SPATIO-TEMPORAL VARIATION AND DISSOLVED ORGANIC CARBON PROCESSING

OF STREAMBED MICROBIAL COMMUNITY:

STABLE CARBON ISOTOPE APPROACH

by

PHILIPS OLUGBEMIGA AKINWOLE

ROBERT H. FINDLAY, COMMITTEE CHAIR AMELIA K. WARD JULIE B. OLSON

BEHZAD MORTAZAVI FRED T. ANDRUS

A DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

in the Department of Biological Sciences in the Graduate School of

The University of Alabama

TUSCALOOSA, ALABAMA

2013

Copyright Philips Olugbemiga Akinwole 2013 ALL RIGHTS RESERVED

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ABSTRACT

Sedimentary microbial communities play a critical ecological role in lotic ecosystems and are

responsible for numerous biogeochemical transformations, including dissolved organic matter

(DOM) uptake, degradation, and mineralization. The goals of this study were to elucidate the

benthic microbes responsible for utilization of humic DOM in streams and to assess overall

variability in microbial biomass and community structure over time and across multiple spatial

scales in stream networks, as DOM quality and quantity will likely change with stream order. In

Chapter 2, multiple spatial patterns of microbial biomass and community structure were

examined in stream sediments from two watersheds; the Neversink River watershed (NY; 1st, 3rd

and 5th order streams sampled) and the White Clay Creek watershed (PA; 1st through 3rd order

streams sampled). Microbial biomass and community structure were estimated by phospholipid

phosphate and phospholipid fatty acids (PLFA) analyses. Multivariate analysis showed that

sedimentary C:N ratios, percent carbon, sediment surface area and percent water content

explained 68% of the variations in total microbial biomass. Overall, the magnitude of within

stream variation in microbial biomass was small compared to the variability noted among

streams and between watersheds. Principal component analysis (PCA) of PLFA profiles showed

that microbial community structure displayed a distinct watershed-level biogeography, as well as

variation along a stream order gradient. Chapter 3 demonstrated that benthic microbial biomass

was seasonally dynamic and significantly correlated to a combination of high and low flood

pulse counts, variability in daily flow and DOC concentration in the White Clay Creek.

Additionally, the seasonal pattern of variation observed in microbial community structure was as

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a result of shift between the ratios of prokaryotic to eukaryotic component of the community.

This shift was significantly correlated with seasonal changes in median daily flow, high and low

flood pulse counts, DOC concentrations and water temperature. Compound-specific 13C analysis

of PLFA showed that both bacterial and microeukaryotic stable carbon isotope ratios were

heaviest in the spring and lightest in autumn or winter. Bacterial lipids were isotopically depleted

on average by 2 - 5‰ relative to δ13C of total organic carbon suggesting bacterial consumption

of allochthonous organic matter, and enriched relative to δ13C algae-derived carbon source. In

Chapter 4, heterotrophic microbes that metabolize humic DOM in a third-order stream were

identified through trace-additions of 13C-labeled tree tissue leachate (13C-DOC) into stream

sediment mesocosms. Microbial community structure was assessed using PLFA biomarkers, and

metabolically active members were identified through 13C-PLFA analysis (PLFA-SIP).

Comparison by PCA of the microbial communities in stream sediments and stream sediments

incubated in both the presence and absence of 13C-DOC showed our mesocosm-based

experimental design as sufficiently robust to investigate the utilization of 13C-DOC by sediment

microbial communities. After 48 hours of incubation, PLFA-SIP identified heterotrophic α, β,

and γ- proteobacteria and facultative anaerobic bacteria as the organisms primarily responsible

for humic DOC consumption in streams and heterotrophic microeucaryotes as their predators.

The evidence presented in this study shows a complex relationship between microbial

community structure, environmental heterogeneity and utilization of humic DOC, indicating that

humic DOC quality and quantity along with other hydro-ecological variables should be

considered among the important factors that structure benthic microbial communities in lotic

ecosystems.

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DEDICATION

I dedicate this piece of work to my family: Taiwo, Susan, Daniella and David for they are there for me every single day.

v

LIST OF ABBREVIATIONS AND SYMBOLS

a Anteiso

ANOVA Analysis of Variance

br Branched

12C Carbon with a mass of 12

13C Carbon with a mass of 13

C18 18 carbon chain

CO2 Carbon dioxide

cm Centimeter

cy Cyclo

df Degrees of freedom: number of values free to vary after certain restrictions have been placed on the data

DI Deionized water

DOC Dissolved organic carbon

DOM Dissolved organic matter

FAMEs Fatty acid methyl esters

FHC High flood pulse count

FIG Figure

FLC Low flood pulse count

ffw Fresh wet weight

g Gram

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gdw Gram dry weight

GC/C/IRMS Gas chromatography-combustion-isotope ratio mass spectrometry

GF/F Glass fiber filters

h Hour

ha Hectare

iso Iso

IHV Indicators of hydrological variation

kDa Kilodalton

km Kilometer

L Liter

ln Natural log

m Meter

m2 Meter squared

m3 Meter cubed

M Mean: the sum of a set of measurements divided by the number of measurements in the set

MBI Base flow index

MDF Mean daily flows

MQ50 Median daily flow

MVD Variability in daily

mg Milligram

min Minute

mL Milliliter

mm Millimeter

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n Number of sample size

14N Nitrogen with a mass of 14

ng Nanogram

nmol Nanomole

NIST National Institute of Standards and Technology

p Probability associated with the occurrence under the null hypothesis of a value as extreme as or more extreme than the observed value

pH Concentration of hydrogen ions

PCA Principle component analysis

PLFA Phospholipid fatty acids

PLP Phospholipid phosphate

ppm Parts per million

r Pearson product-moment correlation

s Second

t Computed value of t test

v Volume

v/v/v Volume to volume

V-PDB Vienna Pee Dee Belemnite standard

< Less than

> Greater than

= Equal to

α Alpha

β Beta

γ Gamma

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δ Delta

µ Micro

µg Microgram

µL Microliter

µm Micrometer

ω Omega

% Percent

‰ Per mille

ºC Temperature in Celsius

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ACKNOWLEDGMENTS

As I hit on the keyboard of my laptop while writing this section of my dissertation, I

realize that I am within a few days of completing my doctoral studies, after a long tiring process

that have presented some challenges. I got to this point because of so many gracious people that

helped nudged me ahead and made valiant efforts to keep me prepared for the next challenges.

My best conceivable advisor: Bob Findlay:

Words cannot express my heartfelt gratitude, appreciation and thanks for all the support,

guidance and time you provided for this research project and my graduate training; right from the

day you picked me up at the Atlanta International Airport to the completion of this dissertation.

You walked me though the dark moments of my academic birth pangs and believed in me; that

worked into something good. Thank you.

My Dissertation Committee- Drs. Ward, Olson, Mortazavi and Andrus:

Your dedication, guidance, consideration and insightful suggestions are greatly appreciated.

Thank you for your constructive comments every step of the way and all the little ‘extra push’ so

that I can become better. Thank you.

Department of Biological Sciences and Graduate School:

I express my thanks to the Department of Biological Sciences for the teaching assistantship that

provided much needed stipend and exposure to classroom settings. I am grateful to the Graduate

school for Dean’s Discretionary Scholarship for the Fall 2009 semester towards the successful

completion of my degree. Thank you.

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Collaborating institute and teams:

Stroud Water Research Centre, Avondale, PA and Stable Isotope Biogeochemistry Lab,

Michigan State University. Thanks to all the people who stayed with me through all the

challenges and pitfalls of experimental work: setting up mesocosms, collecting sediment

samples, putting me through the GC/C/IRMS, including their support in several inches of snow

in Michigan and hiking the Catskill Mountain in New York: Lous Kaplan, Robert Sherman,

Michael Gentile, Peggy Ostrom and Hasand Ghandi. Thank you.

My lab mates and colleagues:

Janna Brown, Prarthana Ghosh, Jen Mosher, Thomas Branan, Joshua Mays, Edwina Clarke,

Brian Shirey, Michael Kendrick and Elise Chapman, for technical and logistic supports, and

taking time out of no time to share information and knowledge at various levels. Thank you.

My awesome family:

Special thanks to my best friend and loving wife; Taiwo and beautiful kids, Susan, Daniella and

David. With your love and support despite my little stipend and my ‘absenteeism’ at home, you

have made this adventure successful. We made it together! Finally, to my families and friends at

‘home,’ thousands of miles away for asking me to finish my studies in time because I need to

start making money. Thank you for those reminders and your understanding all the way.

To Him:

‘…But you are not dead: you lives and abides forever,

For in you we live and move and have our being’

Epimenides of Crete

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TABLE OF CONTENTS

ABSTRACT ................................................................................................ ii

DEDICATION ........................................................................................... iv

LIST OF ABBREVIATIONS AND SYMBOLS ........................................v

ACKNOWLEDGMENTS ......................................................................... ix

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

LIST OF FIGURES ................................................................................. xiii

CHAPTER 1: GENERAL INTRODUCTION ............................................1

CHAPTER 2: SPATIAL PATTERNS OF MICROBIAL SIGNATURE BIOMARKERS IN STREAM NETWORKS ...........12

CHAPTER 3: SEASONALITY IN A STREAMBED MICROBIAL

COMMUNITY: VARIATION IN THE ISOTOPIC COMPOSITION OF LIPID BIOMARKERS……………………..52

CHAPTER 4: ELUCIDATING THE BACTERIA RESPONSIBLE FOR UTILIZATION OF DISSOLVED ORGANIC MATTER IN A THIRD-ORDER STREAM .....................................................96

CHAPTER 5: OVERALL CONCLUSIONS...........................................134

REFERENCES ........................................................................................140

APPENDIX ..............................................................................................147

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LIST OF TABLES

TABLE 1.1 Personal contribution of POA to the NSF funded project and

publications……………………………………………………………….…….…9 TABLE 2.1 Microbial biomass, water chemistry and sediment organic content of

White Clay Creek and Neversink watersheds……………………………………49 TABLE 2.2 Nested ANOVA to test the effects of watershed, streams within watershed,

and stations within streams on microbial biomass…………………………….…50 TABLE 2.3 Multiple regression analysis (best subsets) for natural log biomass as a

function of various physical and chemical stream parameters…………………..51 TABLE 3.1 Seasonal variations in total sedimentary microbial biomass, bacterial

abundance, and physico-chemical parameters of White Clay Creek…………….91 TABLE 3.2 Pearson correlation coefficient matrices between selected hydrological

indices and measured environmental variables…………………………………..92 TABLE 3.3 Multiple regression analysis (best subsets) for natural log biomass as a

function of stream physico- chemical and hydrological indices…………………93 TABLE 3.4 Multiple regression analysis (best subsets) for microbial community

(PC1) as a function of stream physico-chemical and hydrological indices………94 TABLE 3.5 Annual variations in stable carbon isotope signatures for selected

common fatty acids from White Clay Creek sediments. Values shown are mean (±SD) from all sampling sites and months…………………….95

TABLE 4.1 Experimental design of 13C-DOM uptake experiments….....................................131 TABLE 4.2 Microbial PLFA δ13C values (‰; mean ± SD) from 6 mesocosm experiments

determined using DB-1 and DB-23 chromatographic columns…………………132 TABLE 4.3 Phylogenetic affiliation of bacterial fatty acids functional groups extracted

from White Clay Creek sediment…………………………………………….....133

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LIST OF FIGURES

FIG 1.1 Mesocosm setup for 13C leachate uptake measurement, including streamwater-fed bioreactors/ mesocosm chambers containing sediments and water jackets. One chamber without 13C leachate amendment served as experimental control………...…10

FIG 1.2 Close up view of mesocosm chamber showing top surface of a galvanized

sediment box contiginous with the front ramps of the Venturi flumes and associated recirculating pipes system…………………………………………………11

FIG 2.1 Sampling scheme used to examine microbial biomass and community structure across

multiple spatial scales in two watersheds. Sampling within the Neversink watershed consists of four 1st order streams; Biscuit Brook and Pigeon Creek tributaries (Biscuit Brook Tributary A and B [BBTA, BBTB], Pigeon Creek Tributary A and B [PBTA, PBTB], 1a, 1b, 1c, 1d, respectitively), two 3rd order streams (Biscuit Brook [BBR] and Pigeon Creek [PBR], 3a, 3b, respectitively) and one 5th order stream (Neversink River [NRC]). Sampling within the White Clay Creek watershed consists of four 1st order streams (Ledyards Spring Branch [LSB], Water Cress Spring [WTR], Dirty Dog Spring [DDS] and Walton Spring Branch [WSB], 1e, 1f, 1g, 1h, respectitively), two 2nd order streams (East and West Branch White Clay Creek [WCWE, WCCW], 2a, 2b, respectitively) and one 3rd order stream (White Clay Creek [WCC]). Sketches of watersheds are not drawn to scale. Each eclipse represents a reach, which contained 3 stations, each of which was sampled times………………………………………..…42

FIG 2.2 Variation in sediment (a) percent carbon, (b) percent nitrogen and (c) C:N ratio

by stream, order (1st to 3rd/5th order from left to right) and watershed. Vertical bars denote 0.95 confidence intervals. Streams not connected by a horizontal line are significantly different (p = 0.05, Tukey’s Wholly Significant Difference)…………………………………………………………..……43

FIG 2.3 Microbial biomass (mean ± SD) of White Clay Creek and Neversink watershed

sediments at three spatial scales: a; watershed, b; stream and c; station. Stream order (or average order for watershed values) are indicated as: black = 1st order, dark gray = 2nd order, light gray = 3rd order, open = 5th order…………………………………………………………………………….……44

xiv

FIG 2.4 Path diagrams describing the structure of the relationship between sediment microbial biomass and % Carbon, % water content, C:N ratio and sediment surface area. Single-headed arrows indicate casual paths; numbers on arrows are path coefficients (standardized regression coefficients) indicating the relative strength of each path leading to a given response variable. Double-headed arrows represent the correlations among the predictor variables. Arrows connecting environmental variables to the independent variable (microbial biomass) indicate direct effects, while environmental variables linked to the independent variable via other environmental variable constitute indirect effects. Path coefficients calculated by SAS Structural Equation Modeling for JMP 10. *= P <0.01, **=P <0.001………………………………………………………………45

FIG 2.5 Principle Component Analysis of stream sedimentary microbial community

structure of White Clay Creek (open circle) and Neversink (open square) watersheds. The percent variation explained by each axis is indicated on the respective component axis. Identified fatty acids had component loadings of >|0.5| with strong influence on the pattern of variation among samples along the respective component axes. Site abbreviations are as described in the legend to Fig.1…………………………………………………………………46

FIG 2.6 Relationship between Principle Component Analysis factor 1 score and the

calculated percentage that microeukaryotes contribute to total microbial biomass for all stream samples…………………………………………………….…47

FIG 2.7 Spatial variation in sedimentary bacterial community composition in WCC and

NRC watersheds by PLFA analysis after removal of fatty acids assigned a priori to the functional group microeukaryotes and those known to be common to both bacteria and microeukaryotes from the PLFA profiles. Symbols- WCC (circle), NSR (square). Site abbreviations are as described in the legend to Fig.1………………………………………………………48

FIG 3.1 Mean daily discharge at United States Geological Survey (USGS) gauging

station of the study stream during the study period from November 2009 to October 2010. Arrow represents sampling date of streambed sediment samples………………………………………………………………………86

FIG 3.2 PCA of benthic microbial community structure determined by PLFA from the

White Clay Creek seasonal sampling site. Scores are plotted by months: February, F; March, Ma; April, Ap; May, My; June, Ju; July, Jy; August, Au; September, S; October, O; December, D. Scales indicate the degree of difference among samples and influential fatty acids (factor loadings > |0.5|)] are shown along each axis. Symbols indicate mean PC scores (n=9, except Nov. and Dec. where n =3), error bars = ±S.D……………………………………………...87

FIG 3.3 Relationship between PCA factor 1 score and the calculated percentage that

microeukaryotes contribute to total microbial biomass for all samples……………….88

xv

FIG 3.4 Seasonal variability in sedimentary TOC and selected δ 13C PLFAs with

component loadings >0.5 that exerted strong influence on the pattern of variation among samples along the PC 1 (Fig 5). Bars represent standard deviation………….89

FIG 3.5 PCA of all quantified δ

13C of PLFAs of WCC benthic microbial community. Scores are plotted by months: February, FE; March, MA; April, AP; May, MY; June, JU; July, JY; September, SE; October, OC; December, DE. Influential fatty acids (factor loadings > |0.5|)] are shown along each axis. # summed feature includes 16:1ω9, 16:1ω7c, 16:1ω5c, 16:1w13t; *summed feature includes 18:2ω6, 18:3w3, 18:1ω9, 18:1ω7c, 18:1ω5…………………………………………90

FIG 4.1 Changes in a) microbial biomass, b) percent prokaryotes and c) community

structure summarized by PCA axis 1, among treatments and sampling dates for all experiments. Values are mean differences ± SD, (n = 6). T0-TM= Differences attributed to mesocosm effect, TM-T13C= Differences attributed to the effects of 13C-labeled DOM………………………………………130

1

CHAPTER 1

GENERAL INTRODUCTION

Microbes are important players in lotic ecosystems and are responsible for several

biogeochemical transformations, including liberation of essential nutrients via detrital

decomposition and dissolved organic matter uptake, degradation, and mineralization (Kaplan and

Newbold 1993; Pusch et al. 1998; Fischer and Pusch 2001; Tank et al. 2010). They have small

size, ubiquitous distribution, high surface to volume ratio, short generation intervals, high

metabolic diversity and the highest documented intraspecific genetic diversity of any type of

organism (McArthur et al. 1988; Morehead et al. 1996). Microbial processing of terrestrial

particulate organic matter (POM), dissolved organic matter (DOM) and nutrients within the lotic

ecosystems control the material flux that influence higher trophic levels (Dobbs and

Guckert 1988; Hart 1992; Poff and Ward 1992; Pusch et al. 1998; Cotner and Biddanda 2002).

The utilization of DOM in streams by heterotrophic microbial community controls important

lotic ecosystem processes and supports productivity at higher trophic levels. It is, therefore,

important not only to describe microbial community structure and function, but also, to identify

biological processes and environmental variables that influence their assemblages both

temporally and spatially.

DOM is the largest active pool of carbon in lotic ecosystems and is continuously supplied

to the system from both allochthonous (terrestrial) and autochthonous (aquatic) sources (Peduzzi

et al. 2008). Mounting evidence has shown that it plays a significant role in aquatic ecosystems

2

as carbon and energy sources for the microbial food web (Peduzzi et al. 2008; Wiegner et al.

2009; Wong and Williams 2010), and its flux from streams and rivers often dominates organic

loading to estuaries (Amon and Benner 1996). In addition, due to its dynamic role in the

interaction between hydrosphere and biogeosphere, DOM is now seen as an important driver of

ecosystem functions in freshwater environments and a major component in global carbon cycling

and climate change (Amon and Benner 1996; Batin et al. 2008; Besemer et al. 2009). The

interactions between both the quantity and quality of DOM and stream microorganisms are

important to several key ecosystem functions. Variations in the quality and quantity of DOM can

exert pronounced influence on microbial communities altering characteristic such as biomass,

enzymatic activities, and community structure (Bourguet et al. 2009; Freese et al. 2010, Mosher

and Findlay 2011). For example, experimental manipulations of organic matter concentration

and composition have shown marked changes in bacterial metabolic activities (Smith et al. 1995;

see Findlay and Sinsabaugh 1999 and reviews therein). Also, microbial processes directly

influence qualitative and quantitative transformations of DOM in the environment (Bourguet et

al. 2009). Thus, the fate of DOM is intimately associated with microorganisms that are

responsible for carrying out a wide range of processes that are fundamental to ecosystem

success. However, research efforts to understand DOM utilization through microbial processes

have been complicated by the chemical heterogeneity of the DOM pool and a lack of methods

for measuring in situ microbial activities (Kaplan et al. 2008; Bourguet et al. 2009).

In the past, our knowledge has been on bulk microbial processes, generally treating

microbial community as a “black box” (Cottrell and Kirchman 2000; Foreman and Covert 2003).

Studies linking bacterial community structure with functions, such as DOM turnover, are few

and mostly focus on the microbial processing of tracers that are not reflective of natural stream

3

DOM. Early attempts include NaH13CO3 additions in lakes (Kritzberg et al. 2004; Pace et al.

2004) and 13C-enriched sodium acetate additions in streams (Hall and Meyer 1998; Johnson and

Tank 2009; but see Kaplan et al. 2008). However, with the development of new techniques and

substrate (e.g., leachate from composted 13C-labelled tulip poplar tree-tissues; Wiegner et al.

2005a), we can examine what components of DOM are susceptible to degradation and

understand factors affecting taxon-specificity in utilization ability and improve models of carbon

and energy transformation in aquatic habitats. Thus, we can begin to elucidate which microbial

functional group utilizes a given DOM constituent and if humic DOM supports a significant

portion of stream ecosystem metabolism.

Unlike DOM, the scaling of uptake lengths of nutrients with stream size has been widely

documented (Newbold et al. 1981; Peterson et al. 2001; Hall et al. 2002; Alexander et al. 2007;

Tank et al. 2008; but see Kaplan et al. 2008). For example, uptake lengths are shorter in

headwater streams, whereas streams with greater depth and velocity (i.e., 4th order streams and

above) will have longer uptake lengths (Hall et al. 2002). Our understanding of bacterial

functional group utilization of DOM constituents will not be complete without considering the

spatial scale at which these ecological processes occur. Fortunately, the hierarchical nature of

stream networks, in which a series of successively smaller geomorphic units are nested within

each other (Lowe et al. 2006), makes it a prime candidate to test the application of scaling rules

to DOM uptake across stream orders. In effect, riffles/pools are nested within stream reaches,

which are nested within streams, which are nested within watersheds. Both theoretical models

and experimental evidence have demonstrated that the scale at which ecological processes occur

has an effect on microbial community and diversity (Durrett and Levin 1997; Kerr et al. 2002).

However, studies on systematic assessments of variability in biological aspect of DOM and

4

microbial metabolism across multiple spatial scales are rare. Such an approach, when coupled

with measurements of bio-physiochemical variables, could inform how the controls of microbial

utilization of DOM occur across temporal and spatial scales.

The stable isotope ratios of carbon (measured on isotope ratio mass spectrometers) have

the potential to serve as tracers for sources, flow paths and transformations of dissolved and

particulate organic carbon in lentic and lotic ecosystems (e.g., Hall 1995; Hall and Meyer 1998;

Cole et al. 2002). Measurements of δ13C have been used to investigate DOM dynamics in a wide

variety of streams and laboratory mesocosms (Hall and Meyer 1998; Cole et al. 2002; Wiegner et

al. 2005b). An exciting innovation in isotope ratio analysis is the development of gas

chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS). This improved

technique can be used to link microorganisms in environmental samples to utilization of

particular growth substrates (Dumont and Murrell 2005). Presently, lipids, amino acids and

nucleic acids have been used as the biomarker molecules and for compound-specific stable

isotope analysis (Boschker et al. 1998; Radajewski et al. 2000). Phospholipid fatty acid analysis

(PLFA) and lipid profiling are well-established techniques for the identification of

microorganisms and characterization of microbial communities (White et al. 1994; Findlay et al.

1989; 2004). PLFA analysis for assessing the activity of microbial communities in the

environment is greatly augmented by the use of 13C-labelled substrates in conjunction with

GC/C/IRMS (Boschker 2004; Evershed et al. 2006). This approach has proved successful for

linking specific populations within complex microbial consortia with substrate utilization

through 13C enrichment of PLFA biomarkers in various environments and situations (Boschker et

al. 1998). Thus, combining 13C-DOM produced using the technique developed by Wiegner et al.

(2005a) with GC/C/IRMS should provide insights and important information about the dynamics

5

of DOM in aquatic ecosystems and the heterotrophic microorganisms responsible for its uptake

and utilization. The biological lability of DOM, including terrestrial derived humic substances,

and consequently, its importance to bacterial metabolism, underpins the focus of my dissertation.

