock substrates collected from the same upstream and

downstream reach (Spring Creek, USA). On 17 June 2009, biofilms were physically

65

removed from rock substrates in the field, washed into labeled containers, and returned to

the laboratory. Slurries from each site (n= 16) were incubated with the following agents:

1) 10% formaldehyde, 2) 3% glutaraldehyde (Wolfstein et al. 2002), and 3) control (no

inhibitory agents). Sixty minutes after agents were injected into incubation jars, 0.50 mL

carrier-free H333

PO4 radiotracer was injected into each sample. After 10 minutes, slurry

was removed from each incubation jar and filtered. Filters were thoroughly rinsed of any

non-specific 33

P binding, placed into scintillation vials filled with 5 mL Ecolume

scintillation cocktail, and activities estimated via LSC. It should be noted that this was a

first-order estimation, as the effect of formaldehyde/glutaraldehyde fixation on the

possible sorption of P is unknown.

In addition, the effect of increasing levels of disturbance on biofilms was

examined in the field using three different physical scraping techniques with a stiff-

bristled brush (three rocks/ technique): one pass (low disturbance), two passes (medium

disturbance), and multiple passes (high disturbance). For identification of soft algae, 20

mL from each disturbance treatment (n= 9) was preserved with 1% formalin and cells

were identified to genus under 100x and 400x magnifications using a Leica light

microscope (Carrick and Steinman 2001). Large (≈ 300µm) cells were identified to

genus first under 100x magnification using a Palmer-Maloney counting chamber (394 sq.

mm in area, 0.1 mL volume). Remaining cells were counted at 400x magnification

(random fields) to a minimum of 200 cells until 400 cells total were reached under both

100x and 400x. Cells were then grouped by growth form (filamentous, stalked/erect, and

prostrate/adnate). Physiognomic (growth form) classifications of microbial genera

followed Graham and Vinebrooke (1998), Wellnitz and Ward (2000), and Passy (2007).

66

Samples from experiments were filtered (Whatman EPM 2000 glass fiber) for

polyphosphate (poly-P) via hot-water extraction and analyzed using spectrophotometry

following standard methods (Fitzgerald and Nelson 1966, Eixler et al. 2005). These

samples were also analyzed for Chl content using a Turner 10-AU fluorometer (Carrick

et al. 1993).

Autofluorescence was measured (via fluorometry) as a proxy of algal

physiological condition (n= 9, three rocks/disturbance treatment). Sample was poured

into a cuvette and read in vivo on a fluorometer. Cuvettes were then placed in dark

conditions for one minute and 0.1 mL of DCMU (3-(3,4-dichlorophenyl)-1,1-

dimethylurea) –a photosynthetic inhibitor– stock (10-3

M) was added to each sample

(final concentration= 10-5

M) which blocks electron transport from photosystem II to

photosystem I resulting in maximum fluorescence (Prezelin 1981). Cellular fluorescence

capacity (CFC) (proportion of absorbed light being used in photosynthesis) was

calculated as:

(Fa – Fb)/ Fa (Eq. 1)

where Fa is in vivo fluorescence post DCMU addition and Fb is in vivo fluorescence

(Vincent 1980, Vyhnalek et al. 1993). CFC should vary directly with photosynthetic

activity (physiological condition) and inversely with negative effects on photosynthetic

activity (e.g., physical disturbance) (Vincent 1981, Thompson 1997).

Clean techniques

Clean techniques were strictly followed due to the sensitive nature of

radioisotopic experimentation and to avoid any potential contamination which could

influence biofilm P-uptake. These techniques will be briefly discussed here. Small

67

amounts of trace elements can affect phytoplankton (Fitzwater et al. 1982) and

periphyton (Hill et al. 2000) metabolic processes. Contamination during uptake

experiments using radiotracers may include impurities such as metals, inadvertently

added during sampling which may reduce the precision of uptake measurements.

Therefore, clean techniques and sterile instrumentation/apparatus were followed and used

in order to reduce any potential contamination and generate the most accurate uptake

measurements. Following suggestion from Fitzwater et al. (1982), biofilm communities

were incubated in polycarbonate jars instead of glass. Polycarbonate has been shown to

adsorb very little manganese (Mn) and zinc (Zn) compared to glass containers that can

rapidly bind trace metals (Fitzwater et al. 1982). Incubation jars used for each

experiment were cleaned in phosphate free detergent, acid-washed (10% hydrochloric

acid at 20° C for at least 24 hours), and rinsed with deionized water from a Barnstad

Nano-Pure system (Singh et al. 2006) prior to use. Sterile pipette tips constructed of inert

virgin CFR 21 compliant polypropylene (VWR Int.) were used to inject tracer into

samples (Rehder and Borges 2010). Tracer stock solution was stored in a high-density

polyethylene wide mouth bottle at 4° C until use. Additionally, disposable powder-free

gloves (nitrile) and laboratory coats were worn during experiments to protect against

biohazards and radioactivity as well as to prevent any contamination from clothing and/or

skin. By employing clean sampling procedures, using polycarbonate incubation vessels,

and sterile equipment, P-uptake experiments can avoid some of the most serious sources

of trace metal contamination (Fitzwater et al. 1982).

68

Statistical analyses

A mixed-model three-way analysis of variance (ANOVA) was performed to

evaluate variation in P-uptake rate (μgP/μgChl/d) between disturbance treatment (scraped

and intact biofilms), site location (up and downstream), and experiment time (sampling

dates: 17 October 2008, 3 June 2009, and 11 June 2009). Site was treated as a random

effect here, as there were no a priori predictions regarding differences among particular

stream locations, therefore, a mixed-model ANOVA approach was most applicable

(McKone and Lively 1993). For statistical comparisons, I determined the uptake into the

biota from the intact samples by subtracting the activity measured in the water and

activity on the filter (seston) from the total activity. ANOVA was also used to analyze

the differences between controls and poisoning agents in estimating biotic vs. abiotic

uptake. All descriptive statistics and ANOVA analyses were performed using SPSS

software version 19.0 (SPSS Inc., Chicago, IL, USA). M-M parameters (Vmax and Km)

were estimated using an iterative ‘nls’ function (nonlinear least-squares regression) in the

statistical package R (R Development Core Team 2006, Marino et al. 2010).

Observations with DFFITS values greater than 2√(k/n), where k is the number of

predictors (including constant), were removed from regression analyses, following

Belsley et al. (1980). To estimate Cmin, or the threshold [P] at which net incorporation of

P by the biofilm ceases due to insufficient available energy (Aubriot et al. 2000), a plot of

the uptake rate versus the logarithm of the external P concentration (Thellier plot;

Thellier 1970) was made; the intercept on the log [P] axis corresponds to the logarithmic

P threshold concentration (i.e., Cmin) (Wagner et al. 1995).

69

2.4 RESULTS

In general, no overall difference in P-uptake rate (μgP/μgChl/d) estimates was

detected between the two methods, scraped versus intact biofilms (t= 0.69, p= 0.492, df=

33) (Appendix B). The scraping technique yielded a relatively lower coefficient of

variation (x = 18.1%) among P-uptake estimates by the biofilms, while the P-uptake by

intact biofilms yielded a comparably higher coefficient of variation (x = 25.4%).

Because P-uptake was measured for both intact and scraped assemblages across

spatial and temporal scales, I evaluated these influences on the resulting rates (Table 2.1).

As above, scraping was found to have no effect on P-uptakes rates, and thus no treatment

effect (intact, disturbed) was observed. There was a significant temporal component to

the variation (F2, 28= 22.38, p= 0.043), such that uptake by biofilms during the fall

experiment on 10/17/08 (x = 1.03 (±0.57 SD) μgP/μgChl/d) was greater compared with

uptake by biofilms sampled from both the first (x = 0.75 (±0.77 SD) μgP/μgChl/d) and

second (x = 0.68 (±0.58 SD) μgP/μgChl/d) spring experiments on 6/3/09 and 6/11/09,

respectively. Physical disturbance (scraping) did not appear to negatively affect cellular

function as expressed through function of the phototsystems; ANOVA results showed no

significant differences in CFC values among disturbance intensities (F2, 6= 2.73, p=

0.144). The various physical disturbance techniques segregated organisms within the

biofilm (possessing specific internal P storage capacities). Biologically bound P (poly-P)

varied significantly among treatments (F2, 6= 11.34, p= 0.009); specifically the low

treatment averaged 0.133 (±0.028 SD), the medium averaged 0.051 (±0.003 SD), and the

high averaged 0.080 (±0.024 SD) mgP/mgChl. The higher poly-P content in the low

disturbance treatment was likely linked to the higher presence of filamentous taxa in the

70

upper strata of the biofilm (Figure 2.1) (Appendix C, D, E). Killed controls showed that

abiotic sorption yielded significantly lower uptake compared to biotic uptake (F2, 9=

17.51, p= 0.001); specifically P-uptake rates averaged 0.068 (±0.017 SD), 0.029 (±0.01

SD), and 0.025 (±0.00 SD) μgP/μgChl/d by control, formaldehyde, and glutaraldehyde

treatments, respectively. Interestingly, average abiotic sorption (glutaraldehyde and

formaldehyde) across both sites sampled accounted for approximately 40% of total P-

uptake relative to control treatments.

The nonlinear (weighted) least-squares analysis of the M-M model for the scraped

assemblages yielded a Vmax of 2.25 (±0.77 SE) µgP/µgChl/d and Km of 231.36 (±140.92

SE) μgP/L (Appendix F); intact assemblages yielded a Vmax of 2.33 (±0.63 SE)

µgP/µgChl/d and Km of 293.87 (±173.95 SE) µgP/L (Appendix G). A one-way ANOVA

of P-uptake rate (μgP/μgChl/day) versus treatment showed that there was no difference

(F= 0.72, p= 0.408) in uptake rates between scraped and intact assemblages over the 60

minute period. I then tested re-estimated uptake constants (k) by splitting samples

between short time periods (i.e., 5 - 12 minutes) and long time periods (i.e., 30 - 60

minutes) to estimate the effect of experiment duration on the uptake constant and M-M

parameters. For short time periods the scraped assemblages yielded a Vmax of 16.64

(±1.40 SE) µgP/µgChl/d and Km of 1457.10 (±139.35 SE) µgP/L (Appendix H); intact

assemblages yielded a Vmax of 2.31 (±4.70 SE) µgP/µgChl/d and Km of 23.44 (±85.59

SE) µgP/L (Appendix I). For long time periods the scraped assemblages yielded a Vmax

of 8.42 (±16.61 SE) µgP/µgChl/d and Km of 1236.55 (±2831.18 SE) µgP/L (Appendix J);

intact assemblages yielded a Vmax of 1.11 (±1.25 SE) µgP/µgChl/d and Km of 653.45

(±960.90 SE) µgP/L (Appendix K). Additionally, I tested the soil nutrient uptake

71

formulation of Barber (1995) for benthic biofilms by estimating Cmin, the minimum [P]

required for uptake to transpire. The Thellier plots used to estimate Cmin yielded a [P] of

6.80 and 9.95 µg/L for the scraped and intact biofilm treatments, respectively (Figure

2.2).

2.5 DISCUSSION

Measurements made between benthic biofilms subject to physical disturbance

(abrasion/scraping) and undisturbed intact biofilms, showed that single time point

estimates of P-uptake did not vary significantly between the treatments. These findings

indicate that scraping does not negatively affect the ability of biofilms to assimilate P

during brief (≤ 30 minutes) single time-course experiments. In disturbed treatments,

uptake was estimated through direct incorporation of tracer into microbes (on filter),

while in undisturbed treatments, uptake was inferred through tracer loss from overlying

water; either way, both methods appear to be practical and comparable during this

experimental period. A meta-analysis on P-uptake in aquatic microbes supports this

finding, as experiments that derive uptake from water were comparable with those that

derive uptake from direct analyses (Price and Carrick 2011). Further, my data here lend

credence to the numerous experiments that investigate benthic microbial physiologic

responses post-disturbance (e.g., Bothwell 1985, Reuter et al. 1986, Scinto and Reddy

2003, Chapter 3). The M-M parameter estimates did show relative differences when split

by short and long time samplings, with the shorter yielding higher Vmax; this finding

corresponds to earlier research and likely indicates initial transport vs. assimilation

kinetics (Flynn 1998, Price and Carrick 2011). In fact, the rapid uptake revealed during

72

my short time period (i.e., 5 - 12 minutes) is directly in line with Taft et al. (1975), who

found that initial rapid phosphate uptake (attributed to membrane-transport) declined

after 15 - 60 minutes in phytoplankton in the Chesapeake Bay estuary. My Km estimates

were similar to those reported for freshwater periphyton (508 µgP/L) (Scinto and Reddy

2003). In my study, the estimated Km was slightly lower for scraped assemblages and

suggests that microbes liberated from the diffusive boundary layer (see Larned et al.

2004) via physical disturbance, expressed higher affinity (Furihata et al. 1992) for

inorganic P compared to those microbes within an intact biofilm; however, low

replication of these individual experiments precluded more definitive statistical analysis.

Similarly, no level of imposed disturbance caused physiological changes in the

microbes removed from the biofilms (as estimated by CFC). This indicates that, during

brief incubation periods, there are no obvious deleterious effects arresting or impairing

the rate of electron flow between photosystem II and photosystem I and, by extension, the

ability of the scraped biofilms to assimilate P (e.g., Rueter and Ades 1987). Viable cells

sloughing from periphyton (benthic biofilms) have been observed before through

microscopic examination (Naiman and Sibert 1978), and as stream benthic microbial

biofilms are continually subject to the abrasive effects of suspended solids and bed

sediments and consequently periodic sloughing losses (Biggs and Close 1989), it seems

reasonable that they have adapted mechanisms to remain viable during such processes.

The quantity of biologically stored P (poly-P) varied among layers (growth form)

within the biofilm with the highest concentration found in top (filamentous forms; 64%)

and lower for both middle (stalked forms; 16%) and bottom (prostrate forms; 20%).

Cells can store a large amount of phosphorus in poly-P granules (Jacobson and Halman

73

1982) and immediate accumulation in microbes has been observed during P-surplus

(Casadevall et al. 1985, Zeng and Wang 2009). Higher poly-P concentrations in the low

disturbance treatment suggest that surplus P is available to filamentous forms in the

biofilm. This was expected, as filamentous microbes in a biofilm generally have greater

biomass exposed to overlying waters where nutrients may be more bio-available and

further supports work showing that access to phosphate supplies from the water depends

on the position of microbial cells in the biofilm (see Burkholder et al. 1990).

While I found that biofilms subject to both formaldehyde and glutaraldehyde

treatments showed significantly reduced uptake rates compared to controls, the data

suggest that abiotic uptake of P could be important in streams like the one studied here.

These estimates for the abiotic fraction of uptake did show considerable P sorption (40%

of total) and were comparable to previous literature. For example, Aldridge et al. (2010)

found that abiotic interception accounted for more than 70% of the total P-uptake by

epilithic communities across a gradient of unmodified and modified streams. My abiotic

sorption estimate may be artificially elevated due to the absence of flow in my

experiments. That is, a key mechanism of abiotic P retention includes adsorption by

sediments (Reddy et al. 1999) and therefore in a flowing system, that specific mechanism

would be diminished due to added transport and fewer opportunities for nutrient-

sediment encounters (Triska et al. 1989). Abiotic uptake is also dependent on

biogeochemistry and sediment composition of the stream (Stone and English 1993).

Here I measured a single system, but an analysis of abiotic uptake across spatial and

temporal gradients should be further considered to refine models (Dodds et al. 2002).

Despite this need, Price and Carrick (2011) found that only 32% of uptake experiments

74

factor an abiotic fraction of uptake into analysis; I would therefore suggest this be

performed on a more regular basis to avoid overestimation of biotic uptake rates.

Concentrations of substrate below which cells cannot acquire nutrients reflect

important and potentially ecologically meaningful threshold concentrations (Istvánovics

and Herodek 1995). Due to energetic constraints, biotic uptake is then only possible

when external [P] surpasses this threshold, or Cmin, which, in P-limited communities, has

been found to be in the nanomolar range (Falkner et al. 1989). My values averaged 219

nM for the scraped biofilm treatment and 321 nM for the intact treatment and thus appear

to be within ranges previously reported. Previous work has suggested that the threshold

concentration in P-deficient lakes is close to the ambient P concentration (lstvánovics and

Herodek 1995). Biofilms for this experiment were retrieved from an upstream site in

Spring Creek, Pennsylvania where orthophosphate concentrations are <10 µg/L and

biofilm nutrient ratios indicate P limitation (Godwin and Carrick 2008); these conditions

therefore provide further creditability to my results. Nevertheless, rapid nutrient cycling

within biofilms (Riber and Wetzel 1987) may essentially act to insulate microbes and

reduce dependency on bulk surface water nutrients; in such circumstances, Cmin may be

an extraneous parameter. Lastly, given that the magnitude of the Cmin estimates are so

low (nM), including it in the M-M model would not likely alter the shape of the simple

rectangular hyperbolic (Isvánovics et al. 1993) in this set of experiments. Thus my

results seem to indicate that while Cmin parameter estimates can be practical, their

determination and usage in M-M models may not fundamentally alter benthic biofilm

kinetic rate estimates.

75

2.6 ACKNOWLEDGMENTS

I thank B. Eckert and J. Regan for valuable feedback and helpful comments on

this manuscript. Funding for this research was provided to H.J.C. by the Pennsylvania

Department of Environmental Protection (Grant No. 4100034506).


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85

Table 2.1. Three-way mixed-model ANOVA testing the main fixed effects of treatment (n= 2) and

experiment (n= 3) and random effect of site (n= 2) on P-uptake rates (μgP/μgChl/d) for intact vs.

scraped stream biofilms.

Source DF Sequential

sums of squares

Adjusted

mean square

F p

Site 1 10.7368 10.6698 42.83 0.683

Treatment 1 0.2018 0.2493 0.36 0.657

Experiment 2 0.9915 0.4958 22.38 0.043

Site * Treatment 1 0.7827 0.6970 1.48 0.347

Site * Experiment 2 0.0443 0.0222 0.05 0.955

Treatment * Experiment 2 1.4666 0.7333 1.55 0.393

Site * Treatment * Experiment 2 0.9476 0.4738 14.49 0.000

Error 28 0.9158 0.0327

Total 39 16.0873

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Figure 2.1. Stacked column chart of benthic poly-P (mgP/mgChl) vs. disruption treatment according

to growth form. Estimates were made by multiplying mean percent growth form from each

physiognomic classification by the mean poly-P concentration for each disturbance treatment.

87

Figure 2.2. Thellier plots used to estimate Cmin for scraped (left) and intact (right) biofilm

assemblages. The fitted linear regression intercepts the log [P] axis at the logarithmic threshold

concentration (Aubriot et al. 2000).

