A Dissertation. Entitled. Microbial Community, and Nitrogen Availability. Zachary L. Rinkes. The Doctor of Philosophy Degree in Biology (Ecology)

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1 A Dissertation Entitled Understanding the Mechanisms of Decay: Interactive Effects of Litter Chemistry, the Microbial Community, and Nitrogen Availability by Zachary L. Rinkes Submitted to the Graduate Faculty as partial fulfillment of the requirements for The Doctor of Philosophy Degree in Biology (Ecology) Dr. Michael N. Weintraub, Committee Chair Dr. Jared L. DeForest, Committee Member Dr. A. Stuart Grandy, Committee Member Dr. Daryl L. Moorhead, Committee Member Dr. William Von Sigler, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo May 2014

2 Copyright 2014, Zachary L. Rinkes This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

3 An Abstract of Understanding the Mechanisms of Decay: Interactive Effects of Litter Chemistry, the Microbial Community, and Nitrogen Availability by Zachary L. Rinkes Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biology (Ecology) The University of Toledo May 2014 Fundamental questions remain about plant litter decomposition, which is a key control on carbon (C) turnover in terrestrial ecosystems. While it is known that decomposition involves both chemical changes in leaf litter and a succession of microorganisms that consume the various chemical constituents, the underlying biochemical mechanisms are not well understood. Hence, it is difficult to predict the magnitude and often the direction of microbial responses to environmental disturbance. This dissertation examines the changes in microbial community function and composition that occur in response to changes in litter chemistry during decomposition, as well as to environmental factors, in order to quantify the mechanisms regulating plant litter C turnover. In a laboratory incubation of sugar maple litter, decomposers preferentially used soluble substrates, and shifts in functional groups of microorganisms with different enzymatic capabilities and growth rates occurred throughout decay. This provides experimental evidence that microbial uptake of C substrates during decomposition occurs in sequence, with different decomposer groups targeting different substrates. In a iii

4 complementary incubation of sugar maple and white oak litter, peaks in microbial respiration and biomass, low enzyme activities, and nutrient immobilization predominated during the first few days of decomposition across contrasting litter and soil types. This implies that rapid assimilation of soluble substrates by decomposers occurred during early decay. In contrast to these consistent features of early decomposition observed in microcosms, the same leaf litter (i.e., dogwood, sugar maple, and white oak) exhibited strikingly different decay patterns when decomposed under field and lab conditions during long-term decomposition. Nitrogen (N) accumulated in the lab microcosms, but not the field litter bags, and suppressed microbial biomass and activity in mid- and late decay. Nitrogen fertilization also influenced microbial dynamics, but not lignin monomer concentrations, in a separate incubation of three maize genotypes varying in lignin content. Exogenous N decreased oxidative enzyme activities across all maize treatments, which suggests that decomposers degrade lignin to obtain shielded N compounds and decrease production of lignin-degrading enzymes when labile N availability increases. The findings in this dissertation provide evidence that there is a predictable microbial succession tied to litter chemistry during decay, and describe how and why environmental factors alter microbial community dynamics and decomposition rates. iv

5 Dedicated to Madelyn and Anderson. v

6 Acknowledgements I would like to express my sincere gratitude to those individuals that have assisted in this work. First and foremost I would like to thank my advisor Dr. Michael Weintraub for his support, encouragement, and guidance over the last six years. I sincerely appreciate the help and constructive comments from my committee members: Drs. Jared DeForest, Stuart Grandy, Daryl Moorhead, and Von Sigler. I thank the National Science Foundation and Department of Environmental Sciences for funding support throughout the course of this study. Many thanks go to past and present members of the Ecosystem and Soil Ecology Laboratory: Bethany Chidester, Dashanne Czegledy, Anthony Darouzzet-Nardi, Mike Elk, Jared Hawkins, Danielle Kurek, Mallory Ladd, Darian Marinis, Ryan Monnin, Steve Solomon, Heather Thoman, Logan Thornsberry, Eric Wellman, Travis White, and Megan Wenzel. I also thank Jason Witter for help with sample preparation for PLFA analysis and Jonathon Frantz, Russ Friederich, Doug Sturtz, and Bryon Hand from the USDA- ARS for help with CN analysis. I greatly appreciate the continued support and encouragement from my family. In particular, I wish to thank my wife, Katie, who has also endured the stresses and celebrations of graduate life. I definitely could not have finished this journey without you. vi

7 Table of Contents Abstract... iii Acknowledgements... vi Table of Contents... vii List of Tables xii List of Figures... xiii Preface...xv 1 Introduction..1 2 Microbial substrate preference and community dynamics during decomposition of Acer saccharum...8 Abstract Introduction Materials and methods Sample collection Incubation experiment Microbial respiration Enzyme assays Microbial biomass and nutrient analyses PLFA analysis...19 vii

8 2.2.7 Statistics Results C mineralization and biomass dynamics Enzyme activities Nutrient dynamics Microbial community structure Discussion Microbial substrate preference during decay Microbial community dynamics Conclusions Interactions between leaf litter quality, particle size, and microbial community during the earliest stage of decay...43 Abstract Introduction Materials and methods Sample collection Incubation experiment Microbial respiration Enzyme assays Microbial biomass and nutrients PLFA analysis Statistics...56 viii

9 3.3 Results C mineralization Microbial biomass and enzyme activities Nutrient dynamics Phospholipid fatty acid profiles Discussion Microbial dynamics early in decay Controls on early microbial dynamics Priming effect Conclusions Field and lab conditions alter microbial enzyme and biomass dynamics driving decomposition of the same leaf litter...81 Abstract Introduction Materials and methods Study site Litter collection Field litter bag study Laboratory incubation Microbial respiration and mass loss Microbial biomass and nutrients Enzyme assays Data analysis...93 ix

10 4.3 Results Field study - climate and mass loss relationships Lab incubation mass loss Biomass dynamics Inorganic nutrients Enzyme activity Turnover activity Enzyme activity ratios Discussion Temperature control of microbial activity Carbon and phosphorus effects on microbial activity Environmental influences on biomass and enzyme dynamics The influence of edaphic factors on microbial function Implications for decomposition models Conclusions Nitrogen alters microbial enzyme dynamics but not lignin monomer concentrations during maize decomposition Abstract Introduction Materials and methods Maize genotypes Soil collection Incubation experiment x

11 5.2.4 C mineralization Microbial biomass Enzyme assays Phenol chemistry Data analysis Results C mineralization Microbial biomass dynamics Enzyme activities Phenol chemistry Discussion N, ph, and litter quality effects on microbial function N, ph, and litter quality effects on lignin monomer concentrations N, ph, and litter quality effects on microbial respiration and biomass Conclusions Conclusion References.158 xi

12 List of Tables 2.1 MANOVA results for enzyme activities, microbial biomass, and nutrients Predictions for how litter and soil characteristics influence decay General soil properties for Udipsamment soils Cumulative respiration for soil/litter treatments Decay rate coefficients for litters in field and lab Chemical characteristics of maize genotypes Soil properties for soils used in maize incubation xii

13 List of Figures 2-1 Respiration rates and biomass over time for litter treatment and control Hydrolytic enzyme activities over time for litter treatment and control Oxidative enzyme activities over time for litter treatment and control Nutrient concentrations over time for litter treatment and control Enzyme activities versus nutrient concentrations Changes in the most abundant PLFA biomarkers Principal component analysis of the variation in microbial composition Schematic of litter and soil effects on microbial dynamics Respiration rates over the initial 2-weeks of decay Cumulative C respired and the priming effect Dissolved organic C concentrations over the initial 2-weeks of decay Hydrolytic enzyme activities over the initial 2-weeks of decay Nutrient concentrations over the initial 2-weeks of decay mean % mol fraction of the most abundant PLFA s Climatic conditions and mass loss for the field study Biomass to litter mass ratios in the field and lab Nutrient concentrations in field and lab Enzyme activities in the field and lab xiii

14 4-5 Turnover activities during different decay stages C:N and C:P acquisition ratios at different decay stages Cumulative C mineralized with and without added N Microbial biomass in both soils with and without N Phenol oxidase activity for three maize genotypes Cumulative phenol oxidase and chitinase activity S and V unit concentrations over time for three maize genotypes S/V ratios for three maize genotypes Cn unit and total lignin monomer concentrations for three maize genotypes xiv

15 Preface The chapters in this dissertation are organized in the order of completion and publication. Chapter 2 has been previously published as: Rinkes, Z.L., Weintraub, M.N., DeForest, J.L., Moorhead, D.L. (2011) Microbial substrate preference and community dynamics during the decomposition of Acer saccharum. Fungal Ecology, 4, Chapter 3 has been previously published as: Rinkes, Z.L., DeForest, J.L., Grandy, A.S., Moorhead, D.L., Weintraub, M.N. (2014) Interactions between leaf litter quality, particle size, and microbial community during the earliest stage of decay. Biogeochemistry, 117, Chapter 4 has been previously published as: Rinkes, Z.L., Sinsabaugh, R.L., Moorhead, D.L., Grandy, A.S., Weintraub, M.N. (2013) Field and lab conditions alter microbial enzyme and biomass dynamics driving decomposition of the same leaf litter. Frontiers in Microbiology 4: 260 doi: /fmicb Chapter 5 is in preparation for publication. xv

16 Chapter 1 Introduction 1.1 Background Plant litter decomposition is a key control regulating carbon (C) turnover in terrestrial ecosystems (Schlesinger & Andrews, 2000). Recently, there has been much interest in determining the conditions that either accelerate or mitigate plant litter C gains and losses, because potential changes in plant litter decomposition rates could play a role in global warming (Treseder et al., 2011). The dynamics of decomposition have been studied extensively and it is well known that complex interactions between litter chemistry and microbial communities, which change through time in response to each other, regulate the trajectory of decay (Waksman et al., 1928; Berg & Meentemeyer, 2002; Wickings et al., 2012). Although it is recognized that the microbial community and litter chemistry jointly control the amount of litter C metabolized or mineralized at different stages of decomposition, the underlying biochemical mechanisms driving their interactions are not well understood. This dissertation examines the changes in microbial community function and composition that occur in response to changes in litter chemistry 1

17 and nitrogen (N) availability during decomposition in order to quantify the mechanisms regulating plant litter C gains and losses. Litter chemistry changes predictably during decomposition (Aber et al., 1990; Moorhead & Reynolds, 1993; Berg & McClaugherty, 2008). Berg (2000) developed a three phase conceptual model of plant litter decay driven largely by the biological availability of different fractions of organic matter. Rapid decreases in water soluble compounds, such as sugars, low molecular weight phenolics, hydrocarbons, amino acids, and glycerides (Hobbie, 2005; Berg & McClaugherty, 2008; Glanville et al., 2012), which make up the soluble pool, and unlignified holocellulose dominate Phase 1, with no degradation of lignin (Berg, 2000). Phase 2 is characterized by the mineralization of lignified carbohydrates, and lignin decay regulates mass loss rates (Berg, 2000). During Phase 3, the lignin fraction is stabilized and the remaining material becomes recalcitrant and increasingly humified with very slow decomposition rates and eventually reaches its limit of decomposition (Berg et al., 2010). Approximately 5% of the original plant material remains when the limit of decomposition is reached and decomposes very slowly. Degradation of polymeric C substrates is mediated by the activities of microbial extracellular enzymes (Sinsabaugh et al., 2012), which track Berg s (2000) predicted changes in litter chemistry (Moorhead & Sinsabaugh, 2000). Decomposers vary in their enzymatic capabilities and ability to use different litter substrates, and shifts in functional groups of microorganisms occur as different resources become available (Hanson et al., 2008; Chapman et al., 2013; Lunghini et al., 2013). Early stages of decay are dominated by the presence of labile C substrates in the soluble pool that are quickly assimilated by r- 2

18 selected microorganisms such as β -proteobacteria and Bacteroidetes without the aid of extracellular enzymes (Fierer et al., 2007). Once soluble substrates disappear, k-selected decomposers (e.g., Actinomycetes and several fungal taxa) begin producing a wide-range of hydrolytic enzymes, such as -glucosidase and β-glucosidase, to degrade starch and holocellulose (Sinsabaugh et al., 2002; Boer et al., 2005; Snajdr et al., 2011). Basidiomycetes, such as white rot fungi, produce oxidative enzymes that drive lignin degradation during the later stages of decomposition (Sinsabaugh et al., 2002; Baldrian & Valaskova, 2008; Dashtban et al., 2010). Extracellular enzyme activities serve as indicators of microbial substrate preferences and are strongly regulated by environmental nutrient availability (Cusack, 2013; Moorhead et al., 2013). Complete enzymatic degradation of biopolymers requires the synergistic interaction of several C, N, and phosphorus (P) acquiring enzymes. While the activities of enzymes involved in C acquisition (e.g. β-glucosidase and phenol oxidase) can be linked to litter characteristics and decomposition rates, the activities of N and P acquiring enzymes tend to be more tied to the environmental availability of these nutrients (Stursova et al., 2006). For example, proteases and chitinases are needed to degrade protein and chitin/peptidoglycan (two major sources of N), and phosphatases to mineralize P from nucleic acids, ester phosphates, and phospholipids (Sinsabaugh et al., 2009). Studies have shown that if high concentrations of inorganic N and P are available in the environment, decomposers will not allocate resources to the production of enzymes that degrade organic N and P sources (Olander & Vitousek, 2000; Allison & Vitousek, 2005; DeForest et al., 2012). Therefore, the availability of key nutrients, such as N and P, in the environment has a significant effect on the function of the microbial community. 3

19 Atmospheric N deposition has more than doubled the amount of fixed N entering terrestrial ecosystems annually (Vitousek et al., 1997) and increases in soil N availability can influence the trajectory of decay through changes in microbial community function and composition (Craine et al., 2007; Campbell et al., 2010; Ramirez et al., 2010). In general, higher N availability expedites the breakdown of labile litter polymeric compounds, such as cellulose, and accelerates decomposition of low lignin litter, but retards decomposition of high lignin litter (Berg & Meentemeyer, 2002). These effects may reflect changes in extracellular enzyme activities under added N as hydrolytic enzymes responsible for carbohydrate breakdown increase (Saiya-Cork et al., 2002), while oxidative enzyme activities that degrade lignin decrease (Carreiro et al., 2000). However, increased N availability may also alter decomposer community composition, as white-rot fungi and other saprophytic fungi often respond negatively to N addition (Fog, 1988; Entry, 2000). Thus, it is difficult to predict whether N will increase or decrease the decomposition of a specific litter type due to the variable effects of N on microbial community dynamics (Waldrop et al., 2004). Most traditional predictive models of litter decay describe decomposition as a physical process largely driven by changes in litter quality and environmental conditions (Meentemeyer, 1978; Gholz et al., 2000), rather than a microbially mediated process where changes in microbial function and composition determine litter C turnover. Building on efforts to refine decomposition models by including more interactions between microorganisms and substrates, Moorhead and Sinsabaugh (2006) developed the Guild Decomposition Model (GDM). The GDM coupled activities of specific microbial functional groups, or guilds, to a changing litter substrate, and included decomposer 4

20 organisms and their enzyme kinetics as integral drivers of decomposition (Moorhead & Sinsabaugh, 2006). This model hypothesizes regulation of microbial activities within guilds of decomposers, which respond differently to shifts in the environment (i.e., C and N availability), and is consistent with the conceptual model proposed by Berg (2000). The GDM partitions the microbial community into three ecological guilds, each with different physiologies and enzymatic capabilities, and linked these guilds to three C substrate pools (i.e., opportunists consume soluble compounds (early decay) decomposers degrade holocellulose (mid-decay) miners break down lignin (late decay)). However, the predicted microbial succession associated with decomposing litter and the distinct microbial preferences for specific C substrates hypothesized by Moorhead and Sinsabaugh (2006) has yet to be empirically tested. 1.2 Hypothesis and Objective My overarching hypothesis is that during litter decomposition there is a predictable succession of microbial assemblages tied to litter chemistry, and that changes in decomposition rates in response to environmental factors (e.g., nutrient availability) reflect changes in microbial community composition and function. The main objective of this dissertation is to mechanistically test this hypothesis by quantifying the relationships between litter chemistry, N availability, microbial community function and composition, and mass loss under varying environmental conditions at different decay stages. This objective includes four parts, as separate publications, in which I evaluated: 5

21 1) Whether microorganisms have distinct substrate preferences during decay, and if changes in microbial substrate use with decay correlate with shifts in microbial community composition and function. This directly tests the hypothesized succession of decomposers tied to varying C substrates during decomposition (Chapter 2; Rinkes et al., 2011). 2) How differences in litter quality and particle size, and soil type influence microbial biomass and community structure, respiration, enzyme activities, and inorganic nutrient availability during very early decay. This directly tests how alterations in the physical environment influence microbial responses to fresh litter (Chapter 3; Rinkes et al., 2014). 3) The microbial mechanisms underlying the environmental variability (i.e., temperature, moisture, and nutrient fluctuations) associated with field studies by monitoring the decomposition dynamics of contrasting litter types in both the field and lab (Chapter 4; Rinkes et al., 2013). 4) How and why increased N availability influences microbial responses to litter varying in lignin content. Although it has been found that exogenous N retards lignin decay, the biochemical mechanisms giving rise to this inhibitory effect remain unclear (Chapter 5; Rinkes et al., 2014, in prep.). Because global environmental change (i.e., climate change, N deposition) is expected to alter many aspects of the C cycle, empirical data describing how and why microbial communities respond to changes in the environment are needed to refine mechanistic decomposition models. The studies included in the dissertation describe the relationships between litter chemistry, N availability, microbial community function and composition, 6

22 and mass loss under varying environmental conditions. These data were collected in order to provide a more mechanistic understanding of decay processes to better predict the situations in which decomposers may potentially accelerate or mitigate the effects of climate change (Treseder et al., 2011). 7

23 Chapter 2 Microbial substrate preference and community dynamics during decomposition of Acer saccharum This manuscript was originally published as: Rinkes, Z.L., Weintraub, M.N., DeForest, J.L., Moorhead, D.L. (2011) Microbial substrate preference and community dynamics during the decomposition of Acer saccharum. Fungal Ecology, 4, Abstract The Guild Decomposition Model (GDM) hypothesized that temporal shifts in microbial guilds, each with distinct substrate preferences, drive decomposition dynamics and regulate soil carbon (C) losses and sequestration. To test this hypothesis, we established a laboratory incubation of Acer saccharum litter and monitored respiration, microbial biomass and enzyme activities, inorganic nutrients, and shifts in functional groups of decomposers using phospholipid fatty acid (PLFA) analysis. Biomass and respiration peaked within the first two days of incubation, and the Gram negative PLFA biomarker 18:1 7c predominated during the first five days. Hydrolytic 8

24 enzyme activities and two fungal biomarkers (18:2 6,9c and 18:3 6c) increased by Day 25 and lignolytic enzyme activity was detected on Day 68. Our results suggest that decomposers preferentially use labile substrates and that shifts in decomposer groups occur in response to changes in available substrates, which supports the GDM. 2.1 Introduction Globally, terrestrial soils represent a significant carbon (C) reservoir (Berg & McClaugherty, 2008) and have the potential to be either a source or sink for atmospheric C (Schlesinger & Andrews, 2000). Decomposition of plant litter is a key control on soil C sequestration and is mediated primarily by decomposer microorganisms. The trajectory of decay is regulated by complex interactions between the microbial community and litter substrate, which each change through time in response to one another (Berg & Meentemeyer, 2002; Moorhead & Sinsabaugh, 2006). Therefore, litter chemistry and microbial activity jointly control the amount of C mineralized or metabolized at different stages of decay. Litter chemistry changes predictably during decomposition (Aber et al., 1990; Moorhead & Reynolds, 1993; Berg & McClaugherty, 2008). Berg (2000) developed a three phase conceptual model of plant litter decay driven largely by the biological availability of different fractions of organic matter. Rapid decreases in water soluble compounds and unlignified holocellulose characterize Phase 1 with no degradation of lignin (Berg, 2000). Phase 2 is characterized by the mineralization of lignified carbohydrates and lignin decay regulating mass loss rates (Berg, 2000). During Phase 3, 9

25 the relative concentration of lignin increases to the point where mass loss is minimal and litter reaches its limit of decomposition (Berg et al., 2010). Berg (2000) attributed this decrease in litter decomposability over time to chemical changes associated with the substrate and the succession of decomposers capable to compete for recalcitrant, nutrient poor substrates. The primary mechanism by which decomposers mediate the transformation of detrital C, nitrogen (N), and phosphorus (P) compounds is the production of extracellular enzymes (Burns, 1982; Sinsabaugh et al., 1993; Sinsabaugh, 1994). Organic matter decomposition requires the synergistic interaction of several different classes of enzymes, which track Berg s (2000) predicted patterns of turnover in organic matter pools (Moorhead & Sinsabaugh, 2000). For example, the various water soluble compounds and labile C substrates associated with fresh litter can be accessed by decomposers without the aid of extracellular enzymes (Frankland, 1966; Linkins et al., 1990). As these labile substrates disappear, hydrolytic enzymes associated with cellulose degradation, such as β-glucosidase, then increase (Dilly & Munch, 1996; Sinsabaugh et al., 2002). Later stages of decay are closely associated with lignolytic enzyme activity, due to the high concentrations of recalcitrant compounds (Moorhead & Sinsabaugh, 2000; Sinsabaugh et al., 2002). Decomposers vary in their abilities to use different litter substrates, and shifts in functional groups of microorganisms occur as different resources become available (Waldrop & Firestone, 2004; Hanson et al., 2008). For instance, a typical fungal succession includes Zygomycetes, such as sugar fungi, which are commonly associated with the availability of sucrose and cellulose early in decay, followed by Ascomycetes and finally Basidiomycetes that degrade lignin in later stages (Frankland, 1998; Torres et 10

26 al., 2005). Therefore, it has been shown that the predicted patterns of substrate loss during decay are driven by a succession of microorganisms capable of exploiting various organic matter fractions. A recent theoretical model by Schimel & Weintraub (2003) proposed that enzyme production by decomposers is controlled by relative demands for C and nutrients, and that different substrates yield different C and nutrient returns. This suggests that microbial preferences for various substrates are based on their return on investment in enzyme production. More recently, the Guild Decomposition Model (GDM) coupled activities of specific microbial functional groups, or guilds, to a changing litter substrate, and included decomposer organisms and their enzyme kinetics as integral drivers of decomposition (Moorhead & Sinsabaugh, 2006). This theoretical model hypothesizes regulation of microbial activities within guilds of decomposers and is consistent with the conceptual model proposed by Berg (2000). The GDM partitioned the microbial community into three ecological guilds, each with different physiologies, life history traits, and enzymatic capabilities, and linked these guilds to three C substrate pools with varying substrate affinities (Moorhead & Sinsabaugh, 2006). In brief, they hypothesized that opportunist organisms capable of invading quickly and thriving on soluble carbohydrates and other labile C forms (C1) would be first to colonize freshly deposited litter due to their fast growth rate. As soluble substrates disappear, opportunists are replaced by a decomposer guild with the ability to degrade holocellulose (C2) through the release of hydrolytic and oxidative enzymes. As unlignified cellulose is consumed, lignocellulose (lignin encrusted cellulose) predominates in the remaining litter. At this phase a lignin (C3) degrading 11

27 miner guild establishes, but grows very slowly due to the relatively low C and nutrient return on oxidative lignin degradation. Most traditional predictive models of litter decay are largely driven by changes in litter quality and environmental conditions (Meentemeyer, 1978; Gholz et al., 2000), but pay little attention to the activities of decomposers as explicit drivers of decay. Although useful under equilibrium conditions, these models do not sufficiently describe C cycling under variable conditions or fully predict the behavior of soil C flow and microbial dynamics, especially following disturbance (Schimel & Weintraub, 2003). Traditional models also fail to capture changes in microbial community composition and function, which could have substantial impacts on C losses and gains when the entire time-course of decay is considered. Recent mechanistic models of decay, such as the GDM, have incorporated more ecological interactions between the microbial community and litter substrate and should be better able to predict ecosystem responses to perturbations (Schimel & Weintraub, 2003; Moorhead & Sinsabaugh, 2006; Ingwersen et al., 2008). However, high resolution empirical studies are needed to fully test the hypotheses set forth in these theoretical models, as data for many of the parameters are lacking. The first goal of this study was to evaluate microbial substrate preferences to determine if the conceptual model of litter decay based on litter chemistry (Berg, 2000) is supported. Activities of different extracellular enzymes were used as proxies for specific substrate use (e.g. β-glucosidase and phenol oxidase are indicative of cellulose and lignin breakdown, respectively). Our second goal was to determine if changes in litter chemistry due to decay correlates with shifts in microbial community composition and if different functional 12