This study has employed compound-specific stable isotope and PLFA techniques to elucidate the

bacteria responsible for utilization of humic DOM in streams and to assess overall variability in

microbial biomass and community structure temporally and across multiple spatial scales in

stream networks.

The first question addressed is “how similar are stream microbial communities across

multiple spatial scales within and among stream networks” (Chapter 2). Most studies have shown

horizontal variations in microbial abundance, distribution and diversity within a stream

continuum or among streams (Battin et al. 2001; Oda et al. 2003; Crump et al. 2004; Hughes-

Martiny et al. 2006; Fierer et al. 2007), but little attention has been paid to the significance of

multiple spatial scales in stream microbial ecology studies. This research effort investigates

microbial biomass and community structure from streambed sediments in 14 streams within two

forested watersheds across four spatial scales: among individual sediment cores; within reaches

within stream; among streams within watershed and between watersheds. In addition, factors or

set of factors that control the structure of microbial communities in these systems were

investigated. Although some studies have suggested environmental variables that influence

microbial communities in several habitats (Battin et al. 2001; Gao et al. 2005; Fierer et al. 2007),

how environmental heterogeneity structures microbial community composition and distribution

in streambed is not fully understood.

6

Chapter 3 examines seasonal variations in stable carbon isotope signatures of individual

microbial fatty acids of White Clay Creek, a 3rd order piedmont stream in southeastern

Pennsylvania. To the best of our knowledge, there have been no reports describing seasonal

variation in compound specific carbon isotope signatures of individual PLFA in relation to

changes in microbial community structure in stream sediments. Understanding the seasonal

variation of isotopic signatures of specific microbial biomarkers will yield valuable insights into

dynamics of the carbon isotopic composition of the biological and sedimentary substrates in

streams. Unlike studies on seasonal variations in assemblages of stream fishes and invertebrates,

(e.g., Bott and Borchardt 1999; Pires et al. 1999; Cowell et al. 2004; Cleven 2004; Taylor et al.

1996; Hatzenbeler et al. 2000; Davey and Kelly 2007), studies that have investigated the

structure and seasonal dynamics of sedimentary microbial communities are limited in number

(Kaplan and Bott 1989; Smoot and Findlay 2001; Battin et al. 2001; Sutton and Findlay 2003).

Thus, more detailed information on seasonal patterns in benthic microbial community

composition and associated carbon isotope signatures are needed to fully understand the use of

stable isotope probing in adressing the question of DOM use by stream microbiota.

Chapter 4 elucidates which heterotrophic benthic microbes within streams actively utilize

DOM and ultimately control the material flux that influences higher trophic levels. Although

viewed as biologically more recalcitrant and perhaps less energy yielding than monomers (Amon

and Benner 1994), evidence has shown that a portion of the humic substances is biologically

degradable (Moran and Hodson 1990; Carlsson et al. 1999). The research efforts in this chapter

employ bioreactors (Figure 1.1a and b) fitted with undisturbed (as much as possible) sediments

as a laboratory tool to study the incorporation of synthesized tree tissue leachate (Wiegner et al.

2005a) into microbial PLFA biomarkers. The compound-specific 13C analyses of individual

7

PLFAs will allow us to identify the microbial functional group(s) responsible for the uptake of

humic substances in streamwater. Leachate used in this study was synthesized from tulip poplar

seedlings that were earlier grown with 13CO2 at the National Phytotron located at Duke

University, Durham, North Carolina, USA (Wiegner et al. 2005a).

Chapter 5 provides a summary of the major results and conclusions from the

aforementioned chapters and directs attention to their implications in terms of future research

efforts in microbial ecology. This interdisciplinary project contributes to the understanding of the

utilization of labile and semi-labile DOM in streams and provides insight into the efficacy of

mesocosms as tools within microbial ecology. However, my dissertation extends the study of

microbial utilization of labile and semi-labile DOM by including environmental influences that

influence temporal and spatial distributions of microbial community in streams, producing a

better understanding of the importance of headwaters to river networks and important

implications for the protection of forested headwater streams.

8

CONTRIBUTION TO PUBLICATION AND MANUSCRIPTS

My doctoral dissertation was a part of a larger NSF-funded collaborative project to

address the application of scaling rules to energy flow in stream ecosystems; however, my focus

centered on investigating spatio-temporal variations in microbial community in stream networks

and elucidating the bacteria responsible for utilization of dissolved organic matter in streams.

Consequently, my PhD dissertation was interdisciplinary and completed in close collaboration

with Lou Kaplan (biogeochemistry) at Stroud Water Research Centre, Avondale, PA, where

mesocosm experiments were conducted; Peggy Ostrom (geochemistry) at Michigan State

University, East Lansing, MI, where I carried out compound specific stable isotope analysis; and

Robert Findlay (microbial and ecosystem ecology) at University of Alabama, Tuscaloosa, AL,

where I had my graduate training.

The work presented here is based on 3 manuscripts prepared by P.O. Akinwole for

submission to peer-reviewed journals; the close interdisciplinary cooperation will result in a

number of co-authorships. These are:

• Akinwole P.O., L.A. Kaplan and R. H. Findlay. (in prep). Spatial patterns of microbial

signature biomarkers in stream networks. To be submitted to Microbial Ecology

• Akinwole P.O., L.A. Kaplan and R. H. Findlay. (in prep). Seasonal variations in the carbon

isotopic composition of lipid biomarker compounds and structure of a streambed microbial

community. To be submitted to Microbial Ecology

• Akinwole P.O., L.A. Kaplan, P.H. Ostrom and R.H. Findlay. (in prep) Elucidating the

bacteria responsible for utilization of dissolved organic matter (DOM) in a third – order

stream. To be submitted to Ecosystems

9

Personal contributions of P.O. Akinwole to the interdisciplinary project and included

manuscripts are shown in Table 1.

Table 1.1 Personal contribution of POA to the NSF funded project and publications.

xxx = major contribution; xx = moderate contribution; x = minimum contribution

Chapter 2: Spatial pattern of microbial signature biomarkers in stream networks

Chapter 3: Seasonality in a streambed microbial community: variation in the isotopic

composition of lipid biomarker

Chapter 4: Elucidating the bacteria responsible for utilization of dissolved organic matter in a

third-order stream

Activity Chapter 2 Chapter 3 Chapter 4

Experimental design xxx xx x

Experimental/Field work xxx x xx

Laboratory analyses xxx xxx xxx

Data analysis xxx xxx xxx

Manuscript writing xxx xxx xxx

10

Fig. 1.1 Mesocosm setup for 13C leachate uptake measurement, including streamwater-fed

bioreactors/ mesocosm chambers containing sediments and water jackets. One chamber without

13C leachate amendment served as the experimental control.

11

Fig. 1.2. Close up view of mesocosm chamber showing top surface of a galvanized sediment

box contiguous with the front ramps of the Venturi flumes and associated recirculating pipes

system.

12

CHAPTER 2

SPATIAL PATTERNS OF MICROBIAL SIGNATURE BIOMARKERS IN

STREAM NETWORKS

ABSTRACT

The large-scale spatial patterns of microbial community structure and diversity are largely

unknown compared to those of macro fauna and flora. We investigated these patterns in stream

sediments from two watersheds; the Neversink River watershed (NY; 1st, 3rd and 5th order streams

sampled) and the White Clay Creek watershed (PA; 1st through 3rd order streams sampled).

Microbial biomass and community structure were estimated by phospholipid phosphate and

phospholipid fatty acids (PLFA) analyses, respectively. Multivariate analysis showed that C:N

ratio, percent carbon, sediment surface area and percent water content explained 68% of the

variations in total microbial biomass. Overall, the variability of microbial biomass within streams

was low compared to the variability among streams and between watersheds. Principal

component analysis of PLFA profiles showed that microbial community structure displayed a

distinct watershed-level biogeography, as well as variation along a stream order gradient. This

study indicates a non-random distribution of microbial communities and that environmental

heterogeneity and geographical distance can influence microbial distribution.

Key words: benthic microbial community, microbial biomass, Neversink and White Clay Creek

watersheds, multiple spatial scales, phospholipid fatty acids

13

INTRODUCTION

Microbial taxa are the most biologically diverse and ubiquitous taxa on earth and their

metabolic activities largely control biogeochemical cycling and ecosystem processing (Curtis and

Sloan 2004; Tringe et al. 2005; Tank et al. 2010). In stream ecosystems, benthic microbial

communities mediate many of the biochemical transformations, including degradation and

transformation of recalcitrant chemical compounds into biomass or inorganic components,

exerting significant control over the mineralization and downstream exportation of terrestrially-

derived dissolved organic matter (DOM) (Kaplan and Newbold 1993; Pusch et al. 1998; Fischer

and Pusch 2001; Tank et al. 2010). In addition, microbial processing of terrestrial-DOM and

nutrients within the streambed sediments is essential to material flux to higher trophic levels (Hart

1992; Poff and Ward 1992; Pusch et al. 1998; Hall and Meyer 1998). Consequently, microbes are

best described as life’s engines driving biogeochemical processes in streams, as well as on earth

(Falkowski et al. 2008).

There has been limited progress in our understanding of how microbial diversity changes

across spatial gradients and comparable research on the microbial biogeographical patterns have

lagged behind research on plant and animal communities (Fierer and Ladau 2012). Attempts to

investigate microbial biogeography in stream sediments have shown the emergence of clear

biome-level patterns in streambed microbial communities (Findlay et al. 2008). Gao et al. (2005)

compared benthic bacterial community structure among nine streams across the southeastern and

midwestern United States and observed differences attributed to variations in chemical

characteristics of the habitats, rather than a pattern driven by spatial gradients. Other studies have

14

shown that microorganisms vary in abundance, distribution and diversity over various habitats

and that microbial composition across landscapes is nonrandom (Øvreås et al. 1997; Cho and

Tiedje 2000; Battin et al. 2001; Oda et al. 2003; Crump et al. 2004; Hughes-Martiny et al. 2006;

Fierer et al. 2007)

How microbial diversity across spatial scales is related to the physical, chemical and biotic

variables of ecosystems is a fundamental question in microbial ecology. Fierer et al. (2007) found

that a single variable, streamwater pH, could predict much of the variability in bacterial

communities inhabiting fine benthic organic matter across the Hubbard Brook watershed.

Changes in quantities and qualities of carbon availability may also alter microbial community

structure in predictable ways (Fierer et al. 2007; Nemergut et al. 2010). Other studies found that

sediment chlorophyll a (Battin et al. 2001; Gao et al. 2005), dissolved organic carbon and nitrate

concentrations (Gao et al. 2005), stream order and current regime (Molloy 1992) were other

environmental variables influencing the structure of microbial communities.

Although our understanding of microbial biogeography continues to expand, there is a

paucity of information on the spatial distribution of microbial communities attached to streambed

substrata of low-order streams and on the factors that control their distributions (Leff 1994).

Particularly, low-order streams serve as important links between terrestrial and larger aquatic

systems (Hullar et al. 2006). Understanding the ecological coherence (Philippot et al. 2011) of

benthic microbial community in headwater streams may have important implications for

ecological linkages between aquatic and terrestrial systems. Stream networks are inherently

hierarchical in nature with a series of successively smaller geomorphic units nested within each

other (reaches are nested within streams and streams within watersheds) (Tiegs et al. 2009).

Characterization of stream networks and the variation in microbial communities across these

15

networks require a sampling regime that adequately captures this complexity. This study

examined microbial biomass and community structure from streambed sediments in forested

streams within two distinct watersheds. We used a nested sampling design and sampled at four

spatial scales: within a station (individual sediment cores separated by <1m); among stations

within a stream reach (separated by >1m but ≤50m); among streams within watershed (separated

by >50m but ≤10km) and between watersheds (separated by >350km). Our study was designed to

investigate how similar stream microbial communities were across multiple spatial scales within

and among stream networks. We hypothesized that increasing spatial scale of fluvial

geomorphology units, from reaches to watersheds, increases variability in microbial communities

at each hierarchical level. We sampled streambed sediments from 1st, 2nd and 3rd - order streams in

White Clay Creek stream network (Avondale, PA, USA) and from 1st, 3rd and 5th- order streams in

the Neversink stream network (Claryville, NY, USA). We used phospholipid-based techniques to

characterize the microbial biomass and community structure of sediments from these streams. Our

data were subjected to multivariate statistical analyses to compare the patterns of microbial

community structure within and between stream networks.

16

METHODS

Study sites and experimental design

Study streams were located within two stream networks: the 3rd order, 7.3 km2 White

Clay Creek (WCC) watershed in the southern Pennsylvania Piedmont, and the 5th order, 171 km2

Neversink River (NSR) watershed within the Catskill Mountains of New York. The WCC

watershed is located within the Piedmont Province of southeastern Pennsylvania and predominant

land uses are agricultural (52%), hayed/grazed fields (22%) and wooded lands (23%) (Wiegner et

al. 2005; Newbold et al. 1997). Streamflow and streamwater chemistry have been monitored since

the 1970s with mean annual stream flow, stream water temperature, and local precipitation of 115

L/s, 10.6º C, and 105 cm, respectively. Streambed sediments consist of clay-, silt-, and sand-sized

particles in pools and runs, with gneiss- and schist-derived gravel and cobble in riffles. The

dominant tree species reported are beech (Fagus grandifolia), red oak (Quercus rubra), black oak

(Quercus velutina) and tulip poplar (Liriodendron tulipifera). Detailed description is given in

Newbold et al. (1997). The Neversink watershed is contained within a mountainous region in

northeast New York State and elevation ranges from 480 m to 1280 m. The hill slopes are steep

with several deeply incised headwater channels and the soils in the Catskills region are

predominantly acidic inceptisols (Lawrence et al., 2001). Streambed sediments consist of clay-,

silt-, and sand-sized particles and shale-, siltstone-, sandstone- and conglomerate-derived gravel

and cobble in riffles. The watershed is sparsely populated and 95% forested, primarily of mixed

northern hardwood species dominated by American beech (Fagus grandifolia), sugar maple (Acer

saccharum) and yellow birch (Betula alleghaniensis). Balsam fir (Abies balsamea) is common

17

above 1,000-m elevation, and hemlock stands grow in a few areas that have poorly drained soils

(Lawrence et al. 2001; Lovett et al. 2002).

We used a hierarchical design to evaluate spatial patterns of microbial biomass and

community structure along a stream order gradient and across four spatial scales, where stream

order refers to Strahler’s (1957) modification of Horton’s (1945) classification system (headwater

streams with no tributaries are 1st order, two first order streams join to form a 2nd order stream,

when two 2nd order streams combine, they form a 3rd order stream and so on). Our nested

sampling design consisted of four spatial scales: 1) > 350km - distance between the watersheds,

2) 50m-10km - distance between streams within a watershed, 3) 1-50m - distance between

sampling stations within a stream reach, and 4) <1m – the distance between replicate cores within

a sampling station (Fig.1). In White Clay Creek stream network, we sampled 3rd order WCC

adjacent to the Stroud Water Research Center in Avondale, Pennsylvania, two 2nd order streams;

White Clay Creek West (WCCW) and White Clay Creek East (WCCE) and four 1st order streams;

Ledyards Spring Branch (LSB), Water Cress Spring (WTR), Dirty Dog Spring (DDS) and Walton

Spring Branch (WSB). Two 1st order streams flowed into each 2nd order stream (LSB and WRT

into WCCW, DDS and WSB into WCCE). In Neversink stream network, we sampled the 5th

order Neversink River (NRC), two 3rd order streams; Biscuit Brook (BBR) and Pigeon Creek

(PBR) and four 1st order streams; Biscuit Brook Tributary A (BBTA), Biscuit Brook Tributary B

(BBTB), Pigeon Creek Tributary A (PBTA) and Pigeon Creek Tributary B (PBTB). Pigeon

Creek, Biscuit Brook and their tributaries are located within the Frost Valley, Claryville, Ulster

County, NY. Within each stream, three stations within a reach (downstream, midstream and

upstream) were established and triplicate sediment samples collected at each station. In summary,

the design consists of 2 stream networks, 7 streams per stream network, 3 stations per stream and

18

3 replicate sediment samples per station, corresponding to a total of 126 sediment samples.

Within the watershed sampled, both rivers were unregulated. All streams within a watershed were

sampled in the same week, and both watersheds were sampled within a 2-week period in July and

August 2010 to avoid seasonal differences.

Sampling procedures

Samples were delimited with a 100mm diameter Plexiglas ring that was inserted 2cm deep

into the streambed (75mm diameter ring was used for 1st order streams whenever streambeds

were dominated by large rocks, cobbles and stones). Plexiglas plates were slipped under and over

the ring to effectively trap the sediments and allow them to be lifted from the streams without

disturbance. Sediments in the top 2mm within the ring were transferred with a clean spatula to

pre-labeled Whirl-Pak sampling bags and stored on ice prior to subsampling. Within six hours of

sampling, sediments were transferred to a clean plastic weigh boat, thoroughly homogenized and

subsampled for phospholipid, surface area and elemental analyses. Subsamples for phospholipid

and elemental analyses were frozen and shipped to the appropriate laboratory for analysis.

Conductivity and water temperature readings were measured with a YSI model 32 conductance

meter.

Phospholipid analysis

Microbial biomass and community structure were determined using phospholipid

phosphate (PLP) and phospholipid fatty acid (PLFA) analyses following the methods of Findlay

(2004). Briefly, cellular lipids were extracted from the frozen sediment samples by

dichloromethane/methanol/water extraction and partitioned into aqueous and organic fractions.

The organic fraction containing the lipids was subsampled for PLP analysis (Findlay et al. 1989).

PLFA were fractionated from the remaining lipids by silica gel solid phase extraction

19

chromatography using chloroform (neutral lipids), acetone (glycolipids) and a solution of

chloroform:methanol:DI water (5:5:1, v:v:v;) as successive eluents. PLFAs were converted into

their respective methyl esters by base methanolysis and purified by octadecyl bonded silica gel

(C18) reverse-phase column chromatography. Purified fatty acid methyl esters (FAMEs) were

identified and quantified using gas chromatography. The FAMEs were analyzed by gas

chromatography in an Agilent gas chromatograph equipped with an automatic sampler, a 60 m x

0.25 mm non-polar DB-1 column and a flame ionization detector. Hydrogen was used as the

carrier gas at a flow rate of 2.3 ml/min. The initial temperature was 80º C followed by a

temperature rise of 4 ºC/min to 250 ºC which was then held at this temperature for 10 min. FAME

identification was based on relative retention times, coelution with standards, and mass spectral

analysis. The FAME nomenclature used followed Findlay and Dobbs (1993). Using polyenoic

fatty acids as indicators of microeukaryotes, total microbial biomass was partitioned between

prokaryotic and microeukaryotic organisms and the results presented as percentages (Findlay and

Dobbs 1993).

Elemental Analysis

The frozen subsamples for elemental analysis were freeze-dried, finely ground, weighed

(about 1.5 - 2g) and inorganic carbonate removed by exposure to gaseous HCl. Approximately 35

mg of sediment was analyzed on a Costech 4010 elemental analyzer for percent carbon and

nitrogen, and atomic carbon to nitrogen ratio (C:N). Stable isotope ratios (δ13C, δ15N) were

determined using a gas source isotope ratio mass spectrometer (ThermoElectron Delta V

Advantage) connected to the elemental analyzer by a ThermoElectron Conflow III. The isotope

ratios were reported in δ notation (‰) relative to Vienna Pee Dee Belemnite standard (V-PDB)

for carbon and Air-N for nitrogen according to:

20

δX [‰] = (Rsample/Rstandard – 1) x 1000 (1)

where X is 13C or 15N, and R is 13C/12C or 15N/14N. Samples were analyzed in duplicate with an

average of 0.02‰ analytical differences (mean difference between all duplicates). The IRMS was

calibrated using international NIST standards as needed and the calibration checked before and

after each run using working standards consisting of freeze-dried, ground spinach leaves and

cornhusks.

Statistical analysis

Nested analysis of variance (ANOVA; stations nested within streams, and streams within

stream networks) with Turkey’s HSD (p < 0.05) was performed on sediment organic content and

microbial biomass log transformed (n+1) data to determine differences across spatial scales (JMP

10 and MINITAB 16). We reported biomass and abundance per gram of fresh weight sediment,

instead of the customary dry weight of sediment, because the sediments varied greatly in their

percent water content, violating the assumption necessary for standardizing data to sediment dry

weight (that is, sediment dry weight only varies with sample size) (Schallenberg and Kalff 1993).

Relationships among variables were investigated using linear regression and multiple regression

analysis (MINITAB 16). We tested data for normality with the Shapiro-Wilk test and

homogeneity of variance with Bartlett test and applied appropriate transformations as needed. For

multiple linear regression analysis, predictor variables were selected using the ‘best subsets’

algorithm in MINITAB. This algorithm fits a small fraction of all possible regression models and

reports the ‘best subset’; we identified the best model based on several selection criteria including

adjusted r2 and Mallows Cp. We used the structural equations modeling (SEM), more specifically

21

path analysis, to further explore the influence of environmental variables on microbial biomass.

SEM is a multivariate statistical technique that tests the importance of pathways in hypothesized

models, and allows comparison of models to experimental data (Mitchell 1992). Standardized

regression coefficients between variables were calculated and plotted as path coefficients on path

diagrams constructed for microbial biomass. These path coefficients can be used to determine the

direct and indirect impacts of environmental variables on the dependent variable. The SEM was

performed in SAS Structural Equation Modeling for JMP 10. Natural log transformed (ln + 1)

PLFA relative abundance data subjected to principal component analysis (PCA) to identify

patterns of variation in the microbial community structure across spatial scales and stream order

gradient. PCA was performed for the combined data set of Neversink and WCC networks (SPSS

19). PLFA profiles were interpreted using a functional group approach (Findlay and Dobbs 1993).

RESULTS

Water chemistry and sediment organic content

Water temperatures, at the time of sampling, were similar for all streams (Table 1). While

conductivity was only measured for two streams in the White Clay Creek watershed, it is clear

that stream water within the Neversink watershed had significantly lower conductivity. Sediment

% C and % N showed a complex spatial pattern with sediments from the 1st order streams LSB

and WTR showing significantly greater C and N content than all other streams except the WCCW

and no significant differences among WCCW and all other streams (Fig. 2a, 2b). Sediment C:N

ratios showed several patterns. In general, C:N ratios were higher in 1st and 2nd order streams and

lower in 3rd and 5th order steam sediments. In addition, C:N ratios were generally higher in the

22

White Clay Creek watershed (range: 16.6 – 9.1) compared to the Neversink watershed (range:

12.99 – 5.62) (Fig. 2c).

Total microbial biomass

Total microbial biomass ranged from 6.77 ± 0.75 to 52.41 ± 4.87 nmol PLP g-1 fresh

weight sediment (Table 1). ANOVA showed that White Clay Creek sediments contained

significantly greater microbial biomass than Neversink River sediments (p = 0.002; Table 2,

Fig.3a) and that there were significant differences among streams within watersheds (Table 2,

Fig. 3b). Variability in sediment microbial biomass among stations within streams ranged from

moderate (~10%) to 2-fold and showed low to moderate (C.V. = 5.32% to 82.65%) within station

variability (Fig. 3c); we did not detect any consistent pattern of higher biomass by station within a

reach (upstream vs. midstream vs. downstream stations). Prokaryotes comprised between 58 and

96% of total biomass with casual observations indicating that streams with open canopies (WSB

and NRC) showed the highest contribution of eukaryotes to total microbial biomass. Bacterial

abundance ranged from 2.03 x 108 to 1.68 x 109 cells g-1 fresh weight of sediment. In general,

streams from the White Clay Creek watershed showed higher total microbial biomass, percent

prokaryotes and bacterial abundance than those within the Neversink watershed (Table 1).