88

CHAPTER 3

EFFECTS OF NUTRIENT LOADING ON PHOSPHORUS UPTAKE BY BIOFILMS SITUATED

ALONG A STREAM PRODUCTIVITY GRADIENT

89

3.1 ABSTRACT

Biofilms play an important role in the assimilation of phosphorus (P) in streams;

however, they are often treated as a “black box” and relatively few direct measurements

have linked nutrient loadings with assimilation. Herein, I measured P-uptake rates for

resident, benthic biofilms along a stream productivity gradient (n= 8 streams). Realistic

point source nutrient loadings were applied using an in situ enrichment system (ISES):

vials spiked with increasing concentrations of P with and without nitrogen (N) (n= 70 for

each experiment). Vials were capped with porous disks that were removed with attached

biofilms after three weeks of in-stream incubation. A series of short-term radiotracer

(H333

PO4) experiments were then conducted to measure P-uptake by resident biofilms

that grew on the disks collected from each stream experiment. P-uptake versus P-loading

(log10 transformed) was explained by simple linear regression for all streams. Similarly,

uptake efficiency declined with increased P-loading, and the overall slope of the

regressions for these relationships also declined as a function of stream productivity.

Specifically, biofilms in low-productivity streams showed significantly higher uptake

rates for moderate P-loads compared to high-productivity streams (e.g., 1.29 vs. 0.84

(log10(nmolP/μgChl/day)), respectively) (F1,30= 6.21, p= 0.018), indicating that these

communities were more physiologically poised to respond to new P additions. Biofilms

in productive streams appeared to be P-saturated (presumably from P legacy effects), and

thereby had lower demand for experimental P additions. P-uptake rates were greater for

P+N versus sole P-loads; these results indicated that N had a synergistic effect on biofilm

P-uptake ability, and may have been secondarily limiting in streams of higher

productivity (P legacy effect). My results identified the occurrence of P saturation in

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streams and quantified its effect on the uptake of new P additions, as well as the reality of

N as a synergistic nutrient for these resident biofilms.

3.2 INTRODUCTION

Phosphorus (P) and nitrogen (N) are key nutrients that can limit terrestrial

(Vitousek and Howarth 1991) and aquatic primary production (Francouer 2001). P is an

essential nutrient for all living organisms; it is involved in the formulation of the nucleic

acids deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) and the “energy

currency” adenosine triphosphate (ATP) (Versaw and Harrison 2002, Dodson 2005,

Oelkers and Valsami-Jones 2008). Specifically, in primary producers, ADP (adenosine

diphosphate) binds to inorganic P to make ATP which then drives energy-dependent

processes. In aquatic ecosystems, increased anthropogenic inputs of P can be closely tied

to eutrophication (Correll 1998, Mainstone and Parr 2002); however, recent focus on

managing aquatic ecosystems for P alone, without specific concern for N (see Schindler

et al. 2008) has shown great limitations (see Paerl et al. 2010) which underscore the need

for combined P and N management to control freshwater eutrophication (Paerl 2009,

Scott and McCarthy 2010). Strong synergistic effects of simultaneous P and N

enrichment have been identified in other studies (e.g., Elser et al. 2007), such that both

nutrients need to be considered together as important resources that influence the

biological integrity and sustainability of aquatic ecosystems (Karr 1991, Franklin et al.

2005).

Nitrogen is an important nutrient that can regulate primary production (LeBauer

and Treseder 2008), as it is required for biosynthesis of amino acids and proteins as well

91

as photosynthetic pigments (e.g., chlorophyll-a, phycobilins) (Grula 2005). The proposed

mechanisms for the observed combined P and N synergistic effects on primary

production is that cellular stoichiometry of these nutrients are in close balance and that

single enrichments induce limitation by the alternative nutrient (Davidson and Howarth

2007, Elser et al. 2007). For example, Lapointe (1989) found that Gracilaria tikvahiae

(Division Rhodophyta, Order Gigartinales) was limited primarily by P and secondarily by

N when P supply increased. While much research has addressed the synergisms of P and

N additions, the work has focused largely on their effects on net primary productivity

(e.g., Elser et al. 2007), with little regard for physiological processes like uptake, the

initial stage in assimilation of nutrients into organic matter (Beardall et al. 2001).

Stream benthic biofilms have a high affinity for inorganic nutrients (Hoffmann

1998) and have been shown to act as dynamic nutrient sinks (Dodds 2003), much like soil

microbial communities in terrestrial ecosystems (Olander and Vitousek 2004). Biotic

uptake/assimilation by benthic algae transforms reactive inorganic nutrients into

particulate form, thereby rending them unavailable; this transformation thus regulates

nutrient bioavailability to downstream ecosystems (Svendsen et al. 1995). As such,

biofilms are critical in the removal of dissolved nutrients from stream water and key

elements in stream “self-purification” (Gantzer et al. 1988, Sabater et al. 2002, Covich et

al. 2004). Thus, while streams are significant landscape features owing to their role in

nutrient transformations (Peterson et al. 2001), the efficiency of nutrient uptake (relative

to flux) by resident biofilms is paramount in this regard; as such it is important to

understand environmental factors that might act to suppress such efficiency. For

instance, Earl et al. (2006) found that both high ambient [N] and experimental N

92

amendments to streams contributed to decreased uptake efficiency. Excessive N inputs

have generated N saturation responses in forest and aquatic ecosystems (Aber et al. 1989,

1998, Earl et al. 2006, De Schrijver et al. 2008); such that, N availability/supply in excess

of biotic demand limits the retainment of N within the biota and increases the potential

for losses to surrounding environs (Aber et al. 1989). Recent research has focused on

such saturation responses to elevated N loading in lotic ecosystems (e.g., Bernot and

Dodds 2005, Earl et al. 2006, O'Brien and Dodds 2010, Martin et al. 2011), however,

considering the importance of P as a bio-limiting nutrient and the present nutrient debates

(see Paerl 2009), it is critical that both N and P be examined with respect to how single

and dual nutrient loadings can affect biofilm uptake efficiencies.

The research I report on here was designed to evaluate variation in P-uptake as a

function of experimental P and N enrichment among streams of varying productivity; the

experiments were performed in situ to achieve a high degree of environmental realism.

My specific objectives of this study were: (1) derive regression models to describe the

relationship between P-uptake and P-loading, (2) measure the effect (synergistic, neutral,

antagonistic) of concurrent N-loadings with P on biofilm assimilative abilities, and (3)

examine biofilm assimilative capacities across a gradient of stream productivity to

determine the effect of nutrient legacies. I hypothesize that P-uptake by biofilms will

decline as a function of P-loading, and the rate of decline will become less pronounced in

streams that support higher productivity. Further, enrichment with N, a typical secondary

limiting nutrient in aquatic enrichment experiments (Stockner and Shortreed 1978, Marks

and Lowe 1993), will have a synergistic effect with P and augment biofilm response to P-

loading.

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3.3 METHODS

Study sites

I carried out field experiments in eight streams that were a representative subset

from a previous study that evaluated relationships between benthic chlorophyll-a and

nutrient concentrations in 47 streams sampled in Pennsylvania (Carrick et al. 2009)

(Appendix L). Specifically, experiments were conducted in four streams located in each

of the two major geologic provinces that divide Pennsylvania along a southwest to

northeast axis (Appalachian Plateau, Ridge-Valley Piedmont, respectively (PASDA

2005)) (Appendix M). These eight streams were selected in order to capture the broad

range of environmental and biogeochemical conditions reflective of environments in

Pennsylvania and the larger, mid-Atlantic region (Figure 3.1). The streams examined

here exhibited a strong productivity gradient similar to that described elsewhere (Busse et

al. 2006). In fact, my range of chlorophyll-a concentrations reflect trophic conditions

ranging from oligotrophic (<50 mg/m2) to eutrophic (>100 mg/m

2) (see Dodds et al.

1998). Flow velocity can influence the biomass and taxonomic composition of stream

biofilms (Stevenson 1990, Hart and Finelli 1999), and mediate or even override the

effects of nutrient enrichment under certain conditions (e.g., Hullar and Vestal 1989,

Ghosh and Gauer 1994). In all cases, the experiments were conducted during base flow

conditions, where stream discharge was generally < 100 ft3/s and deviated little

throughout the experimental period; therefore, hydrologic mechanisms did not

differentially affect microbial biofilms in my study streams and further, flow rates were

likely too low to affect uptake through increasing basal nutrient flux (cf. Triska et al.

1990).

94

At each stream site, water physicochemical conditions (e.g., temperature,

conductivity, oxygen concentration) were monitored at the beginning and end of the

experiment using an YSI (Yellow Springs Instrument) data Sonde (model 6600); algal

growth (as chlorophyll-a) was also estimated at the start and finish of the experiment on

duplicate collections of natural rocks. Stream flow was measured by nearby United

States Geological Survey monitoring gage stations and recorded through the National

Water Information System. Water chemistry was measured at each site on samples

collected in acid-rinsed, 1-liter amber bottles that were kept cool during transport to the

laboratory (Millie et al. 2010). The concentration of water column phosphorus was

measured using standard digestions and colorimetric reactions (Wetzel and Likens 2000).

Design of field experiments- in situ enrichment system (ISES)

The P-uptake response of benthic biofilms to P-enrichment was directly measured

under natural stream conditions through a series of experiments carried out in 2008 using

an in situ enrichment system (ISES). The ISES design used small, polystyrene vials

(outer dimensions 2.7 x 8.0 cm) that were filled with ultra-pure agar (2% agar noble), to

which five levels of P (P1 - P5) was added (0.00 control, 0.005, 0.025, 0.05, and 0.50 M

as NaH2PO4). In addition, one set of vials received no additional N sources, while a

second set of vials received N additions (0.0 and 0.50 M as NaNO3, respectively). For

each experiment, a total of 70 vials were prepared (7 replicates of 10 treatments), and

each sealed by securing a porous, porcelain crucible disk (2.6-cm diameter, Leco

Corporation, St. Joseph, Michigan) to the top of each vial; the disks acted as both

colonizing substrate and point source for P and N released from the tubes over time

(Gibeau and Miller 1989). Experiments were deployed for a period of 15-18 days in each

95

stream during two intensive field expeditions in late summer 2008. Experiments were

conducted in the Piedmont streams from 11-12 August to 27-29 August, while

experiments were conducted in the Plateau streams from 21-22 August to 3-5 September.

This incubation period was selected to allow for adequate establishment of a growing

stream biofilm to estimate active uptake of inorganic nutrients, while minimizing the

chance of sloughing and space limitation on the experimental substrata (e.g., Stevenson

1996). Experimental vials were placed in epoxy-coated test tube racks, and these were in

turn placed in a protective acrylic sled that was secured to the streambed using rebar.

Following the incubation period, each experimental unit was removed from the

streambed and the experimental vials were processed streamside. Accumulated biofilm

material on each experimental crucible (disk) was scraped with a hard-bristled brush

(e.g., Carlisle and Clements 2003) and washed into labeled 50 mL polypropylene jars

containing ambient stream water; once the experimental samples were collected, the jars

were covered and stored in coolers for transport back to the laboratory. Nutrient loading

rates were estimated from the difference between initial and final concentrations of P and

N in the experimental vials (see Carrick and Price 2011).

Analytical measurements- Biomass and areal nutrient concentrations

Algal biomass was estimated for each crucible disk by concentrating subsamples

onto 47-mm glass microfiber filters (EPM-2000, Whatman International, Maidstone,

UK). The chlorophyll-a (Chl) concentration in each filter was determined using an

organic extraction procedure (50:50 mixture of 90% Acetone to DMSO); Chl

concentrations were subsequently measured using a standard fluorometric technique

(Carrick et al. 1993). Chl was used here as a proxy of the total biofilm biomass (e.g.,

96

Montuelle et al. 2010). However, because Chl has been found to vary in the proportion

of biomass it accounts for, areal carbon (g/m2) was also estimated, as it can provide a

more inclusive means of evaluating particulate organic carbon (Malone and Chervin

1979). Despite that, Chl was found to be strongly correlated with carbon (Pearson

correlation coefficient (r)= 0.768, p< 0.001, r2= 0.590), and therefore was considered an

accurate and appropriate measure of biomass (Appendix N). Total phosphorus (TP) was

measured using wet persulfate digestion (Menzel and Corwin 1965), where liberated

soluble reactive P (as PO43-

) was measured colorimetrically using a spectrophotometer

(American Public Health Association 1992). Carbon (C) and N were measured on

subsamples concentrated onto Whatman glass-fiber filters using a high-temperature

combustion carbon analyzer.

Analytical measurements- P-uptake

P-uptake was estimated from incorporation of carrier-free H333

PO4 into biofilms

exposed to increasing P-loads from the ISES experiments. Use of an isotopic radiotracer

is a common method for measuring phosphate uptake kinetics (e.g., Fuhs et al. 1972,

Cembella et al. 1984) and provides a sensitive marker of activity while adding

insubstantial amounts of P to natural assemblages (Burmaster and Chisholm 1979, Noe et

al. 2003). Additionally, application of radioactive phosphate does not significantly alter

the properties of the uptake system (Falkner et al. 1995). By exposing a biofilm to trace

amounts of 33

P, the isotope can mix with the pool of non-radioactive phosphorus and the

incorporation of nutrient in the cells can be determined. From a diluted stock of 33

P

(PerkinElmer), as carrier-free H333

PO4 (1mL 33

P and 100mL DI H2O), 1.0 mL was

injected into 50 mL, translucent polypropylene (Nalgene) incubation jars containing an

97

ISES disk collected from the experiments (activity of 33P stock= 10 μCi mL

-1). Although

past uptake experiments incubated periphyton communities with magnetic stirring bars

(e.g., Havens et al. 1999), for this experiment the contents of the incubation jars were

hand swirled approximately every minute. I assume that differences in the physical

mixing process were not significant over the short time period of the experiments (e.g.,

Caperon and Meyer 1972, Burmaster and Chisholm 1979). Experimental methods

followed procedures outlined by Steinmann and Mulholland (2006). The kinetics of P-

uptake was assessed by measuring P incorporation into the biofilm over time (30

minutes); this time point was chosen, because previous experiments showed that uptake

is linear up to 30 minutes (K.J. Price, unpublished data). Aliquots (1.0 mL) of microbial

slurry were removed from the incubation jars and injected into a Millipore model 1225

12-place sampling manifold (Millipore Corp., Bedford, MA). Filters (GF/F) were then

removed and placed into 20 mL glass scintillation vials filled with 7 mL Ecolume

scintillation cocktail (ICN Biomedicals) and analyzed for activity using liquid

scintillation counting (LSC) on a Beckman LS3801 liquid scintillation spectrometer

(Beckman Instruments Inc., counting efficiency 95.9%). Uptake is the net transfer of a

chemical constituent across a cell membrane and is typically a fast process that can be

analyzed over tens of minutes (Burmaster and Chisholm 1979). The first-order uptake

rate constant k (min-1

) was determined as the quotient of ln [P0/(P0-X)] and time, where

P0 is the total activity in 1 mL of radio-labeled water sample and X is the radioactivity on

the filter (Hwang et al. 1998, Havens et al. 1999). The rate constant k was multiplied by

the in situ stream phosphate concentration, which then estimates the phosphate uptake

rate, with appropriate corrections for the volume of water used (Lean and Pick 1981,

98

Corning et al. 1989, Havens et al. 1999). Measured uptake rates are expressed as log10

and normalized for chlorophyll-a (nmol P/μg Chl/day). Chl-specific P-uptake

(normalized per unit biomass) is defined here as the assimilation efficiency of stream

biofilms (e.g., Platt et al. 1982, Cloern et al. 1995).

Statistical analyses- Parametric statistics

Variation in the magnitude of P-loading among P treatments was assessed using

one-way analysis of variance (ANOVA) by comparing the overall average of P-loads

(results discussed in Carrick and Price 2011). Simple, descriptive statistics were

calculated for measurements of stream water quality and benthic biofilm biomass for

each of the eight streams where experiments were executed. Quantitative relationships

between the response variable (P-uptake) and the predictor variable (P-loading) were

assessed using simple linear regression; P-uptake was regressed against mass P-loading

rates from each experiment (separate analyses for each stream). The slope and y-

intercept values were estimated from these regression analyses to evaluate changes in P-

uptake vs. nutrient load (efficiency) along said productivity gradient. Slope gradients in

linear regression models relating uptake and nutrient amendments have been used to infer

saturation in streams (cf. Earl et al. 2006). Separate regressions were also performed for

P-uptake versus P-loading with (n= 5) or without (n= 5) simultaneous N- loading.

Biofilm Chl concentration estimates for all eight streams were the average of values

measured on rocks collected at the start and end of experiments and served as a proxy for

stream productivity (trophic state). Tukey multiple means comparisons test, widely used

for pairwise comparisons among group means (Games 1971), was used to evaluate the

least significant difference within the group means for each of the five loading

99

treatments, separated by province (Plateau vs. Piedmont). A paired t-test was performed

to test mean differences between P and P+N treatments for Chl-specific, and bulk P-

uptake. To estimate nutrient legacy effects on biofilm P-uptake I regressed biofilm P-

uptake rates from ISES P1 (control) disks against ambient stream conductivity (dissolved

ion content). I used Cook's distance (Di) estimates to indicate outliers in my least squares

regression analysis; outlier removal was based on Di >4/n (Bollen and Jackman 1990).

Red Clay (Di= 0.62) was the only observation greater than my exclusion Di (0.50) and

was thus removed from the analysis. To integrate potentially interacting effects of

nutrient stoichiometry, N-loading, and biogeochemistry (province) on biofilm P-uptake

capacity, I performed linear regressions on log10 P-uptake vs. log10 N:P and log10 C:P

ratios with discrete regressions for N vs. no N-load for each province. These data were

statistically evaluated using analysis of covariance (ANCOVA). Here, to measure the

influence of points on the fitted regression, mean log10 P-uptake vs. mean log10 N:P (n=

10) and mean log10 C:P (n= 10) observations with DFFITS values greater than 2√(k/n),

where k is the number of predictors (including constant), were removed from the

analysis, following Belsley et al. (1980). Again, Red Clay was the only stream to show a

DFFITS statistic for mean log10 N:P (1.05) and mean log10 C:P (1.27) greater than the

cutoff level (1.00) and was subsequently removed from the analysis. All data were log10-

transformed (for values < 1 I added an arbitrary constant [1] to the entire data set and

then log10 transformed to avoid negative numbers) to meet the assumptions of normality

and homogeneity of variances among treatments. A Kolmogorov-Smirnov test showed

that the data were normally distributed (p> 0.05). Levene's test indicated equal variances

at all predictor values (p> 0.05). I further calculated a White test for homoscedasticity

100

(constant residual variance), which indicated that the residuals were homoscedastic for

both the C:P (χ2= 3.29, df= 2, p> 0.05) and N:P ratios (χ

2= 3.64, df= 2, p> 0.05). Data

were analyzed using the statistical program SPSS 18.0 for Windows (SPSS, Inc.,

Chicago, IL).