28 groups of decomposers correlate with microbial function. This would test the pattern of succession hypothesized by the GDM, and attempt to resolve microbial behaviors that track dynamic litter substrate pools. We hypothesized that shifts in functional groups of microorganisms, each with different enzymatic capabilities and substrate preferences, would occur over the time course of decomposition due to changes in substrate quality and nutrient availability (N and P). To accomplish our goals and test this hypothesis, we established a laboratory incubation of Acer saccharum leaf litter and monitored microbial respiration, extracellular enzyme activities, phospholipid fatty acid biomarkers, and soil inorganic nutrient availability over a 500-day period. 2.2 Methods Sample collection Freshly senesced Acer saccharum leaves were collected in litter traps during the fall of 2008 at The University of Toledo s R. A. Stranahan Arboretum (N 41 o 42, W 83 o 40 ) in Northwest Ohio. All extraneous woody debris and leaves of other tree species were removed from the traps during each weekly collection. Litter was air dried in paper bags and maintained at constant humidity at the University of Toledo until incubation setup. Leaves (including petioles) were ground into a fine powder with the use of a Wiley Mill (20 mesh). Litter was ground to increase surface area available for microbial 13

29 colonization, stimulate decay rate, and minimize the effects of tissue structure during decay. Soil was collected in September of 2008 from Southview Savannah (N 41 67, W ), located in the Oak Openings Region of Northwest Ohio. Soils in the area are typically classified as Udippsamments due to their high sand and low nutrient and C content. We intentionally chose a soil with low C, so that there would be low background noise for this incubation and limited potential for a C priming effect. Several soil cores were collected to a depth of 5 cm (the depth with the highest biological activity) from a 30 m area. Soil (approximately 10 kg) was then immediately transported to the University of Toledo, sieved (2 mm mesh) to remove as much coarse debris and organic matter as possible, homogenized, and pre-incubated at field moisture for 5 months and then under incubation conditions at 40% WHC for 20 days in a dark 20⁰ C incubator in order to metabolize as much labile C as possible, and allow microorganisms to acclimate to the conditions of the experiment Incubation experiment A 500-day laboratory incubation was established in Mason jars (Ball Half Pint Wide Mouth Canning Jars, Jarden Corporation). Twenty litter addition treatment and six nolitter control groups, each with six replicate jars, were incubated together, with each group being harvested at a different time during the experiment. Litter addition jars contained 28 g soil dry weight equivalent adjusted to 40% water holding capacity (WHC) from field moisture and three grams dry weight equivalent ground litter. This soil:litter 14

30 ratio and WHC were selected based on results from a preliminary experiment suggesting these were the conditions that maximized biological activity. Jars without litter contained 28 g soil dry weight equivalent adjusted to 40% WHC from field moisture. Jars were kept in a dark 20⁰ C incubator and left loosely covered to minimize water loss while allowing gas exchange. Jars were weighed initially and deionized water was added gravimetrically on a weekly basis to replace water lost to evaporation. Litter addition treatment groups were destructively harvested on Days 0, 1, 2, 5, 8, 12, 16, 20, 25, 33, 41, 49, 56, 68, 83, 111, 154, 210, 363, and 500. No-litter control groups were destructively harvested on Days 0, 2, 25, 68, 210, and 500. Each destructive harvest included analyses of extracellular enzyme activity, determination of microbial biomass using chloroform fumigation, and extraction with 0.5 M potassium sulfate for subsequent analysis of dissolved organic C (DOC), and ammonium (NH4 + ), nitrate (NO3 - ), and phosphate (PO4 3- ) to determine N and P mineralization in the jars. Although no-litter controls were destructively harvested less frequently than litter treatments, respiration rates were monitored at the same frequency in both Microbial respiration Respiration was quantified by measuring jar headspace CO2 concentrations with a Li- 820 Infra-Red Gas Analyzer (LI-COR Biosciences, Lincoln, Nebraska, USA) set up for static gas injections. Prior to respiration measurements lids were removed and jars were vented with a fan to allow CO2 concentrations to return to ambient conditions, then sealed tightly and incubated at 20 C. Incubation time depended on respiration rate, and 15

31 varied from minutes (first week) to days (fourth month and later). Gas was collected from the headspace of the jars using a 2 ml syringe and injected into the gas analyzer. Absorbance was recorded on a per second basis using Li-820 v software and peak heights for each sample were extracted from the raw data spreadsheet. Peak heights were then converted to concentrations using a three-point calibration (0, 2500, 5000 ppm). Values were blank corrected for ambient air concentrations and converted to μg CO2-C g dry soil -1 day -1. Cumulative respiration was calculated by determining the moving average of rates between measurements and extrapolating this over the entire time course of measurements. A 2400 Series II CHNS/O Analyzer (PerkinElmer, Waltham, Massachusetts, USA) was used to obtain C/N ratios, which served as another method of calculating overall C losses Enzyme assays A suite of extracellular enzymes responsible for C, N, and P cycling were monitored using high throughput microplate assays (Saiya-Cork et al., 2002; Weintraub et al., 2007). Fluorigenic substrates were used to measure activities of the following enzymes: β-glucosidase (BG), N-acetyl-β-glucosaminidase (NAG), leucine peptidase (LAP), and acid phosphatase (PHOS). BG hydrolyzes glucose from cellulose oligomers, especially cellobiose; NAG is a chitinase that hydrolyzes N-acetyl glucosamine from chitin derived oligomers; LAP hydrolyzes leucine and other amino acids from peptides; and PHOS hydrolyzes phosphate from phosphate monoesters such as sugar phosphates. 16

32 Enzyme assays were conducted in 96-well microplates, with 16 replicate wells per sample. Sample slurries were prepared by homogenizing 1g of wet soil (or soil/litter mixture) with 125 ml of 50 mm sodium acetate buffer for 1 minute using a Biospec Tissue Tearer. Buffer was adjusted to the soil ph (5.6 in the no-litter control; 4.5 in the litter addition treatment - soil ph was reduced by litter addition) using a hand held ph meter (Oakton Instruments, Vernon Hills, IL, USA). Soil slurries were constantly mixed, while 200 μl aliquots of slurry were pipetted into appropriate wells. Assays wells included 200 μl slurry and 50 μl substrate (4-MUB-β-D-glucoside for BG; 4-MUB-Nacetyl-β-D-glucosaminide for NAG; L-leucine-7-amino-4-methylcoumarin for LAP; and 4-MUB-phosphate for PHOS). Microplates also contained blank wells (200 μl slurry and 50μl buffer), negative controls (200 μl buffer and 50 μl substrate), quench standards (200 μl slurry and 50 μl of either 10 mm 4-methylumbelliferone (MUB) or 10 mm 7-amino-4 methyl coumarin (MC) in the case of LAP), and reference standards (200 μl buffer and 50 μl MUB or MC standard). Microplates were incubated at 20⁰ C in the dark for 4 hours following substrate addition. Following incubation, 10 μl aliquots of 1.0 M NaOH were added to each well to increase fluorescence of MUB and MC. Plates were read exactly 1 minute following NaOH addition using a Bio-Tek Synergy HT microplate reader (Bio- Tek Inc., Winooski, VT, USA) with 365 nm excitation and 460 nm emission filters. After correcting for quenching and for the negative controls, enzyme activities were expressed as nmol reaction product hour -1 g dry soil -1. High throughput colorimetric assays were conducted for phenol oxidase (Phenox) and peroxidase (Perox) in clear 96-well microplates (Saiya-Cork et al., 2002; Weintraub et al., 2007). Phenox and Perox are both lignin degrading enzymes, with Perox using 17

33 hydrogen peroxide as a terminal electron acceptor instead of oxygen. L-3,4- dihydroxyphenylalanine (L-DOPA) was the substrate used for both Phenox and Perox. Assays consisted of 200 µl aliquots of soil slurry followed by the addition of 50 µl of 25 mm L-DOPA (both Phenox and Perox). The Perox microplates also received 10 µl 0.3% hydrogen peroxide in all wells. Negative controls received 200 µl of soil slurry and 50 µl L-DOPA and blank wells received 200 µl of soil slurry and 50 µl of buffer (each of these also received 10 µl 0.3% hydrogen peroxide for Perox plates). Plates were incubated at 20º C for 4 hours in the dark and a Bio-Tek Synergy HT Plate Reader (Bio-Tek Inc., Winooski, VT, USA) was used to measure absorbance at 460 nm. After correcting for negative controls and blank wells, enzyme activity rates were expressed as μmol h -1 g dry soil -1. Phenox values were subtracted from Perox to obtain net Perox activity Microbial biomass and nutrient analyses Soil nutrient analyses were conducted following Weintraub et al., (2007). Samples (including 2 soil free blanks) were extracted with 25 ml of 0.5 M potassium sulfate and shaken on an orbital shaker for 1 h. Samples were vacuum filtered through Pall A/E glass fiber filters and frozen at -20⁰ C until nutrient analyses were conducted. Microbial biomass carbon (MB-C) was quantified using a modification of the chloroform fumigation-extraction technique (Brookes et al., 1985, Scott-Denton et al., 2006). In brief, 5 g (wet weight) of soil were combined with 2 ml of ethanol free chloroform and incubated at room temperature for 24 hours in a stoppered 250 ml Erlenmeyer flask. Following incubation, flasks were vented in a fume hood for 30 minutes and extracted as 18

34 described above. Fumigated extracts were analyzed for total dissolved organic carbon (DOC) on a Shimadzu total organic carbon (TOC-VCPN) analyzer (Shimadzu Scientific Instruments Inc., Columbia, MD, USA) using the non-purgable organic C protocol. MB- C was calculated as the difference between DOC extracted from fumigated and nonfumigated samples. No conversion factor (kec value) was applied, so values presented represent only extractable C. MB-C is expressed as μg-c g dry soil -1. Inorganic N and P (NH4, NO3 -, and PO4 3- ) were analyzed using colorimetric microplate assays of the unfumigated sample extracts. Soil ammonium concentrations were measured using a modified Berlethot reaction (Rhine et al., 1998). Nitrate was determined using a modification of the Griess reaction (Doane & Horwath, 2003), which involves the reduction of nitrate to nitrite followed by colorimetric determination of nitrite. Ammonium and nitrate concentrations are expressed as μg-n g dry soil -1. Inorganic phosphate was analyzed following the malachite green microplate analysis described by D Angelo et al., (2001). Phosphate concentrations are expressed as μg-p g dry soil -1. Absorbance values were determined on a Bio-Tek Synergy HT microplate reader (Bio- Tek Inc., Winooski, VT) Phospholipid fatty acid (PLFA) analysis Soil (~10 g) samples from Days 2, 5, 25, 68, and 363 (litter addition treatments) and Days 2, 25, and 210 (no-litter controls) were immediately frozen upon harvest, and then freeze dried within 1 week of freezing. Three of the six samples from each harvest date were randomly selected for PLFA analysis. Total lipids were extracted from 5 g of freeze 19

35 dried soil by adding 4 ml of phosphate buffer, 10 ml of methanol, and 5 ml of chloroform (White et al., 1979). To determine analytical recovery, we added a 19:0 PLFA standard. Silicic acid chromatography using solid-phase extraction columns (500 mg 6 ml -1, Thermo scientific) was used to separate neutral lipid, glycolipid, and polar lipid fractions by eluting with chloroform, acetone, and methanol, respectively (Zelles, 1999). Separated polar lipids were then subjected to an alkaline methanolysis to form fatty acid methyl esters (FAMEs). The resulting FAMEs were separated and quantified using a HP GC- FID (HP6890 series, Agilent Technologies, Inc. Santa Clara, CA, USA) gas chromatograph; peaks were identified using the Sherlock Microbial Identification System (v. 6.1, MIDI, Inc., Newark, DE, USA). In addition, an external FAME standard mix (10:0, 12:0, 14:0, 16:0, 18:0, and 20:0; K101 FAME mix, Grace, Deerfield, Il) at five concentrations and analyzed among the samples, was used to determine FAME mass (DeForest et al., 2004). The dominant PLFAs were classified as Gram bacteria (18:1 7c), fungi (18:2 6,9c and 18:3 6c ), and general (common among divergent taxa) (16:0) functional groups (Fierer et al., 2003; DeForest et al., 2004; Waldrop & Firestone, 2004; Potthoff et al., 2006; Amir et al., 2010). PLFA 18:1 9c could be derived from either fungi or Gram bacteria (Fierer et al., 2003; Waldrop & Firestone, 2004). The sum of all PLFAs was used to calculate total microbial biomass, which is expressed as nmol PLFA C g dry soil -1. Biomarker abundance was calculated by dividing individual biomarkers by total biomass (i.e. % mol fraction) Statistics 20

36 Respiration data were analyzed using a two-way repeated measures analysis of variance (ANOVA) with time and litter addition as factors. Respiration was measured on all jars during the first week of sampling. However, no significant differences were found between replicate sets of jars (P = 0.45, F = 0.84 on Day 2, which had the most variation of all days), so all further measurements were made on one of the remaining sets of replicate jars (n = 6). BG, NAG, PHOS, MB-C, NH4 +, NO3 -, and PO4 3- means were compared for the 6 days for which we have data from both the litter addition treatments and no-litter controls using two-way multivariate analysis of variance (MANOVA) with day and litter addition as factors (n = 6 for day and n = 6 for litter addition) followed by Tukey s multiple comparison test. Data were analyzed using SPSS Statistics version The mean concentrations of individual PLFAs (calculated as the mole percentage of the total area of the chromatogram) were compared using one-way MANOVA with day as a factor (n = 5 for the litter addition treatment and n = 3 for the no-litter control). PLFA concentrations, enzyme activities, inorganic nutrients, and biomass were used as input values in a principal components analysis (PCA) that was conducted using R (v , R-Project, vegan package with the biplot.rda function. Bi-plot vectors were displayed at The factor loading scores of each of the individual variables were used to assess their relative importance in calculating the principal component axes. 2.3 Results C mineralization and biomass dynamics 21

37 Litter addition resulted in a significantly higher overall respiration rate when compared to the no-litter control (P < 0.01, F = ). Litter amended jars displayed a fleeting early peak in respiration on Day 2, which was followed by a decrease in C mineralization and prolonged period of stasis beginning around Day 56 (Fig. 2-1A). The peak in respiration measured on Day 2 in the litter addition treatment was significantly higher than all other rates measured in the litter addition treatment and no-litter control (P < 0.01 for all; Fig. 2-1A). Respiration was consistently low in the no-litter control. Rates ranged from 2-16 μg-c g dry soil -1 day -1 and were always significantly lower than the litter amended treatment (P 0.04 for all). We estimate that slightly more than half (approximately 51-56%, based on cumulative respiration calculations and CN analysis, respectively) of the litter C was respired over the 500-day incubation, with 20-24% of the litter C being lost within the first 24 days and 36-40% of the C being respired during the first year. Overall, 10 times more C was respired in the litter addition treatment than the no-litter control (Fig. 2-1A). The initial litter C/N ratio (Day 0) was 48.7 ± 2.9, with this ratio decreasing to 20.4 ± 1.2 on Day 500, based on C and N differences between the litter treatment and no-litter control. The dynamics of MB-C were similar to microbial respiration as MB-C peaked on Day 2 after litter addition (Fig. 2-1B). The peak in MB-C was short-lived as biomass declined quickly and remained between 125 to 330 μg-c g dry soil -1 throughout Days Due to the changes in biomass over time in the litter addition treatment, there was a significant day and litter addition interaction effect (P < 0.01; Table 2.1). Biomass in the no-litter control ranged from μg-c g dry soil -1 and was significantly lower than the corresponding litter amended treatment on all six days (all P values < 0.01; Fig. 2-1B). 22

38 2.3.2 Enzyme activities BG, NAG, and PHOS displayed similar patterns of activity over time following litter addition (Fig. 2-2), although their rates differed in magnitude. BG and NAG were the most active hydrolytic enzymes, with catalytic activities peaking over 400 nmol h -1 g -1 compared to peak activities of ~200 nmol h -1 g -1 for PHOS (Fig. 2-2). Due to the changes in enzyme activities over time in the litter addition treatment, there were significant day and litter addition interaction effects for BG, NAG, and PHOS (P < 0.01; Table 2.1). BG, NAG, and PHOS activities were not significantly affected by litter addition on Days 0 and 2, but activities were significantly higher on Days 25, 68, 210, and 500 (P < 0.01 for all days) when compared to the no-litter control. Although BG and NAG activities were significantly higher with added litter for days 25, 68, 210, and 500, PHOS activities varied and soils with added litter actually had significantly lower activities than the nolitter control on Day 68 (Fig. 2-2). LAP activity was not detectable at any time during the incubation. Phenox activity was not significantly affected by litter addition, overall (P = 0.41; Table 2.1), but did change over time (P < 0.01; Table 2.1). Phenox activity was undetectable in the litter addition treatment until Day 68, but remained measurable throughout the rest of the incubation (Fig. 2-3). Overall, litter addition and day had a significant interaction effect (P < 0.01; Table 2.1) on Perox activity. Similar to Phenox, no detectable Perox activity due to litter amendment was observed until Day 68, when activities were significantly higher than the no-litter control (P < 0.01; Fig. 2-4). Perox 23

39 activity remained detectable throughout the remainder of the incubation, although activities were never higher than Day 68. Throughout the incubation, sporadic oxidative activities were detected in the no-litter control, although activities were never significantly higher than the litter addition treatment Nutrient dynamics There was a significant day and litter addition interaction effect (P < 0.01; Table 2.1) resulting from changes in NH4 + over time in the litter addition treatment. No significant differences between the litter amended treatment and no-litter control were detected on Days 0, 68, 210, and 363, but NH4 + was significantly higher in the no-litter control on Day 2 (P < 0.01) and higher in the litter amended treatment on Day 25 (P < 0.01). NH4 + was low (< 1.0 μg-n g dry soil -1 after Day 5) in both the litter treatment and no-litter control throughout the incubation (Fig. 2-4). No detectable NAG activity was observed until after Day 5 in the litter treatment, when NH4 + concentrations had decreased (Fig. 2-5A). Due to the increases in NO3 - over time in the no-litter control, there was a significant day and litter addition interaction effect (P < 0.01; Table 2.1). Extractable NO3 - approached 2 μg-n g dry soil -1 in the litter addition treatment on Day 0, but decreased considerably by Day 5 and became undetectable throughout the remainder of the incubation, however the no-litter control displayed the opposite pattern (Fig. 2-4). NO3 - in the no-litter control increased over time and concentrations were significantly higher than the litter addition treatment on Days 25, 68, 210, and 500 (P < 0.01 for all). 24

40 Changes in PO4 3- over time in the litter addition treatment resulted in a significant day and litter addition interaction effect (P < 0.01; Table 2.1). PO4 3- in the litter addition treatment was relatively high throughout the experiment, with concentrations approaching 84 μg-p g dry soil -1 during the first few days of incubation and was significantly higher than the undetectable PO4 3- concentrations in the no litter control on Days 0, 2, 25, 68, 210, and 500 (P < 0.01 for all; Fig. 2-4). No detectable PHOS activity was observed until after Day 2, when PO4 3- concentrations had decreased (Fig. 2-5B) Microbial community structure Overall, PLFA richness was 64, which includes all detectable PLFAs in the litter addition treatment and no-litter control. The most abundant PLFAs based on mean % mol fraction in the litter addition treatment were 16:0 (29.74%), 18:1 7c (15.13%), 18:1 9c (12.79%), 18:2 6,9c (11.23%), and 18:3 6c (6.48%). Biomarker concentrations in the litter addition treatment changed over time. PLFA 16:0 (general) was the most abundant throughout the experiment and concentrations did not decrease significantly until Day 363 (P < 0.01, F = 59.65; Fig. 2-6). PLFA 18:1 7c (Gram -) concentrations were highest on Days 2 and 5 (mean % mol fraction of 24.1% and 23.1%, respectively), but decreased significantly throughout the remainder of the incubation (P < 0.01, F = ). PLFA 18:1 9c (general) temporal trends were similar to 18:1 7c (Gram -), with significantly higher concentrations on Days 2 and 5 (mean % mol fraction of 16.7% and 16.0%, respectively) when compared to all other days (P < 0.01, F = ). Fungal PLFA 18:2 6,9c peaked later than the other most abundant biomarkers, with the highest % mol 25

41 fraction found on Day 25 (P < 0.01, F = ; Fig. 2-6). PLFA 18:3 6c (fungi) was also significantly higher on Day 25 when compared to all other days (P < 0.01, F = ) The most abundant three biomarkers in the litter treatment also were among the highest concentrations in the no-litter control. The most abundant PLFA biomarkers based on mean % mol fraction in the no-litter control were 16:0 (12.79%), 20:0 (14.25%), 18:1 7c (5.73%), 18:1 9c (5.43%), and i15:0 (5.44%). No significant differences in PLFA concentrations over time were found for any of these biomarkers (P > 0.05 for all). The PCA of PLFA data suggests a substantial degree of differentiation over time in microbial community structure in the litter addition treatment, but no differentiation in the no-litter control (Fig. 2-7). The first principal component axis (PC1) explained 69.6% of the variance in the data, while the second principal component axis (PC2) explained 15.1%. For the litter addition treatment, Days 2 and 5 were negatively correlated with PC1 and PC2 as was PLFA 18:1 7c (Gram -) and MB-C (r 2 = 0.94, P < 0.01). Days 25 and 68 and PLFAs 18:2 6,9c (fungi) and 16:0 (general) were positively correlated with PC2 and negatively correlated with PC1, as was PO4 - (r 2 = 0.54, P < 0.01), BG (r 2 = 0.71, P < 0.01), and NAG (r 2 = 0.77, P < 0.01). Day 363 was positively correlated with PC1 and PC2. No-litter controls clustered on Days 2, 25, and 210 and were positively correlated with PC1 and negatively correlated with PC2, along with NO3 - (r 2 =0.50, P < 0.01). 2.4 Discussion 26

42 2.4.1 Microbial substrate preference during decay Early decomposition dynamics were characterized by peaks in both microbial respiration and biomass (Day 2) in the litter treatment. There were no corresponding increases in the no-litter control, which suggests that these peaks were the result of soil microorganisms exploiting freshly added litter substrates. These peaks are likely due to the availability of highly labile substrates, such as water soluble carbohydrates, which can be assimilated by organisms that lack enzyme systems capable of degrading cellulose and lignin (Frankland, 1969; Luxhoi et al., 2002). Interestingly, we observed no detectable BG, Phenox, or Perox activity until after the peaks in both respiration and biomass had declined. This suggests that the production of C-acquiring enzymes may be induced by C demand and decomposers will only produce these enzymes if no labile C is available. To our knowledge, these fleeting early peaks in respiration and biomass have not been captured by any previous field studies. Although, soil microcosm studies have found early peaks in respiration (Gunnarsson et al., 2008; Strickland et al., 2009) and have shown that microflora can utilize low molecular weight C sources very easily as experimentally added glucose can be metabolized within 48 hours by bacteria (Coleman et al., 1977; Coleman et al., 1978). Although the height and shape of these respiration peaks may partially be a result of adding finely ground litter to a C-poor sandy soil, we observed a similar pattern in the same type of soil with the addition of larger 5 mm litter pieces (Rinkes, unpublished data), so the size of the particles did not seem to be primarily responsible for the peaks. Our incubation setup also thoroughly mixed litter and soil, which could have created a 27

