Substrate and environmental controls on microbial assimilation of soil organic carbon: a framework for Earth system models.

Ecology Letters, (2014) 17: 547–555

LETTER

Xiaofeng Xu,1* Joshua P. Schimel,2 Peter E. Thornton,1 Xia Song,1 Fengming Yuan1 and Santonu Goswami1 1

Climate Change Science Institute

and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA 2

Department of Ecology, Evolution

and Marine Biology, University of California, Santa

doi: 10.1111/ele.12254

Substrate and environmental controls on microbial assimilation of soil organic carbon: a framework for Earth system models Abstract A mechanistic understanding of microbial assimilation of soil organic carbon is important to improve Earth system models’ ability to simulate carbon-climate feedbacks. A simple modelling framework was developed to investigate how substrate quality and environmental controls over microbial activity regulate microbial assimilation of soil organic carbon and on the size of the microbial biomass. Substrate quality has a positive effect on microbial assimilation of soil organic carbon: higher substrate quality leads to higher ratio of microbial carbon to soil organic carbon. Microbial biomass carbon peaks and then declines as cumulative activity increases. The simulated ratios of soil microbial biomass to soil organic carbon are reasonably consistent with a recently compiled global data set at the biome level. The modelling framework developed in this study offers a simple approach to incorporate microbial contributions to the carbon cycling into Earth system models to simulate carbon-climate feedbacks and explain global patterns of microbial biomass.

Barbara, CA, 93106, USA *Correspondence: E-mail: [email protected]

Keywords Cumulative microbial activity index, microbial annual active period, microbial assimilation, substrate quality. Ecology Letters (2014) 17: 547–555

INTRODUCTION

Microbial assimilation of soil organic carbon is one of the most important processes in global carbon cycling (Bardgett et al. 2008; Schimel & Schaeffer 2012); it determines the magnitude of microbial biomass in soils (Anderson & Domsch 1989) and governs processes that lead to soil carbon stabilisation. The mechanism and controls of microbial carbon assimilation play a fundamental role in regulating land–atmosphere interactions (Bardgett et al. 2008; DeLong et al. 2011); nevertheless, belowground microbial biogeochemistry remains one of the greatest uncertainties in Earth system models for simulating carbon-climate feedbacks (Wieder et al. 2013). Improving these models requires capturing key aspects of microbial mechanisms to better predict the behaviour of the carbon cycle and the climate system (DeLong et al. 2011). One aspect of microbial carbon (C) use that is gaining attention is the efficiency with which microbes convert substrate into biomass. In “classical” models (e.g. CENTURY, ROTHC) this is treated as a C-partitioning function during the flow of C from one soil C pool to another: how much enters the “new” pool vs. being lost as carbon dioxide (CO2). The long-term fate of C is naturally sensitive to this partitioning (Clemmensen et al. 2013). Increasingly, however, this partitioning is viewed explicitly as a physiological process: how much of the C microorganisms assimilate is converted into biomass as opposed to being respired to CO2? This is typically termed as substrate use efficiency (SUE) (Allison et al. 2010; Sinsabaugh et al. 2013) or as microbial growth efficiency (MGE) (Herron et al. 2009). Despite the recognised importance of SUE as an important model parameter, it is not really a single term. Rather, it comprises two separate components: an assimilation

efficiency (AE) and time-dependent maintenance energy, expressed as microbial maintenance respiration (MMR) (van Bodegom 2007; Wang & Post 2012). MMR measures microbial C use to generate energy for metabolic processes, to replace cellular constituents, etc. Because MMR is ongoing, given enough time, all assimilated C will be respired and any molecule’s apparent SUE would necessarily decline to 0. Assimilation efficiency is controlled by substrate quality (i.e. C : N ratio, lignin content etc.) and environmental factors (Pelz et al. 2005). Substrate quality is a key factor controlling carbon mineralisation and assimilation into microbial biomass (Manzoni et al. 2008, 2012b). For example, the initial assimilation efficiency of glucose can be as high as 85%, while for phenolics, it can be as low as 20% (Sugai & Schimel 1993). Thus, high-quality substrate leads to high microbial assimilation and a large microbial biomass (Blagodatskaya et al. 2011). Once a substrate has been assimilated into microbial cells, however, its original quality should have limited effect on its longer term fate. A molecule’s original structure affects the biomolecules it’s converted into, but the turnover of those cell constituents is then a function of environmental condition and microbial activity. The seasonal dynamics of soil temperature and moisture influence microbial maintenance respiration and regulate the duration of the period and magnitude that microbes are active (Koponen et al. 2006). In general, if microbes remain active for a longer period or are more active during that period, cumulative maintenance respiration should be greater, and so the population size should be lower (Brown et al. 2004). Most modelling studies, however, consider the microbial contribution to carbon cycling in an implicit way (Schimel

Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

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2001), for example, most large-scale ecosystem models use a kinetic rate constant to describe the microbial decomposition of soil organic matter (McGuire & Treseder 2010; Schimel 2013). Implicit representation of microbial mechanisms can cause biases in reconstructing ecosystem function, especially in response to disturbances of environmental changes (Lawrence et al. 2009). Although adding complexity to a model carries costs, it is becoming clear that it can be important to incorporate microbial mechanisms into ecosystem models to accurately simulate biogeochemical processes (Allison et al. 2010; Wieder et al. 2013). Mechanisms such as microbial stoichiometry and exoenzyme production appear important to capture ecosystem dynamics and can be modelled without excessively complex parameterisation (Schimel & Weintraub 2003; Schimel & Schaeffer 2012). Explicit representation of microbial assimilation of soil organic carbon requires a modelling framework which considers the balance of the positive effects of substrate quality and the “negative” effects of ongoing microbial activity. Together, these should control the size of the microbial biomass, expressed as the ratio of soil microbial carbon to soil organic carbon. We hypothesise that higher substrate quality and a lower cumulative microbial activity lead to higher ratio of soil microbial biomass to soil organic carbon (Fig. 1a) and vice versa (Fig. 1d); whereas other combinations of substrate quality and cumulative microbial activity index lead to intermediate levels of biomass (Fig 1b,c). To better represent the microbial activity at annual scale cross sites, we use an index of cumulative microbial activity (Cumulative Microbial Activity indeX; CMAX) to quantitatively describe the microbial activity and its contribution to the carbon cycling. In this study, we: (1) developed a simple modelling framework for microbial assimilation of soil organic carbon which is suitable for incorporating into ecosystem models, (2) used the model to theoretically evaluate the effects of substrate quality and CMAX on the ratio of microbial biomass to soil organic carbon, (3) parameterised the model with literaturederived rates for microbial processes, (4) tested it with a recently compiled global data set on soil microbial biomass, and finally (5) used it to summarise the biome-level similarities and dissimilarities of microbial assimilation of soil organic carbon.

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Figure 1 Hypothesis of microbial biomass in response to substrate quality and cumulative microbial activity index (size of the circles indicates the amount of microbial biomass, while the width of the arrows indicates the magnitude of the fluxes)

indicator of soil organic matter quality (Chapin et al. 2011). Microbial C assimilation is controlled by assimilation efficiency (AE) which is described as a function of reference AE (AEref), temperature (T) (Allison et al. 2010), and substrate quality (CNS) (Manzoni et al. 2008). The maintenance respiration of microbial biomass (MMR) was controlled by cumulative microbial activity index (CMAX), a measure of time-integrated microbial activity; the CMAX is multiplier of average microbial activity index with microbial annual active period (MAAP). Because the soil microbial biomass accounts for a small portion of soil organic carbon, often < 2% (Xu

METHODOLOGY

Conceptual framework

We developed a simple modelling framework that includes the fundamental microbial processes for assimilating soil organic carbon (Fig. 2). The key processes include substrate breakdown, carbon assimilation, microbial growth and death and maintenance respiration. The breakdown of substrate (Dc) is simulated as a function of substrate quality, expressed as C : N ratio (Schimel & Weintraub 2003; Zhang et al. 2008). This relationship is supported by many field studies (Gholz et al. 2000) and meta-analyses (Zhang et al. 2008). The C : N ratio is used to represent the substrate quality due to two reasons: the substrate in this study represents a mixture of litter and soil organic matter, and there is no alternative universal