Statistical analyses- Multilevel (hierarchical) model

Slope (change in P-uptake per unit change in loading intensity; efficiency) and

intercept (average P-uptake at 0 loading intensity) coefficients were extracted from linear

modeling; however, the values were varied across the tested streams. Since the model

coefficients have important ecological meanings, I fit a series of multilevel mixed models

to determine important factors explaining P-uptake - loading relationships for stream

biofilms (Qian et al. 2010). Multilevel modeling was utilized here to account for factors

operating at different spatial scales which limit the functionality of linear models. For

instance, measured P-uptake rates are affected both by predictors measured at equivalent

spatial scales (i.e., controlled loading within stream) and by predictors working at

different spatial scales (i.e., varying percent agriculture across stream). A null hypothesis

testing approach was used to determine the significance of fixed and random effects in

model building. Specifically, I was interested in assessing statistical significance of

random intercept and slope effects in describing variability in the P-uptake - loading

relationship. An additive approach to mixed model fitting was followed, where a series

of increasingly complex models were fit and a likelihood ratio test (LRT) was used to

determine the usefulness (importance) of the added variable against a reduced model.

The ‘base’ model was simply a null model containing only a fixed intercept; used for

comparison purposes after addition of terms. Afterwards, a second model with a random

101

intercept (stream effect) was fit to test the hypothesis that there is significant variation

among streams in average P-uptake. In this case, a simple (null) model was compared to

a more complex (stream effect) model to determine if the added parameter should be used

in later analyses. The third model fit contained a random stream and random stream ×

loading interaction. The second model was nested within the third model and thus

allowed use of LRT to analyze the interaction effect. The fourth model added a fixed

effect of percent agriculture and finally the fifth model added a fixed effect interaction of

percent agriculture × loading. The overall intent of this additive fitting was to attain the

most parsimonious model. Lastly, I modeled the coefficients of the best model as

functions of percent agriculture. Significance of fixed effects was determined by

examining the LRT statistics and the p-value for the Chi-square statistic. Statistical

significance for parameter inclusion in these multilevel mixed models was determined at

p< 0.10 because of reduced power to detect differences caused by small sample sizes

(Hebblewhite 2006). All linear mixed-effects models were fit using the R function lmer

(linear mixed effect regression). All mixed model analyses and graphing were performed

using R (R Development Core Team 2010).

3.4 RESULTS

Physicochemical conditions

The streams in which I conducted my ISES experiment exhibited a strong

productivity gradient (Table 3.1). Average biofilm Chl concentrations measured on

natural rocks in each stream spanned more than two orders of magnitude and were

significantly different among the eight streams (F= 5.38, p< 0.001). Specifically, average

102

Chl concentrations were 268.1 (± 393.0 SD) and 64.3 (± 108.0 SD) mg/m2 for streams in

the Piedmont and Plateau provinces, respectively. Conductivity (μS/cm) and salinity

(ppt) varied 7- and 10-fold, respectively, along the stream gradient (Table 3.1).

Conductivity (μS/cm) was plotted against Chl (mg/m2) and showed a significant linear

regression (r2= 0.422, F= 10.21, p= 0.006), suggesting it as a strong proxy for

productivity. A one-way ANOVA for each province showed that the differences between

stream conductivities were significant. Tukey's post hoc test was used to compare

differences between groups according to conductivity measurements and identified a

distribution of the groups into three homogeneous subsets: low (East Hickory and

Tionesta), medium (Cowanesque, Tunkhannock, and Cooks), and high conductivity

(productivity) (Penns, Red Clay, and Spring). Generally higher TN and TP values were

characteristic of Piedmont streams (Table 3.1). TP concentrations were exceedingly high

in Red Clay Creek during experiment retrieval for unknown reasons. Mean discharges

from streams during the ISES experimental period ranged from 11.8 to 72.2 ft3/s and

maximum discharge from the streams were all near base flow conditions (< 100 ft3/s,

range 30.0 to 99.0 ft3/s). Biofilm Chl concentrations on the ISES P1 (control) disks were

not significantly different from Chl concentrations measured from paired samples of

natural rocks collected at the time of the ISES retrieval (two-sample t-test, t= 1.87, p=

0.072), suggesting that ISES disks were a reasonable analogue for natural substrata (see

Carrick and Price 2011).

Effects of increasing P-loadings on biofilm P-uptake

The P treatments spanned an ecologically relevant range of loading rates: the

control (P1) had no appreciable P release from the ISES tubes, P2 and P3 were comparable

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to nutrient export from more pristine, forested watersheds (Binkley et al. 2004), and P4

and P5 reflected loads typical in agricultural watersheds (Peterjohn and Correll 1984) (see

Carrick and Price 2011). The absolute P-uptake rates obtained from the ISES

experimental treatments (x = 0.45 ( 0.69 SD), range= 0.001 - 3.288 µg P/µg Chl/day)

were within the range for values reported in the literature for P-uptake by natural, lotic,

benthic assemblages (range= 0.054 - 5.645 µg P/µg Chl/day, for review see Price and

Carrick 2011). A series of simple, linear regression models were used to describe the

intracellular P-uptake (and incorporation) by stream biofilms as a function of P-loading.

The relative changes among treatments yielded predictable and statistically reliable

results. P-uptake was inversely related to P-loading, and the relationships in six of the

eight streams yielded significant regression models (Table 3.2). The effect of P-loading

on uptake was apparent, such that uptake rates measured in any individual stream were 2-

to 3-fold higher under low P-loading conditions (P1 treatment) compared with high P-

loading (P5 treatment). Background (ambient) P-uptake rates were estimated from the y-

intercepts (0 µg/day P-loading from experiment), and these values declined predictably

with increased stream productivity in both provinces (Figure 3.2). Moreover, background

P-uptake was nearly an order of magnitude higher in streams located in the Appalachian

Plateau (13.6 to 50.4 nmol P/µg Chl/day) compared with the Piedmont (0.5 to 76.6 nmol

P/µg Chl/day) province. For unknown reasons, the y-intercept in Red Clay was higher

relative to the other seven streams. Streams in the Appalachian Plateau yielded

significantly steeper slopes compared to those in the Piedmont (F= 46.94, p< 0.001);

specifically, P-uptake versus P-loading regressions in the Appalachian Plateau streams

yielded 2- to 4-fold steeper slopes (range -0.204 to -0.280) compared to those in the

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Piedmont (range -0.046 to -0.109), and again, these values (slopes) declined with

increasing stream productivity. The steeper negative slopes in the Plateau province

provide evidence of a faster decrease in P-uptake efficiency, and thus more rapid

approach towards saturation, compared to the Piedmont province.

Results from the multilevel model analysis suggested that a model containing a

fixed interaction term for percent agriculture was the best model in describing P-uptake -

loading relationship for stream biofilms (Table 3.3). This model provided a significant

improvement in fit over the reduced (random stream effect) model and provided the best

overall explanation for these data (model 3 vs. model 5; x2= 5.95, df= 1, p= 0.015).

Regressions with percent agriculture as a group level predictor incorporated into the

model showed positive and negative associations with slopes and intercepts, respectively

(Figure 3.3). Further, the percent agriculture model accounted for a greater proportion of

variability in slopes (estimate= 0.003 (±0.001 SE)) compared to intercepts (estimate= -

0.006 (±0.01 SE)).

Effects of N with P-loadings on biofilm P-uptake

Streams in the Piedmont province showed stronger regressions when N was added

concurrently with P (Table 3.4). The addition of N with P-loading explained greater

variation in the physiological response (uptake) of biofilms to increasing P-loads, as

evidenced by the increased coefficient of determination (r2), in all four streams in the

Piedmont province. Conversely, in Plateau streams the addition of N with P-loading

resulted in a moderately reduced coefficient of determination for all four streams.

Ambient P-uptake, as estimated from the y-intercept of the linear regression equation,

increased when N was added along with P in the Piedmont province. Further, when N

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was added concurrently with P, the regression slopes significantly decreased in all

Plateau streams (F= 7.83, p= 0.031) but significantly increased in all Piedmont streams

(F= 14.51, p= 0.009). Bulk P-uptake (not normalized to Chl) and Chl were significantly

higher in P+N treatments (t= -2.82, p= 0.007); suggesting N augments biomass and

biofilm response to P. However, a paired t-test with the data split by geological province,

indicated that biofilm bulk P-uptake showed a significant response to P+N only in the

Piedmont province (t= -2.49, p= 0.020).

Effects of stream productivity and cellular stoichiometry on biofilm P-uptake

Biofilm P-uptake was organized into two groups relative to P-loadings from ISES

experiments performed on the Plateau streams. The 0, 10, 30, and 100 μg/day loading

treatments were separated into one group according to the Tukey’s test, while 30, 100 and

1,180 μg/day loading treatments formed a second group. Alternatively, P-uptake in the

Piedmont province showed no grouping patterns according to P-load. I found a

significant negative linear regression between biofilm P-uptake rates on ISES P1 (control)

disks and ambient stream conductivity (r2= 0.594, F= 7.31, p= 0.040, slope= -1.35 (±

0.50 SE)). For the stoichiometric plot of P-uptake vs. N:P, concurrent N-loadings with P

appeared to have an antagonistic effect on biofilm assimilative abilities in the Plateau

region as the regression declined when N was loaded with P (r2= 0.081, F= 1.60, p=

0.223, slope= 0.254 (± 0.201 SE)) compared to sole P-loadings (r2= 0.324, F= 8.63, p=

0.009, slope= 0.570 (± 0.194 SE)) (Figure 3.4). However, N-loadings with P appeared to

have a synergistic effect in the Piedmont region, such that the regression improved when

N was loaded with P (r2= 0.485, F= 12.25, p= 0.004, slope= 0.426 (± 0.122 SE))

compared to sole P-loadings (r2= 0.245, F= 4.22, p= 0.061, slope= 0.314 (± 0.153 SE)).

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Similarly, for the stoichiometric plot of P-uptake vs. C:P, concurrent N-loadings with P

appeared to have an antagonistic effect on biofilm assimilative abilities in the Plateau

region as the regression declined when N was loaded with P (r2= 0.014, F= 0.252, p=

0.622, slope= 0.102 (± 0.203 SE)) compared to sole P-loadings (r2= 0.263, F= 6.42, p=

0.021, slope= 0.493 (± 0.194 SE)) (Figure 3.4). Conversely, N-loadings with P again

appeared to have a synergistic effect in the Piedmont region, such that the regression

improved when N was loaded with P (r2= 0.560, F= 16.55, p= 0.001, slope= 0.500 (±

0.123 SE)) compared to sole P-loadings (r2= 0.260, F= 4.57, p= 0.052, slope= 0.338 (±

0.158 SE)). An ANCOVA testing the equality of the slopes for N-load and N:P ratio

showed a non-significant interaction term for the Piedmont (p= 0.538) and the Plateau

(p= 0.238), indicating the slopes of the N vs. no N-load regression lines were not

significantly different from each other. Similarly, an ANCOVA for N-load and C:P ratio

showed a non-significant interaction term for the Piedmont (p= 0.383) and for the Plateau

(p= 0.147). P-uptake vs. N:P ratio y-intercepts were 0.592 and 0.781 for N vs. no N-load,

respectively for the Plateau (ANCOVA p= 0.120) and 0.060 and 0.025 for N vs. no N-

load, respectively for the Piedmont (ANCOVA p= 0.657). P-uptake vs. C:P ratio y-

intercepts were 0.534 and 0.579 for N vs. no N-load, respectively for the Plateau

(ANCOVA p= 0.694) and -0.442 and -0.483 for N vs. no N-load, respectively for the

Piedmont (ANCOVA p= 0.591).

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3.5 DISCUSSION

Evidence for saturation of biofilm P-uptake

The incorporation of new P into stream biofilms, as measured here, varied as a

function of P-loading, such that uptake declined in a linear manner. Similarly, in higher

plants, nutrient uptake is often negatively related to supply (Pennell et al. 1990,

Clemensson-Lindell and Asp 1995) owing to inhibitory feedbacks on uptake with

increased internal root nutrient concentration (Jensén and Ktönig 1982). The relationship

I identified here may also be controlled by feedback inhibition, whereby once an internal

cellular pool is filled (and cell quota is achieved) during loading, further uptake processes

are diminished (cf. Jansson 1993). The data further suggests that assemblages growing

on the lowest P treatments (P1 and P2) were likely P-deficient, and thus exhibited higher

uptake rates to overcome this deficiency, a common pattern observed for microorganisms

(see Lean and Pick 1981). This phenomenon has been described in the literature; high

and rapid P-uptake has been observed in algal cells cultivated under nutrient deficient

conditions (Kaya and Picard 1995). The gradient of P to which ISES biofilms were

exposed spanned well beyond a typical starvation (deficient) - satiation (sufficient)

gradient. For instance, under wastewater conditions, 5.5 mg P/L has been used as a

‘starvation’ growth medium for unicellular green microalgae with non-starved (nutrient

sufficient) algae grown under 25.0 mg P/L (4.5 times starvation [P]) (Kaya and Picard

1995). My lowest P treatments (P1, P2) provided 10 to 1000 times lower P-loadings

compared to my highest P treatments (P4, P5).

Organisms experiencing P deficiency generally exhibit rapid uptake of P once a

new source is supplied (Cembella et al. 1984, Raghothama 1999). Graziano et al. (1996)

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found a 10-fold increase in maximum P-uptake rate when uptake was compared between

P-deficient versus P-sufficient cultures of Dunaliella tertiolecta (Division Chlorophyta,

Order Chlorococcales). Further, maximum uptake rates (Vmax) for P-deplete samples

exceeded Vmax of P-enriched samples of the green macroalga Ulva lactuca (Division

Chlorophyta, Order Ulvales) and an estuarine red algal epiphyte Catenella nipae

(Division Rhodophyta, Order Gigartinales) (Runcie et al. 2004). Specifically, Runcie et

al. (2004) found that P-deficient (starved) Catenella nipae exhibited 3-fold higher P-

uptakes compared to those that were P-enriched, a magnitude difference that is in-line

with my findings. In general, periods of P deficiency likely enhance the P-uptake

transport capacity (Jansson 1993). For instance, P transport in the bacterium Escherichia

coli (Division Proteobacteria, Order Enterobacteriales) is regulated by two separate

transport systems, one of which is induced by P deficiency (Rosenberg 1987). In fact,

uptake in P-starved algae can be 10 to 100 times higher than maximal growth rate

(Harrison et al. 1989). This disparity between uptake and growth can partly be attributed

to the ability of some microorganisms to store intracellular pools of P as polyphosphates

(e.g., Harold 1966). My results here suggest that biofilms in the ISES experiment are

non-homeostatic (i.e., plastic), allowing for luxury uptake (storage of P within biomass in

the form of polyphosphate bodies) (Powell et al. 2008, Webster et al. 2009). P-deficient

algae are capable of mobilizing internal stores of P (poly-P) in order to continue growth

and metabolic function (Rhee 1973); these cells can then capitalize on pulsed nutrient

events. This appears to be the case in this set of experiments, as poly-P storage

(discussed in Carrick and Price 2011) increased with P-load and was negatively

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correlated with Chl-specific P-uptake rates (Pearson correlation coefficient (r)= -0.255,

p= 0.023, n= 80).

Phosphorus has been implicated as the nutrient most often controlling (limiting)

primary production in freshwater systems (Schindler 1977) and therefore, the availability

of P in streams is a concern for downstream transport. The high P-uptake efficiency

(uptake/flux) I measured in my low P-loading treatments indicated that biofilm uptake

contributes significantly to the retainment (and thus net transformation into unavailable

soluble species) of dissolved P in these ecosystems. In a general sense, benthic biofilms

may play a similar central role in a variety of other aquatic systems (e.g., Reddy et al.

1999). However, the retentive capacity of streams has specific limits as biofilm P-uptake

was diminished with increasing P-loading; in a sense, P-uptake appeared to become

saturated (Martin et al. 2011). If the biofilms are unable to assimilate new surges of P,

for instance, during storm events (Powers et al. 2009) from point (e.g., domestic sewage)

and non-point (e.g., agricultural fertilizer) sources (Jaworski 1981), this may enhance

transport of P and negatively affect water quality in downstream reaches. These results

are consistent with the N saturation hypothesis proposed by Aber et al. (1998). The

hypothesis suggests that nutrient (N) inputs exceeding biotic (forest) uptake will

accumulate and eventually be in oversupply. In this situation, where nutrient supply is in

excess of demand, losses will increase (Friedland and Miller 1999). In the same manner,

I found that biofilms in low-productivity streams showed significantly higher uptake rates

for moderate P-loads compared with those in high-productivity streams. Chronic and

elevated nutrient loads, likely present in the high-productivity streams, can impose

physiological constraints on organisms (e.g., surpass storage capacity), leading to

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saturation of biological uptake mechanisms (O'Brien and Dodds 2010); predictably, new

P pulses would be passed to downstream regions to a greater degree in the high-

productivity systems.

Biofilm P-uptake rates on ISES P1 (control) disks were negatively associated with

ambient stream conductivity, suggesting the occurrence of a nutrient legacy effect. The

negative relationship between these parameters suggests that as streams increase in

productivity (subjected to higher dissolved ion concentrations), biofilm ability to take-up

P declines. Thus nutrient legacy effects, stemming from land use/form differences across

these sampled provinces, influence microbial physiological responses and ultimately

stream ecosystem dynamics (cf. McColl 1974, Allan 2004). Stream biofilm assemblages

in the Plateau province showed two distinct groupings in uptake capacity according to P-

load, suggesting that a physiological maximum exists between 10 and 1,180 µg/day; a

Tukey post-hoc test suggested that this biological ‘saturation threshold’ can occur at low

P-loadings (i.e., between 10 and 30 µg/day). This was expected as streams in the Plateau

province are better buffered from anthropogenic nutrient inputs and thus biofilm

assemblages would experience lower ambient [P]. Pennsylvania's Allegheny

(Appalachian) Plateau region is heavily forested with white pine, beech, hemlock, and

mixed hardwoods of red maple, yellow and sweet birch, white ash, and black cherry

(Hough and Forbes 1943). Such forested ecosystems are vital in the retention and

entrapment of bio-limiting nutrients carried in surface runoff events that can cause high

productivity in aquatic systems (Bormann et al. 1968, Anbumozhi et al. 2005).

Conversely, in Pennsylvania's Ridge-Valley/Piedmont region, fragmented, mixed-

hardwood deciduous forest with large agricultural and urban clearings is the dominant

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landscape (see Allmendinger et al. 2005). Thus there is a distinct difference in the

prevalence of agriculture between the two major provinces in Pennsylvania which

contributes to the strong gradient of phosphorus delivery to surface waters (Ribaudo and

Johansson 2006). P export rate often increases with land disturbance (agricultural/urban

developments) and proportion of impervious surfaces (Puckett 1995), which in turn can

enhance aquatic productivity (Harvey et al. 1998). The pattern of biofilm response to P-

loads within each province suggests that regional biogeochemical characteristics (e.g.,

geology, land use) are important factors in biofilm nutrient limitation and thus response

to loading events (e.g., Rüegg et al. 2011). My results here also emphasize the

importance of considering the nutrient legacy (cf. Verspoor et al. 2010) in a stream when

trying to predict uptake relative to loading and show that uptake can saturate and

efficiency can decline with even moderate (e.g., 10 and 30 µg/day) nutrient loadings

(Earl et al. 2006).