43 disturbance capable of causing an initial pulse of CO2 from dissolved inorganic C. However, we believe this was highly unlikely due to the length of soil preincubation, the sandy texture, acidic ph, and low initial C (<0.6%) content of soil, and the fact that similar respiration and biomass peaks were not observed in the no-litter control, which received the same initial mixing of soil. Using a soil with a higher clay and/or initial C content may have decreased the relative initial peak in respiration as well as increased enzyme stabilization due to complexation with organic matter or clays, thus increasing enzyme turnover time (Wallenstein & Weintraub, 2008). Our results suggest that the highest concentrations of biomass relative to total system C occur early in decomposition. Biomass was approximately 2.5% of total system organic matter in this incubation on Day 2, which was more than twice as large as the 1.05% mean estimate for terrestrial ecosystems (D.L. Moorhead & J.E. Herman, unpublished). However, this estimate does fall within the 1-3% range often used to characterize microbial biomass pool size (Anderson & Domsch, 1989; Wardle, 1992). Soon after respiration and biomass declined, activities of BG, NAG, and PHOS began to significantly increase. LAP was not detected during the incubation, which may be due to its strong positive correlation with soil ph, while other hydrolytic enzymes tend to track concentrations of soil organic matter (Sinsabaugh et al., 2008). Temporal trends in enzyme activities are often used to estimate turnover of different litter constituents (Sinsabaugh & Moorhead, 1994; Moorhead & Sinsabaugh, 2000). The pattern of enzyme activities we observed indicates that cellulose decay (as indicated by BG activity) occurred before the breakdown of lignin (indicated by Phenox and Perox activities), which requires the production of powerful oxidative enzymes (Sinsabaugh & Liptak, 28

44 1997). Grinding litter increased surface area for microbial colonization, but likely did not make lignin-shielded cellulose more available (the litter was not ground fine enough to disrupt the molecular structure of lignocellulose). A second increase in BG activity was observed between Days , which may be due to an increase in the availability of cellulose that was previously shielded by lignin, since oxidative enzyme activity was first detected on Day 68. We suggest that this pattern in substrate use can be explained based on the highest return on investment microorganisms receive from enzyme production (Schimel & Weintraub, 2003). The patterns in enzyme activities observed in our laboratory incubation correspond well to in-situ decomposition studies that have examined interactions between microbial activities and litter chemistry throughout decomposition. For instance, Snajdr et al., (2011) noted that β-glucosidase activities were high in senescent Quercus petraea leaves and activity did not decrease until litter lost approximately 16% of its initial mass. β- glucosidase activities were highest in initial stages of decay in our incubation and did not decline until approximately 24% of the initial C was respired. Similar to our findings, several in-situ studies have also demonstrated that lignin degradation often remains low until activities of early enzymes, such as β-glucosidase, and cellulose decomposition begin to decline (Dilly & Munch, 1996; Osono & Takada, 2001; Snajdr et al., 2011). Enzyme activities serve as indicators of microbial substrate preferences and are strongly regulated by environmental nutrient availability (Sinsabaugh & Moorhead, 1994; Michel & Matzner, 2003). We found that BG, NAG, and PHOS temporal patterns were similar during decay; however, PHOS had lower overall mean activities than BG and NAG throughout the incubation. This may be explained by the high availability of PO4 3-29

45 in jars with added litter, which, based on the patterns of extractable PO4 3- we observed in the no-litter control, appears to have been released by the ground litter (Fig. 2-4). This raises the question as to why there were detectable PHOS activities throughout the incubation in the litter addition treatment. Previous studies have shown that under high PO4 3- conditions microorganisms immobilize PO4 3-, without producing high levels of P mineralizing enzymes (Moorhead et al., 1996; Olander & Vitousek, 2000; Qualls & Richardson, 2000; Allison & Vitousek, 2005). However, it is possible that inorganic P concentrations were not high enough to satisfy microbial demand considering the large amount of C added at the beginning of the experiment. Therefore, the relatively high PO4 3- that remained throughout the experiment may have been due to PHOS mineralizing organic P from the litter, since strong correlations have been found between PHOS and available organic P (DeForest & Scott, 2010). Inorganic P levels remained elevated throughout the incubation following litter addition, however initial NH4 + and NO3 - were low and immobilized quickly into microbial biomass. The C/N ratio of litter decreased more than twofold throughout the study and was steadily approaching the estimated ratio of 60:7 for microbial biomass (Cleveland & Liptzin, 2007). This suggests that as C availability and the C/N ratio continue to decline, N will begin to accumulate and net nitrification is likely to occur, which is a pattern already apparent in the no-litter control. Overall, we observed a concentration threshold characterizing the relationships between extracellular enzyme activities and NH4 + and PO4 3- concentrations in the litter treatment (Fig. 2-5). No detectable NAG or PHOS activity was observed in the early days of the incubation when NH4 + and PO4 3- were at their highest concentrations. The first 30

46 detectable enzyme activities for PHOS were on Day 5, but substantial measurable activity was delayed until Day 8 for NAG. This indicates that NAG and PHOS activities were separately induced, and suggests that a threshold was reached where the demand for P and N exceeded the readily available supply which prompted the production of enzymes to acquire N and P from organic sources (DeForest & Scott, 2010) Microbial community dynamics Litter addition resulted in distinct temporal shifts in microbial community structure, in a pattern that supports the predictions set forth in the GDM proposed by Moorhead & Sinsabaugh (2006). We observed early peaks in respiration and biomass accompanied by high concentrations of one PLFA (18:1 7c), which is predominantly classified as a Gram bacteria biomarker (Fierer et al., 2003; Waldrop & Firestone, 2004). Gram bacteria tend to be fast growing bacteria (compared to Gram +) that favor low molecular weight compounds (Kao-Kniffin & Balser, 2007). PLFA 16:0, identified as a more general PLFA occurring in a relatively broad range of microorganisms (DeForest et al., 2004; Potthoff et al., 2006), was also abundant early in decay, but its concentrations remained high throughout the incubation, indicating this biomarker was not the major driver of this early peak in respiration and biomass. Studies have shown that protozoan grazers can reduce bacterial biomass (Coleman et al., 1977; Ingham et al., 1985), however we believe this was not the major driver of the early decline we observed, because PLFA 20:4 (protozoa) abundance remained low (mean % mol fraction < 2%) throughout the incubation. A more likely scenario is that an early guild of opportunistic bacteria, with a 31

47 fast growth rate and preference for labile water soluble compounds such as low molecular weight carbohydrates, colonized the freshly deposited litter, but were quickly replaced by a slower growing guild once labile substrates were exhausted, which would be consistent with the GDM. In-situ litterbag studies have often found that fungi do not increase in abundance until after the earliest stages of decay (Osono & Takeda, 2001; Sinsabaugh et al., 2002), however it may not be uncommon to find the opposite (Snajdr et al., 2011). Fungal domination early in decay may be attributed to the physical protection of water soluble compounds by plant cell walls, which require degradation by fungi before they can be made available for bacterial growth (Romani et al., 2006). In our incubation, litter grinding may have made these substrates more accessible to bacteria early in decay and our findings indicate that these substrates will be preferentially used if available. The GDM proposed that groups with slower growth rates and the capability to degrade cellulose (decomposers) and lignin (miners) through release of extracellular enzymes would follow in succession after opportunists. The results of PCA indicate strong clustering on days 25 and 68 characterized by high BG and NAG activity and a relative increase in PLFA biomarker 18:2 6,9c (Waldrop & Firestone, 2004; Amir et al., 2010). These attributes would be characteristic of a decomposer guild dominated by fungi, which tend to have slower growth rates than bacteria and are known to have significant enzymatic capabilities (Bardgett, 2005). Fungal biomarker 18:3 6c also increased by Day 25, but PCA determined that this biomarker was more strongly correlated with the no-litter controls, possibly due to its increase in biomass by Day 210 or strong correlation with NO3 -. Day 363 had a more positive score on axis 1 than Days 25 and 68, which 32

48 could indicate the presence of both decomposer (cellulose and lignocellulose degraders) and miner (fungi that can break covalent bonds of aromatic rings with powerful oxidative enzymes) guilds. Although decomposer and miner guilds may overlap through time, it is likely that miners are primarily dominating the no-litter control. These jars had low microbial biomass, low respiration rates, minimal hydrolytic enzyme activities, and did not display a detectable shift in community composition, as the most dominant PLFA biomarkers (16:0, 20:0, 18:1 7c, 18:1 9c, and i15:0) did not change over time. However, the majority of biomarker vectors point toward the no-litter control groups (e.g. i15:0, 20:0, and 18:3 6c; Fig. 2-7), which indicates that a large portion of the biomarker diversity falls within the miner guild. Our results suggest that biomarker 18:1 7c can be considered a biomarker for members of the opportunist guild, 18:2 6,9c for the decomposer guild, and 18:3 6c for the miner guild. We hypothesize that opportunist and decomposer guilds dominate when substrate is available, but do not necessarily reduce the miner guild biomass. Overall, we did observe sporadic oxidative enzyme activity in the no-litter control, suggesting that the microorganisms present have oxidative enzyme capabilities that provide a low return on investment on the highly recalcitrant organic matter, resulting in lower biomass and a slower growth rate. 2.5 Conclusions Traditional models of litter decomposition suggest a predictable sequence of change in litter chemistry and extracellular enzyme activity over the continuum of decay. Schimel 33

49 and Weintraub (2003) suggest that this pattern is based on the return on investment microorganisms get from producing enzymes. In this study we found that C rich labile substrates were preferentially consumed, likely due to the higher C and nutrient return compared to other litter constituents and because no enzyme investment is required to obtain them. We also observed a shift in functional groups of microorganisms with different enzymatic capabilities and growth rates during the course of Acer saccharum litter decomposition. This was likely due to differing microbial affinities for these various C substrates and the availability of N and P (Turner, 2008). Findings support the pattern hypothesized by the GDM, and provide greater functional resolution of the interactions between the microbial community and litter chemistry during decomposition. Acknowledgements This research was supported by the NSF Ecosystems Program (Grant # ). For field and laboratory assistance, we thank Bethany Chidester, Dashanne Czegledy, Michael Elk, Jared Hawkins, Danielle Kurek, Eric Wellman, and Travis White. We are also grateful to Jason Witter and Alison Spongberg for assistance in freeze-drying samples for PLFA analysis. For help with CN analysis, we thank Doug Sturtz, Russ Friederich, and Jonathan Frantz from the USDA ARS at the University of Toledo. 34

50 Table 2.1. Multivariate analysis of variance (MANOVA) results for all parameters with day and litter addition as factors. Results are significant at P < 0.05 and those significant at P < 0.10 are marked with an asterisk (*). MANOVA results Day Litter Addition Interaction P F P F P F β-glucosidase < < < N-Acetyl-β-glucosaminidase < < < Acid Phosphatase < < < Phenol Oxidase < Peroxidase < * 3.52 < Microbial Biomass Carbon < < < Ammonium < < Nitrate < < < Phosphate < < <

51 Figure 2-1. A. Instantaneous respiration rates over time in the litter addition treatment and no-litter control. Values are expressed as μg-c g dry soil -1 day -1. The diamond ( ) highlights the significant difference (P < 0.01) between the respiration rate in the litter addition treatment on Day 2 and all other days. Cumulative C mineralized is expressed as mg-c g dry soil -1. B. Microbial biomass over time in the litter addition treatment and nolitter control. Values are expressed as μg-c g dry soil -1. The asterisks (*) indicate significant differences (P < 0.05). Error bars show the standard error of the mean (n = 6) for both respiration and biomass. 36

52 Figure 2-2. Timecourses of β-glucosidase, N-Acetyl-β-glucosaminidase, and acid phosphatase activities in the litter addition treatment and no-litter control. Values are expressed as nmol reaction product released h -1 g dry soil -1. Error bars represent the standard error of the mean (n = 6) and asterisks (*) represent significant differences (P < 0.05). Leucine aminopeptidase is not included as there was no detectable activity. 37

53 Figure 2-3. Oxidative enzyme activities measured across time. Values are expressed as nmol reaction product catalyzed h -1 g dry soil -1. Error bars represent the standard error of the mean (n = 6) and asterisks (*) symbolize significant differences (P < 0.05). 38

54 Figure 2-4. NH4 +, NO3 -, and PO4 3- concentrations (expressed as μg-n g dry soil -1 and μg- P g dry soil -1 ) measured across time. Error bars represent standard error of the mean (n = 6) and asterisks represent significant differences (P < 0.05). 39

55 Figure 2-5. A. Mean N-Acetyl-β-Glucosaminidase activity vs ammonium concentration and B. Mean acid phosphatase activity vs phosphate concentration with points labeled by harvest day. The vertical lines represent the concentration threshold characterizing the relationships between (A.) NAG and NH4 + and (B.) PHOS and PO

56 Figure 2-6. Changes in % mol fraction over time for the five most abundant PLFA biomarkers (based on overall mean % mol fraction) in the litter addition treatment. Error bars represent the standard error of the mean (n = 3) and asterisks (*) indicate the day(s) when the concentration of an individual PLFA was significantly higher (P < 0.05) than on all other days for that biomarker. 41

57 Figure 2-7. Principal component analysis of the variation in microbial community composition estimated by PLFA signatures (mol percentages) and correlation with environmental variables through time. 42

58 Chapter 3 Interactions between leaf litter quality, particle size, and microbial community during the earliest stage of decay This manuscript was originally published as: Rinkes, Z.L., DeForest, J.L., Grandy, A.S., Moorhead, D.L., Weintraub, M.N. (2014) Interactions between leaf litter quality, particle size, and microbial community during the earliest stage of decay. Biogeochemistry, 117: Abstract With global change expected to alter aspects of the carbon (C) cycle, empirical data describing how microorganisms function in different environmental conditions are needed to increase predictive capabilities of microbially-driven decomposition models. Given the importance of accelerated C fluxes during early decay in C cycling, we characterized how varying litter qualities (maple vs. oak) and sizes (ground vs cm 2 vs. 1 cm 2 ), and contrasting soils (sandy vs. loamy), altered microbial biomass-carbon and community structure, respiration, enzyme activities, and inorganic nutrients over the 43

59 initial 2-weeks of decomposition. Our hypotheses were 1) mixing ground maple with loam should result in a quicker, more prolonged respiration response than other treatments; and 2) priming, or substrate-stimulated soil organic matter (SOM) turnover, should be minimal over the first few days due to soluble C substrate uptake. Respiration peaks, biomass increases, nutrient immobilization, low enzyme activities, and minimal priming occurred in all treatments over the first 72 hours. These general features suggest soluble C compounds are degraded before polymeric substrates regardless of litter size or type, or soil. Ground litter addition to the high C and microbial biomass loam resulted in a more prolonged respiration peak than the poorly aggregated sand. Priming was greater in loam than the C limited sandy soil after the first 72 hours, likely due to co-metabolism of labile and recalcitrant substrates. We conclude that the general features of early decay are widespread and predictable, yet differences in litter and soil characteristics influence the temporal pattern and magnitude of C flux. 3.1 Introduction Mineralization of freshly senesced leaf litter by decomposer communities generates one of the greatest carbon (C) fluxes in the global C cycle (Schlesinger & Andrews, 2000). However, predicting the magnitude of C flux at different stages of decomposition can be difficult as microorganisms have varying responses to shifts in the environment. In fact, most decomposition models do not explicitly include the decomposer community, because changes in litter chemistry, nutrient availability, and abiotic conditions have variable effects on microbial activity and composition (Allison & Martiny, 2008; Berg & 44

60 McClaugherty, 2008). Although black-boxing the microbial community is common, models that link responses of decomposer functional groups to temporal changes in litter chemistry and nutrient availability may better predict decomposition rates (Moorhead & Sinsabaugh, 2006). With global environmental change (e.g., nitrogen (N) deposition, global warming) expected to alter many aspects of the C cycle, including the size and composition of microbial communities (Frey et al., 2008; Treseder, 2008), empirical data describing how microbial communities respond to changes in the environment are needed to increase predictive capabilities of current microbial-based decomposition models. For instance, data explaining how pulses of litter inputs, varying litter qualities, and contrasting soil types alter C mineralization rates and decomposer dynamics may help predict the situations under which decomposers potentially accelerate or mitigate the effects of climate change (Treseder et al., 2011). In temperate deciduous forests, plant litterfall occurs as a pulse during autumn and is one of the largest annual inputs of labile C into soil (Hibbard et al., 2005). Microorganisms rapidly metabolize soluble C compounds in fresh litter, which significantly increases short-term carbon dioxide (CO2) efflux rates (Gu et al., 2004). For instance, mass loss rates in the first 2-4 weeks of decomposition can be an order of magnitude greater than in later stages, and the total C flux during this early stage can be greater than the following 4-6 months (Alvarez et al., 2008; Jacob et al., 2010). As the quality and quantity of litter changes throughout decomposition, the microbial community also changes (Berg & McClaugherty, 2008). Thus, shifting interactions between the decomposer community and litter chemical constituents regulate decomposition at different stages of decay. Although these types of interactions between 45

61 leaf litter chemical characteristics and decomposer communities have been recently quantified in long-term decomposition studies (Wickings et al., 2011; 2012), we still know little about the dynamic microbial-substrate relations that regulate high rates of C turnover and CO2 flux during early decay (i.e., < 20% litter mass loss). During the initial colonization of litter, the decomposer community is believed to consist mostly of r-selected microorganisms, which take up highly labile low-molecular weight C compounds (i.e., sugars, phenolics, organic acids, and amino acids) that comprise the total soluble pool (TSP) (Berg & McClaugherty, 2008; Van Hees et al., 2005; Glanville et al., 2012). Microorganisms decompose litter types with a large TSP more rapidly than those with a small TSP (Carreiro et al., 2000), especially in early decay, due to the higher C and nutrient return from soluble compounds compared to other litter constituents and because no enzyme investment is required to obtain them (Rinkes et al., 2011). Although microbial substrate use may change in accordance with the return on investment that microorganisms get from producing enzymes to obtain C and nutrients (Schimel & Weintraub 2003; Moorhead et al., 2012), decomposers can degrade plant structural tissues immediately if the quantity and/or quality of the TSP is not sufficient to support microbial C or nutrient demand (Talbot & Treseder, 2012). The surface area of the litter also influences TSP availability to decomposers, and can alter C and nutrient mineralization rates during decomposition (reviewed in Nielsen et al., 2011). For example, meso- and macroinvertebrates (e.g., collembola, nematodes, lumbricids) condition litter by increasing its surface area to mass ratio through comminution, and incorporate fresh substrate into the soil through bioturbation (Frouz et al., 2007; Dungait et al., 2012). This physical fragmentation of litter and mixing with soil 46

62 increases contact with microorganisms, makes soluble C substrates more accessible, and facilitates the activity of soil microbiota (Wolters, 2000; Bradford et al., 2002; Wickings & Grandy, 2011). Additionally, soil physical, biological and chemical properties influence microbial-substrate interactions, especially as faunal mixing increases soil to litter contact. For instance, fine textured soils with a high organic matter content may alter C mineralization rates and microbial responses to fresh litter addition by adsorbing extracellular enzymes, protecting microbial biomass from cell death and predation, and physically shielding labile C compounds from decomposition (Grandy & Neff, 2008; Wallenstein & Weintraub, 2008). While changes in litter quality and particle size, and soil type influence microbialsubstrate interactions, especially TSP availability and C mineralization rates, they may also regulate the turnover of SOM. For instance, fresh organic matter inputs can stimulate the turnover of existing C pools, especially in high C soils, resulting in a priming effect (Sayer et al., 2007; Blagodatskaya & Kuzyakov, 2008; Guenet et al., 2012). The role of microorganisms in priming may be explained by interactions between r- and K-selected decomposers following fresh litter addition. r-strategists thrive on soluble C compounds when in high abundance, however many of these organisms are also capable of releasing extracellular enzymes to degrade polymeric substrates in fresh litter (Fontaine et al., 2003). Metabolites released during the partial degradation of polymeric substrates by r- strategists can stimulate the activity of slow-growing K-strategists, resulting in elevated enzyme production, and further SOM turnover (reviewed in Lambers et al., 2009). Thus, fresh litter and SOM can be co-metabolized by K-strategists, which drives the priming effect in soils (Kuzyakov, 2010). This implies that SOM priming should be magnified 47

63 once the TSP dwindles and polymeric substrate degradation begins, yet there remains a paucity of data explaining temporal relationships between microbial substrate use and priming early in decay. Our goals were: 1) to elucidate the variable effects of litter and soil properties on microbial activity by examining how interactions between litter quality (maple and oak), particle size (ground, 0.25 cm 2, 1 cm 2 ), and soil type (sand and silty loam) alter decomposition dynamics during early decay; and 2) to evaluate temporal relationships between microbial substrate use and the priming effect during the initial 2-weeks of decomposition. We hypothesized that mixing ground maple litter with a loamy soil would result in a quicker and more prolonged C mineralization response to fresh litter addition relative to other litter and soil combinations. This response would be due to high TSP availability in maple and increased surface area of ground litter, and greater microbial biomass and barriers to substrate diffusion (i.e., high aggregation) in loam. We predicted enzyme activities would be low during the first few days in this treatment due to preferential uptake of substrates in the TSP. Additionally, we made predictions for how each individual factor (litter species, particle size, and soil type) should affect C mineralization dynamics, enzyme activities, and microbial biomass based on the mechanisms described above (Table 3.1). We also hypothesized that priming should be higher in the loamy soil than the sand, due to its higher C content. However, priming should be minimal in loamy treatments during the initial days of the incubation and increase over time as the TSP dwindles due to increased enzymatic activity and cometabolism of fresh litter and SOM. 48

64 To accomplish our goals and test these hypotheses, we monitored how varying litter qualities and sizes, and contrasting soils, altered microbial biomass-carbon and community structure, respiration, enzyme activities, inorganic nutrients, and SOM priming over the initial 2-weeks of decomposition in a laboratory incubation (Fig. 3-1). 3.2 Methods Sample collection Acer saccharum (sugar maple) and Quercus alba (white oak) leaves were collected daily in litter traps during October of 2011 at the Oak Openings Preserve Metropark (N 41 o 33, W 83 o 50 ) in Northwest Ohio. These litter species were selected due to their contrasting litter chemistries; sugar maple is reported to have a 40% higher water soluble component and 50% lower lignin fraction than white oak (Aber et al., 1990). Litter was placed in paper bags, air dried, and maintained at room temperature until incubation setup. In order to manipulate substrate accessibility and determine the impact of surface area on microbial dynamics, leaves (including petioles) were either: 1) ground into a fine powder with the use of a Wiley Mill (20 mesh, 0.84 mm), 2) cut into 0.25 cm 2 pieces, or 3) cut into 1 cm 2 pieces. Two soils varying in many soil properties (Table 3.2) were collected in May of A Typic Udipsamment soil (0.4% C) was collected from the Oak Openings Preserve Metropark, which is an area with sandy soils with low nutrient and C content. A Typic Dystrudept soil (4.1% C), which is a silt loam with strong aggregation and high organic 49

65 matter content, was collected from the Waterloo Wildlife Research Area (N 39 20, W ) in Southeast Ohio. These sites were selected in part because of similarities in tree species composition (i.e., oak and maple were dominant trees at both sites), which minimized the potential of a microbial home-field advantage, where decomposers become specialized to litter of certain plant species (Gholz et al., 2000). Soil cores were collected from the top 5 cm (the depth with the highest biological activity) from a 5 m 2 area at both sites, sieved (2 mm mesh) to remove as much coarse debris and organic matter as possible, thoroughly mixed, and pre-incubated for 5 months in a dark 20⁰ C incubator at 45% water-holding capacity (WHC). A preliminary experiment established that this WHC maximizes respiration in both soils (data not shown). The pre-incubation allowed for microorganisms to acclimate to experimental conditions and to metabolize as much extant labile C as possible in order to better isolate the specific response of litter additions. Although this lengthy pre-incubation was necessary, the associated reduction in substrate availability could have resulted in a shift in microbial community composition to predominately K-strategists Incubation experiment A two-week laboratory incubation was established in 237 ml wide mouth canning jars. Thirty-six litter and soil treatments (2 litter types 3 litter particle sizes 2 soil types 3 harvests) and six soil-only control groups (2 soil types 3 harvests) were replicated four times and incubated together. Litter treatment jars contained 50 g dry soil adjusted to 45% WHC and 1 g dry litter. Soil-only control jars contained 50 g dry soil adjusted to 50

