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ECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL COMMUNITY STRUCTURE by MICHAEL SCOTT STRICKLAND (Under the Direction of Mark A. Bradford) ABSTRACT Many processes related to carbon-cycling in terrestrial ecosystems are carried out by soil microbes. Our understanding of the relationship between carbon cycling and microbial community structure is, at best, rudimentary. The role of microbial community structure in carbon-cycling processes has, for the most part, been treated as either functionally redundant or driven by binary distinctions (e.g. fungal:bacterial ratios). It was the purpose of this work to test these generalizations and advance our understanding how soil microbes regulate ecosystem carbon dynamics. This purpose was accomplished using field-based observation and controlled lab-based experimentation. In the first of the field- based studies, I determine the mineralization dynamics of glucose and its relationship to land-use, microbial community and edaphic characteristics across a southeastern U.S. landscape. I find that the size, activity, and fungal:bacterial dominance of the microbial community is unrelated to glucose mineralization but that land-use and the underlying edaphic variable, soil phosphorus, explains spatial variation in this process. In the second field-based study, I examine how an invasive grass affects belowground carbon-cycling in a southeastern forest. Again, I find no role of fungal:bacterial dominance but observe

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Page 1: ECOSYSTEM CARBON CYCLING: …coweeta.uga.edu/publications/10379.pdfECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL COMMUNITY STRUCTURE by MICHAEL SCOTT STRICKLAND (Under

ECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL

COMMUNITY STRUCTURE

by

MICHAEL SCOTT STRICKLAND

(Under the Direction of Mark A. Bradford)

ABSTRACT

Many processes related to carbon-cycling in terrestrial ecosystems are carried out by soil

microbes. Our understanding of the relationship between carbon cycling and microbial

community structure is, at best, rudimentary. The role of microbial community structure

in carbon-cycling processes has, for the most part, been treated as either functionally

redundant or driven by binary distinctions (e.g. fungal:bacterial ratios). It was the purpose

of this work to test these generalizations and advance our understanding how soil

microbes regulate ecosystem carbon dynamics. This purpose was accomplished using

field-based observation and controlled lab-based experimentation. In the first of the field-

based studies, I determine the mineralization dynamics of glucose and its relationship to

land-use, microbial community and edaphic characteristics across a southeastern U.S.

landscape. I find that the size, activity, and fungal:bacterial dominance of the microbial

community is unrelated to glucose mineralization but that land-use and the underlying

edaphic variable, soil phosphorus, explains spatial variation in this process. In the second

field-based study, I examine how an invasive grass affects belowground carbon-cycling

in a southeastern forest. Again, I find no role of fungal:bacterial dominance but observe

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that the presence of the invasive grass accelerates carbon-cycling and depletes soil carbon

stocks, likely via ‘priming’ of the microbial community’s activity. Using lab-based

experimentation, I explore the role of microbial community structure separate to the

influence of confounding variables. I find that litter mineralization rates are dependent on

the microbial community and that these communities exhibit ‘home-field advantage’.

These results refute theories related to functional redundancy. In a follow-up experiment,

I find that the perception of litter quality, of two chemically-distinct litters, is dependent

on the microbial community. Specifically, communities sourced from herbaceous habitats

perceive more chemically-simple litter to be of higher quality than more chemically-

complex litter, whereas communities from forest habitats mineralize both litters similarly.

Together, my findings show that although microbial community structure may appear

unrelated to carbon dynamics in situ, controlled-experimentation reveals that this

structure may play an important role in determining rates of ecosystem processes. Future

work needs to resolve why there is an apparent disconnect between results from

observational and controlled-experimental studies.

INDEX WORDS: carbon-cycling, microbial communities, bacteria, fungi, land-use,

invasive species, glucose, stable isotopes, priming effect,

preferential substrate utilization, leaf litter

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ECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL

COMMUNITY STRUCTURE

by

MICHAEL SCOTT STRICKLAND

B.A., William Jewell College, 2005

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2009

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© 2009

Michael Scott Strickland

All Rights Reserved

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ECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL

COMMUNITY STRUCTURE

by

MICHAEL SCOTT STRICKLAND

Major Professor: Mark A. Bradford

Committee: Mac A. Callaham

Lisa A. Donovan

Noah Fierer

Jacqueline E. Mohan

Electronic Version Approved:

Maureen Grasso

Dean of the Graduate School

The University of Georgia

August 2009

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DEDICATION

I dedicate this dissertation to Delmer and Minnie Strickland, the best parents in

the world. I could not have accomplished this without your love and support.

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ACKNOWLEDGEMENTS

I would like to thank my family, friends, and colleagues who have helped me

make it through this process in more ways than you can ever imagine. Catherine, thank

you for your love and sticking by me during this, I know I can be grumpy at times.

You’re my best friend and I look forward to spending the rest of my life with you. Mom,

Dad, John, Donald, Tisha, Lisa, Sophia, Shawn thanks for being the most loving and

supportive family I could ever hope for. Winston thanks for being a good dog. Paul

thanks for introducing me to ecology and the soil. Thanks to Becky, Bruce, Kristina,

Keith, Carol, Chris, Laura, Jesse, Derek, Chip, Julie, Ching-Yu, Carlye, Carrie, Mark G.,

and the rest of the EcoGhetto and friends for making my time in Georgia a lot of fun.

Thanks to Johannes, Becky, Bruce, Yolima, Krista, and all my other collaborators you

have made me think about the soil in many different ways. Bradford Lab, Chris, Ashley,

Ken, Augustina, Jaya, Tara, Ernie, Chad, Karen, Calley, Brian, and Jimmy thanks for

your help, friendship, and just making the lab a fun place to be. Tom and the ACL thanks

for running all my samples. Special thanks to my committee, Mac, Lisa, Noah, and Jackie

for your guidance. Mark, special thanks to you for putting up with me, pushing me to

achieve, and being a damn good advisor. Thanks to the staff, students, and faculty of the

Odum School. Finally, thanks to everyone else that I may have unintentionally left out.

This is really something that would be impossible to accomplish without the love,

friendship, and support of many people. This dissertation was supported by a grant from

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the Andrew W. Mellon Foundation and a Doctoral Dissertation Improvement Grant from

the National Science Foundation.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .................................................................................................v

CHAPTER

1 INTRODUCTION AND LITERATURE REVIEW .........................................1

2 RATES OF IN SITU CARBON MINERALIZATION IN RELATION TO

LAND-USE, MICROBIAL COMMUNITY AND EDAPHIC

CHARACTERISTICS ................................................................................52

3 GRASS INVASION OF A HARDWOOD FOREST IS ASSOCIATED WITH

DECLINES IN BELOWGROUND CARBON POOLS .............................99

4 TESTING THE FUNCTIONAL SIGNIFICANCE OF MICROBIAL

COMMUNITY COMPOSITION..............................................................147

5 LITTER QUALITY IS IN THE EYE OF THE BEHOLDER: INITIAL

DECOMPOSITION RATES AS A FUNCTION OF INOCULUM

CHARACTERISTICS ...............................................................................184

6 CONCLUSIONS............................................................................................220

APPENDICES

A REFERENCES USED TO CONSTRUCT FIGURE 1.1 ..............................231

B REFERENCES USED TO CONSTRUCT FIGURE 1.2 ..............................244

C CANDIDATE MODEL SET .........................................................................247

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D FIGURE SHOWING SOIL RESPIRATION DURING THE PULSE-CHASE

EXPERIMENT ..........................................................................................250

E PARAMETER ESTIMATES ........................................................................251

F TABLE SHOWING THE INITIAL %C, %N, AND THE C:N RATIOS OF

THE LITTERS AND THE SOIL INOCULA ...........................................253

G BRAY-CURTIS SIMILARITY IN MICROBIAL COMMUNITY

FUNCTION ...............................................................................................254

H EDAPHIC CHARACTERISTICS FOR THE CALHOUN PLOTS .............258

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CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Fungal-to-bacterial dominance in soil – A review1

Soil microbial communities are an integral component of many ecosystem level

processes from N-fixation and methane oxidation to the mineralization of both simple

and complex organic compounds (Bedard and Knowles 1989, Zak et al. 2006, Jackson et

al. 2007). Because of this, the role that these communities play at the ecosystem level has

become a major line of study in both soil and microbial ecology (Jackson et al. 2007).

Although a seemingly straightforward proposition, actually gaining an understanding of

these communities regarding their relationship to environmental factors and ecosystem

function, as well as developing the methods to accurately assess them, has often proven

difficult (Barns et al. 1999). One reason for this is likely the fact that these soil

communities are some of the most diverse on Earth, with in excess of 4 × 104

species

found in a single soil sample (Torsvik et al. 2002, Fierer et al. 2007b). A second

contributor to these difficulties is the opaque nature of the soil environment itself, which

makes the direct observation of soil communities nearly, if not totally, impossible.

Given both of these difficulties and likely others, the predominant approach to

understanding soil microbial communities has often been to simplify the community by

dividing it into two categories of ecologically meaningful groups (Koch 2001). An

example of this is the idea of copiotrophs versus oligotrophs where copiotrophs are

1 Strickland, M.S., J. Rousk. To be submitted to Soil Biology & Biochemistry

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organisms which thrive under high resource conditions and oligotrophs thrive under low

resource conditions (Poindexter 1981, Koch 2001, Fierer et al. 2007a). Other examples of

these sorts of distinctions include Winogradsky‘s idea of autochthonous versus

zymogenous microbial biomass (Winogradsky 1924) and r-versus K-selected organisms

(Fierer et al. 2007a). Although these sorts of distinctions have aided greatly in our

theoretical understanding of soil microbial communities, they all suffer from the fact that

they are largely unmeasurable in the environment. That is, assessing whether or not a

community is largely composed of copiotrophs or oligotrophs, for example, requires

researchers to both know the abundance of specific taxonomic groups in a given soil as

well as their life-history characteristics (Jackson et al. 2007, Green et al. 2008). Such an

understanding of specific organisms is currently lacking, making measurements based on

these community distinctions impossible (Jackson et al. 2007, Green et al. 2008).

One measure of the soil microbial community which is likely to be relevant to

both taxonomy and life history characteristics is the dominance of fungi to bacteria

(Wardle et al. 2004, van der Heijden et al. 2008). Although the methods used to

determine fungal:bacterial dominance should not necessarily be considered easy, fungi

and bacteria in general differ with regards to key traits (e.g. PLFA markers) which allow

researchers to distinguish between them (Anderson and Domsch 1973, Frostegård and

Bååth 1996). Furthermore, these two groups are expected to differ with regards to their

life history characteristics which in turn are likely to influence both their response to

environmental change as well as their impact on ecosystem processes (Hendrix et al.

1986, Holland and Coleman 1987, Yeates et al. 1997, Bardgett and McAlister 1999).

That is the fungal:bacterial dominance of a soil community is both taxonomically and

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likely ecologically relevant (van der Heijden et al. 2008). For this reason as well as the

difficulties associated with making other categorical distinctions of the microbial

community, assessing soil communities based on their fungal:bacterial dominance has

been widely used (Bardgett et al. 1996, Yeates et al. 1997, Bardgett and McAlister 1999,

Rousk et al. 2009, Rousk and Nadkarni 2009). This simple but elegant approach has

provided insight into the opaque soil system and led to concepts which are widely used

and discussed in today‘s soil ecology literature (Figure 1).

The purpose of this review will be to examine many of these concepts. First we

will briefly review and discuss the prominent methods employed to assess

fungal:bacterial dominance (Section I). In Section II, we will examine some of the major

environmental factors which are likely to lead to differences in the fungal:bacterial

dominance of soil microbial communities. The third section will discuss the relationship

between fungal:bacterial dominance and two major ecosystem processes: carbon (C)

sequestration and litter decomposition. Finally, we will conclude by examining our

current understanding of fungal:bacterial dominance and highlight areas where greater

knowledge may lead to advances in this concept. Ultimately, we hope that this review

will provide a general overview on concepts related to the fungal:bacterial dominance of

soil microbial communities.

Section I – Techniques to measure fungal:bacterial dominance

Over the years a wide array of techniques have been developed which allow for the

assessment of soil microbial communities based on their fungal:bacterial dominance

(Joergensen and Wichern 2008). Although many of these measures utilize different

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approaches, they are all driven by the use of key distinctions which can be made between

fungi and bacteria. For example, Frostegård and Bååth (1996) used PLFA‘s which were

distinct to bacteria and fungi to estimate the biomass of each group in soil. Alternatively,

Anderson and Domsch (1973) utilized selective inhibition with antibiotics which inhibit

either fungi or bacteria to similarly estimate their dominance. Both of these techniques, as

well as direct observation (i.e. microscopy) and the use of cell wall derived indicators of

fungi and bacteria (e.g. chitin and muramic acid), were recently reviewed by Joergensen

and Wichern (2008). This review found that across major land-use groups (i.e. cultivated,

grassland, forest, and litter layers) these methods generally gave comparable results

(Joergensen and Wichern 2008). The fact that each of these methods is ultimately an

estimate of the biomass of the fungi and bacteria may have led to these similarities.

Measures of whole microbial biomass using different methods are often found to

correlate across large spatial scales but are not necessarily 1:1 at smaller spatial scales

(Wardle and Ghani 1995). This discrepancy as resolution increases is largely attributed to

the possibility that different biomass measures actually gauge slightly different

components/characteristics of the microbial community (Wardle and Ghani 1995).

Whether this holds true for estimates of fungal:bacterial dominance determined via

different methods remains unknown. Furthermore, it is often difficult to ascertain the

status/activity of the microbial biomass in soil (Rousk and Bååth 2007b). Meaning that

methods such as these make the assumption that the biomass of a given group is a direct

reflection of that groups contribution to a certain ecosystem process or its response to

environmental change (Wright et al. 1995, Bååth 1998). Such assumptions have been

frequently questioned (Wright et al. 1995, Bååth 1998, Rousk and Bååth 2007b).

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A recent alternative measure for assessing fungal:bacterial dominance, is the

use of DNA-based approaches. One of the more widely used of these approaches is

quantitative polymerase chain reaction (qPCR). This approach uses the accumulation of a

florescent reporter molecule during the PCR reaction coupled with primers specific to

either bacteria or fungi (which typically target the 16s or 18s rRNA gene segments,

respectively) to determine the relative abundance of these groups (Fierer et al. 2005).

qPCR represents both a rapid and quantifiable approach to assess fungal:bacterial

dominance in soils (Fierer et al. 2005). However, like with the above biomass measures

there are caveats associated with this technique. Foremost, is that fungal:bacterial

dominance as determined by qPCR may not be indicative of the true abundances of these

groups in soil especially on a per biomass basis. Reasons for this include the fact that

fungal cells may include many or no nuclei, leading to an over or underestimation of

fungal abundance, DNA extraction efficiencies may differ between these two groups, and

the amplification of genes may not be consistent across all fungal and bacterial taxa

(Martin-Laurent et al. 2001). Finally, like with biomass measures, assessments of

fungal:bacterial dominance based on DNA may not necessarily be related to a given

ecosystem process or response to environmental change. This is because DNA is present

in both active and inactive cells (Nocker and Camper 2009). Fungal:bacterial dominance

determined via RNA based approaches may ultimately prove more informative when

assessing relationships which are likely to be driven by active organisms (Poulsen et al.

1993, Aviv et al. 1996, Vestergard et al. 2008, Nocker and Camper 2009).

Another method which is distinct from biomass and DNA based approaches, is

the determination of bacterial and fungal growth (Bååth 1992, Rousk and Bååth 2007b).

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This method uses leucine/thymidine incorporation to measure bacterial growth and

acetate-in-ergosterol incorporation to measure fungal growth (Bååth 1992, Rousk and

Bååth 2007b). Both techniques estimate the biomass production of bacteria or fungi and

may provide a direct estimate of the relationship between these groups and a given

ecosystem process or their response to environmental change (Rousk and Bååth 2007b).

In fact these techniques have been successfully used to determine relative changes in

fungal and bacterial contribution to decomposition (Rousk and Bååth 2007a). However, a

lack of adequate conversion factors have made quantification of bacterial and fungal

growth in units C difficult, although developments suggest progress for both bacteria

(Bååth 1994) and fungi (Rousk and Bååth, 2007b). Furthermore the accuracy of these

assessments of fungal:bacterial dominance are contingent upon certain requirements

being met such as balanced growth or that all bacterial or fungal taxa can take up their

respective indicator molecule through the cell membrane (Winding et al. 2005).

In summary, all the techniques used to determine fungal:bacterial dominance

in soil are by necessity indirect and are thus susceptible to various confounding factors

that should be carefully considered when relating them to either ecosystem processes or a

community‘s response to environmental change. It is also likely that these relationships

will often be dependent on the method used to assess fungal:bacterial dominance. The

possibility that different methodologies lead to different results or at least

stronger/weaker relationships should be addressed in the future. This will likely require

collaborations between researchers whose expertise lay in assessing fungal:bacterial

dominance via differing methods. Currently the wide array of methods used to assess

fungal:bacterial dominance and the fact that researchers often relate this ratio to a wide

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array of ecosystem processes and environmental factors makes a formal meta-analysis

nearly impossible. However, generalizations related to the environmental factors likely to

affect fungal:bacterial dominance and the impact fungal:bacterial dominance has on

ecosystem processes can still be made.

Section II – Environmental factors affecting fungal:bacterial dominance

Much of the rationale for using fungal:bacterial dominance as an assessment of the soil

microbial community rests on the proposition that these two groups of organisms respond

dissimilarly to an array of environmental factors (van der Heijden et al. 2008). Such

environmental factors include physical disturbance of the soil (e.g. tilling), nutrient

availability, soil moisture, and soil pH. As these factors and others differ in direction

and/or intensity then the general expectation is that fungi and bacteria will often exhibit

contrasting responses (Hendrix et al. 1986, Holland and Coleman 1987, van der Heijden

et al. 2008). The general consensus has been that highly disturbed and/or nutrient rich

soils are dominated by bacteria whereas fungi dominate less disturbed and/or nutrient

poor soils (Bardgett and McAlister 1999, van der Heijden et al. 2008). For the remainder

of this section we will explore some of the key environmental factors likely to lead to

differences in the fungal:bacterial dominance of soil communities.

Physical disturbance and tilling effects

Much of the interest in the fungal:bacterial dominance of soils has been related to

agricultural practices (Hendrix et al. 1986, Holland and Coleman 1987, Drijber et al.

2000, Bailey et al. 2002, Helgason et al. 2009). Hendrix et al. (1986) proposed that no-till

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agricultural practices would result in a fungal dominated system as opposed to the

bacterial dominated one expected under conventional tillage practices. This expected

outcome is largely based on key differences in the growth forms of fungi and bacteria

(Hendrix et al. 1986). Most fungi exhibit a hyphal growth form while most soil bacteria

are present as individual cells (Hendrix et al. 1986). The hyphal growth form of fungi

likely conveys several advantages to these organisms. Chief of which is the translocation

of nutrients and resources from microsites where these are abundant to sites where they

are limiting via hyphal networks (Hendrix et al. 1986, Frey et al. 2003). This

phenomenon has been commonly referred to as the hyphal bridge (Hendrix et al. 1986,

Frey et al. 2003). This fungal advantage though may be unfavourable under conventional

agricultural practices where the hyphae are continuously severed or disrupted due to

tillage (Hendrix et al. 1986, Helgason et al. 2009).

In addition to any direct damage caused by tillage, any advantage fungi gain

from hyphal bridges would likely be negated (Hendrix et al. 1986). Bacteria on the other

hand would be relatively unaffected by tillage due to their presence as individual cells

(Hendrix et al. 1986). Furthermore, tillage homogenizes the soil, evenly distributing

nutrients and resources. Just as surely as severing hyphae, this too negates many of the

advantages hyphal bridges convey fungi (Bardgett et al. 1996, Bardgett et al. 1999,

Bardgett and McAlister 1999). Add to this the generally held idea that bacteria are more

efficient colonizers of the organic resources (e.g. newly available SOM and dead

microorganisms) made available post-till than are fungi and it seems safe to conclude that

fungal:bacterial dominance will decrease under conventional tillage practices (Holland

and Coleman 1987). Similar arguments have been made to describe the negative impacts

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earthworms have on fungal dominance as well as other factors which physically disturb

the soil (Butenschoen et al. 2007). Empirical evidence in support of such impacts on

fungal:bacterial dominance are far from definitive however.

Of five studies which explicitly compared the fungal:bacterial dominance of

communities exposed to conventional tillage practices to those exposed to no-till

practices, none found consistent evidence of tillage effects (Frey et al. 1999, Bailey et al.

2002, Pankhurst et al. 2002, Spedding et al. 2004, Helgason et al. 2009). Of these studies,

three found that changes to no-till or a reduction in tillage did lead to an overall increase

in microbial biomass but this increase was proportional for both bacteria and fungi

(Pankhurst et al. 2002, Spedding et al. 2004, Helgason et al. 2009). Bailey et al. (2002)

found that fungal dominance determined via selective inhibition increased under no-till

practices but the opposite was true when fungal dominance was determined using PLFA.

Although the PLFA measurements in this study were not replicated, it does suggest that

different methodologies may lead to dissimilar results (see Section I). Fungal:bacterial

dominance was determined via PLFA by Frey et al. (1999) and they found that fungal

dominance did increase under no-till practices. However, closer examination of these

results showed that tillage covaried with soil moisture and once this was accounted for,

tillage had no impact on fungal:bacterial dominance (Frey et al. 1999). Although studies

such as these raise doubt concerning the impact that physical disturbance of soil will have

on the fungal:bacterial dominance of the microbial community, they also demonstrate

that other factors may well influence the relative dominance of these two groups.

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Direct and indirect effects of nutrients

Another characteristic likely to influence the fungal:bacterial dominance of soil microbial

communities is nutrient availability (Bardgett and McAlister 1999, Suzuki et al. 2009).

This like physical disturbance has been largely related to agricultural based management

decisions such as fertilizer inputs (Bardgett et al. 1996, Bardgett et al. 1999, Bardgett and

McAlister 1999). In many ways the hypothesized impacts fertilizer inputs are likely to

have on fungal:bacterial dominance are similar to those hypothesized for tillage. For

example, fertilizer applications in effect homogenize soil nutrient availability and likely

negate any advantages associated with the fungal growth form.

Stoichiometric differences between bacteria and fungi is another possible

reason why fungal:bacterial dominance is expected to decrease as nutrient inputs into the

soil increase (van der Heijden et al. 2008). Generally the carbon:nitrogen (C:N) ratio of

bacterial biomass is expected to be ~ 3-6 while the C:N ratio of fungal biomass is

expected to be ~ 5-15 (McGill et al. 1981). Because it can generally be assumed that

fungal biomass has a greater C:N ratio than bacterial biomass then nutrient applications

are likely to favour bacteria over fungi (Gusewell and Gessner 2009). This rationale is

not solely limited to explaining fertilizer inputs. It has also been used to explain the

effects of litter quality on fungal:bacterial dominance (see Section III for further details

related to this). However, whether or not the biomass stoichiometry of bacteria is distinct

enough from that of fungi to rationalize such an outcome is relatively unexplored.

Much of what is known about bacterial biomass C:N can be attributed to

cultured organisms and more often than not these organisms were grown on relatively

nutrient rich media (McGill et al. 1981). Bacteria under more natural settings are less

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likely to encounter such nutrient rich environments and their actual C:N ratio may be

much higher than previously assumed. In fact a compilation of studies examining

communities of aquatic bacteria and cultures grown on nutrient poor media have found

that bacteria range in C:N from ~ 3-12 (Fig. 2). Furthermore, assessments of the C:N

ratio of fungi, based largely on sporocarps, show a range of ~3-51 for mycorrhizal fungi

and ~ 4-60 for saprotrophic fungi (Fig. 2). This in the least suggests the possibility that a

large proportion of fungi and bacteria, in general, overlap with regards to biomass C:N

(Fig. 2). In soils, less is known concerning this possible overlap in the fungal and

bacterial C:N ratio largely because of the difficulty in both measuring pure bacterial

biomass in situ and culturing specific soil bacteria (Madsen 2005, Kreuzer-Martin 2007).

Cleveland and Liptzin (2007) have provided some circumstantial evidence that

the C:N ratio of soil bacteria and fungi may not be as distinct a characteristic as

previously thought. They found no difference in microbial biomass C:N ratios across a

range of ecosystem types where fungal:bacterial dominance would be expected to differ

(Cleveland and Liptzin 2007). If bacteria and fungi are distinct with regards to their

biomass C:N ratio then the expectation would have been that total microbial biomass C:N

in fungal-dominated systems would have been greater than in bacteria-dominated

systems. This was not the case and in fact forests which are expected to have greater

fungal dominance than grasslands actually had a lower microbial biomass C:N ratio.

These results are suggestive of either a significant overlap in the C:N ratio of bacteria and

fungi or that the most abundant organisms in either group are stoichiometrically similar.

Regardless of this lack of stoichiometric distinction, several studies have

found that N fertilization impacts fungal:bacterial dominance. A prominent example is

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Bardgett and McAlister (1999), who observed that pastures which received little or no

inputs of fertilizer N typically had a greater fungal:bacterial dominance than did sites

which received large inputs of fertilizer N. An experimental component of Bardgett and

McAlister‘s (1999) study, however, found that fertilizer cessation did not lead to a

significant increase in fungal:bacterial dominance after 6 years. They suggested that this

may have been due to high residual fertility.

Similar studies have also reported conflicting results for both pasture and

forest sites (de Vries et al. 2006, Demoling et al. 2008). de Vries et al. (2006) found using

a 2 year field-based experimental study that low fertilization rates led to an increase in

fungal:bacterial dominance. On the other hand, a follow up field observation found that

fungal:bacterial dominance was not related to changes in management intensity (de Vries

et al. 2007). This was because both bacterial and fungal biomass responded similarly to

changes in management intensity (de Vries et al. 2007). Demoling et al. (2008) found that

across an N deposition gradient in coniferous forests that fungal:bacterial dominance

(estimated using PLFA) increased as deposition decreased. Interestingly however,

Demoling et al. (2008) did not find a concomitant change in fungal growth (estimated

using acetate-in-ergosterol incorporation). The likely explanation for this was attributed

to a shift toward more mycorrhizal fungi as N-deposition decreased since the fungal

growth estimate (i.e. acetate-in-ergosterol incorporation) is likely only relevant to

saprotrophs. This increase in the biomass of mycorrhizal fungi raises an interesting and

less direct means that changes in nutrient levels are likely to affect fungal:bacterial

dominance.

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The nutrient acquisition strategies of plants often change concomitantly with

changes in the concentration of soil nutrients (Lambers et al. 2008). In a review by

Lambers et al. (2008), plants in young soils which are typically rich in P but poor in N

were associated with relatively low levels of mycorrhizae and the same was true for

ancient soils, poor in both nutrients. On the other hand, the association between plants

and mycorrhizae are typically highest in soils where the N:P ratio is greatest (Lambers et

al. 2008). Soils typical of this sort of nutrient status are by large found in temperate zones

around the world where many studies investigating fungal:bacterial dominance have

likely been conducted. Additionally, changes in the status of soil nutrients have been

shown to dramatically impact mycorrhizal fungi with increased nutrient additions often

leading to their decline (Oehl et al. 2004, Cruz et al. 2009, but see Birkhofer et al. 2008).

These findings would suggest that plant nutrient status is likely to be a major factor

impacting the fungal:bacterial dominance of the microbial community (van der Heijden

et al. 2008). If researchers are interested in the effect that nutrient status has on

fungal:bacterial dominance then close consideration of mycorrhizal fungi may not be

necessary. On the other hand, if researchers are interested in relating ecosystem processes

to fungal:bacterial dominance then the distinction between mycorrhizal and saprotrophic

fungi is likely to prove important. Especially given the likely distinct ecological strategies

exhibited by these two groups.

Other effects

Other factors which have been proposed to impact fungal:bacterial dominance include

soil pH, temperature, and moisture (Holland and Coleman 1987, Rousk et al. 2009). Of

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these soil pH, considered a sort of master variable, is likely to have significant impacts on

the fungal:bacterial dominance of microbial communities (Fierer and Jackson 2006).

Recent work has demonstrated that one of the major controls in soil on both the diversity

and composition of bacterial communities is pH (Fierer and Jackson 2006, Lauber et al.

In Review). This work has shown that bacterial diversity is related unimodialy to soil pH

with a peak in diversity found around pH 6-7 (Fierer and Jackson 2006, Lauber et al. In

Review). Soil pH also appears to be related to fungal dominance but this possible

relationship has been less intensively studied (Joergensen and Wichern 2008, Rousk et al.

2009). In general the expectation has been that fungi are likely more acid tolerant than are

bacteria which in turn leads to increased fungal dominance in acidic soils (Hogberg et al.

2007, Joergensen and Wichern 2008, Rousk et al. 2009).

This though does not appear to be a conclusive pattern with alterations in pH

sometimes resulting in increased, decreased, or unchanged levels of fungal dominance

(Marstorp et al. 2000, Bååth and Anderson 2003, Hogberg et al. 2007, Rousk et al. 2009).

One possible explanation for these seemingly inconsistent patterns may be that like soil

bacteria specific fungal taxa are impacted to a greater or lesser degree by changes in pH.

For example, Rousk et al. (2009) suggested that such observed variation may in part be

driven by an increase in ectomycorrhizal fungi in acidic soils due ultimately to a shift in

plant species composition. In this same work, they found that, like bacterial diversity,

fungal dominance followed a similar unimodal pattern (Rousk et al. 2009). They also

suggest that this pattern was largely driven by saprotrophic fungi because no

ectomycorrhizae were present at their study site (Rousk et al. 2009). Another possibility

is that fungal-dominance or the composition of the fungal community is simply not

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impacted to the same degree by pH as the bacterial community is. Work conducted across

a land-use gradient in the Southeastern U.S. found the relative abundance of fungal taxa

were more strongly related to soil P and C:N ratios than any other edaphic characteristic

(Lauber et al. 2008). If this pattern holds true across a range of systems then it might

imply that the fungal component of the soil microbial community is likely to be impacted

to a lesser degree than is the bacterial community by changes in soil pH.

Our understanding of the impact that moisture and temperature have on soil

microbial communities has often been related to their role as mediators of ecosystem

processes such as decomposition (Aerts 1997). However, given the likelihood that

temperature and moisture regimes are apt to change both in degree and frequency across

broad geographical regions in the face of global climate change, these factors and their

effect on fungal:bacterial dominance have garnered increased attention (Allison and

Treseder 2008). In general as with most expected effects of environmental perturbations,

it has been proposed that fungi will exhibit less of a response to changing temperature

and moisture than will bacteria (Holland and Coleman 1987). This is in many ways

related to the fact that fungi have thick cells walls composed of chitin which are apt to be

more resistant to desiccation caused by warm, dry conditions or to the stress caused by

rapid changes in these conditions (Holland and Coleman 1987).

Frey et al. (1999) found, however, that fungal biomass was positively related

to soil moisture in cultivated settings while bacterial biomass remained relatively

constant. Yeilding a fungal:bacterial dominance that was positively related to soil

moisture (Frey et al. 1999). Similarly, Gordon et al. (2008) found that drying and

subsequent rewetting events led to a decrease in fungal:bacterial dominance with no

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change in bacterial biomass but a significant change in fungal biomass. Both studies

show, in contrast to earlier expectations, that fungi are likely to be affected to a greater

extent by changes in soil moisture than are bacteria. Frey et al. (1999) proposed that

because bacteria are generally expected to inhabit soil pore spaces whereas fungal hyphae

are generally found on the exterior of soil aggregates then bacteria are in effect physically

protected from desiccation to some extent. Extending on this idea, bacteria would also be

less affected by mild drying/rewetting events. This is not to say that bacteria are in some

way physiologically more resistant/resilient to moisture effects but the habitat likely

occupied by these organisms may well infer greater resistance/resilience (Six et al. 2006).

If this is true then the protection from moisture stress inferred on the bacterial component

of the microbial community is likely to be influenced greatly by soil texture (Six et al.

2006).

Temperature effects like moisture do not appear to follow the general

consensus. In fact, the response of both bacteria and fungi to changing temperature

appears to largely be in the same direction resulting in little to no change in

fungal:bacterial dominance (Allison and Treseder 2008, Bárcenas-Moreno et al. 2009).

For example, Allison and Treseder (2008) found that the relative abundance (determined

via qPCR) of both bacteria and fungi declined by ~ 50% under experimental soil

warming. This would have resulted in no change in fungal:bacterial dominance although

specific fungal species were affected by warming (Allison and Treseder 2008). Another

study, found that the actual growth rates of fungi and bacteria (determined via

leucine/thymidine and acetate-in-ergosterol incorporation) responded similarly to

changing temperatures (Bárcenas-Moreno et al. 2009). It would appear that, in the

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absence of concomitant moisture effects related to temperature and barring any

differences in acclimatization between these groups, there is no clear evidence that

temperature itself influences fungal:bacterial dominance.

Section III – Functional implications of fungal:bacterial dominance

As detailed in Section II, above, there is the expectation in soil ecology that fungi and

bacteria differ with regards to several key traits (van der Heijden et al. 2008). These

differences on one hand are expected to relate to how each group is likely to respond to

environmental change and can largely be summed up, albeit highly simplified, as a

relationship where what is favorable to bacteria is unfavorable to fungi or vice versa.

