# Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

E.B. Graham, J.E. Knelman, A. Schindlbacher, S. Siciliano, M. Breulmann, A. Yannarell, J.M. Beman, G. Abell, L. Philippot, J. Prosser, et al.

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## To cite this version:

E.B. Graham, J.E. Knelman, A. Schindlbacher, S. Siciliano, M. Breulmann, et al. Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?: Linking microbes to ecosystem processes. Frontiers in Microbiology, 2016, 7, pp.214. 10.3389/fmicb.2016.00214. hal-01588095

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# Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

OPEN ACCESS

**Edited by:**

Gary M. King,
Louisiana State University, USA

**Reviewed by:**

Steffen Kolb,
Landscape Biogeochemistry - Leibniz
Centre for Agricultural Landscape
Research, Germany
Hongchen Jiang,
Miami University, USA
Kristen M. DeAngelis,
University of Massachusetts Amherst,
USA

*Correspondence:

Emily B. Graham
emily.graham@colorado.edu

**Specialty section:**

This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology

Received: 09 December 2015

Accepted: 09 February 2016

Published: 24 February 2016

**Citation:**

Graham EB, Knelman JE, Schindlbacher A, Siciliano S, Breulmann M, Yannarell A, Beman JM, Abell G, Philippot L, Prosser J, Foulquier A, Yuste JC, Glanville HC, Jones DL, Angel R, Salminen J, Newton RJ, Bürgmann H, Ingram LJ, Hamer U, Siljanen HMP, Peltoniemi K, Potthast K, Bañeras L, Hartmann M, Banerjee S, Yu R-Q, Nogaro G, Richter A, Koranda M, Castle SC, Goberna M, Song B, Chatterjee A, Nunes OC, Lopes AR, Cao Y, Kaisermann A, Hallin S, Strickland MS, Garcia-Pausas J, Barba J, Kang H, Isobe K, Papaspyrou S, Pastorelli R, Lagomarsino A, Lindström ES, Basiliko N and Nemergut DR (2016) Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes? *Front. Microbiol.* 7:214. doi: 10.3389/fmicb.2016.00214

Emily B. Graham¹,²*, Joseph E. Knelman¹,³, Andreas Schindlbacher⁴, Steven Siciliano⁵,
Marc Breulmann⁶, Anthony Yannarell⁷, J. M. Beman⁸, Guy Abell⁹, Laurent Philippot¹⁰,
James Prosser¹¹, Arnaud Foulquier¹², Jorge C. Yuste¹³, Helen C. Glanville¹⁴,
Davey L. Jones¹⁴, Roey Angel¹⁵, Janne Salminen¹⁶, Ryan J. Newton¹⁷,
Helmut Bürgmann¹⁸, Lachlan J. Ingram¹⁹, Ute Hamer²⁰, Henri M. P. Siljanen²¹,
Krista Peltoniemi²², Karin Potthast²³, Lluís Bañeras²⁴, Martin Hartmann²⁵,
Samiran Banerjee²⁶, Ri-Qing Yu²⁷, Geraldine Nogaro²⁸, Andreas Richter²⁹,
Marianne Koranda³⁰, Sarah C. Castle³¹, Marta Goberna³², Bongkeun Song³³,
Amitava Chatterjee³⁴, Olga C. Nunes³⁵, Ana R. Lopes³⁵, Yiping Cao³⁶,
Aurore Kaisermann³⁷, Sara Hallin³⁸, Michael S. Strickland³⁹, Jordi Garcia-Pausas⁴⁰,
Josep Barba⁴¹, Hojeong Kang⁴², Kazuo Isobe⁴³, Sokratis Papaspyrou⁴⁴,
Roberta Pastorelli⁴⁵, Alessandra Lagomarsino⁴⁵, Eva S. Lindström⁴⁶, Nathan Basiliko⁴⁷
and Diana R. Nemergut¹,⁴⁸