The final model from the multilevel analysis supports these ideas and suggests

that there is a strong effect of percent agriculture in determining the slope of the P-uptake

- loading relationship; that is, the efficiency of stream biofilms in assimilating a new P

source is explained by the amount of agriculture in the surrounding watershed. Steeper

negative slopes in biofilms situated in the Plateau province provide evidence of a faster

decrease in P-uptake efficiency, and thus a more rapid approach towards saturation,

compared to those in the Piedmont province. Microbial physiological adaptation to P

concentration fluxes depends on previous exposure episodes (Aubriot et al. 2011);

accordingly, those biofilms originating from stream systems adjacent to less agricultural

lands, and less subject to high nutrient loadings (David and Gentry 2000), would likely

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saturate more quickly when exposed to a new nutrient source. Further, greater

physiological costs may be imposed on biofilms attuned to lower ambient [P] (i.e.,

located in minimal percent agriculture environs) when exposed to elevated P, owing to

added adjustment of more machinery (e.g., activation of P-uptake receptors) (e.g., Menge

et al. 2011), which may accelerate the decline in uptake efficiency. Such has been found

in higher plant research as well, where nutrient uptake efficiency decreases considerably

with increasing rates of nutrient loading (Cabrera 2003, Ristvey et al. 2007). Further,

while more variable compared to the slope estimates, the decline in ambient uptake

(intercepts) by biofilms across the agricultural gradient was expected as well. For

instance, studies have shown that [P] strongly affects P-uptake by microalgae such that P

starvation can stimulate uptake and phosphate transport rates (Singh et al. 2007). Thus

microbes located in streams subject to less agricultural surroundings would more likely

be starved for P and demonstrate higher ambient uptake as observed. These findings are

particularly significant given that the forests of the eastern United States, largely under

private ownership, are at high risk of continued fragmentation from parcelization and

Marcellus shale development (Li et al. 2010, PADCNR 2010), which would likely

enhance in-stream nutrient concentrations (Likens et al. 1970, Hobbs 1993).

Interactive effects of N and P-loadings on biofilm P-uptake

My results suggest that bulk P-uptake (uncorrected for Chl) was augmented by P-

enrichment with N; however, Red Clay was the only stream in the study where this effect

proved significant. Red Clay also had the highest ambient [P] which suggests that

biofilm assemblages growing in this stream were likely P-sufficient and only capable of

additional P-uptake when N was also loaded. There is abundant data indicating that

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supplies of both P and N limit algal growth in freshwater ecosystems (Elser et al. 1990).

For instance, Francoeur (2001) performed a meta-analysis of 237 nutrient amendment

experiments, and found 39 (16.5%) displayed N-limitation, 43 (18.1%) displayed P-

limited, and 55 (23.2%) showed combined N- and P-limitation. Furthermore, Tyrell

(1999) dispelled the notion that only N is important in the world’s oceans when he

reviewed the relative importance both P and N have on oceanic primary production. My

study showed that bulk P-uptake was amplified by P+N additions, and suggests that one

nutrient may exert biotic influences on the other. Similarly, P-limitation in the Pearl

River (southern Hong Kong) resulted in low planktonic utilization of nitrate (NO3)

leading to excess NO3 in downstream transport; P regeneration may allow phytoplankton

to take-up additional N in excess of the cellular needs (Yin et al. 2004). I found here that

both P and N are important in mediating physiological response processes, and thus

should be concomitantly managed as “inter-nutrient feedbacks” are possible with single

nutrient reductions (Paerl 2009).

Several authors have discussed the importance of N for benthic algal production

in lotic ecosystems (Francouer 2001, Dodds et al. 2002) and N enrichment has been

shown to enhance certain cellular chemical element concentrations in the diatom

Thalassioseira pseudonana (Division Bacillariophyta, Order Centrales) (Rijstenbil et al.

1998). Cells growing under N enriched conditions may synthesize high concentrations of

proteins (Wang and Dei 2001, Rausch and Bucher 2002), and proteins, especially

membrane and transporter proteins, are critical biochemical compounds needed for

phosphate uptake (Smith 2003). Therefore, it is possible that biofilms growing under N

enrichment may have had higher protein synthesis available for expression of cell surface

114

transporters, thus facilitating efficient P-uptake. This P-uptake enhancement through N

enrichment phenomenon, while novel in benthic microbial ecology, is resonated in the

higher plant literature. For instance, P-uptake was positively influenced by N fertilization

in Spartina alterniflora (Division Angiospermae, Order Poales) whereby single P

additions resulted in a significantly lower P content compared to adding P fertilizer in

conjunction with N treatments (Huberty 2005). My data likewise showed that elevated

[N] provided biofilms a greater ability to respond to a given P-load, suggesting that N is a

controlling (limiting) resource, particularly when P is in high concentrations. For

instance, the average TN concentration in Piedmont streams was estimated to be 3.13

mg/L (Carrick et al. 2009), approximately 2-fold higher than the suggested boundary

between mesotrophic and eutrophic conditions for streams (Dodds et al. 1998). That

said, streams in the Piedmont province (Red Clay, Cooks, Penns, Spring) showed

superior response to highest P-loadings (e.g., P5) compared to Plateau streams, indicating

that such N sufficiency may induce a competitive advantage in P-uptake (Rhee 1974).

Relatedly, with biofilms in the Plateau streams, N-loading augmented P-uptake at highest

P-loading (P5). In higher plants, nitrogen (specifically as ammonium) can enhance

phosphorus uptake by increasing P solubility and transport in root systems or increasing

P-uptake efficiency of roots (Cole et al. 1963, Tisdale et al. 1985, Smith and Jackson

1987, Hoffman et al. 1994).

The effects of N-loadings on biofilm ability to take-up P thus differed across the

stream productivity gradient. For example, the addition of N with P-loading appeared to

hamper the ability of biofilms in Plateau streams to take-up P, as indicated by reduced

intercepts (Table 3.4). Lean and Pick (1981) have discussed energy trade-offs, as the

115

uptake of one resource (e.g., N) may reduce the uptake of a second resource (e.g., P). It

is possible that N-loading events may induce biofilms to dedicate greater cell surface area

to N-uptake, thereby reducing total uptake sites available for P (cf. Ward et al. 2011). As

the number of uptake sites is related to the cell surface area (see Aksnes and Egge 1991,

Kriest and Oschlies 2007), smaller celled microbes in Plateau streams (plankton mean

cell size is strongly correlated with chlorophyll concentrations (Harris et al. 1987)) would

comparatively exhibit a stronger reduction of P-uptake sites when faced with a N-load

and thus demonstrate a lessened uptake potential. In contrast, synergistic effects of N-

loading with P on P-uptake in the Piedmont province suggests that N likely plays a role in

regulating the production of benthic biofilms in these streams, and appears to augment P-

uptake. This phenomenon is reasonable as secondary nitrogen limitation may be more

prominent in eutrophic aquatic systems (e.g., Matthews et al. 2002) and Piedmont

streams were shown to be more productive. This is also in agreement with my

hypotheses, such that nutrient thresholds for the Piedmont region are 3-fold higher for TP

compared to the Plateau region (Carrick et al. 2009) suggesting biofilms here may have

more expansive stores of poly-P. Similarly, phytoplankton in hypereutrophic Lake

Apopka was found to contain large supplies of P as polyphosphates, usable for cellular

growth when supplied with N (Carrick et al. 1993). Polyphosphate storage can also be

important in altering biomass stoichiometry (Sterner and Elser 2002, Makino and Cotner

2004).

That said, the spatial heterogeneity (Plateau vs. Piedmont) in P-uptake I observed

in my experiment may have been modified in some measure by microbial stoichiometry,

such that the biofilm internal nutrient balance facilitated different physiological response

116

(uptake) according to nutrient load. It has been found that microbial nutrient

stoichiometry can influence nutrient processing (e.g., sequestration and cycling) (Sterner

and Elser 2002). For example, biofilm communities with low cellular N:P ratios would

demonstrate a higher demand for N relative to P, and potentially increase the transport

length of P downstream (cf. Frost et al. 2002). However, chronic nutrient loading may

complicate this response; the nutrient content in the biofilm can be enhanced by

experimental nutrient enrichment (Hillebrand and Kahlert 2001). Biogeochemical

differences between the provinces may also have differentially contributed to the N:P and

C:P ratios. For instance, parent material in the Piedmont province is dominated by

limestone-dolomite (Appendix O), which could stimulate sorption of orthophosphate -

calcium and precipitate as dicalcium phosphate (Hargreaves and Tucker 1996). This

particulate inorganic phosphorus complex would then have been present in the chemical

analysis of TP, potentially influencing areal N:P and C:P ratios. Nonetheless, while both

regressions showed that benthic biofilm P-uptake increases as the N:P and C:P

stoichiometric ratios increase (i.e., towards P-limitation), these patterns followed a

different trajectory when N was loaded concurrently with P, suggesting that N

enrichments produce a more complex P-uptake phenomenon mediated by

biogeochemistry (province) and cellular stoichiometric ratios. My results show that the

effects of P- and N-loading on biofilm uptake ability can be interactive, that these effects

can vary considerably across geologic province, and that some of this variation may be

related to microbial nutrient stoichiometry.

Uptake efficiency, however, in the Piedmont was greatly curtailed under higher

nutrient loads, as signified by the increased negative slopes when N was loaded with P.

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These stronger slopes here indicate that concurrent loadings of N with P induce a more

rapid decrease in biofilm P-uptake and uptake efficiency. This then suggests that, while

N can facilitate P-uptake (particularly when [P] is high), biofilm uptake capacity still

saturates when exposed to chronic, high nutrient loadings and in so doing decreases

uptake efficiency and retainment of P (Earl et al. 2006). This is particularly important

considering that one (ultimate) destination for this nutrient transport is the Chesapeake

Bay, and recent preliminary estimates have shown that phosphorus loads to the

Chesapeake Bay from 2009 to 2010 increased from 9 to 16 million pounds (CBPO 2011).

My work here highlights the capacity of biofilms to moderate downstream fluxes of P

through assimilation and the importance of reducing both P- and N-loads in efforts to

increase stream uptake efficiency (retainment) and ultimately achieve sustainable aquatic

management.

3.6 ACKNOWLEDGMENTS

I thank T. Wagner for support with the multilevel model and A. Lashaway, A.

Scanlan, and R. Wagner for assistance in the field. This research was supported by grants

provided to H.J.C. from Pennsylvania Department of Environmental Protection (Grant

No. 4100034506) and Pennsylvania Sea Grant (Grant No. UP69VY0).


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Table 3.1. A summary of geographic and biogeochemical characteristics for streams where ISES experiments were conducted in 2008. Data shown are

means (± SD) for environmental conditions during deployment and retrieval (n= 2) of each ISES to test the effects of P and N-loading on native stream

biofilms. Note: refer to Figure 3.1 for Site ID geographical locations.

Province Stream Site

ID

Temperature

(°C)

Conductivity

(µS/cm)

Chlorophyll-a

(mg/m2)

O2 (%) TP

(mg/L)

Forested

(%)

Agricultural

(%)

Plateau East Hickory 1 17.66± 1.12 74.0± 5.7 30.05± 19.94 84.40±

7.92

0.014±

0.000

98.1 1.9

Tionesta 2 20.35± 1.51 127.0± 19.8 22.20± 15.91 83.10±

8.34

0.011±

0.002

95.7 3.0

Cowanesque 3 23.95± 3.50 260.0± 18.4 62.10± 0.00 108.90±

9.76

0.052±

0.019

63.2 35.9

Tunkhannock 4 17.18± 7.30 261.5± 7.8 142.90±

164.12

99.65±

21.43

0.021±

0.008

65.9 31.0

Piedmont Red Clay 5 19.45± 0.36 528.5± 77.1 163.98± 88.92 47.05±

32.60

0.423±

0.350

30.0 52.5

Cooks 6 15.72± 1.01 363.5± 0.7 57.90± 36.56 67.20±

2.26

0.025±

0.007

60.0 38.8

Penns 7 14.03± 0.49 494.0± 17.0 84.13± 25.00 41.30±

22.63

0.022±

0.006

68.9 30.5

Spring 8 11.40± 0.08 541.5± 7.8 766.50±

667.09

41.45±

5.16

0.002±

0.003

35.1 53.4

137

Table 3.2. Regression statistics for biofilm Chl-specific P-uptake (log10(nmolP/μgChl/day)) versus P-

loading (log10(µg[PO4]/day)) determined from ISES experiments carried out in streams of varying

nutrient content in both Plateau and Piedmont provinces (n= 10 per stream).

Province Stream y-intercept Std. Error

y-intercept

Slope Std. Error

Slope

r2 p

Plateau East Hickory 1.420 0.099 -0.268 0.054 0.757 0.001

Tionesta 1.269 0.101 -0.211 0.055 0.651 0.005

Cowanesque 1.711 0.085 -0.204 0.046 0.711 0.002

Tunkhannock 1.164 0.122 -0.280 0.066 0.690 0.003

Piedmont Red Clay 1.890 0.108 -0.070 0.060 0.148 0.273

Cooks 0.830 0.040 -0.109 0.022 0.753 0.001

Penns 0.659 0.107 -0.090 0.060 0.218 0.174

Spring 0.183 0.029 -0.046 0.016 0.500 0.022

138

Table 3.3. Likelihood ratio test results from iterative model building in determining important

parameters in explaining P-uptake against P-loading. Note: degrees of freedom (DF) is difference in

the number of parameters between the full and reduced models and statistical significance is

determined as p< 0.10.

Model Reduced Model Parameters Full Model Parameters x2 DF p

1 vs. 2 Fixed intercept Fixed intercept

Random stream (intercept)

135.97 1 < 0.001

2 vs. 3 Fixed intercept


Fixed intercept


Random loading (slope)

5.58 2 0.061




Fixed intercept



Fixed percent Ag

0.25 1 0.616




Fixed intercept



Fixed interaction percent Ag

5.95 1 0.015

139

Table 3.4. Regression statistics for biofilm Chl-specific P-uptake (log10(nmolP/μgChl/day)) versus P-

loading (log10(µg[PO4]/day)) without (n= 5) or with (n= 5) simultaneous N-loading.

Stream Treatment y-intercept Std. Error

y-intercept

Slope Std. Error

Slope

r2 p

East Hickory P (without N) 1.464 0.158 -0.290 0.086 0.792 0.043

P (with N) 1.376 0.161 -0.246 0.087 0.725 0.067

Tionesta P (without N) 1.390 0.067 -0.276 0.036 0.951 0.005

P (with N) 1.148 0.197 -0.146 0.107 0.383 0.266

Cowanesque P (without N) 1.733 0.120 -0.242 0.065 0.822 0.034

P (with N) 1.690 0.131 -0.165 0.071 0.641 0.104

Tunkhannock P (without N) 1.306 0.198 -0.324 0.108 0.751 0.057

P (with N) 1.023 0.155 -0.236 0.084 0.725 0.067

Red Clay P (without N) 1.911 0.100 0.002 0.056 0.000 0.973

P (with N) 1.868 0.061 -0.143 0.034 0.856 0.024

Cooks P (without N) 0.762 0.056 -0.072 0.031 0.639 0.104

P (with N) 0.898 0.044 -0.147 0.025 0.923 0.009

Penns P (without N) 0.491 0.195 -0.003 0.110 0.000 0.983

P (with N) 0.826 0.065 -0.178 0.036 0.888 0.017

Spring P (without N) 0.142 0.038 -0.024 0.022 0.285 0.354

P (with N) 0.225 0.042 -0.068 0.024 0.735 0.063

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Figure 3.1. Mid-Atlantic region (left) and Pennsylvania statewide map (right) showing the location of

the eight ISES experiments in both of the major physiographic provinces in Pennsylvania

(Appalachian Plateau and Piedmont). Map was created using ArcGIS 10.0 (Environmental Systems

Research Institute, Inc., Redlands, CA, USA) and displays the boundaries of the US EPA Level III

Ecoregions and county borders.

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Figure 3.2. Scatter plot of Chl-specific P-uptake (log10(nmolP/μgChl/day)) versus P-loading

(log10(µg[PO4]/day)) for each stream tested with ISES (n= 10 per stream) fit with linear regression

and 95% confidence intervals.

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Figure 3.3. Regression slopes (left) and intercepts (right) modeled as linear functions of percent

agriculture across eight streams sampled using ISES. Note scale difference.

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2.01.51.00.50.0 2.01.51.00.50.0

1.6

1.2

0.8

0.4

0.0

3.02.52.01.51.0

1.6

1.2

0.8

0.4

0.0

3.02.52.01.51.0

Log N:P, Plateau

Lo

g C

hl S

p P

-Up

take

Log N:P, Piedmont

Log C:P, Plateau Log C:P, Piedmont

Figure 3.4. Scatter plot of log P-uptake (log10(nmolP/μgChl/day)) vs. log10 N:P (top) and log10 C:P

(bottom) ratios fit with linear regression at subgroups (N-load) for streams in each province. Open

square markers and dotted line regressions indicate concurrent N with P-loadings while closed circle

markers and solid line regressions indicate sole P-loadings. The vertical line at x= 1.204 for log10 N:P

plots indicates the optimal Redfield N:P ratio (16:1) and the vertical line at x= 2.025 for log10 C:P

plots indicates the optimal Redfield C:P ratio (106:1). Note scale difference between N:P and C:P

plots.

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CHAPTER 4

QUALITATIVE EVALUATION OF SPATIO-TEMPORAL PHOSPHORUS FLUXES IN STREAM

BIOFILMS

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4.1 ABSTRACT

Uptake and storage by biofilms facilitates the ability of streams to act as sinks for

inorganic nutrients. P fluxes over short time periods are important for examining

physiological responses to new nutrients inputs. Therefore, I sampled intact stream

biofilm assemblages seasonally and across a spatial productivity gradient and tested their

response to a new P source over the first few minutes of exposure so as to resolve

changes in initial assimilatory kinetics and integrate the singular and interactive effects of

space and time on P fluxes. Intact biofilm communities were sampled seasonally (2009 -

2010) across eight streams between two eco-regions (Appalachian Plateau and Piedmont)

in Pennsylvania. Biofilms were subjected to short-term radiotracer experiments to

estimate uptake rates. Time-series plots were made of P flux over time and data were fit

using LOESS, facilitating estimations of P-uptake and efflux rates. The data revealed

distinct breakpoints in P fluxes; specifically mean max uptake occurred at 1.65 and 2.60

minutes while max efflux occurred at 4.51 and 4.62 minutes in Piedmont and Plateau

provinces, respectively. Stream/biofilm biochemical parameters were strong predictors

of P-uptake and efflux. A MANOVA showed significant differences in P-uptake and P-

efflux by province (F2, 50= 5.91, p= 0.005) and season (F6, 100= 4.23, p= 0.001). These

results demonstrate considerable and rapid exchange processes occurring at early time

periods (i.e., < 5 minutes), the magnitude of which seems to diminish over longer periods

(i.e., 15 - 30 minutes), indicating nutrient processing as a near instantaneous

physiological, dynamic process and further suggesting that experimental time periods

scaled to hours or longer obscure such essential short-term responses. This research

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further indicates that spatio-temporal effects, specifically productivity and seasonality,

are strong determinants of P fluxes.