Extending these ideas has led to the implication of fungal:bacterial dominance as a

determinant of ecosystem processes (Hendrix et al. 1986, Bardgett and McAlister 1999,

van der Heijden et al. 2008). In many ways this is a logical extension of the expected

differences between these two groups and is comparable, for example, to ideas relating

plant community composition to primary production (Tilman et al. 2001). This has led to

the suggestion that changes in fungal:bacterial dominance are likely to be related to such

ecosystem processes as decomposition, nutrient cycling, C-sequestration potential, and

ecosystem self-regulation (Hendrix et al. 1986, Bardgett and McAlister 1999, Bailey et

al. 2002, van der Heijden et al. 2008).

It is the purpose of this section to explore the rationale for these linkages

between fungal:bacterial dominance and ecosystem processes and to discuss the evidence

in favor and against such linkages. Clearly, the rationale for many of the linkages

between fungal:bacterial dominance and ecosystem processes are closely related to the

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environmental response of each group, as described in Section II above, and therefore

some redundancy between these sections is necessary. We have clearly cross referenced

such occurrences. Furthermore, given the wide-array of ecosystem process which

fungal:bacterial dominance may relate to, We have limited ourselves to the exploration of

two processes: C-sequestration and decomposition. Both of these should serve as

sufficient case studies of the rationale linking fungal:bacterial dominance to ecosystem

processes and also provide a broad overview of the other processes expected to be

affected.

C-sequestration and fungal-to-bacterial dominance

The C-sequestration potential of ecosystems has gained increased interest due to current

concerns pertaining to climate change (Bailey et al. 2002). The fungal:bacterial

dominance of a particular site has often been associated with that site‘s C-sequestration

potential with a greater potential associated with fungal dominance and a lesser one

associated with bacterial dominance (Jastrow et al. 2007). Much of the evidence to date

which relates fungal:bacterial dominance to C-sequestration has largely been gained from

comparisons of intensively managed and less intensively managed systems. For example,

Bailey et al. (2002) compared five land-use pairs which differed in management intensity

and found that total soil C and fungal activity were both higher for four of the five pairs

when management intensity was lower. Guggenberger et al. (1999) found the same for

three of six sites but Busse et al. (2009) found almost the opposite with fungal-dominated

sites having lower total soil C than bacterial dominated ones. The hypothesis that greater

fungal dominance is synonymous with a greater C-sequestration potential has become a

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widely held idea in soil ecology and like many ideas linking fungal:bacterial dominance

to ecosystem processes is based on differences in specific traits between fungi and

bacteria (Guggenberger et al. 1999, Bailey et al. 2002, Six et al. 2006, van der Heijden et

al. 2008). Biomass stoichiometry, C-use efficiency (CUE), and the decomposability of

their respective necromass are all examples of fungal and bacterial traits which are

expected to differ and in turn impact C-sequestration.

It has been suggested that on average bacterial and fungal biomass will differ

with regards to their stoichiometry (see Section I). Closely related to these expected

differences in biomass stoichiometry and another trait expected to relate to greater C-

sequestration in fungal-dominated systems is CUE also known as growth yield efficiency

(e.g. Six et al 2006). It is expected that fungi on average produce more biomass-C per

unit of C metabolized than do bacteria which again leads to a greater proportion of C

stored in a fungal-dominated systems (high fungal:bacterial dominance) when compared

to bacterial-dominated systems (low fungal:bacterial dominance).

Six et al. (2006) conducted an extensive review on the CUE of bacteria and

fungi and found a significant degree of overlap between these two groups. More direct

experimental tests conducted by Thiet et al. (2006) found no difference in CUE between

sites differing in fungal:bacterial dominance and concluded that many of the differences

in CUE associated with changes in fungal:bacterial dominance were purely due to

methodological issues. In this experiment, though, Thiet et al. (2006) only determined the

amount of C mineralized and the amount remaining in the soil. They did not determine

the actual accumulation of C into the biomass of the respective groups. Furthermore,

whether or not bacterial and fungal CUE is similar under different environmental stresses

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in soils is currently unknown. It might be expected that fungi or bacteria are more or less

resistant to certain types of stress such as drying/rewetting events (see Section II) and that

these stress events will lead to subsequent impacts on CUE. Another factor to consider is

how substrate C:N ratios influence CUE. It has been shown that as substrate C:N ratios

increase, a decrease in decomposer CUE is noted (Manzoni et al. 2008). Whether fungi in

comparison to bacteria reduce their CUE less as substrate C:N ratios increase is unknown

and like many other factors related to fungal and bacterial CUE warrants further

investigation.

The actual amount of time that C is stored in living fungal or bacterial biomass

is another of the proposed links between fungal:bacterial dominance and C-sequestration

(Six et al. 2006). Although there have been relatively few reports of biomass turnover for

either group, Bååth (1994) estimated that bacterial biomass turnover ranged from days to

weeks, averaging about one week. A similar estimate of turnover times for fungi showed

that turnover was on the order of months (i.e. ~ 130-150 days [Rousk and Bååth 2007b]).

This would suggest that C is stored longer in living fungal biomass than it is in living

bacterial biomass and ultimately may lead to an increased residence time for C associated

with the fungal component of the microbial community.

Like living biomass the decomposition of microbial necromass is another

anticipated factor linking fungal:bacterial dominance to C-sequestration (Guggenberger

et al. 1999). This is based on the expectation that fungal biomass is more chemically

recalcitrant than is bacterial biomass. The rationale for the greater chemical recalcitrance

of fungal biomass is due to cell-wall components which include chitin and membrane

components such as the potentially recalcitrant ergosterol (Mille-Lindblom et al. 2004,

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Zhao et al. 2005). Also, fungal biomass, as discussed above, is expected to have a wider

C:N ratio than is bacterial biomass (McGill et al. 1981). Thus if a greater proportion of a

systems dead microbial biomass is composed of fungi then the decomposition of that

biomass may progress more slowly than if a greater proportion of that biomass were

derived from bacteria (Guggenberger et al. 1999). Concurrently, more C would remain

locked up over the same period of time in fungal rather than bacterial necromass leading

to increased C-sequestration in fungal dominated systems (Guggenberger et al. 1999).

However, there have been relatively few studies which have explicitly looked

at the decomposition rates of bacterial versus fungal biomass but in at least one instance

the decomposition of fungal biomass was equivalent to that of bacterial biomass (Li and

Brune 2005a). Although fungal and bacterial biomass in this study was only sourced from

two cultured organisms and may not represent the full spectrum of possibilities, it none

the less demonstrates that the necromass of certain species of bacteria and fungi may be

equally decomposable (Li and Brune 2005a). On the other hand, Nakas and Klien (1979)

found that the cell wall components of two bacteria species, in general, decomposed at

faster rates than did three fungal species. One possible explanation for this discrepancy

between studies may be that Li and Brune (2005) utilized a gram positive bacterium

whereas Nakas and Klien (1979) utilized a gram negative. Gram positive bacteria have a

thick cell wall composed of peptidoglycan whereas the cell wall of gram negative

bacteria is composed of a much thinner layer of peptidoglycan and an outer

lipopolysaccharide membrane. Since the cell walls of gram positive bacteria are

composed primarily of peptidoglycan then the decomposition of this groups cell wall

constituents may proceed at a slower rate than that of gram negative bacteria and may

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even be slower than the chitinous cell walls of fungi. In fact a comparison, based on two

studies, of the mineralization of chitin and peptidoglycan shows that chitin is mineralized

at an equivalent or ~30% greater rate than peptidoglycan (Li and Brune 2005b, 2005a).

Although it can be argued that there is overlap in many of the characteristics

of bacteria and fungi which are expected to impact C-sequestration, perhaps the most

plausible mechanism leading to enhanced C-sequestration in a fungal-dominated system

can be attributed to the fungal growth form. The majority of fungi exhibit a hyphal

growth form which allows these organisms to grow into new habitats in order to access

new substrates (Hendrix et al. 1986, Guggenberger et al. 1999). In comparison, bacteria,

with the exception of the actinomycetes, are typically relegated to growth on the surface

layers of substrates (Guggenberger et al. 1999). This difference between bacteria and

fungi may lead to greater C-sequestration when a system is fungal-dominated for at least

two reasons. First, if fungal hyphae grow into a habitat such as a soil aggregate and those

hyphae die then they are largely protected from decomposition (Guggenberger et al.

1999, Six et al. 2006). Second, fungal activity may lead to increased aggregation via both

mechanical processes (e.g. entanglement of soil particles by hyphae; Guggenberger et al.

1999, Six et al. 2006) and chemical processes (e.g. the exudation of glomalin

[Guggenberger et al. 1999, Pikul et al. 2009]). An increase in soil aggregation in turn

may lead to incorporation of SOC into the aggregate which is then biologically

unavailable and sequestered (Guggenberger et al. 1999, Wilson et al. 2009). As fungal-

dominance increases then the likelihood of either of these two occurrences may

subsequently increase. Yet, many other physical and chemical properties of the soil are

also likely to influence soil aggregation and fungal-dominance may only be one of many

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at play (Wilson et al. 2009). The importance of fungal-dominance as it pertains to

aggregate formation and possibly C-sequestration is an area where research is needed.

Litter decomposition and fungal-to-bacterial dominance

The decomposition of foliar leaf litter is perhaps one of the most widely studied processes

in terrestrial ecosystems and is expected to be influenced by the microbial community.

The role that fungi play in this process is expected to be distinct from the role played by

bacteria such that the outcome of this process will be dependent on the fungal:bacterial

dominance of the community (de Boer et al. 2005, van der Wal et al. 2006, Meidute et al.

2008). As litter recalcitrance increases then the role of fungi in decomposition is also

expected to increase (van der Heijden et al. 2008). To some degree this is based on the

C:N ratio of the leaf litter in question with a greater initial C:N expected to favor fungi

and a smaller initial C:N expected to favor bacteria (Gusewell and Gessner 2009).

Several mechanisms have been proposed to justify this expectation including

stoichiometric differences between bacteria and fungi, the hyphal growth form of fungi

which allows them to grow into leaf litter bypassing the more recalcitrant outer layers of

the leaf, and that hyphal bridges allow fungi to alleviate nutrient limitations associated

with litter decomposition by mining limiting nutrients from other sources (Hendrix et al.

1986, Holland and Coleman 1987). Also built into the expectation that recalcitrant litter

will favor fungal growth is the lignin degrading ability of fungi (de Boer et al. 2005).

There is a general consensus in the literature that fungi are capable of degrading lignin

whereas bacteria are not (de Boer et al. 2005, van der Heijden et al. 2008). This allows

fungi to exploit these structural components of litter leading to greater decomposition

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rates (de Boer et al. 2005, van der Heijden et al. 2008). In this section we will explore

some of these mechanisms and, as above, assess the validity of relating fungal:bacterial

dominance to leaf litter decomposition as well as the role both bacteria and fungi play in

this process.

The argument that litter with a greater C:N ratio is likely to be primarily

decomposed by fungi is in part based on the expectation that fungi have a greater biomass

C:N ratio than bacteria (McGill et al. 1981). However, as mentioned above (see Section

II) there may be significant overlap between these two groups and further examination

with specific emphasis paid to those organisms inhabiting litter is necessary. In lieu of

such investigations, Güsewell and Gessner (2009) have provided research which suggests

that the role of fungi and bacteria in the decomposition of leaf litter may not be as clear

cut as once expected. In their investigation they found that bacteria dominated leaf litter

with a greater C:N ratio when phosphorous (P) was not limiting and this was attributed to

heterotrophic N-fixation by the litter inhabiting bacteria. Under these conditions bacteria

dominated when litter had a greater C:N ratio and ample P but fungi dominated when the

C:N ratio was lower and P was limiting (Gusewell and Gessner 2009). Comparing

treatments where there was a greater dominance of bacteria to those with a greater

dominance of fungi showed similar overall decomposition rates and in at least one

instance the bacterial-dominated treatment had a higher rate (Gusewell and Gessner

2009). Other studies have demonstrated that the role of bacteria and fungi in the

decomposition of recalcitrant litter resources may be in part dependent on the specific

organisms involved in the process (Hunt et al. 1988, Gholz et al. 2000, Ayres et al. 2009,

Strickland et al. 2009a, Strickland et al. 2009b). In one of these studies the decomposition

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of a recalcitrant litter resource was correlated both positively and negatively to both

bacterial and fungal taxa (Strickland et al. 2009b). Although correlative, such a result

does suggest the possibility that bacteria and fungi may overlap with regards to their

degradative potentials but other explanations are also plausible such as antagonistic

(Mille-Lindblom and Tranvik 2003; Mille-Lindblom et al. 2006) or synergistic

(Bengtsson 1992) interactions between differing groups (Strickland et al. 2009b).

Both of these studies however were short-term (between 5 and 7 weeks) when

compared to most other studies and may not have captured the successional dynamics

associated with litter decomposition. For example, Poll et al. (2008) found in a

microcosm study that initial colonization of litter was dominated by bacteria and latter

stages where dominated by fungi. This was presumably driven by bacteria mineralizing

the soluble, easily-available compounds leaving the more recalcitrant compounds behind

(Poll et al. 2008). Although this may be true in some cases, it is not true for all litter types

or for all microbial communities. Strickland et al. (2009a) found that in some cases

bacterial dominance actually increased during decomposition and this was dependent on

both the microbial community in question and the litter being degraded.

Another key argument made in favor of fungi being the primary decomposers of

recalcitrant litter resources is based on their lignin degrading abilities (Tuomela et al.

2000, de Boer et al. 2005). It has been known for some time that fungi possess the

necessary enzymes capable of degrading lignin while few bacteria have been documented

which are capable of performing this same role (de Boer et al. 2005). Work has been

conducted which demonstrates that in the presence of lignin degrading fungi, bacterial

community composition changes. These bacteria are often ascribed to supporting roles in

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lignin decomposition whereby they scavenge simple C-compounds released by fungal

enzymes (Tornberg et al. 2003, Folman et al. 2008). However, recent work conducted by

Vargas-García et al. (2007) in the field of biodegradation questions this. Vargas-García et

al. (2007) isolated six strains of bacteria and seven strains of fungi from compost heaps

and then measured their lignin degrading abilities. They found that four of the seven

fungal strains led to a significant reduction in lignin. On the other hand, all six strains of

bacteria led to a significant reduction in lignin. Furthermore, they found that one strain of

bacteria (Bacillus licheniformis) demonstrated the greatest lignin degrading capability.

Other results from this field have shown similar capabilities for bacteria but whether

these outcomes will hold under field settings is as yet unknown (Perestelo et al. 1996).

Perhaps, like with C-sequestration, one of the most direct lines of evidence in

support of greater decomposition of recalcitrant litter by fungi can be made by examining

the fungal growth form. On one hand, fungi are expected to grow into litter material

bypassing the recalcitrant outer-layers (Hendrix et al. 1986). This is likely true and is an

ability that most bacteria lack but the actual importance of this mechanism to

decomposition is largely unknown and unexplored. It also disregards the comminuting

effects of other litter inhabiting organisms, such as collembola, mites, and earthworms,

which may increase the surface area of litter available for bacterial colonization (Wardle

2002). Thus if soil fauna are in low abundance then the role of fungi in litter

decomposition may be more important but if soil fauna are in greater abundance then the

role of bacteria may be as important if not more so than that of fungi (Wardle 2002).

Another advantage that the growth form of fungi might incur is related to the idea

of hyphal bridges/networks which allow for the translocation and integration of C and

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nutrients across a wide spatial area (Briones and Ineson 1996, Frey et al. 2000, Frey et al.

2003). Because fungal hyphae can cover a great distance this allows fungi to subsidize

the decomposition of a nutrient poor litter resource with nutrients translocated from a

nutrient rich litter resource (Briones and Ineson 1996, Tiunov 2009). In fact it is this

mechanism which has often been used to explain increased rates of decomposition

associated with recalcitrant litters in litter mixtures (Tiunov 2009). This advantage of the

fungal growth form though may also be negatively impacted by comminuting effects

(Butenschoen et al. 2007). Tiunov (2009) found that in litter mixtures as particle size of

the litters decrease then positive effects on decomposition rates were negated. The

explanation proposed for this result was that inhibitory compounds, such as phenolics,

could more easily diffuse throughout the litter layer thus negatively impacting microbial

decomposers (Tiunov 2009). It is also likely that communation impacts fungi in many of

the same ways that tilling does (see Section II).

To conclude this section, we hope to have touched on how fungal:bacterial

dominance is expected to relate to at least two ecosystem processes, C-sequestration and

decomposition, and these two processes where meant to serve as case studies of this

relationship. Although it should be noted that many of the arguments used in relating

fungal:bacterial dominance to either C-sequestration or decomposition are also used to

relate it to other ecosystem processes. We also hope that this section has pointed out

some of the research needed to evaluate the linkage between fungal:bacterial dominance

and ecosystem processes.

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Section IV – Conclusions

Fungal:bacterial dominance has provided soil ecologists with a metric for assessing both

the environmental impacts on and the functional implications of soil microbial

communities. It has become both widely utilized and has provided many theoretical

advances. It was the goal of this review to assess the implications of fungal:bacterial

dominance as it relates to our current understanding of the soil environment in the hope

of determining its efficacy. We believe that we have highlighted key areas where this

assessment of the microbial community meets the general expectations, areas where it

diverges from these expectations, and areas were more information is warranted.

For example, fungal biomass typically increases as management intensity

decreases (i.e. tillage and fertilization). This result has been by and large the expectation

for fungi under such changes (Hendrix et al. 1986, Bardgett and McAlister 1999). On the

other hand, bacterial biomass also tended to increase as management intensity decreased

typically leading to little if any change in fungal:bacterial dominance (Hendrix et al.

1986, Bardgett and McAlister 1999). This is generally underemphasized and suggests

that management intensity related to both tillage and fertilizer inputs may result in

parallel effects on both fungi and bacteria. Such effects on these two groups may also be

expected due to temperature change. It might also be expected that bacteria and fungi

significantly overlap with regards to the key traits expected to differentiate them such as

biomass stoichiometry, CUE, and even their ability to decompose organic substrates. This

is not a novel idea however. The preeminent microbiologist, Jackson W. Foster stated, ―It

appears incongruous to delineate metabolism of bacteria from that of fungi, since the two

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overlap in so many respects and since each group displays, more than any other groups of

microorganisms, a kaleidoscopic array of metabolic diversity.‖

This may in many ways hold true especially given the large degree of taxonomic

diversity found in both groups. In fact great diversity has often been a major pillar of

arguments related to the functional redundancy of the whole microbial community and

might also be applicable to the environmental response of fungi and bacteria (Andren and

Balandreau 1999, Behan-Pelletier and Newton 1999, Wall and Virginia 1999). Although

functional redundancy of whole soil communities has recently been questioned (Reed and

Martiny 2007, Strickland et al. 2009a), this does not negate the possibility that fungi and

bacteria on a global scale may have significant trait overlap. This might be one reason

why changes in fungal:bacterial dominance are variable under similar conditions.

As more studies are conducted across regions which explore the impacts on

fungal:bacterial dominance, then formal meta-analyses may be conducted which will

ultimately provide broad generalizations related to the impact that environmental factors

have on this measure of microbial community structure. It will also be necessary to

formally assess many of the expected differences between fungi and bacteria under field

conditions. For example, assessments of bacterial and fungal C:N ratios (especially

bacteria) would prove enlightening and demonstrate whether or not there is significant

overlap between these groups. In situ measures of fungal and bacterial biomass C:N

ratios, until recently, have proven difficult if not impossible to attain. However, advances

in the use of nano-scale secondary ion mass spectrometry (NanoSIMS) may allow

researchers to explore these soil communities in ever greater detail (Herrmann et al.

2007). Using this method researcher will be able to determine the elemental composition

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of individual cells and paired with stable isotopes, determine the biomass incorporation

of specific elements (Herrmann et al. 2007).

The functional implications of fungal:bacterial dominance will likely also need

further assessment. we did note that one of the key attributes of fungi likely to influence

ecosystem processes is the fungal growth form. In many instances, when fungal biomass

increased then soil C also tended to increase, although it is worth noting that this was not

always associated with a decline in bacterial biomass. However, given this fact and

evidence that fungi and bacteria are not clearly distinguishable via other characteristics

(e.g. biomass C:N, CUE) then the hyphal growth form of fungi may be the trait most

likely to influence such ecosystem processes. This highlights the need to fully assess the

likely mechanism by which a change in fungal:bacterial dominance might influence a

given ecosystem process and whether or not that mechanism is clearly distinct for

bacteria and fungi. Then measurements clearly related to the assessment of the given

mechanism should be employed to determine if fungal:bacterial dominance is truly at

play. For example, if C-sequestration is the ecosystem process in question and it is

decided that a change in the amount of fungal hyphae are likely to influence this process

then measurements of fungal biomass or hyphae would likely be the most applicable.

Finally, we feel that it is important to realize that the implementation of

fungal:bacterial dominance likely arose because it provided researchers with a

taxonomically and ecologically tractable measure of the microbial community. Although

this has provided researchers with a more complete view of the opaque soil environment,

more recent methodological advances related to the soil community may provide even

further insight. For example, via the use of DNA-based approaches researchers are now

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capable of having an incredibly detailed taxonomic view of the soil microbial

community. Such detail has allowed researchers to relate specific taxonomic groups of

fungi and bacteria to environmental change as well as ecosystem processes (Lauber et al.

2008, Strickland et al. 2009b, Lauber et al. In Review). Also coupling these relationships

to advances in culturing techniques and growth based measures may ultimately allow

researchers to correlate specific fungal and bacterial species to a range of ecological

classifications (Madsen 2005). In the end fungal:bacterial dominance has provided many

insights related to an array of environmental factors and ecosystem processes and has, in

the least, provided a stepping stone which allows for the assessment of soil microbial

communities. It seems that the more manageable complexity of the microbial community

achieved when dividing it into fungi and bacteria historically has enabled a quantitative

and more mechanistic understanding of soil ecology. We predict that this approach has

not yet been exhausted, and that insight may still be gained from it. Yet, future research

will prove if this assessment of the microbial community will prove more reliable and/or

informative in the face of new techniques also aimed at assessing these same soil

communities.

Introduction to the dissertation

The above review was meant to highlight one of the predominant ways in which soil

microbial communities are assessed, that is via its fungal:bacterial dominance. It was this

assessment, at least initially, which was the principal driver of my dissertation research.

In fact it was the relationship between the fungal:bacterial dominance of the microbial

community and ecosystem C-cycling which formed the basis for the two field studies I

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conducted. For these two studies, I wanted to examine the relationship between

fungal:bacterial dominance first under differing land-use regimes and second under an

invasive plant species. My expectation was that the fungal:bacterial dominance of the

microbial community would differ in the face of these changes and would subsequently

influence ecosystem processes related to C-cycling. Although the first of these

expectations did hold some truth often it was other characteristics of the microbial

community which likely influenced C-processes.

For instance in Chapter 2, I set out to examine how changing land-use/land-

management regimes influence the soil microbial community as well as the

mineralization of the low molecular weight organic carbon (LMWOC) compound,

glucose. I found that fungal:bacterial dominance was not influenced by land-use/land-

management regimes. Not surprisingly then was that fungal:bacterial dominance was

unrelated to differences in the mineralization of glucose and other factors such as land-

use and extractable soil P were more strongly related to glucose mineralization. Given

that the microbial community acts as a direct control on the mineralization of glucose,

this was one of the first signs that measures of fungal:bacterial dominance may not

capture all of the intricacies of the soil microbial community which in turn influence

ecosystem C-cycling.

Further evidence of this was found when I explored the impact that the invasive

grass, Microstegium vimineum, had on microbial communities and the C-processes

regulated by them (see Chapter 3). Again I expected that the microbial community would

be dominanted by bacteria in the presence of M. vimineum with the opposite occurring in

the surrounding forest matrix. However, no difference in fungal:bacterial dominance was

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detected but other characteristics related to the microbial community were noted with

distinct effects on C-processes. Results from this study in addition to those described in

Chapter 2 suggested that other factors related to the microbial community were likely at

play and so I decided to investigate these possibilities using controlled lab-based

experimentation.

In the first of these experimental studies (Chapter 4), I explored the functional

redundancy of soil microbial communities. First, I found evidence which suggested that

microbial communities were not functionally redundant with regards to the

mineralization of leaf litter. Additionally, there was evidence which suggested that

communities which shared a common history with a given litter resource often

mineralized a greater proportion of that litter (i.e. ‗home-field advantage‘ [Gholz et al.

2000]). I also noted that across the course of decomposition microbial communities were

taxonomically distinct and this was related to both the bacterial and fungal components of

the microbial community.

With regards to both home-field advantage and taxonomically distinct microbial

communities, I further explored the role of these communities in a follow-up experiment

examining the decomposition of novel litter resources (Chapter 5). In this work, I found

that not all microbial communities perceived litter quality equally. I found that the

perception of litter quality was in part related to the environment that the microbial

community was sourced from as well as the actual composition of that community.

Together, each of these studies has expanded on our understanding of the relationship

between the structure of soil microbial communities and ecosystem C-cycling.

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Figure Legends

Figure 1.1 The prevalence of fungal:bacterial dominance in the literature today showing

(A) the number of publications found using the search terms ―fungal:bacterial‖ and ―soil‖

and (B) the number of citations referring to those same publications from 1991 to 2008.

Both works specifically related to fungal:bacterial dominance and the citations of these

works have increased during this period of time. The number of publications and citations

were identified using Web of Science. See Appendix A for the list of articles used to

construct this figure.

Figure 1.2 The distribution of the C:N ratios of bacteria, saprotrophic fungi, and

mycorrhizal fungi. Shown are box plots and distributions, notably fewer measurements of

the C:N ratio of bacteria have been made. Results were compiled from the literature and

included 1,099 observations from 18 studies. Most of the data on fungi was obtained in

the field from sporocarps whereas most of the data for bacteria was obtained from

cultures or aquatic samples. Closed circles = Mycorrhizal fungi; Open circles =

Saprotrophic fungi; Closed squares = Bacteria. See Appendix B for the list of articles

used to construct this figure.

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Figure 1.1

(A)P

ubli

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Figure 1.2

C:N ratio

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

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CHAPTER 2

RATES OF IN SITU CARBON MINERALIZATION IN RELATION TO LAND-USE,

MICROBIAL COMMUNITY AND EDAPHIC CHARACTERISTICS2

2 Strickland, M.S., M.A. Callaham Jr., C.A. Davies, C.L. Lauber, K. Ramirez, D.D. Richter Jr., N. Fierer,

M.A. Bradford. To be submitted to Soil Biology & Biochemistry.

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Abstract

Plant-derived carbon compounds enter soils in a number of forms; two of the most

abundant being leaf litter and rhizodeposition. Our knowledge concerning the

predominant controls on the cycling of leaf litter far outweighs that for rhizodeposition

even though the constituents of rhizodeposits includes a cocktail of low molecular weight

organic compounds which represent a rapidly cycling source of carbon, readily available

to soil microbes. We determined the mineralization dynamics of a major rhizodeposit,

glucose, and its relationship to land-use, microbial community and edaphic characteristics

across a landscape in the southeastern United States. The landscape consists of cultivated,

pasture, pine plantation, and hardwood forest sites (n=3). Mineralization dynamics are

resolved in both winter and summer using an in situ 13

C-glucose pulse-chase approach.

Mineralization rates of the labeled glucose declined exponentially across the 72 h

measurement periods. This pattern and absolute mineralization rates are consistent across

seasons. An information-theoretic approach revealed that land-use is a moderately strong

predictor of cumulative glucose mineralization. Measures assessing the size, activity,

and/or composition of the microbial community were poor predictors of glucose

mineralization. The strongest predictor of glucose mineralization was soil-extractable

phosphorus. It was positively related to glucose mineralization across seasons and

explained 60% and 48% of variation in cumulative glucose mineralization in the summer

and winter, respectively. We discuss potential mechanisms underlying the relationship

between soil phosphorus and glucose mineralization. Our results suggest that specific soil

characteristics often related to land-use and/or land-management decisions may be strong

predictors of glucose mineralization rates across a landscape. We emphasize the need for

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future research into the role of soil phosphorus availability and land-use history in

determining soil organic carbon dynamics.

Keywords: Soil microbial communities, root exudates, low molecular weight

compounds, fungal-to-bacterial ratios, land-use, rhizosphere, carbon cycling,

decomposition

Introduction

Much of our understanding regarding the cycling of plant-derived carbon (C)-compounds

has been developed from studies that examine the decomposition of leaf litter (Melillo et

al. 1982, Couteaux et al. 1995, Aerts 1997, Gholz et al. 2000, Harmon et al. 2009).

However, leaf litter is not the only plant-derived input of C to terrestrial systems. Of

growing interest is the role that rhizodeposits play in ecosystem level C-processes (van

Hees et al. 2005, Pollierer et al. 2007, Hogberg et al. 2008, Phillips et al. 2008).

Rhizodeposits are in part composed of low molecular weight organic C (LMWOC)

compounds, such as sugars (mono- and disaccharides), amino acids, and organic acids

(Rovira 1969, Grayston et al. 1997, Dakora and Phillips 2002). Although the actual input

of LMWOC compounds into a system have been estimated to account for less than 30%

of total soil respiration per year (van Hees et al. 2005), the overall impact of these

compounds on C-dynamics may be large (van Hees et al. 2005, Hogberg et al. 2008,

Phillips et al. 2008). In part, this is because LMWOC compounds represent a highly

labile, rapidly-cycling, form of C which is immediately available to the soil microbial

community for growth, maintenance and respiration (van Hees et al. 2005, Hogberg et al.

2008). Indeed, the turnover of LMWOC compounds may explain significant variation in

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soil respiration rates (Gu et al. 2004, Bengtson and Bengtsson 2007). Furthermore, the

degree to which rhizodeposition interacts with the microbial community is likely to

influence ecosystem C-sequestration, with increases or decreases in soil organic C pools

expected under differing input rates of LMWOC compounds (Bradford et al. 2008a,

Bradford et al. 2008b, Blagodatskaya et al. 2009). Less well understood is how variation

in land-use, microbial community and edaphic characteristics will impact the

mineralization rates of such rhizodeposits.

Overall there is the expectation that the size, activity, and composition of the soil

microbial community will influence the mineralization rates of LMWOC compounds

(van Hees et al. 2005, Fierer et al. 2007, Allison and Martiny 2008, Green et al. 2008, but

see Kemmitt et al. 2008). An increase in the size and/or activity of the microbial

community is expected to increase mineralization (van Hees et al. 2002, van Hees et al.

2005, Six et al. 2006). The influence of composition, on the other hand, is less clear.

Compositional changes in the soil microbial community, associated with broad

distinctions in life history strategies, are likely to have the greatest impact on

mineralization dynamics. For example, using one of the traditional compositional

distinctions, microbial communities largely composed of zymogenous (i.e. fresh organic

matter decomposers) as opposed to autochthonous (i.e. soil organic matter decomposers)

populations of microbes may have higher rates of LMWOC mineralization (Winogradsky

1924, Fontaine and Barot 2005). A phylogenetic distinction might posit that communities

dominated by bacteria will have greater mineralization rates than those dominated by

fungi (Bardgett and McAlister 1999, Six et al. 2006).

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It is also feasible that microbial communities which have previously been exposed

to a specific LMWOC compound will subsequently influence the future mineralization of

that compound (Saggar et al. 2000). This expectation is similar to the hypothesis of

‗home-field advantage‘, which was developed to explain differences in litter

decomposition rates not accounted for by climate and/or litter quality (Hunt et al. 1988,

Gholz et al. 2000, Ayres et al. 2009, Strickland et al. 2009a). The hypothesis suggests

that communities pre-exposed to a given leaf litter are likely to mineralize that litter more

rapidly than communities for which that litter is novel (Gholz et al. 2000, Ayres et al.

2009). Although the underlying mechanisms for home-field advantage may differ for

mineralization rates of litter and root exudates, the scenario suggests that communities

which derive the bulk of their C from rhizodeposits and/or LMWOC compounds may

have greater mineralization rates than communities deriving their C from another source

(Saggar et al. 2000, Stevenson et al. 2004). Based on this possibility, the mineralization

rates of LMWOC compounds may be dependent on the plant community and factors such

as land-use/land-cover, season, and nutrient availability (Grayston et al. 1997,

Warembourg et al. 2003, van Hees et al. 2005). For example, where the dominant plant

cover is grass (e.g. pastures), inputs of LMWOC compounds may be a more important C

source than in forests, explaining higher mineralization rates of these compounds in

herbaceous communities (Stevenson et al. 2004). Similarly, these inputs may vary

dependent on both season and the nutrient demands of the plant itself (Nguyen 2003, Bais

et al. 2006). Finally, factors typically related to soil C dynamics, such as temperature,

moisture, pH, soil texture and nutrient availability, may all influence mineralization rates

of LMWOC compounds (Saggar et al. 2000, Fierer and Jackson 2006, Yuste et al. 2007,

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Bradford et al. 2008a, Bradford et al. 2008b). Specifically, each of these factors has been

related to effects on the activity and/or composition of the soil microbial community but

their effect on the mineralization rates of LMWOC compounds is lacking (van Hees et al.

2005).

One of the key constituents of LMWOC compounds is glucose (Dakora and

Phillips 2002, Nguyen 2003, van Hees et al. 2005). Glucose is likely to be found in the

rhizodeposits of nearly all plant species under an array of environmental conditions and is

also found in the leachates of forest floor litter (Dakora and Phillips 2002, Nguyen 2003,

van Hees et al. 2005). In light of the significance of glucose as a rhizodeposit and

leachate-constituent, microbial mineralization of glucose has been widely studied. As

early as the 1980s researchers have been adding glucose to soil and tracking the resulting

mineralization dynamics. Notably, Voroney et al. (1989) found that the initial

mineralization of glucose was rapid but that a large proportion (~ 25%) of it was later

stabilized in the system, presumably as microbially-derived products. Using

concentrations more typical of those found in situ, mineralization of glucose has been

shown to follow Michaelis-Menten type kinetics (van Hees et al. 2005, van Hees et al.