Figure 2 Conceptual diagram showing microbial processes in assimilating carbon from substrate (Csub is substrate carbon; Cmic is microbial biomass carbon; Dc is substrate carbon break down catalysed by microbial enzymes; MMR is microbial maintenance respiration; Death of microbial biomass will go to soil organic matter; solid lines indicate fluxes with width for magnitude, and dash lines indicate controls; all processes were influenced by environmental factors including primarily temperature and moisture)


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Controls on microbial C assimilation 549

et al. 2013), and as this study aims to examine the controls on microbial assimilation of soil organic carbon, rather than the pool size of soil microbial biomass, we assumed that soil organic carbon pools remain constant over time and used the steady-state analysis in simulations. Meanwhile it should be noted that soil microbial biomass turnover is one primary source of soil organic matter. dCmic ¼ Dc AE MMR Rd dt Dc ¼ Csub k1 fs ðTÞ fs ðMÞ

ð1Þ Cmic Cmic þ kes

ð2Þ

0:0079 CNS þ 1:2049 365

ð3Þ

MMR ¼ Cmic km fm ðTÞ fm ðMÞ

ð4Þ

k1 ¼

Rd ¼ Cmic kd fm ðTÞ fm ðMÞ AE ¼ AEref AET T Taeref 0 T Tsmin TTsref fs ðTÞ ¼ 10 Q10s T [ Tsmin fm ðTÞ ¼ fs ðMÞ ¼

0 Q10m

TTmref 10

ð5Þ

CNB CNS

T Tmmin T [ Tmmin

logðMsMmin Þ Msmin Þ logðMs max

8 0 M\Mmmin > > > < min logðMm Þ M Mmmin M Mmmax fm ðMÞ ¼ Mmmin > logðMm Þ > max > : M [ Mmmax 1 Z d¼MAAP CMAX ¼ fm ðTÞ fm ðMÞ

0:6 ð6Þ ð7Þ ð8Þ ð9Þ

ð10Þ

ð11Þ

d¼1

Dc is the rate of substrate breakdown; Csub is the concentration of substrate C; k1 is the potential rate of substrate breakdown; fs(T) is the temperature effects following equation 7 and fs(M) is the moisture effects on substrate breakdown following equation 9; fm(T) is the temperature effects following equation 8 and fm(M) is the moisture effects on microbial turnover following equation 10; Cmic is the soil microbial biomass C; Rd is the microbial death and kd is the potential rate of microbial death; kes is the half-saturation constant for microbial effects on substrate breakdown; km is the potential loss rate of microbial biomass through microbial maintenance respiration; Tsmin is the minimum temperature for substrate breakdown and Tmmin is the minimum temperature for microbes remaining active; Msmin is the minimum moisture for substrate breakdown and Mmmin is the minimum moisture for microbes remaining active; Msmax is the maximum moisture for substrate breakdown, moisture effect will be one if moisture is larger than Msmax; Mmmax is the maximum moisture for calculating microbial activity, moisture effect will be one if moisture is larger than Mmmax; CNS is the C : N ratio

of substrate; CNB is the C : N ratio of microbial biomass. The time step of current model is daily, which means all the processes are calculated daily. Parameterisation