Multiple linear regression analysis indicated that sediment percent carbon content, percent

water content, C:N ratio and sediment surface area explained approximately two-thirds of the

variation observed in sedimentary microbial biomass (Table 3, Model 7). Path analysis was used

to investigate the relationships among these variables and indicated that percent carbon content,

percent water content, and C:N ratio had significant direct effects on biomass and that sediment

surface area was correlated, to a greater or lesser extent, with carbon content, percent water

23

content, and C:N ratio (Fig. 4). Two models were investigated, one constrained and a second

unconstrained, to investigate the theoretical linkage and directionality among the variables. The

constrained model links sediment surface area indirectly to biomass via its direct effects on

sediment carbon and water content while the unconstrained model links surface area indirectly to

biomass via its correlations with sediment carbon content, water content and C:N ratio; these two

models yielded very similar results and we present only the unconstrained model. Percent carbon

content showed the greatest direct effect (r2 = 0.393) as well as substantial indirect effects via its

correlation with percent water content and C:N ratio (Fig. 4). Combined, the direct and indirect

effects of carbon accounted for ~ 61% of the variation in total sediment microbial biomass.

Similarly, percent water content and C:N ratio accounted for 56% and 37%, respectively, of the

variation in total sediment microbial biomass. Sediment surface area via indirect effects

accounted for ~12% of total sediment microbial biomass.

Microbial community structure

The major component of variation in microbial community structure of stream sediments

was related to the proportions of prokaryotes and eukaryotes within communities (Fig. 5 & 6).

The 5th order Neversink River and the 1st order stream WSB showed the greatest relative

abundance of fatty acids indicative of phototrophic and heterotrophic eukaryotic microorganisms

(18:3ω3, 20:5 ω3, 20:4ω6, 18:2ω6, 16:1ω13t); samples from these streams showed large negative

loadings along the PC1. All other streams within both systems showed greater relative abundance

of bacterial fatty acids (cy17:0, cy19:0, a17:0, i17:0, i15:0, br17:1a and 10me16:0). The

assignment of the relative contribution of bacteria and microeukaryotes to total biomass as the

major component of variation based on the high correlation (r2 = 0.88) between PC1 factor scores

24

and the percentage that microeukaryotes comprise of total microbial biomass (Fig. 6) and is likely

related to canopy cover as the NSR and WSB stations were observed to have the most open

canopies. Ignoring the two stations with open canopies, the variation in community structure

among 1st order streams, for the most part, bounds the variation within the watershed. PC 2

separated streams from Neversink watershed, except the 5th order Neversink River, from all

stations within the White Clay Creek watershed. In addition, we observed overlapping PC1 and

PC2 scores for sediments from the two Neversink 3rd order streams (Pigeon and Biscuit Brooks)

and their 1st order tributaries, indicating similar sediment microbial community structure for these

streams. In contrast, microbial community structure of sediment from several of the 1st order

streams within the White Clay Creek system showed significant differences among themselves

and with White Clay Creek sediments.

Bacterial community structure

PCA of bacterial fatty acids profiles separated the two watersheds along the PC1 with all

White Clay Creek watershed samples having positive PC1 scores and all Neversink watershed

samples having negative PC1 scores, and PC1 scores for all streams within a watershed being

similar to at least one other stream within that watershed (Fig. 7). PC 2 separated samples from

Neversink River from all other streams in its watershed and separated samples from Watercress

Spring and Dirty Dog Spring from all other streams in the White Clay Creek watershed.

25

DISCUSSION

Stream networks are highly dynamic ecosystems with inherent spatial heterogeneity. This

spatial heterogeneity has important implications on the functions, distribution and composition of

associated microbial communities. In this study, sediment microbial community structure in the

fourteen streams displayed distinct regional scale variations (hundreds of kilometers: i.e.,

watersheds) and among-stream variations at the scale of hundreds of meters within a watershed,

as well as along a stream order gradient. In addition, clear differences in bacterial community

structure among streams and between watersheds were documented. These findings extend

previous studies examining microbial community structure over regional scales indicating that

habitat and geographical distance are important in structuring microbial communities (Hullar et

al. 2006; Findlay et al. 2008).

PCA indicated that the benthic microbial and bacterial communities of the1st and 3rd order

streams in the Neversink system were relatively homogenous (Fig. 5 & 7). These streams were

fully shaded and received appreciable inputs of terrestrial organic matter that could serve as stable

carbon source for bacterial communities that comprised the largest proportion of total microbial

communities in headwater streams. The separation of Neversink River, based on microbial

community structure, from 1st and 3rd order streams within the Neversink system is related to

greater eukaryotic contribution to total microbial biomass in Neversink River. Field observations

showed active dense filamentous green algal streamers that were particularly abundant at the NSR

station, which has the most reduced canopy cover. These observations were generally consistent

with the predominance of chrysophyte and chlorophyte algal biomarkers (18:3ω3, 20:5ω3,

26

16:4ω1) in describing the variation in microbial community structure among samples from the

Neversink system (Fig. 5). This shift in community structure is indicative of increasing exposure

to greater irradiance along forested stream channels (i.e., as stream order increases, the amount of

stream surface shaded by riparian trees decreases and suggests the potential for decreased

importance of allochthonous detrital carbon and increased importance of autochthonous

production downstream; Vannote et al. 1980). Thus, headwater streams provide distinctive

habitats that shape their characteristic microbial communities in a way that is different in several

ways from larger streams. In contrast, the entire benthic microbial community as well as bacterial

community sampled in WCC network displayed high among-streams variation among 1st order

streams, while bacterial communities within sediments from the 3rd order WCC were the most

similar to those found in the two 2nd order streams. The reaches sampled in two 1st order streams,

WTR and WSB, were located in areas where the forest canopy was less dense, which allowed

local increases in light availability. The separation of these streams from all other stations in

forested WCC system is consistent with the increased contribution of phototrophic

microeukaryotes to total microbial biomass and the importance of algal lipid markers (20:4ω6,

20:5ω3, 18:2 ω6, 18:3ω3) in describing the variation in microbial community structure (Fig. 5).

This suggests that discontinuity in corridors of vegetation along streams and/or modern mosaics

of land uses may alter the degree of autotrophy or heterotrophy of a stream. Studies have

attributed greater algal biomass in forested watersheds to local increases in light availability (Hill

and Harvey 1990; Quinn et al. 1997; Kiffney et al. 2004).

At the regional scale, our data indicated nonrandom spatial variations in total microbial

and bacterial communities supporting current evidence for spatial variation in microbial

community structure (Martiny et al. 2006). This pattern was the most evident in bacterial PLFA

27

profiles of community structure, which showed that the major variation in sedimentary bacterial

community structure occurred at the watershed level and that the differences among streams with

similar general geologic features, light availability and terrestrial vegetation within a watershed

were not significant with respect to the major component of variation (Fig. 7). Our data indicated

that the composition of microbial communities were sensitive to watershed scale processes. Our

findings corroborated those of Findlay et al. (2008) who reported unique streambed communities

for each of three biomes. A major difference between our study and that of Findlay et al. (2008)

was that the two watersheds examined in this study occurred within the same biome (Eastern

Deciduous Forest). This implies that spatial variability in microbial communities occurred at a

variety of spatial scales, ranging from the diversity in an individual environmental sample to the

diversity assessed across multiple biomes.

But what processes generates these patterns? The Baas-Becking hypothesis for microbial

taxa postulates that, ‘everything is everywhere, but, the environment selects’ (Bass-Becking

1934). The claim that ‘the environment selects’ implies that contemporary environmental

variation (multiple habitats) maintain distinctive microbial composition. However, the variation in

community structure at regional scales may involve multiple causal pathways. For instance,

differences in watershed characteristics such as water chemistry, flow regime, temperature, point

source inputs, etc., may generate differences in DOM and nutrient qualities and quantities, which

in turn cause variation in microbial productivity and community structure (Battin et al. 2008).

Also, the proximal causes for the observed variation in streambed microbes between the two

watersheds quantified by PCA may be due, in part, to land-cover differences as the Neversink

watershed is 95% forested while the WCC watershed is a mixture of pasture land and forest. Also,

our data suggested significant differences in conductivity and sediment C:N ratios between the

28

two watersheds. A review of microbial biogeography studies showed that spatial distributions

over small scales often reflect local environmental heterogeneity (reviewed in Martiney et al.

2006). For example, the distribution of bacterial communities in soils (Ramette and Tiedje 2007)

and in water column and surface sediments in lakes (Kondo and Butani 2007) at meter to

kilometer scales correlated with environmental heterogeneity, whereas the distribution of

pseudomonads from undisturbed pristine soils sites (Cho and Tiedje 2000) and hotspring

archaeon Sulfolobus assemblages (Whitaker et al. 2003) in similar habitats separated by

>10,000km correlated with geographical distance. These results indicate that environmental

heterogeneity seems to influence microbial community at small spatial scales, whereas at larger

spatial scales (>10,000km), geographical distance can overwhelm effects of environmental

heterogeneity. Interestingly, studies that sampled at intermediate spatial scale (10 – 3000km)

detected the influence of both environmental heterogeneity and geographical distance on

microbial biogeography (Green et al. 2004; Yannarell and Triplett 2005). In our study, a

hierarchical pattern of overall similarity emerged with the highest similarity found among

samples collected within the same stream, especially within the same station, followed by

similarities among samples collected from different streams with similar general geologic features

and terrestrial vegetation within the same watershed, and finally similarities among samples

collected from different watersheds. This implies that the greatest variation in microbial and

bacterial community composition in streams occurs at the largest spatial scales.

Total microbial biomass and bacterial abundances for both watersheds were within the

range of published microbial biomass for temperate freshwater sediments (Bott and Kaplan 1985;

Sutton and Findlay 2003; Findlay et al. 2008) but lower than that reported for an impacted,

channelized riverine system in central Ohio (Langworthy et al. 1998). If discontinuities in stream

29

geomorphology and hydrology occur between streams of different order as predicted by the river

continuum concept (Vannote et al. 1980; Benda et al 2004), one would expect corresponding

changes in microbiota and ecosystem processes. However, in our study, microbial biomass did

not correlate with increasing stream order as might be expected by the river continuum hypothesis

and biomass levels did not differ significantly at all stations within streams for both watersheds.

Ferris et al. (2003) reported similarities in bacterial cell densities from three separate riffles in

each of three streams investigated. In contrast to reach-scale similarity in microbial biomass, our

data showed significant differences at the stream and between watersheds scales (Fig 3).

Significant differences in microbial biomass of streambed sediments among streams have been

reported in coastal plain, temperate to tropical evergreen forest headwater streams (Findlay et al.

2002; Gao et al. 2005; Findlay et al. 2008) and high alpine streams (Battin et al. 2004). Overall,

the magnitude of within stream variation was small compared to the variability noted among

streams and between watersheds and this suggests that microbial biomass within stream reaches is

relatively tightly constrained. Path analysis indicated that these environmental constraints were

percent sediment carbon content, percent water content, C:N ratio and sediment surface area.

These findings are consistent with previous studies (Bott and Kaplan 1985; Findlay et al. 2002;

Fierer et al. 2007b). The model revealed that the primary direct controls on microbial biomass in

this study were sediment organic carbon, C:N ratios and sediment water content (Fig.4).

Utilization of organic carbon by stream communities can be a measure of ecosystem

productivity, while the concentration of organic carbon reflects a combination of several

biogeochemical processes (Hedges 1992; Wang et al. 2007). As such, sediment organic carbon

may influence microbial biomass through its quantity, quality or a combination thereof. This

study revealed that sediment organic carbon influenced total microbial biomass both directly and

30

indirectly via other correlated variables. Previous studies of stream sediments and terrestrial soils

have shown that quantities of carbon can significantly influcene microbial biomass (Schallenberg

and Kalff 1993, Steenworth et al. 2002; Fierer et al. 2007; Nemergut et al. 2010). In addition,

Findlay et al. (2002) showed that variation in quality of sediment detritus, as measured by C:N

ratio, was negatively correlated with bacterial abundance, while Schallenberg and Kalff (1993)

found variable results (either negative or no correlation) in lake sediments. Our results showed

that total microbial biomass (and bacterial abundance, data not shown) was positively correlated

with both sediment organic carbon and C:N ratio. The cause of the difference between our

findings and those of previous researchers is not known, however, within our system there is a

positive correlation between sediment organic carbon and C:N ratio while there was either no

relationship between FBOM organic carbon and C:N ratio (Fierer et al. 2007) or the relationship

between carbon quantity and quality was not determined (Schallenberg and Kalff 1993; Findlay et

al. 2002).

Another important source of variation in this study was sediment percent water content,

which has been implicated by other investigators in studies of microbial communities (Doran

1987; Schallenberg and Kalff 1993). Aqueous connectivity within sediment particles allows

nutrient and substrate transfer between particles and provides microorganisms with a continuous

supply of nutrients and means to move to more favorable locations (Treves et al. 2003).

Schallenberg and Kalff (1993) showed that percent water content was the single most important

factor explaining sediment bacterial biomass in a series of lakes that differed greatly in sediment

grain sizes.

Sediment surface area indirectly affects microbial community biomass via changes in

percent organic carbon, C: N ratios and percent water content (Fig.4). Sediment grain could

31

generate different micro-habitats and increase microscale environmental heterogeneity which has

been observed to structure microbial soil biomass and community structure (Treves et al. 2003).

In addition, it has been noted that grain size, through its effects on flow rates and availability of

nutrients (Bott and Kaplan 1985; Albrechtsen and Winding 1992; Woessner 2000; Vervier et al.

1992) and quantity and quality of organic carbon (Bott and Kaplan 1985; Kaplan and Newbold

2000; Wilcox et al. 2005), can influence microbial biomass.

At the regional scale, sedimentary microbial biomass and community structure from

White Clay Creek tended to be different from those in Neversink. There are several factors that

could account for these regional effects. White Clay Creek and Neversink watersheds differ in

many aspects, including geology (Newbold et al. 1997; Lawrence et al. 2001), landcover and use

(Newbold et al. 1997; Lovett et al. 2000; 2002), anthropogenic impacts such as acid deposition

(Baldigo and Lawrence 2000), and streamwater chemistry (Newbold et al. 1997; Lawrence et al.

2001 and this study). It is reasonable that these environmental factors could influence stream

microbial biomass and community structure through a variety of mechanisms.

In conclusion, the present study indicated that local environmental factors strongly

influence sediment microbial biomass and that the magnitude of within stream variation in

microbial biomass was small compared to the variability noted among streams and between

watersheds. Our results reveal regional-level patterns in microbial community structure and

suggest that regional scale environmental factors influence the biogeography of microbes.

32

Acknowledgments

Sherman Roberts, Michael Gentile and Janna Brown assisted in sample collection and processing.

Chirstina Staudhammer provided invaluable advice on the application of path analysis; however,

the authors take full responsibility for the application and interpretation of all statistical analyses.

Funding for this project was provided by the National Science Foundation DEB-0516235.

33

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Figure 1. Sampling scheme used to examine microbial biomass and community structure across

multiple spatial scales in two watersheds. Sampling within the Neversink watershed consists of

four 1st order streams; Biscuit Brook and Pigeon Creek tributaries (Biscuit Brook Tributary A and

B [BBTA, BBTB], Pigeon Creek Tributary A and B [PBTA, PBTB]

respectitively), two 3rd order streams (Biscuit Brook [BBR] and Pigeon Creek [PBR]

respectitively) and one 5th order stream (Neversink River [NRC]). Sampling within the White

Clay Creek watershed consists of four 1

Cress Spring [WTR], Dirty Dog Spring [DDS] and Walton Spring Branch [WSB]

respectitively), two 2nd order streams (East and West Branch White Clay Creek [WCWE,

WCCW], 2a, 2b, respectitively) and one 3

watersheds are not drawn to scale. Each eclipse represents a

each of which was sampled 3 times.

42

Figure 1. Sampling scheme used to examine microbial biomass and community structure across

multiple spatial scales in two watersheds. Sampling within the Neversink watershed consists of

Biscuit Brook and Pigeon Creek tributaries (Biscuit Brook Tributary A and

B [BBTA, BBTB], Pigeon Creek Tributary A and B [PBTA, PBTB], 1a, 1b, 1c, 1d,

order streams (Biscuit Brook [BBR] and Pigeon Creek [PBR]

order stream (Neversink River [NRC]). Sampling within the White

Clay Creek watershed consists of four 1st order streams (Ledyards Spring Branch [LSB], Water

Cress Spring [WTR], Dirty Dog Spring [DDS] and Walton Spring Branch [WSB]

order streams (East and West Branch White Clay Creek [WCWE,

) and one 3rd order stream (White Clay Creek [WCC]). Sketches of

watersheds are not drawn to scale. Each eclipse represents a reach, which contained 3 stations,

each of which was sampled 3 times.

Figure 1. Sampling scheme used to examine microbial biomass and community structure across

multiple spatial scales in two watersheds. Sampling within the Neversink watershed consists of

Biscuit Brook and Pigeon Creek tributaries (Biscuit Brook Tributary A and

, 1a, 1b, 1c, 1d,

order streams (Biscuit Brook [BBR] and Pigeon Creek [PBR], 3a, 3b,

order stream (Neversink River [NRC]). Sampling within the White

order streams (Ledyards Spring Branch [LSB], Water

Cress Spring [WTR], Dirty Dog Spring [DDS] and Walton Spring Branch [WSB], 1e, 1f, 1g, 1h,

order streams (East and West Branch White Clay Creek [WCWE,

order stream (White Clay Creek [WCC]). Sketches of

contained 3 stations,

Figure.2 Variation in sediment (a) percent carbon, (b) percent nitrogen and (c) C:N ratio by

stream, order (1st to 3rd/5th order from left to right) and watershed.

confidence intervals. Streams not connected by a horizontal line are significantly different (p =

0.05, Tukey’s Wholly Significant Difference).

43

Figure.2 Variation in sediment (a) percent carbon, (b) percent nitrogen and (c) C:N ratio by

order from left to right) and watershed. Vertical bars denote 0.95

confidence intervals. Streams not connected by a horizontal line are significantly different (p =

0.05, Tukey’s Wholly Significant Difference).

Figure.2 Variation in sediment (a) percent carbon, (b) percent nitrogen and (c) C:N ratio by

Vertical bars denote 0.95

confidence intervals. Streams not connected by a horizontal line are significantly different (p =

Figure 3. Microbial biomass (mean ± SD) of White Clay Creek and

watershed, b; stream and c; station. Stream order (or average order for watershed values) are indicated as: black = 1

= 2nd order, light gray = 3rd order, open = 5th order.

44

iomass (mean ± SD) of White Clay Creek and Neversink watershed sediments at three spatial sca

Stream order (or average order for watershed values) are indicated as: black = 1

order.

three spatial scales: a;

Stream order (or average order for watershed values) are indicated as: black = 1st order, dark gray

Figure 4. Path diagrams describing the structure of the relationship between sediment microbial

biomass and % Carbon (%C), % water content, C:N ratio

(SSA). Single-headed arrows indicate casual paths;

(standardized regression coefficients) indicating the relative strength of each path leading to a

given response variable. Double-headed arrows represent the correlations among the predictor

variables. Arrows connecting environmental variables to the independent variable (

biomass) indicate direct effects, while environmental variables linked to the independent variable

via other environmental variable constitute indirect effects

Structural Equation Modeling for JMP 10

45

Figure 4. Path diagrams describing the structure of the relationship between sediment microbial

water content, C:N ratios (C:N) and sediment surface area

rrows indicate casual paths; numbers on arrows are path coefficients

coefficients) indicating the relative strength of each path leading to a

headed arrows represent the correlations among the predictor

ting environmental variables to the independent variable (

iomass) indicate direct effects, while environmental variables linked to the independent variable

via other environmental variable constitute indirect effects. Path coefficients calculate

Structural Equation Modeling for JMP 10. *= P <0.01, **=P <0.001

Figure 4. Path diagrams describing the structure of the relationship between sediment microbial

and sediment surface area

numbers on arrows are path coefficients

coefficients) indicating the relative strength of each path leading to a

headed arrows represent the correlations among the predictor

ting environmental variables to the independent variable (microbial

iomass) indicate direct effects, while environmental variables linked to the independent variable

Path coefficients calculated by SAS

Figure 5. Principle Component A

White Clay Creek (open circle) and Neversink (open squ

explained by each axis is indicated on the respective component axis. Identified fatty acids had

component loadings of >|0.5| with strong influence on the pattern of variation among samples

along the respective component axes. Site abbreviations

46

Analysis of stream sedimentary microbial community structure

White Clay Creek (open circle) and Neversink (open square) watersheds. The percent

explained by each axis is indicated on the respective component axis. Identified fatty acids had

component loadings of >|0.5| with strong influence on the pattern of variation among samples

along the respective component axes. Site abbreviations are as described in the legend to Fig.1

edimentary microbial community structure of

. The percent variation

explained by each axis is indicated on the respective component axis. Identified fatty acids had

component loadings of >|0.5| with strong influence on the pattern of variation among samples

are as described in the legend to Fig.1

47

Figure 6. Relationship between Principle Component Analysis factor 1 score and the calculated

percentage that microeukaryotes contribute to total microbial biomass for all stream samples.

y = -0.0792x + 1.3588R² = 0.8773

-4

-3

-2

-1

0

1

2

0 10 20 30 40 50 60 70

PC

1 S

core

% Eukaryotic Biomass

Figure 7. Spatial variation in sedimentary bacterial community composition in WCC and NRC

watersheds by PLFA analysis after removal of fatty acids assigned a priori to the functional group

microeukaryotes and those known to be common to both bacteria and m

PLFA profiles. Symbols- WCC (circle), NSR (square). Site abbreviations are as described in the

legend to Fig.1.

48

. Spatial variation in sedimentary bacterial community composition in WCC and NRC

watersheds by PLFA analysis after removal of fatty acids assigned a priori to the functional group

microeukaryotes and those known to be common to both bacteria and microeukaryotes from the

WCC (circle), NSR (square). Site abbreviations are as described in the

. Spatial variation in sedimentary bacterial community composition in WCC and NRC

watersheds by PLFA analysis after removal of fatty acids assigned a priori to the functional group

icroeukaryotes from the

WCC (circle), NSR (square). Site abbreviations are as described in the

49

Table 1. Microbial biomass, water chemistry and sediment organic content of White Clay Creek and Neversink watersheds.

Watershed & stream

Biomass/PLP (nmol g-1 fresh wet wt)a

Bacterial abundance (g-1 fww)b

% Eukaryotic/ prokaryoticc

Cond (µS/cm)

Temp (0C)

δ13C δ

15N % C % N C:N ratiod

White Clay Creek

DDS 14.80 ±2.81 5.67 x 108 4/96 176.8 ND -26.89 ±0.65 2.23 ±0.20 0.33 ±0.01 0.02 ±0.55 14.14 ±3.18

WSB 30.34 ±9.81 8.24 x 108 31/69 ND ND -29.36 ±0.79 3.04 ±0.25 0.58 ±0.02 0.04 ±0.54 12.98 ±2.53

LSB 43.29 ±19.60 1.56 x 109 9/91 ND ND -27.65 ±0.46 2.49 ±3.78 2.96 ±0.37 0.31 ±0.51 12.77 ±5.12

WTR 52.41 ±4.87 1.68 x 109 20/80 ND ND -28.25 ±0.59 2.27 ±2.02 3.36 ±0.15 0.21 ±0.34 16.45 ±2.09

WCWE 22.13 ±3.38 8.26 x 108 6/94 ND ND -27.23 ±1.18 2.67 ±0.83 1.15 ±0.04 0.07 ±0.37 16.27 ±2.75

WCCW 26.89 ±2.52 1.01 x 109 6/94 ND 16.2 -26.81 ±0.61 3.27 ±1.52 1.73 ±0.11 0.11 ±0.37 16.59 ±2.71

WCC 12.12 ±4.95 4.25 x 108 11/89 205 16.1 -26.09 ±1.26 -4.70 ±0.55 0.57 ±0.04 0.06 ±5.03 9.08 ±2.96

Neversink

BBTA 6.99 ±2.39 2.27 x 108 19/81 35.1 15.4 -27.32 ±1.01 1.71 ±0.16 0.36 ±0.01 0.03 ±1.08 11.15 ±2.30

BBTB 10.95 ±3.74 3.34 x 108 23/77 32.4 14.8 -25.76 ±1.19 2.29 ±0.21 0.34 ±0.01 0.03 ±0.55 10.25 ±3.15

PBTA 12.18 ±3.78 3.92 x 108 18/82 25.6 16.2 -27.09 ±0.67 2.61 ±0.68 0.72 ±0.04 0.05 ±0.35 12.99 ±3.10

PBTB 16.42 ±6.96 5.73 x 108 12/88 18.8 15.5 -23.54 ±3.22 3.69 ±1.85 1.18 ±0.11 0.08 ±0.38 11.06 ±3.30

BBR 8.54 ±1.79 2.87 x 108 16/84 20.8 16.4 -25.17 ±1.70 1.82 ±0.10 0.18 ±0.00 0.03 ±0.30 5.62 ±2.38

PBR 6.77 ±0.75 2.03 x 108 25/75 24.5 16 -22.97 ±2.83 -0.34 ±0.14 0.16 ±0.01 0.02 ±0.88 6.18 ±3.76

NRC 13.58 ±4.41 2.95 x 108 42/58 33.5 15.3 -25.41 ±2.05 0.60 ±0.08 0.20 ±0.01 0.03 ±0.71 7.69 ±1.05

a Mean ± standard deviation (n = 9). b Calculated from PLP x % prokaryotic (expressed as decimal fraction) and a conversion factor of 100 nmol PLP = 4 x 109 cells c Percentage that microeukaryotic contributes of total microbial biomass, calculated from PLFA profiles (n = 9). d Percent that C and N contribute to total sediment elementary atoms (n = 9) ND= measurement were not taken

50

Table 2. Nested ANOVA to test the effects of watershed, streams within watershed, and stations

within streams on microbial biomass.