2008). Soil microbial communities respond rapidly to glucose additions, even when these

additions are in trace amounts, suggesting that at least certain components of the

microbial community may be adapted to it and/or LMWOC compounds in general (De

Nobili et al. 2001, Jones and Murphy 2007, Hanson et al. 2008, Hoyle et al. 2008). Lab-

based studies of this phenomenon have demonstrated that this adaptation may be

contingent on the source environment of the microbial community, with microbes from

grasslands responding more rapidly than those from forests (Jones and Murphy 2007).

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Such studies have added greatly to our understanding of the microbial response to

glucose but few studies have assessed these dynamics in intact soil systems under field

conditions. The exception is Boddy et al. (2007), who found that glucose mineralization

was more rapid in the field than in the lab; this was attributed to an intact microbial

community in situ associated with the rhizosphere. Less well understood is whether the

mineralization of glucose (or other LMWOC compounds) can be generalized across a

landscape in much the same way that leaf litter decomposition has been (Barlow et al.

2007, Madritch and Cardinale 2007). That is, are there characteristics across space and

time that likely explain variation in the mineralization rates of glucose in situ?

The objective of our study was to determine the landscape-level controls on

glucose mineralization rates. We amended soils with small amounts of 13

C-labeled

glucose and tracked its resultant mineralization, in both the summer and winter, across a

representative rural landscape of the southeastern United States (see Richter et al. 1999,

Richter and Markewitz 2001). This landscape consists of upland soils with four distinct

land-use histories, currently with cultivated crops, pastures, old-field pine stands, and

never cultivated hardwood forests. We expected that the size, activity, and composition

of microbial communities, all of which could be considered potential determinants of

glucose mineralization rates, would vary markedly. Our primary expectation was that

either one or all three of these microbial community characteristics, and/or land-use,

would be a major determinant of in situ glucose mineralization rates. More specifically,

we expected, due to likely differences in microbial community characteristics and

rhizospheric inputs, that those sites currently under herbaceous plant cover (i.e. cultivated

and pasture) would exhibit greater mineralization rates than would those under forest

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cover (i.e. pine and hardwood). To investigate these expectations we used an

information-theoretic approach (Burnham and Anderson 1998) which permitted us to

both determine the probable best model in a suite of candidate models, in addition to the

most important model parameter, for explaining variation in glucose mineralization in

situ. We assessed a secondary set of models that, in addition to microbial community

characteristics and land-use, included a suite of soil characteristics which have been

speculated to influence glucose mineralization (e.g. texture). To justify the use of an

information theoretic approach we include potential explanatory variables for which there

is an established mechanistic rationale for why they might explain variation in the

response variable (see Burnham and Anderson 1998). These rationales are provided in

the Methods. Our overall objective was to compare multiple, ecologically relevant

models to provide a greater mechanistic understanding of mineralization dynamics of

LMWOC compounds across a landscape.

Methods

Site descriptions

Sites used in this study were located in, or adjacent to, the Calhoun Experimental Forest

(CEF), which is managed by the USDA Forest Service and located in the Southern

Piedmont physiographic region (approximately 34.5°N, 82°W) of northwestern South

Carolina, USA (Gaudinski et al. 2001, Callaham et al. 2006). The acidic Ultisols soils at

these sites are classified as fine, kaolinitic, thermic Typic Kanhapludults of the Appling,

Cecil, Hiwassee, and Madison series (Richter et al. 2006, Lauber et al. 2008, Grandy et

al. 2009). All sites are on uplands and on interfluves from similar bedrock, thus

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attempting to control native soil characteristics, geomorphology, and geology. Sites were

within 30 km of each other and each represented one of four land-use practices common

to the region (Lauber et al. 2008, Grandy et al. 2009): annual row-crop agriculture, cattle

pasture, old-field pine forest, and oak-hickory forest (n=3 in all cases).

Management regimes for both the cultivated and pasture sites have been in place

for at least the past 40 years. The cultivated sites are managed by the South Carolina

Department of Natural Resources as wildlife fields with annual crop rotation between

corn, millet, wheat, sorghum, sunflowers, and fallow using conventional tillage practices.

During the winter sampling (start date: 30th

November 2006) two of the three cultivated

sites were fallow while the other (site name: Cultivated 3) was planted in winter wheat. In

the summer sampling (start date: 12 June 2007), cultivated sites were planted with corn

(Cultivated 1), sunflowers (Cultivated 2), and wheat/corn (Cultivated 3). Pasture sites are

dominated by rye and Bermuda grass and are, more or less, continuously cattle-grazed.

Both cultivated and pasture ecosystems are fertilized and limed. Two of the pine

plantations (i.e. Pine 1 and 3) are ~50 years old and consist of an overstory of loblolly

pine with an understory of oak and hickory species. The other pine plantation (i.e. Pine 2)

is a 10-year old loblolly pine monoculture. These pine forests are all growing on old

fields previously cultivated for cotton and have therefore been fertilized and limed but

not during the growth of pine. The hardwood sites are mature oak-hickory stands, ~75

years of age. One hardwood plot (i.e. Hardwood 2) is grazed by cattle and has little

understory vegetation. Average bulk density (for depth 0-7.5 cm) ± 1 S.E. was 1.23±0.15,

1.66±0.10, 1.32±0.09, and 1.16±0.17 g cm-3

for cultivated, pasture, pine, and hardwood

land-uses, respectively.

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This replicated land-use design allowed us to explore how variation in both the

contemporary and historical management of these sites drove differences, which might be

expected to influence glucose mineralization, in soil properties and microbial

communities (Richter et al. 1999, Richter and Markewitz 2001). Differences in dominant

plant cover per land-use could also be a significant determinant of glucose mineralization

(e.g. Stevenson et al. 2004). It was our goal to select sites representative of the land-use

practices and land-use legacies of this region, enabling a practical insight into the

relationship between in situ glucose mineralization and soil chemical, physical and

microbial community characteristics.

13C-glucose pulse-chase

To determine glucose mineralization rates across sites and seasons, we conducted a 13

C-

glucose pulse-chase experiment in both the winter and summer. The pulse-chase was

conducted by making additions of 99 atom% 13

C-labeled glucose and tracking its

mineralization as 13

CO2. This was accomplished by placing two PVC collars (15.4 cm

dia., inserted 5 cm into the soil) in each land-use plot. Water was then added to each core

24 h prior to sampling in order to alleviate any water stress between plots. Soil CO2

efflux rates were determined using a closed-chamber approach (e.g. Bradford et al. 2001),

where CO2 concentrations were determined at the start and end of a 45 min capping

period. We conducted a pilot study to determine the appropriate capping time and found

that headspace CO2 concentrations increase linearly from 0-45 minutes and flux rate

estimates only begin to decrease after 60 minutes. Although this closed-chamber

approach is likely to have caveats associated with it (i.e. underestimated CO2 efflux

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rates), it is nonetheless a widely used approach across an array of ecosystem types (Nay

et al. 1994, Franzluebbers et al. 2002). Similar capping times and protocols as described

here have been employed by other researchers (Iqbal et al. 2008, Mo et al. 2008).

Headspace samples were taken with 20 mL SGE gas syringes, transported to the

laboratory in 12 mL Exetainers, and then CO2 concentrations were determined using an

infra-red gas analyzer (IRGA; Li-Cor Biosciences, Lincoln, NE, USA, Model LI-7000).

A second sample was analyzed using continuous flow, isotope-ratio mass spectrometry

(IRMS; Thermo, San Jose, CA, USA) to determine the δ13

C value of the CO2 in the

sample. The initial headspace sampling provided the natural abundance values for the

isotope mixing equations (see below). After this initial sampling, 1 L of 2.5 mM 13

C-

labeled glucose solution (99 atom%) was added to the collars and permitted to drain. The

capping procedure was repeated post addition at 2, 5, 24, 48 and 72 h, permitting a

negative exponential ‗decay‘ of 13

C label to be tracked in the soil CO2 efflux, from which

cumulative mineralization rates were estimated. The amount of C added to each collar

was relatively small: <25 µg C g dry wt soil-1

, which is <0.0001% of the total soil carbon

(from 0 to 7.5 cm depth in a 15.4-cm dia. PVC collar).

The contribution of 13

C-labeled glucose to soil respiration was estimated using

isotope mixing equations. The amount of CO2 derived from glucose was calculated as

follows (sensu Ineson et al. 1996): Cglucose derived = Ctotal × (δ13

Cafter - δ13

Cbefore)/( δ13

Cglucose

- δ13

Cbefore), where Ctotal is the total amount of C respired, δ13

Cafter is the δ13

C value of the

respired C after glucose was added, δ13

Cbefore is the δ13

C value of respired C before

glucose was added (i.e. the natural abundance value), and δ13

Cglucose is the value for the

glucose itself. Values were calculated per PVC collar but for the statistical analyses the

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mean of the pair of values for each site (per season) was used; in effect treating the pair

of values as ‗experimental repeats‘ and not replicates.

Determination of microbial community structure and soil characteristics

During both the summer and winter pulse-chase, we collected ten individual A horizon

soil cores (8 cm dia., 0 – 7.5 cm depth) from each site using a stratified random approach.

Soil cores were taken from areas adjacent (i.e. within 10 m) to the pulse-chase plots in

each land-use. Soils were sieved (4 mm), homogenized, and stored at +5ºC until

analyzed. Analyses included those used to assess the soil microbial community and key

soil characteristics expected to influence the mineralization of glucose. Mean values per

land-use for both sets of characteristics are presented in Table 2.1 and the rationale for

each is given below.

Although there is debate surrounding the applicability of different methods aimed

at determining the size, activity, and composition of microbial communities, we decided

to use three of the most common (Wardle and Ghani 1995). These included a modified

chloroform fumigation-extraction (CFE) method to assess microbial biomass, substrate-

induced respiration (SIR) as an estimate of activity, and determination of fungal-to-

bacterial dominance via qPCR to estimate composition. All measures of microbial

community size, activity, and composition were determined for both the winter and

summer pulse-chase.

The modified CFE method as described in Fierer and Schimel (2002) and Fierer

and Schimel (2003) allowed us to estimate microbial biomass C. Briefly this was

estimated as the flush of DOC following fumigation with ethanol-free chloroform. Raw

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values are reported for microbial biomass C; no correction factors were used. The SIR

method we used follows Fierer & Schimel (2003) whereby soil slurries are incubated,

after a 1 h pre-incubation with excess substrate (i.e. autolyzed yeast extract), for 4 h at

20ºC. After the 4-h incubation, SIR is determined via infrared gas analysis of headspace

CO2 concentrations using a static incubation technique (Fierer et al. 2005a). Note that

although both the CFE and SIR method both report measures of microbial biomass, these

measures are not necessarily equivalent especially at finer spatial scales (i.e. <100 km)

(Wardle and Ghani 1995). Where CFE is expected to estimate total biomass, SIR has

been suggested to be a measure of active microbial biomass (Wardle and Ghani 1995).

The composition of the microbial community was assessed as the relative

abundance of fungi-to-bacteria. The relative fungal-to-bacterial dominance of the

microbial community is often taken as an indicator of belowground C processes with

fungal-dominated systems being associated with greater C stores and/or slower C-cycling

(Bardgett and McAlister 1999, Six et al. 2006). We determined these ratios using the

quantitative PCR (qPCR) method described by Fierer et al. (2005b) and Lauber et al.

(2008). Briefly, DNA was isolated from soil (kept at -80ºC until use) using the MoBIO

Power Soil DNA Extraction kit (MoBio Laboratories, Carlsbad, CA) with modifications

described in Lauber et al. (2008). Standard curves were constructed to estimate bacterial

and fungal small-subunit rRNA gene abundances using E. coli 16S rRNA gene, or the

Saccharomyces cerevisiae 18S rRNA gene, according to Lauber et al (2008). Ratios of

fungal to bacterial gene copy numbers were generated by using a regression equation for

each assay relating the cycle threshold (Ct) value to the known number of copies in the

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standards. All qPCR reactions were run in quadruplicate. Additional details, including the

specific qPCR reaction conditions, are provided in Lauber et al. (2008).

In addition to measures aimed at determining the size, activity, and composition

of the soil microbial community we also identified and determined several soil

characteristics which are likely to influence the mineralization of glucose both directly

and indirectly. These included in the same surficial 7.5-cm deep samples, soil pH, soil

temperature and moisture, soil texture, soil organic material C:N ratio, and extractable P.

Soil pH, moisture, and temperature were determined for both the winter and summer

pulse-chase. Soil texture, C:N, and extractable P were determined on 7 September 2006

as these measures are not expected to vary greatly across seasons (Richter et al. 2006).

Soil pH (1:1, soil:H2O by volume) was measured with a bench-top pH meter . The

overall rationale for including soil pH as a possible indicator of in situ glucose

mineralization is partially related to its role as an indicator of microbial community

composition and diversity (Fierer and Jackson 2006, Lauber et al. 2008, Jones et al.

2009). At broad spatial scales, as well at our study sites, soil pH is related to the relative

abundance of specific microbial taxa, bacterial diversity, and fungal-to-bacterial

dominance (Fierer and Jackson 2006, Lauber et al. 2008, Jones et al. 2009). Additionally,

soil pH may directly impose stress on the microbial community affecting its activity

and/or it may serve as an integrator of many soil characteristics likely to impact both the

soil microbial community and as a result glucose mineralization (Fierer and Jackson

2006).

Soil temperature (determined at 7.5 cm depth) and moisture (determined across 0-

10 cm depth) were determined at each pulse-chase measurement using hand-held

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moisture and temperature probes. The average of both was estimated for the entire 72 h

period of both the summer and winter pulse-chase. The rationale for determining

temperature and moisture is based on the well known interaction between these factors

and the decomposition of leaf litter (Couteaux et al. 1995, Yuste et al. 2007). This is

likely due to the influence that these two factors have on the activity of the soil microbial

community and it is likely that the interaction between these factors will also influence

the mineralization of glucose.

Soil texture was estimated using a simplified version of the hydrometer method

as described by Gee and Orr (2002). Texture has been shown to relate to the composition

of microbial communities, including across our study landscape (Lauber et al. 2008).

Generally, finer textured soils may protect microbial biomass from both predation and

environmental stress, such as desiccation, and may influence the activity of the microbial

community (Six et al. 2006). This in turn typically leads to a lower turnover of the

microbial biomass, lower activity, possible shifts in community composition, and in turn

likely differences in glucose mineralization (Six et al. 2006).

Soil C:N was determined using an NA1500 CHN Analyser (Carlo Erba

Strumentazione, Milan, Italy). Extractable P was measured on an Alpkem auto-analyzer

(OI Analytical, College Station, TX) using Murphy-Riley chemistry after extraction with

Mehlich I double-acid (H2SO4 –HCl) using a 1:4 mass:volume ratio (Kuo, 1996). Both

soil C:N and extractable P are suggestive of soil nutrient status and have been shown to

influence the mineralization of an array of C-compounds (Bradford et al. 2008a, Bradford

et al. 2008b, Gusewell and Gessner 2009, Strickland et al. 2009b). Both of these soil

characteristics have also been found to relate to the composition of soil microbial

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communities, specifically fungal communities, at the CEF as well as other ecosystems

(Lauber et al. 2008, Clare et al. 2009, Gusewell and Gessner 2009, Hrynkiewicz et al.

2009). Due to the fact that both soil C:N and P are tied to nutrient availability as well as

microbial community characteristics then they may also influence glucose mineralization.

Statistical analyses

Linear mixed-effects models (Pinheiro and Bates 2000) were used to examine the

relationships between glucose mineralization and soil microbial community

characteristics (i.e. size, activity, and composition), land-use, and select soil

characteristics. For this approach microbial community characteristics, land-use, soil

characteristics, and season (when applicable) were treated as fixed effects. Site identity

was included as a random effect to account for repeated sampling (i.e. season).

Cumulative glucose mineralization was loge-transformed to conform to assumptions of

homoscedasticity (verified using model checking). Parameter estimates were calculated

using restricted maximum likelihood. However, models compared using the information

theoretic approach were constructed using maximum likelihood estimates as the fixed

effects of these models varied (MacNeil et al. 2009). Further assessment of model fit was

done using linear regression within a given season (i.e. the same linear model was fit to

either data gathered in the winter or summer). Finally, analyses were also conducted

which examined the effect of land-use on cumulative glucose mineralization and

mineralization dynamics. We analyzed cumulative glucose mineralization using a linear

mixed effects model where land-use and season were treated as fixed effects and site

identity was treated as a random effect to account for repeated sampling across seasons;

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land-use and season were allowed to interact in this model. Land-use was further

examined within each season using ANOVA with land-use as a discrete variable and the

Tukey method was used to assess differences between means. We analyzed the

mineralization dynamics across the 72 h period using a linear mixed-effects model where

land-use, season, and time since glucose addition were treated as fixed effects and site

identity was treated as a random effect to account for repeated sampling across the 72 h.

Time was treated as a continuous variable and all three variables were allowed to interact.

Mineralization dynamics were also analyzed within a given season using a similar mixed-

effects model except in this case there was no fixed effect of season. All analyses were

conducted using the freeware statistical package R (http://cran.r-project.org/).

Model selection and comparison was conducted using an information-theoretic

approach (Burnham and Anderson 1998). We initially compared models which related

soil microbial community characteristics and land-use to cumulative glucose

mineralization. This resulted in a set of 30 candidate models plus an additional intercept

only model. This model set included a full factorial combination of soil microbial

community characteristics with and without both land-use and season (see Appendix C

for the full model list). Land-use was also included in this candidate set as a single

parameter and in combination with season. Model parameters were only included as

additive terms in order to both keep the number of candidate models manageable and

because there was no a priori rationale for the inclusion of interactions. Akaike‘s

information criterion for small sample size (AICc) was used as the model selection

criterion (Burnham and Anderson 1998). Model comparisons are based on the difference

in AICc (Δi) with models <2 AICc units apart considered nearly indistinguishable

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(Burnham and Anderson 1998). Models >10 AICc units from the model with the

minimum AICc value have no support (Burnham and Anderson 1998). We also

calculated Akaike weights (ωi) which allowed us to quantify the support for one model

relative to another, calculate the 95% confidence set of models, and determine the

importance of a given model variable (Burnham and Anderson 1998). The last of these

was calculated as the Σ ωi for all models containing the variable in question (Burnham

and Anderson 1998).

In addition to the initial 30 candidate models we also looked at the role of specific

soil characteristics. The overall rationale for this was to determine, if any, which specific

soil characteristics regardless of their relationship to land-use likely drove differences in

cumulative glucose mineralization. In order to assess this possibility we incorporated five

soil characteristics likely to impact the soil microbial community: pH, the interaction

between soil temperature and moisture, silt + clay content, soil organic material C:N

ratio, and extractable P. The rationale for inclusion of each is given above (see

Determination of microbial community structure and soil characteristics). Each of these

was included as an additive variable in models containing microbial community

characteristics as well as a single factor and as an additive factor with season (see

Appendix C for the full model list). This resulted in a total of 110 candidate models (plus

an intercept only model) which included the original 30 candidate models. Model

selection for this set of candidate models was conducted in the same manner as described

above.

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Results

Temporal dynamics and cumulative mineralization of glucose

Overall the addition of glucose caused relatively little if any change in total soil

respiration within a land-use, regardless of season (Appendix D). The amount of glucose

respired as 13

CO2 declined across the 72 h measurement period with the highest

mineralization values recorded ~2 h after the addition of glucose and the lowest values

occurring at ~48 or ~72 h (Fig. 2.1). This change in glucose mineralization rates across

time was highly significant (F1, 96 = 278.93; P<0.0001) and an interaction between these

temporal dynamics and season was detected (F1, 96 = 5.27; P<0.05). This interaction was

likely explained by the similar mineralization rates regardless of season at the 2 and 5 h

measurement periods but greater mineralization rates in the winter at the 24, 48, and 72 h

measurement periods. It is worth noting that glucose mineralization rates tended to be

similar across seasons in spite of the fact that total soil respiration was greater in the

summer when compared to the winter (Fig. 2.1, Appendix D). Land-use was also a

significant factor impacting glucose mineralization rates (F3, 8 = 9.89; P<0.01) and this

was consistent for both the winter (F3, 8 = 10.08; P<0.01) and summer (F3, 8 = 9.57;

P<0.01) (Fig. 2.1a,b). In general we noted that pasture and cultivated sites typically had

higher glucose mineralization rates across 72 h than did pine and hardwood sites,

although this pattern was clearer in the summer than in the winter (Fig. 2.1).

Overall, land-use had a significant influence on cumulative glucose mineralization

(F3, 8 = 10.34; P<0.01). Neither season effects, (F1, 8 = 0.45; P = 0.52), nor an interaction

between season and land-use (F3, 8 = 1.36; P = 0.32), were observed for the cumulative

amount of glucose mineralized. Generally, cumulative mineralization was higher in

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pasture sites while both the pine and hardwood sites tended to be lower (Fig. 2.1). The

cultivated sites were not as easily generalized given that cumulative mineralization

tended to be lower in the winter when compared to the summer. Additionally, during the

summer there was a large degree of variation associated with this land-use, likely due to

differences in crop cover between the three sites (see Methods and Discussion). The high

variation in cultivated sites in the summer may explain why the land-use effect on

cumulative glucose mineralization (Fig. 2.1c, d) was not consistent across seasons. That

is, in the winter a clear land-use effect was noted (F3, 8 = 18.22; P <0.001) but this was

not true for the summer (F3, 8 = 2.64; P = 0.12), where larger error variances were

observed (compare Fig. 2.1c, d). The large variation in cumulative glucose mineralized

across cultivated sites may also explain why the temporal dynamic data (Fig. 2.1b)

revealed a statistically significant land-use effect but the cumulative data (Fig. 2.1d) did

not.

Relatively little of the added glucose was recovered as CO2 and this was true

regardless of season or land-use. The amount of C recovered as CO2 ranged from a low

of 0.57% (Hardwood 3 in the winter) to a high of 4.29% (Cultivated 3 in the summer)

and across all land-uses and both seasons averaged 1.93%. This suggests that

approximately 95-99% of the added glucose-C remained within the soil system. Of

course, given that we did not begin tracking the mineralization of glucose until 2 h after

its addition, and the fact that some of the respired glucose may not have been captured by

our chamber method; the actual amount of C remaining may have been lower. However,

low recovery rates of glucose-C as CO2 are typical when glucose solutions are added to

soils in low concentrations (Boddy et al. 2007).

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Glucose mineralization and its relationship to microbial community structure and land-

use

We initially set out to determine the importance of land-use and the size, activity, and

composition of the microbial community as they relate to cumulative glucose

mineralization in situ. In order to do this we utilized an information-theoretic approach to

assess a series of 30 candidate models (Burnham and Anderson 1998). Given that land-

use was significantly related to cumulative glucose mineralized (Fig. 2.1), it is not

surprising that the best model (i.e. lowest AICc) included land-use as the sole explanatory

variable (Table 2.2). The probability that this model was the actual best model in this set

was 53% suggesting moderate support for this outcome. However, a model which only

included an intercept term was within 2 AICc units of the top model suggesting that it

was also a plausible top model candidate (Table 2.2). Furthermore, the evidence ratio (i.e.

ω1/ ω2 = 1.89) when comparing these two models is ~2 and we must then assume a high

level of model selection uncertainty when considering these two models (Burnham and

Anderson 1998). Additionally, all of the remaining models, including those that

accounted for the size, activity, or composition of the microbial community, were > 4

AICc units from the top model indicating that there was overall considerably less support

that any of these models was the ‗actual‘ best model (Burnham and Anderson 1998). In

fact when calculating the importance of each model parameter, we found that the size,

activity, and composition of the microbial community were all relatively unimportant in

the prediction of glucose mineralization (ωi<0.10 for all 3; Fig. 2.2a) while land-use was

moderately important (ωi = 0.55; Fig. 2.2a). Parameter estimates for models within the

95% confidence limit are reported in Appendix E.

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Glucose mineralization and its relationship to microbial community structure, land-use,

and soil characteristics

Given that only marginal support for any one model or model parameter was found in the

initial candidate set, we decided to explore the relationship between several key soil

characteristics at our sites and the cumulative mineralization of glucose. We found that

the best model (i.e. lowest AICc) in this candidate set included soil P concentrations as

the sole explanatory variable (Table 2.3). The probability that this model was the actual

best model in this set was 73% suggesting moderately strong support for this outcome. In

fact no other model was within 2 AICc units of this model and in comparison to the

second ranked model in this set, the evidence ratio was 10.11. This suggests that given

this set of candidate models that it is fairly certain that this model is in fact the top model.

Further supporting this is the fact that no other models were <4 AICc units from this top

model which indicates that there was overall considerably less support for any of the

other 109 models (Burnham and Anderson 1998). Parameter estimates for all models

within the 95% confidence limit are reported in Appendix E.

We also found that soil extractable P was the most important model parameter

and was significantly related to land-use (F3, 8 = 9.53; P<0.01). This is not surprising

given that soil P was both the sole parameter in the top model and was also found in all of

the other top models (i.e. all models within 10 AICc units of the top model). Soil P also

had an extremely high weight of evidence (ωi = 0.99; Fig. 2.2b), again demonstrating its

importance as an indicator of cumulative glucose mineralization. No other parameter was

found to be as important. In fact all other model parameters were > 10 times less likely to

be as important as soil P (Fig. 2.2b). Soil P and cumulative glucose mineralized, after

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accounting for variation among plots, were positively related (β = 0.48 ± 0.09; F1,10 =

30.04; P<0.001). Regression analysis found that this was generally consistent across

seasons (Fig. 2.3) with soil P explaining 60 and 48% of the variation in cumulative

glucose mineralized in the winter and summer, respectively (Fig. 2.3).

Discussion

In this study we amended four land-uses representative of a rural upland landscape with

13C –labeled glucose and tracked its mineralization across the course of 72 h in both the

summer and winter in order to determine what factors, if any, were related to the

mineralization of this common LMWOC compound in soils. We expected that

cumulative glucose mineralization would be related to land-use and/or the size, activity,

or composition of the soil microbial community. We also evaluated a second set of

putative explanatory variables related to specific soil characteristics. We expected that

these soil characteristics either alone or in combination with microbial community

characteristics would affect cumulative glucose mineralization. Both of these sets of

variables were examined using an information-theoretic approach that allowed us to

determine the probable best model as well as the most important individual parameter for

explaining glucose mineralization. The approach, when applied to evaluate and compare

potential explanatory variables for which there is an established mechanistic rationale

detailing why they might cause variation in a response variable, generates ecologically-

relevant, regression models (see Burnham and Anderson 1998). Our overarching

objective was to advance the understanding of factors that influence the in situ

mineralization of LMWOC compounds across landscapes.

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Across the course of 72 h we found that glucose mineralization rates decreased

exponentially and this pattern was consistent across land-use and season (Fig. 2.1a, b).

Such results are similar to the findings of an array of lab-based studies (Bremer and

Vankessel 1990, Bremer and Kuikman 1994, Hoyle et al. 2008, van Hees et al. 2008), as

well as the findings of field-based studies where relatively high concentrations of glucose

have been applied (Voroney et al. 1989). The mineralization dynamics observed in this

study where similar to those observed by Boddy et al. (2007), the only other in situ study

which simulated glucose additions at amounts and concentrations comparable to those

expected under root exudation. This may suggest that across a wide array of settings the

mineralization dynamics of glucose and, perhaps other LMWOC compounds, follow a

predictable pattern.

Of the glucose added, we found that as much as 4% was recovered as CO2

meaning that ~96% of glucose-derived C remained in the soil. This is not an uncommon

occurrence, with comparable studies such as Voroney et al. (1989) finding that ~25% of a

large glucose addition remained after 7 years and Boddy et al. (2007) finding that ~70-

80% of a small glucose addition remained after 48 h. In light of such findings, it has been

suggested that relatively large amounts of added/exuded glucose are cycled through the

microbial biomass and may to some degree be stabilized as microbial byproducts in the

soil organic matter (Voroney et al. 1989). However, such processes may be slow and

occur over decadal time scales (Richter et al. 1999). The stabilization (or not) of

LMWOC in SOC sinks is an area requiring considerably more research attention, given

the importance of the inputs of these compounds to soils.

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We found that land-use affected the cumulative amount of glucose mineralized

(Fig. 2.1c, d). Like temporal mineralization dynamics (Fig. 2.1a, b), we found that

pasture sites typically had greater cumulative glucose mineralization than did pine or

hardwood sites. These results concur with those of Stevenson et al. (2004), who showed,

using lab-based catabolic response profiling, that grassland soils are typically associated

with greater glucose mineralization than forest soils. Cumulative glucose mineralized in

the cultivated sites was on average lower in the winter than in the summer and this was

likely due to the presence of established crop cover in the summer. Specifically,

cumulative mineralization in the cultivated sites in summer was often comparable to

cumulative values for pastures. In the winter two of the cultivated sites were fallow and

the third (Cultivated 3) had freshly sprouted winter wheat. Interestingly, variation in

mineralization rates across cultivated sites in the summer was high (Fig. 2.1d) and this

variation may be due to differences in crop species cover. However, the role of plant-

cover species identity in regulating mineralization dynamics of LMWOC compounds is

relatively unexplored (see Paterson et al. 2007).

Not surprising given that land-use was significantly related to cumulative glucose

mineralized, the best explanatory model in the primary set of models analyzed using the

information theoretic approach contained land-use as the sole parameter (Table 2.2).

Land-use was also found to be the most important parameter for the prediction of

cumulative glucose mineralization (Fig. 2.2a). Surprisingly, all models containing

parameters related to the size, activity, or composition of the microbial community were

likely poor predictors of cumulative glucose mineralization and overall they were not

important parameters (Table 2.2, Fig. 2.2a). A possible explanation for the overall lack of

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importance for all of these model variables may be the high functional redundancy in the

microbial community with regards the mineralization of glucose (Jones and Murphy

2007, Hanson et al. 2008). However, we only used one method each to determine the

size, activity, and composition of the microbial communities. Although these methods are

commonly and widely employed, they are coarse and may not be indicative of the actual

characteristics of the microbial community involved in the mineralization of glucose.

Future research will need to assess the role of methodology as it pertains to measures of

the microbial community that might explain glucose mineralization dynamics in situ.

Land-use and land-management regimes are likely to transform soil systems in

many ways and this is apparent at the CEF (Richter and Markewitz 2001). Research at

these sites has demonstrated that a suite of edaphic properties (e.g. soil P, texture) are

affected by both the contemporary land-use as well as the legacy of land-management

decisions (Richter and Markewitz 2001, Richter et al. 2006, Lauber et al. 2008, Grandy et

al. 2009). For example, Richter et al. (2006) demonstrates across the same landscape that

soil P is strongly influenced by management history such as fertilization, even for

decades after it was last practiced. Notably, we found that soil P was a parameter of

major importance in explaining cumulative glucose mineralization; it was over 10 times

more important than any other parameter considered in the entire model set (Fig. 2.2b).

Soil P was positively related to cumulative glucose mineralized and this was consistent

across both the winter and summer (Fig. 2.3). These results may suggest that both past

and present land-use/management decisions ultimately influence ecosystem C dynamics

through influences on underlying variables including soil P. Saggar et al. (2000), using a

lab-based study of pasture soils with different fertilizer input histories, observed that

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glucose mineralization was positively related to soil P concentrations. They suggested

that this occurs because the microbial biomass becomes less efficient at utilizing C-

compounds as soil P becomes limiting. Similarly, Cleveland and Townsend (2006) found

that when labile C (i.e. leached from litter) was plentiful then P-availability was the

primary control on its mineralization under both field and lab conditions. There is also

the likelihood that increasing P-availability increases overall plant biomass, including

root biomass (Saggar et al. 1997, Saggar et al. 2000), leading to an increase in the size of

the microbial community geared toward the mineralization of LMWOC compounds like

glucose. A recent study conducted with soils from our CEF sites demonstrated that the

decomposition of recalcitrant leaf litter material decreased as soil P increased (Strickland

et al. 2009b). Together these studies, and our new results, suggest that soil P may be an

indicator of shifts in the microbial community from (under higher P) organisms which

specialize in utilizing LMWOC compounds to those (under lower P) which utilize more

complex substrates such as leaf litter. This may be similar to distinctions relating certain

components of the microbial community to r- and K-strategists (Fierer et al. 2007,

Blagodatskaya et al. 2009). Alternatively, soil P may simply be co-correlated with

characteristics of the soil microbial community that determine C-dynamics. For example,

it has been shown that soil P is related to the relative abundance of different microbial

taxa (Lauber et al. 2008). Increased resolution beyond the level of fungal-to-bacterial

dominance may provide more relevant indices of community composition when

attempting to predict glucose mineralization (e.g. Hanson et al. 2008).

Soil P may also be related to the ATP supply rate of the microbial community

(Bradford et al. 2008b, Hoyle et al. 2008). Under this expectation soil P is associated with

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an increased ATP store within the microbial community which may subsequently lead to

greater glucose mineralization. On the other hand, a greater ATP supply rate may favor

soil microbes that maintain a high intracellular concentration of ATP (De Nobili et al.