66 45% WHC. Respiration was monitored frequently and destructive harvests occurred on days 0, 3, and 14. Each destructive harvest included extracellular enzyme analyses, determination of microbial biomass-c and analyses for dissolved organic C (DOC), ammonium (NH4 + ), nitrate (NO3 - ), and phosphate (PO4 3- ). We used the component integration method to quantify total primed C. This technique physically separates different C pools contributing to CO2 fluxes and measures specific rates of CO2 efflux from each pool (Kuzyakov, 2005). Specific rates of CO2 efflux from each component are multiplied by their respective masses and summed to calculate integrated CO2 efflux. Respiration was measured in twelve litter-only control groups (2 litter types 3 litter particle sizes 2 soil types) that were incubated alongside the soil + litter treatments and soil-only controls. Separate litter controls were established for each litter size class, each containing 1 g dry litter adjusted to 45% WHC mixed with 0.1 g of soil, which was added as a microbial inoculum. All jars were sealed and kept in a dark 20⁰ C incubator. The amount of respiration attributable to SOM priming was calculated as the difference in cumulative respiration for a given soil and litter combination and the sum of the cumulative respiration from the soil-only control and the same size, litter-only control. A 2-day lag in peak respiration rate occurred in all litter controls (not shown) when compared to the treatments (Fig. 3-2). Therefore, estimates of cumulative littercontrol respiration were adjusted by adding two additional days of C mineralization at the rate observed on the final harvest date. In addition, priming values were adjusted for this 2-day lag. By reducing the calculated difference in respiration between treatments, this adjustment makes the estimated amount of priming more conservative. 51

67 3.2.3 Microbial respiration Respiration was measured after 24, 46, 66, 86, 130, 154, and 325 hours in litter addition treatment groups, soil-only controls, and litter controls. Sodium hydroxide (NaOH) traps captured CO2 produced during decomposition. The amount of NaOH in each trap was optimized based on the anticipated respiration rate on each harvest day (e.g., 8 ml of 1M NaOH per trap on day 2, but only 2 ml of 1M NaOH on day 7). Traps were analyzed using the BaCl2/HCl titration method (Snyder & Trofymow, 1984). In brief, 2 ml of BaCl2 were added to the NaOH to precipitate the trapped CO2 as BaCO3, followed by 5 drops of thymophthalein ph indicator. The carbonic acid trapped in the NaOH was then back titrated with 0.1 N HCl. The calculation of C trapped required subtracting the equivalents of acid used in titrating a sample from the equivalents used to titrate a blank Enzyme assays High throughput microplate assays were conducted following the procedures outlined by Saiya-Cork et al. (2002). β-1,4-glucosidase (BG), α-1,4-glucosidase (AG), β-1,4-nacetyl-glucosaminidase (NAG), and acid phosphatase (PHOS) activities were monitored in the litter + soil treatments and soil-only controls using fluorigenic substrates. BG hydrolyzes glucose from cellulose oligomers, especially cellobiose; AG degrades starch oligomers into glucose monomers; NAG hydrolyzes N-acetyl glucosamine from chitin and peptidoglycan derived oligomers; and PHOS hydrolyzes phosphate from phosphate 52

68 monoesters such as sugar phosphates. We selected these enzymes because they catalyze terminal reactions that release assimilable nutrients from organic C, N, and P sources (Sinsabaugh et al., 2010). Sample slurries for the enzyme assays were prepared to maintain a consistent 50:1 soil:litter ratio, which allowed for direct comparison between the ground and larger particle size treatments. Soil slurries were made using 1.02 g wet soil/litter mixture for the ground litter treatment, 1 g wet soil + 20 mg wet litter for the larger size treatments, and 1 g wet soil for the soil-only controls homogenized with 125 ml of 50 mm ph 5.5 sodium acetate buffer for 1 minute using a Biospec Tissue Tearer (BioSpec Products, Bartlesville, OK). For the two larger litter size treatments, any adhering soil particles were removed from the litter using a 2 mm brush before being added to the slurry. Soil/litter slurries were stirred continuously on a magnetic stir plate, while 200 μl aliquots of slurry were pipetted into 96-well black microplates. Sixteen replicate wells were created for each assay and sample. Assay wells included 200 μl slurry and 50 μl substrate (4-MUB-β-D-glucoside for BG; 4-MUB-α-D-glucoside for AG; 4-MUB-N-acetyl-β-D-glucosaminide for NAG; and 4-MUB-phosphate for PHOS). Blank wells were created using 200 μl slurry and 50 μl buffer. Negative controls were created using 200 μl buffer and 50 μl substrate. Quench standards were created using 200 μl slurry and 50 μl of 10 mm 4-methylumbelliferone (MUB). Reference standards were created using 200 μl buffer and 50 μl MUB standard. Following substrate addition, microplates were incubated at 20⁰ C in the dark for 2-3 hours. Plates were read using a Bio-Tek Synergy HT microplate reader (Bio-Tek Inc., Winooski, VT, USA) with 365 nm excitation and 460 nm emission filters. After 53

69 correcting for quenching and for the negative controls, enzyme activities were expressed as nmol reaction product hour -1 g dry soil Microbial biomass and nutrients Litter + soil treatment and soil-only control samples (including 3 sample-free blanks) were extracted for DOC, NH4 +, NO3 -, and PO4 3- by adding 25 ml of 0.5 M potassium sulfate and agitating on an orbital shaker for 1 h. Samples were vacuum filtered through Pall A/E glass fiber filters and frozen at -20⁰ C until nutrient analyses were conducted. Microbial biomass carbon (MB-C) was quantified using a modification of the chloroform fumigation-extraction method described by Scott-Denton et al. (2006). For the litter treatments, 5.1 g (wet weight) of the ground soil/litter mixture and 5 g wet soil mg wet soil-free litter from the larger size treatments were combined with 2 ml of ethanol free chloroform and incubated at room temperature for 24 hours in a stoppered 250 ml Erlenmeyer flask. This fumigation procedure was replicated for the soil-only controls using 5 g wet soil. Following incubation, flasks were vented in a fume hood for 30 minutes and extracted as described above. Fumigated extracts were analyzed for DOC on a Shimadzu TOC-VCPN analyzer (Shimadzu Scientific Instruments Inc., Columbia, MD, USA). MB-C was calculated as the difference between DOC extracted from fumigated and non-fumigated samples. No correction factor for extraction efficiency (kec) was applied, as it is unknown for these soils. NH4 +, NO3 -, and PO4 3- concentrations were analyzed using colorimetric microplate assays of the unfumigated sample extracts. NH4 + concentrations were measured using a 54

70 modified Berthelot reaction (Rhine et al., 1998). NO3 - was determined using a modification of the Griess reaction (Doane & Horwath, 2003), which involves the reduction of nitrate to nitrite followed by colorimetric determination of nitrite. PO4 3- was analyzed following the malachite green microplate analysis described by D Angelo et al. (2001). Absorbance values were determined on a Bio-Tek Synergy HT microplate reader (Bio- Tek Inc., Winooski, VT) Phospholipid fatty acid (PLFA) analysis Ground maple and oak treated samples from both soils were immediately frozen after the day 0 and day 3 harvests, and then freeze dried within 5 days. Total lipids were extracted from 5 g freeze dried soil using 4 ml of phosphate buffer, 10 ml of methanol, and 5 ml of chloroform (White et al., 1979). A 19:0 PLFA standard was added to determine analytical recovery. Silicic acid chromatography using solid-phase extraction columns (500 mg 6 ml -1, Thermo scientific) was used to separate neutral lipid, glycolipid, and polar lipid fractions by eluting with chloroform, acetone, and methanol, respectively (Zelles, 1999). Separated polar lipids were then subjected to an alkaline methanolysis to form fatty acid methyl esters (FAMEs). FAMEs were separated and quantified using a HP GC-FID (HP6890 series, Agilent Technologies, Inc. Santa Clara, CA, USA) gas chromatograph; peaks were identified using the Sherlock Microbial Identification System (v. 6.1, MIDI, Inc., Newark, DE, USA). In addition, five concentrations of an external FAME standard mix (10:0, 12:0, 14:0, 16:0, 18:0, and 20:0; K101 FAME mix, Grace, 55

71 Deerfield, Il) were analyzed among the samples to determine FAME mass (DeForest et al., 2012). Overall, the dominant PLFAs (highest relative abundance) were classified as bacteria (19:0 iso) and general, meaning common among divergent taxa, (16:0 and 20:0) functional groups (DeForest et al., 2004; Potthoff et al., 2006; Steenwerth et al., 2006). The classification of PLFA 18:1 9c varies between studies, as this biomarker could be derived from either fungi or Gram bacteria (Fierer et al., 2003; Waldrop & Firestone, 2004). Biomarker abundance was calculated by dividing individual biomarkers by total biomass (i.e., % mol fraction) Statistics Instantaneous respiration data were analyzed using a 3-way repeated measures analysis of variance (rmanova) with litter species, litter size, and soil type as factors. Cumulative respiration data were compared among treatments using a 3-way analysis of variance (ANOVA). Cumulative respiration was also compared between each soil and litter combination treatment and the sum of the cumulative respiration from the soil-only control and the same size, litter-only control to determine if there was a priming effect using a 4-way ANOVA with litter species, litter size, soil type, and experimental group (i.e., treatment or controls) as factors. MB-C, DOC, BG, NAG, PHOS, NH4 +, NO3 -, and PO4 3- means were compared using 3-way multivariate analysis of variance (MANOVA). Biomass to total system C (B:C) ratios on days 3 and 14 were compared with a 4-way ANOVA with day, litter species, litter size, and soil type as factors. Mean % mol 56

72 abundance for the 4 most abundant PLFAs (16:0, 18:1ω9c, 20:0, and 19:0 iso) were compared using 2-way MANOVA with litter species and soil type as factors. PLFA biomass increased from day 0 to day 3, however day was not a significant factor overall (P = 0.29, F = 0.80) when included in a 3-way MANOVA with day, litter species, and soil type as factors. Therefore, day 0 and day 3 values were combined for the 2-way MANOVA. For all analyses, differences among groups were considered significant if P Respiration data were log10 transformed to meet assumptions of normality and homogeneity of variance. Differences between groups were compared using a Tukey multiple comparison post-hoc test. Data were analyzed using SPSS Statistics version Results C mineralization Litter species, litter particle size, and soil type had significant main effects on respiration rates (P < 0.01 for all) with no significant interactions among factors. Mixing ground litter with sand resulted in peak respiration rates for both maple and oak between hours, which were significantly higher than for the 0.25 cm 2 and 1 cm 2 size pieces, respectively (P < 0.01 for all; Fig. 3-2A, B). Respiration for the 0.25 cm 2 and 1 cm 2 sizes peaked between hours for both litters in sand with the 0.25 cm 2 pieces having the higher peaks (Fig. 3-2A, B). 57

73 In the loam, peak respiration rates for most sizes of both litter types occurred between hours (Fig. 3-2C, D), the only exception being ground maple litter, which had consistently high rates over hours There were no significant differences between peak rates of different maple litter sizes in loam, but peak rates for all sizes of oak litter were significantly different from one another (ground > 0.25 cm 2 > 1 cm 2 ). Overall, maple had significantly higher peak respiration rates than oak for all litter sizes in sand (P < 0.05 for all), but not in loam. The total amounts of C mineralized over the study period varied between litter types, soil types and occasionally, litter particle sizes resulting in significant 3-way interaction between factors (Table 3.3). In sand, post-hoc tests found no significant differences in cumulative respiration for the three sizes of oak litter. In contrast, the total C respired significantly decreased with increasing size of oak litter in loam (Table 3.3). There was a significant decrease in total C respired in 1 cm 2 maple litter compared to the ground and 0.25 cm 2 pieces in sand, but not in loam (Table 3.3). Litter mass loss ranged from 6-8% in sandy soil to 9-15% in loam for both maple and oak. There was a significant 4-way interaction between species, particle size, soil type, and experimental group (P < 0.01; F = 9.57) on the total amount of C mineralized. Although the total C respired varied between litter species and particle sizes in both soils, the amount of respiration for a given soil and litter combination treatment was only significantly higher than the sum of the cumulative respiration from the soil-only control and the same size, litter-only control (i.e., a priming effect ) in loam (P < 0.05 for all; Fig. 3-3). No significant priming occurred over the first 3 days in any treatment, except for when ground maple was mixed with loam (P = 0.01; F = 15.6), although priming was 58

74 33% higher after day 3 than before in this treatment. Priming was significant in the ground maple (P = 0.02; F = 11.4), 0.25 cm 2 maple (P < 0.01; F = 163.5), 1 cm 2 maple (P < 0.01; F = 358.5), ground oak (P < 0.01; F = 42.3), and 0.25 cm 2 oak (P = 0.02; F = 11.4) loamy treatments between days Microbial biomass and enzyme activities Although MB-C was greater in the loam soil-only control compared to sand on day 0 (Table 3.2), there were no significant differences in MB-C between treatments after values were corrected for soil-only controls. However, MB-C changed significantly over time (P < 0.01; F = 22.7), as MB-C increased from day 0 to day 3 for all treatments and decreased (not always significantly) between days 3 and 14 (data not shown). On day 14, MB-C was significantly higher than day 0 in oak litter only. Peak values did not differ between treatments on day 3 and ranged from μg-c g dry soil -1. DOC also changed significantly over time (P < 0.01; F = 48.7), as concentrations decreased between day 0 and day 14 for all treatments (P < 0.05 for all; Fig. 3-4). B:C ratios were significantly higher in sand than loam (P < 0.01; F = 20.9). Overall, B:C values did not differ between litter particle sizes, but were significantly higher on day 3 than day 14 (P < 0.01; F = 17.7). Values ranged between % in the 0.25 cm 2 and 1 cm 2 particle sizes in sand on day 3, which were coincident with peak respiration rates. B:C ratios ranged from % in the ground litter and sand treatments on day 3, which was the day following the respiration peak for both ground maple and oak. On day 14, B:C ratios decreased to < 1.6% in all sandy treatments. Ratios averaged 0.73% on day 59

75 3 and decreased to 0.32% on day 14 in loamy treatments. However, all B:C estimates are conservative as no correction factor for microbial biomass extraction efficiency was applied. AG activity was not detectable in this study. All other enzyme activities significantly increased over time, but showed relatively few effects of treatments or interactions between treatment factors after values were corrected for soil-only controls (Fig. 3-5). BG activity for the 1 cm 2 maple and oak litter in loam was twice as high as all other treatments on day 14 (Fig. 3-5A, B). This resulted in a significant overall effect of litter size on BG activity (P = 0.02, F = 4.18). NAG activity did not differ between treatment factors. PHOS showed a significant interaction between litter size and soil type (P = 0.03, F = 3.68). PHOS was low in maple litter in sand throughout the incubation (Fig. 3-5E). In maple, PHOS was significantly higher for all sizes in loam compared to sand on day 14 (P < 0.03 for all). On day 14, PHOS activity in oak litter was significantly higher for 1 cm 2 pieces than ground litter in loam (Fig. 3-5F), and both these activity levels were significantly higher than in all other oak litter size soil combinations (Fig. 3-5F) Nutrient dynamics NH4 +, NO3 -, and PO4 3- concentrations significantly decreased over time (P < 0.05 for all) in all treatments that had detectable concentrations on Day 0 (Fig. 3-6). NH4 + showed a significant interaction between litter and soil type (P < 0.01, F = 7.44). NH4 + was low (< 5.0 μg-n g dry soil -1 ) in sand for all litter treatments and the no-litter controls throughout the incubation (Fig. 3-6A). The loam had three times higher extractable NH4 + 60

76 on day 0 (between 15.0 and 30 μg-n g dry soil -1 ), but significantly decreased in all treatments to < 5 μg-n g dry soil -1 by day 14 (P < 0.04 for all). On day 0 in loam, NH4 + was significantly higher in oak than maple for all litter sizes (Fig. 3-6B). On day 3 in loam, ground maple litter had significantly lower NH4 + than all other treatments (P < 0.01; Fig. 3-6B). Also in loam, NH4 + increased in the soil-only control from approximately 15 to 45 μg-n g dry soil -1 from day 0 to day 3, respectively, and then decreased to 5 μg-n g dry soil -1 on day 14 (Fig. 3-6B). Extractable NO3 - only showed a significant difference between soil types (P < 0.01, F = ). On day 0 in sand, NO3 - approached 20 μg-n g dry soil -1 but decreased by day 3 and was undetectable throughout the remainder of the incubation (Fig. 3-6C). NO3 - was significantly higher in loam and more variable, decreasing in the no-litter control from day 0 to day 3, and then increasing from day 3 to day 14 (Fig. 3-6D). PO4 3- also was only significantly different between soil types (P < 0.01, F = 26.44). It ranged from 35 μg-p g dry soil -1 to 45 μg-p g dry soil -1 on day 0 in sand for all litter treatments, but decreased to < 10 μg-p g dry soil -1 by day 3 (P < 0.01 for all; Fig. 3-6E). Initial PO4 3- was significantly lower in the loam + litter treatments (< 15 μg-p g dry soil -1 ) than sand, and decreased to an undetectable level by day 3 for all treatments (Fig. 3-6F) Phospholipid fatty acid profiles Overall, 57 PLFA biomarkers were identified. The most abundant PLFAs based on mean % mol fraction (relative biomarker abundance) were 16:0 (general), 18:1ω9c (Gram bacteria or fungi), 20:0 (general), and 19:0 iso (bacteria) (Fig. 3-7). The only 61

77 biomarkers to differ between treatments were PLFAs 16:0 and 18:1ω9c (P < 0.01 for both; Fig. 3-7). Concentrations of PLFAs 16:0, 18:1ω9c, and 19:0 iso were higher in the loamy than the sandy soil (P < 0.01 for all), while PLFA 20:0 showed a difference between soil types approaching significance (P = 0.09, F = 2.95). 3.4 Discussion Microbial dynamics early in litter decay Rapid increases in respiration and microbial biomass occurred during the first 72 hours in all litter treatments, indicating that fast-growing microorganisms quickly degraded C substrates in newly added litter. Most likely these C substrates are soluble and can be quickly assimilated by decomposers, including those that lack complex enzymatic capabilities (Van Hees et al., 2005). This conclusion is supported by a decrease in DOC concentrations, low enzyme activity and minimal priming until after the peaks in respiration and biomass occurred, despite high initial abundance of two bacterial PLFAs (18:1ω9c and 19:0 iso), and NH4 +, NO3 - and PO4 3- immobilization over the first three days. Thus, our results provide experimental evidence in support of conceptual models illustrating that microbial uptake of C substrates during decomposition occurs in a step-wise fashion, where soluble substrates are rapidly consumed before the enzymatic breakdown of polymeric substrates begins (i.e., Berg, 2000). No study that we are aware of has examined individual and combined effects of factors such as litter quality, particle size, and soil properties and their interactions with 62

78 microbial communities during early stage decomposition. Rinkes et al. (2011) observed rapid increases in C mineralization and MB-C, high rates of N and P immobilization, and high abundance of a Gram - bacterial PLFA over the first two days of decomposition when ground maple litter was mixed with a sandy soil. However, the present study demonstrates that uptake of soluble C compounds, DIN, and DIP by opportunistic decomposers in early decay is not just an artifact of adding ground maple litter to a C- limited sandy soil, but can also occur in litter with a lower soluble content, larger particle sizes, and in a higher C soil. Therefore, we believe this initial pattern is likely to be a general feature of decomposition. Our results show that while differences in litter quality and particle size, and soil type influence the temporal pattern and magnitude of C flux during early stage decomposition, the uptake of soluble C compounds, DIN, and DIP by opportunistic microorganisms is a predictable and robust process occurring in different litter and soil types Controls on early microbial dynamics Rapid decreases in the TSP characterize the initial phase of fresh litter decay, with the sharpest declines typically observed in low lignin litter because it has a higher initial TSP (Moorhead & Reynolds, 1993; Berg, 2000). Maple is reported to have as much as a 40% higher TSP than oak (McClaugherty et al., 1985; Aber et al., 1990) and we found that maple C mineralization rates were greater than oak for every litter size within each soil type over the first 66 hours. Additionally, maple has high concentrations of soluble phenolics (Lovett et al., 2004) and a portion of the phenolic content can be easily 63

79 degraded, as indicated by the commonly reported positive relationships between the soluble phenolic content of litter and early respiration rates (Reber & Schara, 1971; Bernhard-Reversat et al., 2003; Meier & Bowman, 2008). Furthermore, MB-C was significantly higher in all oak treatments on day 14 compared to day 0, although no differences in MB-C occurred in maple treatments between these days. Therefore, early stage microbial dynamics likely were stimulated by higher maple TSP quality and quantity, which resulted in more rapid C mineralization and microbial turnover. In addition to litter quality, particle size also directly affects microbial activity (Hanlon & Anderson, 1980; Ekschmitt et al., 2005; Xin et al., 2012) by altering litter surface area available for microbial colonization (Anderson et al., 1983). Furthermore, litter grinding may increase the availability of the TSP by disrupting plant cell walls that shield these compounds (Romani et al., 2006). To determine the effects of litter particle size on microbial biomass and activity and because the relationship between respiration and biomass was not constant, we examined ratios of microbial biomass to total soil C (B:C). In all our treatments, B:C ratios peaked early (day 3) and were close to the 1-3% range often used to characterize the relative microbial biomass pool size (Anderson & Domsch, 1989). B:C ratios did not differ between particles sizes on day 3, which was the day respiration and biomass peaked in the larger sizes. However, respiration peaked earlier in most ground litter treatments and it is likely that MB-C and the peak in B:C ratios occurred before day 3 in the highest surface area litter treatments due to enhanced microbial colonization (Yang et al., 2012). B:C values were also higher in sand than loam, likely due to its lower soil C content and inability to physically and/or chemically protect C substrates from microorganisms (Ahn et al., 2009). For instance, labile C 64

80 compounds can become bound within aggregates or adsorbed to mineral surfaces in finer textured soils, thus decreasing vulnerability to microbial degradation (Six et al., 2002; Plante et al., 2006; Grandy & Neff, 2008). It is also possible that soluble substrates had fewer barriers to diffusion in the low C sandy soil, thus making them more available as a primary resource to decomposers. Oxygen (O2) availability may have also limited B:C ratios in the loam, as non-uniform O2 distribution and anaerobic aggregate centers have been found in otherwise aerobic silty loam soils (Sexstone et al., 1985). Grinding litter substantially increased the surface area to mass ratio of litter, and when mixed with sand, accelerated the peak in respiration by 24 hours and increased the total amount of C mineralized by as much as 15% over the study period compared to the larger sand particle sizes. The rapid peak in respiration in sand mixed with ground litter was coincident with high concentrations of PLFA 18:1ω9c, a biomarker associated with Gram bacteria, which are likely r-strategists (Fierer et al., 2007). Therefore, opportunistic microorganisms responded quickly to the availability of fresh substrate in this C-limited soil. However, these early biomass and respiration peaks were fleeting, likely due to depletion of soluble C compounds (TSP), or perhaps depletion of soluble N and/or P, and the poor competitive ability of r-strategists when resources become limited (Bouvy et al., 2011; Rinkes et al., 2011; Mula-Michel & Williams, 2012). As soluble C substrates and inorganic nutrients dwindle, community composition can shift to decomposers that produce a wider array of enzymes targeting more complex litter substrates and SOM (Snajdr et al., 2010). Our results are consistent with other studies in that C, N and P acquiring enzymes were induced as soluble C substrates and inorganic nutrients were depleted in sandy treatments (Olander & Vitousek, 2000; DeForest et al., 2012). 65

81 Therefore, it is likely that a threshold was reached when the demand for C, N and P exceeded the available supply, which triggered enzyme production and the enzymatic breakdown of C and nutrient rich organic compounds in the sandy soil (DeForest & Scott, 2010; Rinkes et al., 2011). In contrast to enzyme activities, respiration in loam increased within the first 24 hours and remained elevated for 66 hours when ground litter was added. We suggest that the higher initial microbial biomass and soil C content in loam (both were 10 times higher than sand) allowed for quicker initial litter colonization by opportunistic bacteria and subsequent use of soluble C substrates. This is likely as concentrations of PLFAs 18:1ω9c and 19:0 iso, both potentially representative of r-strategists (Fierer et al., 2007), were greater in loam than sand. NH4 + immobilization was also significantly greater in the ground maple and loam mixture on day 3 compared to all other treatments, which further supports the hypothesis that higher TSP quantity/quality, greater surface area, and higher soil C promotes microbial growth and colonization. It is possible that the prolonged uptake of these soluble compounds from ground litter was due to the greater physical (e.g., occlusion within aggregates) and chemical (e.g., sorption to minerals) protection of labile C substrates and by-products of early decay (Plante et al., 2006; Moni et al., 2010). Alternatively, soluble substrates from fresh litter may have not been as important as a resource due to the greater C substrate pool in loam compared to sand, thus prolonging their uptake. BG activity was lower in ground and 0.25 cm 2 than 1 cm 2 litter of both types in the loamy soil on day 14. It is probable that the low surface area per unit mass associated with larger fragments decreased microbial access to soluble C substrates in the TSP. 66