Source DF Adj SS Adj MS F P

Watershed 1 5.119 5.119 15.18 0.002

Stream (Watershed) 12 4.047 0.337 7.38 0.000

Stations (Watershed*Stream) 28 1.280 0.046 1.46 0.095

Error 84 2.628 0.031

51

Table 3. Multiple regression analysis (best subsets) for natural log biomass as a function of

various physical and chemical stream parameters

Model Vars R-Sq R-Sq(adj) Mallows Cp SE %Water SSA %C %N C:N

1 1 60.7 60.4 30.2 0.2052 X

2 1 56.4 56.0 46.3 0.2161 X

3 2 65.0 64.4 15.9 0.1943 X X

4 2 64.6 64.0 17.6 0.1955 X X

5 3 67.6 66.7 8.4 0.1879 X X X

6 3 66.9 66.0 11.1 0.1899 X X X

7 4 69.2 68.1 4.3 0.1839 X X X X

8 4 68.1 67.0 8.5 0.1872 X X X X

9 5 69.3 68.0 6.0 0.1845 X X X X X

52

CHAPTER 3

SEASONALITY IN A STREAMBED MICROBIAL COMMUNITY: VARIATION IN THE

ISOTOPIC COMPOSITION OF LIPID BIOMARKERS

ABSTRACT

Microbial biomass and community structure can show dramatic seasonal variability in temperate

stream ecosystems and understanding links between this variability and in-stream biotic and

abiotic processes is an important goal for stream ecologists. Stable isotopic composition of

organisms and related biomolecules has increasingly become an important tool for ecologists

probing stream processes. To test if the seasonal changes in the structure of stream sediment

microbial communities altered the stable carbon isotope signatures of microbial phospholipid

fatty acids, we collected sediments from White Clay Creek from December 2009 through October

2010. Sedimentary microbial biomass, measured as total phospholipid phosphate, ranged from 10

to 29 nmol PLP g-1 dry weight sediment and was significantly correlated with high and low flood

pulse counts, variability in daily flow and dissolved organic carbon (DOC) concentrations.

Principal component analysis of phospholipid fatty acid (PLFA) profiles indicated that the

sedimentary microbial communities displayed seasonal patterns of change as a result of a shift

from dominance of prokaryotes during times of cold water to increased importance of

phototrophic microeukaryotes during times of warm water. This shift was significantly correlated

with seasonal changes in median daily flow, DOC, high and low flood pulse counts and water

temperature. Microbial carbon isotope signatures using compound-specific 13C analysis of PLFA

53

showed that both bacterial and microeukaryotic stable carbon isotope ratios were heaviest in the

spring and lightest in autumn or winter. Bacterial PLFAs were isotopically depleted on average by

2-5‰ relative to δ13C of total organic carbon suggesting bacteria consumption of terrestrial

organic matter. Most bacterial PLFAs were enriched on average by 9-12‰ compared with algal

PLFA indicating an uncoupled algal-bacteria system. However, δ13C values of bacterial PLFA of

summed feature 1 (16:1ω9, 16:1ω7c, 16:1ω5c, 16:1w13t) were enriched compared with algal

PLFA by 5.7‰ suggesting utilization of autochthonous DOC, along with allochthonous detritus

as carbon sources. Our study revealed seasonal fluctuations in microbial biomass, community

structure and their lipid isotopic signatures examined in stream sediments, and also demonstrated

the potential influences of various hydrological indices on microbial biomass and community

composition in lotic ecosystems.

54

INTRODUCTION

Stable isotope analysis, in particular those of carbon and nitrogen, is a rapidly expanding

tool used by ecologists to examine diet (Ben-David et al. 1997; Karlsson et al. 2003), foraging

ecology (Rubenstein and Hobson 2004; Cherel et al. 2007), ecophysiological processes (Gannes

et al. 1998; Cernusak and Hutley 2011), and trophic position and food-web analysis (Kwak and

Zedler 1997; McNabb et al. 2001; Post 2002), as well as evaluating the structure and dynamic of

ecological communities (Vander Zanden et al. 1999; Post et al. 2000). Also, stable isotope

analysis has been successfully used to assess spatial and seasonal variability in the isotopic

composition of organisms in various ecosystems (Riera and Richard 1997; Vizzini et al. 2002;

Finlay 2004). Moreover, advances in bulk stable isotope analysis have improved methodological

approaches (Tobias et al. 2008) and mathematical models (Logan et al. 2008; Bond and Diamond

2011), enhanced linking processes at multiple scales (Martínez del Rio et al. 2009), and addressed

more complex ecological questions, such as determining the primary controls of high variability

observed in lotic algal δ13C values (Finlay 2004; Ishikawa et al. 2012).

Although, bulk stable isotope analysis has been extensively used in ecology, compound-

specific stable isotope analysis is a more robust tool in microbial ecology that enables the

molecular specificity and isotopic signature of individual compounds to be exploited concurrently

(Boschker et al. 1998). Recent technological advances in the development of gas

chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for compound

specific stable isotope analysis have increased our ability to link microorganisms in

environmental samples to utilization of particular growth substrates (Rieley et al. 1991; Dumont

55

and Murrell 2005). Presently, phospholipid fatty acid (PLFA), amino acids and nucleic acids have

been used as biomarker molecules and for compound-specific stable isotope analysis (Boschker et

al. 1998; Radajewski et al. 2000). Investigation of carbon isotope signature of individual PLFAs

allows for identification of carbon source use by microorganisms. This methodological approach

has several appealing advantages including short incubation times and use of trace-level additions

(< 5% of total carbon). These advantages arise from the high precision of GC/C/IRMS, which

allows for the detection of changes of as little as 4‰ to be considered a significant change in

stable isotope ratio. However, this sensitivity requires an understanding of the variation in

isotopic signatures of specific biomarker compounds of microbial communities, both temporally

and among the various components of the community. As the availability of this advanced

technique is relatively recent, studies investigating variations in the compound specific isotopic

composition of lipid biomarkers of organic matter and microbial composition are scarce and, to

the best of our knowledge, only conducted in estuarine sediments (Zimmerman and Canuel 2001;

Dai and Sun 2007); such studies are still lacking in stream sedimentary habitats.

Seasonal dynamics in sedimentary microbial biomass and community structure in lotic

ecosystems have been documented (Kaplan and Bott 1989; Smoot and Findlay 2001; Battin et al.

2001; Sutton and Findlay 2003; Hullar et al. 2006). In addition, several environmental variables

have been considered to influence microbial community composition in several habitats

(reviewed in Horner-Devine et al. 2003). For example, Smoot and Findlay (2001) reported that

benthic microbial communities were dynamically responsive to physico-chemical and biological

parameters that varied seasonally in a riverine-reservoir ecosystem. In addition, temperature, pH

and dissolved organic carbon (DOC) have been associated with temporal shifts in

bacterioplankton communities in lakes and streams (Crump et al. 2003; Lindström et al. 2005;

56

Hullar et al. 2006; Mueller-Spitz et al. 2009). Environmental variables such as climate,

topography, underlying geology, inorganic water chemistry and riparian vegetation are more

constrained and significantly influence stream processes. Other variables, such as streamflow

velocity, light, temperature, quantity and quality of allochthonous and autochthonous inputs (e.g.

DOC) and high frequency of storms, can vary dramatically throughout the year and play an

important role in structuring the stream communities and hydrological conditions of temperate

streams (Kaplan and Bott 1989; Autio 1998; Gremm and Kaplan 1998; Olapade and Leff 2005;

Hullar et al. 2006).

Although it is known that seasonal variation in sedimentary microbial community

structure occurs in streams (Hullar et al. 2006), there have been no reports describing seasonal

variation in carbon isotope signatures of individual PLFAs in relation to temporal changes

observed in microbial community structure in stream sediments. In this study, we aimed to

investigate if this variation in sedimentary microbial community structure contributes to any

observable variation in isotope profiles of microbial PLFAs. Our study approach was to

investigate the seasonal differences in microbial biomass, community structure and microbial

PLFA isotopic signatures in order to compare patterns in stable isotope profiles and community

structure and to assess the relationships to varying hydrological indices. We sampled replicate

microbial community within a single reach over an annual cycle in order to constrain potentially

confounding environmental variables such as climate, topography, underlying geology, inorganic

water chemistry, and riparian vegetation. Seasonal variations in benthic microbial community

structure and stable carbon isotope signatures of individual microbial fatty acids were determined

using PLFA and compound specific stable isotope analyses, respectively. Annual stream

discharge data for 5-year period were obtained from the US Geological Survey Water Resources

57

database. Our data were subjected to multivariate statistical analyses to characterize flow regime

and elucidate the dominant sources of variation and correlation of microbial community structure

with seasonal changes in hydro-ecological parameters.

MATERIALS AND METHODS

Study site

The study site was located within the 3rd order reach of 7.3 km2 White Clay Creek (WCC)

watershed directly adjacent to the Stroud Water Research Center, Avondale, PA. The WCC

stream is 2,400m long, occurs within riparian woodlands in the Piedmont Province of

southeastern Pennsylvania and northern Delaware and joins the Christina River near the

Christina's discharge to the Delaware Bay. Upstream of our sampling station, WCC has a

protected riparian zone and drains a 725 ha watershed comprised of approximately 52% of

agricultural, 22% of tilled/hayed and 23% of wooded lands (Wiegner et al. 2005; Newbold et al.

1997). Streamflow and streamwater chemistry have been monitored at regular intervals since the

1970s with mean annual stream flow, stream water temperature, and local precipitation of 115

L/s, 10.60C, and 105 cm y-1, respectively. Streambed sediments consist of clay-, silt-, and sand-

sized particles in pools and runs, with gneiss- and schist-derived gravel and cobble in riffles. The

dominant tree species reported are beech (Fagus grandifolia), red oak (Quercus rubra), black oak

(Quercus velutina) and tulip poplar (Liriodendron tulipifera). Detailed description is given in

Newbold et al. 1997.

58

Study design

At the study site, streambed sediments were collected at monthly intervals (with the

exception of January, 2010) over eleven months during December 2009 and October 2010. The

stream annual hydrograph showed higher flows during the spring of the year, lowest flows during

late summer-early fall and was punctuated by storm-driven high flow events. There were no zero-

flow days (i.e., surface water not present or present in isolated pools). Sampling was done pre-

and post- high flow events, and during average flow and low flow periods (Fig.1). Sampling

periods coincided with summer (June to August 2010), autumn (Sept and Oct 2010), winter (Dec

09, Feb and Mar 2010) and spring (April and May 2010). Samples were delimited with a 100mm

diameter Plexiglas ring that was inserted 2 cm deep into the streambed. Plexiglas plates were

slipped under and over the ring to effectively trap the sediments and allowed them to be lifted

from the streams with minimum disturbance. Sediments in the top 2mm within the ring were

transferred with a clean spatula to pre-labeled Whirl-Pak sampling bags and transported on ice to

the laboratory. In December 2009, three replicate samples were collected from left-mid-right

locations across the stream, however, from February to October 2010 nine samples, three from

each station within the reach were collected with stations separated by approximately 10m. In the

laboratory, sediments were transferred to a clean plastic weigh boat and thoroughly homogenized,

and subsampled for phospholipid and elemental analyses.

Streamflow data and hydrological indices

Daily streamflow data for WCC at Avondale, PA (Lat 39`49'42", long 75`46'52") were

acquired from the US Geological Survey Water Resources database (http://waterdata.usgs.gov).

There is little or no flow regulation and the drainage area is 11.3 m2. The flow data consisted of a

59

5-year period from 1 October 2007 to 30 September 2012. We examined 108 hydrologic indices

to identify a subset of biologically relevant indices that can best explain critical attributes of the

flow regime and variation in microbial biomass and community structure. These indices were

distributed into four categories following Richter et al. (1996) and Poff et al. (1997). These are

magnitude (n=79), frequency (n=10), duration (n=12) and rate of change (n=7). Timing of flow

such as predictability and constancy, which are known to be sensitive to the length of record used

in their calculation (Gan et al. 1991; Clausen and Biggs 2000), were excluded from analysis

because of the short period of record used in this study.

Our general approach to hydrological assessment was first to identify a series of

ecologically relevant hydrological indices that characterized dominant inter-annual variation in

flow conditions and then analyze these ‘unique’ hydrological attributes as the predictors for

variation observed in microbial biomass and community structure. The approach involved five

steps:

1- Compute values for hydrological indices. We calculated values for each 108 streamflow

variables for the WCC for each year of the five-year period. The hydrological indices were

distributed into four categories: the magnitude of flow, frequency of occurrence of flows above a

given magnitude, duration of flow for specific flow conditions and the rate of change or flashiness

of flows (see http://nj.usgs.gov/projects/2454C2R/EcoFlow/definitions.html, Olden and Poff 2003

and Clausen and Biggs 2000 for hydrologic index definitions, and supplementary data for

hydrologic index values calculated for this study).

2- Compute inter-annual statistics. We performed data ordination by using principal component

analysis (PCA) to elucidate major patterns of intercorrelation among the hydrologic variables and

identify relevant subsets of indices that structure the interannual variation. Standardized PCA

60

based on correlation matrix was obtained by centering and standardization by ‘species’ (in

CANOCO 4.5) since hydrological variables were measured in different units. We selected six

representative descriptors of the hydrograph as unique ecological relevant hydrological indices

that best characterized inter-annual variation in flow conditions; referred to as indicators of

hydrological variation (IHV).

3- Compute IHV values prior to sampling. We calculated values for each IHV for 2-week period

prior to stream sediment sampling for the sampling year (2009/2010) in order to evaluate the

sensitivity of microbial biomass and community structure to certain types of hydrological

impacts.

4- Calculate multicollinearity of the IHV. To test whether collinearity existed within the IHV we

compared the correlation matrices of the IHV calculated for 2-week period prior to stream

sediment sampling and eliminate two highly correlated indices.

5- Compute multiple regression analysis of IHV and environmental variables. Multiple regression

analysis (‘best subsets’ in MINITAB 16) was performed on uncorrelated indices of IHV and other

environmental parameters measured in this study.

The 108 hydrologic indices for 5-year period used in this study are presented in Appendix 1 of the

supplementary data. The results from the biplot of PCA are presented in Appendix 2 of the

supplementary data.

Phospholipid fatty acids analysis

Microbial biomass and community structure were determined using phospholipid analysis

following the methods of Findlay (2004). Briefly, lipids were extracted from frozen sediment

samples in the dark at 4°C in 50ml screw-cap glass tubes with 27ml of a 1:2:0.6 (v/v/v)

61

dichloromethane-methanol-50 mM phosphate buffer (pH 7.4) solution. The solution was

partitioned into organic and aqueous phases with 7.5ml dichloromethane and 7.5ml deionized

water, after which the organic phase (containing total lipid) was collected through a predried 2V

filter (Whatman, Schleicher & Schuell) into 15ml test tubes and the solvent dried under Nitrogen

at 37°C. The dried lipid was dissolved in 2ml chloroform and two 100µl subsamples were

oxidized with potassium persulfate at 100 °C overnight in sealed ampoules to release

orthophosphate. Phosphate content was determined spectrophotometrically (610nm) using a dye-

coupled reaction between ammonium molybdate and malachite green. The remainder of the

dissolved lipid was fractionated into neutral, glyco-, and phospholipid with silica gel solid phase

extraction chromatography. Phospholipid fatty acids (PLFAs) were converted into their

respectively methyl esters by base methanolysis and purified by octadecyl bonded silica gel (C18)

reverse-phase column chromatography. Fatty acid methyl esters (FAMEs) were identified and

quantified using Agilent gas chromatograph-flame ionization detection (GC-FID). FAME

identification was based on relative retention times, coelution with standards, and mass spectral

analysis. Individual fatty acids were analyzed for both absolute and relative abundance. Absolute

abundance data (µg FAME g-1 dry weight) allowed the determination of functional group biomass

within the microbial community, while relative abundance or weight percent data (gram

individual fatty acids x gram-1 total fatty acids x 100) allowed the determination of community

structure (Findlay and Dobbs 1993). The FAME nomenclature used followed Findlay and Dobbs

(1993). Standard nomenclature was used to refer to the fatty acids: the total number of carbon

atoms is followed by a colon, and the number of double bonds. The position of the first double

bond is indicated by ω and the number of carbon atoms from the aliphatic end. For example, the

fatty acid 18:2ω6, is 18 carbons long, and has two double bonds that occur at the sixth carbon

62

from the omega end of the molecule. The suffixes c and t specify the cis and trans configurations

of the double bond, respectively. Methyl branching at the iso and anteiso positions and at the 10th

carbon atom from the carboxyl end is designated by the prefixes i, a, and 10Me, respectively. The

prefix cy denotes cyclopropane fatty acids.

Isotopic Analysis of Biomarker Compounds

Isotopic analysis of individual FAMEs were determined using a Delta V Advantage

(ThermoElectron) isotope ratio mass spectrometer coupled with an Agilent 6890N gas

chromatograph via a GC/C III (ThermoElectron) combustion interface. Gas chromatographic

separation of FAMEs utilized a BPX70 column (50m x 0.32mm, 0.25µm film thickness, insert

source). The separation and combustion of fatty acids are described in detail in Abraham and

Hesse (2003). All samples were run in duplicate. Precision for isotopic measurements of

individual compounds was ± 0.6‰. Stable isotope composition was expressed in the δ notation

with V-PDB as standard:

δ13C = [(R sample / R standard) -1] x 1000 (1)

where R is 13C/12C in the samples and standard.

Derivatization of the fatty acids introduced an additional carbon to the molecule altering isotope

ratios. Therefore, calculation of the carbon isotope ratio of FAMEs included a correction for the

isotope ratio of the methyl moiety to obtain the original isotope ratio of fatty acids using the

equations taken from Abraham et al. (1998):

δ13CFA = [(Cn + 1)* δ13CFAME - δ13CMeOH]/Cn (2)

63

where δ13CFA is the δ13C of the fatty acid, Cn is the number of carbons in the fatty acid, δ13CFAME

is the δ13C of the FAME, and δ13CMeOH is the δ13C of the methanol used for the methylation

reaction, which was determined to be -43.23‰.

Elemental Analysis

The frozen subsamples for elemental analysis were freeze-dried, finely grounded to

provide a homogenous sample, weighed (about 1.5 - 2g) into glass vials, placed uncovered in a

desiccator containing 2N HCl and evacuated with a sink aspirator. Fumed samples were freeze-

dried, weighed and re-fumed until constant weight. Approximately 35mg of dried sediment

sample were weighed in tin boats and analyzed on a Costech 4010 elemental analyzer for δ13C,

δ15N, percent carbon and nitrogen, and atomic carbon to nitrogen ratio (C:N). Samples were

analyzed at least in duplicate with an average of 0.02‰ analytical differences.

Statistical analysis

One-way analysis of variance (ANOVA) with Turkey’s HSD (p < 0.05) was performed on

sediment organic content and microbial biomass log transformed (n+1) data to determine

temporal differences (QI Macros 2012 for Excel). Significant differences were assessed at an α

error level of p = 0.05. To investigate the interrelationships between the biological (microbial

biomass and community structure) and hydro-ecological variables, linear regression and multiple

linear regression analyses (MINITAB 16) were used. For multiple linear regression analysis,

predictor variables were selected using the ‘best subsets’ algorithm in MINITAB. This algorithm

fits a small fraction of all possible regression models and selects the ‘best subset’ based on several

selection criteria such as Mallow Cp and adjusted r2.

64

Natural log transformed (ln+1) PLFA relative abundance data were subjected to principal

component analysis (PCA; SPSS 21) to identify patterns of seasonal variation in the microbial

community structure. PLFA profiles were interpreted with a functional group approach (Findlay

and Dobbs, 1993). PCA biplots (CANOCO 4.5) were used to examine major patterns of

intercorrelation among the hydrologic variables and identify representative descriptors that

structure the interannual variation. Path analysis was used to investigate the relationships among

environmental variables and explore the directions of influence of these variables on microbial

biomass. The path analysis was performed in SAS Structural Equation Modeling for JMP 10.

RESULTS

Physical and chemistry stream characteristics

Mean monthly flows ranged from 0.61 – 1.22 m3 s-1 during winter and decreased gradually

in spring to an annual low in late summer (0.15 m3 s-1), before dramatically increasing in October

2010 to 0.55 m3s-1 (Fig. 1). The hydrograph was also characterized by spikes in flow, associated

with storm events, where mean daily flow can increase to as high as 52 m3 s-1. During the study

period, water temperature varied from a minimum of 2.8°C in December to a maximum of 20.3°C

in August showing clear annual trends typical of temperate streams (Table 1). DOC

concentrations varied throughout the year, with the annual low in mean monthly concentration

occurring in late winter (February 2010) and highest monthly mean concentration observed in

October 2010. There was no significant correlation between mean monthly streamflow and mean

monthly DOC concentration.

65

There was a high degree of inter-correlation (either positive or negative) among many of

the 108 calculated hydrologic indices for White Clay Creek over the 5-year period examined.

Principal component analysis was used to reduce the number of variables and to determine which

parameters best explained the differences among each year. Representative descriptors of the

hydrograph that were significantly associated with inter-annual variations were used as putative

drivers of microbial biomass; these were high flood pulse count (FHC1), mean daily flows

(MDF), low flood pulse count (FLC), median daily flow (MQ50), base flow index (MBI) and

variability in daily flow (MVD1).

Total Microbial Biomass

Total microbial biomass, measured as phospholipid phosphate, ranged from 10.05 ± 6.95

to 28.96 ± 12.13 nmol PLP g-1 dry weight sediment and increased during spring to its peak in

June before declining during summer and early fall (Table 1). Total sediment microbial biomass

was significantly greater during May and June compared to December and February. The

proportion that microeukaryotes comprised of total sediment microbial biomass was significantly

smaller (6 – 7%) during the winter (December 2009 and February 2010) compared to remaining

months (21 – 30) % (Table 1). Bacterial abundance estimates ranged from 3.78 × 108 in

December 2009 to 9.19 × 108 cells g–1 sediment (dry wt) in June 2010 and showed a similar

seasonal pattern as observed for total microbial biomass.

Microbial Community Structure

Principal component analysis of PLFA profiles revealed that the samples formed three

clusters that suggest a seasonal pattern in microbial community structure in WCC sediments (Fig.