2001, Hoyle et al. 2008). These types of microorganisms have been described as

exhibiting a ‗metabolically alert‘ life history strategy (De Nobili et al. 2001). Such

organisms might be expected to dominate in systems where LMWOC compounds are the

predominant form of C inputs. If either of these possibilities hold true then assessments

of ATP may be a better indicator of microbial activity with regards to glucose

mineralization than the SIR method employed in this study (but see Landi et al. 2006).

In conclusion, it was the goal of this study to determine the in situ mineralization

of the common rhizodeposit and leachate-constituent, glucose, and its relationship to

land-use, soil microbial community and edaphic characteristics across a landscape.

Surprisingly no measure of the microbial community was related to glucose

mineralization. However, soil P and land-use were predictors of glucose mineralization.

Given that land-use and soil P are intimately related at these sites, there is the indication

that land management decisions which impact soil P may in turn lead to altered

mineralization rates of LMWOC compounds (Richter and Markewitz 2001, Richter et al.

2006). This is not likely a trivial matter given that a large proportion of the Earth‘s soils

are similar to the low P Ultisols at our sites and that many of these same soils are apt to

be or are already impacted by management decisions which increase soil P (Richter and

Markewitz 2001). Such management decisions may lead to increased mineralization of

LMWOC compounds, altering ecosystem C-cycling and C source-sink dynamics. Future

studies will need to address these possibilities and decipher the underlying mechanisms

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that explain the relationship between soil P and C dynamics. Such studies will further our

understanding of the factors which control the cycling of belowground plant-C inputs and

may ultimately lead to an understanding of LMWOC compound dynamics comparable to

our current understanding of leaf litter dynamics.

Acknowledgements

We gratefully acknowledge funding from the Andrew W. Mellon Foundation to N.F. and

M.A.B., and to M.A.B from the Office of Science (BER), U.S. Department of Energy.

We thank Tom Maddox in the Analytical Chemistry Laboratory of the Odum School of

Ecology, Univ. of Georgia, for element and isotope analyses; Jimmy Blackmon for field

assistance; and Mr. Jack Burnett for access to his property.

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Table 2.1 Mean values for soil and microbial community characteristics per land-use in the winter and summer. Values in parentheses

are standard errors across the three sites in each land-use. Silt + clay content, soil C:N, and soil P were determined in September 2006

only as these measures are not expected to vary greatly across seasons.

Land-use

Microbial

biomass

(g C m-2

)

SIR

(g CO2-C

m-2

h-1

)

F:B pH

Soil

Temperature

(ºC)

Soil

Moisture

(%)

Silt + Clay

(%) Soil C:N

Ext P

(g m-2

)

Cultivated

Winter 5.17

(2.33)

0.08

(0.008)

0.022

(0.013)

5.39

(0.14)

14.2

(0.23)

28.94

(2.72)

38.8

(2.87)

14.68

(1.19)

1.62

(0.22)

Summer 5.26

(1.95)

0.13

(0.011)

0.002

(0.000)

4.51

(0.22)

25.4

(0.52)

19.50

(0.52) -- -- --

Pasture

Winter 4.77

(1.40)

0.11

(0.007)

0.047

(0.027)

5.62

(0.18)

14.2

(0.04)

27.67

(0.04)

21.0

(4.16)

14.11

(0.38)

1.94

(0.30)

Summer 8.81

(3.36)

0.22

(0.024)

0.004

(0.001)

5.15

(0.09)

24.3

(0.08)

15.29

(0.08) -- -- --

Pine

Winter 2.86

(0.58)

0.09

(0.003)

0.008

(0.001)

5.37

(0.33)

14.9

(0.21)

14.12

(0.21)

17.7

(3.13)

23.90

(4.95)

0.55

(0.39)

Summer 6.38

(1.92)

0.15

(0.036)

0.008

(0.003)

4.63

(0.36)

19.9

(0.31)

13.46

(0.31) -- -- --

Hardwood

Winter 5.87

(2.28)

0.12

(0.021)

0.239

(0.159)

6.09

(0.50)

14.2

(0.20)

20.61

(0.20)

30.1

(3.18)

21.78

(2.96)

0.17

(0.11)

Summer 8.83

(6.36)

0.16

(0.028)

0.006

(0.002)

5.15

(0.30)

20.7

(0.42)

17.04

(0.42) -- -- --

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Table 2.2 The 10 best models (i.e. ΔAICc<10 ) of the initial 30 candidate models considered which explain cumulative 13

C-glucose

mineralized across 72 h (13

CO2).

Model K log(L) AICc Δi AICc ωi Evidence

ratio

Rank

13CO2 = Land*

6 -7.08 40.15 0.00 0.53 1.00 1

13CO2 = 1* 3 -16.38 41.42 1.27 0.28 1.89 2

13CO2 = MicC* 4 -15.73 44.47 4.32 0.06 8.65 3

13CO2 = F:B* 4 -16.25 45.51 5.36 0.04 14.57 4

13CO2 = SIR* 4 -16.36 45.72 5.57 0.03 16.18 5

13CO2 = MicC + SIR 5 -14.67 47.90 7.75 0.01 48.24 6

13CO2 = MicC + Seas. 5 -14.82 48.21 8.06 0.01 56.16 7

13CO2 = Land + MicC 7 -6.29 48.98 8.83 0.01 82.73 8

13CO2 = MicC + F:B 5 -15.39 49.36 9.21 0.01 99.92 9

13CO2 = Land + Seas. 7 -6.82 50.04 9.89 0.00 140.35 10

The table shows the number of parameters (K), maximized log-likelihood (log(L)), AICc, AICc differences (Δi AICc), Akaike weights

(ωi), evidence ratio (ω1/ ωn), and the model rank. In bold are the best top models in this candidate set (ΔAICc<2). Models within the

95% confidence interval are denoted with an * and parameter estimates for these models are given in Appendix E. All models had plot

as a random effect in order to account for repeated sampling at the plot level.

SIR = substrate induced respiration, a measure of microbial activity; MicC = microbial biomass C as determined via CFE, a measure

of the size of the microbial biomass; F:B = fungal-to-bacterial dominance as determined via qPCR, a measure of community

composition; Seas. = Season, either winter or summer when the pulse-chase experiment was conducted; Land = land-use.

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Table 2.3 The 6 best models (i.e. ΔAICc<10) of the initial 104 candidate models considered which explain cumulative 13

C-glucose

mineralized across 72 h (13

CO2).

Model K log(L) AICc Δi AICc ωi Evidence

ratio

Rank

13CO2 = P*

4 -8.10 29.20 0.00 0.73 1.00 1

13CO2 = P + MicC* 5 -7.63 33.83 4.63 0.07 10.11 2

13CO2 = P + SIR* 5 -7.84 34.26 5.06 0.06 12.53 3

13CO2 = P + Seas.* 5 -7.87 34.30 5.10 0.06 12.82 4

13CO2 = P + F:B 5 -8.06 34.68 5.48 0.05 15.50 5

13CO2 = P + MicC + SIR 6 -5.52 37.04 7.84 0.01 50.31 6

The table shows the number of parameters (K), maximized log-likelihood (log(L)), AICc, AICc differences (Δi AICc), Akaike weights

(ωi), evidence ratio (ω1/ ωn), and the model rank. In bold are the best top models in this candidate set (ΔAICc<2). Models within the

95% confidence interval are denoted with an * and parameter estimates for these models are given in Appendix E. All models had plot

as a random effect in order to account for repeated sampling at the plot level.

SIR, MicC, F:B, Land, and Seas. are the same as in Table 2.2. P = extractable phosphorous.

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Figure 2.1 Results of the 13

C-glucose pulse-chase conducted during the winter

(December 2006) and summer (June 2007) associated with land-use. Panels a and b show

glucose mineralization dynamics across 72 h for each land-use. Land-use differences

were detected for these dynamics across seasons (P<0.01) and during both the winter

(P<0.01) and summer (P<0.01). Panels c and d show the cumulative amount of glucose

mineralized during the entire 72-h period. Although there was a significant land-use

effect across seasons on cumulative glucose mineralization (P<0.01) this was not

consistent within seasons. That is, a statistically significant land-use effect was observed

in the winter (P<0.001) but not in the summer (P=0.12) for cumulative amounts. Values

are means ± 1S.E. per land-use (n=3) and letters in panel c indicate significant differences

between land-uses based on pair-wise comparisons. Note that mineralization dynamics

and cumulative values were roughly equivalent for a given land-use regardless of season.

Figure 2.2 The relative importance of variables from the primary (a) and secondary (b)

set of candidate models which explain cumulative glucose mineralized. Relative

importance was determined via the sum of Akaike weights (∑ωi) for the models in which

a given variable was present. The closer to 1 a variable is the more important it is in this

given set. Numbers to the right of each bar indicate the number of models that a given

variable was in (e.g. land-use was in 16 of the candidate models).

Figure 2.3 The relationship between cumulative glucose mineralized and soil P for the

winter (a) and summer (b). Notably this relationship was very similar in both the winter

(y = 0.51x + 2.73) and summer (y = 0.45x + 2.90). Cumulative glucose mineralized was

loge-transformed in the regression analysis but untransformed values are shown here.

Each symbol represents a specific plot with Cu1-3, Pa1-3, Pi1-3, and Ha1-3 representing

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the individual sites (n=3) in the cultivated, pasture, pine, and hardwood land-uses,

respectively.

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Figure 2.1

(a) Winter (December) Land-use effect: P <0.01

Res

pir

ed 1

3C

O2-C

(m

g m

-2 h

-1)

0

1

2

3

4

Cultivated

Pasture

Pine

Hardwood

(b) Summer (June) Land-use effect: P <0.01

Time since glucose added (h)

0 20 40 60 80

0

1

2

3

4

(c) Winter (December) Land-use effect: P <0.001

Cu

mu

lati

ve

glu

cose

min

eral

ized

(m

g 1

3C

O2-C

m-2

)

0

10

20

30

40

50

60

70

a

c

ab b

(d) Summer (June) Land-use effect: P =0.12

Land-use

Cultivated Pasture Pine Hardwood

0

10

20

30

40

50

60

70

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Figure 2.2

i

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Model

par

amet

er

P

MicC

SIR

Seas.

F:B

M*T

C:N

Land

pH

S+C 16

16

16

16

16

16

56

56

56

55

Land

MicC

SIR

F:B

Seas. 16

16

16

16

16

(a) Initial candidate model set

(b) Secondary candidate model set

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Figure 2.3

Winter (December)

0

10

20

30

40

50

60

70

80

Cu1

Cu2

Cu3

Pa1

Pa2Pa3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Summer (June)

Extractable P(g m-2

)

0.0 0.5 1.0 1.5 2.0 2.5

Cu

mu

lati

ve

glu

cose

min

eral

ized

(m

g 1

3C

O2-C

m-2

)

10

20

30

40

50

60

70

80

Cu1

Cu2

Cu3

Pa1 Pa2

Pa3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

r2 = 0.60

P<0.01

r2 = 0.48

P<0.01

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CHAPTER 3

GRASS INVASION OF A HARDWOOD FOREST IS ASSOCIATED WITH

DECLINES IN BELOWGROUND CARBON POOLS 3

3 Strickland, M.S., J.L. DeVore, J.C. Maerz, M.A. Bradford. Submitted to Global Change Biology,

4/23/2009.

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Abstract

Invasive plant species affect a wide range of ecosystem processes but their impact on

belowground carbon pools is still relatively unexplored. This is particularly true for grass

invasions of forested ecosystems. Such invasions may alter both the quantity and quality

of inputs to forest floors. Dependent on both, two dominant theories, ‗priming‘ and

‗preferential substrate utilization‘, suggest these changes may decrease, increase, or leave

unchanged native plant-derived soil carbon. A decrease is expected under ‗priming‘

theory due to increased activity of the soil microbial community degrading soil carbon.

Under the theory of ‗preferential substrate utilization‘, either an increase or no change is

expected because the invasive plant‘s inputs are used by the microbial community instead

of soil carbon. Here we examine how the widespread invasive Microstegium vimineum

affects belowground carbon-cycling in a southeastern U.S. forest. Following predictions

of priming theory, the presence of M. vimineum is associated with decreases in native-

derived, soil carbon pools. For example, in September 2006 M. vimineum is associated

with 24, 34, 36 and 72% declines in total organic, particulate organic matter,

mineralizable, and microbial biomass carbon, respectively. Soil carbon formed from M.

vimineum inputs does not fully compensate for these decreases, meaning that the sum of

native- and invasive-derived carbon pools are smaller than native-derived carbon pools in

uninvaded plots. Supporting our inferences that carbon-cycling is accelerated under

invasion, the microbial community is more active per unit biomass: added 13

C-glucose is

respired more rapidly in invaded plots. Our work suggests that this invader may

accelerate carbon-cycling in forest soils and deplete carbon stocks. The paucity of studies

investigating impacts of grass invasion on soil carbon-cycling in forests highlights the

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need to further study M. vimineum and other invasive grasses to assess their impacts on

carbon sink strength and forest fertility.

Key-words: Microstegium vimineum, Japanese stiltgrass, Nepalese browntop, stable

isotopes, carbon sequestration, carbon sink, priming effects, preferential substrate

utilization, annual grass, exotic species

Introduction

Invasive plant species are capable of altering a range of ecosystem processes and

properties from nutrient cycling to species interactions (Mack et al. 2000; Liao et al.

2008). In some cases, these impacts are the result of specific traits that are novel to the

recipient community. For example, nitrogen (N)-fixing invasive species can dramatically

impact decomposition rates and soil fertility in ecosystems lacking native N-fixers

(Vitousek et al. 1987; Vitousek & Walker 1989; Allison & Vitousek 2004). Similarly, the

introduction of invasive grass species can increase rates of fire disturbance and fire

intensity, altering rates of carbon (C)-cycling (Mack et al. 2001; Mack & D'Antonio

2003; Bradley et al. 2006). We know far less about the more general effects of invaders,

particularly the many species that do not exhibit particular traits such as N-fixation.

Consequently, it is difficult to predict whether many invasive plant species will

significantly alter ecosystem processes and/or the communities they invade.

Blumenthal (2006) noted that many invasive plant species are ―high-resource‖

species. That is, these species show lower investment in defensive compounds and tend

to be of higher chemical quality (e.g. lower C:N) than native plant species. Others have

made similar observations (Pattison et al. 1998; Baruch & Goldstein 1999; D'Antonio &

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Corbin 2003). Evidence also suggests that the presence of invasive plant species is, more

often than not, associated with increased detrital inputs to native ecosystems (Ehrenfeld

2003; Liao et al. 2008). While such changes may not be true for all invasive plants, this

highlights the likelihood that many invaders may alter ecosystem processes simply via

changes in the quantity and/or quality of detrital inputs within a system (D'Antonio &

Corbin 2003). This would be synonymous to those studies which demonstrate that

changes in the composition of native detrital resources within an ecosystem affects that

system‘s decomposition rates (Ball et al. 2008; Harguindeguy et al. 2008). Specifically,

the presence of an invasive plant species may alter the composition of detrital inputs

entering a system via an increase in both the quality and quantity of inputs. The impacts

of these altered inputs on belowground processes and properties, specifically with regard

to soil C, is an area of debate in soil theory (Bradford et al. 2008c) and such impacts as

they relate to invasive plant species are little studied (Hook et al. 2004; Valery et al.

2004; Batten et al. 2005; Litton et al. 2006, 2008).

Evidence on one hand suggests that increases in the degradability of C inputs, as

perceived by the microbial community (Strickland et al. 2009), will have a negligible and

in some cases a positive impact on belowground C pools (Dalenberg & Jager 1981; Wu

et al. 1993; Fontaine & Barot 2005). This is referred to as the ‗preferential substrate

utilization‘ theory and is based on the premise that high quality C sources, such as leaf

litter and rhizodeposits, will be utilized before lower quality soil C sources (Fontaine et

al. 2004a; Fontaine & Barot 2005). Although the mechanism leading to preferential

substrate utilization is debatable, it has been largely assumed that if novel inputs are of

both a high enough quality and quantity in comparison to background inputs then a shift

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in the microbial community toward organisms which specialize on less recalcitrant

compounds will occur (Blagodatskaya & Kuzyakov 2008; Bradford et al. 2008c). Under

such an expectation the component of the microbial community responsible for the

degradation of more recalcitrant soil C sources may go unchanged leading to little change

in soil C pools (Fontaine et al. 2003). Under more extreme cases, the increase in

organisms specializing on less recalcitrant compounds may negatively impact (e.g.

through competition) those specializing on more recalcitrant resources (e.g. soil C)

leading to a decline in the latter‘s population and an increase in the size of soil C pools

(Fontaine et al. 2003). In contrast, ‗priming effects‘ theory posits that high-quality inputs

give rise to disproportionate impacts on soil C (Fontaine et al. 2003; Fontaine et al.

2004b; Dijkstra & Cheng 2007). Priming is theorized to occur because high quality inputs

stimulate the activity of the microbial community, in particular those groups which

specialize in decomposition of recalcitrant soil C pools. The result is enhanced

degradation of belowground C pools, leading to declines in their size (Fontaine et al.

2004b). It seems reasonable to assume that the impact of invasive plant species on

belowground C pools in native ecosystems will be dependent on the quality and quantity

of invasive inputs relative to native ones, the proportion of labile invasive inputs to

recalcitrant ones, and whether or not the outcomes of preferential substrate utilization or

priming effects theory are realized. Regardless of which outcome occurs, and given that

both theories suggest that a change in soil C-cycling will occur, altered C inputs with

plant invasions will likely alter a site‘s fertility, drought tolerance, C storage potential,

and possibly the aboveground-belowground interactions between organisms (Lal 2004;

Lal et al. 2007).

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We studied an advancing invasion of Microstegium vimineum (Trin.) A. Camus

(commonly named Japanese stiltgrass or Nepalese browntop) to measure associated

changes in soil C-cycling and C pools. Microstegium vimineum is an annual C4 grass,

which produces relatively little root biomass, and invades the understory of a variety of

forest types across a large portion of the southern and eastern United States (U.S.). The

U.S. Department of Agriculture reports M. vimineum invasions in 25 states including all

states east of the Mississippi River except Vermont, New Hampshire and Maine

(http:/plants.usda.gov/). Research in northeastern U.S. forests demonstrates that M.

vimineum alters soil properties, including the activities of microbial exoenzymes that may

affect soil C-cycling (Kourtev et al. 1998; Ehrenfeld et al. 2001; Kourtev et al. 2002). As

yet no work has examined how M. vimineum invasion impacts belowground C pools. In

fact, little is known generally about the impact of grass invasions on soil C in forest

ecosystems (Kourtev et al. 2003; Mack & D'Antonio 2003; Litton et al. 2008). A recent

meta-analysis of invasive plant impacts on C and N cycling shows only 23% of 94

studies measured some form of belowground C, and of those only two studies looked at

the effect of grass invasion on belowground C pools in forests (Liao et al. 2008). The first

of these studies examined how perennial grass replacement of forest, due to an increase

in fire occurrence, impacted soil C (Mack & D'Antonio 2003). The second, a laboratory

study, compared changes in soil C between invasive understory species (including M.

vimineum) and a single native understory species (Kourtev et al. 2003). Mack &

D'Antonio (2003) found no significant difference in mineralizable soil C between forest

sites where an exotic grass had and had not been removed but did note a greater

percentage of mineralizable soil C in the invaded sites during the dry season. Kourtev et

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al. (2003) found that soils planted with M. vimineum when compared to soils planted with

a native understory species had a greater percentage of soil organic matter but whether

this difference held for an intact forest invaded by M. vimineum was not determined. A

third and more recent study (Litton et al. 2008) assessed the impacts of invasive grass

removal on soil C. The researchers found decreased rates of soil C flux for forests where

the invasive grass was removed but did not find changes in soil C pools between removal

and invaded plots. Despite the ubiquity of M. vimineum invasions of eastern U.S. forest

understories and recent calls for research investigating understory invaders (Martin et al.

2009), as far as we are aware there has been no assessment of the impacts on soil C pools

of intact, forest ecosystems where uninvaded and grass-invaded areas are compared.

What M. vimineum‘s impact on belowground C pools in an intact forest landscape

will be is unknown, but because the plant has invaded forests over a large portion of the

eastern U.S. understanding its impact is important (Morrison et al. 2007). The objectives

of this study were to compare the size of multiple soil C and N pools across an active M.

vimineum invasion front, measure the contribution of native plant species and M.

vimineum C to soil C pools, and evaluate differences in soil C pools across the invasion

front with changes in soil microbial activity. In our study system C inputs from M.

vimineum can be distinguished from native inputs using stable C isotope ratios because

M. vimineum is the only species to use the C4 photosynthetic pathway. Our overarching

hypothesis was that M. vimineum invasion would alter belowground carbon cycling and

we expected this alteration to be explained by one of two hypotheses (H1 or H2). We

expected that in invaded plots either the predictions of (H1) preferential substrate

utilization theory would be observed through no change or an increase in native plant-

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derived, soil C pools, or (H2) priming effect theory would be observed by decreases in the

same soil C pools. A significant strength of our study is the investigation of an active

invasion front in an intact forest ecosystem. At the same time this ‗observational‘

approach means we need to consider alternative mechanisms that may contribute to those

responses we document; we explore these through additional measurements and in the

Discussion.

Methods

Site description

Twelve 2 m × 2 m study plots divided into 6 pairs (1 uninvaded and 1 invaded) were

established across the edges of a rapidly progressing M. vimineum invasion front in a

riparian forest within the Whitehall Experimental Forest, Athens, GA, USA (N33º53.27‘

W 83º21.93‘). The site is a former wood lot, managed as mixed hardwood for the past 70

years (Rhett Jackson pers. comm.). There is little evidence that these particular sites were

actually farmed but much if not all of the surrounding land was (Rhett Jackson pers.

comm.). Soils at this site are a sandy loam in the Madison series (Soil Survey Staff 2009).

Bulk density was consistent across the site and was 1.08 g cm-3

. Anecdotal reports

indicate M. vimineum became apparent within the Whitehall Experimental Forest ~15

years ago, but we do not know the exact time of invasion for these study sites but given

their size they are likely < 15 years old. It is likely that M. vimineum went largely

unnoticed until it formed dense lawns and so pinpointing the exact invasion date is

difficult (Martin et al. 2009). Notably, the invaded plot in each pair appeared to have

developed from a discrete, invaded patch and remained discrete while our work was

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conducted in 2006 and 2007. In the 2008 growing season these patches were joining-up

to form a contiguous cover of M. vimineum. No confounding issues related to invasive

earthworms were noted between invaded and uninvaded sites (see Results). The forest

overstory was composed of Acer rubrum, Quercus nigra, Platanus occidentalis, and

Liquidambar styraciflua. The uninvaded areas of the study site were generally

depauperate in understory plants. The understory plant community was composed of <

5% cover of Vitis rotundifolia, Acer negundo, A. rubrum, Q. nigra, Parthenocissus

quincifolia, Carex sp. Ligustrum sinense, Lonicera japonica, Smilax bona-nox,

Toxicodendron radicans, Euonymous sp., Chasmanthium latifolium, Bignonia capriolata,

and Aster sp. (unpub. data). In the invaded areas, M. vimineum covered >90% of the

understory on average with an average dry green biomass of ~63.04 g m-2

(unpub. data).

M. vimineum began to invade the uninvaded plots towards the end of our study in

2007 and in 2008 had invaded all of these control plots. This suggests that our uninvaded

study plots were hospitable to M. vimineum and the plant had simply not invaded these

areas yet. This is an important strength of our study. We deliberately worked across an

advancing invasion to minimize the likelihood that invaded and uninvaded areas might

exhibit differences in soil C cycling because of an historical factor other than the M.

vimineum invasion. We do recognize, however, that our study design is not a controlled

experiment and our inferences should be interpreted with this in mind. Further, although

our sample plots are spatially independent and our results pronounced, they are limited to

the description of a single forest site. Whether they can be generalized to other similar

sites or to other grass invasions of forest understories is not yet known.

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Standard sampling regime

Standard sampling consisted of taking three individual A horizon soil cores (8 cm dia., 0

- 10 cm depth) from each plot using a stratified random approach. Soils were sieved (4

mm), homogenized, and stored at +5ºC until analyzed. Standard samples were taken six

times between September 2006 and July 2007 (12 September 2006, 13 November 2006,

19 January 2007, 14 February 2007, 8 May 2007, and 24 July 2007). Microstegium

vimineum was actively growing during the sampling months of September, May, and July

but had senesced by November and did not germinate prior to the January and February

samplings.

Measurements taken during the seasonal sampling regime consisted of

gravimetric soil moisture, pH, soil temperature (taken at 10 cm depth), soil CO2 efflux,

substrate induced respiration (SIR; a measure of microbial biomass), and mineralizable C

(a measure of microbially-available C). Measurements of both moisture and pH were

conducted on two analytical repeats per sample. Gravimetric soil moisture was

determined by drying a soil sub-sample at 105ºC for 24 h and pH (1 : 1, soil : H2O by

volume) was determined using a bench-top pH meter. Soil CO2 efflux, which is a

measure of both heterotrophic and autotrophic respiration, was taken in the field using an

infrared gas analyzer (IRGA; Li-Cor Biosciences, Lincoln, NE, USA, Model LI-8100).

Specifically, given the small plot size, one PVC collar was used per plot (20 cm dia.,

inserted 5 cm into the soil), and one measurement per plot was taken during the late

afternoon. Collars were placed on each sample day and measurements were taken at a

minimum of 1 h after placement. The SIR method we used follows Fierer & Schimel

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(2003) whereby soil slurries are incubated, after a 1 h pre-incubation with excess

substrate, for 4 h at 20ºC.

Mineralizable C was determined using 60-day C-mineralization assays and is

often used to estimate the pool size of labile C in the soil. This is similar to measurements

of dissolved organic carbon (DOC; see below) but the mineralizable C assays provide a

more direct measure of microbially available C than do measurements of DOC (e.g.

Bradford et al. 2008b). Mineralizable C was determined by maintaining soils at 20C and

65% water-holding capacity (WHC) for 60 d with periodic determinations of respiration

rates using a static incubation technique (Fierer et al. 2005a) and infra-red gas analysis of

headspace CO2 concentrations. Mineralizable C was estimated as the area under the curve

derived by plotting CO2 production against time.

Intensive sampling regime

Measurements for the intensive sampling regime were taken before (12 September 2006)

and after (13 November 2006) M. vimineum had senesced. Due to the elongated growing

season in the southeastern United States, M. vimineum biomass was still green in

September. For the intensive sampling regime, we measured the mineral-associated and

particulate organic matter (POM) C and N pools, the DOC pool, CFE microbial biomass

C and N, and extractable inorganic and organic N. This fractionation approach, whereby

we resolved C pools with different turnover times, provided a powerful approach for

detecting changes in soil C that a single blanket measure of total soil C might have

obscured (see Bradford et al. 2008b; Bradford et al. 2008c).

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To determine the mineral-associated and POM C and N pools, the fractionation

method described in Bradford et al. (2008c) was used. Briefly, duplicate soil samples (10

g of air-dry soil) from each site were dispersed with NaHMP (30 mL sample-1

) via

shaking for 18 h and then passed through a 53 m sieve. Material < 53 m is considered

mineral-associated and material > 53 m is considered POM. Both mineral and POM

material were dried (105C), ball-milled to a fine powder, and percentage C and N

determined using an NA1500 CHN Analyser (Carlo Erba Strumentazione, Milan, Italy).

Of these two fractions, mineral-associated C pools are expected to have slower turnover

times than are POM C pools (Schlesinger & Lichter 2001). Mineral-associated C pools

are presumed to be primarily microbial-derived C whereas POM pools are presumed to

be primarily plant-derived C (Grandy & Robertson 2007). Given the multi-annual

turnover times of these pools we only measured them during the September sampling; i.e.

further changes in pool size would not have been observed across the time course of our

study.

During both the September and November sampling, DOC and organic and

inorganic N pools were determined on two analytical repeats per sample. Samples were

shaken with 0.5 M K2SO4 for 4 h, filtered, and DOC concentrations determined using a

Total Organic Carbon Analyzer (Shimadzu, Columbia, USA). Dissolved organic N

(DON) and inorganic N pools were quantified on a Lachat autoanalyzer (Milwaukee, WI,

USA). The size of the DOC pool is often determined because it is one estimate of labile

soil C (Bradford et al. 2008c). However, although its turnover time is more rapid than

either POM or mineral-associated C pools, DOC is a mixture of both high and low

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molecular-weight C compounds and so is likely less labile than the C pool resolved using

our 60-day mineralization assays described above.

Microbial biomass C and N was estimated using a modified chloroform

fumigation-extraction (CFE) method as described in Fierer & Schimel (2002) and Fierer

& Schimel (2003). Microbial biomass C or N was estimated as the flush of DOC or

DON, respectively, following fumigation. Raw values are reported for microbial biomass

C and N; no correction factors are used. By determining microbial biomass C using the

CFE method, we were able to determine the proportion of microbial biomass which was

derived from M. vimineum (see below). Note that the relationship between SIR and the

CFE method for microbial biomass is not necessarily one to one (Wardle & Ghani 1995).

For example, where CFE is expected to estimate total biomass, SIR is suspected to

measure active microbial biomass (Wardle & Ghani 1995). We used the SIR method for

the standard seasonal sampling because it likely provides greater resolution of differences

in soil microbial biomass within a site (Wardle & Ghani 1995). It could not though

provide the information on native versus invasive-derived plant C in the microbial

biomass that the CFE method could.

Finally, during intensive sampling we determined earthworm biomass, litter

chemistry, and the fungal-to-bacterial dominance of the microbial community. M.

vimineum invasion is linked to increased nonnative earthworm biomass in northeastern U.

S. forests (Kourtev et al. 1998; Kourtev et al. 1999; Nuzzo et al. 2009) so we wanted to

account for this potential confounding issue in our study. Earthworm biomass was

determined by hand-extracting earthworms from 20 cm dia., 20 cm deep soil cores.

Hand-extracting is commonly considered to be the most accurate sampling technique for

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earthworm biomass (Lee 1985; Eisenhauer et al. 2008), which we report here as wet

mass.

Total percentage C, nitrogen (N), cellulose, hemi-cellulose, and lignin were

determined for native forest litter, M. vimineum leaf material, and M. vimineum stem

material (Table 3.1). We also determined the ratio of leaf-to-stem material for M.

vimineum which allowed us to determine whole plant litter chemistry. Total C and N

were determined using an NA1500 CHN Analyzer (Carlo Erba Strumentazione, Milan,

Italy). Fiber concentrations were determined using an Ankom A200 Fiber Analyzer

(Ankom, Macedon, USA). Quantitative inputs of M. vimineum litter and native forest

litter were assessed by taking 0.25 0.25 m quadrats from each plot post-senescence (i.e.

November), drying the sample at 65ºC, hand-sorting the material, and determining its

mass (Table 3.1).

Fungal-to-bacterial dominance of the microbial community is often taken as an

indicator of belowground C processes with fungal-dominated systems being associated

with greater C stores. We determined these ratios using the quantitative PCR (qPCR)

method described by Fierer et al. (2005b) and Lauber et al. (2008). Briefly, DNA was

isolated from soil (kept at -80ºC until use) using the MoBIO Power Soil DNA Extraction

kit (MoBio Laboratories, Carlsbad, CA, USA) with modifications described in Lauber et

al. (2008). Standard curves were constructed to estimate bacterial and fungal small-

subunit rRNA gene abundances. After soil DNA concentrations were normalized, PCR

reactions were carried out which allowed us to generate the ratios of fungal to bacterial

gene copy numbers by using a regression equation for each assay relating the cycle

threshold (Ct) value to the known number of copies in the standards. All qPCR reactions

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were run in quadruplicate. Additional details, including the specific PCR conditions, are

provided in Lauber et al. (2008).

Determination of M. vimineum derived C

To establish the amount of C derived from M. vimineum, we determined the δ13

C value of

the following C pools: mineralizable C, CFE microbial biomass, DOC, total soil C, POM

C, mineral-associated C, and the litter layer. For mineralizable C, a gas sub-sample taken

on the first day of each 60-day incubation was analyzed. The δ13

C value of the CO2 in the

sample was determined using continuous-flow isotope-ratio mass spectrometry (IRMS;

Thermo, San Jose, CA, USA). For the DOC and CFE biomass a TOC analyzer was

coupled to the IRMS and the remaining solid samples were introduced to the IRMS via

an elemental analyzer. Once δ13

C values were determined, we were able to estimate the

amount of M. vimineum derived C in each of the C pools by using the difference in the

natural abundance, δ13

C values of either the plant or soil C pools. The pre-invasion δ13

C

value for each C pool was determined from the plots where M. vimineum was absent and

due to an absence of any native C4-plant species these pools all exhibited a C3-

photosynthetic value which ranged from -27.72 ± 0.25 ‰ (mean ± 1SE; n =6) for native

POM C to -24.29 ± 0.32 ‰ (mean ± 1SE; n =6) for the September DOC measurement.

M. vimineum has a C4-photosynthetic value (n =13; mean ± 1SE = -14.31 ± 0.14 ‰). The

difference in the C isotope composition between M. vimineum and native C is sufficient

to discriminate sources (Staddon 2004). The amount of C derived from M. vimineum was

calculated as follows(sensu Ineson et al. 1996): CM. vimineum derived = Cpool × (δ13

Cinvaded -

δ13

Cuninvaded)/( δ13

CM. vimineum - δ13

Cuninvaded), where Cpool is the measured size of the pool

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(total, POM, Mineral, DOC, Microbial C, mineralizable, or litter), δ13

Cinvaded is the δ13

C

value of the pool in plots where M. vimineum is present, δ13

Cuninvaded is the δ13

C value of

the pool in plots where M. vimineum is absent, and δ13

CM. vimineum is the value for M.

vimineum itself.