4.2 INTRODUCTION

Uptake and storage by organisms growing attached to submerged substrata

(biofilms) facilitates the ability of streams to act as sinks for inorganic nutrients that can

cause eutrophication (Vollenweider 1968, Cardinale et al. 2002, Jöbgen et al. 2004).

Existing at the interface between sediments and the water column, microbial biofilms can

regulate benthic/pelagic nutrient cycling by increasing nutrient flux from the water

column into the sediments (Confer 1972, Spears et al. 2007). As such, biofilm uptake

capacity for key, bio-limiting nutrients (i.e., carbon (C), nitrogen (N), and phosphorus

(P)) is of great importance, particularly for P which has been identified as the primary

limiting nutrient in freshwater systems (Correll 1998, Karl 2000, Schindler et al. 2008).

For example, P-uptake and adsorption by biofilms attached to duckweed (Lemna gibba

L.) accounted for 31 - 71% of the total P removal in a wastewater treatment system

(Körner and Vermaat 1998); and biofilms on submerged artificial substrata have proven

effective in P removal from eutrophic lakes (Jöbgen et al. 2004). Michaelis-Menten

kinetics have been used to describe nutrient uptake into biofilms (Reuter et al. 1986),

facilitating estimations of the maximum uptake rate (Vmax) and half-saturation constant

(Km); although there are many instances where this model is not supported (e.g.,

Tarapchak and Herche 1986). While Michaelis-Menten kinetic estimations are useful,

there are however, several types of error induced by experimental protocol during

determination of Vmax and Km (Flynn 1998). Estimations of nutrient uptake parameters

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can be confounded by both temporal and spatial factors. For instance, in a meta-analysis

of P-uptake experiments, Price and Carrick (2011) found that methodological variation in

sample time can influence P assimilation, such that sampling over shorter time scales

yields higher uptake rates. While likely more related to membrane transport kinetics,

short term processes assuredly affect assimilatory kinetics as well (Wheeler et al. 1982,

Flynn 1998), which highlights the importance of experimentally investigating short-term

P flux phenomena. Similarly, microbe physiological pre-history is crucial in determining

kinetic uptake responses (Halterman and Toetz 1984); however, little research has

compared biofilm uptake capacity across stream trophy (biogeochemical history). P

export rate often increases with land disturbance (agricultural or urban lands) and

proportion of impervious surfaces (Byron and Goldman 1989, Puckett 1995) causing

variation in P-loads (and thus nutrient history) across land-use; therefore, information on

spatial heterogeneity of biofilm uptake capacities could be beneficial to predicting

nutrient effects on stream environs.

The physical and chemical architecture of biofilms may act to enhance the uptake

and storage of nutrients in dynamic and turbulent stream ecosystems (Neu and Lawrence

1997). A polysaccharide matrix surrounding biofilm communities provides a mechanism

for entrapment and concentration of nutrients (Lock et al. 1984, Laspidou and Rittmann

2002), effectively increasing local nutrient availability compared to overlying waters

(Freeman et al. 1995). There is thus contention that uptake is a coupled process: 1)

adsorption, or the movement of P into such surface-adsorbed P pools, and 2) absorption,

or the movement of adsorbed P into intercellular pools (Short and McRoy 1984, Sañudo-

Wilhelmy et al. 2004, Yao et al. 2011). Nonetheless, uptake is not necessarily

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unidirectional. For example, Kuenzler et al. (1979) found that microbial recycling

(efflux) of reactive phosphorus accounted for 37 - 90% of microbial uptake in the

Pamlico River Estuary. This association of P-uptake with efflux is resonated in the

higher plant literature as well (Bieleski 1973, Elliot et al. 1984). Therefore, to the extent

that surface-adsorption and recycling (efflux) of phosphate may affect uptake processes

(e.g., Yao et al. 2011), examination of P flux patterns is necessary, particularly through

short-term experiments so as to estimate the influence of initial assimilatory processes on

biofilm P dynamics (cf. Wheeler et al. 1982). Moreover, given that nutrient uptake has

shown strong relationships with nutrient (P) content of cells (Riegman and Mur 1984) as

well as microbial biomass (Riedel et al. 1996), such predictive factors might as well

explain some variation in efflux and thus warrant further correlative consideration.

The objective of this study was to sample intact stream biofilm assemblages

seasonally and across a spatial productivity gradient and test their response to a new P

source over the first few minutes of exposure so as to resolve changes in initial

assimilatory kinetics and integrate the singular and interactive effects of space and time

on P fluxes.

4.3 METHODS

To test my hypotheses on natural, intact biofilm communities, a set of unglazed

ceramic tiles (surface area= 8.42 cm2) were secured to cement blocks and established in

eight streams of varying nutrient content and relative productivity; temporal effects were

estimated by sampling streams seasonally (Fall 2009 - Summer 2010; n= 64 experimental

units) (Appendix P). The eight streams were divided evenly between two eco-regions

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(Appalachian Plateau and Piedmont) in the state’s Water Quality Network (see Carrick et

al. 2009). Specifically, streams located in the Plateau province were East Hickory

(41.6160, -79.3701), Tionesta (41.6019, -79.0503), Cowanesque (42.0014, -77.1278), and

Tunkhannock (41.5581, -75.8950); streams in the Piedmont province were Spring

(40.7917, -77.7980), Penns (40.8590, -77.5796), Cooks (40.5854, -75.2061), and Red

Clay (39.81667, -75.69194) (see Carrick and Price 2011 for additional stream

physicochemical information). After a 30 day incubation period, tiles with adhered

biofilms were removed from each stream and placed into 60 mL translucent

polypropylene (Nalgene) incubation jars with site-specific water. Once tiles were

returned to the laboratory, incubation jars with intact biofilms were enriched with 200 μg

of KH2PO4 to prevent potential isotope-dilution, or recycling of substrate (e.g., Harrison

and Harris 1986) and to fulfill latent requirements of extracellular potassium (K) for PO4

uptake (e.g., Weiden et al. 1967). For fine resolution into the time sensitivity of biofilm

uptake and estimations of short-term P fluxes, 1 mL of water overlying biofilms was

removed from each replicate incubation chamber using a sterile pipette at 6 - 11 time

periods ≤ 30 minutes after 0.75 mL injection of radiotracer (H333

PO4) (activity= 10

μCi/L). Water samples were then placed into labeled 20 mL scintillation vials filled with

5 mL Ecolume scintillation cocktail (ICN Pharmaceuticals, Costa Mesa, CA, USA) and

read for activity using liquid scintillation counting (LSC) (model LS 6000 IC; Beckman-

Coulter, Fullerton, CA). No obvious signs of seston were present in the incubation jars.

Scintillation counts per minute (CPM) from the LSC were converted into disintegrations

per minute (DPM) using an internal quench curve.

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Samples from the experiments were filtered (Whatman EPM 2000 glass fiber) for

particulate phosphate (part-P) and polyphosphate (poly-P). One-half of each filter was

used for poly-P analysis and the other half for part-P analysis. Filters were analyzed

using spectrophotometry for poly-P (hot-water extraction) and part-P (persulfate

digestion) following standard methods (Fitzgerald and Nelson 1966, Eixler et al. 2005).

Additionally, for all experiments, water physicochemical conditions (e.g., temperature,

conductivity, oxygen concentration) were monitored using an YSI (Yellow Springs

Instrument) data Sonde (model 6600). All samples were also analyzed for chlorophyll-a

(Chl) content; samples were extracted in a mixture of dimethyl sulfoxide (DMSO) (CAS

67-68-5) and 90% acetone at -10°C for 1 h (Carrick et al. 1993). Chl concentrations were

determined using a Turner 10-AU fluorometer. Chl accumulation rates on artificial

substrata (mgChl/m2/day) were used as a proxy for microbial biofilm production

(Godwin and Carrick 2008).

Statistical analyses

For this fine resolution uptake analysis on intact biofilms, data were first

converted to µgP/L from DPM using a ratio of total DPM to enriched [P] (200 µgP/L)

assuming the distribution of tracer estimates the distribution of phosphate (Lean and

Nalewajko 1976, Hessen et al. 2012); this value was then multiplied by total sample

volume and normalized to Chl (µg) (final units= µgP/µgChl). Data were incorporated

into R (version 2.10.1) and analyzed using the ‘segmented’ package (piecewise

regression, Muggeo 2008) to estimate breakpoints or thresholds in uptake responses over

time. Additionally, data were fit using locally weighted regression (LOESS), a robust

nonparametric regression technique that fits linear regressions over localized subsets of

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the data (Cleveland and Devlin1988). A LOESS smoothing parameter (λ) of 0.3 was

chosen to provide adequate local regression sensitivity to variation in the data while

limiting ‘over-fitting’ (Jacoby 2000). LOESS regression analyses are useful for

ecological time-series data (Trexler and Travis 1993) and plots have proven effective in

aiding selection of candidate breakpoints (e.g., MacNulty et al. 2009). Breakpoints are

defined here as distinct divergences in the time-series such that the slope of the trend

changes sign (Gao et al. 2010). Precise coordinates of maximum trough (uptake) and

maximum peak (efflux) were then derived from the LOESS regressions in Minitab

statistical package version 14.0 (Minitab, State College, PA). Specifically, breakpoints

were visually estimated (Ryan and Porth 2007) using the crosshair mode in Minitab to

show precise coordinates of trough/peak points on the data region of each graph. Max

uptake was determined by the difference between initial [P] and max trough over elapsed

time and efflux was determined by the difference between max trough and subsequent

peak (typically max) over elapsed time. Further, the duplicate tiles were analyzed using a

two-way analysis of variance (ANOVA) to test the effects of province, season, and the

interaction of province and season on P-uptake and P-efflux (log10(µgP/µgChl/min)) (n=

64). Additional analyses were performed using SPSS software version 19.0 (SPSS Inc.,

Chicago, IL, USA). A preliminary Kolmogorov-Smirnov test indicated that the data

were normally distributed; statistical significance levels were determined as p< 0.05.

4.4 RESULTS

Fine resolution into the time sensitivity of uptake was accomplished in an

experiment focused on intact biofilm P-uptake over the first few minutes of exposure to

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new P. Phosphorus flux estimates from LOESS plots of P-uptake (log10(µgP/µgChl)) vs.

time (minute) conducted on intact biofilm assemblages from eight Pennsylvania streams

of varying productivity over four seasons (2009 - 2010) revealed distinct breakpoints in P

fluxes (Figures 4.1 - 4.4); specifically mean max uptake occurred at 1.65 and 2.60

minutes while max efflux occurred at 4.51 and 4.62 minutes in Piedmont and Plateau

provinces, respectively. These results show considerable and rapid exchange processes

occurring at early time periods (i.e., < 5 minutes), the magnitude of which seems to

diminish over longer periods (i.e., 15 - 30 minutes) (Table 4.1). The breakpoints

estimated using the R algorithm were largely in agreement with those from the LOESS

plots (t= 1.56, p= 0.125, df= 49). P flux estimates derived from the LOESS plots

regressed against stream/biofilm biochemical parameters showed that uptake and efflux

had significant inverse linear relationships with part-P (mgP/m2), and Chl accumulation

(mgChl/m2/d) for biofilm assemblages established on artificial substrata (tiles) (Figure

4.5, Table 4.2). Similarly uptake and efflux had significant inverse linear relationships

with part-P and Chl (mgChl/m2) for biofilm assemblages established on natural substrata

(rocks) (Figure 4.6, Table 4.2). Coefficients of determination (r2) were generally higher

for efflux than uptake; further, uptake and efflux were more strongly related to predictors

from biofilm assemblages established on natural substrata (rocks) compared to those on

artificial substrata (tiles).

Additionally, no differences were observed between biofilm assemblages from

duplicate tiles for uptake (F1,60= 0.150, p= 0.699) or efflux (F1,57= 0.010, p= 0.909).

Further, uptake and efflux were found to be strongly correlated (Pearson correlation

coefficient (r)= 0.848, p< 0.001); as such, a two-way multivariate analysis of variance

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(MANOVA) was performed for uptake (log10(µgP/µgChl/min)) and efflux

(log10(µgP/µgChl/min)) by province and season (n= 64). The MANOVA showed

significant differences in uptake and efflux by province (Wilks's lambda= 0.809, F2, 50=

5.91, p= 0.005), season (Wilks's lambda= 0.636, F6, 100= 4.23, p= 0.001), and the

interaction of province and season (Wilks's lambda= 0.758, F6, 100= 2.48, p= 0.028).

Further two-way ANOVA analysis indicated a significant effect of province on both

uptake (F1, 51= 8.19, p= 0.006), averaging 0.039 ±0.016 (SE) (log10(µgP/µgChl/min)) in

the Piedmont and 0.099 ±0.030 (SE) (log10(µgP/µgChl/min)) in the Plateau province, and

efflux (F1, 51= 12.06, p= 0.001), averaging 0.015 ±0.007 (SE) (log10(µgP/µgChl/min)) in

the Piedmont and 0.065 ±0.018 (SE) (log10(µgP/µgChl/min)) in the Plateau province

(Table 4.3). The two-way ANOVA furthermore showed a significant effect of season on

uptake (F3, 51= 6.40, p= 0.001) and efflux (F3, 51= 5.13, p= 0.004). A Tukey post-hoc test

revealed that biofilm (log10) uptake rates in winter (x = 0.158) and spring (x = 0.099)

seasons were significantly higher compared to summer (x = 0.026) and fall (x = 0.003)

(Table 4.4). Similarly, the Tukey post-hoc test revealed that biofilm (log10) efflux rates

in winter (x = 0.094) and spring (x = 0.039) seasons were significantly higher compared to

fall (x = 0.025) and summer (x = 0.014) (Table 4.5). Finally, a strong interaction between

province and season for P-uptake was also observed (F3, 51= 3.20, p= 0.031), although

these sources showed no such interaction for P-efflux. Descriptive statistics for P fluxes

(µgP/µgChl/min) (n= 64) revealed that biofilm P-uptake (0.364 ±0.125 SE) and P-efflux

(0.194 ±0.060 SE) were greater in the Plateau province compared to P-uptake (0.126

±0.063 SE) and P-efflux (0.0404 ±0.0214 SE) in the Piedmont province (Table 4.6,

Figure 4.7).

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4.5 DISCUSSION

Initial assimilatory processes

I have run a time-course experiment of the disappearance of P from water

overlying intact benthic biofilm assemblages to observe the pattern of uptake with time

(Harrison et al. 1989, Pérez-Lloréns and Niell 1995). This assay provided essential

insight into the short-term uptake kinetics of these important freshwater microbes. The

results of the experiments using P-uptake estimations from radiotracer loss in water

showed that active P-uptake is not unidirectional, such that there appears to be both

biofilm uptake and significant efflux of P back into the incubation medium over time.

The results appear to conform to a phosphate exchange process, whereby orthophosphate

is exchanged between the biofilm protoplasm and the surrounding medium. This

dynamic response of biofilms to new a P source further showed consistency across space

(stream) and time (season).

These findings are in accord with previous research that has found phosphorus is

not only assimilated rapidly by microbes, but exchanged between intracellular

compartments and the water (Goldberg et al. 1951, Rice 1953), given that cytoplasmic P

is readily exchangeable with external P (Cembella et al. 1982). In fact, Lean and

Nalewajko (1976) found that P exchange between organisms and their environment

might considerably exceed net uptake. Microbes in the biofilm saturated with P or

internally replete will not necessarily ‘turn off’ their uptake systems, but rather exchange

phosphate between the external medium and an internal pool of inorganic phosphorus

(Jansson 1988). I did find that there was a linear fraction of uptake < 3 minutes at most

sites, whereas afterwards a strong efflux was observed (> 5 minutes). Similarly, Harrison

155

et al. (1989) and Jansson (1993) found a two-phase uptake process with an initial rapid

(surge) uptake associated with a high-affinity system which fills an internal vacuole and a

secondary slower uptake associated with a low-affinity system which fuels metabolic

requirements. These kinetic patterns have been found as well in complex rooted seed-

producing plants (Pérez-Lloréns and Niell 1995). Taft et al. (1975), studying

phytoplankton uptake and release rates for inorganic phosphate in the Chesapeake Bay

estuary, found that P exchange in membrane transport was the principle process during

the initial phase of uptake (< 15 min), whereas net incorporation of phosphate was

dominant thereafter (> 15 min). This rapid surface sorption followed by slower trans-

membrane incorporation (Karlson 1989) supports my findings. That is, the LOESS plots

of P flux over time showed a similar rapid and considerable uptake occurrence and (after

an efflux period) a period of stabilized uptake. My time-course experimental results

expound on these findings and suggest that P-uptake into benthic biofilms may be a three

step kinetic process: rapid initial adsorption onto the biofilm matrix, efflux to conserve

membrane potential (see below), and slower incorporation. Fernández et al. (1997)

proposed a five compartment model (with components of intracellular and extracellular)

in explaining phosphorus fluxes in phytoplankton, underscoring the complexity of

microbial-nutrient dynamics.

As observed in my experiment, initial P-uptake was high, but fleeting (ca. 2

minutes). It is possible that the initial uptake observed in my experiment for the first few

minutes could be explained by two coupled processes in nutrient uptake: adsorption and

absorption (Short and McRoy 1984). Adsorption here is referring to the movement of P

into surface-adsorbed phosphorus pools, a fraction of which can be internalized and used

156

for growth (Sañudo-Wilhelmy et al. 2004). The biofilm assemblage may be well adapted

structurally for rapid uptake of P into surface areas. The heterogeneous architecture of

benthic biofilms with channel networks, interstitial spacings, and highly hydrated

matrixes (Sutherland 2001) allow them to function as living zones of transient storage

and biochemical processing (Battin et al. 2003). The chemical composition of microbial

cell surfaces typically generates a net negative charge at neutral pH (Sherbet 1978,

Dolowy 1984). Although phosphate (PO43-

) and most phosphate groups (mono- and

diesters) also have a negative charge above pH 3 (Beveridge and Murray 1980), the

biofilm matrix contains cell-surface-bound metal hydroxides and oxides (Sañudo-

Wilhelmy et al. 2004). Oxyanions such as phosphate can form (by means of ligand

exchange) surface complexes with these hydrous oxides (Sigg and Stumm 1981). Initial

flux/binding of phosphate ions by biofilms is then a largely adsorptive process, governed

by electrostatic interaction between negatively charged phosphate groups in the medium

and positively charged alkali metal hydroxides on the biofilm surface. The change from

the first phase, delineated by an initial rapid or surge uptake, to the second phase, with

decreased uptake, could be explained on the basis of a feedback inhibition (from filling

an inter-cellular pool) and/or a regulation of the phosphate efflux.