66

2). Samples from December and February formed one cluster and were taken during a period of

low water temperatures (mean daily temperatures; 2.8 - 3.8 oC) and high stream flows (mean daily

flows; 0.61 – 1.14 m3 s-1) (Table 1). Samples from March and April formed a second cluster and

were taken during a period of moderate water temperatures (7.6. - 12.3 oC) and high stream flows

(0.72 – 1.22 m3 s-1). Samples from May through October formed the third cluster and were taken

during a period of moderate to high water temperatures (11.8. - 20.3 oC) and low to moderate

stream flows (0.15 – 0.55 m3 s-1). Samples from December and February showed positive PC1

component scores and were enriched in PLFAs a15, cy17:0, 10me16, cy19:0, i15:0, br17:1a and

i17:0. These fatty acids are typically considered bacterial in origin, and can be indicative of

several bacterial functional groups. Samples collected in March and April had negative PC1

component scores and were enriched in PLFAs 16:1ω13t, 20:5ω3 and 16:4ω1. These fatty acids

are consistent with the presence of phototrophic microeukaryotes. Samples collected from May

through October had positive PC2 component scores (December through April samples showed

negative PC2 scores) and were enriched with PLFAs 20:4ω6, br19:1a, 18:2w6, 18:3ω3, 15:0 and

18:1ω9. The even-chain length fatty acids in this group are consistent with the presence of

heterotrophic microeukaryotes. The importance of the seasonal variation in proportions of

prokaryotes and eukaryotes to the patterns of change within microbial community structure was

further accentuated by the high correlation (r2 = 0.54) between PC1 factor scores and the

percentage that microeukaryotes comprise of total microbial biomass (Fig. 3).

Relationships between microbial biomass, community structure and environmental variables

Analysis of Pearson correlation coefficients between indicators of hydrological variation,

temperature and DOC concentrations showed that base flow index (MBI) and mean daily flows

67

(MDF) were highly correlated (r > 0.7) with MQ50, MVD1and FLC (Table 2) and MBI and MDF

were removed prior to multiple linear regression analysis of the relationship between total

microbial biomass and environmental descriptors. Multiple linear regression analysis indicated

that high flood pulse count provided the best single variable model explaining the variation in

total sediment microbial biomass (Table 3, Model 1; adjusted R2 = 0.33). Best subset multiple

linear regression indicated that a combination of high flood pulse count, low flood pulse count

and variability in daily flow best explained the observed annual variation in sedimentary

microbial biomass in WCC sediments (Table 3, Model 5; adjusted R2 = 0.49).

A simple linear regression analysis indicated that the percentage that microeukaryotes

comprise of total microbial community explained approximately half of the variation observed in

PC1 scores (Fig. 3). The multiple component model to explained most of the variation observed

in sedimentary microbial community structure indicated that combination of temperature, DOC,

MQ50, FLC and FHC1 explained 93% of the variation (Table 4, Model 9). Path analysis showed

that temperature, median daily flow and high flood pulse count were negatively correlated with

PC1 scores while DOC concentration and low flood pulse count showed positive correlations with

this descriptor of seasonal variations in community structure.

General patterns and seasonal dynamics in PLFAs stable carbon isotopes

Phospholipid fatty acid isotope ratios ranged from a low of -43.66 ± 1.41 (20:5w3,

September) to a high of -28.23 ± 1.45 (i16:0/15:1, February) (Table 5). Total sediment organic

carbon (TOC) stable isotopic ratios averaged -26.50‰ ± 0.73‰ (although this data set is

incomplete as 4 months worth of samples were lost in transit; Table 1). Differences in isotopic

signatures between bacterial PLFA and TOC ranged between 2 - 5‰, depending on the fatty acid.

68

The phototrophic microeukaryotic marker 20:5w3 showed δ13C values of -36 to -44‰ and

differences in isotopic signatures between 20:5w3 and TOC were substantial and ranged from 9 -

17‰ (Fig. 4).

Principal component analysis of compound-specific stable carbon isotope ratios profiles of

the White Clay Creek sedimentary microbial community indicated that for much of the year

(December, March, May and June) profiles were similar (Fig. 5). However, three months (April,

July and September) showed significant variation from the majority of the samples and two

months (February and October) showed high variability among replicates. The samples collected

in April showed positive PC1 scores while sediment samples collected in summer (July and

September) showed negative PC1 scores. PC1 scores for samples collected in October spanned

those for July and September and December, March, May and June. Similarly, February samples

showed PC1 scores that spanned those from April and December, March, May and June.

DISCUSSION

Microbial biomass, community structure and PLFAs stable carbon isotope ratios were

seasonally dynamic in White Clay Creek sediments. Total microbial biomass was lowest during

winter months and peaked during May and June, a period of moderate temperatures and stream

flow. Microbial community structure was dominated by prokaryotes during winter months but

then showed an increase in the relative abundance of several PLFA (16:1w13t, 20:5w3, 16:4w1)

typically associated with phototrophic microeukaryotes during March and April. The relative

importance of microeukaryotes remained high during May through October, however, PCA of

69

PLFA profiles suggested that heterotrophic microeukaryotes gained in relative importance during

this period. Compound specific stable isotope analysis revealed that most bacterial PLFAs (e.g.

i15:0, a15:0) closely tracked δ13C values of sediment total organic carbon, and varied little over

the course of the study. The PLFA 20:5ω3 was the most depleted of all fatty acids and was one

of most variable ranging from a low of -43.89 in December to a high of -36.17 in April..

Total microbial biomass and bacterial abundance for WCC exhibited seasonal fluctuations

and closely reflected variations in stream physicochemistry, indicating a tightly coupled response

by microbial biomass to environmental variables. We found that a significant amount of the

variance (49%) in total microbial biomass was explained by a combination of high and low flood

pulse counts, variability in daily flow and DOC concentration. This suggests that flood

disturbance frequency (as depicted by high and low flood pulse counts) and flow rate (mean or

median flow) should be major factors influencing benthic microbial productivity in streams. Our

results extend to total microbial biomass the findings of Poff et al. (1990), who demonstrated that

benthic algal biomass was sigificantly impacted by current regime. For example, sediment

microbial biomass in WCC showed peak total biomass in late spring/early summer when mean

monthly flows were moderate, variability in daily flow was low and DOC was in steady supply.

These findings extend to heterotrophic microbial communities earlier reports that current regime

can structure the development of benthic communities in streams (Poff and Ward 1989; Biggs

1996; Clausen and Biggs 1997).

Total microbial biomass and bacterial abundance were within the range of published

microbial biomass for temperate freshwater sediments (Bott and Kaplan 1985; Sutton and Findlay

2003; Findlay et al. 2008) but lower than that reported for an impacted, channelized riverine

system in central Ohio (Langworthy et al. 1998; 2002) and contaminated subsurface riverine

70

sediments (Mosher et al. 2006). Total microbial biomass was low and microeukaryotic

contribution to total biomass was significantly reduced during the winter when water temperature

was below 4ºC. At this temperature, metabolisms of both (phototropic) microeukaryotes and

macrofauna are minimal (Lencioni 2004). Insufficient light to support photosynthesis could

explain the depression of phototropic biomass (6-7% of total biomass) in winter, while lack of

grazing pressure on heterotrophic prokaryotes could explain their absolute dominance of total

microbial biomass during the coldest period of the year. With the onset of spring in March, the

phototrophic eukaryotes increased in biomass, while microbial biomass reached periods of peak

total biomass in late spring and summer (Table 1). In spring water has warmed sufficiently, mean

monthly flow decreased and changes in light levels likely enhanced phototropic growth rates such

that total microeukaryotic biomass accounted for 21% to 28% of total microbial biomass.

However, increasing water temperature also increased macrofauna activities (Tande 1988) with

resultant grazing pressure on microbial biomass as evidenced by the decline in total microbial

biomass into the autumn.

Seasonal patterns in microbial community structure have previously been reported in

several lotic environments (Smoot and Findlay 2001; Sekiguchi et al. 2002; Crump et al. 2003),

and two studies described recurring seasonal patterns of microbial communities in streambed

sediments through several annual cycles (Sutton and Findlay 2003; Hullar et al. 2006). The

sediment microbial community composition of WCC displayed distinct seasonal patterns

supporting earlier findings and paralleled the annual cycle in biomass loss and accrual. Our data

showed that the seasonal pattern of variation was, in part, the result of shift between the ratios of

prokaryotic to eukaryotic component of the community. This shift, quantified as PC1 score, was

significantly correlated with seasonal changes in median daily flow, DOC concentration, high and

71

low flood pulse counts and water temperature. The correlation of variation in streamflow and

temperature with changes in community structure of sediment communities observed in our study

was consistent with other studies of temperate ecosystems that reported associated changes in

productivity and community structure (Kaplan and Bott 1989; Shiah and Ducklow 1995; Sutton

and Findlay 2003; Hullar et.al. 2006). The negative effects of low temperature on

microeukaryotic component of microbial community may be associated with decreased affinity

for substrates or inability to sequester substrates from their environment at very low temperatures

(Nedwell 1999). Also, variability in daily flow and high flood pulse count could reduce contact

time with highly bioavailable DOC, which can alter microbial community structure and function.

The positive effects of DOC on microbial community structure via changes in quantities and

qualities of carbon availability have been documented to alter microbial community structure in

predictable ways in both field and mesocosm studies (Falchini et al. 2003; Waldrop and Firestone

2004; Fierer et al. 2007; Nemergut et al. 2010).

The winter sediments (December - February) were dominated by prokaryotes; including

functional groups of fatty acids indicating the presence of gram-positive, gram-negative,

anaerobic, and sulfate-reducing bacteria. Earlier molecular assessments (Hullar et al. 2006) and a

mesocosm study (Chapter 4) of WCC sediments showed that these fatty acids are indicative of a

wide range of bacteria found in the α, β, γ and δ subclasses of proteobacteria and in several genera

of Firmicutes, Acidobacteria, Bacterroidetes and Gammatimonadetes, which is consistent for

other freshwater bacterioplankton communities (Zwart et al. 2002; Eiler and Bertilsson 2004).

Storm events in winter accounted for over 30% (Table 1) of the yearly discharge, and

consequently, a major driver that contributed to low microbial biomass through erosion and

transport of streambed particles. Reductions in sediment bacterial biomass have been reported for

72

riverine sediments following storm events (Holmes et al. 1998; Eisenmann et al. 1999). However,

benthic bacterial composition exhibited a lower disturbance threshold compared to eukaryotic

biomass, which may reflect individual differences in attachment to streambed structure.

Microeukaryotic functional groups, including diatoms and other phototrophic

microeukaryotes (as represented by 20:5w3, 18:3w3, and 16:4w1), at our site responded to the

moderate warmer conditions in the spring and helped defined microbial community structure.

Dominant vernal photoautotrophs corresponding to Haslea, seasonally varying Navicula and

cyanobacterial (Phormidium subfuscum) populations were previously determined in WCC based

on a molecular assessment (Hullar et al. 2006). Kjeldsen et al. (1996) reported benthic algal

spring development peaking in late April/mid May in a Danish lowland stream. This is in concert

with Iversen et al. (1991) who reported the same pattern in a channelized stream. In several

studies (Sand-Jensen et al. 1988; Horner et al. 1990; Kjeldsen et al. 1996), the initiation of spring

blooms has been explained by increased irradiance and decreased water velocity and light

attenuation. As streamwater continued to warm into the summer, studies of temperature-

dependent metabolic activity suggests increases in macroinvertebrate activity (Tande 1988), thus,

increased grazing pressure on phototrophs may have caused the decrease in phototrophic biomass

observed in summer months (Table 1 and Fig. 2). This finding is in agreement with other studies

showing that invertebrate grazing can regulate phototrophic biomass (Kohler 1992; Feminella and

Hawkins 1995; Lambert 1996). Functional groups of fatty acids indicating the presence of fungi,

protozoan, and bacteria progressively replaced the community and were more dominant in

summer (Fig. 2). Saprophytic fungi and terrestrial plant detritus (plant root and shoot tissue)

represented by 18:2w6 and 18:1w9 (Frostegård and Bååth, 1996 and Olsson and Johansen, 2000)

73

assumed dominance in the summer/late summer months, possibly due to an increased input of

plant detritus towards the end of the growing season.

There were no seasonal differences in stable carbon isotope ratios for most bacterial

PLFAs (e.g., i15:0, a15:0), but there were seasonal differences in summed feature 1 (16:1ω9,

16:1ω7c, 16:1ω5c, 16:1w13t) and the fatty acid 20:5ω3. The fatty acids in summed feature 1 are

all monounstaturated fatty acids and are found in a wide range of aerobic bacteria, and

heterotrophic and phototrophic eukaryotes (Findlay 2004). The fatty acid 20:5ω3 is most often

associated with phototrophic microeukaryotic (in particular, diatoms; Findlay 2004) and showed

annual range in isotopic ratios of approximately -36‰ in spring (April) to -44‰ in Autumn

(September) and Winter (December), indicating that isotopic fractionation was not constant

seasonally (Ishikawa et al. 2012). Published values of carbon isotopic signatures for freshwater

algae ranged between -47 and -12‰ (-25.7 ‰ ± 6.8, mean ± S.D.) (Finlay 2001; Zah et al. 2001;

Ishikawa et al. 2012). Studies investigating factors that influence isotopic fractionation in

phytoplankton have suggested that spatial and temporal variation in δ13C of CO2 can play a major

role in determining microalgal δ13C in streams (Finlay 2004). Since photosynthesis in spring is

high and respiration on terrestrial organic matter is low, resulting in reduced input of CO2 from

decomposing 13C-depleted terrestrial detritus, the biological carbon fixation results in less

negative δ13C for PLFA 20:5w3 (Golterman and Meyer 1985; Hellings et al. 2001 ). Moreover,

some phototrophs such as diatoms can utilize bicarbonate, which is isotopically heavier than

dissolved CO2 (Law et al. 1995; Boschker et al. 2005; Ishikawa et al. 2012), thereby explaining

the higher δ13C of 20:5w3 in spring. Overall, increased algal productivity in spring was partly due

to moderate stream temperature and flow appears to be the strongest predictor of the variability in

δ13C of PLFAs.

74

On average, the δ13C values of many bacterial PLFAs were depleted by 2-5‰ relative to

δ13C of sediment total organic carbon, which was consistent with the findings of Canuel et al.

(1997) that δ13C values of lipids are generally depleted by 3-5‰ relative to δ13C of sediment total

organic carbon. This implied that bacteria were utilizing a carbon source with a δ13C of -26 to -

28‰, which is comparable to δ13C of sediment total organic carbon in this study and a previous

determination of the δ13C of WWC DOC (-26.10; Wiegner et al, unpublished). The

correspondence among the δ13C of many bacterial PLFAs and sediment organic carbon and

streamwater DOC supports previous studies concluding that bacterial carbon in forested streams

is derived from streamwater DOC and particulate detritus (Bott et al. 1984; Hall 1995; Webster et

al.1999). These fatty acids were enriched in 13C, on average by 9-10‰ compared to 20:5w3. This

difference support the utilization of terrigenous detrital carbon and suggests minimal utilization of

autochthonous DOC. Boschker et al. (2005) reported uncoupled algal-bacterial system (also based

on PLFA) with terrestrial organic matter or sewage as subsidies supporting bacteria growth during

a spring bloom in the upper Scheldt estuarine. In contrast, δ13C values of bacterial PLFA of

summed feature 1 (16:1ω9, 16:1ω7c, 16:1ω5c, 16:1w13t) showed enrichment in δ13C by 5.77‰

relative to 20:5w3 suggesting that some heterotrophic prokaryotes within the system may utilize

autochthonous DOC, along with allochthonous detritus as carbon sources. The observed δ13C

difference among microbial taxa suggested that further studies of carbon substrate dynamics in

lotic systems are needed.

In conclusion, our results show an overall seasonality within freshwater sediment

microbial communities and their associated lipid carbon isotope ratios. Bacterial lipids were

isotopically depleted on average by 2‰ and 5‰ relative to δ13C of total organic carbon and

enriched relative to δ13C algae-derived carbon source. In winter, the lowest δ13C PLFA values

75

were observed likely due to enhanced input of CO2 from detrital decomposition. During spring

bloom, when litter input is lowest, photosynthesis is high, input of CO2 from detrital

decomposition is low resulting in less negative δ13C PLFAs. Thus, the annual variation of the

δ13C of biota is sensitive in providing indication of changes resulting from biological carbon

fixation and from degradation and respiration from aquatic biota or terrestrial detritus. As such,

isotopic measurements may serve as an early warning signal of ecological changes related to

ecological processes in natural ecosystems. Furthermore, these findings support an emerging

picture in stream ecosystem that hydro-ecological parameters are important factors that structure

streambed microbial communities. Seasonal changes in microbial community structure are largely

predictable and allow the community to take advantage of the changes in carbon and energy input

sources over an annual cycle. Several microbial biogeography studies now provide evidence for

non-random patterns in microbial distribution and show that local environmental heterogeneity

and geographical distance regulate microbial distribution (Battin et al. 2001; Oda et al. 2003;

Crump et al. 2004; Hughes-Martiny et al. 2006; Fierer et al. 2007; Findlay et al. 2008). More

studies and assessments of microbial seasonal patterns may help facilitate better monitoring

strategies for detecting immediate as well as long-term impacts of anthropogenic stressors on the

microbial communities of lotic ecosystems.

Acknowledgments

The authors wish to acknowledge Sherman Roberts for help obtaining samples, and Janna Brown

for assistance in obtaining IRMS data. This research was supported, in part, by NSF grants DEB –

DEB-0516235.

76

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Figure 1. Mean daily discharge at United States Geological Survey (USGS) gauging station of the

study stream during the study period from November 2009 to October 2010. Arrow represents

sampling date of streambed sediment samples.

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Figure 2. PCA of benthic microbial community structure determined by PLFA from the White

Clay Creek seasonal sampling site. Scores are plotted by months: February, F; March, Ma; April,

Ap; May, My; June, Ju; July, Jy; August, Au; September, S; October, O; December, D.

Scales indicate the degree of difference among samples and influential fatty acids (factor loadings

> |0.5|)] are shown along each axis. Symbols indicate mean PC scores (n=9, except Nov. and Dec.

where n =3), error bars = ±S.D. Elipeses drawn by hand to emphase clusters.

88

Figure 3. Relationship between PCA factor 1 score and the calculated percentage that

microeukaryotes contribute to total microbial biomass for all samples.

y = -0.0763x + 1.5829R² = 0.543

-3

-2

-1

0

1

2

3

0 5 10 15 20 25 30 35 40

PC

Sco

re 1

% Eukaryotic Biomass

89

Figure 4. Seasonal variability in sedimentary TOC and selected δ13C PLFAs with component

loadings >0.5 that exerted strong influence on the pattern of variation among samples along the

PC 1 (Fig 5). Bars represent standard deviation.

-50

-45

-40

-35

-30

-25

-20

Feb Mar Apr May Jun Jul Sep Oct Dec

δ1

3 C

Month

i15:0 a15:0 18:2w6 20:5w3 TOC

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Figure 5. PCA of all quantified δ13C of PLFAs of WCC benthic microbial community. Scores are

plotted by months: February, FE; March, MA; April, AP; May, MY; June, JU; July, JY;

September, SE; October, OC; December, DE. Influential fatty acids (factor loadings > |0.5|)] are

shown along each axis. # summed feature includes 16:1ω9, 16:1ω7c, 16:1ω5c, 16:1w13t;

*summed feature includes 18:2ω6, 18:3w3, 18:1ω9, 18:1ω7c, 18:1ω5

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Table 1. Seasonal variations in total sedimentary microbial biomass, bacterial abundance, and physico-chemical parameters of White

Clay Creek

Sampling

date

Biomass/PLP

(nmol g-1dw)

Bacterial

abundance

(g-1 dry wt)

Percent

Eukaryotic/

prokaryotic

Mean daily

temperature

(°C)

Mean

Monthly

Flow (m3s-1)

Mean

DOC

(µ g/L)

Sediment

δ13C

Sediment

δ15N

Sediment

%C

Sediment

%N

C:N ratio

December 10.05 ± 6.95 3.78 x 108 6/94 2.82 1.14 1260 LIT LIT LIT LIT LIT

February 10.64 ± 3.67 3.96 x 108 7/93 3.82 0.61 956 LIT LIT LIT LIT LIT

March 18.34 ± 8.47 5.66 x 108 23/77 7.56 1.22 1632 LIT LIT LIT LIT LIT

April 16.23 ± 3.29 5.33 x 108 18/82 12.31 0.72 1199 -27.22 ± 0.70 2.87 ± 1.75 1.20 ± 0.35 0.11 ± 0.03 10.79

May 27.19 ± 9.00 7.80 x 108 28/72 16.13 0.46 1470 LIT LIT LIT LIT LIT

June 28.96 ± 12.13 9.19 x 108 21/79 16.64 0.27 1274 -26.69 ± 0.64 -0.81 ± 2.70 0.95 ± 0.52 0.06 ± 0.03 14.74

July 19.91 ± 11.95 7.10 x 108 11/89 19.83 0.32 1757 -26.09 ± 1.27 -5.45 ± 4.83 0.57 ± 0.53 0.06 ± 0.04 9.92

August 13.94 ±1.45 3.90 x108 30/70 20.33 0.16 1932 -25.27 ± 0.25 2.00 ± 1.15 0.26 ± 0.02 0.02 ± 0.00 13.02

September 13.35 ± 1.29 4.14 x 108 23/77 14.3 0.15 1357 -27.17 ± 0.85 2.77 ± 0.49 0.49 ± 0.23 0.04 ± 0.03 12.66

October 16.92 ± 6.76 4.93 x 108 27/73 11.82 0.55 2550 -26.55 ± 1.78 2.96 ± 0.77 0.43 ± 0.26 0.03 ± 0.02 12.77

LIT= Lost in transit

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Table 2. Pearson correlation coefficient matrices between selected hydrological indices and measured environmental variables.

Biomass Tempt (°C) DOC (ug/L) MDF MQ50 MVD1 MBI FLC FHC1

Biomass 1

Temperature (C) 0.519 1

DOC (ug/L) 0.078 0.385 1

MDF -0.320 -0.702 -0.389 1

MQ50 -0.066 -0.510 -0.276 0.887 1

MVD1 -0.552 -0.465 -0.283 0.519 0.093 1

MBI 0.488 0.350 0.181 -0.527 -0.134 -0.974 1

FLC -0.192 -0.220 -0.532 0.549 0.342 0.650 -0.706 1

FHC1 -0.628 -0.534 -0.122 0.365 0.204 0.401 -0.382 0.477 1

Bold mean r statistics for variable > |0.7| Italic means variables removed prior to ‘best subset’ multiple regression analysis

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Table 3. Multiple regression analysis (best subsets) for natural log biomass as a function of stream

physico- chemical and hydrological indices

Model Vars R-Sq R-Sq(adj) Mallow Cp SE Temp DOC MQ50 MVD1 FLC FHC1

1 1 39.4 32.7 0.8 4.9865 X

2 1 30.5 22.8 1.9 5.3405 X

3 2 50.2 37.7 1.4 4.7965 X X

4 2 44.1 30.2 2.2 5.0783 X X

5 3 64.3 49.0 1.6 4.3381 X X X

6 3 51.5 30.8 3.2 5.0561 X X X

7 4 65.7 42.8 3.4 4.5952 X X X X

8 4 64.7 41.2 3.5 4.6588 X X X X

9 5 66.2 32.5 5.3 4.9926 X X X X X

10 5 65.9 31.9 5.4 5.0151 X X X X X

11 6 68.8 22.1 7.0 5.3628 X X X X X X

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Table 4. Multiple regression analysis (best subsets) for microbial community (PC1) as a function of stream physico-chemical and

hydrological indices

Model Vars R-Sq R-Sq (adj) Mallow Cp SE %

Eukaryotic

Temp DOC MQ50 MVD1 FLC FHC1

1 1 57.1 51.8 28.1 0.62343 X

2 1 31.0 22.4 48.8 0.79067 X

3 2 78.3 72.1 13.3 0.47435 X X

4 2 65.1 55.2 23.7 0.60108 X X

5 3 85.6 78.3 9.5 0.41777 X X X

6 3 84.3 76.4 10.5 0.43577 X X X

7 4 90.4 82.8 7.6 0.37224 X X X X

8 4 88.7 79.7 9.0 0.40484 X X X X

9 5 97.0 93.2 4.4 0.23342 X X X X X

10 5 92.8 83.7 7.8 0.36226 X X X X X

11 6 97.4 92.2 6.1 0.25024 X X X X X X

12 6 97.1 91.4 6.3 0.26313 X X X X X X

13 7 97.5 88.7 8.0 0.30210 X X X X X X X

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Table 5. Annual variations in stable carbon isotope signatures for selected common fatty acids from White Clay Creek sediments.