¹ Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA, ² Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA, ³ US Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA, ⁴ Department of Forest Ecology, Federal Research and Training Centre for Forests, Bundesforschungs- und Ausbildungszentrum für Wald, Vienna, Austria, ⁵ Department of Soil Science, University of Saskatchewan, Saskatoon, SK, Canada, ⁶ Helmholtz Centre for Environmental Research – Centre for Environmental Biotechnology, Leipzig, Germany, ⁷ Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA, ⁸ Life and Environmental Sciences and Sierra Nevada Research Institute, University of California – Merced, Merced, CA, USA, ⁹ School of Medicine, Flinders University, Adelaide, SA, Australia, ¹⁰ Institut National de la Recherche Agronomique – Agroecology, Dijon, France, ¹¹ Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK, ¹² Irstea, UR MALY, Centre de Lyon-Villeurbanne, Villeurbanne, France, ¹³ Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas, Madrid, Spain, ¹⁴ Environment Centre Wales, Bangor University, Gwynedd, UK, ¹⁵ Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria, ¹⁶ Häme University of Applied Sciences, Hämeenlinna, Finland, ¹⁷ School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA, ¹⁸ Department of Surface Waters, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland, ¹⁹ Centre for Carbon, Water and Food, The University of Sydney, Sydney, NSW, Australia, ²⁰ Institute of Landscape Ecology, University of Münster, Münster, Germany, ²¹ Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland, ²² Natural Resources Institute, Vantaa, Finland, ²³ Institute of Soil Science and Site Ecology, Technische University, Dresden, Germany, ²⁴ Institute of Aquatic Ecology, Facultat de Ciències, University of Girona, Girona, Spain, ²⁵ Institute for Sustainability Sciences – Agroscope, Zurich, Switzerland, ²⁶ CSIRO Agriculture Flagship, Crace, ACT, Australia, ²⁷ Department of Biology, University of Texas at Tyler, Tyler, TX, USA, ²⁸ EDF R&D, National Hydraulics and Environmental Laboratory, Chatou, France, ²⁹ Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria, ³⁰ Division of Terrestrial Ecosystem Research, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria, ³¹ Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA, ³² Centro de Investigación y Docencia Económicas – Consejo Superior de Investigaciones Científicas, Valencia, Spain, ³³ Department of Biological Science, Virginia Institute of Marine Science, Gloucester Point, VA, USA, ³⁴ AES School of Natural Resources Sciences, North Dakota State University, Fargo, ND, USA, ³⁵ LEPABE – Laboratory for Process Engineering, Environmental, Biotechnology and Energy, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal, ³⁶ Southern California Coastal Water Research Project Authority, Costa Mesa, CA, USA, ³⁷ UMR, Interactions Sol Plante Atmosphère, INRA Bordeaux, Villenave d'Ornon, France, ³⁸ Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, Uppsala, Sweden, ³⁹ Department of Biological Sciences, Virginia Polytechnic Institute, State University, Blacksburg, VA, USA, ⁴⁰ Centre Tecnològic Forestal de Catalunya, Solsona, Spain, ⁴¹ Centre de Recerca Ecològica i Aplicacions Forestals, Cerdanyola del Vallès, Barcelona, Spain, ⁴² School of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea, ⁴³ Department of Applied Biological Chemistry, The University of Tokyo, Tokyo, Japan, ⁴⁴ Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Puerto Real, Spain, ⁴⁵ Research Centre for Agrobiology and Pedology, Florence, Italy, ⁴⁶ Department of Ecology and Genetics/Limnology, Uppsala University, Uppsala, Sweden, ⁴⁷ Vale Living with Lakes Centre and Department of Biology, Laurentian University, Sudbury, ON, Canada, ⁴⁸ Biology Department, Duke University, Durham, NC, USA
Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

**Keywords:** microbial diversity, functional gene, statistical modeling, microbial ecology, ecosystem processes, respiration, nitrification, denitrification

## INTRODUCTION

The links between complex environmental microbial communities and ecosystem processes remain unclear (Carney and Matson, 2005; Prosser et al., 2007; van der Heijden et al., 2008; Petersen et al., 2012; Wallenstein and Hall, 2012; Graham et al., 2014; Martiny et al., 2015), and an emerging field of research has begun to investigate the utility of microbial data for improving predictions of carbon (C) and nitrogen (N) cycling beyond estimates based solely on environmental data (Todd-Brown et al., 2012; Wieder et al., 2013; Reed et al., 2014; Powell et al., 2015). While researchers have attempted to enhance ecosystem process models by parameterizing microbial physiological properties such as drought tolerance (Manzoni et al., 2014), growth efficiency (Hagerty et al., 2014), dormancy (Wang et al., 2015), and turnover rates (Wieder et al., 2013), these models often fail to consider variation in microbial community structure that may regulate ecosystem process rates (Bouskill et al., 2012; Kaiser et al., 2014). As such, we still lack an integrated understanding of the interactions between microbial communities and ecosystem function, and a central question in ecosystem science remains: 'Under what circumstances does information on microbial communities add to our predictive power of ecosystem processes?' Addressing this question is essential not only for improving knowledge on how critical