82 Therefore, microorganisms likely increased BG activity to degrade cellulose, as there is a tight correlation between BG and the bioavailability of C sources (Canizares et al., 2011). However, BG activity was not elevated in the 1 cm 2 treatments in sand, perhaps due to lower microbial biomass, which in turn reduces microbial C demand. Interestingly, NAG activity also increased in the loamy treatments on day 14 even though nitrate concentrations remained elevated throughout the incubation, possibly a result of increased nitrification due to high ammonium concentrations. Although NAG is a primary means of acquiring N from chitin, decomposers can produce this enzyme to obtain C or as an antagonistic response towards fungi (Talbot & Treseder, 2012), which may explain why NAG activity increased in the presence of mineral N Priming effect Overall, priming was greater for most litter treatments in loam compared to the C limited sandy soil. For instance, sandy soil treatments and the 1 cm 2 oak treatment in loam had no priming response or even displayed an antagonistic response after litter addition. This was likely due to the scarcity of readily accessible high quality substrates, which typically increase the priming effect in soils (Kuzyakov & Bol, 2006). Additionally, priming effects in C rich soils are larger than those in C poor soils (reviewed in Kuzyakov et al., 2000), possibly due to both a higher microbial inoculum and higher potential C substrate pool. Kuzyakov (2002) summarized a series of longerterm experiments where priming was found to be times greater in a 4.7% C loamy soil than a 0.7% sandy soil. In our two-week study, priming was as much as 15 times 67

83 greater in the 4.1% C loam than the 0.4% C sand. Likewise, we observed more priming in treatments with smaller particle sizes, higher quality litter, and higher soil C. The relationship between microbial substrate use and priming in loamy treatments may be explained by co-metabolism of fresh litter and SOM by K-strategists. r-strategists respond rapidly to increases in soluble C substrates (Fig. 3-2), but these compounds are metabolized quickly and microbial turnover occurs when the readily available C supply is exhausted (Blagodatskaya et al., 2007). Many r-strategists also produce enzymes to decompose polymeric litter substrates, such as cellulose, however enzyme production is typically not induced until the TSP dwindles (Fig. 3-5). In contrast, K-strategists metabolize poor quality substrates, such as SOM, and, although they grow too slowly to compete with r-strategists for compounds in the TSP, benefit from metabolites released when higher energy polymeric substrates in fresh litter are degraded (Fontaine et al., 2003). Thus, K-strategists stimulated by polymeric substrates in fresh litter may increase enzymatic activity and SOM decomposition rates, further driving the priming effect (Fig. 3-3) in soils (Kuzyakov, 2010). We hypothesize that r-strategists outcompeted K- strategists for soluble C substrates, which can be taken up without enzymatic breakdown, during the first 66 hours of our incubation, as priming was minimal in most treatments. However, the significant priming effect throughout the remainder of the incubation was likely a result of increased K-strategist populations, elevated enzyme activities, and cometabolism of labile and recalcitrant substrates. 3.5 Conclusions 68

84 Litter decomposition models often poorly characterize early decay, primarily because we lack a clear understanding of the mechanisms that regulate the temporal dynamics of microbial responses to changes in the environment. Given the potential for accelerated litter and soil C fluxes during early decay and the importance of these fluxes to ecosystem-scale C cycling and global climate change, we provide high-resolution data needed to understand how microbial communities respond to fresh litter addition in varying litter qualities, particle sizes, and soil types. Our results suggest that soluble C compounds are preferentially consumed following fresh litter addition to soil, as we observed rapid increases in respiration and MB-C, immobilization of N and P, low enzymatic activities, and a minimal priming effect over the first 72 hours in all treatments. However, differences in litter quality and particle size and soil type influenced the temporal pattern and magnitude of C flux following fresh litter addition. Soil type was a strong determinant of the magnitude and longevity of increased C flux in response to litter addition, especially when mixed with ground litter (i.e., high TSP availability), which suggests differences in C substrate availability, microbial biomass, and the soil s capacity to protect SOM can regulate microbial access to the TSP. Furthermore, our data suggest that priming is a more important phenomenon in finer textured soils, and even litter types with a minimal TSP (i.e., oak) and larger particle sizes (i.e., 0.25 cm 2 and 1 cm 2 ) can accelerate SOM turnover and magnify CO2 flux during early decay when added to a high C soil. Acknowledgements 69

85 This research was supported by the NSF Ecosystems Program (Grant # ). For field and laboratory assistance, we thank Mallory Ladd, Ryan Monnin, Steve Solomon, Heather Thoman, Logan Thornsberry, and Megan Wenzel. We are also grateful to Jason Witter for assistance in freeze-drying samples for PLFA analysis. For help with CN analysis, we thank Doug Sturtz, Russ Friederich, and Jonathan Frantz from the USDA ARS at the University of Toledo. We also thank two anonymous reviewers whose suggestions greatly improved this work. 70

86 Table 3.1 Predictions for how different litter species, particle sizes, and soil types should influence decomposition rates and microbial dynamics throughout the 2-week incubation. Factor Respiration Peak Height Total C Mineralized Enzyme Activities Microbial Biomass Litter Species Maple > Oak Maple > Oak Oak > Maple Maple > Oak Particle Size Ground > 0.25 cm 2 = 1 cm 2 Ground > 0.25 cm 2 = 1 cm cm 2 = 1 cm 2 > Ground Ground > 0.25 cm 2 = 1 cm 2 Soil Type Sand > Loam Loam > Sand Loam > Sand Loam > Sand 71

87 Table 3.2 General soil properties averaged (± SE) for the Typic Udipsamment and the Typic Dystrudept soils (n = 4). Soil Properties Udipsamment Dystrudept Texture Sand Silty loam ph 5.5 ± ± 0.3 % C 0.4 ± ± 0.5 % N 0.04 ± ± 0.1 Biomass 11.3 ± ± 14.9 Ammonium 0.2 ± ± 0.2 Nitrate 14.4 ± ± 10.1 Phosphate < 0.1 < 0.1 Units are µg-c g soil -1 for biomass and µg-n g soil -1 or µg-p g soil -1 for inorganic nutrient concentrations. All soil properties were significantly different between soil types except ph and initial PO

88 Table 3.3 Cumulative carbon (C) respired (mg C/kg soil) for each litter/soil treatment at the conclusion of the two-week incubation. Data are corrected for soil-only controls. Litter Treatment Ground C respired in sandy soil (mg C/kg soil) C respired in loamy soil (mg C/kg soil) Maple ± a ± x Oak ± 9.39 b ± 5.93 x 0.25 cm 2 Maple ± a ± x Oak ± b ± y 1 cm 2 Maple ± b ± x Oak ± b ± z Values represent the mean cumulative C respired (± SE) for each treatment (n = 4) and were compared among the different treatments with a 3-Way ANOVA. Differences between groups within each soil type were compared with Tukey s post-hoc test. Lowercase letters designate significant differences between treatments within each soil type. 73

89 Figure 3-1 A schematic illustrating how interactions between litter and soil characteristics may influence the microbial mechanisms underlying decomposition processes during early decay. 74

90 Figure 3-2 Respiration rates over the time intervals during which CO2 was captured in NaOH traps over the two-week incubation for the (A) sugar maple and sand treatment, (B) oak and sand treatment, (C) sugar maple and loam treatment, and (D) oak and loam treatment. Values are expressed as μg-c g dry soil -1 day -1 and were corrected for soil-only controls. Error bars show the standard error of the mean (n = 4). Tukey s posthoc test was used to determine significant differences between treatments on each day. Lowercase letters were used to designate significant differences between treatments. 75

91 Figure 3-3 Cumulative carbon (C) respired in each litter + soil treatment (Trt) and soil + litter (S+L) control group at the conclusion of a two-week incubation. Cumulative C respired for each S+L control group was calculated as the sum of the total C mineralized in each of 4 litter-only and soil-only control replicates. A priming effect is apparent when cumulative respiration in a given soil and litter combination treatment was significantly higher than the sum of the cumulative respiration from the soil-only control and the same size, litter-only control. Error bars show the standard error of the mean (n = 4). Values were compared among the different treatments with a 4-Way ANOVA followed by Tukey s post-hoc test. Lowercase letters were used to designate significant differences between treatments. 76

92 Figure 3-4 Dissolved organic carbon (DOC) concentrations over time during the twoweek incubation for (A) sugar maple and (B) oak treatments. Values are expressed as μg- C g dry soil -1. Error bars show the standard error of the mean (n = 4). Tukey s posthoc test was used to determine significant differences between treatments. Lowercase letters were used to designate significant differences between treatments. 77

93 Figure 3-5 β-glucosidase (BG), N-acetyl-β-glucosaminidase (NAG), and acid phosphatase (PHOS) activities measured at three harvest dates over a two-week period. BG activity is displayed in panels A (maple) and B (oak), NAG activities in panels C (maple) and D (oak), and PHOS activities in panels E (maple) and F (oak). Values are expressed as nmol reaction product h -1 g -1. Activities are corrected for soil-only controls. Error bars show the standard error of the mean (n = 4). Lowercase letters were used to designate significant differences between treatments. 78

94 Figure 3-6 Ammonium (NH4 + ), nitrate (NO3 - ), and phosphate (PO4 3- ) concentrations measured at three harvest dates over a two-week period. NH4 + concentrations are displayed in figures A (sand) and B (loam), NO3 - concentrations in figures C (sand) and D (loam), and PO4 3- concentrations in figures E (sand) and F (loam). Values are expressed as µg-n g soil -1 (NH4 + and NO3 - ) or µg-p g soil -1 (PO4 3- ). Soil-only controls were included in the figures for comparison, but were not analyzed in the MANOVA. Litter treatments were not corrected for soil-only controls. Error bars show the standard error of the mean (n = 4). Lowercase letters were used to designate differences between treatments. 79

95 Figure 3-7 Mean % mol fraction (day 0 and day 3 data were combined) for the 4 most abundant PLFAs in the soil + ground litter treatments. The mean % mol fraction data were compared among the different treatments with a 2-Way MANOVA followed by Tukey s post-hoc test. Error bars show the standard error of the mean (n = 3). Lowercase letters were used to designate differences between treatments. 80

96 Chapter 4 Field and lab conditions alter microbial enzyme and biomass dynamics driving decomposition of the same leaf litter This manuscript was originally published as: Rinkes, Z.L., Sinsabaugh, R.L., Moorhead, D.L., Grandy, A.S., Weintraub, M.N. (2013) Field and lab conditions alter microbial enzyme and biomass dynamics driving decomposition of the same leaf litter. Frontiers in Microbiology 4: 260 doi: /fmicb Abstract Fluctuations in climate and edaphic factors influence field decomposition rates and preclude a complete understanding of how microbial communities respond to plant litter quality. In contrast, laboratory microcosms isolate the intrinsic effects of litter chemistry and microbial community from extrinsic effects of environmental variation. Used together, these paired approaches provide mechanistic insights to decomposition processes. In order to elucidate the microbial mechanisms underlying how environmental conditions alter the trajectory of decay, we characterized microbial biomass, respiration, enzyme activities, and nutrient dynamics during early (< 10% mass loss), mid- (10-40% 81

97 mass loss), and late (> 40% mass loss) decay in parallel field and laboratory litter bag incubations for deciduous tree litters with varying recalcitrance (dogwood < maple < maple-oak mixture < oak). In the field, mass loss was minimal (< 10%) over the first 50 days (January-February), even for labile litter types, despite above-freezing soil temperatures and adequate moisture during these winter months. In contrast, microcosms displayed high C mineralization rates in the first week. During mid-decay, the labile dogwood and maple litters in the field had higher mass loss per unit enzyme activity than the lab, possibly due to leaching of soluble compounds. Microbial biomass to litter mass (B:C) ratios peaked in the field during late decay, but B:C ratios declined between midand late decay in the lab. Thus, microbial biomass did not have a consistent relationship with litter quality between studies. Higher oxidative enzyme activities in oak litters in the field, and higher nitrogen (N) accumulation in the lab microcosms occurred in late decay. We speculate that elevated N suppressed fungal activity and/or biomass in microcosms. Our results suggest that differences in microbial biomass and enzyme dynamics alter the decay trajectory of the same leaf litter under field and lab conditions. 4.1 Introduction The mineralization of newly senescent leaf litter contributes approximately half of the annual carbon dioxide (CO2) efflux from soils in temperate deciduous forests (Schlesinger & Andrews, 2000). Complex interactions between litter quality and microbial communities regulate the magnitude of this carbon (C) flux and determine the trajectory of decay (Berg & McClaugherty, 2008). For instance, the same litter exposed 82

98 to different microbial communities frequently displays pronounced differences in chemistry, even after substantial mass loss (Wallenstein et al., 2011; Wickings et al., 2011, 2012). Additionally, the complexity and diversity of litter chemical composition and its impact on microbial community function may explain why diverse plant litter mixtures often follow different decay trajectories than the average of the component species alone (Meier & Bowman, 2010). However, we lack detailed data on how microbial communities respond to labile and recalcitrant litter types at progressive stages of decomposition under field and laboratory conditions. Decomposition rates of the same litter vary widely across terrestrial ecosystems. For instance, Cornus (dogwood) and Quercus (oak) mass loss can range from 50-75% and 25-55%, respectively, between field studies after one year of decomposition (Blair, 1988; Blair, 1992; Carreiro et al., 2000; Knoepp et al., 2005; Piatek et al., 2010). Pinus (pine) litter displayed highly variable decay rates over time after five years of field decomposition over 28 sites throughout North America (Gholz et al., 2000). Thus, it appears that site-specific factors influence microbial-substrate interactions and C flux patterns. We need to explain this underlying variability between field studies, especially how microbial behavior and decay rates of different litter types change in response to variations in climate and edaphic factors, to enhance the accuracy of decomposition models. The influence of environmental factors on microbial dynamics and decomposition patterns is difficult to predict. For instance, soil temperature and moisture fluctuate seasonally in the field (Aerts, 1997; Liski et al., 2003), which influences microbial growth, as well as extracellular enzyme pools and activities (Baldrian et al., 2013). 83

99 Variations in quality of plant litter, soil organic matter content and ph, and even wind velocity can alter microbial activity and decomposition rates (Berg & McClaugherty, 2008). Filamentous decomposers (i.e., actinomycetes and fungi) influence decomposition by physically integrating substrates that differ in C and nitrogen (N) availability, thus overcoming local nutrient limitation through translocation (Boberg, 2010). Therefore, the magnitude of decomposer responses to litter quality under field conditions is not consistent across sites due to the variable effects of biotic and abiotic factors on microbial-substrate interactions and C flux rates (Carreiro et al., 2000; Treseder, 2008; Snajdr et al., 2010). In contrast to the influences of environmental conditions on decomposition in the field, laboratory microcosms isolate the intrinsic effects of litter and soil chemistry and microbial community from extrinsic effects of climatic variation. For instance, constant temperature and moisture conditions in the laboratory optimize CO2 production and decay rates (Risk et al., 2008). Laboratory microcosms also eliminate variable C and nutrient subsidies to microbial activity (Salamanca et al., 1998). For instance, microcosms prevent decomposers from using adjacent litter resources, exclude new microbial colonizers (i.e., fungi) from translocating nutrients from external sources during later stages of decay, and disregard interactions between plant roots and nutrients (Teuben & Verhoef, 1992). In addition, microcosms disrupt in-situ microbial consortia and networks, and homogenize microbial functional behavior that otherwise is spatially compartmentalized or heterogeneous under field conditions (Kampichler et al., 2001). Thus, laboratory microcosm studies describe interactions between microbial communities and leaf litter with much lower variability than in the field, and provide mechanistic 84

100 information describing underlying processes of decomposition needed to refine decomposition models (McGuire & Treseder, 2009). Traditional predictive models of litter decay assume that microbial communities are black boxes functioning in the same way in different environments (Bradford & Fierer, 2012). While useful in stable environments, these models do not sufficiently describe C flow or microbial dynamics under variable conditions (Schimel & Weintraub, 2003; Manzoni & Porporato, 2009). For instance, conventional models fail to capture how changes in microbial function impact C gains and losses throughout decay (Treseder et al., 2011). In contrast, mechanistic decomposition models that incorporate both decomposers and their enzymes as explicit drivers of decay predict C dynamics under variable conditions better than earlier models that simulated litter decay as a first-order process without microbial decomposers (Moorhead & Sinsabaugh, 2006; Moorhead et al., 2012). However, parameterizing mechanistic models is difficult due to the highly interactive and variable factors that influence microbial activity. Our goal was to elucidate the microbial mechanisms underlying the variability associated with field litter bag studies by monitoring the decomposition dynamics of contrasting litter species in both field and lab settings. To accomplish this goal, we conducted parallel field and laboratory experiments using three litter species that varied in initial recalcitrance (dogwood < sugar maple < white oak) and an equal maple-oak mixture. We monitored mass loss, microbial biomass and enzyme activity, and inorganic nutrient dynamics during decomposition. We established separate hypotheses for early (< 10% mass loss), mid- (10-40% mass loss), and late (> 40% mass loss) stages of decay: 85

101 Early Decay: We hypothesized that decomposition in early decay is regulated by the availability of water-soluble labile compounds. For instance, soluble substrates are preferentially consumed in fresh litter, primarily by decomposers with limited enzymatic capabilities. Thus, we expected that differences in mass loss between litter types in both the field and lab would increase with initial litter soluble content (dogwood > maple > mixture > oak). However, we predicted decay rates, microbial biomass to litter mass (B:C) ratios, and microbial demand for N and phosphorus (P) relative to C for all litter types to be greater in the lab than field, because of high water-soluble C availability in fresh litter combined with optimal temperature and moisture conditions for biological activity. Mid Decay: We hypothesized that decomposition in mid-decay is regulated by the depletion of water-soluble C and labile nutrients, which would be reflected by increased extracellular enzyme activities targeting organic polymeric substrates. We expected microbial N and P demand to be greater in the lab than field, due to rapid depletion of nutrients from the limited amount of low-nutrient content soil used in the incubation. We predicted that nutrient limitation would decrease decay rates and B:C ratios for all litter types in the lab relative to the field, as decomposers cannot use external resources or translocate nutrients from adjacent litter in microcosms. Late Decay: We hypothesized that the increasing relative proportion of lignin regulates decomposition in late decay. For instance, we used oxidative enzyme activity as a proxy 86

102 for microbial breakdown of lignin. We expected that lab decomposition rates, B:C ratios, and microbial N and P demand would decrease in comparison to field incubations in response to the accumulation of nutrients in the lab microcosms. We predicted that lignolytic activity and biomass would be greater in the field, especially in the high lignin oak litter, due to increased fungal activity and the limited potential for excess nutrients to inhibit lignolytic activity. Thus, we established separate hypotheses for early, mid-, and late decay to determine how environmental conditions and edaphic factors alter microbial-substrate interactions during decay. 4.2 Methods Study site The study site was an oak-maple forest within the Oak Openings Region (N 41 o 33, W 83 o 50 ) of Northwest Ohio. Our study area was in the 1,500 ha Oak Openings Preserve Metropark. The mean annual temperature is 9.2 C and annual precipitation is 840 mm (DeForest et al., 2009). Soils have a low ph (~4.5) and are sandy, mixed, mesic Spodic Udipsamments (Noormets et al., 2008). Within the study area, we continually measured 30-minute mean soil temperature (5 cm depth; CS107, Campbell Scientific Inc. (CSI), Logan, UT, USA) and soil water content (SWC (%); CS616, CSI) in the top 20 cm from 87

103 probes buried within 500 m of a micrometeorological tower operated by the University of Toledo Litter collection Cornus florida (dogwood), Acer saccharum (sugar maple) and Quercus alba (white oak) leaves were collected weekly in litter traps during October of 2009 within the study area. Litter was placed in paper bags, air dried, and maintained at room temperature at the University of Toledo. Using the same litter in both experiments minimized the potential for variation in litter chemistry to influence decomposition dynamics. Leaves (including petioles) were cut into 1 cm 2 pieces to control how much litter mass went into each litter bag and to facilitate use in lab microcosms Field litter bag study Eight replicate 30 m 2 plots were randomly selected (i.e., randomized block design) within the study area at least 150 m apart and within 500 m of the micrometeorological tower. Litter bags were constructed from nylon mesh measuring cm (1 mm 2 mesh size) and were filled with 5 g of either dogwood, sugar maple, white oak, or a 50% maple-oak mixture. Thirty litter bags (6 dogwood, 8 maple, 8 oak, and 8 of the mixture) were deployed in January 2010, into each of the 8 plots for a total of 240 litter bags. Litter bags were placed 3 m apart in direct contact with the soil surface and secured at their corners with 15 cm ground staples. Litter bags were collected during January

104 (0 d), February 2010 (50 d), May 2010 (120 d), August 2010 (220 d), December 2010 (337 d), June 2011 (512 d), October 2011 (641 d), and May 2012 (849 d). Dogwood litter bags were harvested only 6 times (June 2011 and May 2012 were excluded) due to its rapid decomposition. Destructive harvests included analyses for mass loss, extracellular enzyme activities, microbial biomass-c (MB-C), dissolved organic C (DOC), ammonium (NH4 + ), nitrate (NO3 - ), and phosphate (PO4 3- ), described below Laboratory incubation The soil used in the laboratory incubation was a sandy soil low in C (0.6% ± 0.01) and nutrient content, collected from a 10 m 2 area where the field litter bag study was conducted. Soil cores were taken from the top 5 cm (the depth with the highest biological activity), sieved (2 mm mesh) to remove coarse debris and organic matter, thoroughly mixed, and pre-incubated for 1 month in a dark 20⁰ C incubator at 45% water-holding capacity (WHC). This is the water content that maximizes microbial respiration (Rinkes et al., 2013). The pre-incubation allowed microorganisms to acclimate to the conditions of the experiment and to metabolize labile soil C. A 376-day laboratory incubation was established in 473-ml canning jars using the same litter as the field study (see above). Nylon mesh 6 cm 6 cm litter bags (1 mm 2 mesh size) were constructed to lay flat inside each canning jar. Each litter bag contained 1 g litter and 1 g dry soil, which was used as a microbial inoculum to enhance colonization. Treatment jars included a litter bag placed in the middle of 99 g dry soil adjusted to 45% WHC. Soil-only control jars contained 100 g dry soil adjusted to 45% 89

105 WHC. Eight sets of 32 litter bag + soil jars (each set with 4 jars each of dogwood, maple, oak, and the mixture) and 4 sets of soil-only control jars (each set with 4 replicate jars), were incubated together. Jars were kept in a dark 20⁰ C incubator with lids left loosely covered, which minimized water loss but allowed gas exchange. Jars were weighed initially and deionized water was added gravimetrically on a weekly basis to replace water lost to evaporation. Litter bags were destructively harvested after 0 d, 2 d, 34 d, 99 d, 161 d, 230 d, 312 d, and 376 d of decomposition. Adhering soil particles were removed from the litter with a 2-mm brush before analyses for mass loss, enzyme activities, MB-C, DOC, NH4 +, NO3 -, and PO4 3-. Soil-only controls were destructively harvested less frequently than litter treatments, but respiration rates were monitored frequently in both litter bag + soil treatments and soil-only controls Microbial respiration and mass loss Respiration was quantified in the lab by measuring jar headspace CO2 concentrations with a Li-820 infra-red gas analyzer (LI-COR Biosciences, Lincoln, Nebraska, USA) according to the manufacturer s protocol. Jars were vented, sealed in canning jars with septae (No.: Wheaton grey butyl stoppers) installed in the lids, and incubated at 20 C for minutes (early decay) to hours (late decay). Respiration was measured at 0 d, 1 d, 2 d, 3 d, 5 d, 7 d, 8 d, 25 d, 43 d, 53 d, 78 d, 99 d, 139 d, 161 d, 230 d, 259 d, and 376 d of incubation. Cumulative C mineralization was calculated for the laboratory experiment by determining mean rates between measurements and interpolating over 90