Values shown are mean (±SD) from all sampling sites and months.

SF1= summed feature including 16:1ω9, 16:1ω7c, 16:1ω5c, 16:1w13t; SF2 = summed feature including 18:1ω9, 18:1ω7c, 18:1ω5. ND = not detected

FAMEs FEB MAR APR MAY JUN JUL SEP OCT DEC

14:0 -37.64 ±1.53 -37.24 ±2.75 -32.03 ±1.60 -35.69 ±0.67 -36.34 ±1.01 -34.56 ±1.97 -37.47 ±1.55 -34.50 ±1.30 -34.98 ±1.00

i15:0 -30.36 ±0.83 -30.19 ±0.51 -29.61 ±0.46 -30.53 ±0.55 -31.20 ±0.63 -31.59 ±1.41 -31.74 ±0.70 -30.69 ±0.56 -29.70 ±0.94

a15:0 -28.95 ±1.72 -29.72 ±0.86 -29.34 ±0.63 -29.20 ±0.91 -30.24 ±0.53 -30.41 ±0.72 -30.78 ±0.54 -29.97 ±0.60 -29.32 ±0.91

15:0 -30.97 ±2.71 -35.34 ±0.90 -30.66 ±2.08 -30.73 ±1.82 -32.11 ±1.50 -29.53 ±0.50 -32.86 ±1.67 -30.00 ±0.84 ND

i16:0+15:1 -28.23 ±1.45 -29.53 ±0.75 -29.10 ±0.59 -28.36 ±1.25 -30.44 ±0.51 -31.94 ±1.33 -31.14 ±0.62 -29.63 ±0.79 -28.43 ±1.00

16:0 -32.65 ±2.24 -34.32 ±1.21 -31.90 ±2.05 -34.45 ±0.57 -32.92 ±2.53 -34.28 ±1.98 -35.20 ±0.84 -34.68 ±2.24 -33.79 ±1.65

SF1 -32.58 ±1.33 -33.91 ±1.31 -31.70 ±1.71 -34.43 ±0.79 -30.99 ±2.67 -35.32 ±1.00 -35.47 ±1.17 -35.26 ±1.76 -36.08 ±1.27

cy17:0 -32.49 ±4.80 -30.04 ±0.64 -30.16 ±0.68 -31.30 ±0.80 -30.28 ±1.15 -32.56 ±1.06 -31.50 ±0.90 -32.14 ±2.39 -30.78 ±1.73

18:0 -30.82 ±0.32 -31.91 ±0.94 -30.64 ±0.29 -32.97 ±0.82 -32.28 ±2.02 -33.54 ±1.23 -33.81 ±1.14 -32.97 ±1.57 -31.27 ±0.14

SF2 -30.74 ±1.46 -32.03 ±0.69 -31.26 ±0.38 -31.85 ±0.55 -31.18 ±1.13 -32.35 ±0.45 -32.43 ±0.53 -33.00 ±1.71 -31.94 ±0.17

18:2w6 -33.43 ±3.31 -37.64 ±2.32 -32.63 ±1.44 -36.16 ±1.08 -36.05 ±3.06 -37.77 ±2.06 -38.32 ±2.70 -38.22 ±4.70 -38.94 ±1.00

18:3w3 -32.10 ±0.73 -32.37 ±0.65 -32.31 ±0.53 -33.10 ±0.36 -33.12 ±0.58 -34.62 ±0.79 -33.58 ±0.42 -34.37 ±1.71 -32.05 ±0.33

20:0 -36.60 ±1.62 -42.52 ±1.78 -38.57 ±0.95 -40.78 ±1.73 -41.68 ±1.49 -42.06 ±2.05 -42.12 ±2.42 -41.90 ±3.69 ND

20:4w6 -37.60 ±3.25 -40.32 ±1.78 -36.08 ±0.87 -38.46 ±1.33 -40.94 ±0.86 -38.92 ±1.24 -41.50 ±2.01 -39.83 ±2.24 ND

20:5w3 -41.18 ±1.75 -38.90 ±1.34 -36.17 ±2.77 -39.61 ±0.75 -42.56 ±0.88 -40.36 ±1.73 -43.66 ±1.41 -39.23 ±2.32 -43.89 ±2.03

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CHAPTER 4

ELUCIDATING THE BACTERIA RESPONSIBLE FOR UTILIZATION OF

DISSOLVED ORGANIC MATTER IN A THIRD-ORDER STREAM

ABSTRACT

Terrigenous dissolved organic matter (DOM) has long been considered recalcitrant to bacterial

biodegradation although current research has shown its susceptibility to microbiological and

photolytic oxidations and that it contributes significantly to the energy flow in aquatic

ecosystems. To determine the microbial groups actively utilizing terrestrially derived streamwater

DOM, we characterized sediment microbial biomass and community structure using phospholipid

phosphate and phospholipid fatty acids analysis and identified metabolically active members

using phospholipid fatty acid stable isotope probing. Prokaryotes comprised 61% of the

streambed microbial community and consisted of aerobic, facultative anaerobic and anaerobic

bacteria while microeukaryotes comprised the remaining 39%. Streambed sediments were

incubated in re-circulating mesocosm chambers amended with leachate from composted 13C-

labelled tulip poplar tree-tissues (a process that yields 13C-labeled DOM with size and lability

fractions approximating streamwater DOM) and examined for 13C incorporation into microbial

phospholipid fatty acids (PLFAs). The structure of stream sediment microbial communities prior

to and after mesocosm incubation, in both the presence and absence of 13C-labeled DOM, showed

no significant differences and indicated our mesocosm-based experimental design as sufficiently

97

robust to investigate the utilization of 13C-DOM by sediment microbial communities. After 48

hours of incubation, bacterial fatty acids i15:0, a15:0, 16:0, 16:1ω9, 18:1ω9c, 18:1ω7c, 10me16

and cy19:0 showed increased abundance of 13C. This identified the aerobic, facultative anaerobic

and anaerobic bacteria as actively utilizing the 13C-labeled DOM. A single dark 48 h incubation

showed incorporation into both baterial and and microeukaryotic fatty acids (20:4ω6, 20:5ω3)

suggesting that the microeukaryotic predators consumed bacteria that utilized 13C-labeled DOM.

Our data support the hypothesis that streamwater DOM is utilized by stream bacteria, and

substantially contributes to the energy flow in aquatic ecosytems.

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INTRODUCTION

Dissolved organic matter (DOM) plays a significant metabolic role in aquatic ecosystems as

carbon and energy sources for the microbial food web (Peduzzi et al. 2008; Wiegner et al. 2009;

Wong and Williams 2010). It influences the availability of dissolved nutrient and metals, and

modifies the optical properties of aquatic ecosystems (Findlay and Sinsabaugh 1999; Sulzberger

and Durisch-Kaiser 2009). In addition, DOM is now seen as an important driver of ecosystem

functions in freshwater environments and a major component in global carbon cycling and

climate change (Amon and Benner 1996; Batin et al. 2008; Besemer et al. 2009). Dissolved

organic carbon (DOC) is the largest pool of organic carbon in aquatic ecosystems, is

heterogeneous in nature being comprised of humic high-molecular-weight (HMW >1 kDa) and

non-humic low-molecular-weight (LMW <1 kDa) fractions (Amon and Benner 1996; Rosenstock

et al. 2005) and may contain upwards of 5500 individual organic compounds between 300 and 1

kDa Da (Mosher et al. 2010). This complex mixture is present in all natural waters and is

continuously supplied to aquatic ecosystems from both allochthonous (terrestrial) and

autochthonous (aquatic) sources (Peduzzi et al. 2008). Amon and Benner (1996) reported that

humic substances of terrestrial origin are the major constituents of the DOC pool in stream

ecosystems and comprise up to 88% of the DOC in the high molecular weight fraction in the

Amazon River water. These findings agree with other studies, which found that, in most cases,

terrigenous DOC comprised a large portion of DOM in streams and rivers (Benner and Hedges

1993; Hedges et al. 1994; Peduzzi et al. 2008). Conventionally, terrigenous DOC has been

considered recalcitrant to bacterial biodegradation and to move conservatively through aquatic

ecosystems due to the apparent biochemical refractory nature of humic substances (Mantoura and

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Woodward 1983; Thurman 1986; Rosenstock et al. 2005). However, Volk et al. (1997) found that

humic substances account for 75% of the biodegradable fraction of DOM in White Clay Creek.

Similar studies confirm the susceptibility of terrigenous DOC to microbiological and photolytic

oxidations, as well as their value as microbial substrates and their significant contribution to

energy flow in aquatic ecosystems (Amon and Benner 1996; Bano et al. 1997; Carlsson et al.

1999; Frazier et al. 2005; Kaplan et al. 2008; Battin et al. 2008; Fagerberg et al. 2009).

Heterotrophic benthic bacteria are important organisms in lotic ecosystems and are

responsible for several biogeochemical transformations, including DOC uptake, degradation, and

mineralization (Kaplan and Newbold 1993; Pusch et al. 1998; Fischer and Pusch 2001; Tank et al.

2010). While it is clearly established that bacteria provide an important trophic linkage between

DOM and many stream fauna (Hall and Meyer 1998), their relative importance in overall stream

carbon processing remains relatively understudied (Tank et al. 2010). In particular, little is

known about which heterotrophic benthic bacteria drive these ecosystem dynamics. Moreover,

research efforts to understand DOM utilization through microbial processes have been

complicated by the chemical heterogeneity of the DOM pool (Mosher et al. 2010) and a lack of

methods for measuring in situ microbial activities (Kaplan et al. 2008; Bourguet et al. 2009). Few

studies have attempted to identify substrates that would be representative of natural molecules

providing realistic data regarding DOM utilization. Such attempts include NaH13CO3 additions in

lakes (Kritzberg et al. 2004; Pace et al. 2004) and 13C-enriched sodium acetate additions in

streams (Hall and Meyer, 1998; Johnson and Tank 2009). However, being chemically much

simpler than terrestrially derived DOM, these tracers are not reflective of natural stream water

DOM. This major drawback has been addressed by the production of a terrestrial DOC tracer;

13C-labeled tree tissue leachate from tulip poplar tree leaves, small twigs and roots with polymeric

100

and monomeric constituents and lability fractions approximating those of stream water DOC

(Wiegner et al. 2005a). Using this 13C-DOC tracer, Wiegner et al. (2005b) reported that labile

DOM is taken up quickly at or near its point of entry to the stream, whereas intermediately labile

/humic DOM is consumed more slowly and a substantial proportion is exported to downstream

reaches, thus humic DOM serves as an important energy link between upstream and downstream

systems (Kaplan et al. 2008). These results indicated that 13C-labeled tree tissues leachate is the

most representative tracer of allochtonous DOC inputs to streams that has been used to date and is

well suited to investigate the utilization of DOC by heterotrophic benthic microbes.

The goal of our study was to elucidate the heterotrophic benthic microbes within stream

sediments that actively utilize terrestrial DOC, thereby controlling C flux to higher trophic levels

and to downstream reaches. We examined incorporation of terrestrial DOC into microbial

biomass by incubating stream sediment in recirculating mesocosms with natural stream water to

which tracer-levels of 13C-labeled tree tissues leachate were added. We used phospholipid fatty

acid (PLFA) analysis to characterize the benthic microbial biomass and community structure

(Findlay 2004) and PLFA stable isotope probing (SIP) to determine the metabolically active

community members (Boschker et al. 1998; Boschker 2004). We have extended the specificity of

functional assignments of PLFA-SIP by utilizing clone libraries produced from White Clay Creek

sediments by Hullar et al. (2006) and published phenotypic descriptions of identified species (e.g.,

Hahn et al. 2010, Jin et al., 2012).

101

MATERIALS AND METHODS

Study site

Streamwater and sediments were collected from 3rd order White Clay Creek (WCC)

adjacent to the Stroud Water Research Center in Avondale, Pennsylvania, and two 2nd order

streams; the West (WCCW) and East (WCCE) branches of White Clay Creek. The White Clay

Creek watershed is agriculturally dominated with upstream riparian forests and is within the

Piedmont Province of southeastern Pennsylvania and northern Delaware (39ο53’N, 75ο47’W),

joining the Christina River near the Christina's discharge to the Delaware Bay. White Clay Creek

drains 725 ha of approximately 52% of agricultural, 22% of tilled/hayed and 23% of wooded

lands (Newbold et al. 1997; Wiegner et al. 2005b). The immediate area surrounding the three

study sites is forested and the local drainage is a patchwork of pasture lands grazed by horses and

cattle. The dominant tree species reported are tulip poplar (Liriodendron tulipefera), beech

(Fagus grandifolia), red oak (Quercus rubra), and black oak (Quercus velutina) (Wiegner et al.

2005b). Streamflow and streamwater chemistry has been monitored at regular intervals since the

1970s with mean annual stream flow, stream water temperature, and local precipitation of 115

L/s, 10.6ºC, and 105 cm y-1, respectively (Newbold et al. 1997). Streambed sediments consist of

clay-, silt-, and sand-sized particles in pools and runs, with gneiss- and schist-derived gravel and

cobble in riffles (Kaplan et al. 1980).

102

Synthesis of 13C-labeled DOM

Wiegner et al. (2005a) describes the generation of the 13C-labeled stream DOM. Briefly,

thirty-two 1-y-old tulip poplar seedlings (Liriodendron tulipefera L.) were grown with 13CO2 at

the National Phytotron located at Duke University, Durham, North Carolina, USA. 13C-tree tissue

leachate was generated by leaching approximately 4 g of dried, ground tulip poplar seedlings

tissues (60.9% leaves, 24.6% stems, 14.5% roots; % weight of new tissues) in 4 L of sterile-

filtered (0.2-µm, Gelman Supor) C-free de-ionized cold water in the dark at 4° C for 24h. The

mixture of all tree tissue types (leaf, stem, and root) was used to generate a leachate representative

of fresh tree litter inputs (organic matter inputs) from trees to streams. Tree tissue leachate was

Tyndallized in a 70 °C water bath for 0.5 h twice, separated by 24 h at room temperature to

ensure biological stability, and stored in 2-L sterile plastic containers in the dark at 4 °C (Wiegner

et al. 2005a) until the experiment began.

Mesocosms

Two 15-L recirculating plug-flow bioreactors with an empty bed contact time of 150 min

were used to elucidate the microbes responsible for utilization of DOM in streams. Bioreactors

accepted a 0.014 m2 galvanized box used to sample stream sediments such that the stream

sediments were level with the bioreactor bed. Bioreactors could be operated in either open or

recirculating mode and were contained within a 1000 L flowing stream water tank to maintain

ambient stream temperature. These tanks also served as the source of streamwater during open

mode operation. Sondes with dissolved O2 and temperature/conductivity probes (YSI Model 600

XL, Yellow Springs, Inc., Yellow Springs, Ohio) were inserted into the recirculation line of each

103

mesocosm. Mesocosms were set up in the experimental greenhouse facility of Stroud Water

Research Centre under natural photoperiod except for experiment 1 (see below).

Sediment Collection and Mesocosm Experiments

Streambed sediments were collected in 2009 and 2010; four samples were collected at

each sampling period. Sediments were collected from White Clay Creek in October 2009 (twice),

April 2010 and August 2010, and from the west and east branches in November 2009. To collect

stream sediments with a minimum of disturbance, a galvanized box (surface area 0.014 m2)

perforated by 0.32-cm diameter holes (bottom only) that allowed streamwater to escape as the box

was inserted into the sediments until the bottom just touched the sediment surface. Plexiglas

plates were slipped under and over the box trapping the sediments and allowed them to be lifted

from the stream intact. A second box, just large enough to accommodate the first, was place over

the first box, the core inverted and the inner (first) box removed. This process yielded a

rectangular core or sample of intact stream sediment that was placed into a bioreactor in the

proper vertical orientation. Two samples were processed immediately and served as a reference

for sediment and microbial community characteristics before the 13C-DOM uptake experiment

(stream control; referred to as T0). The other two boxes were placed into 15-L recirculating

bioreactors (one per bioreactor), which were operated for 24 h in open mode with a flow of 0.06

cm/s (equivalent to measured stream velocities). The chambers were switch to recirculate mode

and one of the chambers (experimental; referred to as T13C) received 13C DOM while the other

chamber (mesocosm control; referred to as TM) received no 13C DOM.

104

Addition of the tracer increased total DOM by approximately 1-5%. Sediments were incubated in

recirculation mode for 48h or 50h; 13C exposure period was based upon the effective depth of the

chambers (ratio of chamber volume to sediment tray area) and the mass transfer coefficient for the

13C tracer (estimated from a prior whole-stream injection). At the end of the incubation period, the

core was removed from the bioreactor, the upper 2 mm of sediments were removed with a clean

spatula, placed in an aluminum weigh boat, well mixed, and subsampled for total microbial

biomass, microbial community structure, δ13C of PLFAs, organic matter contents and particle-size

analyses. In total six mesocosms experiments were conducted over the two-year period.

Experiment 1 used White Clay Creek sediments and the experiment was run in the dark for 48 h;

the mesocosm chambers were covered with 2.2 cm Styrofoam sheets and black plastic sheeting to

exclude light (Table 1). Experiment 2 was a replicate of experiment 1, except sediments were

incubated uncovered (exposed to natural sunlight at natural photoperiod) and for 50 hours.

Experiments 3 and 4 were replicates of experiment 2 except sediments from west and east

branches of WCC, respectively, were used. Experiment 5 and 6 replicated experiment 2. The

concentrations of the 13C- tree tissue leachate used and exposure time are shown in Table 1. In

August 2010, sediments were also collected from White Clay Creek at the end (T72) of the

mesocosm incubations to assess natural changes in the microbial community over the period of

the mesocosm experiment.

Phospholipid fatty acids analysis

Samples for lipid analysis were stored at -80°C until lyophilized. Microbial biomass and

community structure of freeze-dried sediments (approximately 10 g dry weight) were determined

using phospholipid analyses following the methods of Findlay (2004). Briefly, stream and

105

mesocosm sediments were extracted in the dark at 4°C in 50ml screw-cap glass tubes with 27ml

of a 1:2:0.6 (v/v/v) dichloromethane-methanol-50 mM phosphate buffer (pH 7.4) solution. The

solution was partitioned into organic and aqueous phases with 7.5ml dichloromethane and 7.5ml

deionized water, after which the organic phase (containing total lipid) was collected through a dry

2V filter (Whatman, Schleicher & Schuell) into 15ml test tubes and the solvent dried under

nitrogen at 37°C. The dried lipid was dissolved in 2ml chloroform and two 100µl subsamples

were oxidized with potassium persulfate at 100 °C overnight in sealed ampoules to release

orthophosphate. Phosphate content was determined spectrophotometrically (610nm) using a dye-

coupled reaction between ammonium molybdate and malachite green. The remainder of the lipid

was fractionated into neutral, glyco-, and phospholipid with silica gel solid phase extraction

chromatography. Phospholipid fatty acids were converted into their respectively methyl esters by

base methanolysis and purified by octadecyl bonded silica gel (C18) reverse-phase column

chromatography. Purified fatty acid methyl esters (FAMEs) were identified and quantified using

Agilent gas chromatograph equipped with an automatic sampler, a 60m x 0.25 mm non polar DB-

1 column and a flame ionization detector. Hydrogen was used as the carrier gas at a flow rate of

2.3ml/min. The initial chromatograph oven temperature was 80°C followed by a temperature rise

of 4 °C/min to 250 °C which was then held at this temperature for 10min. FAME identification

was based on relative retention times, coelution with standards, and mass spectral analysis.

Standard nomenclature was used to refer to the fatty acids: the total number of carbon atoms is

followed by a colon, and the number of double bonds. The position of the first double bond is

indicated by ω and the number of carbon atoms from the aliphatic end. For example, the fatty acid

16:1ω7, is 16 carbons long, and has one double bond that occurs at the seventh carbon from the

omega end of the molecule. All double bounds are cis, unless designated as trans configuration

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using a suffix of t. Methyl branching at the iso and anteiso positions and at the 10th carbon atom

from the carboxyl end is designated by the prefixes i, a, and 10Me, respectively. The prefix cy

denotes cyclopropane fatty acids. Individual fatty acids were analyzed for both absolute and

relative abundance. Relative abundance or weight percent data (gram individual fatty acids x

gram-1 total fatty acids x 100) was used to determine community structure (Findlay and Dobbs

1993). In addition to the standard combination of functional group and marker fatty acid

assignments (Findlay 2004), we increased the specificity of PLFA taxonomic assignments using a

previously published 16S rRNA gene library constructed from WCC sediments (Hullar et al.

2006) and the taxonomic descriptions of these OTUs or closely related species.

Microbial community utilization of 13C DOM

Stable carbon isotope ratios of individual FAMEs were determined using an Agilent 6890

GC coupled to a to a PRISM (GV Instruments, Manchester, UK) stable isotope mass

spectrometer. Analyses were conducted on two different gas chromatographic columns (DB-1

and DB-23; 60m x 0.25mm, 0.25µm film thickness) to allow the analysis of a greater number of

resolved fatty acid methyl esters. For FAMEs that were not resolved by either column during GC-

IRMS analysis, we report the δ13C of the summed feature. Stable carbon isotope ratios were

expressed as:

δ13C = [(R sample / R standard) -1] x 1000 (1)

where R is 13C/12C in the samples and standard. Data were reported relative to Vienna PeeDee-

Belemnite (VPDB). Incorporation of 13C into PLFAs was estimated using the equations (Abraham

et al. 1998):

δ13CFA = [(Cn + 1)* δ13CFAME - δ13CMeOH]/C (2)

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where δ13CFA is the δ13C of the fatty acid, Cn is the number of carbons in the fatty acid, δ13CFAME

is the δ13C of the fatty acid methyl ester (FAME), and δ13CMeOH is the δ13C of the methanol used

for the methylation reaction, which was determined to be -43.23‰.

Statistical analysis

We used comparisons of total microbial biomass and community structure to assess the

efficacy of our mesocosm approach with a comparison of stream sediments (T0) to the control

mesocosm sediments (TM) used to assess mesocosm effects and comparison of control mesocosm

sediments to treatment mesocosm sediments (T13C) used to check for unwanted stimulation due to

tracer-level DOM additions. Potential differences in microbial biomass and percent eukaryotes vs.

prokarotes were examined with matched pairs t-tests with a α-level of 0.05 using SPSS 19. Fatty

acid profiles for the bioreactors and WCC sediments were subjected to principal component

analysis (PCA) after log transformation [ln (x + 1)] of weight percent fatty acid data and analyzed

using SPSS 19. Changes in microbial community structure were examined by comparing

principal component 1 scores as above. Increases in δ13C values of individual fatty acids, or

when necessary summed features, upon exposure to δ13C-DOM were detected using a matched

pairs t-tests of the difference in δ13C values from control and treatment mesocosm sediments.