13C-glucose pulse-chase

To determine whether or not the presence of M. vimineum was associated with more

rapidly cycling carbon pools, we conducted a 13

C pulse-chase in November 2006. The

pulse-chase was conducted by making additions of 13

C-labeled glucose and tracking its

mineralization as 13

CO2. This was accomplished by placing two PVC collars (15.4 cm

dia., inserted 5 cm into the soil) in three plots where M. vimineum was present and 3 plots

where it was absent. Water was then added to each core 24 h prior to sampling in order to

alleviate any water stress between plots. Soil CO2 efflux rates were determined using a

closed-chamber approach (e.g. Bradford et al. 2001), where CO2 concentrations were

determined at the start and end of a 45 min capping period. We conducted a pilot study to

determine the appropriate capping time and found that headspace CO2 concentrations

increase linearly from 0 to 45 min; flux rate estimates only began to decrease after 60

min. Although this closed-chamber approach is likely to have caveats associated with it

(i.e. underestimated CO2 efflux rates), it is nonetheless a widely used approach across an

array of ecosystem types (Nay et al. 1994; Franzluebbers et al. 2002). Similar capping

times and protocols as described here have been employed by other researchers (Iqbal et

al. 2008; Mo et al. 2008). Headspace samples were taken with 20 mL SGE gas syringes,

transported to the laboratory in 12 mL Exetainers, and then CO2 concentrations were

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determined using an IRGA (Li-Cor Biosciences, Lincoln, NE, USA, Model LI-7000). A

second sample was analyzed using continuous flow, IRMS to determine the stable C

isotope composition (see above for details). The initial headspace sampling provided the

natural abundance values for the isotope mixing equations. After this initial sampling, 1 L

of 2.5 mM 13

C-labeled glucose solution (99 atom %) was added to the collars and

permitted to drain. The capping procedure was repeated post addition at 2, 5, 24, 48 and

72 h, permitting a negative exponential ‗decay‘ of 13

C label to be tracked in the soil CO2

efflux, from which cumulative mineralization rates were estimated. The contribution of

13C-labeled glucose to soil respiration was estimated using isotope mixing equations

similar to those described above.

Statistical analysis

Linear mixed-effects models were used to analyze the effect of M. vimineum

presence/absence (Pinheiro & Bates 2000). For this approach the presence of M.

vimineum and month (when applicable) were treated as fixed effects. Pair and plot were

treated as random effects with plot nested within pair. The pair identity was included as a

blocking variable to account for spatial heterogeneity among paired plots where M.

vimineum was present and absent. If the removal of this blocking variable resulted in a

more parsimonious model, as determined by AIC, then it was dropped from the analysis.

Plot identity was included as a random effect to account for repeated sampling across

months. When reported as such, data were loge-transformed to conform to assumptions of

homoscedasticity (verified using model checking). All analyses were conducted using the

freeware statistical package R (http://cran.r-project.org/). In all cases we considered

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results statistically significant at P < 0.05 and marginally significant at P < 0.10. Notably,

it is acceptable practice to consider changes in soil C pools at P < 0.10 to be statistically

significant because of the large spatial variation associated with them (e.g. Carney et al.

2007). However, we took a more conservative stance in this work.

Results

Of the standard measures taken at six time points spanning from September 2006 to July

2007, we observed an average decrease in mineralizable C of 35% in the presence of M.

vimineum (F1,5=14.05; P<0.05; Fig. 3.1a). Average declines in mineralizable C of 36, 18,

36, 39, 43, and 37% during the months of September, November, January, February,

May, and July, respectively, were noted when the invader was present (Fig. 3.1a). A

significant effect of sampling month was also detected (F5,50=2.59; P<0.05) but the

relative difference between sites where M. vimineum was present and where it was absent

remained the same across all six sampling points (interaction: F5,50=1.29; P<0.28).

Furthermore, of the initial mineralizable C (i.e. CO2 evolved during the first of 60

incubation days), M. vimineum derived inputs accounted for approximately 10% on

average across all sampling months (Fig. 3.1a). A high of approximately 20% was found

for the May sampling and a low of approximately 4% was found for the November

sampling (Fig. 3.1a). The presence of M. vimineum was not associated with a change in

SIR biomass (F1,10=3.10; P=0.11; Fig. 3.1b).

The presence of M. vimineum did not affect soil CO2 efflux (F1,10=1.66; P=0.23;

Fig. 3.2), but did interact with soil moisture (F5,50=3.30; P<0.05), temperature

(F5,50=2.55; P<0.05), and pH (F5,50=4.17; P<0.01). Soil CO2 efflux did vary across

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sampling month (F5,50=63.8; P<0.001; Fig. 3.2) with greater efflux during the growing

season (i.e. September, May, and July) and lower efflux when plant species were not

actively growing (i.e. November, January, and February). Soil moisture tended to be

seasonally more stable in the presence of M. vimineum than in its absence. Soil

temperature tended to be lower in the presence of M. vimineum during the months of

November, January, and February but during the months of September, May, and July

sites where it was present had similar or higher temperatures to sites where it was absent.

In general, pH was typical higher in the presence of M. vimineum. This was dependent on

sampling month with higher pH values for soils where M. vimineum was present during

September, January, and May but similar pH values regardless of M. vimineum

presence/absence for the months of November, February, and July.

Several soil C and N pools were intensively sampled in September and/or

November 2006. Of the soil C pools, we noted a significant decrease of 64% and 41%

(September and November, respectively) in CFE microbial biomass when M. vimineum

was present (P<0.05; Table 3.2). Regardless of the invader‘s status microbial biomass

was marginally greater post-senescence (F1,10=3.69; P=0.08). We also noted marginally

significant declines of 22% and 29%, respectively in both total soil organic C (P=0.09)

and POM-associated C (P=0.08; Table 3.2). The marginally significant decrease in total

soil C in the presence of M. vimineum was likely driven by the decrease in POM C given

that total C is the sum of POM and mineral-associated C. No significant change in

mineral-associated soil C was noted, although the pool size was lower in invaded plots

(P=0.13; Table 3.2). Microstegium vimineum presence also was not associated with a

significant effect on DOC but again this pool was lower where the invader was present

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(P=0.19; Table 3.2). There were no obvious differences in any of the measured N pools

(P>0.12 in all cases).

Inputs derived from M. vimineum contributed between 1 and 29% of the C found

in the belowground pools of invaded plots (Table 3.2). These inputs did not, however,

compensate fully for the observed declines in several native plant-derived C pools (Table

3.2). Specifically we noted significant declines in both native-derived CFE microbial

biomass C (P<0.05) and POM-associated C (P<0.05), as well as marginally significant

declines in native-derived total soil organic C (P =0.07) and DOC (P =0.10) in the

presence of M. vimineum (Table 3.2). The presence of M. vimineum was associated with a

72% decline in native derived CFE microbial biomass C in September and a 43% decline

in November (Table 3.2). Overall, native-derived CFE microbial biomass C tended to

increase post-senescence in November (F1,10=5.40; P<0.05). Microstegium vimineum‘s

presence was also associated with a 25% decline in native derived DOC (P=0.10; Table

3.2) and this was independent of sampling month (F1,10=0.00; P=0.93). Native derived

POM-associated soil C was 34% lower and total soil organic C was 24% lower in the

presence of M. vimineum but there was no statistical support for changes in the mineral-

associated soil C, albeit this pool was lower on average in invaded plots (Table 3.2).

Of the remaining measures taken in September and November 2006, no

statistically significant differences in earthworm biomass m-2

were detected between

invaded and uninvaded plots (F1,10=0.04; P=0.84), nor in fungal-to-bacterial ratios

(F1,10=0.00; P=0.99). In contrast, the cumulative amount of 13

C-glucose mineralized to

13CO2 was lower in sites where M. vimineum was absent and higher in sites where it was

present (F1,2=19.6; P<0.05; Fig. 3.3). Analysis of the time-series data showed that 13

C-

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glucose mineralized to 13

CO2 generally followed a pattern of exponential decay across the

72 h sampling period. The significant difference in cumulative values between sites was

likely due to higher rates of 13

C-glucose mineralized to 13

CO2 observed in the plots

invaded by M. vimineum during the 2 h and 5 h sampling periods (Fig. 3.3).

Discussion

We evaluated whether the abundant invader, M. vimineum, impacted belowground C

pools. We expected that if it impacted belowground C pools, the mechanism could likely

be framed around the theories of preferential substrate utilization or priming. If

preferential substrate utilization was the mechanism then our expectation (H1) was that

there would be either no change or an increase in belowground C pools, meaning that soil

C turnover rates would likely remain the same or decrease (Fontaine et al. 2004a;

Fontaine & Barot 2005; Bradford et al. 2008c). In contrast, if a priming effect (H2) is the

potential mechanism then our expectation was that M. vimineum‘s presence would be

associated with a decrease in the size of soil C pools and an increase in their turnover

rates (Fontaine et al. 2004b). We observed that the presence of M. vimineum was

associated with increased C turnover rates and declines in several belowground C pools.

Our findings suggest that M. vimineum invasion is altering soil C cycling via a priming

effect. We do, however, recognize that our study was observational and so causation

could be attributed to other response variables. Given that both moisture and temperature

were neither consistently higher nor lower in invaded sites and earthworm biomass was

similar across invaded and uninvaded plots we discuss below why priming may be the

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most plausible explanation and contrast our results with other studies that appear to have

found preferential substrate utilization manifesting in grass-invaded forest soils.

We found a marginal decrease in both total organic and POM C associated with

the invasion but mineral-associated C was not lower (Table 3.2). The decrease in total

soil C appeared to be largely an effect of the decrease in POM C, which is part of the

total C pool. The reason that a decrease in POM C was observed while no statistically

measurable decrease in mineral-associated C was observed may be due to the greater

stability of the mineral pool and its slower turnover rates (Schlesinger & Lichter 2001;

Grandy & Robertson 2007; Bradford et al. 2008c). These declines in both POM and total

soil C represent an average 29 and 22% decrease under M. vimineum. If expressed on an

areal basis, this would be equivalent to a decline of ~375 g m-2

of total C which

represents a decrease of ~25 g m-2

year-1

, conservatively assuming M. vimineum has been

present at this site for 15 years. We recognize that we are basing some of our inferences

on statistically marginal differences (see Table 3.1). However, we think this is legitimate

given that a power analysis indicated the likelihood of detecting a significant effect

associated with a 22% total or 29% POM C decline was extremely low (0.18 and 0.29,

respectively) and even lower power would be associated with smaller percentage declines

(e.g. a 10% decline is associated with a power of 0.08). In both instances to achieve the

recommended power of 0.80 would have required a sample size of > 40 and > 20

replicates for total and POM C, respectively (Thomas 1997).

The lack of power to detect significant changes when measuring soil C pools,

given their large spatial variation at fine scales, is a common issue (Throop & Archer

2008). Recent work by Saby et al. (2008) indicates that the ability to detect changes in

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soil C pools is largely site specific and that annual changes in these pools are extremely

difficult to detect. This may have prompted the acceptance of studies conducted at fine

spatial and temporal scales to deem changes in soil C pools at P<0.10 significant (e.g.

Carney et al., 2007). We followed the more conventional approach of P<0.05 and

recognize this may be overly conservative. Given the widespread occurrence of M.

vimineum invasion and a call for increased emphasis on understory invaders of intact

forest systems (Martin et al. 2009), clearly there is a need for more studies such as ours

that assess the impacts of invasion on forest soil C pools. Use of fractionation and stable

isotope approaches will increase the ability to resolve changes in soil C stocks and

cycling. In conducting future studies, investigators need be aware that the power to detect

statistically significant changes in soil C pools is generally below recommended values

when logistically feasible replicate numbers are used. The failure to detect statistically

significant effects should not simply be interpreted as a null effect of grass invasion on

forest soil C pools. Undoubtedly, more studies and then meta-analysis of their collective

results will indicate the significance, direction, and size of the effect on soil C associated

with grass invasion of forest understories. The use of meta-analysis to explore the result

of multiple studies using relatively small sample sizes is successfully and widely-adopted

in medical fields (Gurevitch & Hedges 1999).

After accounting for the contribution of M. vimineum-derived C in invaded plots

there was relatively little change in the contribution of native-derived C to the mineral

pool (Table 3.2). Microstegium vimineum-derived C only accounted for ~1% of mineral-

associated C, which again is indicative of the greater stability of this pool and its slower

turnover time (Schlesinger & Lichter 2001; Grandy & Robertson 2007; Bradford et al.

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2008c). It may also suggest that a lesser proportion of M. vimineum-derived C when

compared to native-derived sources is microbially processed to the extent that it becomes

stabilized in the mineral pool and more C derived from this invader may be lost as CO2.

This is simply speculation based in part on the knowledge that mineral-associated C may

often be largely microbially-derived (Grandy & Robertson 2007). Alternatively, it may

be that M. vimineum has not been present (i.e. <15 years) at this site long enough to affect

the mineral-associated C pool to an extent that marked differences can be resolved;

similar explanations have been suggested by others (e.g. Litton et al., 2008).

In contrast to the mineral-associated C pool, C derived from native sources found

in total organic and POM C were lower in the presence of M. vimineum. Although the

POM C pool was smaller in the presence of M. vimineum, the invader accounted for ~6%

of this pool (compared to 1% for the mineral-associated C pool). Thus, most of the

decline in the POM C pool is from the loss of native-derived sources. Declines in native-

derived POM C may indicate that native-derived C is cycling faster in plots where the

invader is present due to invader inputs priming its decomposition. Furthermore, native-

derived POM C decreased by ~34% and given that M. vimineum-derived C only accounts

for ~6% of this pool then further declines in this pool may be expected. This outcome

will be largely contingent on whether or not the declines in native-derived POM C have

stabilized and/or inputs derived from M. vimineum remain the same. Future research will

need to address these possibilities if we are to understand the long term impacts of this

invader on soil C pools.

Total DOC (i.e. both native and M. vimineum derived) was not significantly lower

in the presence of M. vimineum. However, DOC derived from native sources was

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marginally lower in the presence of M. vimineum. Interestingly, DOC derived from M.

vimineum was greater on average pre-senescence than post-senescence (Table 3.2). This

may indicate that rhizodeposition from M. vimineum is an important source of C inputs to

soils, despite their small root to shoot ratios (unpub. data). This possibility remains to be

tested.

Like DOC, DON was not significantly impacted by the presence of M. vimineum

and we found no significant difference in inorganic N pools. This last result contrasts

with other studies conducted in the northeastern U.S. which showed that the presence of

M. vimineum was associated with changes in available NO3- and nitrification rates

(Kourtev et al. 1999; Ehrenfeld 2003; Kourtev et al. 2003). We found that M. vimineum

was not associated with a statistically significant change in available NO3- although it is

worth noting that pH, which typically increases when NO3- is the dominant form of plant

available N (Nye 1981; Ehrenfeld 2003), was significantly greater in the invader‘s

presence. One possible reason for the contrasting results between our study and studies

conducted in northeastern forests may be the association between non-native earthworm

biomass and M. vimineum. Non-native earthworm biomass is often associated with

altered N availability and mineralization in soils (Scheu 1987; Kourtev et al. 1999;

Eriksen-Hamel & Whalen 2008) and may have contributed to changes in N pools in

northeastern US forests invaded by M. vimineum (Bohlen et al. 2004; Marhan & Scheu

2006; Eisenhauer et al. 2007). Given that there was no difference in earthworm biomass

between our plots, we were able to explore the effect of M. vimineum in the presence of a

uniform earthworm gradient.

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Extractable microbial biomass C, both total and native-derived, was found to be

significantly lower in plots invaded by M. vimineum. Microbial biomass C was

determined using a modified CFE method and is expected to relate to the total active and

inactive biomass (Wardle & Ghani 1995; Bradford et al. 2008c). Declines in microbial

biomass C have often been associated with declines in soil C pools (Wardle & Ghani

1995; Bradford et al. 2008a; Bradford et al. 2008b). Since microbial biomass C is likely

to be one of the more responsive belowground C pools then its decrease may be an

indicator of an overall decline in soil C associated with M. vimineum invasion. We

observed that a greater proportion of microbial biomass C was derived from M. vimineum

pre-senescence than post-senescence (Table 3.2). Under actively growing M. vimineum,

the microbial community derived ~30% of its C from the invader (Table 3.2). This is a

remarkable percentage because it is much larger than the aboveground biomass of M.

vimineum at the study site (relative to native plant biomass), and M. vimineum also has a

rather superficial root system. That C derived from M. vimineum in the microbial biomass

was much higher pre-senescence may suggest that the microbial community is attaining

the bulk of M. vimineum-derived C via root exudates rather than foliar litter. Root

exudates are in part composed of an array of low-molecular weight compounds, such as

glucose, which are expected to be highly labile (van Hees et al. 2005). Such compounds

have been shown to cause priming effects when applied to soils and may be the direct

link between M. vimineum invasion and associated declines in soil C (Dalenberg & Jager

1981; Blagodatskaya et al. 2007; Carney et al. 2007). Further work is necessary to

establish this linkage between M. vimineum root exudates, the microbial community, and

declines in soil C pools.

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Although the presence of M. vimineum was associated with a decrease in

microbial biomass C, no change in the fungal-to-bacterial dominance of the microbial

community was found. This result was somewhat unexpected because a shift toward

bacterial dominance might be expected, due to this invasion, since M. vimineum

represents inputs of higher chemical quality (Table 3.1) and its presence was also

associated with a more neutral pH. Both factors are expected to favor bacteria over fungi

(Bardgett & McAlister 1999; Six et al. 2006; van der Heijden et al. 2008). Changes in

fungal-to-bacterial dominance may not have had long enough to occur (Bardgett et al.

1996; Bardgett & McAlister 1999) but some studies have noted changes across short time

intervals (Carney et al. 2007). Another possibility is that fungal-to-bacterial dominance

may be too coarse an estimate of microbial community structure in this case. A finer

measure of the community may have shown differences associated with M. vimineum

which have been reported elsewhere for this species (Ehrenfeld et al. 2001; Kourtev et al.

2002). Indeed, evidence is growing that specific taxa within fungal and bacterial

communities are associated with specific ecological strategies, such as r- versus K-

strategists (Fierer et al. 2007; Lauber et al. 2008). Shifts in the community at this level

would not necessarily be detected simply by determining fungal-to-bacterial ratios. Such

a shift (below the Kingdom level) might explain why we observed a decrease in the size

of the microbial biomass pool but saw no concomitant decline in SIR biomass (Table 3.2;

Fig. 3.1b). SIR biomass differs from CFE biomass because it may be more indicative of

physiologically active microbial biomass rather than absolute biomass (Wardle & Ghani

1995; Bradford et al. 2008c). If true, then on a per unit biomass basis, the microbial

community associated with M. vimineum was more active.

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The mineralizable C pool, a measure of microbially available C (Bradford et al.

2008c), was significantly lower when associated with M. vimineum invasion (Fig. 3.1a).

The decrease in the size of this pool under M. vimineum may indicate that it is cycling

more rapidly. The soil CO2 efflux data also support the idea that C is cycling more

rapidly under M. vimineum invasion. Indeed, soil CO2 efflux did not decrease under M.

vimineum but yet declines in mineralizable C as well as other soil C pools were observed

(Fig. 3.2; Table 3.2). This discrepancy may be explained by more rapid turnover of

belowground C pools (assuming autotrophic respiration is unchanged). That is, if soil C

pools were simply smaller and turnover rates were unchanged then soil CO2 efflux would

be smaller in invaded plots. Instead, soil CO2 efflux rates were equivalent in the presence

and absence of M. vimineum, indicating that labile C pools under M. vimineum may be

turning over more rapidly (i.e. C atoms have reduced residence times in the soil in

invaded plots). This scenario is synonymous with depletion of fast-cycling soil C pools

under experimental soil warming, where soil CO2 efflux rates in control and warmed

plots are equivalent when plots reach equilibrium due to faster cycling of active soil C

pools in the warmed plots (Kirschbaum 2004; Knorr et al. 2005; Bradford et al. 2008a).

The possibility that an increase in autotrophic respiration is associated with M. vimineum

presence seems unlikely given that, if this were the case, soil CO2 efflux in invaded plots

should have been lower than in uninvaded plots once M. vimineum had senesced, but it

was not. Although, if after M. vimineum senesced decomposition increased due to the

invader‘s litter inputs then the lack of a difference in CO2 efflux between plots might be

attributed to increased autotrophic respiration pre-senescence and increased heterotrophic

respiration post-senescence. To help decipher the mechanism we conducted an in situ

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pulse-chase with 13

C-glucose. The more rapid mineralization of this added substrate

supports our inference that soil C is turning over more rapidly in plots invaded by M.

vimineum. Specifically, when M. vimineum was present more glucose was mineralized to

CO2 during the first 72 h after addition (Fig. 3.3). Notably the difference in cumulative

values was largely driven by higher rates of glucose mineralization during the first five

hours after glucose addition (Fig. 3.3).

The results of our study show that the presence of M. vimineum was associated

with declines in several belowground C pools and these declines were likely associated

with more rapid cycling. Microstegium vimineum represents a relatively minor

quantitative detritus input (< 3% of litter layer biomass; Table 3.1) at our site but the

higher chemical quality of M. vimineum litter (Table 3.1), in addition to its root exudates,

appears to stimulate soil C decomposition through a possible priming effect. Other

studies of invasive plant effects on belowground C pools, similar in context to our own

(Stock et al. 1995; Mack et al. 2001; Mack & D'Antonio 2003; Bradley et al. 2006), have

observed relatively little change in soil C pools (Hook et al. 2004; Valery et al. 2004;

Koutika et al. 2007; Litton et al. 2008). We speculate that what appear to be

inconsistencies between our study results and those of others may actually be consistent

in the context of preferential substrate utilization versus priming effects. That is, the

impact that an invasive plant species has on belowground C pools may be dependent on

the quality and quantity of its C inputs relative to the quality and quantity of native

inputs. If invasive plant species‘ inputs are of high chemical quality (i.e. low C:N or low

Lignin:N) but low quantity relative to native inputs then a priming effect may occur

leading to a decrease in belowground carbon pools; however, if quantity is high then

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preferential substrate utilization may instead occur. One key example of this is work

conducted by Litton et al. (2008). They found that grass invasion in Hawaiian forests

increased soil CO2 efflux, represented a very large increase in litter inputs (i.e. 16 to 44

fold increase), but did not impact soil C pools. Such results are at least indicative of

preferential substrate utilization. In our own study we found that the presence of M.

vimineum did not change soil CO2 efflux, its litter inputs only represented an ~3%

increase in total litter inputs, and we noted declines in several soil C pools. These results

are indicative of a priming effect and similar to other field based studies which have

found and concluded the same (Carney et al. 2007). Furthermore, when considering both

Litton et al.‘s (2008) study and our own there is some support for the proposition that the

strength of preferential substrate utilization versus priming may be dependent on the

relative increase in inputs (Blagodatskaya & Kuzyakov 2008; Bradford et al. 2008c).

This would be in agreement with Blagodatskya & Kuzyakov (2008) who found that

priming effects tended to decrease as relative inputs increase. It is also important to

recognize that effects may not be consistent across all belowground C pools, with both C

input rates and the nutrient content of these inputs affecting distinct soil C pools

differentially (Bradford et al. 2008c). By incorporating priming and preferential substrate

utilization theories with our current understanding of invasive plant species, we may be

better able to understand and predict how invasive plants are likely to impact soil C

cycling and hence long-term soil fertility.

In conclusion, we have demonstrated that at our study site M. vimineum invasion

is associated with declines in the size of several belowground C pools and C inputs

derived from M. vimineum do not offset the loss of native, plant-derived soil C. This loss

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of C appears to be the result of a priming of the microbial community from higher quality

M. vimineum foliar litter inputs and root exudates, which leads to increased soil C

turnover rates where the invader is present. Whether our results can be generalized to M.

vimineum invasions at other similar sites is not yet known. Assuming they are

generalizable, then the results of this study indicate that invasions by M. vimineum may

have long term implications for forest soil fertility, carbon storage, and potentially the

flow of energy through detrital pathways to aboveground components of terrestrial

foodwebs.

Acknowledgements

We thank C.A. Davies for his assistance with the 13

C-glucose pulse-chase, N. Fierer and

C. Lauber for assistance with qPCR, and B.A. Snyder and M. Callaham for assistance

with earthworm sampling. This research was supported by the Andrew W. Mellon

Foundation and by National Science Foundation grants to the Coweeta LTER Program.

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Table 3.1 Litter chemistry and percentage of native and M. vimineum derived inputs into M. vimineum invaded plots. Microstegium

vimineum litter was divided into leaf material and stem material because of expected differences in their chemical recalcitrance. The

proportion of M. vimineum stem and stem material was used to calculate whole litter chemistry for M. vimineum. Native litter was a

mixture of several species found at this site. For litter chemistry the mean ± 1 S.E. is reported (n=3 analytical repeats). For the

percentage of litter inputs the mean ± 1 S.E. is reported for all 6 invaded plots.

Litter Source Litter input

(%)

C (%) N (%) Lignin (%) Cellulose (%) Hemi-cellulose

(%)

Native 97.25 ± 0.98 48.52 ± 0.72 0.87 ± 0.01 25.58 ± 1.81 18.66 ± 0.23 12.40 ± 0.52

M. vimineum* 2.75 ± 0.98 42.50 1.10 17.97 25.17 21.03

Leaves 42.33 ± 1.81 1.34 ± 0.06 20.04 ± 2.38 20.54 ± 0.76 20.76 ± 0.54

Stems 42.83 ± 0.09 0.62 ± 0.03 13.99 ± 2.08 34.06 ± 1.09 21.54 ± 0.64

*Leaves represented 65.74 ± 0.02% of M. vimineum litter and stems represented 34.26 ± 0.02% (n=3).

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Table 3.2 The mean ± 1S.E. of the Total (native + M. vimineum) and Native forest derived total soil organic C, POM associated C,

mineral associated C, DOC, and microbial biomass C for plots where M. vimineum is absent and where it is present (n = 6). Where

applicable, values are shown both before and after M. vimineum senesced (i.e. September and November samplings, respectively).

Also, shown is the mean percentage ± 1S.E. that M. vimineum derived C contributed to the total C in each pool. Analyses compared

both Total (native + M. vimineum) pool sizes between plots where M. vimineum was present/absent and Native (native only) C pool

sizes. In uninvaded plots Total = Native and so a dash is shown in the table for these entries. Total C, POM C, and Mineral C pools

were only measured pre-senescence. F-values, degrees of freedom, and P-values are reported for the main effect of M. vimineum

presence/absence. Where measures were taken both pre- and post-senescence (e.g. DOC) these values are reported for the main effects

across the two sampling points. Units for Total, POM, and mineral C pools are mg g dry wt soil-1

and µg g dry wt soil-1

for DOC and

microbial biomass C. Where significant (P<0.05) P-values are reported in bold. Marginally significant (P<0.10) P-values are

italicized.

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Table 3.2 Cont.

Pre-senescence Post-senescence

M. vimineum

absent

M. vimineum

present

%

M. vimineum

derived C

M. vimineum

absent

M. vimineum

present

%

M. vimineum

derived C

Fd.f P-value

Total C

Total 22.21 ± 3.20 17.24 ± 3.56 NA NA NA 4.561,5 0.086

Native − 16.80 ± 3.50 2.87 ± 0.64 NA NA NA 5.281,5 0.070

POM C

Total 8.46 ± 1.24 5.99 ± 1.22 NA NA NA 4.791,5 0.080

Native − 5.60 ± 1.16 6.01 ± 1.34 NA NA NA 6.831,5 <0.05

Mineral C

Total 13.75 ± 2.41 11.25 ± 2.44 NA NA NA 3.371,5 0.126

Native − 11.13 ± 2.43 1.29 ± 0.51 NA NA NA 3.511,5 0.120

DOC

Total 102.97 ± 12.96 87.10 ± 15.55 113.13 ± 18.32 87.46 ± 21.68 2.341,5 0.186

Native − 77.16 ± 14.62 9.80 ± 2.12 − 85.09 ± 21.47 3.12 ± 1.48 4.071,5 0.100

Microbial biomass C

Total*

68.56 ± 22.61 25.03 ± 10.48 74.50 ± 16.24 44.20 ± 6.86 2.341,10 <0.05

Native*

− 19.11 ± 9.57 29.41 ± 7.50 − 42.55 ± 7.34 5.25 ± 3.63 10.081,10 <0.05

*Data were loge transformed.

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Figure 3.1 Mineralizable C (a) and SIR microbial biomass (b) for plots where M.

vimineum was present and where it was absent (n = 6). Panel a shows the mean

cumulative values ± 1S.E. for mineralizable C across each 60-day incubation.

Mineralizable C is expected to represent microbially-available, labile C. Also shown in

(a) is the percentage ± 1S.E. of CO2-C derived from M. vimineum on the first day of each

60-day incubation for sites where M. vimineum was present (sub-set bar plot). Panel b

shows the mean ± 1S.E. for SIR microbial biomass which may be an indicator of

physiologically active microbial biomass. Microstegium vimineum was active during the

sampling months of Sep., May, and Jul. (note it is an annual). * = P<0.05; ** = P<0.01;

*** = P<0.001; ns = not significant.

Figure 3.2 Soil CO2 efflux (mean ± 1S.E.) measured in situ across all sampling dates for

plots where M. vimineum was present and where it was absent (n = 6). This is a measure

of both heterotrophic and autotrophic respiration. Microstegium vimineum was active

during the sampling months of Sep., May, and Jul. (note it is an annual). Symbols are the

same as in Fig. 3.1.

Figure 3.3 Results of the 13

C-glucose pulse-chase experiment conducted during the

November sampling. The panel insert shows the cumulative amount of 13

C-glucose

mineralized to 13

CO2-C (mean ± 1S.E.) across 72 h for plots where M. vimineum is

present and where it is absent (n = 6). The main panel shows the time course of glucose

mineralization. A marginally significant effect of M. vimineum presence was detected at 2

h (F1,2 = 10.5, P = 0.084 ) and 5 h (F1,2 = 9.98, P = 0.087) but no effect was detected at 24

h (F1,2 = 0.13, P = 0.76), 48 h (F1,2 = 0.01, P = 0.94), or 72h (F1,2 = 0.11, P = 0.77).

Symbols are the same as in Fig. 3.1.

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Figure 3.1

Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul.

Month

SIR

(

g C

O2-C

g d

ry w

t so

il-1

h-1

)

0

1

2

3

4

5

a

b

M. vimineum presence *Sample month *Interaction ns

M. vimineum presence nsSample month ***Interaction ns

%

0

10

20

30M. vimineum derived

Min

eral

izab

le C

(mg

CO

2-C

g d

ry w

t so

il-1

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2M. vimineum absent

M. vimineum present

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Figure 3.2

Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul.

Month

Soil

CO

2 e

fflu

x (

g C

O2-C

m-2

day

-1)

0

10

20

30

40

50M. vimineum absent

M. vimineum present

M. vimineum presence nsSample month ***Interaction ns

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Figure 3.3

Time since glucose added (h)

0 20 40 60 80

Glu

co

se m

inera

lizat

ion

(mg

13C

O2-C

m-2

h-1

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Absent Present

Cu

mu

lati

ve

Min

eral

izat

ion

(mg

13C

O2-C

m-2

)

0

2

4

6

8

10M. vimineum presence*

P=0.08

ns

nsns

P=0.09

M. vimineum absentM. vimineum present

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CHAPTER 4

TESTING THE FUNCTIONAL SIGNIFICANCE OF MICROBIAL COMMUNITY

COMPOSITION4

4 Strickland, M.S., C. Lauber, N. Fierer, M.A. Bradford. Ecology. 90(2):441-451. Reprinted here with

permission of publisher.

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Abstract

A critical assumption underlying terrestrial ecosystem models is that soil microbial

communities, when placed in a common environment, will function in an identical

manner regardless of the composition of that community. Given high species diversity in

microbial communities and the ability of microbes to adapt rapidly to new conditions,

this assumption of functional redundancy seems plausible. We test the assumption by

comparing litter decomposition rates in experimental microcosms inoculated with distinct

microbial communities. We find that rates of carbon dioxide production from litter

decomposition were dependent upon the microbial inoculum, with differences in the

microbial community alone accounting for substantial (~20%) variation in total carbon

mineralized. Communities that shared a common history with a given foliar litter

exhibited higher decomposition rates when compared to communities foreign to that

habitat. Our results suggest that the implicit assumption in ecosystem models (i.e.,

microbial communities in the same environment are functionally equivalent) is incorrect.

To predict accurately how biogeochemical processes will respond to global change may

require consideration of the community composition and/or adaptation of microbial

communities to past resource environments.