106 time. The initial C content ranged from 40-45% for all litter types and was used to calculate overall C losses. A 2400 Series II CHNS/O Analyzer (PerkinElmer, Waltham, Massachusetts, USA) was used to obtain the litter C content. In addition, litter mass loss was calculated as the difference in dry weight before and after incubation in both the field and lab Microbial biomass and nutrients To extract litter samples for DOC, dissolved inorganic N (DIN), and dissolved inorganic P (DIP), 15-ml aliquots of an aqueous 0.5 M solution of potassium sulfate were added to each homogenized sample and agitated at 120 rpm on an orbital shaker for 1 h. Samples were vacuum filtered through Pall A/E glass fiber filters and frozen until analysis. Replicate samples were also fumigated with chloroform to quantify MB-C using a modification of the chloroform fumigation-extraction method (Brookes et al., 1985) described by Scott-Denton et al. (2006). Ethanol-free chloroform (2 ml) was added to 0.25 g (wet weight) of litter (including 3-litter free blank flasks) and incubated at room temperature for 24 hours in a stoppered 250-ml Erlenmeyer flask. Following incubation, flasks were vented in a fume hood for 30 minutes and extracted as described above. Fumigated extracts were analyzed for total DOC on a Shimadzu total organic carbon (TOC-VCPN) analyzer (Shimadzu Scientific Instruments Inc., Columbia, MD, USA) using the non-purgable organic C manufacturer s protocol, which eliminates inorganic C prior to analysis. MB-C was calculated as the difference between DOC extracted from fumigated and non-fumigated samples. No extraction efficiency constant (kec) was 91

107 applied because it is unknown for these samples. Colorimetric microplate assays of the unfumigated sample extracts were used to analyze NH4 +, NO3 - and PO4 3- concentrations. NH4 + concentrations were measured using a modified Berlethot reaction (Rhine et al., 1998). NO3 - was determined using a modification of the Griess reaction (Doane & Horwath, 2003), which involves the reduction of nitrate to nitrite for colorimetric determination. PO4 3- was analyzed following the malachite green microplate analysis described by D Angelo et al. (2001). Absorbance values were determined on a Bio-Tek Synergy HT microplate reader (Bio- Tek Inc., Winooski, VT) according to the manufacturer s protocol Enzyme assays Fluorimetric enzyme assays were conducted using procedures defined by Saiya-Cork et al. (2002). We measured β-1,4-glucosidase (BG), β-1,4-n-acetyl-glucosaminidase (NAG), leucine amino peptidase (LAP), and acid phosphatase (Phos) activities in 96-well microplates using methyl umbelliferyl linked fluorimetric substrates. BG hydrolyzes glucose from cellulose oligomers, especially cellobiose; NAG (a.k.a. chitinase) hydrolyzes N-acetyl glucosamine from chitin and peptidoglycan-derived oligomers; LAP hydrolyzes leucine and other amino acids from peptides; and Phos hydrolyzes phosphate from phosphate monoesters such as sugar phosphates. These enzymes were selected because they catalyze terminal reactions that release assimilable nutrients from organic C, N, and P sources (Sinsabaugh & Follstad Shah, 2012). Slurries were made using 0.25 g of wet litter homogenized with 50 mm sodium 92

108 acetate buffer (ph 4.5) using a Biospec Tissue Tearer (BioSpec Products, Bartlesville, OK) according to the manufacturer s protocol. For the lab litter bags, adhering soil particles were removed from the litter using a 2-mm brush before addition to the slurry. We used a 200 µm substrate solution (4-MUB-β-D-glucoside for BG; 4-MUB-N-acetylβ-D-glucosaminide for NAG; L-leucine-7-amino-4-methycoumarin for LAP; and 4- MUB-phosphate for Phos) to ensure saturating substrate concentrations (German et al., 2011). After substrate addition, microplates were incubated at 20⁰ C in darkness for at least 2 hours. High-throughput colorimetric assays were conducted in 96-well microplates for phenol oxidase (Phenox), which is a lignin-degrading enzyme, using 2,2ʹ-azino-bis-3- ethylbenzothiozoline-6-sulfononic acid (ABTS) as a substrate (Floch et al., 2007). Assay wells included 200 µl aliquots of soil slurry followed by the addition of 100 µl of 1 mm ABTS. Negative controls received 200 µl of buffer and 100 µl ABTS and blank wells received 200 µl of soil slurry and 100 µl of buffer. Plates were incubated at 20º C in darkness for at least 2 hours Data analysis The relationship between the remaining leaf litter mass and time (days) was fit to a simple negative exponential model of decay (Olson, 1963). Decay rate constants were determined from the equation Xt = X0e (-kt) where Xt is the mass remaining at time = t, X0 is the initial mass, t is time in days and k is a decay rate constant in days -1. Decay rate coefficients (k values) were calculated at different stages of decay for both the field and 93

109 lab studies and k values for each stage were compared with a one-way analysis of variance (ANOVA) including litter type as the fixed effect. Ratios of microbial biomass to litter mass (B:C) were compared at different stages of decay in both the field and lab studies using a two-way ANOVA with stage and litter type as fixed effects. B:C ratios were examined in early decay (January and February 2010 field harvests and days 0-2 in lab), mid-decay (May 2010 through December 2010 field harvests, and days in lab), and late decay (June 2011 through May 2012 field harvests, and days 230 through 367 in the lab). Enzyme activities were expressed as μmol reaction product hour -1 g dry litter -1. These values were subsequently integrated over all sample dates according to Sinsabaugh et al. (2002) to derive cumulative enzyme activities and calculate turnover activities. Turnover activity, or the cumulative amount of enzyme activity per unit mass loss, provides a basis for comparing microbial allocation toward extracellular enzymes and the efficiency of decomposition (higher turnover activity = lower mass loss per unit enzyme activity) across different litter types. In brief, cumulative enzyme activity was calculated by multiplying the average activity of an enzyme between harvest dates over a specific time period. Turnover activity for BG, NAG, LAP, Phos, and Phenox was calculated by dividing cumulative enzyme activities by total mass loss for each sample replicate over time. Mean turnover activity (mol) was calculated from the turnover activities for all replicates (n = 8 in field, n = 4 in lab). Separate two-way ANOVA s were performed at each stage of decay with litter type and enzyme as fixed factors. Turnover activities were examined in early decay (January through February 2010 field harvests and days 0-2 in lab), mid-decay (May 2010 through December 2010 field harvests, and days in 94

110 lab), and late decay (June 2011 through May 2012 field harvests, and days 230 through 367 in the lab) and overall. We used BG:(NAG + LAP) and BG:Phos ratios as indicators of microbial demand for C relative to nutrients, and (NAG + LAP):Phos ratios as an indicator of microbial demand for N relative to P in both studies at each decay stage. Ratios of these C:N:P acquiring enzyme activities broadly converge on a 1:1:1 ratio across a wide range of sample types and ecosystems, and deviations from this ratio indicate relatively more or less decomposer demand for C, N, or P (Sinsabaugh et al., 2008). C:N:P ratios were examined in early decay (January and February 2010 field harvests and days 0-2 in lab), mid-decay (May 2010 through December 2010 field harvests, and days in lab), and late decay (June 2011 through May 2012 field harvests, and days 230 through 367 in the lab) and overall. In addition, data from the field litter bag study were analyzed using a mixed-model two-way multivariate analysis of variance (MANOVA) with day and litter type as fixed effects and subplot (block) as a random effect. Mass loss, NH4 +, NO3 -, PO4 3-, BG, NAG, Phos, and Phenox means were compared for the 6 harvest dates for which we have data for all litter types (including dogwood). The block effect was not significant overall (Wilk s λ = 0.89, P = 0.73) or for each individual response variable, therefore the MANOVA was rerun without block as a factor (Scheiner & Gurevitch, 1993) and results from this final analysis were reported. For the laboratory incubation, mass loss, NH4 +, NO3 -, PO4 3-, BG, NAG, and Phos means were compared using a two-way MANOVA with day and litter type as fixed effects. Differences among groups were considered significant if P Differences 95

111 between groups were compared using Tukey multiple comparison post-hoc tests. Enzyme activities were log-transformed to meet assumptions for normality and homogeneity of variance (Levene s test). Data were analyzed using SPSS Statistics version Results Field study - climate and mass loss relationships Average weekly soil temperatures ranged from 1 to 24 C, while mean weekly SWC ranged from 2 10% over the course of the field study (Fig. 4-1A). Over the initial 2 months of decomposition, litter mass loss was low for dogwood (5.8% ± 3.7), maple (4.7% ± 3.2), oak (5.4% ± 3.6), and the maple-oak mixture (1.7% ± 1.3) (Fig. 4-1B). During this time, soil temperatures averaged 5.6 C ± 0.13 and SWC averaged 6.7% ± Dogwood, maple, oak, and the litter mixture lost 37% ± 6.6, 22.9% ± 6.5, 23.4% ± 4.5, and 24.6% ± 6.3 of their original masses, respectively, during May through August 2010 (Fig. 4-1B). Mass loss then decreased for dogwood (27.3% ± 6.1), maple (19.1% ± 5.5), oak (20.4% ± 4.6), and the mixture (22.9% ± 3.5) between August 2010 and October 2011 (Fig. 4-1B). SWC was highly variable but generally increased during the winter and decreased during the summer (Fig. 4-1A). Mass loss was < 7% for maple, oak, and the mixture between October 2011 and May Decay rate coefficients were higher for dogwood than the other litter types during mid- and late decay and overall in the field (Table 4.1). In addition, decay rate coefficients were higher for dogwood and maple in the field than lab during mid-decay 96

112 (Table 4.1). Overall, mass loss was greatest for dogwood (76.5% ± 2.7) followed by maple (60.9% ± 1.9), the maple-oak mixture (58% ± 2.6), and oak (56.1% ± 7.5). This resulted in a significant litter type effect on mass loss (P < 0.01; F = 5.69) Lab incubation - mass loss In the lab, decay rate coefficients were significantly higher for dogwood than the other litter types during early decay, but similar among all litter types over the rest of the study and overall (Table 4.1). In addition, C mineralization rates for all litter types peaked within the first week and were higher on day 2 in dogwood than the other litter types (P < 0.01 for all; data not shown). Dogwood, maple, oak, and the litter mixture lost 20.7% ± 0.6, 15.9% ± 0.3, 16.8% ± 0.5, and 14.8% ± 0.5 of their original masses, respectively, between days Over the next 127 days, mass loss was 15.6% ± 0.9 for dogwood, 18.3% ± 1.5 for maple, 21.3% ± 1.5 for oak, and 20.7% ± 1.2 for the mixture. Over the remainder of the incubation, dogwood, maple, and the mixture lost approximately 9% of the original mass, while oak lost 12.9% ± Biomass dynamics In the field, B:C ratios for all litter types in late decay were significantly higher than values in early or mid-decay (P < 0.02 for all; Fig. 4-2). Maximum B:C ratios in the field ranged from 2-3% (Fig. 4-2). B:C ratios in the lab were significantly higher in mid- 97

113 decay than late-decay for all litter types (P < 0.05 for all; Fig. 4-2). Maximum B:C values ranged from 1-3.5% in the lab (Fig. 4-2) Inorganic nutrients NH4 + significantly changed over time in both the field (P < 0.01; F = 4.57) and lab (P < 0.01; F = 10.29). NH4 + decreased during early decay for all litter types and remained low (< 20 μg-n g dry litter -1 ) through mid-decay in the field (Fig. 4-3A), but increased during late decay for maple, oak, and the maple-oak mixture. In the lab, NH4 + decreased during early decay and then increased during mid-decay for all litter types, but then decreased during late-decay and remained low (< 20.0 μg-n g dry litter -1 ) between days 230 and 376 (Fig. 4-3B). Extractable NO3 - was low (< 25.0 μg-n g dry litter -1 ) for all litter types throughout the field study (Fig. 4-3C). In contrast, NO3 - increased quickly during mid- and late decay in the lab for all litter types (Fig. 4-3D) and was significantly higher in dogwood compared to most other litters on days 161 and 230, which resulted in a significant litter type by day interaction for NO3 - (P < 0.01; F = 3.22). PO4 3- significantly decreased for all litter types during early decay in the field (Fig. 4-3E). PO4 3- remained low for all litter types throughout mid-decay, but increased during late decay. PO4 3- was significantly higher in the mixture during the initial harvest and in dogwood during the October 2011 harvest than other litter types (P < 0.01 for all; Fig. 4-3E). In the lab, PO4 3- concentrations increased for most litter types during mid- and late decay and were significantly higher in dogwood on day 230 than other litter types (P < 98

114 0.01 for all; Fig. 4-3F). Overall, there were significant litter type by day interactions on PO4 3- in the field (P < 0.01; F = 7.49) and lab (P < 0.01; F = 4.37) Enzyme activity In the field, BG activities peaked during late decay for maple, oak, and their mixture (Fig. 4-4A). BG activity increased following the initial harvest in the lab (Fig. 4-4B) and was significantly higher in dogwood compared to other litter types on day 230 (not shown), which resulted in a significant litter type by day interaction (P < 0.01; F = 5.45). NAG activity increased following the initial harvest in the field and lab, but did not differ between litter types in the field (Fig. 4-4C, D). NAG activity was higher in dogwood than other litter types on day 230 (not shown) in the lab, which resulted in a significant litter type by day interaction (P < 0.01; F = 2.46). Phos activities peaked for maple, oak, and the mixture during late decay and were significantly higher in oak than dogwood during the December 2010 harvest and the mixture compared to maple during the October 2011 field harvest (Fig. 4-4E). Phos also increased rapidly following the initial harvest in the lab and was significantly higher in oak than other litter types on day 2 (Fig. 4-4F). Overall, there were significant litter type by day interactions on Phos in the field (P = 0.01; F = 2.73) and lab (P < 0.01; F = 2.30). LAP activity remained relatively low (< 0.3 µmol hour -1 g dry litter -1 ) for all litters throughout both studies (data not shown). Phenox activities were low in dogwood and maple throughout the field study and were significantly higher in mixed litter compared to other litter types during the October 2011 harvest (P < 0.01 for all; Fig. 4-4G). Phenox activity was also elevated in oak during late 99

115 decay (June 2011 and May 2012) (Fig. 4-4G). Due to increasing Phenox activity in oak and the maple-oak mixture, there was a significant litter type by day interaction (P < 0.01; F = 4.90) on Phenox activity in the field. Phenox was not detectable at any time during the lab incubation Turnover activity Turnover activities were low (< 15 mol) and did not differ between litter types or enzymes in the field during early decay (Fig. 4-5A). In the lab, turnover activities were also low (< 25 mol), but were significantly higher in oak and the mixture than other litters for Phos (P < 0.01; Fig. 4-5B). BG and NAG turnover activities were > 60 mol in the lab, but < 50 mol in the field for all litter types during mid-decay (Fig. 4-5C, D). Turnover activity differed between enzymes (P = 0.01; F = 3.44) and litter types (P = 0.04; F = 2.87) during late decay in the field due to higher Phenox and lower dogwood turnover activities compared to most other enzymes and litter types (Fig. 4-5E). However, turnover activity for dogwood > maple, oak, and their mixture in the lab (Fig. 4-5F), which produced a significant litter type effect (P = 0.03; F = 3.20). Turnover activities were as much as an order of magnitude higher in the lab than field during late decay (Fig. 4-5E, F). Overall, turnover activity in dogwood < maple < oak for BG, NAG, and Phos in the field (P < 0.04 for all; Fig. 4-5G). Maple decomposition required 2.2X more BG per unit mass loss, 1.8X more NAG, and 2.1X more Phos than dogwood, while oak and the maple-oak mixture required approximately 1.5X more BG, NAG, and Phos than maple. In the lab, turnover activity in dogwood > maple and oak for BG and NAG (P <

116 across all enzymes), but did not differ between litter types for Phos (Fig. 4-5H). Dogwood decomposition required approximately 2X more BG and NAG than other litter types. Phenox turnover activity was undetectable in early decay and significantly higher than most other enzymes during mid-and late decay and overall in the field (P < 0.01 across all enzymes; Fig. 4-5A, C, E, G). Overall, Phenox was 2-4X higher in oak compared to dogwood, maple, and the maple-oak mixture. LAP turnover activity was significantly lower than most other enzymes and did not differ between litter types in both the field and lab (Fig. 4-5). Differences in enzyme turnover activities between litter types resulted in significant enzyme type by litter type interactions during mid-decay (P < 0.01; F = 2.39) and overall in the field (P < 0.01; F = 6.63) and during early decay (P < 0.01; F = 8.75) and overall in the lab (P < 0.01; F = 3.56) Enzyme activity ratios In early decay, C:N acquisition ratios were > 1 for all litter types in both studies. C:P acquisition ratios were also > 1 for all litter types in the field, but in the lab, C:P acquisition ratios were < 0.5 for maple, oak, and their mixture and > 1 for dogwood (Fig. 4-6A). Additionally, N:P ratios were < 1 (data not shown) for all litter types in the lab during early decay, but ratios increased to > 1 during mid- and late decay. Field C:N and C:P ratios in mid-decay were > 1. In the lab, C:N acquisition ratios were < 1 for dogwood, maple, and oak, although the maple-oak mixture C:N and C:P ratios were > 1 (Fig. 4-6B). During late-decay, all litter types in the field had C:N and C:P ratios < 1, but 101

117 these patterns were reversed in the lab (Fig. 4-6C). Overall, C:N and C:P ratios were > 1 for all litter types in both studies (Fig. 4-6D). 4.4 Discussion Temperature control of microbial activity In contrast to expectations, mass loss during the initial 2 months of field decomposition (January-February) was < 6%, even in the highly labile dogwood litter. We also found that enzyme activities and B:C ratios were lower than those found in later decay stages. Although temperature and moisture are strong controls on heterotrophic respiration (Schimel et al., 1994; Ise & Moorcroft, 2006; Suseela et al., 2012), field conditions during early decay included above-freezing soil temperatures (5.6 C), adequate moisture (32% WHC), and a substantial snowpack during most of February that minimized soil freezing. There was likely abundant labile C in fresh litter, and lower microbial demand for nutrients than C in all litters except oak (co-limited by C and P) suggests that either the high abundance of cellulose in fresh litter stimulated BG activity or N and P limitations were not acute. Additionally, greater mass loss for all litters under much lower moisture conditions during May through August 2010 suggests that field litter decomposition was not water-limited early in decay. Thus, we conclude that low temperatures suppressed decay rates during January February. Temperature sensitivities are commonly reported to be inversely proportional to litter quality (Mikan et al., 2002; Fierer et al., 2006), with low temperatures inhibiting the degradation of easily available C 102

118 polymers (Koch et al., 2007). For instance, Prescott (2010) proposed that microbial activity is uniformly low at temperatures below 10 C regardless of other factors, which is consistent with our findings, even with abundant labile C. In addition, it is known that the Q10 for respiration increases toward low temperatures and is about 4.5 at 10 C and 2.5 at 20 C (Kirschbaum, 1995, 2006). Thus, our finding that field decay rates were approximately 11 times lower than the lab during early decay is compatible with progressively higher Q10 toward lower temperatures, as suggested by developments in the understanding of how temperature affects microbial rates during decomposition (Kirschbaum, 2013) Carbon and phosphorus effects on microbial activity Under lab conditions, respiration peaked within the first week for all litter types, with the highest peak in dogwood. Dogwood has high concentrations of water-soluble compounds, cellulose, and nutrients (Moorhead & Sinsabaugh, 2000), and was the only litter type where microbial demand for C was greater than P during early decay (Fig. 4-6A). It is possible that greater labile C availability in dogwood stimulated BG activity, as cellulose degradation increased more rapidly in dogwood than other litters during the first few days of decomposition (Fig. 4-4B). However, litter types with high labile C concentrations often decompose rapidly when microorganisms are not P-limited because fast growing decomposers have proportionately higher P requirements (Gusewell, 2005). Microbes were less efficient at hydrolyzing phosphate during early decay in our maple and oak litters (Fig. 4-5B). Thus it is possible that higher dogwood P availability 103

119 increased C mineralization rates and mass loss relative to more recalcitrant litters. These contrasts in demand for C and P between litter types suggest that litter quality and/or P availability influenced decomposer responses to leaf litter under temperature controlled conditions Environmental influences on microbial biomass and enzyme dynamics Mass loss per unit enzyme activity was greater in the field than lab during mid-decay (Fig. 4-5C, D). During this time, higher decay rates occurred in dogwood and maple under field than lab conditions (Table 4.1). Blair (1988) found that the total soluble content of dogwood and maple litter (> 30% higher than oak) was the most important factor in first-year mass loss. This is likely due in part to the acceleration of mass loss by precipitation and leaching of soluble substrates. Decomposers also preferentially metabolize compounds in the soluble pool, as highly labile low-molecular weight substrates (i.e., sugars, phenolics, amino acids) are taken up with no enzymatic breakdown (Rinkes et al., 2011; Glanville et al., 2012). Thus, we speculate that the combination of leaching and microbial uptake of soluble compounds increased decay rates and mass loss per unit enzyme activity in the more labile litters under field conditions. It is unlikely leaching was as significant in microcosms because litter bags were placed in the middle of pre-wetted soil and only small amounts of water (1-2 ml) were added to the soil on a weekly basis to maintain moisture conditions. Thus, our findings suggest that environmental factors and litter soluble content influenced decay rates and mass loss per unit enzyme activity between studies during mid-decay. 104

120 It is also possible that greater faunal colonization and resulting fragmentation in the field increased the proportion of litter accessible to microbial attack and decreased the enzymatic effort needed to degrade dogwood and maple compared to more recalcitrant litters in the field (Cornelissen et al., 1999; Yang et al., 2012). For instance, lignified litters have high structural integrity that reduces soil faunal activity and fragmentation rates (Holdsworth et al., 2008). However, the exclusion of key soil macrofauna (e.g., non-native earthworms) due to the 1 mm 2 mesh size of litter bags likely limited overall fragmentation in both studies compared to natural conditions. For example, litter decomposition rates are often more than 8-times greater in earthworm-accessible large mesh litter bags than in fine mesh bags that exclude them in temperate deciduous forests (Holdsworth et al., 2008; Fox et al., 2010). Therefore, we likely underestimated decay rates in both studies compared to natural conditions by excluding various soil faunal fragmenters. Decomposer demand for N was greater than C in the lab for most litters during middecay (Fig. 4-6B). Consistent with our mid-decay hypothesis, isolation from external nutrient subsidies and the limited amount of low nutrient soil used in the incubation likely reduced N availability and increased decomposer N limitation (Boberg, 2010). We also found higher BG activities (Fig. 4-4A, B), but lower cellulose degradation per unit enzyme activity during mid-decay and overall in the lab than field for most litter types (Fig. 4-5). It is possible that decomposers degraded soluble compounds and hemicellulose more rapidly under optimal lab conditions during early decay, especially in dogwood. If so, cellulose was a more important C source to decomposers in the lab after early decay. This likely stimulated BG activity, but decreased mass loss relative to the 105

121 field. These differences in mass loss per unit enzyme activity and enzymatic effort directed toward C- and N- acquisition between decomposition studies suggest that both C and N availability constrain decomposer responses to litter quality. Strong contrasts in mass loss per unit enzyme activity occurred between studies during late decay (Fig. 4-5E, F). In addition, biomass to litter mass (B:C) ratios peaked in all litters during late decay in the field. However, B:C ratios declined between mid- and late decay in the lab (Fig. 4-2). Coupled field and microcosm experiments demonstrate that field soils behave differently following disturbance (Teuben & Verhoef, 1992; Salamanca et al., 1998), primarily due to alterations in microbial community abundance, activity and composition. It is possible that soil collection decreased saprophytic fungal abundance (Coleman et al., 1988) and/or disrupted the N- and P- transporting mycelial network of mycorrhizae, likely increasing enzymatic effort to obtain nutrients from organic sources (Hart & Reader, 2004; Sheng et al., 2012). For instance, there is often an inverse relationship between total enzyme pool size and fungal biomass (Sinsabaugh et al., 2002). However, it is likely that hyphal transfer of nutrients by actinomycetes between soil and litter bags still occurred in both studies. Another potential explanation is that NO3 - accumulation (Fig. 4-3D) suppressed ectomycorrhizal and saprotrophic fungal abundance (Carfrae et al., 2006; Wallenstein et al., 2006) and/or decreased overall microbial biomass in the lab (Ramirez et al., 2012). However, it is possible that high N throughputs occurred in the field as well, as our finding of consistently low field NO3 - concentrations are based on in-situ snap-shot measurements. Thus, our findings suggest relationships between microbial biomass and litter quality were not constant between the 106