Parameter values are mainly from previous studies on the microbial physiology of soil organic carbon assimilation (Devevre & Horwath 2000; Manzoni et al. 2008, 2012b; Manzoni & Porporato 2009; Chapin et al. 2011). The AE dependence on temperature was calculated based on a previous study (Devevre & Horwath 2000): AE decreases 0.012 unit when temperature increases 1 °C. The AE for “average” organic matter at reference temperature of 15 °C is set to 0.43, following a global analysis (Manzoni et al. 2008), but is modified by the C : N ratio of the substrate (relative to biomass) to capture variation in substrate quality (Eq. 6). The half-saturation constant for microbial effects on substrate breakdown (kes) is determined at 72 mg C kg1 soil which is based on a previous conceptual analysis and a realistic data set. In Schimel & Weintraub (2003), the efficiency of enzyme effects on substrate breakdown was assumed to be 0.3 mg C g1, and the enzyme C was simulated to be 5–10% of microbial C; in addition, a recently compiled data set shows that the globally averaged microbial biomass C is c. 700 mg C Kg1 soil (Xu et al. 2013). A further sensitivity analysis confirms a 4.4% variation in simulated microbial C when the kes increases or decreases 50%, inferring the reliability of the determined kes value. The basal maintenance respiration of microbial biomass carbon and the potential rate of microbial death are set as 0.048 day1, which is calibrated on a basis of an hourly rate of 0.0027 h1 used in a previous ecosystem model (Blomback & Eckersten 1997), and scales with temperature. Because microbes have relatively broad tolerance on temperature, the minimum temperature for microbes to remain active is set to 2 °C, which is lower than the minimum temperature for substrate breakdown at 1 °C. Given that the substrate breakdown is a mixture of physical, chemical and biological process, the minimum soil moisture for substrate breakdown is 0.01 (volumetric water content), smaller than 0.05 (volumetric water content), the minimum soil moisture for microbial activity. The value was set based on previous knowledge of water content for microbial activity (Chapin et al. 2011). The microbial activity will remain maximum when soil moisture is large than 0.6 volumetric water content, Mmmax. These conditions represent the range where ecologically significant activity can occur, rather than the extremes of microbial life, which span from near boiling to as low as 39 °C (Panikov et al. 2006). The detailed information of all parameters and their values and reference sources can be found in the Table 1. Defining cumulative microbial activity index and microbial annual active period

Microbial processes are controlled by environmental factors including temperature (Bradford et al. 2008), moisture (Yuste et al. 2007), soil type (Schimel & Schaeffer 2012), etc. Attributing microbial assimilation of soil organic carbon to each


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Table 1 The key parameters used and their ecological meanings, values and sources

Parameter AE AET Taeref AEref k1 km kd kes Tsref Tmref Q10s Q10m Tsmin Tmmin Msmin Msmin Mmmin Mmmax CNB CNS

Ecological meaning Assimilation efficiency AE dependence on temperature Reference temperature for AE AE at reference temperature First-order decomposition rate of substrate, litter and soil organic matter Potential rate of microbial maintenance respiration Potential rate of microbial death Half-saturation constant for microbial enzymes on substrate Reference temperature for substrate decomposition Reference temperature for microbial biomass decomposition Temperature sensitivity of substrate decomposition Temperature sensitivity of microbial biomass decomposition Minimum temperature for substrate decomposition Minimum temperature for microbes remain active Minimum moisture for substrate decomposition Minimum moisture for substrate decomposition Minimum moisture for microbial activity Maximum moisture for calculating microbial activity; effect will be 1 above this threshold CN ratio for microbial biomass CN ratio for substrate

individual environmental factor is challenging due to the heterogeneity of soil conditions and the complexity of these processes. In this study, we synthesised the environmental controls on microbial functions into a single term, CMAX which considers the microbial activity and the duration when microbes remain active for assimilating organic carbon and regulating cycling of soil nutrients, particularly nitrogen and phosphorus. The CMAX is easy to use to compare sites that vary in how long microbes remain active and in how active they are. The term “microbial annual active period” was used to describe the time span when microbes remain active; it is easily to be quantified across sites and to be linked with vegetation activity. Similar to the growing season length, which regulates the vegetation’s role in the Earth system (Myneni et al. 1997), and physiological time to describe duration of animal’s activity (Taylor 1981), the length of microbial annual active period is fundamentally important for the large-scale contribution of microbial metabolism to ecosystem-level fluxes (Schimel et al. 2007). Normally, microbes are more tolerant than plants to extremes of temperature and moisture (Paul 2007) and may remain active over a broader range of temperature and soil moisture than plants (Chapin et al. 2011). Therefore, the length of the active season for microbes is normally longer than that for vegetation growth; for this work, we defined it as 100 days longer (Table 2). Meanwhile, based on the model results of Community Land Model, we calculated the microbial annual active period based on Eq. 11 (Fig. S1 and S2); the values for nine key biomes are consistent with what we set in Table 2. For example, the calculated microbial annual active period is 207 days with a 90% confidence range of 20 days to 345 days for natural wetlands; the average microbial annual active period is consistent with 200 days used in this study (Table 2). The