Keywords: bacteria, carbon mineralization, community composition, decomposition,

functional diversity, functional equivalence, functional redundancy, fungi, leaf litter,

microorganisms

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Introduction

Many of the ecosystem processes critical to the functioning of ecosystems, such as

decomposition, are carried out in whole or in part by microorganisms. Ecosystem models

designed to explain and predict how the rates of these ecosystem processes change in

response to changing environmental conditions, such as temperature and moisture,

typically treat the microbial community as a single, homogenously functioning entity

(Parton et al. 1983). Yet it is increasingly being recognized that microbial community

composition plays a role in determining ecosystem process rates (Hendrix et al. 1986,

Schimel and Gulledge 1998, Reed and Martiny 2007). However, even in those models

(Hunt and Wall 2002) that explicitly consider microbial community composition (e.g.,

bacterial vs. fungal dominance), the assumption is still that the environment ultimately

controls ecosystem process rates. That is, changes in microbial community composition

exert proximate control on process rates but the contemporary environment ultimately

structures the community. This hypothesis, which we refer to as ―functional

equivalence,‖ has recently been challenged (Reed and Martiny 2007) given the finding

that historical contingencies, and not just the contemporary environment, structure

microbial communities both at local and regional scales (Martiny et al. 2006, Ramette

and Tiedje 2007). The counter-hypothesis, which we refer to as ―functional

dissimilarity,‖ is significant because it proposes that the responses of ecosystem

processes to global changes are not ultimately fixed by environmental conditions (Balser

and Firestone 2005). On the contrary, it proposes that microbial community composition

(i.e., the whole community genotype) may, in combination with the environment,

ultimately (not just proximally) determine ecosystem process rates.

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The hypothesis of functional equivalence does appear intuitive. For example,

given that there are many thousands of species of microorganisms in a given soil (Fierer

et al. 2007) and that microbes can rapidly adapt to new environments (Goddard and

Bradford 2003), one might assume that this equates with a high degree of functional

redundancy (Andrén and Balandreau 1999, Behan-Pelletier and Newton 1999, Wall and

Virginia 1999). Any spatial or temporal change in community composition would then

produce a negligible change in ecosystem processes regulated by microbes (Bell et al.

2005, Franklin and Mills 2006, Wertz et al. 2006, Cardinale et al. 2007, Jiang 2007,

Verity et al. 2007). There is an expectation that the greatest functional redundancy will be

observed for biogeochemical processes performed by a broad array of soil

microorganisms, such as the mineralization of carbon compounds during litter

decomposition, rather than for processes such as nitrogen fixation (Schimel 1995, Bell et

al. 2005) that are performed only by specific microbial groups. Work by Balser and

Firestone (2005) tends to support this divide. They observed functional differences for

microbial processes such as nitrogen mineralization but not for carbon mineralization.

Indeed, equivalence in organic carbon degradation has been suggested by others among

both local and regional heterotrophic microbial communities in both aquatic

(Langenheder et al. 2005, 2006) and terrestrial habitats (Balser and Firestone 2005, Ayres

et al. 2006, Wertz et al. 2006).

There is evidence from both plant and animal communities that changes in

community composition impact ecosystem process rates (Tilman et al. 1996, 2001,

Naeem and Wright 2003, Taylor et al. 2006, Cardinale et al. 2007). Similarly, evidence is

now accumulating that microbial community composition influences ecosystem process

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rates, albeit the identification of causation, as opposed to correlation, is not always

conclusive (Schimel and Gulledge 1998, Behan-Pelletier and Newton 1999, Wall and

Virginia 1999, Reed and Martiny 2007). To identify causation both common garden and

reciprocal transplant approaches are required, where both ecosystem processes and

community composition are monitored across time in different environments (Reed and

Martiny 2007).

Here we used a long-term (300-day) experimental litter–soil system that combines

both a common garden and a reciprocal transplant approach to test between the following

competing hypotheses for litter mineralization: (1) different soil microbial communities

are functionally equivalent vs. (2) different soil microbial communities are functionally

dissimilar. The hypothesis of ―functional equivalence‖ is based on the expectation that

the high diversity and/or adaptive ability of soil microbes confer functional redundancy

across different microbial communities (i.e., only the environment impacts function). The

competing hypothesis of ―functional dissimilarity‖ is based on the expectation that

historical contingencies play a role in shaping the functioning of microbial communities.

To test between the hypotheses, we collected soil inocula from three locations in the

continental United States where we might expect the microbial community composition

to differ either because of geographic distance (a surrogate for history) and/or

contemporary habitat differences (Martiny et al. 2006). Litters from the dominant plant

species at each of the three locations were also collected, sterilized, and then milled to

generate three, distinct, common garden environments. We inoculated each litter in a

factorial design with each of the three soils. If different inocula yielded distinct rates of

litter decomposition then the hypothesis of functional dissimilarity was supported, and if

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rates were similar then this supported the hypothesis of functional equivalence. We posed

a sub-hypothesis under the hypothesis of functional dissimilarity that stated that soil

inocula that shared a common history with a litter (i.e., soils collected from the same

location as the litter) would mineralize that litter more rapidly than soil inocula without a

shared history (the ―home field advantage‖ hypothesis [Gholz et al. 2000]). Our findings

support the hypotheses of functional dissimilarity and home field advantage, suggesting

that differences in microbial community composition may contribute, as ultimate and not

just proximal controls, to spatial and temporal variation in ecosystem carbon dynamics.

Methods

Microcosm design

Litter and soil were collected from three sites within the continental United States (see

Plate 4.1). Grass litter (Hordeum murinum) was collected from Sedgwick Reserve,

California (34°42′ N, 120°03′ W), pine (Pinus taeda) from Duke Forest, North Carolina

(35°58′ N, 79°05′ W), and rhododendron (Rhododendron maximum) from the Coweeta

Long Term Ecological Research site, North Carolina (35°00′ N, 83°30′ W). Pine and

rhododendron were collected as recent litterfall and grass litter as standing-dead material.

Litter was oven-dried (65°C) and then milled (2 mm). Litter was sterilized by autoclaving

(121°C, 20 min) twice in succession and again 24 h later. Mineral soil (0–5 cm) was

collected together with the litter. Soils were passed through a 2-mm sieve, homogenized,

and then stored at 5°C until use as an inoculum. We refer to each soil inoculum as

rhododendron, pine, or grass inoculum and each litter type as rhododendron, pine, or

grass litter.

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To 1 g of litter we added 0.5 g dry mass equivalent of soil inoculum and mixed

them by vortexing in a 50-mL plastic centrifuge tube. The mixture was adjusted to and

maintained at 50% water-holding capacity, which is favorable for microbial activity.

Tubes were incubated at 20°C and 100% humidity during the 300-d experiment. Our

design was a 3 × 3 combinatorial setup (i.e., all litters crossed with all soil inocula) plus

additional ―no-litter‖ soils. In all, 24 replicates per treatment were constructed, permitting

destructive harvest of replicates on incubation days 25, 99, and 300 for chemical and

microbial analyses (see Clone library construction and sequencing). The soil inoculum

accounted for an average of <5% of the total CO2 flux across all treatment combinations,

and soil carbon (much of which is not likely to be readily bioavailable) accounted for

<5% of total carbon in each microcosm (see Appendix F).

Determination of carbon mineralization rates and litter chemistry

Carbon mineralization rates were determined across 300 days using a static incubation

procedure as described in Fierer et al. (2003). Carbon mineralization rates were

determined on eight replicate samples per treatment combination at 22 time points during

the course of the incubation, and carbon dioxide production rates were corrected for the

contribution of the corresponding soil inoculum by subtracting the carbon mineralization

rates of the ―no-litter‖ soils. Since samples were harvested on days 25, 99, and 300 in

order to assess the composition of the microbial community (see Clone library

construction and sequencing below), we decided that those harvested microcosms should

correspond to the same eight replicates per treatment measured up to a given harvest date.

Specifically, those microcosms harvested on days 25, 99, or 300 were the same

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microcosms on which we measured carbon mineralization rates during days 2–25, 26–99,

or 100–300 of the incubation, respectively. This resulted in three incubation periods

corresponding to days 2–25, 26–99, and 100–300. Since a treatment within each of these

incubation periods was composed of different replicates, we measured all microcosms

regardless of their specified incubation period on days 25 and 99 in order to insure that

each set of replicates had similar carbon mineralization rates. It should be noted that

microcosms from all three incubation periods (i.e., three sets of eight replicates) were

measured on day 25, but only microcosms from the last two incubation periods (i.e., two

sets of eight replicates) were measured on day 99, since the first set of microcosms had

already been destructively harvested. No significant difference in mineralization rates

was detected for samples from differing incubation periods measured on the same day

(F2, 357 = 0.12; P = 0.89). Since no differences in mineralization rates were detected

between samples measured on the same day but differing in incubation period we felt

confident in using the entire data set to calculate cumulative carbon mineralized across

the entire 300-d period of this experiment (See Statistical analyses below).

Total carbon, total nitrogen, and C:N ratios for each inocula and litter were

determined using an NA1500 CHN analyzer (Carlo Erba Strumentazione, Milan, Italy).

Clone library construction and sequencing

For each of the nine unique litter–soil inoculum combinations, DNA was extracted from

samples at three time points (days 25, 99, and 300 of the incubation periods) using the

PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, California, USA), after

grinding 1 g of each sample with a mortar and pestle in liquid nitrogen. We monitored

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microbial community composition at each of these three time points in order to determine

whether microbial community composition ever converged, an approach suggested by

Reed and Martiny (2007). The DNA was extracted from three replicate samples per

litter–soil inoculum combination at each time point, and these were pooled prior to

polymerase chain reaction (PCR) amplification. This yielded a total of 27 unique DNA

samples from which the clone libraries were constructed.

The DNA was also extracted and clone libraries were constructed from an

additional 12 samples (two of the grass litter–grass soil inoculum treatments and two of

the rhododendron litter–rhododendron soil inoculum treatments at each of the three time

points). This was done to assess variability in microbial community composition within a

given treatment at a given time point. In all cases, the replicate samples (the clone

libraries constructed from the same litter–soil inoculum–time point combination) were

essentially identical with respect to microbial community composition, and therefore we

present only the results from the 27 clone libraries generated by the different treatments

(nine litter type/soil inoculum combinations at three time points).

Clone libraries were constructed by amplifying the DNA samples with the

universal primer pair 515f (5′-GTGCCAGCMGCCGCGGTAA-3′) and 1391r (5′-

GACGGGCGGTGWGTRCA-3′). This primer pair amplifies small subunit rRNA genes

from all three domains (Archaea, Bacteria, and Eukarya) (Baker et al. 2003, Cullen and

MacFarlane 2005, Martiny et al. 2006) yielding a PCR product that is 850–1100 base

pairs (bp) in length. Although this primer set may not necessarily amplify all taxonomic

groups equally, any amplification bias should be consistent across all of the samples.

Each 50-μL PCR reaction contained 1× PCR buffer, 2.5 mmol/L MgCl2, 0.2 mmol/L of

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each dNTP, 0.2 μmol/L of each primer, 0.5 units of Taq DNA polymerase (Invitrogen,

Carlsbad, California, USA), and 2 μL of template DNA. Each DNA sample was

amplified with three replicate PCR amplifications (30 cycles each, annealing temperature

of 52°C) with the amplicons from each sample pooled prior to cloning. Amplicons were

cleaned using the Promega WizardSV Gel Extraction and PCR Clean Up kit (Promega,

Madison, Wisconsin, USA).

Amplicons were cloned using TOPO TA for Sequencing kit (Invitrogen)

following the manufacturer's instructions. Plates were grown overnight at 37°C before

plasmid amplification and sequencing. A total of 32 clones were sequenced per sample at

Agencourt Bioscience (Beverly, Massachusetts, USA). Despite the small size of these

libraries, they were sufficiently large to allow us to compare quantitatively community

structure at a general level of phylogenetic resolution (see Sequence analyses).

Sequence analyses

Sequences were binned into major taxonomic groups (i.e., metazoan, bacteria, and fungi)

using the BLAST algorithm (Altschul et al. 1997) against the complete European

Ribosomal RNA database (http://www.psb.ugent.be/rRNA/index.html). Sequences with

expect (E) values greater than 1 × 10−100

to nearest neighbors were not included in the

analyses. Of the clones sequenced, ~3% of the sequences were not included in the

analyses as they were either of low quality, chimeric, or were neither bacterial nor fungal.

The taxonomic classification of the fungal sequences was determined by BLAST search

against the European Ribosomal RNA database. The bacterial sequences were aligned

against the Greengenes database (DeSantis et al. 2006b) using the NAST aligner

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(DeSantis et al. 2006a) and classified into taxonomic groups with the Greengenes

―classify‖ utility (http://greengenes.lbl.gov/cgi-bin/nph-classify.cgi). Only those

sequences sharing >90% similarity to reference sequences over a 600-bp region were

classified. Taxonomic characteristics of the microbial communities are provided in Table

4.1. Sequences were deposited in GenBank under the accession numbers EU729748–

EU730583 and EU725929–EU726201 for bacteria and fungi, respectively.

The bacterial and fungal sequences were combined together and aligned against

bacterial and fungal guide sequences downloaded from the Silva ssu RNA database

(http://silva.mpi-bremen.de/). Sequences were aligned using MUSCLE (Edgar 2004) and

a neighbor-joining tree containing all bacterial and fungal sequences from the constructed

libraries. In addition, an outgroup (Haloferax volcanii) was constructed in MEGA

(Tamura et al. 2007).

We used the weighted UniFrac algorithm (Lozupone et al. 2006) to determine

whether soil inocula affected the composition of the microbial communities on each litter

type. UniFrac provides an overall estimate of the phylogenetic distance between each pair

of communities by examining the fraction of the total branch length within a single

phylogenetic tree that is unique to either of the two communities (as opposed to being

shared by both; Fierer et al. 2007).

Statistical analyses

Statistical analyses of mineralization data were performed using S-Plus 7.0 (Insightful,

Seattle, Washington, USA). To examine treatment effects on mineralization rates across

time, a linear mixed-effects model was used in which time, inoculum source, and litter

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type (all discrete variables) were treated as fixed effects and permitted to interact.

Microcosm identity was included as a random effect to account for the repeated sampling

across time of microcosms. We analyzed the cumulative carbon mineralization rates

using ANOVA with litter type, inoculum source, and block as discrete variables;

inoculum source and litter type were permitted to interact. Cumulative carbon

mineralization rates were determined by integrating values under the curve of the 300-d

sampling period. Since replicates were destructively harvested for molecular analyses, to

generate cumulative values we blocked replicates within a treatment by mineralization

rates and generated the cumulative values from samples within a block. This approach

enabled us to determine whether litters crossed with their home (as opposed to foreign)

soil inoculum resulted in the highest, cumulative carbon mineralization, permitting us to

explore our ―home field advantage‖ hypothesis. Pairwise comparisons of means were

explored using the Tukey method to assess differences between inocula within the same

litter type. For statistical significance we assumed an α level of 0.05 and, when reported

as such, data were log10-transformed to conform to assumptions of homoscedasticity

(verified using model checking).

Results

Carbon mineralization

Across the course of the incubation we noted that in most cases, and regardless of the

inoculum–litter combination, that carbon mineralization dynamics followed an

approximately similar three-stage pattern. This consisted of an initial peak in

mineralization rates during the first 25 d, a secondary peak lasting from about day 30 to

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as much as day 150, and finally a decline phase characterized by a gradual decrease in

mineralization rates over time (Fig. 4.1).

When examining each litter in turn, for rhododendron litter we found a significant

two-way interaction between inoculum and time on carbon mineralization rates for each

incubation period (P < 0.0001 in all cases; Table 4.2, Fig. 4.1A). The interactions, across

all incubation periods (i.e., days 2–25, 26–99, and 100–300), showed that mineralization

dynamics were different between at least two of the inocula and that these differences

between the inocula were dependent upon time. For pine litter a significant two-way

interaction between inoculum and time was found for the first two incubation periods (P

< 0.0001 in both cases; Table 4.2, Fig. 4.1B). In the third incubation period (days 126–

300), the interaction was not significant but the main effects of inoculum (P < 0.0001)

and time (P < 0.0001) were (thus, relative differences between inocula were time

independent). For grass litter we found a significant interaction between inoculum and

time on carbon mineralization rates for incubation periods one, two, and three (P < 0.05,

P < 0.0001, and P < 0.001, respectively; Table 4.2, Fig. 4.1C).

To examine further the dynamics we saw in the time course data, we looked at

cumulative mineralization across the 300-d experiment (Fig. 4.2). This examination

showed a significant two-way interaction term of inoculum and litter on cumulative

carbon mineralization rates (P < 0.001; Table 4.3). We investigated this result by looking

at the inoculum effect for each litter type individually to test our sub-hypothesis of ―home

field advantage.‖

For each litter type we found a significant effect of inoculum on cumulative

carbon mineralization (P < 0.001 in all cases). Post hoc analyses on rhododendron litter

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showed that all three inocula differed, with the rhododendron inoculum yielding a higher

cumulative value than either the pine or grass inoculum (Fig. 4.2A). For pine litter, all

three inocula again differed except in this instance the pine inoculum yielded a higher

cumulative value than either of the other two inocula (Fig. 4.2B). For grass litter, both

grass and rhododendron inocula yielded a higher cumulative value than did the pine

inoculum (Fig. 4.2C).

Microbial community

For the UniFrac analyses we examined each litter type separately (building a separate tree

for each litter type), combining the sequences for each soil inoculum from the three time

points. By doing this, we could examine the overall influence of the inoculum source on

microbial community composition in each litter type across the entire incubation period.

We found that, as expected, litter type influenced microbial community composition

(data not shown). However, within each litter type, we also found a significant influence

(P < 0.01) of soil inoculum on microbial community composition (Fig. 4.3), as

determined by both the UniFrac significance test and the phylogenetic (P) test (Martin

2002). In most instances, the inoculum effects on the microbial communities were

apparent at even the coarsest levels of taxonomic resolution (e.g., shifts in

bacterial:fungal ratios or shifts in the dominance of particular bacterial phyla; Table 4.1).

Notably, the resulting microbial community composition from the different inocula was

most dissimilar in the grass litter microcosms and most similar in the rhododendron litter

microcosms. That is, the more chemically complex the litter habitat, the more similar the

resulting community compositions from the different inocula (Fig. 4.3, Table 4.1).

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Furthermore, microbial communities on the same litter habitat never converged

compositionally across the course of this experiment (Fig. 4.3, Table 4.1).

Discussion

The three-stage pattern observed in mineralization dynamics across time (Fig. 4.1) is

consistent with a recently proposed mathematical model (Moorhead and Sinsabaugh

2006) that incorporates three guilds of microbial decomposers. However, we found that

the magnitude and characteristics of each stage within a given litter habitat were strongly

determined by the inoculum source (Fig. 4.1, Table 4.2). Indeed, the main effect of

inoculum explained 20% of the variation in cumulative carbon mineralization across litter

types, and its interaction with litter type explained an additional 25% of the variation

(Fig. 4.2, Table 4.3). When examining the inoculum effect for each litter separately, it

explained between 22% and 86% of the variation in cumulative carbon mineralization

(Fig. 4.2, Table 4.3). The significant inoculum effects and inoculum interactions with

time refute the hypothesis that microbial communities are functionally equivalent. These

results are contrary to those studies (Balser and Firestone 2005, Langenheder et al. 2005,

2006, Ayres et al. 2006, Wertz et al. 2006) that did not observe differences in carbon

mineralization rates between different microbial communities, and these results

demonstrate that microbial communities may impact processes that are generally

presumed to be carried out equivalently by a wide array of organisms (Schimel 1995).

Indeed, the competing hypothesis of functional dissimilarity was supported across the

course of the entire 300-day experiment. Given that the composition of the communities

in a given litter habitat, from different inocula, were consistently distinct across time (Fig.

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4.3, Table 4.1), differences in function in the same litter habitat may be attributed to the

composition of the microbial community (Reed and Martiny 2007).

When the rhododendron and pine inocula were combined with their ―home‖

litter resources, the combinations yielded significantly greater cumulative mineralization

than instances in which the inocula were ―foreign‖ to the litter (Fig. 4.2A, B, Table 4.3).

The grass inoculum yielded cumulative mineralization values that were equivalent to the

rhododendron inoculum and higher than the pine inoculum only with grass litter (Fig.

4.2C, Table 4.3). These results support the ―home field advantage‖ hypothesis (Gholz et

al. 2000, Ayres et al. 2006), suggesting that soil microbial communities display some

adaptation (or pre-adaptation) to their past resource environment.

If adaptation to the past resource environment explains the ―home field

advantage‖ phenomenon that we observed (Fig. 4.2), this implies that decomposer

communities may be selected for, at least in part, by the specific characteristics of past

litter inputs as both Gholz et al. (2000) and Ayres et al. (2006) have suggested. More

specifically, litter characteristics may lead to changes in the composition of a given

microbial community, as observed in this study (Fig. 4.3, Table 4.1). Indeed, we chose

three litter resources that presented a gradient in litter chemical complexity and

recalcitrance, as indicated by both C:N ratios (Appendix F) and by in situ decomposition

rates reported for identical or very similar litter types (Andren et al. 1992, Sanchez 2001,

Ball et al. 2008), with rhododendron representing the most, and grass the least, complex

and recalcitrant litter. Potentially then, these differences in resource quality could account

for the strong home field advantage observed for both the rhododendron and pine inocula

on their respective home litters and the weaker home field advantage observed for the

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grass inoculum on the grass litter (Fig. 4.2, Table 4.3). Notably, Langenheder et al.

(2005) observed that a microbial community inoculum from a lake with high-quality

dissolved organic carbon (DOC) yielded respiration rates lower than inocula from low-

quality DOC environments, when exposed to common garden environments with low-

quality DOC. Also, soils pre-exposed to a pesticide or herbicide may degrade that

compound more rapidly in subsequent applications (Taylor et al. 1996, Laha and Petrova

1997). Together, these results suggest that the ability of microbial communities to

decompose carbon compounds may be contingent on past exposure to similar compounds

(Taylor et al. 1996, Laha and Petrova 1997) and that the more chemically complex litter

carbon is, the more likely community function will be contingent on history and not

simply on chemical quality alone.

Our study cannot unambiguously disentangle whether the functional

differences between the inocula were the result of differences in the abundance of

different taxa or the degradative capacities of individuals from the same taxa from

different inocula. If only the former mechanism applied then communities of more

similar composition would function more similarly. We did not observe this relationship;

instead, those communities that developed on rhododendron litter were most similar in

composition (Fig. 4.3) and less similar with regards to their function (Figs. 4.1 and 2.2).

This observation was investigated statistically and the similarity plots are shown in

Appendix G. Intriguingly, these data suggest that the manner in which microbial

communities respond compositionally to environmental variation may be a poor predictor

of their impacts on ecosystem function and communities that are more similar in

taxonomic composition may not necessarily be more similar with regards to their

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functional capabilities. Or to rephrase that statement using the definitions from the

biodiversity–ecosystem functioning literature, functional response groups may not

correlate with functional effect groups (Naeem and Wright 2003). This may make

prediction of the manner in which changing microbial community composition will

impact ecosystem functioning highly uncertain. However, at least from the functional

response group perspective, the microbes behaved like animal and plant communities.

That is, the harsher environmental filter (i.e., rhododendron > pine > grass litter)

generated communities of more similar phylogenetic composition (Fig. 4.3).

Ecosystem models are generally designed without incorporation of the

potential effects of variation in microbial community composition or the functional

capacities of individuals from the same taxonomic level on ecosystem processes (Zak et

al. 2006). Yet, under similar environmental conditions, our results show that the effects

of different microbial inocula can account for >85% of the variation in litter carbon

mineralization (Table 4.3) and significantly alter the temporal dynamics of this process

(Fig. 4.1). Before exploring the consequences of these findings and their possible

implications for predicting ecosystem carbon dynamics, we propose two necessary areas

of research. First we should resolve whether the taxonomic composition of microbial

communities or the past exposure of communities to specific resource conditions

predominately impacts ecosystem function through time. That is, does community

composition impact future functioning and/or is functioning a product of the selection

environment (Loehle and Pechmann 1988, Goddard and Bradford 2003)? Second, and

building on ideas relating to adaptation, future work should resolve whether microbial

communities from different selection environments functionally converge when placed in

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a similar environment. Although our experiment ran for 300 days, to reliably test this

possibility we would need an experimental design in which microbial communities

sourced from differing environments were exposed to continuous inputs of the same

resource. By using such an approach we may be able to determine how rapidly microbial

communities adapt to new resource conditions and whether different communities

functionally converge. Such studies might then inform us as to how rapid land use

conversion may impact microbially mediated biogeochemical processes and would be

vital to our understanding of the degree to which ecosystem functions are resistant and

resilient to environmental change.

Our findings suggest that soil microbial communities of differing

composition are functionally dissimilar. We propose future work to explore further the

assumption of functional equivalence for soil microbial communities. If the weight of

evidence supports the alternate hypothesis of functional dissimilarity, then the historical

factors that shape microbial community functioning must be explored in ecosystem

models to predict accurately how the carbon cycle will respond to global change.

Acknowledgements

We thank Tom Maddox in the Analytical Chemistry Laboratory of the Odum School of

Ecology, University of Georgia, for element analyses. We also thank two anonymous

reviewers who helped to significantly improve an earlier version of this manuscript. The

authors acknowledge funding from the Andrew W. Mellon Foundation and from the

National Science Foundation to the Coweeta LTER.

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Wertz, S., V. Degrange, J. I. Prosser, F. Poly, C. Commeaux, T. Freitag, N. Guillaumaud,

and X. Le Roux. 2006. Maintenance of soil functioning following erosion of

microbial diversity. Environmental Microbiology 8:2162–2169.

Zak, D. R., C. B. Blackwood, and M. P. Waldrop. 2006. A molecular dawn for

biogeochemistry. Trends in Ecology and Evolution 21:288–295.

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Table 4.1 Taxonomic description of the microbial communities of each litter type/soil inoculum combination at each of the three time

points.

L

itte

r ty

pe

Soil

Inocu

lum

Incu

bat

ion D

ay

% B

acte

ria

(all

gro

ups)

% F

ungi

(all

gro

ups)

% P

rote

obac

teri

a

% A

cidobac

teri

a

% A

ctin

obac

teri

a

% C

FB

% F

irm

icute

s

% O

ther

bac

teri

a

% A

scom

yco

ta

% O

ther

Fungi

Rhod. Grass 25 62.1 37.9 24.1 13.8 6.9 17.2 0.0 0.0 37.9 0.0

Pine 25 51.6 48.4 25.8 9.7 3.2 9.7 0.0 3.2 38.7 9.7

Rhod. 25 36.7 63.3 23.3 9.4 3.0 0.0 0.0 1.0 45.2 18.1

Grass 99 58.6 41.4 37.9 13.8 0.0 3.4 0.0 3.4 34.5 6.9

Pine 99 40.0 60.0 6.7 20.0 6.7 6.7 0.0 0.0 46.7 13.3

Rhod. 99 47.3 52.7 31.6 11.4 2.2 1.1 0.0 1.0 44.5 8.2

Grass 300 46.4 53.6 28.6 7.1 3.6 3.6 0.0 3.6 50.0 3.6

Pine 300 76.7 23.3 23.3 30.0 0.0 23.3 0.0 0.0 20.0 3.3

Rhod. 300 82.7 17.3 30.0 36.6 3.6 4.4 0.0 8.0 11.6 5.7

Pine Grass 25 72.4 27.6 55.2 17.2 0.0 0.0 0.0 0.0 27.6 0.0

Pine 25 78.3 21.7 56.5 21.7 0.0 0.0 0.0 0.0 21.7 0.0

Rhod. 25 71.4 28.6 46.4 25.0 0.0 0.0 0.0 0.0 21.4 7.1

Grass 99 85.2 14.8 37.0 40.7 3.7 0.0 3.7 0.0 14.8 0.0

Pine 99 70.4 29.6 40.7 14.8 0.0 14.8 0.0 0.0 29.6 0.0

Rhod. 99 87.1 12.9 51.6 35.5 0.0 0.0 0.0 0.0 12.9 0.0

Grass 300 68.4 31.6 57.9 5.3 0.0 0.0 0.0 5.3 31.6 0.0

Pine 300 75.0 25.0 41.7 8.3 0.0 12.5 0.0 12.5 25.0 0.0

Rhod. 300 93.3 6.7 50.0 40.0 0.0 0.0 0.0 3.3 6.7 0.0

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Table 4.1 Cont.

Taxonomic description was determined by the 30-32 clones sequenced per library. Firmicutes refers to the ‗Bacillus-Clostridium‘

group and CFB refers to the ‗Cytophaga-Flavobacterium-Bacteriodes’ group. The ―other bacteria‖ category consists primarily of

sequences identified as either Planctomycetes or Verrucomicrobia. The ―other fungi‖ category is evenly divided between sequences

identified as Basidiomycota or Zygomycota.

Lit

ter

type

Soil

Inocu

lum

Incu

bat

ion D

ay

% B

acte

ria

(all

gro

ups)

% F

ungi

(all

gro

ups)

% P

rote

obac

teri

a

% A

cidobac

teri

a

% A

ctin

obac

teri

a

% C

FB

% F

irm

icute

s

% O

ther

bac

teri

a

% A

scom

yco

ta

% O

ther

Fungi

Grass Grass 25 87.1 12.9 32.7 4.0 3.8 41.7 1.4 3.6 8.9 4.0

Pine 25 89.7 10.3 55.2 6.9 3.4 17.2 3.4 3.4 6.9 3.4

Rhod. 25 86.2 13.8 65.5 3.4 13.8 0.0 3.4 0.0 13.8 0.0

Grass 99 92.7 7.3 21.6 1.1 4.8 47.2 3.5 14.6 7.3 0.0

Pine 99 96.8 3.2 35.5 0.0 12.9 35.5 9.7 3.2 0.0 3.2

Rhod. 99 87.5 12.5 68.8 6.3 12.5 0.0 0.0 0.0 12.5 0.0

Grass 300 93.0 7.0 22.5 1.2 1.1 25.3 32.8 10.1 7.0 0.0

Pine 300 96.8 3.2 22.6 0.0 3.2 67.7 0.0 3.2 3.2 0.0

Rhod. 300 100.0 0.0 76.7 6.7 3.3 3.3 0.0 10.0 0.0 0.0

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Table 4.2 Linear mixed-effects model results for the effect of inoculum and time on

carbon mineralization rates.

Litter type

Incu

bat

ion

per

iod

(day

s)

Inoculum effect Inoculum Time effect

Num

d.f.

Den

d.f.

F P Num

d.f.

Den

d.f.

F P

Rhododendron 2-25 2 21 6.12 <0.01 16 168 5.08 <0.0001

26-99 2 21 285.33 <0.0001 10 105 18.73 <0.0001

100-300 2 21 51.85 <0.0001 12 126 6.34 <0.0001

Pine 2-25 2 21 3.2 0.061 16 168 3.65 <0.0001

26-99 2 21 59.16 <0.0001 10 105 4.03 <0.0001

100-300 2 21 93.63 <0.0001 12 126 ns ns

Grass 2-25 2 21 6.61 <0.01 16 168 1.85 <0.05

26-99 2 21 4.40 <0.05 10 105 6.22 <0.0001

100-300 2 21 13.01 <0.001 12 126 3.15 <0.001

Notes: Results were analyzed based on litter type and incubation period. F and P values

are reported for significant (P < 0.05) model terms; ns: not significant. All data were

log10-transformed to correct for heteroscedasticity. F-values and P-values are estimated in

linear mixed-effects models using, in this case, restricted maximum likelihood (REML)

estimates and are not calculated using least squares methods (Pinheiro and Bates 2000).

Hence, means and sums of squares values are not calculated.

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Table 4.3 ANOVA tables with percentage sums of squares explained (%SS) for the

effects of inoculum across all litter types, and for the effect of inoculum within each litter

type, on cumulative carbon mineralization.

Litter Type Source of

Variation

d.f. SS % SS F P

All Inoculum 2 5.6 19.6 179.8 <0.001

Litter 2 13.2 46.1 422.2 <0.001

Block 7 1.8 6.2 16.3 <0.001

Inoculum × Litter 4 7.1 24.9 114.12 <0.001

Error 56 0.9 3.0

Rhododendron Inoculum 2 10.2 86.2 145.0 <0.001

Block 7 1.1 9.7 4.6 <0.01

Error 14 0.5 4.2

Pine Inoculum 2 2.4 82.5 114.5 <0.001

Block 7 0.4 12.4 4.9 <0.01

Error 14 0.1 5.1

Grass Inoculum 2 0.1 22.3 17.6 <0.001

Block 7 0.4 68.9 15.6 <0.001

Error 14 0.1 8.9

All data were log10-transformed.

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Figure legends

Figure 4.1 Time course of carbon mineralization for each litter type and soil inoculum

combination. In all cases across all time periods there was a significant inoculum by time

interaction on CO2 production rates (P<0.05), except in the case of pine litter between

incubation days 126 and 300 where there were significant main effects of inoculum

source and time only (P<0.0001 and P<0.0001, respectively, see Table 2.2). In (A)

mineralization dynamics for rhododendron litter are shown and in this instance

rhododendron inoculum is native to this litter type. In (B) pine litter is represented and

pine inoculum is native to this litter type. In (C) grass litter is represented and grass

inoculum is native to this litter type. Means are shown for carbon mineralization rates ± 1

SE (n=8).

Figure 4.2 Cumulative carbon mineralized for each litter type by inoculum across the

300 day experiment. The inoculum effects for each litter type were highly significant

(P<0.001). Letters denote significant differences between treatments within each litter

type using a Tukey post-hoc pair-wise comparison and those letters in bold denote the

home litter by home inoculum combination, as do grey bars. In (A) cumulative carbon

mineralization values are shown for rhododendron litter with rhododendron inoculum

being native to this litter type. In (B) pine litter is represented and in (C) grass litter is

represented. Means are shown ± 1 SE (n=8).

Figure 4.3 Relative UniFrac distances between the decomposer communities developing

from the three soil inocula in rhododendron litter (A), pine litter (B), and grass litter (C).