Efflux rates for biofilms in my study averaged 39 and 66% of mean uptake in the

Piedmont and Plateau provinces, respectively, and correspond well to previous research.

Harrison (1983b) found that that soluble phosphorus regeneration (efflux) by

microplankton populations ranged from 45 - 68% of uptake rates during a seasonal study

in a coastal embayment of Nova Scotia. Similarly, Wen et al. (1997) showed that about

half of the PO4 taken up was excreted in freshwater algae. Moreover, higher plant

157

literature has shown too that P influx is associated with efflux (Elliot et al. 1984, Bieleski

and Läuchli 1992). It has been observed that ammonium influx must be coupled with an

active cation efflux in order to maintain membrane potential (Parslow et al. 1984).

Uptake of phosphate, as a polyatomic anion, may instigate similar effects as observed

with ammonium, such that a portion of the PO43-

incorporated into a cell must be

eliminated in order to maintain a stable electrical potential between the cellular interior

and the exterior. That said, it could be more energetically favorable for microbes to

excrete phosphate rather than activate and deactivate the entire phosphate transport

system at short intervals (see Jansson 1988). Phosphorus assimilation by Escherichia

coli, for instance, requires regulation of at least 31 genes (Wanner 1993). Efflux of P

possibly allows microbes to assimilate P and efflux excess while maintaining the same

transport systems and membrane potential. Nevertheless, physiological explanations for

high P-efflux when P-uptake and demand are also high remain uncertain (Nalewajko and

Lean 1980, Whitton 1992).

Non-linear time-courses observed in other nutrient uptake experiments have been

attributed to substrate exhaustion (e.g., Fisher et al. 1981). Moreover, Goldman et al.

(1981) suggested that substrate depletion may be important in explaining non-linear

nutrient uptake kinetics in natural microplankton populations. However, this

experimental artifact of isotope availability does not provide an adequate explanation for

my results here. According to computations, on average approximately 82% of the P

available was taken up during any of the time-course experiments. Therefore, the

evidence suggests that active efflux by the biofilm assemblages contributed to the time-

course patterns observed rather than methodological artifacts. That said, the rate at which

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biofilm assemblages actively process (uptake/efflux) P may be somewhat disparate,

thereby differentially affecting the time-course patterns. For example, the LOESS plots

of P fluxes over time showed large variability among replicate experimental units (tiles).

Such inter-experimental variation is not uncommon (especially given the highly

heterogeneous nature of stream systems), and may be attributable to diverse taxonomic

composition, even in adjacent samples. For instance, Wen et al. (1997) found substantial

variability in the measurement of uptake kinetics between axenic freshwater algal

cultures. My study employed natural biofilm assemblages that likely differed, albeit

moderately, in community composition which may have contributed to the observed

variation in P fluxes. While biofilm samples for this experiment were preserved (1%

formalin) for future taxonomic analysis, examination was beyond the scope of the current

objectives, and thus such hypotheses remain to be tested.

My selected time-courses, with 11 sampling periods prior to 30 minutes (five

periods prior to five minutes) is in agreement with past phytoplankton research which

suggested that short-term nutrient uptake experiments be conducted in the range of

seconds to minutes to examine important physiological processes (Goldman and Glibert

1982); this indicates nutrient processing is a near instantaneous physiological dynamic

and further suggests that experimental time periods scaled to hours or longer obscure

such essential short-term responses (Harrison 1983a). Nonetheless, the literature remains

replete with rate measurements from nutrient uptake experiments of varying and often

arbitrary durations (Goldman et al. 1981). For instance, “short-term” nutrient uptake in

the literature has varied from ≤ 3 hours (Pérez-Lloréns and Niell 1995), to 45 - 120

minutes (O’Brien and Wheeler 1987), to < 1 minute (Parslow et al. 1984). This trend has

159

continued into current literature where “very fast” uptake kinetics of Synedropsis

(Division Ochrophyta, Order Fragilariales) was just recently estimated as sampling after

10 - 100 minutes (Hessen et al. 2012). My research here has shown that mean max

uptake occurred under three minutes while max efflux occurred under five minutes in

both Piedmont and Plateau provinces over four seasons. There are thus considerable P

exchange processes occurring at early time periods (i.e., < 5 minutes) in these biofilm

assemblages, the majority of which is being missed during many “short-term” nutrient

uptake experiments. Such experiments thus reflect net uptake, or the result of several

interacting and counteracting processes involved in microbial phosphate assimilation

(Jansson 1988). While these experiments are certainly valuable in their own right and

likely more applicable to total water column uptake rates, they are of limited value for

discerning the significance of initial assimilatory processes to nutrient uptake dynamics

(Wheeler et al. 1982). True “short-term” experiments and time-course incubations are

important to understand the physiological characteristics of nutrient uptake (Goldman et

al. 1981). Investigations into rapid initial uptake processes could be of ecological

importance, particularly if natural nutrient perturbations are on the time scale of minutes

(Collos 1983). In addition, initial uptake (≤ 5 minutes) was a significant linear predictor

of assimilatory (stable) P-uptake (30 minutes) (Figure 4.8; r2= 0.811, slope= 0.0495, p<

0.001), suggesting that indeed the magnitude of short-term processes affect longer term

kinetics (Dortch 1990). Moreover, Cembella et al. (1984) found that PO4 Vmax values

vary over six orders of magnitude; as kinetic parameters of microbial uptake systems can

fluctuate over experimental conditions (e.g., time), uniformity of “short-term” nutrient

uptake research may likely provide greater consistency of determined kinetic parameters

160

(Wagner and Falkner 2001). For instance, the average maximum uptake rate for benthic

biofilms was 0.126 and 0.364 µgP/µgChl/min for the Piedmont and Plateau provinces,

respectively. These values, while within the benthic range specified in Price and Carrick

(2011), are on the high end, suggesting that short-term uptake estimates yield generally

higher rates (also in agreement with their findings) and further demonstrate the effect of

experimental sample time on kinetics.

While the occurrence of non-linear nutrient disappearance over time has been

reported before in phytoplankton (Goldman et al. 1981, Wheeler et al. 1982, Parslow et

al. 1984, Cochlan and Harrison 1991), prokaryotic nannoplankton (Lehman and Sandgren

1982), seaweeds (Thomas and Harrison 1987), and submerged aquatic angiosperms

(Pérez-Lloréns and Niell 1995), much of it has focused on short-term N dynamics with

little regard for P and little research has been devoted to freshwater benthic biofilms. As

such, this work illuminates the short-term uptake potential for benthic stream

assemblages and highlights the need for time-dependent assays to ascertain community

physiological responses to added nutrients. Overall, the data demonstrate that non-

linearity in uptake is a common occurrence, detectable only in rapid time-course studies,

and that excretory phenomena are actively occurring together with uptake in a dynamic

coupling that may be undetected with longer time samplings.

Nutrient legacies

Regressions of P-uptake and P-efflux vs. part-P estimated from biofilms on

natural (rock) substrata were stronger compared to those biofilms on artificial (tile)

substrata. The biofilms on the natural substrates would comparatively have developed

for a longer period of time and thus subjected to nutrient legacies (cf. Verspoor et al.

161

2010). These experimental results indicate that while stream biofilms may act as a

reservoir for dissolved P, their uptake and efflux magnitude is rapidly reduced in

response to prolonged high P inputs (legacy), further suggesting that downstream

transport of nutrients could be enhanced in streams that are already enriched (see Chapter

3). The strong positive correlation between part-P and Chl accumulation (Pearson

correlation coefficient (r)= 0.812, p< 0.001) further suggests that as productivity

increases and biofilms accumulate P, the capacity of the population to assimilate new P

declines. Research has shown that a fraction of cellular stored P (polyphosphate A)

serves as a noncompetitive inhibitor on the uptake system (Rhee 1973). My data reflect

this finding, such that those biofilms containing high concentrations of part-P showed a

much lowered capacity to take-up new P. The LOESS plots (Figures 4.5, 4.6) further

support these data and indicate a sharp decline (threshold) in uptake/efflux magnitude as

biomass and productivity increase. Further, the breakpoints estimated by the LOESS plot

for uptake and efflux correspond along the predictor axes, suggesting that as uptake

diminishes efflux, is moderated as well.

Despite my effort for quantitative rigor in this experiment, precise internal P

exchange(s) could not be estimated given that a single radiotracer was used, and thus

explicit quantification of the [P] being effluxed from microbes within the biofilm was not

possible. I therefore utilized my chemical analysis of poly-P (intercellular phosphate

stores (Kromkamp 1987)), as an estimation of internal [P]. I then regressed poly-P

against P-efflux in order to determine the effect of internally stored P on efflux processes.

Observations with DFFITS values greater than 2√(k/n), where k is the number of

predictors (including constant), were removed from the analysis, following Belsley et al.

162

(1980). Overall, P-efflux yielded a significant negative regression with poly-P (slope= -

0.0195 (±0.004 SE), p< 0.001, r2= 0.332, Appendix Q) which suggests that as poly-P

decreases (or internal stores of P are reduced), these smaller internal P stores can be more

readily and swiftly exchanged with the external medium. Further, the higher efflux rates

occurring in biofilm assemblages with low polyphosphate content could be attributable to

elevated mobilization/utilization of stored P as an energy source by microbes in the

biofilm; some research has found evidence that P release during the utilization of

polyphosphates by benthic microorganism can contribute to enhanced P fluxes

(Sannigrahi and Ingall 2005).

For biofilms in the Plateau province, efflux comprised approximately 66% of

uptake, whereas in the Piedmont province efflux averaged only 39%. This finding is in

accord with the expected difference in electrochemical potential of cell membranes

between biofilms located in the two provinces. For instance, (log10) poly-P was

significantly (F1, 49= 14.65, p< 0.001) lower in biofilms situated in Plateau streams (x =

0.68 ±0.51 (SD) mgP/m2) compared to those in Piedmont streams (x = 1.26 ±0.56 (SD)

mgP/m2). Further, TP (mg/L) concentrations were more than 3-fold lower in the Plateau

compared with the Piedmont streams (see Carrick et al. 2009). Thus, once biofilms were

returned to the laboratory and subjected to a new elevated, yet realistic P source (33

P

carrier), P ions would face a more unfavorable electrochemical gradient for uptake in

those biofilms situated in the Plateau province, resulting in more considerable leakage or

efflux of acquired P (Elliott et al. 1984, Britto and Kronzucker 2006).

The differential in efflux rates between biofilms in the Plateau versus Piedmont

provinces might further be explained through mineral composition analyses. For

163

instance, both calcium (Ca) and zinc (Zn) have been found to influence uptake processes

in higher plants. Ca sufficiency can enhance P-uptake in mung beans (Vigna radiata)

(Tanada 1955), and Zn deficiencies can up-regulate expression of genes encoding high-

affinity P transporters in barley (Hordeum vulgare L. cv Weeah) (Huang et al. 2000).

Biofilms in the Piedmont province had significantly (F1, 57= 6.95, p= 0.011) higher Ca

concentrations, averaging 120.5 ±189.3 (SD) g/m2, compared to biofilms in the Plateau

province (x = 5.5 ±3.4 (SD) g/m2) (see PADEP 2005, Carrick et al. 2009). Moreover,

biofilms in the Piedmont province had moderately (p> 0.05) lower Zn concentrations,

averaging 0.177 ±0.141 (SD) g/m2, compared to biofilms in the Plateau province (x =

0.230 ±0.156 (SD) g/m2). Therefore, higher Ca and lower Zn concentrations in biofilms

located in the Piedmont province might facilitate greater retention of P, thereby reducing

the magnitude of efflux events.

Spatial-temporal effects

While both province and season were significant factors in the ANOVAs for P-

uptake and P-efflux, the F-ratio for province was greater, suggesting that variation

between the provinces more strongly overrides variation within each and that spatial

effects may more effectively influence differences in uptake/efflux. This is in accord

with past studies demonstrating the strong variability of uptake across stream systems

(e.g., Orr et al. 2006). For example, Ensign et al. (2006) reported over a 7-fold range (1.0

to 7.2 mm min-1

) in PO43-

retention between adjacent second- and third-order streams,

draining into the South River estuary in North Carolina. Such spatial variability in

nutrient retention has thus been linked with even moderately heterogeneous

biogeochemistry of streambeds. Certainly the Pennsylvania Plateau to Piedmont

164

transition I tested against here reflects the prominent geology of the provinces as well as

a strong gradient of environmental conditions (see Carrick et al. 2009). Nonetheless,

Martí and Sabater (1996) suggested that differences in nutrient retention efficiencies

across streams might be related to differences in nutrient availability. This was also the

case in the streams I selected here, such that TP concentrations were more than 3-fold

higher in the Piedmont compared with the Plateau streams (see Carrick et al. 2009).

Despite the strength and influence of spatial effects, season remained a significant factor

in my study and revealed interesting trends in the P-uptake and P-efflux over time. I

found that uptake and efflux were particularly elevated in winter; other research has

found as well that microbes in winter can exhibit strong assimilation capacities— PO43-

uptake was highest in the Pamlico estuary in late winter (Kuenzler et al. 1979). Further,

inorganic nutrients were likely most limiting during winter and spring in my experiment,

as suggested by stream conductivity data (e.g., Sharpe 2007), and therefore, biofilm

communities were likely more physiologically poised to respond to a new P source.

Microbial adaptive responses to altered phosphate concentrations can initiate new uptake

potentials (Aubriot et al. 2011). For instance, under nutrient stressed (limiting)

conditions, microbes are capable of inducing high-affinity uptake systems (Mann 1995)

which exhibits strong anion selectivity and contains high affinity P-binding proteins

(Dignum et al. 2005). Such systems have been found in higher plants as well during

periods of nutrient stress, such that high-affinity inorganic P-uptake systems are induced

in cluster roots of P-deficient plants (Vance et al. 2003). Further, high uptake and

retention of nutrients that I recorded in my experiment during spring has been observed

previously. Mulholland (1992) found that uptake by stream autotrophs contributes

165

significantly to nutrient retention in Walker Branch (east Tennessee, USA), during

periods of high ambient light prior to leaf emergence (e.g., spring). My spring

experimental period occurred before complete leaf-out at all sites (personal observation),

and thus biofilms were growing during conditions (high light/low leaf density) favorable

for uptake.

4.6 ACKNOWLEDGMENTS

I thank J. Carlson and J. Lynch for helpful discussions and comments on a

previous version of this manuscript. Funding for this research was provided to H.J.C. by

the Pennsylvania Department of Environmental Protection (Grant No. 4100034506).


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Table 4.1. P-uptake and P-efflux estimates from LOESS plots of P flux (log10(µgP/µgChl)) vs. time

(minute) and breakpoint estimates from the ‘segmented’ package in R for experiments conducted on

intact biofilm assemblages from eight Pennsylvania streams of varying productivity over four

seasons 2009 - 2010. Note * indicates failure of LOESS to detect distinct divergences in the time-

series or inability of the R algorithm to converge on a breakpoint.

Season Province Stream Time Max

Uptake

(min)

Time Max

Efflux

(min)

Segmented

Breakpoint

(min ±SE)

Uptake Rate

(µgP/µgChl/min)

Efflux Rate

(µgP/µgChl/min)

Fall Plateau Cowanesque 5.15 10.06 1.61 ±2.09 0.0021 0.0015

Fall Plateau East Hickory 2.04 5.17 * 0.1259 0.1131

Fall Plateau Tionesta 2.14 5.04 2.44 ±4.36 -0.1024 0.1780

Fall Plateau Tunkhannock 1.21 1.88 2.26 ±2.58 0.0089 0.0036

Fall Piedmont Cooks 1.91 9.96 * 0.0142 0.0032

Fall Piedmont Penns 0.90 2.02 5.29 ±1.53 0.0052 0.0063

Fall Piedmont Red Clay 2.04 5.17 * 0.0191 0.0094

Fall Piedmont Spring 2.10 9.93 * 0.0138 0.0046

Winter Plateau Cowanesque 0.99 2.05 6.19 ±2.11 0.0495 0.0876

Winter Plateau East Hickory * * * * *

Winter Plateau Tionesta 5.14 10.05 2.88 ±1.31 0.3851 0.1275

Winter Plateau Tunkhannock 1.94 5.14 * 0.9763 0.5189

Winter Piedmont Cooks 2.15 5.14 6.38 ±2.80 1.7616 0.2480

Winter Piedmont Penns 0.99 2.27 6.97 ±9.03 0.0049 0.0037

Winter Piedmont Red Clay 2.05 5.26 1.26 ±1.10 0.0278 0.0080

Winter Piedmont Spring 0.87 2.15 * 0.0606 0.0404

Spring Plateau Cowanesque 4.94 * 4.91 ±6.92 0.0044 *

Spring Plateau East Hickory 2.28 4.74 1.60 ±0.60 0.5901 0.1937

Spring Plateau Tionesta 1.04 1.93 * 1.9093 0.7379

Spring Plateau Tunkhannock 1.00 2.12 * 0.0181 0.0063

Spring Piedmont Cooks 4.84 9.53 4.22 ±6.35 0.0283 0.0080

Spring Piedmont Penns 0.92 2.04 2.57 ±2.66 0.0285 0.0065

Spring Piedmont Red Clay 0.94 4.96 * 0.1663 0.0333

Spring Piedmont Spring 1.00 1.23 5.71 ±10.8 0.0121 0.0101

Summer Plateau Cowanesque 0.99 2.27 2.44 ±0.46 0.0417 0.0058

Summer Plateau East Hickory 3.97 5.04 5.69 ±2.67 0.0622 0.0497

Summer Plateau Tionesta 2.15 4.07 4.01 ±1.92 0.0355 0.0133

Summer Plateau Tunkhannock 3.86 5.14 5.48 ±2.53 0.0392 0.0214

Summer Piedmont Cooks 1.08 2.36 2.11 ±1.30 0.0877 0.0404

Summer Piedmont Penns 1.63 3.98 1.01 ±0.07 0.0933 0.0340

Summer Piedmont Red Clay 2.05 4.19 1.52 ±0.67 0.0078 0.0022

Summer Piedmont Spring 0.87 1.94 1.15 ±0.70 0.0940 0.0252

178

Table 4.2. Linear regression statistics for (log10) P-uptake (µgP/µgChl/min) and (log10) P-efflux

(µgP/µgChl/min) vs. (log10) part-P (mgP/m2) and (log10) Chl accumulation (mgChl/m

2/d) for biofilm

assemblages established on tiles, and (log10) part-P (mgP/m2) and (log10) Chl (mgChl/m

2) for biofilm

assemblages established on natural substrata (rocks) from eight Pennsylvania streams of varying

productivity over four seasons 2009 - 2010.

Response Predictor y-

intercept

Std.

Error y-

intercept

Slope Std.