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RESULTS

Utilization of 13C-labeled DOM by sediment microbial community

The combined DB-1 and DB-23 columns allowed the quantification of the δ13C values of

15 features; these were either individual PLFAs, two co-eluting PLFAs or summed features (more

than 2 co-eluting PLFAs). For PLFAs that were resolved by both columns, values presented are

the mean of both analyses (Table 2). PLFA δ13C values for stream and bioreactor control

sediments (treatments T0 and TM) ranged from -37.26 to -28.53‰ and -35.92 to -28.70‰,

respectively, with the most depleted values found in the microeukaryotic biomarkers 20:4ω6 and

20:5ω3, and the most enriched values found in cy17:0 (Table 2). No significant differences were

observed between the δ13C values of PLFAs from T0 and TM treatments. The δ

13C values of

PLFAs from the 13C-labeled bioreactor (T13C) ranged from -34.75 to -24.69‰. Eight features (5

individual PLFAs, two co-eluting pairs and 1 summed feature) showed significant 13C enrichment

(p< 0.05); these were i15:0, a15:0, 16:0, 10me16:0, cy19:0, 16:0/16:1ω9, 18:1ω9/18:1ω7 and

summed feature 2 (18:2ω6, 18:3ω3, 18:1ω9, 18:1ω7c, 18:1ω5). The PLFA 18:2ω6 and 18:3ω3

were resolved by the DB-23 and did not show significant enrichments in T13C, suggesting that

significant enrichment in summed feature 2 detected using the DB-1 column was driven by

enrichment of 18:1ω9/18:1ω7. Two polyenoic fatty acids, 20:5ω3 and 20:4ω6, indicative of

microeukaryotes were significantly labeled in the dark-incubated bioreactor with a 4.15‰ and

3.45‰ differences (TM vs T13C) respectively, while labeling of these fatty acids was not detected

when incubations were conducted using the natural photoperiod. Assigning microbial identity to

the enriched PLFAs using functional group approach (Findlay 2004), bacteria actively

metabolizing stream water DOM were aerobic gram-negative bacteria (16:0/16:1ω9,

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18:1ω9/18:1ω7), gram-positive or facultative anaerobic gram-negative bacteria (i15:0, a15:0), and

anaerobic gram-negative bacteria (10Me16:0, cy19:0).

Hullar et al. (2006) produced bacterial 16S rRNA gene clone libraries from White Clay

Creek sediment (same site as used in this study). Using the highest available taxonomic

resolution (species, genius, or phylum) and published phenotypic descriptions, we matched

bacterial taxa known to inhabit White Clay Creek to the PLFAs showing 13C enrichment during

incubation with 13C-labeled DOM (Table 3). The fatty acid i15:0 is a major fatty acid in several

described bacteria closely related to those from the White Clay Creek sediment clone libraries.

These include: Lysobacter antibioticus, (Gammaproteobacteria, aerobic, gram-negative), Bacillus

niacina, B. silvestris (Firmicutes, aerobic, gram-positive), Acidobacterium capsulatum

(Acidobacteria, facultative anaerobic, gram-negative), Flavobacterium aquatile, Dysgonomonas

gadei, Runella slithyformis (Bacteroidetes, aerobic, gram-negative), and Gemmatimonas

aurantiaca (Gemmatimonadetes, aerobic, gram-negative). The fatty acid a15:0 is a major fatty

acid in two bacteria closely related to those from the White Clay Creek sediment clone libraries -

Bacillus niacina and B. silvestris (Firmicutes, aerobic, gram-positive). The fatty acid 16:0 is

widely distributed and found in many of the described bacteria that are closely related to those

from the White Clay Creek sediment clone libraries. The fatty acids 10me16:0 is not a major

fatty acid of any described bacteria that are closely related to those identified from the White Clay

Creek sediment clone libraries. The fatty acid 16:1w9 is a major fatty acid in one bacterium

(Nitrospira cf. moscoviensis (Nitrospirae, aeorobic, gram-negative)) that is closely related to

those identified from the White Clay Creek sediment clone libraries. The fatty acid 18:1w9 is a

major fatty acid in two bacteria (Acidobacterium capsulatum, Acidobacteria, facultative

anaerobic, gram-negative; Blastopirellula marina, Planctomycetes, facultative anareobic, gram-

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negative) that is closely related to those identified from the White Clay Creek sediment clone

libraries. The fatty acid 18:1ω7 is a major fatty acid in several described bacteria closely related

to those from the White Clay Creek sediment clone libraries. These include: Burkholderia

cepacia, Herbaspirillum rubrisubalbicans, Rhodoferax ferrireducens, Variovorax paradoxus

(Betaproteobacteria, aerobic, gram-negative), Nevskia ramosa (Gammaproteiobacteria, aerobic,

gram-negative) and Filomicrobium fusiforme (Alphaproterobacteria, areobic, gram-negative).

The fatty acid cy19:0 is a major fatty in two described bacteria (Burkholderia cepacia and

Filomicrobium fusiforme) closely related to those from the White Clay Creek sediment clone

libraries. Assigning microbial identity to the enriched PLFAs using this extended approach,

bacteria actively metabolizing stream water DOM were aerobic gram-negative bacteria including

species related to Burkholderia cepacia, Nevskia ramosa, Lysobacter antibioticus, Kofleria flava,

Filomicrobium fusiforme, Flavobacterium aquatile, Runella slithyformis, Blastoperillula marina

Gemmatimonas aurantiacca and Nitrospira cf. moscoviensis; gram-positive or facultative

anaerobic gram-negative bacteria including species related to Bacillus niacini and B. silvestris,

Rhodoferax ferrireducens, Variovorax paradoxus, Herbaspirillum rubrisubalbicans,

Dysgonomonas gadei, and Acidobacterium capsulatum; and anaerobic gram-negative bacteria.

Evaluation of mesocosm approach

Total sediment microbial biomass was increased by removal from White Clay Creek and

incubation within the mesocosms; however, this increase was not significant (Figure 1a).

Sediment microbial community structure was unchanged by incubation within the mesocosms as

neither the percentage that prokaryotes comprised of the total community nor the PC1 score from

a PCA analysis of PLFA profiles were significantly different for stream and mesocosm control

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sediments. The addition of 13C-DOM to the natural stream water DOM did not affect either total

microbial biomass or microbial community structure. Particularly, the concentration of

phospholipid phosphate, the percentage that prokaryotes comprised of the total community and

the PC1 score from a PCA analysis of PLFA profiles were not significantly different for

mesocosm control and treatment sediments (Figure 1).

DISCUSSION

Current studies of DOM metabolism within freshwater streams acknowledge that the

structure of sediment microbial communities may modulate the degradation and use of this

important carbon and energy resource (e.g. Mineau et al. 2013). However, few, if any, studies

directly examine the bacteria that are responsible for the utilization of DOM in streams. In this

study, the 13C incorporated into the microbial phospholipid fatty acids revealed that many, but not

all, heterotrophic microorganisms present in White Clay Creek sediments assimilated components

from an allochthonous detrital source. The tracer used was designed to mimic, to a far greater

extent than any previously used DOC tracer, stream water DOM (Wiegner et al. 2005a). It

contains humic and polysaccharide components (Wiegner et al. 2005a), both labile and semi-

labile fractions (Wiegner et al. 2005b) and has been used for direct measurement of stream DOC

uptake rates coefficients (Kaplan et al. 2008). Kaplan et al. (2008) determined that uptake rate

coefficients for the labile and semi-labile fractions were 4.20 and 0.22 km-1, respectively, and

calculated labile tracer uptake was 272 mg C m-2 d-1 and semi-labile tracer uptake was 40 mg C

m-2 d-1. These rates were sufficient that all labile tracer DOC and ~25% of semi-labile fraction

were taken up during our mesocosm incubations. PLFA-SIP revealed that aerobic gram-negative

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bacteria, gram-positive and/or facultative anaerobic gram-negative bacteria, and anaerobic gram-

negative bacteria utilized stream water DOM.

Among the microbial PLFAs that showed significant 13C enrichment, there were two

trends - those that were enriched, on average, by 3.5‰ to 5.9‰ and those that were enriched, on

average, by 0.8‰ to 1.1‰. The fatty acids showing the strongest enrichment were i15:0, a15:0,

16:1ω9, 16:0, 18:1ω7c and 18:1ω9. Coupling these PLFA-SIP findings with the results of

previously constructed 16S rRNA gene sequence clone libraries via published phenotypic species

descriptions indicated that several organisms closely related to several described species actively

utilized 13C-labeled leaf leachate (Table 3). Virtually all of these species are known for

possessing versatile metabolisms. For example, the Burkholderia cepacia complex (Vandamme

et al. 1997) is well known for its extraordinary degradative abilities, possessing broad substrate

mono- and dioxygenases (Lessie et al. 1996). Herbaspirillum rubrisubalbicans, best known as a

nitrogen-fixing, plant-growth-promoting rhizobacteria, is also a plant pathogen capable of

penetrating plant cell walls (Monteiro et al. 2012). This genus contains a number of aquatic

species (e.g. H. aquaticum) that can metabolize a wide variety of sugars and other low molecular

weight compounds (Dobritsa et al. 2010). Nevskia ramosa is considered a neuston bacterium

although related OTUs have been identified among the active bacteria present in drinking water

biofilms (Keinanen-Toivola et al. 2006). Nevskia ramosa is capable of digesting complex organic

polymers including starch and cellulose, as well as, many low molecular weight compounds

(Stu rmeyer et al. 1998). Species of genus Lysobacter are typically found in soil and water

habitats and L. antibioticus is capable of degrading a wide variety of complex substrates including

carboxymethyl cellulose, chitin, gelatin, laminarin, protein, Tween-20, Tween-80 and yeast cell

walls (Sullivan et al. 2003). 13C DNA-SIP has shown utilization of 2,4,6-trinitrotoluene by an

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OTU related to L. taiwanensis in Norfolk Harbor sediments (Gallagher et al. 2010).

Acidobacteria are one of the most common bacterial phyla in soil and can also be among the

dominant taxa aquatic sediments (Rawat et al. 2012, Spring et al. 2000). Acidobacterium

capsulatum is known to degrade cellobiose, starch and xylan, and contains homologs to enzymes

required for pectin degradation (Rawat et al. 2012). Bacillus niacini and B. silvestris are known to

utilize a host of simple organic molecules, as well as degrade several complex organic molecules.

Flavobacterium aquatile digests casein and aesculin, and exhibits cystine arylamidase esterase,

esterase lipase, and α-glucosidase activity. Runella slithyformis is capable of growth on

glycogen, D-arabitol, dulcitol, inositol, mannitol, sorbitol, ribose and sorbose and can hydrolyze

starch (Copeland et al. 2012). Blastopirellula marina digests DNA, aesculin, gelatin and starch,

exhibits lipase activity and growth on fructose, glycerol, glutamic acid and chondroitin sulfate

(Schlesner et al. 2004). Gemmatimonas aurantiaca is capable of growth on yeast extract,

polypeptone, succinate, acetate, gelatin, benzoate, glucose, sucrose, galactose, melibiose, maltose,

formate and b-hydroxybutyrate (Zhang et al 2003). Genomic sequencing of Rhodoferax

ferrireducens indicates that this species possesses highly diverse metabolic capacities including

utilization of sugars, acetate and aromatic compounds under both aerobic and anaerobic

conditions (Risso et al. 2009). Variovorax paradoxus is capable of digesting a wide range of

complex organic compounds including (but not limited to) amino acids, polychlorinated

biphenyls, dimethylterephthalate, linuron, 2,4-dinitrotoluene, homovanillate, veratraldehyde, 2,4-

dichlorophenoxyacetic acid, anthracene, poly(3-hydroxybutyrate), chitin, cellulose, and humic

acids. Dysgonomonas gadei is known to utilize a wide range of sugars, to hydrolyze starch and

aesculin, and to exhibit a wide range of derivative enzyme activities including N-acetyl-b-

glucosaminidase, acid phosphatase and trypsin (Hofstad et al. 2000). Combined, the 13C-

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enrichment of many of the abundant fatty acids present in these species, the recovery of gene

sequences closely related to these cultured species from clone libraries developed from White

Clay Creek and their utilization of labile organic compounds strongly suggests that bacteria

related to the species discussed above utilized the labile tracer DOC. The capacity of several

species, notably Rhodoferax ferrireducens, Variovorax paradoxus, Lysobacter antibioticus,

Burkholderia cepacia and Nevskia ramosa to digest complex organic compounds suggests that

bacteria related to these species are responsible for the utilization of the semi- labile tracer DOC.

Among the cultured relatives most closely related to recovered sequences from the sediment

community only Nitrospina moscoviensis exhibits 16:1ω9 as a dominant fatty acid. Nitrospina-

like bacteria are nitrite-oxidizing bacteria and members of the deep-branching bacterial

phylum Nitrospirae with only one class Nitrospira (Bock and Wagner 2006). The enrichment of

16:1ω9 following sediment incubation with 13C-labeled leaf leachate suggests that either species

containing 16:1ω9 but not identified from the clone libraries also utilized components of the 13C-

labeled leaf leachate or that bacteria closely related to Nitrospina moscoviensis exhibit

mixotrophic, rather than lithoautotrophic growth in White Clay Creek.

The PLFAs 10me16:0 and cy19:0 showed moderate 13C enrichment following incubation

with 13C-labeled leaf leachate. The fatty acid 10me16:0 is viewed as a marker fatty acid for

members of the genus Desulfobacter within the delta subclass of proteobacteria (Findlay 2004).

Macalady et al. (2000) analyzed fatty acid profiles for 100 strains of bacteria including 12 genera

of sulfate-reducing bacteria and several other anaerobic species. They concluded that

Desulfobacter was the major sources of 10me16:0 in environmental samples. The Macalady et al.

(2000) analysis also indicated that the PLFA cy19:0 was also strongly associated with

Desulfobacter. This suggests that sulfate-reducing bacteria, and in particular members of the

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genus Desulfobacter, utilized some component of 13C-labeled leaf leachate, albeit at a lesser

extent compared to those bacteria represented by the strongly labeled PLFAs.

On one occasion, we incubated sediments in the dark and during this trial the δ13C of the

PLFAs 20:4ω6 and 20:5ω3 increased by 3.45‰ and 4.15‰ respectively, compared to an average

increase of 1‰ when sediments were incubated under the natural photoperiod. It is important to

note that we conducted but a single dark incubation and that 20:4ω6 and 20:5ω3 are found in both

heterotrophic and autotrophic protists. Nevertheless, the presence of label in fatty acids 20:4ω6

and 20:5ω3 in the dark suggests that protozoan grazers used bacteria that are utilizing 13C-DOM

as food source and may contribute significantly to the transfer of allochthonous carbon via the

microbial loop in stream ecosystems. As the system contains both heterotrophic and autotrophic

protists, synthesis of 20:4ω6 and 20:5ω3 by algae during incubations under the natural

photoperiod would produce a second, non-labeled source of these fatty acids, which would serve

to isotopically dilute those produced by trophic interactions within the sediment microbial

community.

Total sediment microbial biomass measured as phospholipid phosphate (nmol PLP gdw-1)

for the control and 13C-labeled bioreactors (Table 3) fall within the range (2-280nmol PLP g-1 dry

wt) of PLP concentrations quantified in freshwater sediments of eastern deciduous forest and

those previously published for White Clay Creek (Bott and Kaplan 1985; Sutton and Findlay 2003;

Findlay et al. 2008). The increase in microbial biomass in the mesocosm sediments (Figure 1a;

comparison T0-TM), though not significant, is likely a response of stream microbial communities

to placement within a mesocosm setting. While infrequently assessed and even less frequently

discussed (but see Mortazavi et al. 2013, Suárez-Suárez et al. 2011), a ‘mesocosm effect’ appears

to be an increase in sediment bacterial abundance. Sediments, in general and streams sediments in

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particular, are dynamic and sediment microbial communities experience frequent disturbances

associated with storm flows, hydraulic turbulence, or macrofauna activities (Fisher et al., 1982;

Schwendel et al., 2011) and are best viewed as rarely, if ever, at maximum biomass. The natural

response of sediment microbial communities to disturbance is regrowth leading to increased

biomass (Findlay et al., 1990; Traunspurger et al., 1997; Langworthy et al., 2002). In this study,

sediments were obtained with the utmost care; however, obtaining a “disturbance free” microbial

sediment sample is very difficult, if not impossible. In addition, once placed in the mesocosms

sediments were protected from further in-stream disturbances, which, combined, with any

disturbance during removal from the stream, likely led to the small observed increase in biomass

(Riemann et al. 2000). Stream and mesocosm sediments (T0, TM, and T13C) showed little change

in ratios of prokaryotic to eukaryotic biomass. On average, in the bioreactors, ~60 % of the

microbial biomass was prokaryotic and the remaining 40 % was eukaryotic, which is well within

the range of estimates reported for stream sediments from several low-order forested streams

(Sutton and Findlay 2003; Mosher and Findlay 2011) and is similar to previously reported ratios

for WCC (Findlay et al. 2008). Thus, there was no change in community structure, or in microbial

biomass in these mesocosms in response to sampling procedure or mesocosm effect.

In this study, the additions of 8.73 to 17.47 µg/L of 13C-DOM did not significantly alter

the structure of the sediment microbial community during any of the mesocosm incubations. In a

study where 50 µg C g-1 soil of universally 13C labeled glucose, glutamine, oxalate or phenol were

added to samples of soil, no detectable changes in the soil PLFA profiles were found (Brant et al.

2006). Also, Griffiths et al. (1999) detected no changes in the soil PLFA profiles until rates of

additions of a model root exudate exceeded 375µg C g-1d-1 in a 14-d experiment. In other studies,

the additions of 400 µg C g-1 soil of vanillin (Waldrop and Firestone 2004) and 726 µg C g-1

117

oxalate and glutamate to grassland sandy loam soil (Falchini et al. 2003) resulted in changes in

microbial community composition or PLFA profile. The trend in these studies was that as

substrate loading increased, relative abundance of specific PLFAs increased leading to changes in

total microbial community composition. Our tracer-level substrate addition was significantly

smaller than additions used in studies where significant changes in biomass or community

composition were observed; this was intentional and designed to avoid changes in biomass and

PLFA profiles that could compromise our use of mesocosm-based experimental design.

Though mesocosm experiments have increased our understanding of community ecology,

ecosystem dynamics and provided insight into global processes (Fraser and Keddy 1997; Jessup

et al. 2004; Cardinale et al. 2006; Benton et al. 2007; Duffy 2009), mesocosms have been

criticized as being unrealistic simplifications of natural systems with restricted utility (Carpenter

1996; Schindler 1998; Haag and Matschonat 2001). However, with appropriate scaling, accurate

conclusions can be made (Spivak et al. 2011). Our analyses of microbial biomass and community

structure indicate that our mesocosm-based experimental design, particularly the comparison of

the TM mesocosm control to the T13C mesocosm treatment samples, was sufficiently robust to

warrant examination of individual fatty acids for the incorporation of 13C with the goal of

determining the role of sediment microbes in processing streamwater DOM. Also, the significant

13C-enrichment detected in microbial lipids in the T13C bioreactors clearly demonstrated the high

sensitivity of stable isotope probing of PLFA as a technique to elucidate which microbial

communities are responsible for the utilization of terrestrial DOC.

In conclusion, the present study provides direct experimental evidence that terrestrial

DOM is readily utilized by a broad range of benthic heterotrophic aerobic, facultative anaerobic

and anaerobic bacteria in forested headwater streams. We posit that terrestrially derived DOM

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exported from forested watersheds is not entirely lost to downstream systems but rather is

assimilated and mineralized by a variety of heteroorganotrophic bacteria which, in turn, are

grazed by heterotrophic eukaryotes transferring allochthonous carbon and energy to higher

trophic levels through the microbial loop (Meyer 1994). Thus, our data have important

implications for protection of forested headwater streams where much of the DOM in transport is

derived from the surrounding terrestrial ecosystem. As terrestrial DOM is an important source of

carbon and energy for stream microbial communities, any human activity that disrupts or

accelerates the delivery of terrestrial DOM to headwater streams may need regulation, since

perturbation to ecological linkages between aquatic and terrestrial systems could have pronounced

effects on microbial community structure and function. This is particularly true where the

terrestrial and aquatic ecosystems are tightly linked by large internal fluxes of DOM in the

forested landscape (McDowell and Likens 1988; Aitkenhead-Peterson et al. 2003).

Acknowledgements

We thank Janna Brown (University of Alabama, Tuscaloosa, AL) and Sherman Roberts (Stroud

Water Research Center, Avondale, PA) for laboratory assistance. Funding for this project was

provided by the National Science Foundation (award number DEB-0516235).

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Figure 1. Changes in a) microbial biomass, b) percent prokaryotes and c) community structure summarized by PCA axis 1, among treatments and sampling dates for all experiments. Values are mean differences ± SD, (n = 6). T0-TM= Differences attributed to mesocosm effect, TM-T13C= Differences attributed to the effects of 13C-labeled DOM

-120

-90

-60

-30

0

30

60C

hang

e in

PL

P (n

mol

PO

4 gd

w)

-15

-10

-5

0

5

10

15

Cha

nges

in %

Pro

kary

ote

b

-0.8

-0.6

-0.3

-0.1

0.2

0.5

0.7

T0 - TM TM - T13C

Cha

nge

in P

C 1

Comparision

c

a

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Table 1. Experimental design of 13C-DOM uptake experiments.

Experiment/ Mesocosm condition

Stream Order/ Sampling Date

13C DOM injected (µg/L)

Experiment duration (h)

1. Dark 3rd order WCC / 12-15 Oct., 2009

17.47 48

2. Natural photoperiod 3rd order WCC / 20-23 Oct., 2009

17.47 50

3. Natural photoperiod 2nd order WCCE/ 3-6 Nov., 2009

17.47 50

4. Natural photoperiod 2nd order WCCW/ 9-12 Nov., 2009

8.73 50

5. Natural photoperiod 3rd order WCC/ 20-23 April 2010

8.73 50

6. Natural photoperiod 3rd order WCC/ 9-12 August 2010

17.47 50

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Table 2. Microbial PLFA δ13C values (‰; mean ± SD) from 6 mesocosm experiments determined using DB-1 and DB-23

chromatographic columns.