Values

Unit

Sources 1

Varied 0.012 15 0.43 Varied

gCgC °C1 °C g C g C1 –

Manzoni et al. (2012b) Calculated based on Devevre & Horwath (2000) Calculated based on Devevre & Horwath (2000) Manzoni et al. (2008) Zhang et al. (2008)

0.048 0.048 72 20 25 2 2.5 1 2 0.01 1 0.05 0.6

– – mg C Kg1 °C °C – – °C °C – – – –

Blomback & Eckersten (1997) Blomback & Eckersten (1997) Schimel & Weintraub (2003) Craine et al. (2010) Craine et al. (2010); Chapin et al. (2011) Yuste et al. (2007) Davidson & Janssens (2006) & Yuste et al. (2007) Reference on Paul (2007) Reference on Paul (2007) Reference on Paul (2007) & Chapin et al. (2011) Reference on Paul (2007) & Chapin et al. (2011) Reference on Gulledge & Schimel (1998) & Paul (2007) Shaffer et al. (2001) & Chapin et al. (2011)

Varied Varied

g C g N1 g C g N1

Chapin et al. (2011) & Xu et al. (2013) Post et al. (1985) & Xu et al. (2013)

calculated wider range reflects the large spatial heterogeneity of the environmental conditions. The CMAX is the cumulated microbial activity reflected as environmental control on microbial functions by considering the soil moisture and soil temperature when microbes are active. As the soil temperature in nature is normally < 40 °C, the highest value for microbial activity index will be c. 4, therefore the range of microbial activity index will be in the range of [0,4] for 1 day. High CMAX indicates high microbial activity and small CMAX indicates low microbial activity over 1 year (Fig. S3). It should be noted that the microbes defined in this study represent a mixture of groups of bacteria and fungi that drive the key soil biogeochemical processes; we do not calculate the environmental tolerance of specific microbial taxa. The biome-level values for microbial annual active period were set as a wider range of reported vegetation growing season length (Chapin et al. 2011). Model Implementation

In this study, the model was used for theoretical analysis and for biome-level comparisons; both analyses are based on steady-state simulations. All simulations run in total 36500 days with 1 year’s climate data iterated 100 times. It should be noted that most simulations reach steady state in c. 3650 days. Long-term simulations were used to ensure steady state and consistency among simulation for all biomes. The model was firstly initialised with the values in Table S1; then the model was driven by daily soil temperature and moisture for 100-year simulations. The theoretical analysis was conducted with globally averaged soil temperature and soil moisture, and soil carbon concentration. Totally 1170 simulations were run with a combination of 15 C : N ratios (10, 15,


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Table 2 Biome-level parameters and microbial assimilation of soil organic carbon

Biome

Microbial annual active period (Days)

Cumulative microbial activity index (Days)

Substrate C : N ratio*,†

95% confidence range of modelled Cmic/ Csub (%)

Cmic/Csub (%) in a global data set*

Boreal forest Temperate coniferous forest Temperate broadleaf forest Tropical/subtropical forest Grassland Shrubland Tundra Desert Natural wetlands

100–200 200–300 200–300 365 150–250 150–250 50–150 0–200 150–250

12–34 37–57 36–54 ~ 198 22–48 22–55 5–24 0.5–37 22–64

16–35 20–35 18–26 10–18 8–20‡ 16–23 16–30 9–15 17–21

1.1–2.4 1.3–1.9 1.3–1.8 1.0–1.6 2.1–4.6 1.7–3.0 1.6–3.6 0.5–7.1 1.5–2.9

1.5–2.1 0.9–1.1 1.1–1.3 1.6–2.0 2.0–2.2 1.1–1.8 1.3–2.2 3.8–6.5 1.0–1.5

*Xu et al. (2013) Global Ecology and Biogeography. †Post et al. (1985) Nature. ‡Aitkenhead & McDowell (2000).