Error

Slope

r2 p

(log10)

Uptake

(log10) Part-P

(tile) 0.183 0.046 -0.097 0.032 0.254 0.005

(log10) Chl

Accumulation

(tile)

0.117 0.027 -0.144 0.046 0.261 0.004

(log10)

Efflux

(log10) Part-P

(tile) 0.066 0.011 -0.034 0.008 0.443 0.000

(log10) Chl

Accumulation

(tile)

0.041 0.007 -0.047 0.011 0.399 0.000

(log10)

Uptake

(log10) Part-P

(rock) 0.202 0.038 -0.088 0.020 0.407 0.000

(log10) Chl

(rock) 0.245 0.050 -0.095 0.023 0.371 0.000

(log10)

Efflux

(log10) Part-P

(rock) 0.072 0.009 -0.030 0.005 0.586 0.000

(log10) Chl

(rock) 0.072 0.014 -0.026 0.006 0.376 0.000

179

Table 4.3. Two-way ANOVA results for P-uptake (log10(µgP/µgChl/min)) and P-efflux

(log10(µgP/µgChl/min)) by season and province for replicate measurements on intact biofilms across

eight Pennsylvania streams of varying productivity over four seasons 2009 - 2010.

Source Dependent

Variable

Type III Sum

of Squares

df Mean

Square

F p

Province (log10) Uptake 0.102 1 0.102 8.185 0.006

(log10) Efflux 0.051 1 0.051 12.060 0.001

Season (log10) Uptake 0.240 3 0.080 6.399 0.001

(log10) Efflux 0.065 3 0.022 5.132 0.004

Province * Season (log10) Uptake 0.120 3 0.040 3.202 0.031

(log10) Efflux 0.031 3 0.010 2.444 0.075

Error (log10) Uptake 0.637 51 0.012

(log10) Efflux 0.217 51 0.004

Corrected Total (log10) Uptake 1.050 58

(log10) Efflux 0.341 58

180

Table 4.4. Homogenous subsets based on Tukey's HSD post-hoc test for (log10) P-uptake by season.

Means for groups in homogeneous subsets are displayed.

Season N Subset

1 2

Fall 15 0.0027

Summer 16 0.0260

Spring 16 0.0991 0.0991

Winter 12 0.1583

Sig. 0.106 0.487

Table 4.5. Homogenous subsets based on Tukey's HSD post-hoc test for (log10) P-efflux by season.

Means for groups in homogeneous subsets are displayed.

Season N Subset

1 2

Summer 16 0.0137

Fall 15 0.0248

Spring 16 0.0388 0.0388

Winter 12 0.0944

Sig. 0.727 0.111

181

Table 4.6. Descriptive statistics of P-uptake and P-efflux (µgP/µgChl/min) for biofilms on duplicate

tiles in eight streams situated across two geologic provinces over four seasons (n= 64).

Province Season N Mean Std. Error Minimum Maximum

Plateau Fall Uptake 8 0.0058 0.0421 -0.19 0.25

Efflux 8 0.1189 0.0663 0.00 0.54

Winter Uptake 6 0.8587 0.3795 0.02 2.58

Efflux 6 0.4636 0.2140 0.00 1.30

Spring Uptake 8 0.6628 0.3036 0.01 2.13

Efflux 8 0.2019 0.1057 0.00 0.82

Summer Uptake 8 0.0528 0.0112 0.01 0.11

Efflux 8 0.0330 0.0089 0.01 0.07

Piedmont

Fall

Uptake 8 0.0124 0.0027 0.00 0.02

Efflux 8 0.0055 0.0015 0.00 0.01

Winter

Uptake 8 0.3612 0.2392 0.01 1.88

Efflux 8 0.0962 0.0796 0.00 0.65

Spring

Uptake 8 0.0559 0.0276 0.01 0.24

Efflux 8 0.0185 0.0072 0.00 0.05

Summer

Uptake 8 0.0726 0.0222 0.01 0.18

Efflux 8 0.0315 0.0105 0.00 0.07

182

Table 4.7. Pearson correlation matrix of all LOESS derived uptake estimations and biochemical parameters tested in eight Pennsylvania streams over

four seasons (n= 32). Note: ** and * indicates correlation is significant at the 0.01 and 0.05 level (2-tailed), respectively.

Variable Max

Uptake

Time

Max

Uptake

Max

Efflux

Time

Max

Efflux

(log10)

Uptake

Rate

(log10)

Efflux

Rate

(log10)

Poly-P

(log10)

Part-P

(log10)

Conductivity

(log10) Chl

Accumulation

Max Uptake Pearson 1 0.220 0.996** 0.227 0.813** 0.752** -0.350 -0.617** -0.508** -0.588**

Sig. (2-tailed) 0.235 0.000 0.228 0.000 0.000 0.086 0.000 0.004 0.001

Time Max Uptake Pearson 1 0.253 0.776** -0.046 -0.043 -0.417* -0.248 -0.329 -0.309

Sig. (2-tailed) 0.178 0.000 0.806 0.823 0.038 0.179 0.071 0.091

Max Efflux Pearson 1 0.215 0.817** 0.784** -0.356 -0.627** -0.520** -0.592**

Sig. (2-tailed) 0.255 0.000 0.000 0.088 0.000 0.003 0.001

Time Max Efflux Pearson 1 -0.023 -0.068 -0.222 -0.210 -0.172 -0.299

Sig. (2-tailed) 0.902 0.722 0.297 0.265 0.364 0.108

(log10) Uptake Rate Pearson 1 0.866** -0.178 -0.529** -0.375* -0.537**

Sig. (2-tailed) 0.000 0.395 0.002 0.038 0.002

(log10) Efflux Rate Pearson 1 -0.474* -0.598** -0.552** -0.558**

Sig. (2-tailed) 0.019 0.000 0.002 0.001

(log10) Poly-P Pearson 1 0.738** 0.637** 0.675**

Sig. (2-tailed) 0.000 0.001 0.000

(log10) Part-P Pearson 1 0.581** 0.812**

Sig. (2-tailed) 0.001 0.000

(log10) Conductivity Pearson 1 0.590**

Sig. (2-tailed) 0.000

(log10) Chl

Accumulation

Pearson 1

Sig. (2-tailed)

183

Time (min)

(lo

g1

0)

µg

P/

µg

Ch

l

30150

0.30

0.25

0.20

0.15

30150

0.08

0.07

0.06

0.05

0.04

30150

0.045

0.040

0.035

0.030

0.025

30150

0.14

0.12

0.10

0.08

0.06

30150

0.010

0.008

0.006

0.004

0.002

30150

0.08

0.07

0.06

0.05

0.04

30150

0.10

0.08

0.06

0.04

0.02

30150

0.08

0.06

0.04

0.02

0.00

East Hickory Tionesta C owanesque Tunkhannock

Red C lay C ooks Penns Spring

Figure 4.1. Scatter plot of P flux (log10(µgP/µgChl)) vs. time (minute) conducted on intact biofilm

assemblages from eight Pennsylvania streams of varying productivity in Summer 2010. The lines

show locally weighted scatter plot smoothing (LOESS) curve fitting. Time samplings were conducted

at 0, 1, 2, 4, 5, 8, 10, 12, 16, 25, 30 minutes. Note scale differences for streams.

184

Time (min)

(lo

g1

0)

µg

P/

µg

Ch

l

30150

0.60

0.55

0.50

0.45

0.40

30150

0.85

0.80

0.75

0.70

0.65

30150

0.025

0.020

0.015

0.010

0.005

30150

0.016

0.014

0.012

0.010

0.008

30150

0.14

0.12

0.10

0.08

0.06

30150

0.22

0.20

0.18

0.16

0.14

30150

0.04

0.03

0.02

0.01

30150

0.0100

0.0075

0.0050

0.0025

0.0000




assemblages from eight Pennsylvania streams of varying productivity in Spring 2010. The lines show

locally weighted scatter plot smoothing (LOESS) curve fitting. Time samplings were conducted at 0,

1, 2, 5, 10, 30 minutes. Note scale differences for streams.

185

Time (min)

(lo

g1

0)

µg

P/

µg

Ch

l

353025

0.50

0.25

0.00

-0.25

-0.50

30150

1.00

0.95

0.90

0.85

30150

0.24

0.21

0.18

0.15

0.12

30150

0.95

0.90

0.85

0.80

0.75

30150

0.07

0.06

0.05

0.04

0.03

30150

1.0

0.9

0.8

30150

0.010

0.009

0.008

0.007

0.006

30150

0.060

0.045

0.030

0.015

0.000




assemblages from eight Pennsylvania streams of varying productivity in Winter 2010. The lines show


1, 2, 5, 10, 30 minutes. Note scale differences for streams; also, samples for East Hickory were

irretrievable due to ice-coverage.

186

Time (min)

(lo

g1

0)

µg

P/

µg

Ch

l

30150

0.40

0.35

0.30

30150

0.6

0.5

0.4

0.3

0.2

30150

0.0200

0.0175

0.0150

0.0125

0.0100

30150

0.0240

0.0225

0.0210

0.0195

0.0180

30150

0.05

0.04

0.03

0.02

0.01

30150

0.025

0.020

0.015

0.010

0.005

30150

0.0150

0.0125

0.0100

0.0075

0.0050

30150

0.025

0.020

0.015

0.010

0.005




assemblages from eight Pennsylvania streams of varying productivity in Fall 2009. The lines show


1, 2, 5, 10, 30 minutes. Note scale differences for streams.

187

(lo

g1

0)

µg

P/

µg

Ch

l/m

in

1.000.750.500.250.00

0.4

0.3

0.2

0.1

0.0

2.01.51.00.50.0

0.100

0.075

0.050

0.025

0.000

(log)Uptake*(log)Particulate-P (log)Uptake*(log)Chl Accumulation

(log)Efflux*(log)Particulate-P (log)Efflux*(log)Chl Accumulation

Figure 4.5. Scatter plot with linear regression (solid-line) and LOESS (dashed-line) of (log10) P-

uptake (µgP/µgChl/min) (top) and (log10) P-efflux (µgP/µgChl/min) (bottom) vs. (log10) part-P

(mgP/m2) (left) and (log10) Chl accumulation (mgChl/m

2/d) (right) for intact biofilm assemblages

established on artificial substrata (tiles) from eight Pennsylvania streams of varying productivity

over four seasons 2009 - 2010. Note scale differences for P-uptake vs. P-efflux.

188

(lo

g1

0)

µg

P/

µg

Ch

l/m

in

3.02.52.01.51.0

0.60

0.45

0.30

0.15

0.00

3210

0.10

0.05

0.00

-0.05

(log)Uptake*(log)Particulate-P (log)Uptake*(log)Chl

(log)Efflux*(log)Particulate-P (log)Efflux*(log)Chl

Figure 4.6. Scatter plot with linear regression (solid-line) and LOESS (dashed-line) of (log10) P-

uptake (µgP/µgChl/min) (top) and (log10) P-efflux (µgP/µgChl/min) (bottom) vs. (log10) part-P

(mgP/m2) (left) and (log10) Chl (mgChl/m

2) (right) for biofilm assemblages established on natural

substrata (rocks) from eight Pennsylvania streams of varying productivity over four seasons 2009 -

2010. Note scale differences for P-uptake vs. P-efflux.

189

Province

(lo

g1

0(µ

gP

/µ

gC

hl/

min

))

P lateauP iedmont P lateauP iedmont

0.60

0.45

0.30

0.15

0.00

P lateauP iedmont

0.60

0.45

0.30

0.15

0.00

P lateauP iedmont

(log) Uptake, F all (log) Uptake, Winter (log) Uptake, Spring (log) Uptake, Summer

(log) Efflux, F all (log) Efflux, Winter (log) Efflux, Spring (log) Efflux, Summer

Figure 4.7. Box plot of (log10) P-uptake (top) and (log10) P-efflux (bottom) (µgP/µgChl/min) across

eight streams situated in two geological provinces in Pennsylvania over four seasons (n= 64).

Rectangular boxes represent the middle 50% (interquartile range (IQR)) of the data, the top and

bottom of the boxes represent the third (Q3) and first (Q1) quartiles, respectively, lines (“whiskers”)

extending to either side indicate the general extent of the data (upper limit (Q3+1.5(IQR)) and lower

limit (Q1–1.5(IQR))), lines within the boxes designate the median value, and asterisks indicate the

outliers (data lying outside the defined “whisker” bounds).

190

(log10) Initial Uptake

(lo

g1

0)

Assim

ilato

ry U

pta

ke

0.60.50.40.30.20.10.0

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0.000

Figure 4.8. Scatter plot with linear regression of (log10) assimilatory (stable) P-uptake (30 minutes)

vs. (log10) initial P-uptake (≤5 minutes) (µgP/µgChl/min) for intact biofilm assemblages established

on artificial substrata (tiles) from eight Pennsylvania streams of varying productivity over four

seasons 2009 - 2010 (n= 64). Regression statistics: F= 248.44, p< 0.001, r2= 0.811.

191

CONCLUSION

Lotic systems play a critical role in Earth’s hydrologic cycle, transportation of

nutrients and organic matter, and maintenance of biological diversity (Allan and Flecker

1993). Moreover, based on average global value of ecosystem services, lake and river

systems are among the most important biomes in the world (cf. Costanza et al. 1997).

Obviously, any potential determent to the proper functioning of such systems (e.g.,

anthropogenic nutrient loading) could have serious environmental and economic impacts.

This dissertation provides insight into the efficiency of stream ecosystems in removing

nutrients through investigating the uptake capacity of benthic biofilms, which constitute

the dominant form of microbial life in most aquatic ecosystems (Hall-Stoodley et al.

2004). The overall objective of this four chapter work was to conduct a series of

empirical and experimental research endeavors examining the P-uptake potential of

benthic biofilms by building upon preceding chapters’ discoveries and continually

refining hypotheses in an effort to help identify and understand sources of variation in

uptake rates (Figure C.1).

In Chapter 1, I performed a synthesis and analysis of peer-reviewed aquatic P-

uptake rates which helped in developing a model explaining variation in nutrient uptake

rates among aquatic microbes. This research highlighted the significant difference

between planktonic and benthic P-uptake rates and suggests that the lower affinity for P

by benthic microbes could be attributed to their adnate growth forms, which can create

boundary layers separating cells from ambient P. Further, I found that uptake rates

measured in experiments on cultured microbes were generally higher compared with wild

samples; however, this trend was not significant and suggests that cultured microbes in

192

these studies represented a reasonable surrogate for natural microbes. Such an extension

may be useful for studies where in situ experimentation is not feasible. Lastly, this

research showed that experiments sampling microbes over shorter times yielded over 3-

fold higher P-uptake rates and appear to represent more accurate estimates of gross

uptake, a concept explored further in Chapter 4.

The results from Chapter 1 suggested that differences in experimental conditions

may be driving some of the substantial variation observed in uptake rates. Chapter 2

sought to build on these findings by verifying the effects of physical disturbance (a

common biofilm experimental processing technique) on biofilm viability and P-uptake

potential. The experiments showed no difference in P-uptake rates between (physically)

scraped (x = 0.77 ±0.11 (SE) μgP/μgChl/d) and intact (x = 0.91 ±0.17 (SE) μgP/μgChl/d)

biofilms (t= 0.69, p= 0.492, df= 33). Further, microbial physiology was not depressed by

physical disturbance. These data thus lend confidence to the numerous experiments that

investigate benthic microbial physiologic responses post-disturbance and highlight the

potential of uptake following common physical disturbances that occur in turbulent

(stream) environments.

Given the findings in Chapter 2, specifically that physical abrasion (disturbance)

of the biofilm does not negatively affect uptake capacity or physiological condition, I

utilized such sampling methods in Chapter 3 to facilitate estimations of multiple

biochemical parameters (e.g., biomass, areal nutrient concentrations) from a single

sample that would otherwise be impractical to measure from an intact radioactive sample.

Overall, results from Chapter 3 experiments showed that benthic stream biofilm uptake

declined linearly with increasing experimental P-loadings across all streams. Results

193

further showed a pattern of decreasing uptake efficiency with higher point source nutrient

enrichment and stream productivity. These experimental results indicate that while

stream biofilms may act as a reservoir for dissolved P, their uptake efficiency is rapidly

reduced in response to prolonged high P inputs (legacy). My data also showed that N

enrichment provided biofilms a greater ability to respond to a given P-load, suggesting

that N may become a controlling (limiting) resource particularly when P is in high

concentrations. While N enrichments did produce a complex biofilm P-uptake

phenomenon, influenced by biogeochemistry (province) and cellular stoichiometric

ratios, my experiments provide direct, cause-effect results that underscore the need to

control loads of both P and N in streams throughout Pennsylvania (and likely the greater

Mid-Atlantic region) and present a much needed mechanistic understanding of microbial

resource availability - resource demand (cf. Davidson and Howarth 2007).

Expounding upon the findings in Chapter 1, specifically that microbes sampled

over shorter times (i.e., < 10 minutes) yielded considerably higher P-uptake rates, results

in Chapter 4 showed substantial and rapid uptake/exchange processes occurring at early

time periods (i.e., < 5 minutes), the magnitude of which seemed to diminish over longer

periods (i.e., 15 - 30 minutes). Qualitative evidence found here suggests that many

aquatic microbial kinetic uptake studies with initial samples ≥ 5 minutes (e.g., Wolfe and

Lind 2010, Yao et al. 2011) may fail to measure initial assimilatory kinetics –essential in

generating longer term uptake rates (see Figure 4.8)– and that many contemporary “short-

term” phosphate uptake experiments are generating rates that reflect net uptake (net result

of counteracting uptake/efflux events) rather than gross. Additionally, research from

Chapter 4 supported earlier results from Chapter 3 regarding the influence of nutrient

194

legacy (between provinces) on biofilm P-uptake rates. Here I found that while both

province and season were significant factors in the ANOVAs for P-uptake and P-efflux,

the F-ratio for province was greater, suggesting that spatial effects may more effectively

influence differences in uptake/efflux. Certainly the Pennsylvania Plateau to Piedmont

transition I tested against here reflects the prominent geology of the provinces as well as

a strong gradient of environmental conditions and nutrient availability.

Overall, my dissertation research provides greater understanding of stream

nutrient dynamics through these empirical and experimental approaches aimed at

examining biofilm community uptake rates. Future research might focus on microbial

taxonomic composition (see section 4.5) and diversity indices to provide further insights

into nutrient uptake - availability relationships (see Baker et al. 2009).

LITERATURE CITED

Allan, J.D. and Flecker, A.S. 1993. Biodiversity conservation in running waters:

Identifying the major factors that affect destruction of riverine species and


Baker, M.A., de Guzman, G., and Ostermiller, J.D. 2009. Differences in nitrate uptake

among benthic algal assemblages in a mountain stream. Journal of the North


Costanza, R., d’Arge, R., deGroot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K.,

Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., and van den Belt,

M. 1997. The value of the world’s ecosystem services and natural capital. Nature

387:253-260.

195

Davidson, E.A. and Howarth, R.W. 2007. Environmental science: Nutrients in synergy.