122 field and lab during late decay, likely due to differences in microbial community composition and/or N availability The influence of edaphic factors on microbial function Oxidative enzyme activities only increased substantially in the high lignin oak litter and the maple-oak mixture in the field during late decay. Lignin is an aromatic polymer that is highly resistant to biological degradation, surrounds holocellulose in plant cell walls, and blocks microbial access to cell membrane proteins (Berg & McClaugherty, 2008). Thus, it is possible that a higher abundance of fungi specializing in lignin degradation in the field increased oxidative enzyme production in oak and the mixture to obtain protected labile N compounds (Talbot & Treseder, 2012). This is supported by our observation that microbial demand for N relative to C was greater for all litter types in the field after 40% mass loss (Fig. 4-6C). Because the C return on investing in lignin degrading enzymes is low (Kirk & Farrell, 1987; Dashtban et al., 2010), our findings also suggest that decomposers likely degraded lignin to acquire shielded cellulose. For instance, the peak in BG activity was concomitant with the peak in oxidative enzyme activity in oak (Fig. 4-4). Given that specialized fungal groups produce potent oxidative enzymes driving lignin degradation during later decay stages, an increase in their abundance and activity under field conditions likely influenced lignin degradation in our study (Bending & Read, 1997; Baldrian & Valaskova, 2008). Oxidative enzyme activities never increased for any litter type in the lab. Consistent with our late-decay hypothesis, NO3 - accumulated in the lab (Fig. 4-3D) and possibly 107

123 decreased microbial demand for N relative to C across all litter types (Fig. 4-6C). Although N can positively influence fungal colonization in fresh litter (Rousk & Bååth, 2007), we speculate that excess N suppressed ectomycorrhizal and saprotrophic fungal activity during late-decay (Waldrop & Zak, 2006), which decreased lignin decomposition rates (Grandy et al., 2008). Additionally, the inability of lignin degrading fungi to colonize from adjacent litter in the lab probably limited lignin degradation, as spatial and temporal variations in fungal abundance and activity in field settings are common (Lindahl et al., 2007; Osono, 2007; Feinstein & Blackwood, 2013). It is also possible that cellulose was a more important resource than lignin to decomposers during late decay in the lab, which increased C:N acquisition ratios. For instance, mass loss was never greater than 50% for any litter type in microcosms, suggesting that unshielded cellulose may not have been completely depleted. Overall, however, our results suggest that decomposer isolation from external resources, and/or N inhibition of microbial activity decreased litter mass loss rates in the lab during late decay. We found that mass loss of the maple-oak mixture was the average rate of the two individual litters, and hypothesize that this additive effect may be explained by N availability influences on microbial function. For instance, it is possible that the relative changes in microbial activity and decomposition rates of maple and oak due to changes in N availability were equal and the decomposition rate of the mixture was the average of the individual rates (Berglund & Ågren, 2012). Furthermore, unequal proportions of litter commonly result in non-additive effects (Mao & Zeng, 2012). Thus, the observed additive effect in our study was likely a result of N movement between the equal mixture of oak and maple litter used in our experiment (Schimel & Hattenschwiler, 2007). 108

124 4.5 Implications for Decomposition Models Our findings suggest that variable influences of climate and edaphic factors on microbial biomass, enzyme dynamics, and decomposition rates alter the trajectory of decay of varying leaf litter types. Although different microorganisms respond to temperature increases differently, our findings imply that microbial activity is predictably low across all decomposer groups below a temperature > 5.6 C regardless of litter composition. Although not explicitly measured, we speculate that leaching and fragmentation increase access to soluble C compounds that do not require enzymatic hydrolysis prior to uptake, resulting in greater mass loss per unit enzyme activity in labile litter types. Additionally, our study demonstrates that N availability alters microbial responses to litter composition, with potentially strong effects on microbial abundance, activity, and enzymatic effort per unit mass loss in mid- and late decay. Different functional groups of decomposers may respond differently to N availability and target different substrates depending on their relative demand for C and N at different decay stages. Therefore, linking the N demands of different functional groups of decomposers to microbial behavior and decay rates of different litter substrates is likely to enhance the predictive capabilities of decomposition models. 4.6 Conclusions 109

125 This study demonstrates that decomposition of the same leaf litter under field and lab conditions can result in strikingly different decay patterns due to the variable influences of climate and edaphic factors on microbial-substrate interactions. For instance, low temperatures decreased microbial activity early in decay, even in highly labile litters. These results in combination with previous findings of low microbial activity below 10 C and a higher Q10 at lower temperatures imply that even a small increase at lower temperatures may cause a substantial increase in CO2 flux. Furthermore, sharp contrasts in enzymatic effort directed toward C-, N-, and P- acquisition between litter types and studies indicate the importance of nutrient constraints on decomposer responses to litter quality. Finally, microbial biomass did not have a constant relationship to litter chemistry between the field and lab, which was likely caused by differences in nutrient availability and/or microbial community composition and function. Therefore, linking the C and N demands of different decomposer groups to decay rates of varying C substrates is likely to enhance mechanistic decomposition models. Acknowledgements This research was supported by the NSF Ecosystems Program (Grant # ). We thank Metroparks of the Toledo Area for use of the study site. For field and laboratory assistance, we thank Bethany Chidester, Dashanne Czegledy, Michael Deal, Michael Elk, Russ Friedrich, Mallory Ladd, Ryan Monnin, Steve Solomon, Heather Thoman, Logan Thornsberry, Eric Wellman, Travis White, and Megan Wenzel. We are grateful to the 110

126 Landscape Ecology Lab for providing climate data. We also thank Johannes Rousk and two reviewers whose comments greatly improved this work. 111

127 Table 4.1. Decay rate coefficients (k) for dogwood, maple, oak, and the maple-oak mixture during early, mid-, and late decay, and over the entire duration of the 849-day (641-day for dogwood) field and 376-day laboratory litter bag studies. Early Mid Late Overall Field k (day -1 ) k (day -1 ) k (day -1 ) k (day -1 ) R 2 Dogwood a b d f 0.97 Maple a c e g 0.91 Oak a c e g 0.88 Mix a c e g 0.93 Lab k (day -1 ) k (day -1 ) k (day -1 ) k (day -1 ) R 2 Dogwood h j k l 0.85 Maple i j k l 0.89 Oak i j k l 0.92 Mix i j k l 0.89 Decay coefficients for each decay stage in the field and lab were compared among the different litter types with 1-way ANOVAs and differences between litter types were compared with Tukey s post-hoc tests, when necessary. Lowercase letters designate significant differences between litter types within each decay stage and study. R 2 values represent the variance explained by the regression model and all P values are <

128 Figure 4-1. A) Soil temperature ( C) and soil water content (%) weekly averages and B) % mass remaining for dogwood, maple, oak, and the maple-oak mixture over a 2 ½ year field litter bag study. Error bars show the standard error of the mean (n = 8). 113

129 Figure 4-2. Biomass to litter mass (B:C) ratios for the field and laboratory incubations. Ratios were examined in early decay (January and February 2010 field harvests and days 0-2 in lab), mid-decay (May 2010 through December 2010 field harvests, and days in lab), and late decay (June 2011 through May 2012 field harvests, and days 230 through 367 in the lab). Error bars show the standard error of the mean (n = 8 for field and n = 4 for lab per harvest). B:C ratios changed over time (P < 0.05) and were higher in late decay in the field and during mid-decay in the lab for all litter types. 114

130 Figure 4-3. NH4 +, NO3 -, and PO4 3- concentrations for dogwood, maple, oak, and the maple-oak mixture in the field litter bag study (A, C, E) and laboratory incubation (B, D, F) during early (< 10% mass loss), mid- (10-40% mass loss), and late (> 40% mass loss) decay. Error bars show the standard error of the mean (n = 8 for field and n = 4 for lab). Asterisks (*) denote significant differences (P < 0.05) between litter types on a harvest date. 115

131 Figure 4-4. BG, NAG, Phos, and Phenox activities over time for dogwood, maple, oak, and mixed litter in the field litterbag study (A, C, E, G) and laboratory incubation (B, D, F) during early (< 10% mass loss), mid- (10-40% mass loss), and late (> 40% mass loss) decay. Phenox activity was not detected in the lab. Error bars show the standard error of the mean (n = 8 for field and n = 4 for lab). An * designates differences between litter species at each harvest date. 116

132 Figure 4-5. BG, NAG, LAP, Phos, and Phenox turnover activities (mol) for dogwood, maple, oak, and the maple-oak mixture in the field litter bag study and laboratory incubation during early (A, B), mid- (C, D), and late (E, F) decay, and overall (G, H). No Phenox activity was detected in the lab or during early decay in the field. Turnover activities (high turnover activity = high enzyme activity per unit mass loss) were compared with separate two-way ANOVA s (field and lab) during each decay stage and lowercase letters designate significant differences within and across enzymes for each study. Error bars show the standard error of the mean (n = 8 for field and n = 4 for lab). No significant differences between enzymes or litter types occurred during early decay in the field. Phenox turnover activity was significantly higher than most other enzymes during late decay in the field, while dogwood turnover activity was significantly higher than other litter types during late decay in the lab. 117

133 Figure 4-6. C:N and C:P acquisition ratios at different stages of decay during the field and lab incubations. C:N:P acquisition converges on a 1:1:1 ratio, therefore BG:(NAG+LAP) or BG:Phos ratios deviating from 1 in each scatterplot indicate differences in relative microbial demand for C and nutrients. Ratios were examined in A) early decay (January through February 2010 field harvests and days 0-2 in lab), B) middecay (May 2010 through December 2010 field harvests, and days in lab), and C) late decay (June 2011 through May 2012 field harvests, and days 230 through 367 in the lab) and D) overall. 118

134 Chapter 5 Nitrogen alters microbial enzyme dynamics but not lignin monomer concentrations during maize decomposition Abstract Increases in nitrogen (N) availability reduce decay rates of highly lignified plant litter. However, the microbial and chemical mechanisms that give rise to this inhibitory effect are still unclear. Here, we ask: Why does increased N availability inhibit lignin decomposition? We hypothesized that either 1) decomposers degrade lignin to obtain protected N compounds and stop producing lignin-degrading enzymes if mineral N is available, or 2) chemical reactions between lignin and mineral N make lignin more recalcitrant and limit the ability of decomposers to break it down. In order to test these hypotheses, we tracked changes in carbon (C) mineralization, microbial biomass and enzyme activities, and lignin monomer concentrations over a 478-day laboratory incubation of three maize genotypes differing in litter quality (F292bm3 (recalcitrant) > F2 (intermediate) > F2bm1 (labile)). Maize stem internodes of each genotype were mixed with either an acidic or neutral ph sandy soil, both with and without added N. Nitrogen 119

135 addition reduced C mineralization, microbial biomass, and lignin-degrading enzyme activities across all treatments. We speculate that mineral N suppressed fungal growth and reduced microbial acquisition of lignin-shielded proteins. In contrast to oxidative enzyme activities, N-acetyl-β-glucosaminidase (NAG; a chitin/peptidoglycan degrading enzyme) activity was elevated in the low ph soil following N addition, primarily in the recalcitrant genotype. It is possible that faster-growing decomposers responded positively to N and produced NAG to acquire C from sources other than lignocellulose. However, N addition did not significantly alter the quantity or quality of lignin monomers in any treatment. This suggests that abiotic interactions between N and phenolic compounds did not strongly influence the ability of decomposers to degrade lignin. Our findings indicate that N fertilization alters microbial enzyme dynamics, but not lignin monomer concentrations during maize decomposition. 5.1 Introduction Increases in mineral nitrogen (N) availability suppress decay rates of highly lignified plant litter (Berg, 2000; Wang et al., 2004; Gallo et al., 2005). Soil mineral N is known to inhibit microbial growth and respiration, and decrease lignin-degrading enzyme activity during the late stages of litter decomposition (Gallo et al., 2004; Waldrop & Zak, 2006; Ramirez et al., 2012). Although it has been found that mineral N retards lignin decay, the microbial and chemical mechanisms giving rise to this inhibitory effect remain unclear (reviewed in Burns et al., 2013). With N deposition expected to alter many aspects of carbon (C) cycling (Waldrop et al., 2004; Treseder, 2008; Ochoa-Hueso et al., 2013), we 120

136 need to understand how and why increased N availability inhibits lignin decay to better predict how organic matter turnover rates are affected by N deposition. Microorganisms receive a low C return on investment from lignin-degrading enzymes (Sugai & Schimel, 1993; Schimel & Weintraub, 2003; Moorhead & Sinsabaugh, 2006). Decomposers preferentially degrade more labile C substrates (i.e., water-soluble compounds and a fraction of soluble carbohydrates) before lignin in fresh litter, primarily due to the higher C and nutrient returns from less recalcitrant substrates (Berg, 2000; Rinkes et al., 2011). However, lignin degradability also varies with its chemical composition. For example, syringyl (S-) lignin subunits are more susceptible to enzymatic oxidation than vanillyl (V-) type phenols (Otto & Simpson, 2006). Thus, not only lignin quantity but also lignin quality impacts residue decomposition (Klotzbücher et al., 2011; Machinet et al., 2011). Given that the initial quality and quantity of lignin varies between and within plant species (Amin et al., 2013), we need to better understand how N availability influences the biochemical mechanisms driving the degradation of lignin (Neff et al., 2002; Talbot et al., 2012). Lignin is generally not used as a sole C source because decomposers do not receive a positive energy return on lignin degradation (Kirk et al., 1976; Kirk & Farrell, 1987). Thus, microorganisms must get something else in return for producing lignin-degrading enzymes. Because lignin forms a resistant shield around cellulose (i.e., lignocellulose) in plant cell walls (Dashtban et al., 2010; Amin et al., 2013; Moorhead et al., 2013), decomposers may increase their investment in lignin-degrading enzymes to acquire C from shielded cellulose. However, lignin may not be worth the cost of breaking down for increased C acquisition if it is particularly recalcitrant or in high concentrations, yet 121

137 decomposers may still degrade lignin to acquire N if no other N is available. Indeed, many laccases function with proteolytic and chitinolytic enzymes to mine N from humic complexes (reviewed in Sinsabaugh et al., 2010). Decomposers can also be stimulated by labile C to break down lignin to acquire shielded N compounds (Craine et al., 2007; Talbot et al., 2012). This microbial N mining hypothesis implies that decomposers may at times degrade lignin primarily to acquire N, and oxidative enzyme activities and lignin decay can therefore be suppressed by high soil N concentrations (Keyser et al., 1976). Thus, decomposers no longer need to invest resources in producing lignin-degrading enzymes to acquire shielded cell membrane proteins if inorganic N is readily available (Couteaux et al., 1995; Moorhead & Sinsabaugh, 2006), resulting in a suppressive effect of mineral N on lignin-degrading enzyme activities (Carreiro et al., 2000). This might explain decreased lignin turnover rates with elevated N (Berg & Matzner 1997; Wang et al., 2004). In addition to the potential effects of N on microbial production of lignin degrading enzymes, Fog (1988) hypothesized that non-enzymatic reactions between polyphenols and amino compounds form brown, toxic and highly recalcitrant compounds that could suppress lignin degradation. Lignin depolymerization products may interact with nitrogenous compounds to create toxic oligomers that inhibit fungal activity and growth (Hodge, 1953). Similarly, it is possible that reactions between ammonium or amino acids and phenolic groups may produce recalcitrant aromatic groups in degraded lignin (Stevenson, 1982). Thus, it has been hypothesized that reactions between lignin derived phenolics and labile N compounds decrease lignin-degrading enzyme efficiency and further reduce the C return that decomposers get from oxidative enzyme production 122

138 (Schimel & Weintraub, 2003; Tu et al., 2014). The extent that browning (i.e., production of recalcitrant or toxic compounds) impacts lignin oxidation tends to vary with soil ph. In general, the rate of browning reactions increases with soil ph, possibly due to greater N availability in neutral than acidic ph soils (Soderstrom et al., 1983; reviewed in Fog, 1988). Therefore, the influence of external factors such as soil ph on browning can further exacerbate microbial C limitation (Treseder, 2004) by increasing the energy required for lignin degradation and/or decreasing the C return. However, we lack the detailed data needed to disentangle the extrinsic and intrinsic factors underlying why increased N availability often inhibits lignin decay. Our goal was to elucidate how interactions between N availability, soil ph, and litter quality alter 1) microbial lignolytic enzyme activities and 2) lignin quality during maize decomposition. To accomplish this goal, we conducted a 478-day laboratory incubation using stem internodes from three maize genotypes varying in lignin quality and quantity (F292bm3 (recalcitrant) > F2 (intermediate) > F2bm1 (labile)) mixed with either an acidic or neutral ph soil, both with and without added N. We monitored C mineralization, microbial biomass and enzyme activities, and lignin chemistry throughout decomposition. We tested two hypotheses aimed at understanding the microbial and chemical mechanisms underlying why and how added N alters microbial dynamics and lignin monomer concentrations: Hypothesis #1: Decomposer microorganisms degrade lignin to acquire N, but if N is otherwise available they stop producing lignolytic enzymes to acquire N. Hypothesis #2: Chemical reactions between lignin and N make lignin more recalcitrant, 123

139 which decreases the ability of decomposers to break it down by lignin degrading enzymes. We predicted that added N would decrease C mineralization, microbial biomass, and oxidative enzyme activities in all treatments after the early stages of decay, with the greatest inhibitory effect occurring in the recalcitrant litters. We also predicted that chemical reactions between N and lignin would have the largest effect on total lignin monomer concentrations (i.e., syringyl (S) + vanillyl (V) + cinnamyl (Cn) units) at a higher soil ph, as previous studies demonstrate that browning is more pronounced in neutral ph soils where N is more available. In general, S and Cn monomeric units are more labile than V units. Thus, we expected S/V and Cn/V ratios to decrease more rapidly in the more recalcitrant litter types, due to the lack of labile litter constituents that are preferentially consumed by decomposers. 5.2 Methods Maize genotypes Three maize genotypes differing in lignin quality and quantity (Table 5.1) were cultivated in experimental fields at the INRA Lusignan experimental station (N 49 o 26, E 0 o 07, Vienne, France) and were harvested at physiological maturity. The second internodes from one normal line genotype (F2) and two brown midrib mutants (F2bm1 and F292bm3) were used in the incubation. The total C and N content of each genotype was determined by elemental analysis (NA 1500, Fisons Instruments) and the soluble and cell 124

140 wall content was determined by neutral detergent fiber (NDF) extraction following the method described by Goering & Van Soest (1970). In brief, the soluble fraction was removed by boiling 1 g of maize internodes in deionized water at 100 C for 60 minutes. The remaining NDF fraction corresponded to the cell walls. Total sugar content of the residue (i.e., soluble and cell wall fraction) was determined by the method described by Blakeney et al. (1983) using a two-step sulfuric acid hydrolysis. Lignin content of the residue was approximated as the acid-unhydrolyzable residue remaining after sulfuric acid hydrolysis according to the Klason lignin (KL) determination (Monties, 1984). Results of these analyses are found in Table 5.1. Based on initial litter chemistry, we refer to the three maize genotypes as follows: 1) F2bm1 = Labile Genotype, 2) F2 = Intermediate Genotype, 3) F292bm3 = Recalcitrant Genotype. These designations are based on the decomposability (i.e., initial soluble and lignin content) of each genotype Soil collection Two Typic Udipsamment soils varying in ph but similar in many soil properties (Table 5.2) were collected in September of The acidic ph soil (i.e., 4.9 ± 0.2) was collected from the Kitty Todd Nature Preserve (N 41 37, W ) and the neutral ph soil (6.7 ± 0.3) from the Oak Openings Preserve Metropark (N 41 33, W ), which are both located in Northwest Ohio. These sites were selected due to their low C and nutrient sandy soils. Soil cores were collected from the top 5 cm (the depth with the highest biological activity) from a 10 m 2 area at both sites, sieved (2 mm mesh) to remove as much coarse debris and organic matter as possible, thoroughly mixed, and pre- 125

141 incubated for 3 months in a dark 20⁰ C incubator at 45% water-holding capacity (WHC). This is the WHC that maximizes respiration in both soils (Rinkes et al., 2013, 2014). The pre-incubation allowed for microorganisms to acclimate to experimental conditions and to metabolize as much extant labile C as possible Incubation experiment A 478-day laboratory incubation was established in 473 ml wide mouth canning jars. One hundred-twenty litter and soil treatments (3 litter types 2 soils 2 N addition levels 10 harvests) and forty soil-only control treatments (2 soil types 2 N addition levels 10 harvests) were replicated four times and incubated together. Litter treatment jars contained 50 g dry soil adjusted to 45% WHC mixed with dry maize stem internode fragments (0.5 cm length / cm diameter) at a rate of 3000 mg C kg -1 soil. Soil-only control jars contained 50 g dry soil adjusted to 45% WHC. Nitrogen (ammonium nitrate, 70 mg N kg -1 soil) was added to half of the treatment and soil-only control jars as an initial pulse at the beginning of the experiment. The control groups received an equivalent amount of water, but did not receive supplemental N addition. Nitrogen addition decreased the ph in the acidic (4.6 ± 0.1) and neutral soil (6.0 ± 0.2). Respiration was monitored frequently (see below) and destructive harvests occurred on days 0, 27, 58, 84, 120, 172, 238, 316, 390, and 478. Each destructive harvest included enzyme assays, determination of microbial biomass-c, and dissolved organic C (DOC) C mineralization 126

142 The total amount of C mineralized was measured after 7, 11, 18, 31, 53, 89, 123, 159, 219, 306, and 478 days in litter addition treatment groups, soil-only controls, and four sample-free jars (blanks). Sodium hydroxide (NaOH) traps captured CO2 produced during decomposition. The amount of NaOH in each trap was optimized based on the anticipated respiration rate on each harvest day. Traps were changed less frequently as respiration rates decreased and became less dynamic over time. Traps were analyzed using the BaCl2/HCl titration method (Snyder & Trofymow, 1984). In brief, 2 ml of BaCl2 were added to the NaOH to precipitate the trapped CO2 as BaCO3, followed by 5 drops of thymophthalein ph indicator. The carbonic acid trapped in the NaOH was then back titrated with 0.1 N HCl. The calculation of C trapped required subtracting the equivalents of acid used in titrating a sample from the equivalents used to titrate traps in the sample-free blanks. Litter treatment traps were saturated on the day 7 harvest. However, jar headspace CO2 concentrations were also measured with a Li-820 infra-red gas analyzer (LI-COR Biosciences, Lincoln, Nebraska, USA) over the first week of the experiment, which served as another method to calculate cumulative C mineralization Microbial biomass Maize internodes (removed from each litter + soil treatment and brushed free of soil particles with a 2 mm paint brush) and 3 sample-free blanks were extracted for DOC by adding 8 ml of 0.5 M potassium sulfate and agitating on an orbital shaker for 1 h. Samples were vacuum filtered through Pall A/E glass fiber filters and frozen at -20 C 127

143 until analyses were conducted. Microbial biomass carbon (MB-C) was quantified using a modification of the chloroform fumigation-extraction method described by Scott-Denton et al. (2006). For the treatments, approximately mg (wet weight) of internodes were combined with 0.5 ml of ethanol free chloroform and incubated at room temperature for 24 hours in a stoppered 250 ml Erlenmeyer flask. Following incubation, flasks were vented in a fume hood for 30 minutes and extracted as described above. Fumigated extracts were analyzed for DOC on a Shimadzu TOC-VCPN analyzer (Shimadzu Scientific Instruments Inc., Columbia, MD, USA). MB-C was calculated as the difference between DOC extracted from fumigated and non-fumigated samples. No correction factor for extraction efficiency (kec) was applied, as it is unknown for these samples Enzyme assays We measured β-1,4-n-acetyl-glucosaminidase (NAG; a.k.a chitinase, which hydrolyzes chitin and peptidoglycan oligomers into N-acetyl-glucosamine) activity in 96- well microplates following the fluorometric enzyme assay outlined by Saiya-Cork et al., (2002). Sample slurries were prepared by homogenizing approximately mg of wet internodes with 50 mm sodium acetate buffer (ph 5). The internode and buffer mixture was homogenized for 1 minute using a Biospec Tissue Tearer (BioSpec Products, Bartlesville, OK). We used a 200 µm substrate solution (4-methyl umbelliferyl-n-acetyl-β-d-glucosaminide) to ensure saturating substrate concentrations (German et al., 2011). After substrate addition, microplates were incubated at 20 C in darkness. Following incubation, 10 μl aliquots of 1.0 M NaOH were added to each well 128