20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80) and 78 MAAPs (1, 2, 3, 4, 5, 6, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365). The MAAP was used to calculate the CMAX in the simulations; for specific MAAP, soil temperature and moisture will be adjusted by increasing and reducing one value across the year to remain microbes active based on the parameters (Fig. S4). These simulations are used to produce Fig. 3. The biome-level analyses were conducted by using biome-specific carbon concentrations and C : N ratio for substrate and soil microbial biomass, primarily from Xu et al. (2013), as well as daily soil temperature and soil moisture averaged across each biome. Figure S5 shows the work flow of these uncertainty analyses. Specifically,

Figure 3 Diagram showing dependence of microbial assimilation of soil organic carbon on cumulative microbial activity index (days) and substrate quality (shown as C : N ratio)

simulations were run for each biome with 200 combinations of C : N ratio and MAAP; the ranges of C : N ratio and MAAP for each biome are shown in the Table 2, and the Latin Hypercube sampling method is used to generate the 200 combinations of parameters.

RESULTS AND DISCUSSION

Effects of substrate quality and microbial annual active period on microbial assimilation of soil organic carbon

In this model, the ratio of microbial biomass carbon to soil organic carbon is a function of substrate C : N ratio, following an exponential decay curve (Fig. 3). Theoretically, highquality substrate, comprised molecules that require limited energy to convert them into forms that are easily diverted into anabolic biochemical pathways, is assimilated into biomass with high efficiency (Nadelhoffer et al. 1991; Sugai & Schimel 1993; Paul 2007; Allison et al. 2010). In contrast, substrates that require substantial catabolic reworking (e.g. many aromatics) lead to lower microbial biomass accumulation (e.g. Sugai & Schimel 1993; Fig. 3). For this study, we used C : N ratio as an index of substrate quality because it often correlates with the nature of C-skeletons and because N is required to allow microbes to assimilate C, rather than respiring it through overflow metabolism (Schimel & Weintraub 2003). The effect of microbial annual active period on the microbial biomass proportion follows a single-peak curve (Fig. 3). As shown in the Figure, when temperatures are low or soils are dry, microbial activity is close to zero and the length of the microbial annual active period is zero, so no microbial assimilation of soil organic carbon occurs (Fig. 3). When environmental conditions become more favourable for microbial physiology, the microbial annual active period lengthens and assimilation of soil carbon increases to its maximum; when the microbial annual active period passes a certain threshold; however, the increasing demands of maintenance respiration slowly decreases the amount of C in the biomass. The observed correlation between microbial annual active period and microbial C assimilation can be explained by metabolism theory (Brown et al. 2004). If microbes are active for only a very short period, they may not have time to fully


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assimilate available carbon into biomass, resulting in a low ratio to soil organic carbon (which would be largely unprocessed detritus). As annual active period increases, microbes have more time to assimilate potential substrate, but maintenance demands will drain C. Thus, as predicted by metabolism theory (Brown et al. 2004), a very long annual active period and high microbial activity should cause a larger amount of carbon to be respired through maintenance metabolism, leading to a low microbial biomass. Combining the substrate and environmental controls on microbial assimilation of soil organic carbon into one figure develops a surface plot that shows a curved surface of microbial carbon relative to soil organic carbon as a function of substrate quality (C : N ratio) and the microbial annual active period (Fig. 3). Microbial biomass responds more strongly to changes in substrate quality than to microbial annual active period, suggesting that internal biological mechanisms regulating microbial assimilation are stronger than external environmental factors. Biome-level substrate and environmental controls on ratio of microbial carbon to soil organic carbon