FAMEs T0 TM T13C Mean of da nb significancec

i15:0 -29.01 ± 0.75 -28.69 ± 0.84 -24.69 ± 3.30 4.00 ± 3.01 6 0.02

a15:0 -29.34 ± 0.99 -29.12 ± 0.86 -25.58 ± 3.08 3.54 ± 2.82 6 0.02

16:0 -32.35 ± 2.12 -31.89 ± 1.51 -28.12 ± 3.16 3.77 ± 2.92 6 0.02

summed feature 1* -33.16 ± 0.86 -32.50 ± 1.23 -30.20 ± 4.23 2.30 ± 4.57 6 0.23

16:0, 16:1w9 -33.17 ± 1.81 -32.84 ± 1.49 -28.73 ± 2.81 4.11 ± 2.84 6 0.01

10me16:0 -30.60 ± 1.76 -29.88 ± 0.49 -28.80 ± 0.72 1.13 ± 0.64 5 0.02

cy17:0 -28.53 ± 2.18 -28.70 ± 2.44 -27.84 ± 2.40 0.86 ± 2.10 6 0.55

summed feature 2* -31.54 ± 1.52 -31.60 ± 1.40 -27.36 ± 3.36 4.24 ± 2.88 6 0.02

18:2w6 -33.07 ± 2.11 -32.83 ± 1.32 -32.09 ± 2.00 0.24 ± 1.85 5 0.50

18:1w9c, 18:1w7c -30.35 ± 0.91 -30.56 ± 0.97 -24.70 ± 3.16 5.87 ± 3.03 6 <0.01

cy19:0 -30.46 ± 0.80 -30.84 ± 0.23 -30.03 ± 0.50 0.82 ± 0.54 6 <0.01

20:4w6, 20:5w3 -36.69 ± 1.52 -35.92 ± 1.95 -34.59 ± 1.62 1.33 ± 2.62 6 0.23

20:4w6, coelluter -35.25 ± 1.71 -33.85 ± 1.75 -32.42 ± 4.29 0.40 ± 4.73 5 0.50

20:5w3, coelluter -37.26 ± 1.44 -35.92 ± 2.01 -34.75 ± 1.38 1.17 ± 2.81 6 0.27

22:6w3 -33.85 ± 1.49 -33.40 ± 1.62 -33.29 ± 1.68 0.05 ± 2.85 4 0.92

T0 = natural sediment control, TM= experimental control, T13C= treatment sediment. * Summed feature 1 includes 16:1ω9, 16:1ω7c,

16:1ω5c, 16:1ω13t; summed feature 2 includes 18:2ω6, 18:3w3, 18:1ω9, 18:1ω7c, 18:1ω5, a=difference in δ13C values of T13C- TM,

b= number of time when peak was present in all three treatments per experiment, c=p of paired t-test

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Table 3. Phylogenetic affiliation of bacterial fatty acids functional groups extracted from White Clay Creek sediment*

*fatty acids that showed 13C enrichment in this study are in bold type # Hullar et al., (2006) bacterial 16S rRNA gene clone libraries

Phylum or Subphylum Species # Fatty acids Morphology Metabolism Citation

Betaproteobacteria Rhodoferax ferrireducens 16:0, 16:1w7, 18:1w7 Gram -ve aerobic, facultative anaerobic Hahn et al., 2010

Variovorax paradoxus 16:0, 16:1w7, cy17:0,

18:1w7

Gram -ve aerobic, facultative anaerobic Jin et al., 2012

Herbaspirillum rubrisubalbicans 16:1w7, 16:0, 18:1w7 Gram -ve aerobic, facultative anaerobic Jung et al. 2007

Burkholderia cepacia 16:0, 16:1w7, cy19:0 Gram -ve strict aerobe Stead, 1992

Gammaproteobacteria Lysobacter antibioticus i15:0, i17:1w9 Gram -ve aerobic Srinivasan et al., 2010

Nevskia ramosa 16:1w7, 16:0, 18:1w7 Gram -ve aerobic Losey et al., 2013

Deltaproteobacteria “Polyangium vitellum” [Kofleria

flava] (Haliangium ochraceum)

i16:0,16:0 Gram -ve strict aerobe Fudou et al., 2002

Alphaproteobacteria “Pedomicrobium fusiforme”

[Filomicrobium fusiforme]

18:1w7, 16:0, cy19:0 Gram –ve aerobe Wu et al., 2009

Bacteroidetes

Flavobacterium aquatile i15:0, a15:0, 15:1w6 Gram -ve strict aerobic Lee et al., 2012

Dysgonomonas gadei i14:0, i15:0, 16:0 Gram -ve aerobe, facultative anaerobic Hofstad et al., 2000

Runella slithyformis a15:0, i15:0, 16:1w5 Gram -ve strict aerobe Copeland et al., 2012

Firmicutes Bacillus niacini a15:0, i15:0, 16:0,

16:1w11, 18:0

Gram +ve facultative anaerobe Hong et al., 2012

Bacillus silvestris i15:0, i16:1 Gram +ve aerobic Reddy et al., 2008

Dendrosporobacter quercicolus 15:1, 17:1 Gram -ve anaerobic Strompl et al., 2000

Acidobacteria Acidobacterium capsulatum i15:0, 18:1w9 Gram -ve facultative anaerobic Kulichevskaya et al., 2012

Planctomycetes Pirellula (Blastopirellula)

marina

16:0, 18:1w9 Gram -ve strict aerobe Schlesner et al., 2004

Nitrospirae Nitrospina moscoviensis 16:1w9, 16:0

(11Me16:0)

Gram -ve aerobe Lipski et al., 2001,

Spieck et al., 2006

Gemmatimonadetes Gemmatimonas aurantiaca i15:0, 16:1, 14:0 Gram -ve aerobe Zhang et al., 2003

Actinobacteria Kutzneria kofuensis i16:0 (10Me17:0) Gram +ve aerobe Stackebrandt et al., 1994,

Suriyachadkun et al., 2013

134

CHAPTER 5

OVERALL CONCLUSIONS

Microbial communities are important players in lotic ecosystems and provide a critical

link to higher trophic levels through the microbial loop (Pomeroy 1974; Hall and Meyer 1998).

They are responsible for several biogeochemical transformations, including DOC uptake,

degradation, and mineralization (Kaplan and Newbold 1993; Pusch et al. 1998; Fischer and

Pusch 2001; Tank et al. 2010). Thus, DOC is a major energy source for benthic microbial

metabolism and drives lotic ecosystem processes and maintains secondary production of

consumers. To better understand the ecology of microbial communities, it is important not only to

describe the community composition and identify environmental factors that regulate their spatial

and temporal variations, but also to elucidate which of these microbes within the community

actively participate in DOC uptake, degradation, and mineralization. Studies that have

investigated bacterial utilization of DOC have indicated its heterogeneous nature being comprised

of both labile components, which turn over rapidly, and refractory components, which turn over

more slowly (Moran and Hodson 1990). The refractory DOC pool is composed of larger

molecules, primarily humic in nature, and often assumed to be largely inert to bacterial

degradation. However, few studies have shown that a portion of the humic substances is

biologically degradable (Moran and Hodson 1990; Carlsson et al. 1999).

135

The goals of this interdisciplinary study were to elucidate the bacteria responsible for

utilization of humic DOC in streams and to assess overall variability in microbial biomass, carbon

isotope signatures and community structure over time and across multiple spatial scales in stream

networks. Moreover, I examined the role of environmental heterogeneity and hydrological indices

in structuring benthic microbial communities. This dissertation was arranged into five chapters

with chapters one and five (this chapter) providing a comprehensive introduction and summary,

respectively.

Chapter two examined spatial variations in sedimentary microbial biomass and

community structure of forested streams within two distinct watersheds. A nested sampling

design was used to sample sediments from 1st- through 3rd - order streams in White Clay Creek

watershed and from 1st, 3rd and 5th- order streams in the Neversink watershed across multiple

spatial scales. PLFA analysis was used to characterize the microbial biomass and communities

found in the samples from these streams. In general, streams from the White Clay Creek

watershed showed higher total microbial biomass, percent prokaryotes and bacterial abundance

than those within the Neversink watershed. Also, C:N ratios and conductivity were generally

higher in the White Clay Creek watershed compared to the Neversink watershed. In addition, C:N

ratios were higher in 1st and 2nd order streams and lower in 3rd and 5th order steam sediments. The

variation in microbial biomass in stream sediments correlated with C:N ratio, sediment grain size,

percent carbon and percent water content. In contrast to reach-scale similarity in microbial

biomass, there were large significant differences in biomass at stream-scale and between

watersheds. Sediment microbial community structure in the fourteen streams investigated

displayed distinct watershed-scale variations at the scale of hundreds of kilometers and among-

stream within a watershed variation at the scale of hundreds of meter. Also, the shift from

136

predominance of bacteria in lower order streams to phototrophic microeukaryotes in higher order

stream may be explained by decreased importance of terrestrial organic inputs from riparian

vegetation and increased importance of algal production downstream. Overall, the magnitude of

within stream variation in microbial biomass was small compared to the variability noted among

streams and between watersheds. Furthermore, this study and others (Oda et al. 2003; Crump et

al. 2004; Hughes-Martiny et al. 2006; Fierer et al. 2007) conducted over a range of spatial scales

implies non-random distribution of microbial community and that environmental heterogeneity

and geographical distance can structure microbial biomass and distribution.

Chapter three provided baseline information on the natural abundance and seasonal

variation in compound specific stable carbon isotope signatures of individual microbial PLFAs of

White Clay Creek. Also, seasonal variation of microbial biomass and community structure was

investigated with assessment of the effect of hydrological and environmental variables on

microbial communities in headwater stream sediments. This work demonstrated that sedimentary

microbial biomass was seasonally dynamic and significantly correlated to a combination of high

and low flood pulse counts, variability in daily flow and DOC concentrations. The seasonal

pattern of variation observed in microbial community structure was as a result of a shift between

the ratios of prokaryotic to eukaryotic component of the community. This shift was significantly

correlated with seasonal changes in median daily flow, high and low flood pulse counts, DOC

concentrations and water temperature. Stable carbon isotopes signatures of some PLFAs varied

significantly over an annual cycle. Both bacterial and microeukaryotic stable carbon isotope

signatures were heaviest in the spring and lightest in autumn or winter. Some bacterial lipids were

isotopically depleted on average by 2- 5‰ relative to δ13C of total organic carbon and enriched

relative to algae PLFAs. During spring bloom, increased algal productivity partly due to moderate

137

stream temperature and flow, and reduced input of CO2 from detrital decomposition appears to

result in a more negative δ13C PLFAs (Finlay 2004). Thus, the annual variation of the δ13C of

biota is sensitive in providing indication of changes resulting from ecological processes related to

ecosystem functions. In addition, this findings support emerging picture in microbial ecology for

non-random patterns in microbial biomass and community structure and that local environmental

heterogeneity regulate microbial distribution over annual cycles.

Chapter four elucidates which heterotrophic benthic microbes within streams actively

utilize humic DOC and ultimately control the material flux that influences higher trophic levels.

Streambed sediments were incubated in re-circulating mesocosm chambers amended with

leachate from composted 13C-labelled tulip poplar tree-tissues (a process that yields 13C-labeled

DOM with size and lability fractions approximating streamwater DOM) and examined for 13C

incorporation into microbial PLFA.Total community structure and metabolically active

community members from the mesocosm incubated sediments were elucidated through PLFA and

13C isotopic analysis of the microbial PLFAs respectively. This work demonstrated that the

mesocosm-based experimental design, particularly the comparison of the TM mesocosm control

to the T13C mesocosm treatment samples, is sufficiently robust to warrant examination of

individual fatty acids for the incorporation of 13C labeled DOC into microbial lipids. Bacterial

fatty acids i15:0, a15:0, 16:0, 16:1ω9, 18:1ω9c, 18:1ω7c, 10me16 and cy19:0 (aerobic, anaerobic

and facultative anaerobic bacteria biomarkers) and fatty acids 20:4ω6, 20:5ω3 (microeukaryotic

biomarkers) showed increased abundance of 13C.The fatty acids showing the strongest enrichment

were i15:0, a15:0, 16:1ω9, 16:0, 18:1ω7c and 18:1ω9. These are marker fatty acids for members

of major bacterial groups such as Alphaproterobacteria, Gammaproteobacteria, Firmacutes,

Acidobacteria, Bacteroidetes, Gemmatimonadetes and Nitrospirae. These species are known for

138

versatile metabolisms and PLFA-SIP findings suggest that they actively utilized 13C-labeled leaf

leachate. The PLFAs 10me16:0 and cy19:0 showed moderate 13C enrichment and are marker fatty

acids for Desulfobacter. This suggests that sulfate-reducing bacteria, in particular, members of the

genus Desulfobacter utilized some component of 13C-labeled leaf leachate. In addition, the

presence of label in fatty acids 20:4ω6 and 20:5ω3 suggests that protozoan grazers used bacteria

that are utilizing 13C-DOM as food source and may contribute significantly to the transfer of

allochthonous carbon via microbial loop in stream ecosystems. This work suggests that these

benthic microbes are important players within steam ecosystems with regards to humic DOC

uptake, degradation, respiration, and transfer to higher trophic levels.

The overall results of this study shows evidence that the sedimentary microbial

community displayed seasonal pattern of variation in structure and isotopic signatures, distinct

watershed-level biogeography, as well as variation along a headwater streams-large stream

gradient. The variation observed in sedimentary microbial community structure in time and space

was significantly correlated to local environmental heterogeneity. Further, this study provides

direct experimental evidence that benthic microbial communities in forested headwater streams

readily utilize humic DOM. In summary, this study depicts a complex relationship between

microbial community structure, environmental heterogeneity and utilization of humic DOM,

indicating that terrestrial DOM quality and quantity along with other hydro-ecological variables

should be considered among the important factors that structure benthic microbial communities in

lotic ecosystems.

For future directions, investigation should be focused on examining spatial patterns in

sedimentary microbial distribution at larger spatial scales, such as within and among watersheds

in a biome and in different biomes with samplings at different times of the year. Results from

139

such extensive studies ranging from small scales (<10km) to intermediate (10 – 3000km) and at

larger spatial scales (>10,000km) would provide addition information about the role of

environmental heterogeneity and geographical distance in structuring microbial community

structure. Also, such study can be complemented with research efforts to identify which microbial

individual populations are unique to each habitat using molecular techniques. Results from such

undertakings can provide useful insights into the long held assumption in microbiology of

“everything is everywhere, but the environment selects” (Bass-Becking, 1934).

Further investigations on the mesocosm experiment should be focused on scaling up to

whole-stream tracer- level isotope addition with sufficiently enriched 13C-DOC to examine the

incorporation of labeled 13C-DOC into stream sediment microbial lipids. Undoubtedly, this will

be a challenging task but such study would provide a strong support for the elucidation of the

metabolically active microbial communities within stream sediments involved in humic DOC

metabolism as shown in this study. Furthermore, results from such study will advance our

understanding of methods for measuring in situ microbial and stream dynamics.

140

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APPENDIX

Supplementary Data Appendix 1. Values of the 108 hydrologic indices used in the study

Code Hydrologic Index Unit Year 1 Year 2 Year 3 Year 4 Year 5

Magnitude of flow events Average flow condition

MDF Mean daily flows m3s-1 0.37 0.35 0.56 0.58 0.43

MQ50 Median daily flow m3s-1 0.31 0.28 0.42 0.31 0.38

MVD1 Variability in daily flow1 (--) 1.16 1.12 1.35 2.28 1.02

MVD2 Variability in daily flow 2 (--) -0.38 -0.28 -0.63 -0.49 -0.48

MDSK Skew ness in daily flows (--) 1.19 1.27 1.32 1.86 1.12

MR1 Ranges in daily flows 1 (--) 0.28 0.36 0.17 0.25 0.24

MR2 Ranges in daily flows 2 (--) 0.42 0.58 0.29 0.41 0.31

MR3 Ranges in daily flows 3 (--) 0.48 0.67 0.35 0.45 0.41

MS1 Spreads in daily flows 1 (--) -1.08 -0.81 -2.09 -1.20 -1.49

MS2 Spreads in daily flows 2 (--) -0.75 -0.42 -1.44 -0.76 -1.22

MS3 Spreads in daily flows 3 (--) -0.63 -0.32 -1.23 -0.69 -0.94

MM1 Mean monthly flow-October

m3s-1 0.28 0.20 0.63 0.55 0.51

MM2 Mean monthly flow-November

m3s-1 0.25 0.24 0.34 0.29 0.69

MM3 Mean monthly flows- December

m3s-1 0.48 0.62 1.14 0.39 0.86

MM4 Mean monthly flows- January

m3s-1 0.31 0.36 0.69 0.31 0.69

MM5 Mean monthly flow- February

m3s-1 0.77 0.34 0.61 0.70 0.50

MM6 Mean monthly flow - March m3s-1 0.62 0.27 1.22 0.96 0.43

MM7 Mean monthly flow - April m3s-1 0.43 0.41 0.72 0.71 0.39

MM8 Mean monthly flow - May m3s-1 0.42 0.49 0.46 0.46 0.33

MM9 Mean monthly flow - June m3s-1 0.25 0.37 0.27 0.27 0.26

MM10 Mean monthly flow - July m3s-1 0.24 0.22 0.32 0.16 0.16

MM11 Mean monthly flow - August

m3s-1 0.16 0.36 0.16 0.88 0.15

MM12 Mean monthly flow - September

m3s-1 0.27 0.37 0.15 1.29 0.17

148

MMV1 Variability in monthly flows-October

(--) 1.57 0.43 1.50 2.82 0.23

MMV2 Variability in monthly flows-November

(--) 0.27 0.51 0.25 0.48 1.25

MMV3 Variability in monthly flows-December

(--) 0.76 1.55 1.52 0.94 0.87

MMV4 Variability in monthly flows-January

(--) 0.23 0.71 0.91 0.53 0.75

MMV5 Variability in monthly flows-February

(--) 1.52 0.35 0.50 0.59 0.28

MMV6 Variability in monthly flows-March

(--) 0.63 0.09 0.83 1.40 0.28

MMV7 Variability in monthly flow - April

(--) 0.31 0.52 0.23 0.62 0.66

MMV8 Variability in monthly flows-May

(--) 0.34 1.09 0.19 0.28 0.42

MMV9 Variability in monthly flows-June

(--) 0.32 0.64 0.19 0.16 0.46

MMV10 Variability in monthly flows-July

(--) 0.52 0.23 0.98 0.18 0.17

MMV11 Variability in monthly flows-August

(--) 0.29 0.71 0.19 3.37 0.22

MMV12 Variability in monthly flows-September

(--) 1.23 1.38 1.25 2.00 0.63

MV1 Variability across monthly flows1

(--) 4.87 5.33 5.34 12.46 3.74

MV2 Variability across monthly flows2

(--) 0.29 0.32 0.37 0.38 0.21

MV3 Variability across monthly flows3

(--) 0.84 1.06 1.11 1.42 0.87

MV4 Variability across monthly flows4

(--) 75.01 79.30 82.53 145.59 62.08

MMSK Skewness in monthly flows (--) 0.23 0.31 0.35 0.61 0.23

MAR Mean annual runoff m3s-1

km-2 0.01 0.01 0.02 0.02 0.01

Low flow condition

MML1 Mean minimum monthly flow-October

m3s-1 0.12 0.14 0.19 0.21 0.40

MML2 Mean minimum monthly flow-November

m3s-1 0.19 0.17 0.26 0.21 0.37

MML3 Mean minimum monthly flow-December

m3s-1 0.24 0.22 0.34 0.24 0.51

MML4 Mean minimum monthly flow-January

m3s-1 0.22 0.25 0.45 0.22 0.48

MML5 Mean minimum monthly m3s-1 0.31 0.25 0.42 0.28 0.40

149

flow-February

MML6 Mean minimum monthly flow-March

m3s-1 0.40 0.23 0.65 0.40 0.34

MML7 Mean minimum monthly flow-April

m3s-1 0.31 0.26 0.54 0.45 0.27

MML8 Mean minimum monthly flow-May

m3s-1 0.28 0.27 0.34 0.31 0.24

MML9 Mean minimum monthly flow-June

m3s-1 0.17 0.23 0.18 0.22 0.16

MML10 Mean minimum monthly flow-July

m3s-1 0.15 0.17 0.15 0.12 0.12

MML11 Mean minimum monthly flow-August

m3s-1 0.12 0.19 0.10 0.12 0.12

MML12 Mean minimum monthly flow-September

m3s-1 0.12 0.20 0.08 0.26 0.09

MBI Base flow index (--) 0.32 0.41 0.17 0.22 0.24

MBV Variability in base flow index

(--) 3.00 2.47 5.42 2.57 9.88

MAL Specific mean annual minimum flows

m3s-1

km-2 0.00 0.00 0.00 0.00 0.00

High flow condition

MMH1 Mean maximum monthly flow-October

m3s-1 2.66 0.54 4.47 8.92 0.85

MMH2 Mean maximum monthly flow-November

m3s-1 0.51 0.76 0.68 0.96 4.98

MMH3 Mean maximum monthly flow-December

m3s-1 2.24 5.55 7.87 1.87 3.28

MMH4 Mean maximum monthly flow-January

m3s-1 0.57 1.67 3.96 1.05 3.37

MMH5 Mean maximum monthly flow-February

m3s-1 6.46 0.74 1.56 2.18 1.19

MMH6 Mean maximum monthly flow-March

m3s-1 2.44 0.31 6.03 7.73 0.99

MMH7 Mean maximum monthly flow-April

m3s-1 0.93 1.19 1.33 2.44 1.59

MMH8 Mean maximum monthly flow-May

m3s-1 0.82 3.23 0.65 0.88 1.02

MMH9 Mean maximum monthly flow-June

m3s-1 0.57 1.42 0.37 0.45 0.76

MMH10 Mean maximum monthly flow-July

m3s-1 0.85 0.40 1.87 0.23 0.23

MMH11 Mean maximum monthly flow-August

m3s-1 0.34 1.02 0.27 16.68 0.31

MMH12 Mean maximum monthly flow-September

m3s-1 1.93 2.94 1.10 13.56 0.59

150

MHF1 High flow discharge1 (--) 0.39 0.53 0.23 0.41 0.28

MHF2 High flow discharge2 (--) 0.49 0.65 0.36 0.62 0.37

MHF3 High flow discharge3 (--) 0.65 0.82 0.51 0.77 0.54

MHA Specific mean annual maximum flows

m3s-1

km-2 0.22 0.19 0.27 0.57 0.17

MHV1 High flow volume1 days 5.17 7.55 9.61 17.43 7.04

MHV2 High flow volume2 days 11.70 10.85 14.24 26.54 13.86

MHV3 High flow volume3 days 13.79 28.16 21.46 52.88 20.24

MHP1 High peak flow 1 (--) 1.82 1.99 2.10 3.08 1.65

MHP2 High peak flow 2 (--) 7.25 5.98 6.60 10.05 6.52

MHP3 High peak flow 3 (--) 10.42 14.08 12.03 20.97 9.57

MHP4 High peak flow 4 (--) 2.50 2.61 2.99 5.01 2.32

Frequency of flow events FLC Low flood pulse count year-1 78.00 88.00 87.00 77.00 91.00

FLS Frequency of low flow spell year-1 16.00 15.00 19.00 16.00 12.00

FHC1 High flood pulse count 1 year-1 78.00 78.00 91.00 80.00 76.00

FHC2 High flood pulse count 2 year-1 10.00 16.00 12.00 29.00 10.00

FHC3 High flood pulse count 3 year-1 5.00 3.00 7.00 10.00 4.00

FRE1 Flood frequency 1 year-1 164.00 136.00 192.00 174.00 183.00

FRE2 Flood frequency 2 year-1 10.00 16.00 21.00 29.00 10.00

FRE3 Flood frequency 3 year-1 5.00 3.00 7.00 10.00 4.00

FRE4 Flood frequency 4 year-1 271.00 261.00 270.00 270.00 273.00

FRE5 Flood frequency 5 year-1 78.00 102.00 91.00 80.00 76.00

Duration of flow events Average flow conditions

DLE1 Low exceedence flows 1 (--) 1.36 1.22 1.47 1.73 1.33

DLE2 Low exceedence flows 2 (--) 1.73 1.84 2.13 2.51 1.56

DL0 Number of zero-flow days year-1 0.00 0.00 0.00 0.00 0.00

DLP0 Percent of zero-flow months (--) 0.00 0.00 0.00 0.00 0.00

High flow conditions

DFD Flood duration 1 (--) 1.83 1.55 2.10 1.90 1.79

DHP High flow pulse duration days 0.78 0.72 1.27 1.56 0.89

DHPV Variability in high flow pulse duration

(--) 102.98 102.20 98.67 166.34 85.89

DHF1 High flow duration 1 days 0.57 0.55 0.89 0.96 0.63

DHF2 High flow duration 2 days 2.26 1.66 2.80 3.13 2.49

DHF3 High flow duration 3 days 3.25 3.91 5.11 6.53 3.66

DHF4 High flow duration 4 days 0.45 0.42 0.70 0.70 0.52

151

DHF5 High flow duration 5 days 0.78 0.72 1.27 1.56 0.89

Rate of change in flow events RRM Rise rate m3s-1d-1 0.14 0.13 0.24 0.34 0.14

RRV Variability in rise rate (--) 332.15 335.78 331.38 462.39 334.06

RFM Fall rate m3s-1d-1 -0.27 -0.26 -0.51 -0.62 -0.30

RFV Variability in fall rate (--) -269.79 -224.63 -249.75 -309.36 -214.31

RD0 Number of day rises (--) 0.52 0.52 0.53 0.55 0.47

RCF1 Change of flow 1 m3s-1 0.35 0.22 0.19 0.26 0.20

RCF2 Change of flow 2 m3s-1 -0.51 -0.56 -0.40 -0.55 -0.43

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Appendix 2. Ordination from the principal component analysis of 108 streamflow variables for

the WCC. Standardized PCA based on correlation matrix was obtained by centering and

standardization by ‘species’ (in CANOCO 4.5) since ‘species’ were measured in different units.

Some of the data points were jittered (where overlapping occurred) to improve clarity.