The ―home‖ soil inoculum is highlighted in bold type. The sequence data from the three

sampling dates are combined for these analyses to illustrate overall differences in the

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microbial communities. For each litter type, the three inocula always yielded distinct

communities (P<0.01), as determined by the UniFrac significance test and the

Phylogenetic (P) test (see text for details).

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Figure 4.1

(A) Rhododendron litter

0

10

20

30

40

(B) Pine litter

Ca

rbo

n m

ine

raliz

atio

n r

ate

(

g C

-CO

2 h

-1)

0

5

10

15

20

25

Rhododendron inoculum

Pine inoculum

Grass inoculum

0 25 50 75 100 125 150 175 200 225 250 275 300

(C) Grass litter

0

10

20

30

40

50

Time (incubation day)

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Figure 4.2

(A) Rhododendron litter

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

(B) Pine litter

Cu

mu

lative

ca

rbo

n m

ine

raliz

atio

n (

g C

-CO

2)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

(C) Grass Litter

Inoculum source

Rhododendron Pine Grass

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

a

b

c

a

b

c

a a

b

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Figure 4.3

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Plate legends

Plate 4.1. Sites (U.S.) where litter and soil inocula were collected: (top left) Sedgwick

Reserve, California, (lower left) Duke Forest, North Carolina, and (right) Coweeta Long

Term Ecological Research site, North Carolina (Photo credits: B. A. Ball).

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Plate 4.1

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CHAPTER 5

LITTER QUALITY IS IN THE EYE OF THE BEHOLDER: INITIAL

DECOMPOSITION RATES AS A FUNCTION OF INOCULUM

CHARACTERISTICS5

5 Strickland, M.S., E. Osburn, C. Lauber, N. Fierer, M.A. Bradford. Functional Ecology. 23:627-636.

Reprinted here with permission of publisher.

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Abstract

1. The chemical composition of plant litter is commonly considered to indicate its

quality as a resource for decomposer organisms. Litter quality, defined in this way, has

been shown to be a major determinant of litter decomposition rates both within and

across terrestrial ecosystems. Notably, the structure of the microbial community that is

directly responsible for primary decomposition is rarely considered as an empirical

predictor of litter decay rates.

2. Microbial communities are generally assumed to perceive litters of the same chemical

composition to be of equivalent resource quality but evidence from field studies suggests

that these same communities may adapt to the prevalent litter types at a given site. Here,

we tested this assumption by assessing how microbial communities sourced from

different forest- and herbaceous-dominated ecosystems perceive the quality of novel,

foliar litters derived from a tree (Rhododendron maximum) and from a grass (Panicum

virgatum) species. Based on chemical composition, we would expect R. maximum litter

to be of lower quality than P. virgatum litter.

3. We used an experimental litter-soil system which employs a ‗common garden‘

approach and measured rates of CO2 production across 50 days; higher rates of

production were assumed to indicate higher quality (i.e. more easily degradable) litter.

4. We found that communities sourced from habitats under differing plant cover

perceived litter quality differently. Those communities sourced from herbaceous habitats

perceived the grass litter to be of higher quality than the tree litter, whereas communities

from forest habitats decomposed both litter types similarly. Within a litter type,

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differences in both community composition and nutrient availability of the source habitat

were related to decomposition rates.

5. Our results suggest that litter quality cannot necessarily be predicted solely from

chemical characteristics; instead the perceived quality is dependent on the quality of past

resource inputs a community has experienced and the structure of those microbial

communities responsible for the initial stages of litter decomposition.

Key-words: Bacteria, Carbon mineralization, Fungi, Resource history, Soil properties

Introduction

The decomposition of plant litter by soil microbial and faunal communities is one of the

most heavily researched areas in modern ecology and this process is one of the primary

pathways in the terrestrial carbon cycle (Raich & Schlesinger 1992; Couteaux et al. 1995;

Aerts 1997). Research on litter decomposition has shown that, at the global scale,

decomposition rates of plant litter are strongly related to mean annual temperature (MAT)

and precipitation (MAP) (Meentemeyer 1978; Vitousek et al. 1994; Aerts 1997). Within

individual ecosystem or biome types, litter quality becomes a better determinant of

decomposition rates than climate (Meentemeyer 1978; Aerts 1997). At the ecosystem

scale, litter quality is most often related to the chemical characteristics of the litter, for

example carbon-to-nitrogen ratios and/or lignin content (Aber et al. 1990; Aerts 1997).

Support for this relationship is derived from a multitude of litter decomposition studies

which often employ experimental set-ups in which a range of litter types are decomposed

across a variety of different biomes (Vitousek et al. 1994; Gholz et al. 2000; Hobbie et

al. 2006; Hobbie et al. 2007; Parton et al. 2007). Based on this evidence most models of

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ecosystem carbon dynamics rely heavily on the effects of climate (i.e. MAT and MAP) as

well as estimates of quality based on litter chemistry (Parton 1983; Heal 1997).

Models of decomposition based on climate and litter chemistry effectively ignore

the potential influence of microbial community structure on rates of litter decomposition

(Zak et al. 2006; Reed & Martiny 2007). This omission is partly due to the difficulty

associated with manipulating microbial communities in situ and is also largely based on

the assumption that given their abundance, diversity, and ubiquity soil microbial

communities are, for the most part, redundant with regards to their functional attributes

(Cardinale et al. 2007; Jiang 2007; Verity et al. 2007). This assumption has been

questioned (Schimel 1995; Martiny et al. 2006; Zak et al. 2006; Ramette & Tiedje 2007;

Reed & Martiny 2007) but there are surprisingly few studies directly testing how distinct

microbial communities may perceive the decomposability of a given litter type. Indeed,

we do know that microbial communities are not homogeneous, varying across space and

time in composition due to environmental factors and/or historical contingencies (Andrén

& Balandreau 1999; Behan-Pelletier & Newton 1999; Wall & Virginia 1999; Fierer &

Jackson 2006; Martiny et al. 2006; Ramette & Tiedje 2007; Reed & Martiny 2007).

Furthermore recent work has demonstrated both implicitly and explicitly that structural

differences in the microbial community can influence ecosystem function (Balser &

Firestone 2005; Reed & Martiny 2007; Strickland et al. 2009). Such results demonstrate

that, like comparisons between individual species of microorganisms (Newell 1984;

Dowson et al. 1989; Osono & Takeda 2002), whole microbial communities also differ

with regards to their functional capacity and these differences may influence the process

of litter decomposition.

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The specific mechanisms which lead to functional dissimilarity (see Strickland et

al. 2009) in litter decomposition amongst microbial communities remains poorly

understood. One possible reason that functional dissimilarity may arise is due to a

microbial community‘s past exposure to litters of differing chemistry (Hunt et al. 1988;

Gholz et al. 2000). Implicit evidence of this phenomenon has been shown for both the

degradation of subsequent applications of pesticides and litter residues (Taylor et al.

1996; Laha & Petrova 1997; Cookson et al. 1998) as well as in reciprocal litter transplant

experiments (Hunt et al. 1988; Gholz et al. 2000; Castanho & de Oliveira 2008; Vivanco

& Austin 2008). However, it is unknown whether or not the past exposure of a microbial

community to litter inputs of differing chemistry also relates to that community‘s ability

to decompose novel litters which themselves differ chemically. Such knowledge will

improve our ability to understand, and possibly predict, the factors which influence litter

decomposition rates.

To test whether or not past exposure of microbial communities to high or low

quality litters influences the decomposition of novel litters (to those communities) which

differ in litter chemistry, we used a ‗common garden‘ experimental approach. We paired

one of 12 soil inocula, six sourced from herbaceous habitats and six sourced from forest

habitats, with one of two litters which differed in their litter chemistry. We hypothesized

that soil microbial inocula from forested habitats would perceive the lower-nutrient,

higher-lignin litters, taken from a tree species, to be of higher ‗quality‘ than would

incoula sourced from habitats dominated by herbaceous cover. Similarly, we reasoned

that the higher-nutrient, lower-lignin litters, taken from a grass species, would be

perceived of equivalent ‗quality‘ by all inocula. This hypothesis was based on the

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expectation that microbial communities which develop in forest habitats will be adapted

(or pre-adapted) to decompose lower-nutrient, higher-lignin litters due to a history of

exposure to them, which will have acted as a selection pressure and/or an environmental

filter (sensu Lambers 1998). Hunt et al. (1988) present field data which may be explained

by this hypothesis. To test the hypothesis our microcosm, experimental design permits

effects associated with the microbial community to be separated from co-varying factors

in the field. Henceforth, we use the term ‗perceived quality‘. This is, essentially, a biotic

definition of litter quality (see Fierer et al. 2005) because a litter‘s decomposition rate in

our experiment is a function of how it is ―perceived‖ by the microbial community.

Methods

Microcosm design

The tree foliar litter was taken from Rhododendron maximum collected from the Coweeta

Long Term Ecological Research (LTER) site, North Carolina, USA (35º00‘N, 83º30‘W)

as recent, senesced litterfall. The herbaceous litter was taken from Panicum virgatum and

was collected from the University of Georgia, Georgia, USA (33º53‘N, 83º21‘W) as

recent, standing dead material. We chose these species because they have litters of low

and high chemical quality, respectively (Table 5.1). That is, R. maximum litter had a C:N

ratio which was nearly twice as great as, and a lignin concentration greater than twice that

of, P. virgatum litter. Neither litter was present at the sites from where the inocula were

sourced. This design ensured that effects of differing litter chemistries were not

confounded by prior exposure to the litter species used (i.e. ‗home-field advantage‘,

sensu Gholz et al. 2000). Litters were air-dried, passed twice through a Wiley mill (2 mm

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mesh), and then sterilised by autoclaving twice in succession and again 24 h later (121°C,

20 min), before being dried to constant mass at 65C. The milling was done to increase

surface area for microbial colonization, to make the material between replicates more

homogenous, and to remove the influence of physical litter structure on decomposition

rates. For example, the slow decomposition rate of R. maximum foliar litters in the field

(e.g. Ball et al. 2008) is thought to result from both its chemical composition and

physically-tough leaves.

Soils for use as inocula were collected from, or adjacent to, the Calhoun

Experimental Forest (CEF), which is managed by the US Department of Agriculture and

located in the Piedmont region (approximately 34.5°N, 82°W) of northwestern South

Carolina, USA (Gaudinski et al. 2001; Callaham et al. 2006). Twelve soil inocula were

collected from four land-uses (cultivated, pasture, pine, and hardwood) within this region,

each land-use being represented three times (4 land-uses 3 locations = 12 inocula). This

provided six inocula from forest habitats (pine and hardwood) and six from habitats

dominated by herbaceous vegetation (cultivated and pasture). Each inoculum was a

subsample from soil samples composed of 10 individual A horizon soil cores (8 cm dia.,

0 - 7.5 cm depth), which were collected from a 100 m2 plot within each of the 12 sites

using a stratified random sampling approach. Soils were sieved (4 mm), homogenized,

and stored at +5ºC until use.

Microcosms were constructed by adding 0.5 g dry mass equivalent of soil

inoculum to 1 g of litter, which was then mixed by vortexing in a 50 mL plastic

centrifuge tube. The mixture was adjusted to and maintained at 50% water-holding

capacity, which is favourable for microbial activity. Tubes were incubated at 20°C and

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100% humidity during the experiment. Our design was a 2 12 combinatorial set-up (i.e.

all litters crossed with all soil inocula) plus additional ―no-litter‖ soils. This resulted in

two inocula treatments, herbaceous and forest. The herbaceous inocula were composed of

three inocula sourced from cultivated sites and three sourced from pasture sites giving a

total of 6 herbaceous replicates. The forest inocula were composed of three inocula

sourced from hardwood stands and three inocula sourced from pine stands giving a total

of 6 forest replicates. Six analytical repeats were constructed for each inoculum × litter

combination giving 144 experimental units (i.e. 12 inocula × 2 litters × 6 analytical

repeats). To avoid pseudoreplication the mean was taken across the 6 analytical repeats

for a given inoculum × litter combination resulting in 6 herbaceous and 6 forest replicates

per litter type. The soil inocula accounted for an average of ~ 5% of the total carbon (C)

dioxide (CO2) flux across all treatment combinations and soil C (much of which is not

likely to be bioavailable) accounted for <20% of total C in each microcosm (see Table

5.1, see Appendix H).

Determination of decomposition rates and litter chemistry

Litter decomposition rates were estimated by periodic measurement of CO2 production

rates from the microcosms. This was done across 50 days using a 24 h static incubation

procedure described in Fierer et al. (2003). Before the start of the incubation period, soil-

litter mixtures were incubated for 10 days to allow microbial colonization of the litters.

Following this period CO2 production rates were measured on days 1, 5, 10, 20, 30, 40,

and 50 and were corrected for soil contributions by subtracting the production rates

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measured from the corresponding ‗no-litter‘ soils. Cumulative CO2 production was

calculated by integrating values under the curve for the incubation period.

Total percentage C, nitrogen (N), non-fibrous material, hemi-cellulose, cellulose,

lignin, and pH, were determined for both litter types (Table 5.1). Total C and N were

determined using an NA1500 CHN Analyzer (Carlo Erba Strumentazione, Milan, Italy).

Non-fibrous material, hemi-cellulose, cellulose, and lignin concentrations were

determined using an Ankom A200 Fiber Analyzer (Ankom, Macedon, USA). Litter pH

was determined in water (2:1 ratio of water-to-litter) using a benchtop pH meter.

Determination of inoculum source edaphic characteristics

For each site from which an inoculum was sourced, we determined a series of edaphic

factors. Carbon and nutrient pools, microbial biomass, pH, soil texture, bulk density, and

cation concentrations were calculated from three analytical replicates per soil sample

from each inoculum source.

Total, POM (particulate organic material) associated, and mineral associated C

and N were determined using an NA1500 CHN Analyser (Carlo Erba Strumentazione,

Milan, Italy). Carbon and N associated with POM and mineral material were determined

using the fractionation method described in Bradford et al. (2008). Microbial biomass C

and N, and dissolved organic C (DOC), were determined using the method described in

Fierer and Schimel (2003). Ammonium (NH4+) and nitrate (NO3

-) were determined

colorimetrically following Fierer and Schimel (2002). Extractable phosphorus (P) was

measured on an Alpkem auto-analyzer (OI Analytical, College Station, TX) using

Murphy-Riley chemistry after extraction with Mehlich I double-acid (H2SO4 –HCl) using

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a 1:4 mass:volume ratio (Kuo, 1996). Soil pH was determined in water (1:1 ratio of

water-to-soil) using a benchtop pH meter. Silt and clay contents were measured using a

simplified version of the hydrometer method as described by Gee and Orr (2002). Soil

bulk density was calculated after correcting for the mass and volume of roots and stones

(Culley 1993). Exchangeable cations (Ca+, Mg

+, and K

+) were measured by atomic

absorption in the presence of LaCl3 after extraction with 1 M NH4OAc (pH 7) using a

1:10 mass:volume ratio (Sumner & Miller 1996). Edaphic data for each inoculum source

are provided in Appendix H.

Determination of the starting community composition of the soil inocula

Soils for community analyses were collected and treated the same as those collected for

edaphic characterization. A DNA-based rather than an RNA-based assessment of

microbial communities was used because it probably better reflects the potential pool of

microbes that might develop on our microcosm litter environments from the soil incolua;

also DNA-based assessments are less variable and hence provide a more integrated

assessment of differences in microbial community inocula. DNA was isolated from 3

replicate sub-samples of fresh soil per plot using the MoBio Power Soil DNA Extraction

kit (MoBio Laboratories, Carlsbad, CA) with the modifications described in Lauber et al.

(2008). Each DNA sample was amplified in triplicate using both bacterial and fungal

specific primer sets described in Lauber et al. (2008) in order to assess bacterial and

fungal community composition in each sample. For each plot, we constructed an

individual clone library and sequenced 80-100 cloned amplicons per library following

standard protocols. Additional details on the PCR conditions, clone library construction,

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and sequencing are provided in Lauber et al. (2008). To classify the fungal sequences, we

used the BLAST algorithm (Altschul et al. 1990) to compare the fungal sequences to an

in house data base of 100 fungal 18S sequences derived from the AFTOL data set

(Lutzoni et al. 2004). Bacterial sequences were aligned against the Greengenes data base

(http://greengenes.lbl.gov/cgi-bin/nph-index.cgi ) using the NAST alignment utility

(DeSantis et al. 2006a) and fungal sequences were aligned using MUSCLE 3.6 (Edgar

2004a). The sequences were chimera-checked using utilities available on the Greengenes

website (DeSantis et al. 2006b). Sequences which were of poor quality or suspected to

be chimeric were eliminated from the analysis (less than 9% of the sequences).

Sequences were aligned using MUSCLE 3.6 (Edgar 2004b) and a neighbor-joining

phylogenetic tree containing either all of the bacterial or all of the fungal sequences was

generated with PHYLIP 4.0. The phylogenetic distance between the bacterial and fungal

communities within each soil inoculum was determined using the weighted-normalized

UniFrac algorithm (Lozupone & Knight 2005). For full details and results concerning the

composition of microbial communities, see Lauber et al. (2008). Briefly, Lauber et al.

(2008) found that bacterial community composition was related to both soil pH and soil

texture whereas fungal community composition was related to both extractable P and soil

C:N ratios.

Statistical analyses

Statistical analyses of cumulative CO2 production were performed in S-Plus 7.0

(Insightful Corp., Seattle, USA), using ANOVA with litter type and inoculum source as

discrete variables that were permitted to interact. Using this approach, a significant

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inoculum or inoculum by litter type interaction would support our hypothesis that

resource history influences the perception of litter quality whereas only a litter type effect

would refute this hypothesis. We further explored our results using correlation and

regression approaches (see below). For statistical significance we assumed an -level of

0.05. When reported as such, data were loge-transformed to conform to assumptions of

homoscedasticity (verified using model checking).

Relationships between cumulative CO2 production, edaphic characteristics of the

source environment, and the starting community composition of the inocula were

assessed using Plymouth Routines in Multivariate Ecological Research v5 software

(Primer v5, Lutton, UK). Mantel tests were performed to determine if there were

significant correlations between community function on either R. maximum or P.

virgatum litter, edaphic characteristics of the source environment, and the starting whole

fungal or whole bacterial community composition of the inoculum (individual taxa were

examined using regression analyses, see below). Correlations between these factors were

considered significant when P<0.05.

Linear regression analyses were performed to look at the relationships between

cumulative CO2 production on either R. maximum or P. virgatum and the starting edaphic

characteristics of the inocula. This was only conducted for those edaphic characteristics

which Mantel tests showed were significantly correlated to cumulative CO2 production.

Regression analyses were also used to look at relationships between cumulative CO2

production and the dominant bacterial and fungal taxa in the starting inocula. We were

able to look at the relationships between cumulative CO2 production and the dominant

bacterial and fungal taxa by performing phylogenetic independent analyses which

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recorded the distribution of taxa for each site and normalized these values to represent the

percentage of each taxon within a given inoculum. Regression analyses, like ANOVAs,

were performed using S-Plus 7.0.

Results

Litter decomposition

During this 50 day incubation, between 2.4 and 3.1% of R. maximum litter C was lost as

CO2 and between 3.1 and 4.2% of P. virgatum litter C was lost as CO2. When examining

cumulative CO2 production, we found a significant inoculum by litter type interaction

(P<0.01; Fig. 5.1). This interaction suggested that the quality of past resources influences

how the community inocula perceive the quality of the two litters. This difference is

clearly represented in Fig. 5.1 - cumulative CO2 production differs from one litter type to

the other for the different inocula. Specifically, cumulative CO2 production was nearly

identical for the forest inoculum on either litter resource suggesting that there was no

difference in perceived quality. In contrast, the herbaceous inocula perceived the P.

virgatum litter to be of higher quality than the R. maximum litter. To further explore this

interaction we examined each litter type separately. We did not detect a significant

inoculum effect on cumulative CO2 production for P. virgatum litter (F1,10 = 2.40;

P=0.15) but did detect a significant inoculum effect on cumulative CO2 production for R.

maximum litter (F1,10 = 25.80; P<0.001). This suggested that the interaction between

inoculum and litter type was driven by minimal differences in CO2 production on P.

virgatum litter while CO2 production did differ between inocula on R. maximum litter. To

further explain these results and to assess other cover-type related differences in the

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perception of litter quality, we explored relationships between cumulative CO2

production and the edaphic and microbial community characteristics of each inoculum

(see next).

Relationship of litter decomposition rates to inoculum characteristics

When examining how cumulative CO2 production from R. maximum litter was related to

the microbial community composition of the inocula, we found significant relationships

with both fungal and bacterial community composition (P<0.05 for fungi and P<0.01 for

bacteria; Table 5.2). Regression analyses showed that the relative abundances of

Sordariomycete, Leotiomycete, Chaetothyriomycete, and Agaricales (the dominant

fungal taxa in these inocula; Lauber et al. (2008)) were significantly related to cumulative

CO2 production (Fig. 5.2a-d). The relative abundances of Leotiomycete and

Sordariomycete were negatively related to cumulative CO2 production (Fig. 5.2a-b). The

relative abundances of Agaricales and Chaetothyriomycete were positively related (Fig.

5.2c-d). Of the bacterial taxa, the relative abundances of Acidobacteria in the starting

inocula were positively related to cumulative CO2 production on R. maximum (Fig. 5.2e).

We also found that site specific differences in the soil chemical and physical

characteristics from which an inoculum was sourced were related to cumulative CO2

production on R. maximum (Table 5.2). More specifically, we found that NO3-, NH4

+,

extractable P, K+, and percentage clay were all significantly related to cumulative CO2

production on R. maximum (P<0.01 for NO3- and NH4

+, P<0.05 for P, K

+, and %clay;

Table 5.2). All except for NH4+ were negatively related to cumulative CO2 production on

R. maximum (Fig. 5.3).

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When examining how cumulative CO2 production from P. virgatum litter was

related to the microbial community composition of the inocula, we found that only the

composition of the bacterial community was significantly related (P<0.05; Table 5.2),

while the fungal community composition was not (Table 5.2). Regression analyses of

bacterial taxa showed that only the relative abundance of Actinobacteria was positively

related to cumulative CO2 production (Fig. 5.2f). Of the soil chemical and physical

characteristics, only K+ concentration was positively related to cumulative CO2

production (P<0.05; Table 5.2, Fig. 5.3f).

Discussion

We initially hypothesized that resource quality history would influence how microbial

communities perceived litters which differed in chemical quality. Our expectation was

that microbial communities which develop in forest habitats will be adapted (or pre-

adapted) to decompose lower chemical quality litters due to their history of exposure to

lower quality litters (Hunt et al. 1988; Gholz et al. 2000). We also expected that

microbial communities, regardless of their previous history, would be equally well

adapted to decompose high chemical quality litters (Hunt et al. 1988). As hypothesized,

we found that those microbial communities sourced from forest habitats perceived the

low chemical quality R. maximum litter to be of higher quality than did the communities

sourced from herbaceous habitats (Fig. 5.1). This result is similar to results from

reciprocal litter transplant studies which have shown that litters often decompose more

rapidly when placed in their native forests (Cookson et al. 1998; Castanho & de Oliveira

2008; Vivanco & Austin 2008). Also confirming our original hypothesis was the

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observation that communities sourced from either habitat generally perceived the high

chemical quality P. virgatum litter equally (Fig. 5.1), a result also observed in studies

which have found no differences in decomposition rates across sites for the same litter

type (Prescott et al. 2000; Ayres et al. 2006).

Initial litter decomposition rates with inocula sourced from herbaceous sites

confirmed the expectation (Aber et al. 1990; Aerts 1997) that litter chemistry is a good

predictor of decomposition rates. That is, the R. maximum litter with its greater C:N ratio

and higher lignin content decomposed more slowly than the P. virgatum litter with its

lower C:N ratio and lower lignin content (Fig. 5.1). In contrast to the inocula sourced

from the herbaceous sites, the decomposition rates observed with the forest inocula were

similar for the two litter types (Fig. 5.1). This result agrees with those studies which have

found that litter chemistry is not a strong predictor of litter decomposition rates (Hunt et

al. 1988; Gholz et al. 2000) and provides empirical support for the suggestion from these

previous studies that the decomposer community characteristics affect litter

decomposition rates. Overall, our results imply that an understanding of the microbial

community and/or its past resource environment may increase our ability to predict

decomposition dynamics. Indeed, the community or the resource history of that

community may account for some of the unexplained variation in decomposition models

where climate and litter quality are used as explanatory variables (e.g. Gholz et al. 2000,

Parton et al. 2007). Our microcosm experiment demonstrates the potential for both

decomposer communities and the resource history of the decomposer community to

influence decomposition rates, but it remains to be determined if our results are relevant

to decomposition dynamics observed in the field.

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To further explore how differences in microbial community structure and resource

quality history impact the perception of litter quality we assessed the relationship

between cumulative CO2 production of both litter types and the biological, chemical, and

physical characteristics of the sites from which each inoculum was sourced. When

examining the biological factors of the inocula sources which were related to cumulative

CO2 production from either R. maximum or P. virgatum litters, we found that both fungal

and bacterial community composition was related to the cumulative CO2 production from

R. maximum litter but only the bacterial community composition was significantly related

to the cumulative CO2 production from P. virgatum litter (Table 5.2). The fact that the

fungal community was related to cumulative CO2 production on R. maximum but not on

P. virgatum litter may be due to the ability of fungi to degrade litters of lower-nutrient

and higher-lignin content, or at least out-compete bacteria in such environments (Six et

al. 2006). When more closely examining the fungal community, we found that four

fungal taxa (Sordariomycete, Leotiomycete, Chaetothyriomycete, and Agaricales) of the

inocula sources were related to the cumulative CO2 production on R. maximum litter. We

found that those inocula sources which had comparably higher relative abundances of

either Sordariomycetes or Leotiomycetes had lower cumulative CO2 production on R.

maximum litter than sites with lower abundances of these groups (Fig. 5.2a, 5.2b).

Conversely, those inocula sourced from sites having a higher relative abundance of

Chaetothyriomycete or Agaricales tended to have higher cumulative CO2 production on

R. maximum litter than did sites with lower abundances of either of these fungal taxa

(Fig. 5.2c, 5.2d). This finding is similar to observations which have demonstrated

differences in the litter degrading abilities of fungal phyla (Osono & Takeda 2002). Our

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observations suggest that such differences may also occur within phyla given that within

the phylum Ascomycota the abundance of one taxon (Chaetothyriomycete) was

positively related, while two other taxa (Sordariomycetes and Leotiomycetes) were

negatively related, to cumulative CO2 production from R. maximum litter (Fig. 5.2a-d).

When considering the bacterial community, we found that those inocula sourced

from sites with higher relative abundances of Acidobacteria and Actinobacteria were

positively related to cumulative CO2 production on R. maximum litter and P. virgatum

litter, respectively (Fig. 5.2e, 5.2f). Acidobacteria have recently been associated with

resource environments of lower quality (Fierer et al. 2007) and this may explain why a

positive relationship was observed on R. maximum litter which has both a high C:N ratio

and lignin content (Fig. 5.2e). When considering the Actinobacteria, relatively little is

known concerning their ecological attributes. However, due to their filamentous growth

form and a presumed ability to effectively scavenge nutrients (Goodfellow & Williams

1983; Steger et al. 2007), those inocula which started with a higher relative abundance of

Actinobacteria may be better at decomposing P. virgatum litter across the course of our

experiment.

Many more chemical and physical factors of the inocula sources were related to

cumulative CO2 production on R. maximum than on P. virgatum (Table 5.2). Of those

factors that significantly correlated with CO2 production on R. maximum litter we found

that all but one was negatively related (Fig. 5.3a-e). Specifically, we observed that NO3-,

extractable P, K+, and percentage clay were negatively related to cumulative CO2

production on R. maximum while NH4+ was positively related. Only K

+ was related to

cumulative CO2 production on P. virgatum and this relationship was positive (Table 5.2,

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Fig. 5.3f). Soil physical and chemical factors are often products of management. In the

region where our inocula were sourced NO3-, extractable P, and K

+ are generally higher

in cultivated and pasture sites (than forests) due to annual fertilizer inputs (Richter et al.

2000). Percentage clay is generally higher in cultivated sites due to long-term intensive

agriculture and weathering (Richter & Markewitz 2001; Callaham et al. 2006) and NH4+

is generally higher in forest sites due to lower levels of nitrification (Adams 1986). These

relationships between chemical and physical variables and the source environments of the

soil microbial inocula make it difficult to determine whether the chemistry of the litter

inputs in the source environments, the nutrient status of the environments, or both were

important factors in structuring the function of the microbial inocula in our litter

microcosms. Notably, differences between the inocula in their chemical and physical

characteristics, and microbial community composition, did not follow land use

differences exactly (Table S1 and Lauber et al. 2008). This may suggest that litter

chemistry (i.e. herbaceous vs. woody plant litters) was the over-riding cause of

differences in the functioning of our inocula on the R. maximum and P. virgatum litters.

Our results provide a foundation upon which future studies can investigate how

historical factors that may shape microbial communities may influence their function. In

future work, investigation of how microbial community composition develops on

different litter types and across time may facilitate a more detailed understanding of the

relationships between the taxonomic composition of microbial communities and their

function (Osono 2005; Trinder et al. 2008; Strickland et al. 2009). Such studies might

address whether compositionally distinct communities, when faced with similar

environments, develop in the same manner. They should consider which characteristics

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(e.g. lignin content) of litter chemistry likely influence this development both at earlier

and later stages of decomposition. They should also ask how long differences in the

functioning of microbial communities might persist once different communities are

exposed to the same environment. Given the rapid generation times of microbes it seems

plausible that microbial community functioning might rapidly converge in the same

environment (Strickland et al. 2009). Such information will add to our understanding of

the process of decomposition and may also enable prediction of how a given microbial

community will respond to environmental change.

Our study involved a short-term, laboratory, common garden approach to

determine the significance of microbial community structure and its resource history for

the initial decomposition rate of leaf litters of differing chemical composition. There are a

number of criticisms that can be leveled at our approach. These include the fact that we

do not know whether microfauna, which affect the activities of microbial decomposers,

were present in our inocula or not. Certainly, the functional symbiosis of plants and

mycorrhizal fungi, which may play a crucial role in decomposition (Talbot et al. 2008),

was absent. In addition autoclaving, as any sterilization technique, impacts soil and

potentially litter properties (Berns et al. 2008). However, in our study such non-target

effects would have been consistent within a given litter type or inoculum and hence

would not have been able to explain our most significant finding. That is litter chemistry

impacting decomposition rates of litters inoculated with soils from herbaceous but not

forested environments.

What our approach did provide was a tightly-controlled experiment which

demonstrated that the past resource environment of a microbial community impacted its

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ability to decompose litters differing in their chemistry. We cannot conclude to what

extent this effect influences decomposition rates in the field, but it does offer one

explanation for why different litter chemistries do not always lead to expected differences

in decomposition rates in situ (Hunt et al. 1988; Gholz et al. 2000; Castanho & de

Oliveira 2008; Vivanco & Austin 2008). Our work suggests that the differential

perception of litter chemistry by different microbial inocula may be explained by the

starting, community composition of microbial inocula and/or the resource status of the

environment from which they were derived. To fully understand decomposition dynamics

in the environment across space and time may require recognition and further exploration

of the role of microbial community composition and that community‘s resource history as

a driving variable.

Acknowledgements

We gratefully acknowledge funding from the Andrew W. Mellon Foundation, and from

the National Science Foundation to the Coweeta LTER. We thank Tom Maddox in the

Analytical Chemistry Laboratory of the Odum School of Ecology, Univ. of Georgia, for

element analyses; Dan Richter and Mac Callaham for establishing the permanent plots in

the Calhoun Experimental Forest; and Mr. Jack Burnett for allowing us to collect samples

on his property.

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Table 5.1 Initial %C, %N, C:N ratios, %Lignin, and pH of the litters.

Litter

Type

%C %N C:N %Non-

fibrous

%Hemi-

cellulose

%Cellulose %Lignin pH

R.

maximum

48.9 ±

0.24

0.42 ±

0.01

116 ±

1.70

60.91 ±

1.23

8.81 ±

0.22

17.73 ±

0.22

12.54 ±

1.15

4.5 ±

0.01

P.

virgatum

41.8 ±

0.07

0.62 ±

0.02

68 ±

2.32

38.14 ±

0.25

27.53 ±

0.52

29.08 ±

0.18

5.24 ±

0.38

5.2 ±

0.03

Values shown are means ± 1 SE (n=3).

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Table 5.2 Mantel test results showing correlations between either inoculum source

edaphic factors or initial inoculum community composition and cumulative CO2

production on either R. maximum or P. virgatum litter. Spearman‘s correlation

coefficients relate the calculated Euclidian distance between cumulative CO2 production

and edaphic factors; UniFrac distance is used for the community factors. Significant

correlation coefficients are reported in bold (P<0.05).