Nature 449:1000-1001.

Hall-Stoodley, L., Costerton, J.W., and Stoodley, P. 2004. Bacterial biofilms: From the

natural environment to infectious diseases. Nature Reviews Microbiology 2:95-

108.

Wolfe, J.E. III and Lind, O.T. 2010. Phosphorus uptake and turnover by periphyton in the

presence of suspended clays. Limnology 11:31-37.

Yao, B., Xi, B., Hu, C., Huo, S., Su, J., and Liu, H. 2011. A model and experimental

study of phosphate uptake kinetics in algae: Considering surface adsorption and

P-stress. Journal of Environmental Sciences 23:189-198.

196

Figure C.1. Schematic of my four chapter dissertation research illustrating the refinement and

concentration of hypotheses through the successive applications of preceding research findings.

197

Appendix A. Summary table of experiments testing the effects of physical disturbance on biofilm

phosphorus uptake, displaying stream geographic coordinates and sampling date(s) for each

experiment (Chapter 2).

Experiment Stream Name Latitude Longitude Sampling Date(s)

Uptake by intact vs.

scraped biofilms

Spring Creek 40.7786

40.8222

-77.7696

-77.8369

10/17/2008

6/3/2009

6/11/2009

Effect of increasing levels

of disturbance

Spring Creek 40.7786 -77.7696 5/11/2009

Estimates of M-M

parameters

Spring Creek 40.7786 -77.7696 8/5/2009

Estimates of abiotic

sorption

Spring Creek 40.7786

40.8222

-77.7696

-77.8369

6/17/2009

198

Appendix B. Descriptive statistics for site, treatment, and experiment (date) factors for intact vs.

scraped stream biofilm P-uptake rates (μgP/μgChl/d) (Chapter 2). Note: SE Mean is the standard

error of the mean and CV is the coefficient of variation.

Site Treatment Experiment (date) n Mean SE Mean CV

Upstream Intact 10/17/08 4 0.336 0.030 17.627

Upstream Scrape 10/17/08 4 0.749 0.045 12.118

Downstream Intact 10/17/08 4 1.715 0.156 18.239

Downstream Scrape 10/17/08 4 1.329 0.077 11.593

Upstream Intact 6/3/09 3 0.250 0.011 7.639

Upstream Scrape 6/3/09 3 0.115 0.017 26.039



Upstream Intact 6/11/09 3 0.153 0.055 62.379

Upstream Scrape 6/11/09 3 0.197 0.018 15.819



199

Appendix C. Genera list from increasing levels of disturbance experiment (Chapter 2). Cells

identified under 100x magnification. Physiognomic (growth morphology) classifications of these

genera follow Graham and Vinebrooke (1998), Wellnitz and Ward (2000), and Passy (2007).

Genera Growth

Morphology

Disturbance Treatment

Low Low Low Medium Medium Medium High High High

Melosira Filamentous 1101 187 113 1419 693 793 678 406 1018

Closterium Stalked 22 5 2 10 5 12 6 7 66

Cosmarium Stalked 46 4 6 52 6 76 24 26 48

Cymbella Stalked 5 4 0 6 2 1 6 4 6

Diatoma Stalked 0 0 0 0 0 0 0 0 0

Gomphonema Stalked 46 9 7 142 40 206 108 71 54

Nitzschia Stalked 0 0 0 0 0 1 1 0 0

Surirella Stalked 0 0 0 0 0 0 0 0 0

Synedra Stalked 1 3 0 0 0 0 0 0 0

Achnanthes Prostrate 0 0 0 0 0 0 0 0 0

Amphora Prostrate 0 0 0 0 0 0 0 2 0

Cocconeis Prostrate 17 9 0 24 14 10 2 15 33

Cyclotella Prostrate 11 2 0 3 4 3 4 1 6

Eunotia Prostrate 0 0 0 0 0 0 0 0 0

Frustulia Prostrate 10 8 2 2 7 5 7 12 14

Gyrosigma Prostrate 0 0 0 0 0 0 1 0 0

Navicula Prostrate 0 3 0 0 0 0 2 2 12

Rhoicosphenia Prostrate 0 0 0 0 0 0 0 1 0

Total - 1259 234 130 1658 771 1107 839 547 1257

200

Appendix D. Genera list from increasing levels of disturbance experiment (Chapter 2). Cells

identified under 400x magnification. Physiognomic (growth morphology) classifications of these

genera follow Graham and Vinebrooke (1998), Wellnitz and Ward (2000), and Passy (2007).

Genera Growth

Morphology

Disturbance Treatment

Low Low Low Medium Medium Medium High High High

Melosira Filamentous 29 12 15 57 18 12 25 15 24

Closterium Stalked 2 0 0 1 0 0 0 0 2

Cosmarium Stalked 3 0 0 0 0 4 0 0 0

Cymbella Stalked 0 0 0 1 0 0 3 7 3

Diatoma Stalked 0 0 0 0 0 0 3 0 2

Gomphonema Stalked 2 1 1 3 1 2 9 1 5

Nitzschia Stalked 0 0 0 1 1 0 1 0 0

Surirella Stalked 0 0 0 0 1 0 0 0 0

Synedra Stalked 0 1 0 0 0 0 0 0 0

Achnanthes Prostrate 0 0 0 0 0 0 4 10 8

Amphora Prostrate 0 0 0 0 0 1 2 1 4

Cocconeis Prostrate 1 0 0 1 0 0 4 3 4

Cyclotella Prostrate 0 0 0 2 1 0 1 2 1

Eunotia Prostrate 0 0 0 0 0 0 1 0 0

Frustulia Prostrate 1 1 0 0 0 0 0 0 1

Gyrosigma Prostrate 0 0 0 0 0 0 0 0 0

Navicula Prostrate 1 1 0 0 1 2 1 11 8

Rhoicosphenia Prostrate 0 1 0 0 0 2 3 1 0

Total - 39 17 16 66 23 23 57 51 62

201

Fra

cti

on

of

To

tal

HighMediumLow

1.0

0.8

0.6

0.4

0.2

0.0

Growth Form

Filamentous

Stalked

Prostrate

Appendix E. Fraction (of total) of biofilm growth forms (examined at 400x) recovered from each of

the three disturbance treatments applied to resident biofilms growing on natural substrates in Spring

Creek (Chapter 2).

202

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 50 100 150 200 2500.0

0.5

1.0

1.5

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km

95% Confidence Intervals

Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

2.247

231.4

0.7662

140.9

-1.050 to 5.544

0.0 to 837.8

2

0.9893

Appendix F. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

scraped biofilm assemblages estimated over a 60 minute time period (Chapter 2). Data were fitted by

nonlinear regression according to the Michaelis-Menten equation using GraphPad Prism 5

(GraphPad Prism Software Inc., San Diego, CA, USA).

203

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 200 400 6000.0

0.5

1.0

1.5

2.0

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km


Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

2.332

293.9

0.6299

173.9

0.3271 to 4.336

0.0 to 847.4

3

0.9757

Appendix G. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

intact biofilm assemblages estimated over a 60 minute time period (Chapter 2). Data were fitted by

nonlinear regression according to the Michaelis-Menten equation using GraphPad Prism 5

(GraphPad Prism Software Inc., San Diego, CA, USA).

204

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 50 100 150 200 2500.0

0.5

1.0

1.5

2.0

2.5

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km


Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

16.64

1457

1.400

139.4

10.62 to 22.67

857.5 to 2057

2

1.000

Appendix H. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

scraped biofilm assemblages estimated over short (i.e., 5 - 12 minutes) time periods (Chapter 2). Data

were fitted by nonlinear regression according to the Michaelis-Menten equation using GraphPad

Prism 5 (GraphPad Prism Software Inc., San Diego, CA, USA).

205

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 10 20 300.0

0.5

1.0

1.5

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km


Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

2.301

23.37

4.685

85.24

-57.23 to 61.83

0.0 to 1106

1

0.6448

Appendix I. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

intact biofilm assemblages estimated over short (i.e., 5 - 12 minutes) time periods (Chapter 2). Data



206

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 50 100 150 200 2500.0

0.5

1.0

1.5

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km


Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

8.422

1237

16.61

2831

-63.05 to 79.90

0.0 to 13419

2

0.9934

Appendix J. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

scraped biofilm assemblages estimated over long (i.e., 30 - 60 minutes) time periods (Chapter 2). Data



207

[P] (µgP/L)

P U

pta

ke (

µg

P/µ

gC

hl/d

)

0 50 100 150 200 2500.0

0.1

0.2

0.3

Michaelis-Menten

Best-fit values

Vmax

Km

Std. Error

Vmax

Km


Vmax

Km

Goodness of Fit

Degrees of Freedom

R square

1.106

653.4

1.251

960.9

-4.276 to 6.489

0.0 to 4788

2

0.9904

Appendix K. Michaelis-Menten plot and parameter estimates of P-uptake vs. P concentration for

intact biofilm assemblages estimated over long (i.e., 30 - 60 minutes) time periods (Chapter 2). Data



208

Appendix L. Pennsylvania statewide map depicting the locations of the 47 streams (within distinct

physiographic provinces) sampled during a previous study (see Carrick et al. 2009) from which eight

streams (numbered in diamonds) were selected as a representative subset to deploy the in situ

enrichment system (ISES) in order to evaluate the effects of P- and N-loading on native stream

biofilm P-uptake capacity (Chapter 3).

209

Appendix M. Summary table of in situ enrichment experiments (ISES) conducted in 2008 (Chapter

3). All streams are part of the Pennsylvania Department of Environmental Protection’s Water

Quality Network (WQN). The summary includes the county where each stream is located, WQN

number, Ecoregion, geographic coordinates, and sampling dates for the deployment and retrieval of

each ISES experiment.

County Stream Name WQN Ecoregion Latitude Longitude Sampling Dates

Warren East Hickory Creek 877 Appalachian

Plateau

41.6160 -79.3701 8/22/2008

9/05/2008

Forest Tionesta Creek 830 Appalachian

Plateau

41.6019 -79.0503 8/22/2008

9/05/2008

Cumberland Cowanesque River 320 Appalachian

Plateau

42.0014 -77.1278 8/21/2008

9/03/2008

Wyoming Tunkhannock Creek 317 Appalachian

Plateau

41.5581 -75.8950 8/21/2008

9/03/2008

Chester Red Clay Creek 150 Lower

Piedmont

39.8167 -75.6919 8/11/2008

8/27/2008

Bucks Cooks Creek 187 Lower

Piedmont

40.5854 -75.2061 8/11/2008

8/27/2008

Synder Penns Creek 229 Upper

Piedmont

40.8590 -77.5796 8/12/2008

8/29/2008

Centre Spring Creek 415 Upper

Piedmont

40.7917 -77.7980 8/12/2008

8/29/2008

210

Areal C (g/m2)

Are

al C

hl (g

/m

2)

35302520151050

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Appendix N. Scatter plot of areal chlorophyll-a (Chl) (g/m2) vs. areal carbon (C) (g/m

2) estimated

from biofilms recovered from ISES experiments in both Plateau and Piedmont provinces (n= 80)

(Chapter 3). Pearson correlation coefficient (r)= 0.768, p< 0.001, r2= 0.590.

211

Appendix O. Total alkalinity measured in eight Pennsylvania streams of varying productivity over four seasons 2009 - 2010 (Chapter 4). Alkalinity was

determined by colorimetric titration (phenolphthalein and mixed indicator of bromocresol green-methyl red) to pH 4.6 with 0.02 N H2SO4 following

Wetzel and Likens (2000).

Stream Province Season Date Hydroxide

(mg CaCO3/L)

Carbonate

(mg CaCO3/L)

Bicarbonate

(mg CaCO3/L)

Total alkalinity

(mg CaCO3/L)

East Hickory Plateau Fall 11/11/2009 0 0 16 16

East Hickory Plateau Winter 1/13/2010 0 0 10 10

East Hickory Plateau Spring 5/14/2010 0 0 4 4

East Hickory Plateau Summer 9/15/2010 0 0 30 30

Tionesta Plateau Fall 11/11/2009 0 0 22 22

Tionesta Plateau Winter 1/13/2010 0 0 14 14

Tionesta Plateau Spring 5/14/2010 0 10 10 20

Tionesta Plateau Summer 9/15/2010 0 0 40 40

Cowanesque Plateau Fall 11/18/2009 0 0 68 68

Cowanesque Plateau Winter 1/18/2010 0 0 66 66

Cowanesque Plateau Spring 5/17/2010 0 24 21 45

Cowanesque Plateau Summer 9/17/2010 0 20 50 70

Tunkhannock Plateau Fall 11/18/2009 0 0 48 48

Tunkhannock Plateau Winter 1/18/2010 0 0 42 42

Tunkhannock Plateau Spring 5/17/2010 0 0 36 36

Tunkhannock Plateau Summer 9/17/2010 0 20 32 52

Red Clay Piedmont Fall 11/15/2009 0 0 92 92

Red Clay Piedmont Winter 1/15/2010 0 0 84 84

Red Clay Piedmont Spring 5/12/2010 0 36 3 39

Red Clay Piedmont Summer 9/20/2010 20 80 0 100

Cooks Piedmont Fall 11/18/2009 0 0 122 122

Cooks Piedmont Winter 1/15/2010 0 0 108 108

Cooks Piedmont Spring 5/12/2010 10 20 0 30

Cooks Piedmont Summer 9/20/2010 0 20 90 110

Penns Piedmont Fall 11/13/2009 0 0 188 188

Penns Piedmont Winter 1/16/2010 0 0 162 162

212

Penns Piedmont Spring 5/15/2010 0 36 9 45

Penns Piedmont Summer 9/18/2010 0 0 170 170

Spring Piedmont Fall 11/13/2009 0 0 204 204

Spring Piedmont Winter 1/16/2010 0 0 160 160

Spring Piedmont Spring 5/15/2010 0 20 100 120

Spring Piedmont Summer 9/18/2010 0 0 240 240

213

Appendix P. Summary table of sampling dates for the deployment and retrieval of each spatio-

temporal experiment (Chapter 4). Additional stream information (i.e., county, Ecoregion, geographic

coordinates) is located within Appendix M.

Plateau Piedmont

Stream Season Sampling Dates Stream Season Sampling Dates

East Hickory Fall 10/14/2009

11/11/2009

Red Clay Fall 10/9/2009

11/15/2009

Winter 12/16/2009

1/13/2010

Winter 12/18/2009

1/15/2010

Spring 4/16/2010

5/14/2010

Spring 4/14/2010

5/12/2010

Summer 8/11/2010

9/15/2010

Summer 8/16/2010

9/20/2010

Tionesta Fall 10/14/2009

11/11/2009

Cooks Fall 10/9/2009

11/15/2009

Winter 12/16/2009

1/13/2010

Winter 12/18/2009

1/15/2010

Spring 4/16/2010

5/14/2010

Spring 4/14/2010

5/12/2010

Summer 8/11/2010

9/15/2010

Summer 8/16/2010

9/20/2010

Cowanesque Fall 10/12/2009

11/18/2009

Penns Fall 10/10/2009

11/13/2009

Winter 12/21/2009

1/18/2010

Winter 12/19/2009

1/16/2010

Spring 4/19/2010

5/17/2010

Spring 4/17/2010

5/15/2010

Summer 8/13/2010

9/17/2010

Summer 8/14/2010

9/18/2010

Tunkhannock Fall 10/12/2009

11/18/2009

Spring Fall 10/10/2009

11/13/2009

Winter 12/21/2009

1/18/2010

Winter 12/19/2009

1/16/2010

Spring 4/19/2010

5/17/2010

Spring 4/17/2010

5/15/2010

Summer 8/13/2010

9/17/2010

Summer 8/14/2010

9/18/2010

214

Poly-P ((log10)mgP/m2)

P-e

fflu

x (

(lo

g1

0)

µg

P/

µg

Ch

l/m

in)

2.01.51.00.50.0

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Appendix Q. Scatter plot with linear regression of (log10) P-efflux (µgP/µgChl/min) vs. (log10) poly-P

(mgP/m2) for intact biofilm assemblages established on artificial substrata (tiles) from eight

Pennsylvania streams of varying productivity over four seasons 2009 - 2010 (Chapter 4).

KEITH J. PRICE 125 Cardinal Dr. (484) 238-8730

Conshohocken, PA 19428 [email protected]

EDUCATION

Ph.D. Wildlife and Fisheries Science, The Pennsylvania State University,

University Park, PA, 2012

o Dissertation: “Phosphorus Uptake by Stream Benthic Biofilms: Empirical

and Experimental Approaches to Explaining Variation”

M.S. Biology, West Chester University, West Chester, PA, 2005

o Thesis: “Nutrient Limitation of Periphyton Biomass in Westtown Lake”

B.S. Biology, Philadelphia University, Philadelphia, PA, 2002

o Minor: Economics

EXPERIENCE

The Pennsylvania State University, Dept. of Ecosystem Science and Management

o Teaching Assistant- Vertebrates Laboratory (WFS 301): Fall 2011

o Research Assistant: Spring 2007 - 2011

o Teaching Assistant- Limnology (WFS 435): Fall 2007 - 2010

o Teaching Assistant- Limnological Methods (WFS 436): Fall 2010

PEER-REVIEWED PUBLICATIONS

Price, K. J. and H. J. Carrick (in prep) Effects of nutrient loading on phosphorus

uptake by biofilms situated along a stream productivity gradient.

Carrick, H. J., K. L. Dananay, R. A. Eckert, and K. J. Price (2012) Decomposition

during autumn foliage leaf-fall in wetlands situated along a biogeochemical

gradient in Pennsylvania, USA. Journal of Freshwater Ecology 27:1-17 (Editor’s

Choice Article).

Price, K. J. and H. J. Carrick (2011) Meta-analytical approach to explain variation

in microbial phosphorus uptake rates in aquatic ecosystems. Aquatic Microbial

Ecology 65:89-102.

TECHNICAL PUBLICATIONS

Carrick, H. J. and K. J. Price (2011) Determining Variation in TMDL Reduction

Criteria. Final Report, Pennsylvania Department of Environmental Protection,

Harrisburg, PA, 84 p.

Carrick, H. J., R. A. Eckert, M. K. May, and K. J. Price (2011) Changes in

Biofilm Stoichiometry and Diatom Taxonomic Composition in Response to

Ecosystem-Level, Experimental Enrichment with P. Final Report, Pennsylvania

Department of Environmental Protection, Harrisburg, PA, 23 p.

Carrick, H. J., K. J. Price, M. K. May, and J. M. Regan (2009) Developing

Numeric Criteria to Guide Nutrient Controls for Streams in Pennsylvania. U.S.

Geological Survey State Water Resources Research Institute Program Report

2009PA103B, 5 p.

Price, K. J. (2005) Nutrient Limitation of Periphyton Biomass in Westtown Lake.

Masters Thesis, West Chester University, 140 p.

Documents

PHOSPHORUS UPTAKE BY STREAM BENTHIC BIOFILMS …