We evaluated this model by comparing against a recently compiled global data set (Xu et al. 2013). The biome-level soil organic carbon and its key parameter values are listed in Table 2 and Table S1. The flow chart is shown in Fig. S5. We used Latin-Hypercube sampling method for two parameters, substrate C : N ratio and microbial annual active period with an assumed normal distribution. We resampled 200 pairs of the parameters and used these parameters to drive 200 independent simulations for each of nine biomes. The long-term daily soil temperature and moisture data set are derived from an equilibrium-state simulation of the Community Land Model; the soil temperature and moisture for the top six layers were averaged based on weight of soil layer thickness. The observed ratios of microbial biomass to soil organic carbon at the biome level are reasonably consistent with our model predictions, as shown in Fig. 4. Further comparison between the simulated and observed results from the global data set shows that our modelling framework is able to capture the range of the ratio of soil microbial biomass to soil organic carbon (Fig. 4 & Table 2). For example, compared with shrubland, grassland has a low C : N ratio carbon inputs, which lead to higher microbial assimilation of soil organic carbon and a higher ratio of microbial carbon to soil organic carbon. Deserts possess the highest ratio of microbial carbon to soil organic carbon because of a short microbial annual active period and lower C : N ratio substrates (Fig. 4 & Table 2). A low ratio of microbial carbon to soil organic carbon results from some combination of low carbon assimilation (low-quality substrate) and high microbial maintenance respiration. For example, temperate conifer forests had some of the lowest biomass ratios (Table 2), presumably as a result of both low substrate quality and a relatively long active period. Tropical forests and shrublands hold intermediate values for microbial carbon, primarily resulting from high microbial respiration although relatively high microbial assimilation of soil organic carbon (Xu et al. 2013).

Figure 4 Effects of cumulative microbial activity index and substrate quality on microbial assimilation of soil organic carbon for nine major biomes (BorF: boreal forest; ConF: temperate coniferous forest; BrodF: temperate broadleaf forest; TropF: tropical forest; Grass: grassland)

The patterns of microbial carbon across biomes reflect the varying microbial dynamics, and illustrate the importance of incorporating critical microbial mechanisms in models of carbon processes. The high fraction of microbial carbon in some “stressful” biomes, for example, desert and tundra, suggests that dormancy may be important in maintaining high levels of biomass. Thus, modest changes that might make the environment less stressful could actually reduce microbial populations. This could be important for carbon cycling in these ecosystems and further for carbon-climate feedbacks (Singh et al. 1989; Schimel & Schaeffer 2012); for example, by shifting the role of microbial biomass as a key source of nutrient for a dry tropical forest and savanna (Singh et al. 1989). Seasonality of soil microbial biomass

The seasonality of soil microbial biomass is an important factor for soil carbon cycling. Because microbial biomass can act as a buffer for nutrients, its seasonality has been intensively studied (Yao et al. 2011). In this study, we found that favourable soil conditions lead to higher microbial respiration and so lower biomass, while stressful environments can reduce basal maintenance respiration and so allow a greater biomass (Fig. S6). The seasonality of simulated soil microbial biomass is high in early spring and low in fall, while it starts to increase in later fall which is consistent with seasonality observed in Yao et al. (2011) (Fig. S6). However, due to microbial dormancy in winter, the model does not simulate continued increase in soil microbial biomass in winter, indicating the importance of more exact information or model representation of seasonality of microbial biomass and its activity, particularly in winter. Better understanding and model representation of microbial


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activity in response to stressful condition is needed to improve model simulations and predictions (Schimel et al. 2007; Manzoni et al. 2012a). High microbial biomass in a dormant period while biomass is lower in an active period has been proposed to be an advantage for vegetation (Singh et al. 1989) and has been observed in a California annual grassland with a long dry summer (Parker and Schimel 2011). Singh et al. (1989) proposed that under the stressful conditions, the microbial biomass stores nutrients and then releases them during conditions favourable for plant growth. Yao et al. (2011) found that the nitrogen availability in the soil, rather than the climate, is the determinant for the seasonality of soil microbial biomass. Although this study does not consider nitrogen dynamics, the seasonal microbial physiology does show the hypothesised pattern. Nitrogen effects on the seasonality of microbial biomass deserve further investigation. Microbial annual active period and cumulative microbial activity index