144 to increase fluorescence of MUB before being read on a Bio-Tek Synergy HT Plate Reader (Bio-Tek Inc., Winooski, VT, USA) with 365 nm excitation and 460 nm emission filters. High-throughput colorimetric assays were conducted in 96-well microplates for phenol oxidase (Phenox), which is a lignin-degrading enzyme, using 2,2ʹ-azino-bis-3- ethylbenzothiozoline-6-sulfononic acid (ABTS) as a substrate (Floch et al., 2007). Assay wells included 200 µl aliquots of soil slurry followed by the addition of 100 µl of 1 mm ABTS. Negative controls received 200 µl of buffer and 100 µl ABTS and blank wells received 200 µl of soil slurry and 100 µl of buffer. Plates for both enzymes were incubated at 20º C in darkness and a Bio-Tek Synergy HT Plate Reader (Bio-Tek Inc., Winooski, VT, USA) was used to measure absorbance at 420 nm. After correcting for negative controls and blank wells, enzyme activity rates were expressed as μmol h -1 g dry litter Phenol chemistry Litter treatments (internodes + soil) were oxidized with cupric oxide (CuO) after 0, 120, and 478 days following the procedure of Hedges & Ertel (1982) and adapted from Kogel (1986). One g of CuO, 100 mg ammonium iron (II) sulfate hexahydrate and 15 ml of 2M NaOH purged with N gas were added to Teflon-line bombs containing samples. After heating for 2 hours at 170 C, the bombs were cooled at room temperature and methanol water (1:1, v/v) solutions of ethylvanillin and 3, 4, 5 trimethoxy-trans-cinnamic acid were added to the reaction mixture as internal standards. The reaction mixture was 129

145 acidified to ph 1-2 using 6 M HCl and centrifuged (10 minutes at rpm). The residues were washed with 10 ml of deionized water prior to centrifugation. The combined supernatants were extracted three times with 25 ml of dichloromethane. Anhydrous Na2SO4 was added to the combined organic phases to remove any remaining water. The dichloromethane extract was evaporated at 40 C to dryness under reduced pressure. The monomers recovered by CuO oxidation were determined by HPLC (Waters, 2690). The dried extracts were dissolved in 1 ml methanol:water (1:1, v/v) and filtered (0.45 µm) prior to injection onto a Spherisorb S5ODS2 (Waters, RP-18, mm) column. The lignin oxidation products were detected using a photodiode array UV detector (Waters, 996), The vanillyl (V) and syringyl (S) units were quantified at 280 nm using the ethylvanillin as an internal standard while cinnamyl (Cn) units were quantified at 302 nm using 3, 4, 5 trimethoxy-trans-cinnamic acid (Chen, 1992). All monomers were quantified using appropriate reference standards. The concentration of V-type monomers was calculated as the sum of the concentrations of vanillin, acetovanillone, and vanilic acid. The concentration of S-type monomers was calculated as the sum of the concentrations of syringic acid, syringaldehyde, and acetosyringone. The concentration of Cn-type monomers was calculated as the sum of the concentrations of ferulic and p-coumaric acids (Hedges & Ertel, 1982). The sum of V, S, and Cn type phenols released from lignin macromolecules was used to approximate the amount of lignin phenols (Kogel, 1986; Bahri et al., 2006). For all samples, the mean values were obtained from two replicate CuO oxidations Data analysis 130

146 Differences in the initial characteristics of the three maize genotypes were analyzed by analysis of variance (ANOVA). Cumulative C mineralization and Phenox means were analyzed using separate three-way ANOVA s with maize genotype, soil ph, and N addition (+ or -) as factors. Microbial biomass-c was analyzed at day 0 and between days (mean values were reported) using separate three-way ANOVA s with maize genotype, soil ph, and N addition as factors. Cumulative NAG and Phenox activities were analyzed separately with three-way ANOVA s. V, S, and Cn monomers, S/V ratios, and the sum of V+S+Cn monomers were analyzed on days 120 and 478 using a threeway MANOVA with maize genotype, soil ph, and N addition as factors. Values for lignin monomers and ratios on day 0 (n = 4 for F2 and F2bm1; n = 3 for F292bm3) were not included in the statistical analyses as only a few of the initial samples were analyzed, but are included in figures for comparison and to denote initial differences between genotypes. Differences among groups were considered significant if P 0.05 and were compared using Tukey multiple comparison post-hoc tests. All statistical analyses were performed on untransformed data. Data were analyzed using SPSS Statistics version Results C mineralization 131

147 C mineralization rates peaked within the first week in all genotypes, with the highest peak rates (~ µg-c g dry soil -1 day -1 ) in the labile and intermediate litters (P < 0.01; data not shown). Nitrogen addition increased the amount of C remaining at the end of the experiment in all treatments by 3-11% (P < 0.01; F = 14.57), but had the weakest effect on the recalcitrant genotype (Fig. 5-1). On average, 6% more of the initial litter C added was respired in neutral than acidic treatments. Thus, the amount of C mineralized was significantly higher in the neutral than acidic ph treatments (P < 0.01; F = 14.68) Microbial biomass dynamics MB-C significantly changed over time when day was included as a factor in the ANOVA (P < 0.01; F = 14.62), primarily due to higher MB-C on day 0 in the labile and intermediate genotypes compared to days (Fig. 5-2). On day 0, there were significant genotype (P = 0.04; F = 3.55) and ph (P = 0.05; F = 3.92) effects on MB-C. Post-hoc tests revealed MB-C was higher in the labile and intermediate than recalcitrant genotype (P < 0.01). Additionally, MB-C was greater in the neutral than acidic ph treatments on day 0 (Fig. 5-2). There were significant ph (P < 0.01; F = 13.1) and N (P < 0.01; F = 7.91) effects on MB-C throughout the remainder of the incubation (days ). This was primarily due to lower MB-C in the N+ than N- treatments in the acidic ph soils (Fig. 5-2) Enzyme activity 132

148 Phenox activity was significantly higher in the more recalcitrant litters than the labile genotype (P < 0.01 for all; Fig. 5-3). Phenox activity was significantly lower in the N+ than N- treatments and in the neutral than acidic ph treatments, primarily due to higher activity in the acidic ph, N- treatment for the recalcitrant genotype and lower activity in the neutral ph, N+ treatment for the intermediate genotype on day 58 (Fig. 5-3). This resulted in significant genotype (P < 0.01; F = 12.26), ph (P < 0.01; F = 7.52), and N (P < 0.01; F = 13.87) effects on Phenox activity. If day was included as a factor in the MANOVA, Phenox activity significantly changed over time for all genotypes, primarily due to the peak in activity for most treatments on day 58 (Fig. 5-3). Cumulative Phenox activity in the labile litter was lower than in the intermediate and recalcitrant genotypes (P < 0.01). In addition cumulative Phenox activity was greater in the acidic than neutral ph treatments (P < 0.01). Nitrogen addition significantly decreased cumulative Phenox activity compared to ambient conditions and suppressed cumulative activity by more than 50% in the low ph, recalcitrant treatment (Fig. 5-4). This resulted in a significant N genotype interaction effect (P = 0.04; F = 3.43). Cumulative NAG activity in the acidic ph, N+ treatment was greater than in the acidic and neutral ph, N- treatments for the recalcitrant genotype (Fig. 5-4). This resulted in a significant genotype ph N interaction effect (P = 0.04; F = 3.41) Phenol chemistry There was a significant genotype effect on the quantity of S monomers (P < 0.01; F = 6.57) and an effect approaching significance for V monomers (P = 0.07; F = 2.77), as 133

149 both monomers were more abundant in the intermediate than recalcitrant genotype between days 120 and 478 (Fig. 5-5). There was also a significant genotype effect (P < 0.01; F = 25.66) on the S/V ratio, primarily due to lower ratios in recalcitrant litter than in the labile and intermediate genotypes (Fig. 5-6). The acid to aldehyde (Ad/Al) ratios of the vanillyl and syringyl units were highly variable, but typically increased between days 0 and 478 for most treatments (data not shown). Additionally, both S and V monomers decreased over time for most genotypes, as concentrations were higher on day 0 than day 478 in the intermediate and recalcitrant genotypes (Fig. 5-5). There was a significant genotype effect (P < 0.01; F = 10.25) on the quantity of Cn monomers, as concentrations in the labile litter were lower than the intermediate and recalcitrant genotypes between days 120 and 478 (Fig. 5-7). Additionally, Cn monomer concentrations were 1.5 higher in the acidic than neutral treatments when averaged over days 120 and 478, resulting in a significant ph effect (P < 0.01; F = 14.24). The ratio of Cn to V monomers decreased between day 0 and day 478 for most treatments (data not shown). There was also a significant genotype effect (P = 0.02; F = 4.47) on the total amount of lignin monomers (i.e., V+S+Cn), primarily due to higher monomer concentrations in the intermediate litter than labile and recalcitrant genotypes when averaged over days 120 and 478 (Fig. 5-7). Overall, Cn monomers represented a small percentage (~5-10%) of the total amount of lignin monomers (Fig. 5-7). In general, the total amount of lignin monomers decreased over time, as concentrations were higher on day 0 than day 478 in most treatments (Fig. 5-7). 134

150 5.4 Discussion N, ph, and litter quality effects on microbial function We hypothesized that under N enrichment, lignin degradation would decrease due to lower microbial N demand (Moorhead & Sinsabaugh, 2006; Craine et al., 2007). Indeed, N addition decreased oxidative enzyme activities across all treatments (Fig. 5-4). This ubiquitous decline supports our hypothesis that decomposers were using labile C substrates in maize litter to mine lignin and obtain N shielded by the cell wall phenolic fraction (Talbot et al., 2012). Given that the acid insoluble fraction of litter does not provide sufficient C and energy yields upon degradation, it is likely decomposers decreased production of energetically costly lignin-degrading enzymes when the availability of mineral N increased (Kirk & Farrell, 1987; Craine et al., 2007). We speculate that microorganisms (presumably fungi) degraded lignin primarily to access lignin-shielded N compounds rather than shielded C compounds in this maize litter. Thus, our findings provide support for the microbial N mining hypothesis as a potential noncompetitive microbial mechanism underlying N inhibition of lignin decay (Craine et al., 2007). Another possibility is that N suppression of lignin-degrading enzyme activity was due to competitive interactions between various groups of decomposers. For instance, N addition increased cumulative NAG activity in the intermediate and recalcitrant genotypes under acidic conditions in our study (Fig. 5-4). We speculate that fast-growing decomposers responded positively to N and increased NAG production as a mechanism 135

151 to acquire C, as chitin and peptidoglycan are polysaccharides, making them primary sources of C as well as N. Increases in N availability likely elevated microbial C demand and increased C acquisition from NAG. However, this effect was not apparent in the labile genotype, possibly due to greater C availability from sources other than chitin or peptidoglycan. We hypothesize that exogenous N caused a shift in microbial community composition from slow-growing lignin degraders (possibly N miners) to decomposers with higher N assimilation efficiencies in our low ph, intermediate and recalcitrant litter treatments (Treseder, 2004; Allison et al., 2007), decreasing fungal abundance and oxidative enzyme activities compared to ambient conditions. The possibility that interactions between N availability and lignin content structured competitive dynamics between decomposers in our study provides an alternative mechanism to the N mining hypothesis by which exogenous N can suppress lignin decay (Talbot & Treseder, 2012). Although decreases in lignin-degrading enzyme activity following N enrichment are well-documented (Carreiro et al., 2000; Waldrop et al., 2006; Grandy et al., 2008), we do not have the data to rule out either the competitive or non-competitive mechanism as the driver underlying N inhibition of phenol oxidase activity in our study N, ph, and litter quality effects on lignin monomer concentrations We hypothesized that abiotic reactions between lignin and N make lignin more recalcitrant to microbial degradation. Despite a statistically insignificant trend toward being higher in most N addition treatments, the quantity (i.e., abundance of S, V, and Cn monomers) and quality (i.e., S/V ratios) of lignin monomers did not significantly vary 136

152 between N addition and ambient treatments for any genotype over the 478-day incubation. In fact, N had little effect on lignin monomer abundance in our neutral ph soil treatments, contradicting the Fog (1988) hypothesis that browning reactions especially reduce lignin degradability in higher ph soils. For example, a higher concentration of lignin monomers would have been expected in our N addition treatments compared to controls if added N decreased the degradability of lignin. Thomas et al. (2012) also found that chronic N addition had little effect on the biochemical composition of lignin-derived molecules in a sugar maple forest stand. In addition, Gallo et al. (2005) concluded that reactions directly incorporating N into soil organic matter (SOM) was not a major mechanism influencing ecosystem responses to mineral N addition in temperate hardwood forests. Furthermore, the weakest effect of N fertilization on C mineralization occurred in the highest lignin genotype in our study (Fig. 5-1). This provides further evidence that elevated N did not increase lignin recalcitrance to the point that mass loss stopped. Thus, our findings suggest that chemical reactions between by-products of lignin and N did not significantly increase lignin recalcitrance or limit the ability of decomposers to break down phenolic compounds. In contrast to the effects of mineral N, we found strong differences in the quantity and quality of lignin monomers between maize genotypes. The sum of phenyl propane units (i.e., V+S+Cn) is commonly used to measure lignin concentrations, even if extraction yield may be low (Klotzbücher et al., 2011; Machinet et al., 2011), while decreases in S/V and Cn/V ratios over time represent a gradual decline in lignin quality (Otto & Simpson, 2006). We found that the VSCn proportions tended to remain constant in the labile and intermediate genotype treatments over the first 120 days of incubation (Fig

153 7). This suggests that minimal lignin degradation occurred in the more labile genotypes as labile litter constituents were preferentially degraded (Bertrand et al., 2006; Machinet et al., 2009). Typically, labile substrates are efficiently used by decomposers during early stages of decay (Moorhead et al., 2013; Rinkes et al., 2014) and are a dominant source of microbial products contributing to stable SOM (Cotrufo et al., 2013). Given the ratio of more degradable Cn- and S- to more recalcitrant V-units (Kogel, 1986; Otto & Simpson, 2006) did not significantly change in the labile and intermediate genotypes over the first 120 days in our study, our results suggest the labile litters became enriched in lignin during the earlier part of the incubation. VSCn yields and S/V ratios were lower on days 120 and 478 compared to initial values in the recalcitrant genotype. In addition, a 25% greater decrease in Cn- units occurred in the recalcitrant litter compared to more labile genotypes over the incubation. These results are comparable to other studies, as bm3 mutants are characterized by a significant decrease in labile-ether linked syringyl units (Barriere et al., 2004; Machinet et al., 2011). In addition, we found higher cumulative Phenox activity in the recalcitrant than labile genotype. Thus, lignin shielded cellulose was likely a more important resource to decomposers earlier in decay in the most recalcitrant litter. In addition to our previous finding that decomposers likely degrade lignin to obtain N, these results suggest that microorganisms may also use lignin to obtain shielded C sources when necessary N, ph, and litter quality effects on microbial respiration and biomass dynamics 138

154 Litter chemistry, soil ph, and N availability influence microbial dynamics, but their effects vary based on the stage of decay. For instance, rapid increases in microbial biomass occurred in the labile and intermediate genotypes immediately following litter addition (within 8 hours), with the highest concentrations in the neutral ph soil (Fig. 5-2). In addition, microbial respiration rates were elevated, but lignin-degrading enzyme activities were low (Fig. 5-3) during the first few days of incubation for these litters. This suggests that fast-growing microorganisms preferentially degraded water-soluble compounds and labile polymeric C substrates during very early decay (Glanville et al., 2012; Rinkes et al., 2014). Machinet et al. (2009) found that early colonizing microorganisms were influenced by the chemical quality of maize roots, as they colonized genotypes with a high cell wall sugar content more rapidly than those with high lignin content. Similarly, we found the lowest microbial biomass and activity in the most recalcitrant genotype during this early colonization phase. We also found that microbial biomass during the initial harvest was more than 70% lower in the acidic than neutral ph soils across all labile and intermediate genotype treatments. We speculate that the ph response in these litters was due to higher bacterial activity and growth in the neutral ph soil. For instance, bacterial abundance is positively correlated with soil ph and frequently doubles between ph 4 and 8 (Rousk et al., 2009, 2010). Thus, it is likely that the neutral ph soil accelerated bacterial colonization and assimilation of abundant water-soluble substrates in the more labile genotypes. Overall, our findings suggest that litter chemistry and soil ph influence how quickly decomposers colonize fresh litter. Nitrogen addition and soil ph were strong controls on microbial activity, growth, and possibly community composition during later decay stages. Nitrogen fertilization 139

155 decreased the total amount of C mineralized across all of our maize treatments and reduced microbial biomass by approximately 50% over days in our acidic ph treatments (Figs. 5-1, 5-2). During this same time period, N addition only reduced microbial biomass by an average of 8% across all neutral ph soil treatments, which generally had the highest amounts of C mineralized. It is not uncommon for N fertilization to consistently reduce microbial respiration (Ramirez et al., 2010) or to suppress microbial biomass by as much as 20-35% across a wide range of soil types (Treseder, 2008 and references therein; Ramirez et al., 2012). For instance, white-rot fungi and other saprophytic fungi often respond negatively to N addition, especially in microcosms (Fog, 1988; Entry, 2000). Thus, chronic N amendments tend to decrease fungal: bacterial ratios and reduce fungal degradation of lignin (Bowden et al., 2004; Frey et al., 2004; Wallenstein et al., 2006; Treseder, 2008). Given that fungi tend to be more acid tolerant than bacteria, it is possible that fungi were a more active and dominant component of our lower ph soils (Joergensen & Wichern 2008; Strickland & Rousk 2010). We speculate that the inhibitory effect of N on fungal composition and activity had a greater influence in our acidic soil, which may have driven the strong contrasts in microbial dynamics between soil types. 5.5 Conclusions We provide data describing the microbial and chemical mechanisms that give rise to the inhibitory effect of N on lignin decay. This study suggests that N availability alters microbial enzyme and biomass dynamics, but not the recalcitrance of by-products of 140

156 lignin decay during decomposition of maize. Our results provide support for the microbial N mining hypothesis, which suggests that decomposers degrade lignin to obtain shielded N compounds and decrease production of lignin-degrading enzymes when mineral N availability increases. Indeed, N fertilization inhibited lignin-degrading enzyme activities across all our maize treatments. However, it is also possible that differences in chitin/peptidoglycan-degrading enzyme activity between treatments decreased lignin-degrading enzyme activity, as competition between varying decomposer groups for C and/or N may influence lignin decay by altering the abundance and/or activity of lignin decomposers. Thus, an alternative explanation for decreased lignolytic enzyme activity following N addition is that N miners were outcompeted by decomposers employing other strategies for resource acquisition when N was abundant. Furthermore, our results indicating greater lignin degradation in the recalcitrant genotype suggest that decomposers will degrade lignin to acquire C in the absence of high energy labile C compounds. However, we found no evidence that chemical reactions between N and lignin made lignin more recalcitrant to decomposition in maize, even in highly lignified genotypes. Acknowledgements This research was supported by an NSF Ecosystems Program Grant (# ) to MNW and the NSF Research Coordination Network on Enzymes in the Environment Grant (# ). For field and laboratory assistance, we thank Bethany Chidester, Dashanne Czegledy, Michael Elk, Mallory Ladd, Ryan Monnin, Steve Solomon, Heather 141

157 Thoman, Logan Thornsberry, Eric Wellman, Travis White, and Megan Wenzel. 142

158 Table 5.1. Chemical characteristics of three different genotypes of maize internodes. Values represent the mean (± SE) for each genotype chemical category (n = 2). Lowercase letters designate significant differences between genotypes for each chemical characteristic. Cultivars F2bm1 Labile C to N ratio F2 F292bm3 Intermediate Recalcitrant Total C (% dry matter) 44.5 ± 0.12 a 44.1 ± 0.08 a 45.2 ± 0.01 a Total N (% dry matter) 0.93 ± 0.03 b 0.98 ± 0.02 b 0.91 ± 0.02 b C:N ratio (% dry matter) 47.8 ± 1.80 c 44.9 ± 0.06 c 49.2 ± 0.70 c Labile Content Soluble (% dry matter) 39.2 ± 2.2 d 44.8 ± 1.8 d 25.7 ± 1.2 e Total Sugars (% dry matter) 45.0 ± 1.6 f 44.0 ± 0.2 f 44.3 ± 0.3 f Cell Wall Fraction Cell wall (% dry matter) 60.3 ± 2.2 g 55.2 ± 1.8 g 74.3 ± 1.2 h Klason lignin (% cell wall) 10.4 ± 0.2 i 15.0 ± 0.6 j 13.0 ± 0.4 k Klason lignin (% dry matter) 6.3 ± 0.1 l 8.3 ± 0.3 m 9.7 ± 0.3 n S/V ratio 0.86 ± 0.03 o 0.81 ± 0.02 o 0.31 ± 0.02 p 143

159 Table 5.2. General soil properties averaged (± SE) for the Udipsamment soils used in the 478-day incubation (n = 4). Soil Properties Acidic Neutral ph 4.9 ± 0.2 a 6.7 ± 0.3 b % C 0.7 ± 0.07 c 0.6 ± 0.03 c % N 0.06 ± 0.01 d 0.05 ± 0.01 d C/N Ratio 12.1 ± 0.62 e 12.7 ± 0.87 e Microbial Biomass 27.2 ± 4.3 f 43.8 ± 1.6 g Ammonium 1.3 ± 1.1 h 0.2 ± 0.1 h Nitrate 1.8 ± 0.1 i 0.76 ± 0.05 j Units are µg-c g soil -1 for microbial biomass and µg-n g soil -1 for inorganic nutrient concentrations. Lowercase letters designate significant differences between soils for each property. 144

160 Figure 5-1. The % of initial C mineralized for all maize genotypes in the two Udipsamment soils with and without added N at the conclusions of a 478 day incubation. Values represent the mean ± SE for each treatment (n = 4). 145

161 Figure 5-2 Microbial biomass-c (MB-C) for all genotypes in both soil types (acidic and neutral ph) with and without added nitrogen. MB-C peaked on day 0 for the labile and intermediate genotypes and did not significantly change over the remainder of the incubation (days ). MB-C values for the recalcitrant genotype did not differ over time. Values represent the mean ± SE for each treatment on day 0 (n = 4) and over the final 9 incubation harvests (n = 36). Units are mg-c g dry litter -1. Lowercase letters indicate significant differences within and between genotypes for each treatment on each day. 146

162 Figure 5-3. Phenol oxidase activities over a 478 day laboratory incubation for the A) labile, B) intermediate and C) recalcitrant maize genotypes mixed with either an acidic or neutral ph soil, both with and without added N. Error bars show the standard error of the mean (n = 4). Phenox activity was significantly lower in labile litter than other genotypes, in N+ compared to N- treatments, and in the neutral compared to acidic ph treatments. Asterisks (*) denote significant differences (P < 0.05) between treatments on a harvest date. 147

163 Figure 5-4. Cumulative enzyme activities (Phenol oxidase and N-acetyl-βglucosaminidase) for all maize genotypes in the two Udipsamment soils with and without added N. Cumulative activity was calculated by integrating enzyme activity over time; the results were expressed in units of mmol of substrate oxidized per g of litter (mmol g - 1 ). Lowercase letters indicate significant differences between treatments for each enzyme. 148

164 Figure 5-5. Concentration of S and V units on days 0, 120, and 478 during a laboratory incubation of labile (A, D), intermediate (C, E), and recalcitrant (C, F) maize genotypes. Each genotype was mixed with either an acidic or neutral ph sandy soil, both with and without added N. S monomers in the intermediate > recalcitrant genotype over days 120 and 478. Nitrogen addition had a weak effect on S and V unit concentrations over time. 149

165 Figure 5-6. Ratio of S to V units on days 0, 120, and 478 during a laboratory incubation of the A) labile, B) intermediate, and C) recalcitrant maize genotypes that were mixed with two soils of varying ph and N availability. S/V ratios decreased over time only in the recalcitrant genotype. Asterisks (*) denote treatments where S/V ratios were significantly greater (P < 0.05) on day 120 than day 478. Nitrogen addition had a weak effect on S/V ratios over time. 150

166 Figure 5-7. Concentration of Cn units and total lignin monomers (S+V+Cn units) on days 0, 120, and 478 during a laboratory incubation of the labile (A, D), intermediate (C, E), and recalcitrant (C, F) maize genotypes. Each genotype was mixed with either a low or neutral ph sandy soil, both with and without added N. Added N had a weak effect on lignin chemistry. 151