R. maximum litter P. virgatum Litter

Spearman's

correlation

coefficient

P-value Spearman's

correlation

coefficient

P-value

Edaphic factors

DOC (mg g dry weight soil-1

) 0.104 ns -0.063 ns

NO3- (μg g dry weight soil

-1)* 0.750 <0.01 0.044 ns

NH4+

(μg g dry weight soil-1

)* 0.480 <0.01 -0.20 ns

Total C (mg g dry weight soil-1

) 0.089 ns -0.137 ns

POM C (mg g dry weight soil-1

) 0.034 ns -0.108 ns

Mineral C (mg g dry weight soil-1

) -0.072 ns -0.029 ns

Total N (mg g dry weight soil-1

) -0.013 ns -0.012 ns

POM N (mg g dry weight soil-1

) 0.069 ns -0.073 ns

Mineral N (mg g dry weight soil-1

) 0.062 ns 0.203 ns

Extractable P (μg g dry weight soil-

1)*

0.314 <0.05 -0.032 ns

Ca+

(mg g dry weight soil-1

) 0.026 ns 0.034 ns

Mg+

(mg g dry weight soil-1

) -0.03 ns 0.120 ns

K+

(mg g dry weight soil-1

)* 0.251 <0.05 0.379 <0.05

C:N 0.094 ns 0.114 ns

C:P 0.152 ns -0.14 ns

N:P 0.101 ns -1.38 ns

Sand (%) 0.134 ns 0.032 ns

Silt (%) -0.145 ns -0.196 ns

Clay (%)* 0.244 <0.05 0.189 ns

pH -0.061 ns -0.014 ns

SIR (μg g dry weight soil-1

) 0.088 ns -0.077 ns

Microbial C (μg g dry weight soil-1

) 0.060 ns -0.145 ns

Microbial N (μg g dry weight soil-1

) 0.136 ns -0.167 ns

Community factors

Bacteria 0.417 <0.01 0.356 <0.05

Fungi 0.327 <0.05 0.017 ns

*Data were loge transformed for both litters, except for K+ which was only transformed

for R. maximum.

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Figure legends

Figure 5.1 Cumulative CO2 production from microcosms consisting of microbial inocula

sourced from four land-uses representing two cover-types combined with either R.

maximum or P. virgatum litters. Land-uses representing herbaceous cover-types are the

cultivated and pasture inocula and land-uses representing forest cover-types are the pine

and hardwood inocula. Values are means±1SE (n = 6). ANOVA results are based on

cover-type. A significant interaction between inoculum and litter was detected for

cumulative CO2 production (F1,20=12.71; P<0.01). Main effects of litter type and

inoculum were significant (F1,20=10.39; P<0.01) and not significant (F1,20=0.583;

P=0.454), respectively.

Figure 5.2 Relationships between cumulative CO2 production and the relative

abundances of three fungal taxa (Sordariomycete, Leotiomycete, and Agaricales) and two

bacterial taxa (Acidobacteria and Actinobacteria) on R. maximum (a-e) and P. virgatum

litter (f). Circles, inverted triangles, diamonds, and squares represent cultivated, pasture,

pine, and hardwood land-uses, respectively. Each individual plot within a given land-use

is indicated by a different shade (e.g. Cultivated plot 1 (Cu1 in the legend) is represented

by a white circle).

Figure 5.3 Relationships between cumulative CO2 production and the edaphic

characteristics of the site from which the inocula were sourced on R. maximum (a-e) and

P. virgatum litter (f). Symbols are as in Fig. 5.2.

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Figure 5.1

Inoculum ns

Litter **

Inoculum x Litter **

Litter type

P. virgatum R. maximum

Cum

ula

tive

CO

2-C

(m

g g

lit

ter-1

)

0

2

4

6

8

10

12

14

16

18

20

22Cultivated inoculum

Pasture inoculum

Pine inoculum

Hardwood inoculum

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Figure 5.2

Source habitat

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Source habitat

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Cu1

Cu2

Cu3

Pa1

Pa2

Pa3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Pi1

Pi2

Pi3

Ha1

Ha2

Ha3

Acidobacteria (%)0 10 20 30 40 50 60

Agaricales (%)0 20 40 60 80 100

Cu

mu

lati

ve C

O2-C

(m

g g

lit

ter-1

)

Leotiomycete (%)0 10 20 30 40 50

11

12

13

14

15

16

Actinobacteria (%)0 10 20 30 40 50

12

13

14

15

16

17

18

19

R. maximum

r2=0.42

P<0.05

Sordariomycete (%)0 20 40 60 80

11

12

13

14

15

16

R. maximum

r2=0.46

P<0.01

R. maximum

r2=0.36

P<0.05

R. maximum

r2=0.41

P<0.05

P. virgatum

r2=0.45

P<0.01

Chaetothyriomycete (%)

0 10 20 30

11

12

13

14

15

16

R. maximum

r2=0.57

P<0.01

a

b

c f

e

d

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Figure 5.3

NH4

+ (mg g dry weight soil

-1)

0 2 4 6 8

11

12

13

14

15

16

R. maximum

r2=0.54

P<0.01

NO3

- (mg g dry weight soil

-1)

0 10 20 30 40 50

11

12

13

14

15

16

Extractable P (mg g dry weight soil-1

)

0 5 10 15 20

Cum

ula

tive

CO

2-C

(m

g g

lit

ter-1

)

11

12

13

14

15

16

Clay (%)

0 5 10 15 20 25 30

K+

(mg g dry weight soil-1

)

0.02 0.06 0.10 0.14 0.18

12

13

14

15

16

17

18

19

K+

(mg g dry weight soil-1

)

0.02 0.06 0.10 0.14 0.18

R. maximum

r2=0.69

P<0.001

R. maximum

r2=0.63

P<0.01

R. maximum

r2=0.63

P<0.01

R. maximum

r2=0.33

P<0.05

P. virgatum

r2=0.30

P<0.05

a

b

c

d

e

f

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CHAPTER 6

CONCLUSIONS

The role that soil microbial community structure plays in regulating ecosystem processes

has been thought redundant, or dependent on binary distinctions of microbes (e.g.

fungal:bacterial dominance). In part the presumed functional redundancy of these

communities began with the famous quote, ―Everything is everywhere, the environment

selects‖ (Beijerinck 1913, Baas-Becking 1934). Whether this was the intention of

Beijerinck / Baas-Becking or not, this statement has been taken to suggest that either due

to their incredible diversity or adaptive abilities that microbial community function is

largely driven by the environment. Others have taken a more mechanistic approach to the

role soil microbial communities‘ play. Often this has relied on slotting components of

these communities into one of two groups such as zymogenous vs. autochthonous,

copiotrophs vs. oligotrophs, r-selected vs. K-selected, and bacteria vs. fungi

(Winogradsky 1924, Koch 2001, Fierer et al. 2007, van der Heijden et al. 2008).

Although such distinctions made an incredibly diverse community of organisms more

manageable, these binary distinctions overlook a grand spectrum of organisms and their

respective traits. Ultimately this has left us with the question, ―How is soil microbial

community structure related to ecosystem function?‖

My doctoral work was intended to begin addressing this question by examining

how microbial communities are related to ecosystem carbon (C)-processes. In the first of

these studies (Chapter 2) I set out to describe the role that soil microbial communities

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played in the mineralization of glucose. Glucose is a low molecular weight organic

compound found in root exudates, as well as leachates derived from plant litter, and

likely represents a broadly available form of C to microbes (van Hees et al. 2005). I

expected that the mineralization of this compound would likely be related to some

component of the microbial community such as its size, activity, or fungal:bacterial

dominance. Surprisingly, glucose mineralization was related to none of these variables

but was related to the land-use that these communities were found in and more

specifically land-use associated variation in soil phosphorus. This may well be an

indication of the land-use legacy that these communities have endured (Richter and

Markewitz 2001). That is, the land-use regimes found at the Calhoun Experimental Forest

are for the most part products of 100 years of extensive cotton agriculture but upon

cessation of these practices have been managed differently (Callaham et al. 2006).

Specifically, that these communities share a similar history prior to cotton agriculture and

at the time were likely broadly similar with regards to their structure and function.

However, post-cotton these communities were exposed to differing land-use/management

regimes which ultimately influenced these characteristics. In light of this finding it

appears that at least microbial community function was influenced by its environment

and further collaborative work at these sites indicated that community composition below

the level of fungal:bacterial dominance was also influenced (Lauber et al. 2008).

Results from my examination of Microstegium vimineum invasion of hardwood

forests revealed that changes in the soil microbial community altered C-processes

(Chapter 3). Like my work in the Calhoun, I found that there was little change in the

fungal:bacterial dominance of the microbial community but in spite of this, these

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communities were functionally distinct. This functional distinction resulted in more rapid

C-cycling associated with those communities where M. vimineum was present. This study

also suggested that the function of the soil microbial communities was primed by inputs

from the invasive plant. This outcome was likely contingent on the chemical quality of

M. vimineum in comparison to the chemical quality of native-derived plant C (Kuzyakov

et al. 2000, Litton et al. 2008). This may indicate that the functional response of soil

microbial communities is in part related to their contemporary environment. This

invasion may also indicate the sort of events which lead to functionally distinct microbial

communities. Like the research at the Calhoun, the forest microbial community in this

study shared a similar historical legacy but the invasion of M. vimineum altered this

legacy for a spatial subset of the community. Whether this will lead to permanent

functional and/or compositional divergence in these communities is as yet unknown. It

does though suggest one mechanism, the historic resource environment, which is likely to

influence the function of soil microbial communities with regards to C-processes.

The influence of the historic resource environment was resolved in the first of

my lab-based experimental studies (Chapter 4). In this work I found that microbial

communities were not functionally redundant and that communities paired with their

‗home‘ litter resource typically exhibited the highest litter mineralization rates for that

litter. This suggested that microbial communities which are pre-exposed to a given litter

substrate are subsequently better at decomposing it. This phenomenon has been termed

‗home-field advantage‘ (Hunt et al. 1988, Gholz et al. 2000, Ayres et al. 2009).

Interestingly, I also found that communities sourced from different environments in

combination with the most chemically-complex litter resource were compositionally the

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most similar but functionally the most dissimilar. This may imply that the contemporary

environment is a structuring force with regards to the composition of microbial

communities but that the historical environment is the dominant structuring force with

regards to function.

In my second lab-based experiment (Chapter 5) I found further confirmation of

the influence of the historic environment on function. I paired two novel litter resources

with microbial communities sourced from either forest or herbaceous habitats. We found

that the mineralization of each litter (i.e. the perception of litter quality) was dependent

on the environment that the microbial community was sourced from. Specifically,

communities sourced from forest environments perceived no differences in quality

between the chemically-complex and -simple litters. In contrast, communities sourced

from herbaceous habitats perceived the chemically-simple litter to be of greater quality

than the chemically-complex one. This indicates that the function of soil microbial

communities might be further generalized with regards to their past exposure to different

C-compounds (Table 6.1).

Madsen (2005) argued that one of the major unknowns in microbial ecology was

the link between microbial communities and C-processes. Although my dissertation work

does not link specific soil microorganisms to specific C-processes, it does suggest that the

functioning of soil microbial communities is shaped by a history of differing C-inputs

(Fig. 6.1). This provides us with one possible mechanism which leads to functional

dissimilarity in soil microbial communities (Fig. 6.1). Although we may never be able to

determine if everything is everywhere, we can say that in part the environment does

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select with regards to function but that this selection is contingent on both the

contemporary and historical environment of the soil microbial community.

Future Directions

One of the unanswered questions from my work is whether or not soil microbial

communities from differing historic environments are likely to converge functionally,

given enough time, when placed in the same contemporary environment. This is an area

of research which I am currently exploring. I plan to first determine if functional

convergence occurs. Second, if it does how long it takes and, finally, whether

convergence is associated with loss of function in alternate environments (i.e. a

‗functional trade-off‘). The results from this work will likely inform us as to the impact

that a changing environment is likely to have on the role that soil microbial communities

play in regulating ecosystem processes.

Other avenues of future research involve determining the legacy of historical

inputs on the function of soil microbial communities. Specifically, if communities share a

common history but a dissimilar contemporary environment then is function still broadly

similar amongst these communities? For example, in the Calhoun microbial communities

likely shared a common history but are now found in distinct contemporary

environments. So, we can ask if the function of these Calhoun communities is more

similar to each other than to historically-distinct communities, or whether it is the

contemporary environment which exerts the strongest influence on function.

Finally, I believe it is important to determine whether it is the input history of C-

compounds alone which impacts soil microbial community function. The structure of soil

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microbial communities is influenced by an array of factors besides input history, such as

soil pH and nutrient status. However, whether or not these structuring forces also impact

the function of microbial communities has for the most part not been explored. I believe

that controlled-experimentation should be first used to explore these questions, but

ultimately that there is a need to explore the significance of these findings for the

regulation of ecosystem processes in situ.

References

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226

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227

van Hees, P. A. W., D. L. Jones, R. Finlay, D. L. Godbold, and U. S. Lundstomd. 2005.

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Winogradsky, S. 1924. Sur la microflora autochtone de la terre arable. Comptes Rendus

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228

Table 6.1 Generalized functional implications for soil microbial communities with

distinct histories of high and low chemical quality inputs as they relate to C-cycling. The

realization and strength of these generalized outcomes will likely be contingent on the

resource history of one microbial community relative to another. For example, a

microbial community from an unfertilized grassland may be considered to have a high

quality resource history when compared to an old growth forest but is likely low quality

when compared to a fertilized grassland.

Resource History

High quality Low quality

1. Mineralization of low molecular

weight organic compounds

(LMWOC) such as glucose is high.

Possibly because LMWOC

compounds are major C inputs in

these systems or because

communities are not C-limited.

2. Perception of litter quality is

related to the litter‘s chemical

recalcitrance.

3. Increasing labile C inputs may lead

to an increase or no change in soil

C pools due to preferential

substrate utilization.

4. Relatively poor decomposers of an

array of litter resources.

1. Mineralization of LMWOC

compounds is low. Possibly because

LMWOC compounds are minor C

inputs in these systems or because

communities are C-limited.

2. Perception of litter quality is not or

less strongly related to the litter‘s

chemical recalcitrance.

3. Increasing labile C inputs may lead

to a decrease in soil C-pools due to a

priming effect.

4. Relatively good decomposers of an

array of litter resources but this may

be confounded by home-field

advantage.

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Figure Legends

Figure 6.1 A schematic showing how alterations in the quality of C-compounds may

impact the function of soil microbial communities across time. Community X represents

the ancestral community. Community X1, 2 … represent communities distinct with regards

to their function. Each node (●) represents environmental change leading to an alteration

in the quality of C inputs a given subset of the ancestral community is exposed to. In this

example, subsets of Community X are impacted by an environmental change which alters

the quality of C inputs they are exposed to leading to two functionally dissimilar

communities, Community X1 and X2. Communities X1 and X2 are again impacted by an

environmental change leading to the functionally distinct communities of X1.1, X1.2, X2.1,

and X2.2. Finally, Communities X1.1 and X1.2 are impacted by the same change

experienced by the ancestral community (i.e. X1) leading to convergence of these two

communities and a return to the functioning of the ancestral community. On the other

hand communities X2.1 and X2.2 experience a similar perturbation but never functionally

converge. Environmental change may be as dramatic as land-use change or fertilizer

inputs or even as minor as a change from one tree species to another within a forest. The

question marks represent a change in input quality to those found one community back

and is related to whether or not microbial communities will converge functionally.

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Figure 6.1

Community X

?

Community X1

Community X2.1

Community X2.2

Community X2

Community X1.1

Community X1.2

Community X1

Community X2.1.1

Community X2.2.1

?

Time

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APPENDIX A

REFERENCES USED TO CONSTRUCTUCT FIGURE 1.1

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242

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243

Vong, P. C., S. Piutti, S. Slezack-Deschaumes, E. Benizri, and A. Guckert. 2008. Sulphur

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Wallenstein, M. D., S. McNulty, I. J. Fernandez, J. Boggs, and W. H. Schlesinger. 2006.

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long-term experiments. Forest Ecology and Management 222:459-468.

Weyman-Kaczmarkowa, W., and Z. Pedziwilk. 2000. The development of fungi as

affected by pH and type of soil, in relation to the occurrence of bacteria and soil

fungistatic activity. Microbiological Research 155:107-112.

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miospores in buried Middle Devonian (Givetian) paleosols: Indicators of

weathering, oxidation and maturity. Catena 28:1-19.

Williamson, W. M., D. A. Wardle, and G. W. Yeates. 2005. Changes in soil microbial

and nematode communities during ecosystem decline across a long-term

chronosequence. Soil Biology & Biochemistry 37:1289-1301.

Yeates, G. W., and H. vanderMeulen. 1996. Recolonization of methyl-bromide sterilized

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21:1-6.

Zhang, W. J., W. Y. Rui, C. Tu, H. G. Diab, F. J. Louws, J. P. Mueller, N. Creamer, M.

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APPENDIX B

REFERENCES USED TO CONSTRUCTUCT FIGURE 1.2

References

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1251.

Chrzanowski, T. H., and M. Kyle. 1996. Ratios of carbon, nitrogen and phosphorus in

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245

Fagerbakke, K. M., M. Heldal, and S. Norland. 1996. Content of carbon, nitrogen,

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Microbial Ecology 10:15-27.

Fukuda, R., H. Ogawa, T. Nagata, and I. Koike. 1998. Direct determination of carbon and

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Hart, S. C., C. A. Gehring, P. C. Selmants, and R. J. Deckert. 2006. Carbon and nitrogen

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Norland, S., K. M. Fagerbakke, and M. Heldal. 1995. Light-Element Analysis of

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APPENDIX C

CANDIDATE MODEL SET

Table C.1 List of candidate models used to describe cumulative glucose mineralized

(13

CO2). The first 30 models and the intercept only model were the initial set of candidate

models used. All models were compared as the secondary set of candidate models.

# Model

13CO2 = 1

1 13CO2 = MicC

2 13CO2 = SIR

3 13CO2 = F:B

4 13CO2 = MicC + SIR

5 13CO2 = MicC + F:B

6 13CO2 = SIR + F:B

7 13CO2 = MicC + SIR + F:B

8 13CO2 = Seas. + MicC

9 13CO2 = Seas. + SIR

10 13CO2 = Seas. + F:B

11 13CO2 = Seas. + MicC + SIR

12 13CO2 = Seas. + MicC + F:B

13 13CO2 = Seas. + SIR + F:B

14 13CO2 = Seas. + MicC + SIR + F:B

15 13CO2 = Land

16 13CO2 = Land + Seas.

17 13CO2 = Land + MicC

18 13CO2 = Land + SIR

19 13CO2 = Land + F:B

20 13CO2 = Land + MicC + SIR

21 13CO2 = Land + MicC + F:B

22 13CO2 = Land + SIR + F:B

23 13CO2 = Land + MicC + SIR + F:B

24 13CO2 = Land + Seas. + MicC

25 13CO2 = Land + Seas. + SIR

26 13CO2 = Land + Seas. + F:B

27 13CO2 = Land + Seas. + MicC + SIR

28 13CO2 = Land + Seas. + MicC + F:B

29 13CO2 = Land + Seas. + SIR + F:B

30 13CO2 = Land + Seas. + MicC + SIR + F:B

31 13CO2 = Moist.*Temp.

32 13CO2 = Moist.*Temp. + Seas.

33 13CO2 = Moist.*Temp. + MicC

34 13CO2 = Moist.*Temp. + SIR

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Appendix C (cont.)

# Model

35 13CO2 = Moist.*Temp. + F:B

36 13CO2 = Moist.*Temp. + MicC + SIR

37 13CO2 = Moist.*Temp. + MicC + F:B

38 13CO2 = Moist.*Temp. + SIR + F:B

39 13CO2 = Moist.*Temp. + MicC + SIR + F:B

40 13CO2 = Moist.*Temp. + Seas. + MicC

41 13CO2 = Moist.*Temp. + Seas. + SIR

42 13CO2 = Moist.*Temp. + Seas. + F:B

43 13CO2 = Moist.*Temp. + Seas. + MicC + SIR

44 13CO2 = Moist.*Temp. + Seas. + MicC + F:B

45 13CO2 = Moist.*Temp. + Seas. + SIR + F:B

46 13CO2 = Moist.*Temp. + Seas. + MicC + SIR + F:B

47 13CO2 = C:N

48 13CO2 = C:N + Seas.

49 13CO2 = C:N + MicC

50 13CO2 = C:N + SIR

51 13CO2 = C:N + F:B

52 13CO2 = C:N + MicC + SIR

53 13CO2 = C:N + MicC + F:B

54 13CO2 = C:N + SIR + F:B

55 13CO2 = C:N + MicC + SIR + F:B

56 13CO2 = C:N + Seas. + MicC

57 13CO2 = C:N + Seas. + SIR

58 13CO2 = C:N + Seas. + F:B

59 13CO2 = C:N + Seas. + MicC + SIR

60 13CO2 = C:N + Seas. + MicC + F:B

61 13CO2 = C:N + Seas. + SIR + F:B

62 13CO2 = C:N + Seas. + MicC + SIR + F:B

63 13CO2 = S+C

64 13CO2 = S+C + Seas.

65 13CO2 = S+C + MicC

66 13CO2 = S+C + SIR

67 13CO2 = S+C + F:B

68 13CO2 = S+C + MicC + SIR

69 13CO2 = S+C + MicC + F:B

70 13CO2 = S+C + SIR + F:B

71 13CO2 = S+C + MicC + SIR + F:B

72 13CO2 = S+C + Seas. + MicC

73 13CO2 = S+C + Seas. + SIR

74 13CO2 = S+C + Seas. + F:B

75 13CO2 = S+C + Seas. + MicC + SIR

76 13CO2 = S+C + Seas. + MicC + F:B

77 13CO2 = S+C + Seas. + SIR + F:B

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Appendix C (cont.)

# Model

78 13CO2 = S+C + Seas. + MicC + SIR + F:B

79 13CO2 = pH

80 13CO2 = pH + Seas.

81 13CO2 = pH + MicC

86 13CO2 = pH + SIR + F:B

87 13CO2 = pH + MicC + SIR + F:B

88 13CO2 = pH + Seas. + MicC

89 13CO2 = pH + Seas. + SIR

90 13CO2 = pH + Seas. + F:B

91 13CO2 = pH + Seas. + MicC + SIR

92 13CO2 = pH + Seas. + MicC + F:B

93 13CO2 = pH + Seas. + SIR + F:B

94 13CO2 = pH + Seas. + MicC + SIR + F:B

95 13CO2 = P

96 13CO2 = P + Seas.

97 13CO2 = P + MicC

98 13CO2 = P + SIR

99 13CO2 = P + F:B

100 13CO2 = P + MicC + SIR

101 13CO2 = P + MicC + F:B

102 13CO2 = P + SIR + F:B

103 13CO2 = P + MicC + SIR + F:B

104 13CO2 = P + Seas. + MicC

105 13CO2 = P + Seas. + SIR

106 13CO2 = P + Seas. + F:B

107 13CO2 = P + Seas. + MicC + SIR

108 13CO2 = P + Seas. + MicC + F:B

109 13CO2 = P + Seas. + SIR + F:B

110 13CO2 = P + Seas. + MicC + SIR + F:B

SIR = substrate induced respiration, a measure of microbial activity; MicC = microbial

biomass C as determined via sCFE, a measure of the size of the microbial biomass; F:B =

fungal-to-bacterial dominance as determined via qPCR, a measure of community

composition; Seas. = Season, either winter or summer when the pulse-chase experiment

was conducted. Land = land-use. Moist.*Temp. = the interaction between moisture and

temperature. C:N = the soil C:N ratio. S+C = silt plus clay content of the soil. pH = Soil

pH. P = extractable phosphorous.

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APPENDIX D

FIGURE SHOWING SOIL RESPIRATION DURING THE PULSE-CHASE

EXPERIMENT

Figure D.1 Mean soil respiration ±1 S.E. per land-use (n=3) during the summer (a) and

winter (b) across the 72h pulse-chase. The x-axis denotes the time since glucose was

added. Glucose was added at time zero as indicated by the arrow.

(b) Summer (June)

Time since glucose added (h)

-2 0 2 4 6 20 40 60 80

CO

2-C

(m

g m

-2 h

-1)

0

50

100

150

200

250

300

350

400

(a) Winter (December)

0

20

40

60

80

100

120

140

160

180Cultivated

Pasture

Pine

Hardwood

Glucose added

Glucose added

CO

2-C

(m

g m

-2 h

-

1)

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APPENDIX E

PARAMETER ESTIMATES

Tables showing the restricted maximum likelihood (REML) estimates for models from

Tables 2.2 and 2.3 which are within the 95% confidence interval.

Table E.1 Restricted maximum likelihood (REML) estimates for models from Table 2.2

which are within the 95% confidence interval.

Model Factor Estimate SE d.f. t P 13CO2 = Land*

random effects Intercept 0.05 Residual 0.35

fixed effects

Intercept 3.50 0.15 12 23.85 < 0.001 Land-Ha -0.72 0.21 8 -3.46 <0.01 Land-Pa 0.38 0.21 8 1.85 0.10 Land-Pi -0.35 0.21 8 -1.69 0.13 13CO2 = 1*

random effects

Intercept 0.41

Residual 0.36

fixed effects

Intercept 3.33 0.14 12 24.09 < 0.001 13CO2 = MicC*

random effects

Intercept 0.42

Residual 0.35

fixed effects

Intercept 3.48 0.19 11 18.06 < 0.001 MicC -0.02 0.02 11 -1.13 0.28 13CO2 = F:B*

random effects

Intercept 0.40

Residual 0.37

fixed effects

Intercept 3.35 0.14 11 23.73 < 0.001 F:B -0.39 0.82 11 -0.48 0.64 13CO2 = SIR*

random effects

Intercept 0.40

Residual 0.37

fixed effects

Intercept 3.29 0.27 11 12.08 < 0.001 SIR 0.32 1.78 11 0.18 0.86

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Table E.2 Restricted maximum likelihood (REML) estimates for models from Table 3

which are within the 95% confidence interval.

Model Factor Estimate SE d.f. t P 13CO2 = P*

random effects

Intercept 0.02

Residual 0.35

fixed effects

Intercept 2.81 0.12 12 23.72 <0.001 P 0.48 0.09 10 5.48 <0.001 13CO2 = P + MicC*

random effects

Intercept 0.09

Residual 0.34

fixed effects

Intercept 2.91 0.16 11 18.31 <0.001 P 0.48 0.09 10 5.22 <0.001

Mic C -0.02 0.02 11 -0.95 0.36 13CO2 = P + SIR*

random effects

Intercept 0.00

Residual 0.36

fixed effects

Intercept 2.68 0.23 11 11.88 <0.001 P 0.48 0.09 10 5.43 <0.001

SIR 0.98 1.45 11 0.68 0.51 13CO2 = P + Seas.*

random effects

Intercept 0.00

Residual 0.36

fixed effects

Intercept 2.77 0.14 11 19.68 <0.001 P 0.48 0.09 10 5.42 <0.001

Seas. (Winter) 0.09 0.15 11 0.64 0.53

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APPENDIX F

TABLE SHOWING THE INITIAL %C, %N, AND THE C:N RATIOS OF THE

LITTERS AND THE SOIL INOCULA

Table F.1

Source %C %N C:N

Rhododendron

Litter 48.9 ± 0.24 0.42 ± 0.01 116 ± 1.70

Pine Litter 47.3 ± 0.67 0.66 ± 0.01 96.7 ± 1.81

Grass Litter 43.4 ± 0.27 0.49 ± 0.02 66.3 ± 1.60

Rhododendron

Inoc. 4.5 ± 0.16 0.18 ± 0.01 25.3 ± 1.42

Pine Inoc. 2.7 ± 0.24 0.13 ± 0.01 21.5 ± 0.57

Grass Inoc. 3.3 ± 0.54 0.32 ± 0.03 10.1 ± 0.73

Values shown are means ± 1 SE (n=3).

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APPENDIX G

BRAY-CURTIS SIMILARITY IN MICROBIAL COMMUNITY FUNCTION

Table G.1 Bray-Curtis similarity (%) matrix between the cumulative mineralization for

each inoculum for rhododendron litter during the entire incubation period (All periods)

and for each incubation period (Days 2-25, 26-99, and 100-300). Similarities were

calculated from the mean of eight replicates per treatment. The home inoculum

(Rhododendron) is in bold.

Incubation

period

Inoculum Source Rhododendron Pine Grass

All periods Rhododendron 100.00

Pine 65.13 100.00

Grass 33.23 58.41 100.00

Days 2-25 Rhododendron 100.00

Pine 93.81 100.00

Grass 99.88 93.69 100.00

Days 26-99 Rhododendron 100.00

Pine 54.23 100.00

Grass 39.56 79.73 100.00

Days 100-300 Rhododendron 100.00

Pine 67.15 100.00

Grass 22.79 40.56 100.00

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Table G.2 Bray-Curtis similarity (%) matrix between the cumulative mineralization for

each inoculum for pine litter during the entire incubation period (All periods) and for

each incubation period (Days 2-25, 26-99, and 100-300). Similarities were calculated

from the mean of eight replicates per treatment. The home inoculum (Pine) is in bold.

Incubation

period

Inoculum Source Rhododendron Pine Grass

All periods Rhododendron 100.00

Pine 84.42 100.00

Grass 76.56 62.36 100.00

Days 2-25 Rhododendron 100.00

Pine 99.83 100.00

Grass 98.28 98.45 100.00

Days 26-99 Rhododendron 100.00

Pine 57.11 100.00

Grass 84.15 45.00 100.00

Days 100-300 Rhododendron 100.00

Pine 95.38 100.00

Grass 65.89 70.04 100.00

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Table G.3 Bray-Curtis similarity (%) matrix between the cumulative mineralization for

each inoculum for grass litter during the entire incubation period (All periods) and for

each incubation period (Days 2-25, 26-99, and 100-300). Similarities were calculated

from the mean of eight replicates per treatment. The home inoculum (Grass) is in bold.

Incubation

period

Inoculum Source Rhododendron Pine Grass

All periods Rhododendron 100.00

Pine 93.67 100.00

Grass 97.70 91.38 100.00

Days 2-25 Rhododendron 100.00

Pine 93.00 100.00

Grass 86.12 93.05 100.00

Days 26-99 Rhododendron 100.00

Pine 89.48 100.00

Grass 84.05 94.48 100.00

Days 100-300 Rhododendron 100.00

Pine 95.14 100.00

Grass 83.94 79.24 100.00

Page 267: ECOSYSTEM CARBON CYCLING: …coweeta.uga.edu/publications/10379.pdfECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL COMMUNITY STRUCTURE by MICHAEL SCOTT STRICKLAND (Under

257

Figure G.1 Bray-Curtis similarity (%) between the decomposer community function

across all incubation periods for the three soil inocula in rhododendron litter (A), pine

litter (B), and grass litter (C). The ―home‖ soil inoculum is highlighted in bold type.

Pine

Group average

DS

DC

DD

Sa

mp

les

100908070605040

Similarity

Resemblance: S17 Bray Curtis similarity

Grass

Group average

SD

SC

SS

Sa

mp

les

100908070605040

Similarity

Resemblance: S17 Bray Curtis similarity

Grass Inoculum

Rhodo. Inoculum

Grass Inoculum

Rhodo. Inoculum

Pine Inoculum

A

B

C

Rhodo

Group average

CS

CC

CD

Sa

mp

les

100806040

Similarity

Resemblance: S17 Bray Curtis similarity

Rhodo. Inoculum

Pine Inoculum

40 60 80 100

Similarity (%)

Pine Inoculum

Grass Inoculum

Page 268: ECOSYSTEM CARBON CYCLING: …coweeta.uga.edu/publications/10379.pdfECOSYSTEM CARBON CYCLING: RELATIONSHIPS TO SOIL MICROBIAL COMMUNITY STRUCTURE by MICHAEL SCOTT STRICKLAND (Under

258

APPENDIX H

EDAPHIC CHARACTERISTICS FOR THE CALHOUN PLOTS

Table H.1 Edaphic characteristics measured at each plot. Cultivated and pasture sites were classified as herbaceous-cover and pine

and hardwood plots were classified as forest-cover. Means of three analytical repeats are shown.

Land-use Plot DOC (μg g

dry wt soil-1)

NO3- (μg

g dry wt

soil-1)

NH4+ (μg

g dry wt

soil-1)

Total C

(mg g

dry

weight

soil-1)

POM C

(mg g

dry

weight

soil-1)

Mineral

C (mg g

dry

weight

soil-1)

Total N

(mg g

dry

weight

soil-1)

POM N

(mg g

dry

weight

soil-1)

Mineral

N (mg g

dry

weight

soil-1)

Total P

(μg g dry

wt soil-1)

Cultivated Cu1 166.91 11.54 0.64 15.64 6.03 9.61 0.95 0.24 0.71 17.13

Cultivated Cu2 129.94 18.04 0.58 10.96 3.57 7.39 0.88 0.19 0.69 16.74

Cultivated Cu3 183.88 46.45 0.75 10.42 4.17 6.25 0.69 0.16 0.53 18.59

Pasture Pa1 72.95 10.09 0.84 9.14 5.21 3.93 0.68 0.30 0.39 9.91

Pasture Pa2 59.59 9.50 0.95 9.22 5.38 3.84 0.64 0.27 0.37 18.86

Pasture Pa3 54.95 26.34 1.71 11.27 7.67 3.61 0.78 0.43 0.35 18.94

Pine Pi1 38.64 0.04 5.47 8.21 4.52 3.69 0.33 0.12 0.21 1.02

Pine Pi2 73.56 0.02 4.05 8.18 4.66 3.53 0.56 0.24 0.31 13.45

Pine Pi3 114.33 0.00 0.77 9.52 5.20 4.32 0.30 0.12 0.18 2.30

Hardwood Ha1 146.66 0.17 7.54 13.88 7.07 6.80 0.77 0.25 0.52 1.07

Hardwood Ha2 242.11 0.00 3.23 24.94 14.73 10.21 0.90 0.45 0.45 3.55

Hardwood Ha3 96.95 10.33 4.13 16.10 7.93 8.17 0.82 0.27 0.55 0.74