Although microbes are recognised as the engine for global biogeochemical cycles (Falkowski et al. 2008), their metabolic dynamics throughout the year are not well represented in ecosystem-level models (Schimel & Weintraub 2003; McGuire & Treseder 2010). The microbial annual active period defined in this study reflects the seasonality of microbial activity and dormancy; it extends the “active factor” concept that can enhance simulation accuracy (Blagodatsky & Richter 1998). It combines microbiological properties and environmental factors into a simple term that will be more easily used in ecosystem models and that offers a platform to bridge microbial and vegetation processes. In addition, the ability of drought to induce dormancy was demonstrated in a meta-analysis (Manzoni et al. 2012a) – below 13 MPa, respiration essentially ceases. The cumulative microbial activity index is defined to quantitatively describe and compare the activity of microbes for maintenance respiration and on ecosystem functions. In this study, it is directly related to the soil temperature and moisture over the active period through a year. In this study, we separated the effects of substrate quality out and used soil temperature and soil moisture to calculate the cumulative microbial activity index. The microbial annual active period and cumulative microbial activity index are two terms suitable for large-scale modelling but may not be applicable at fine scales because the variation among microbial species might be large. Other mechanisms might be important for microbial assimilation of soil organic carbon

The model presented here includes a number of key biological processes and controls of microbial assimilation of soil organic carbon. There are other processes that might be important for a few biomes or specific conditions; for example, photodegradation of litter is important in arid lands, while microfaunal grazing may regulate microbial biomass in some ecosystems (Chapin et al. 2011; Schimel & Schaeffer

2012). Further inclusion of these processes would benefit model improvements and applications for better simulating microbial control on carbon cycle. Microbial maintenance respiration may consist of a number of components, as it is “the energy consumed for functions other than the production of new cell material” (van Bodegom 2007). This study does not separate the components of maintenance respiration because we want to keep it simple to be suitable for large-scale models. Meanwhile another study compared three models and argued that the microbial maintenance respiration should come from substrate and biomass (Wang & Post 2012); this study assumes that microbial maintenance respiration is solely from C that has been assimilated into biomass, whereas the carbon for growth respiration is released to the atmosphere before becoming microbial biomass (Fig. 2). Through this treatment, we completely separate the substrate use efficiency into assimilation efficiency and maintenance respiration, but if microbes are not growing, they may use all C taken up for maintenance needs with no assimilation. Microbial community structure may regulate carbon assimilation and the quality of soil organic matter produced. Thus, improving our understanding and model representation of microbial community structure and its impacts on carbon and nutrient cycling is important for modelling studies and field experiments. CONCLUSIONS

The proposed modelling framework is simple and practical, and thus is suitable for incorporation into Earth System models to simulate microbial assimilation of soil organic carbon at large scales. It has been recognised that microbial substrate use efficiency (or growth efficiency) is a critical term in converting detritus into stable soil carbon in Earth System models (Cotrufo et al. 2013). This modelling approach addresses the inherent limitation of a single use efficiency value – it is fundamentally two processes, assimilation and maintenance, which have different controls and time courses. The potential implications of the proposed modelling framework are multiple. First, the simple framework offers an opportunity to study the microbial contribution to the climate system in Earth System models. Second, although the microbial annual active period is first introduced in this study as a specific term, it reflects basic information on annual microbial activities at a large scale. Third, the finding that substrate quality is more important than the microbial annual active period in regulating microbial assimilation of soil carbon suggests that changes in the vegetation will be more directly important to changing soil C storage than climate effects on microbial activity. ACKNOWLEDGEMENTS

We thank Drs. Taniya Roy Chowdhury, Jonathan Chase and three anonymous referees for their critical comments which greatly improved this manuscript. This research was sponsored by the U.S. Department of Energy, Office of Science, Biological and Environmental Research (BER) program and


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performed at Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC, for the U.S. Department of Energy, and this manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. AUTHORSHIP

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SUPPORTING INFORMATION

Additional Supporting Information may be downloaded via the online version of this article at Wiley Online Library (www.ecologyletters.com).

Editor, Richard Bardgett Manuscript received 8 October 2013 First decision made 4 November 2013 Manuscript accepted 8 January 2014


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