biogeochemical cycles may respond to current and impending environmental change, but also for allowing us to identify factors that determine microbial community structure and activity in space and time.

The specific conditions in which microbial community structure – broadly defined here as information on diversity and/or abundance of taxa within a community – may improve predictions of ecosystem process rates beyond models based on environmental and physiological attributes varies with a myriad of biotic and abiotic factors (Knelman and Nemergut, 2014; Nemergut et al., 2014). Research has demonstrated global patterns in microbial communities that correlate with environmental factors such as salinity (Lozupone and Knight, 2007; Auguet et al., 2010), pH (Lauber et al., 2009), and habitat type (Dinsdale et al., 2008; Nemergut et al., 2011; Fierer et al., 2012), and links between microbial community and ecosystem processes have been observed within numerous individual studies (van der Heijden et al., 1998; Torsvik and Øvreås, 2002; Bardgett and van der Putten, 2014; Wagg et al., 2014). However, these relationships and the underlying ecological principles that generate them may vary among environments, relevant microbial traits, and processes of interest (Wallenstein and Hall, 2012; Nemergut et al., 2014). For example, stochastic assembly processes (Hubbell, 2001; Rosindell et al., 2011; Stegen et al., 2012; Nemergut et al., 2013), phenotypic plasticity
(DeWitt et al., 1998), and priority effects (Fukami, 2004; Fukami et al., 2010) can decouple microbial community structure from environmental conditions, and under such conditions, microbial community structure should be central to explaining ecosystem process rates. A unifying perspective that accounts for the dynamic relationships between microbial community structure and ecosystem function is vital for improving predictions of ecosystem process rates, yet recent work has been largely unable to decipher coherent trends in such relationships across studies (Rocca et al., 2014; Bier et al., 2015).

Thus, to assess the importance of microorganisms in
explaining ecosystem processes, we used statistical modeling
to evaluate the value of environmental variables and microbial
community structure both alone and in conjunction for
explaining rates of C and N cycling processes within global
datasets. Although the literature reporting environmental
variables, microbial community data, and biogeochemical
processes in a single study is sparse (Rocca et al., 2014), we
collected and re-analyzed 82 datasets spanning a multitude
of diverse environments. We hypothesized that while the
environment is a strong driver of most ecosystem processes,
microbial community structure adds to our understanding of
processes under certain circumstances, and we discuss patterns
across ecological, biogeochemical, and phylogenetic subsets of
data. Our analysis generates new insights about the relationship
between microbial community structure and a variety of
ecosystem processes.

MATERIALS AND METHODS

We completed an exhaustive literature review for studies
measuring environmental variables, microbial community
structure, and biogeochemical processes across a range of
ecosystems (Supplementary Table S1). We then constructed
statistical models for three predictor sets and their additive
combinations – environmental data, microbial community data,
and microbial biomass measurements (Supplementary Figure
S1). Because the measurement of microbial biomass (defined as
microbial C and/or N content) was not a criterion for dataset
selection, only 28 datasets included biomass measurements. To
incorporate differences in microbial community structure into
our models, we used ordination-based techniques to condense
multivariate community data into vectors that we included
as predictors in our models (Supplementary Methods). The
Shannon diversity index of each sample was also included as
a measure of alpha diversity. In total, these taxonomic metrics
are referred to as ‘community diversity’ when applied to 16S
rRNA genes or PLFAs and ‘functional diversity’ when applied to
functional genes. Functional gene abundances were included as
measures of total gene abundance per gram of soil when possible
(n = 17), although five datasets only listed gene abundances as
normalized relative to 16S rRNA gene abundance.

Within each type of data, methodology to collect
environmental, microbial, and process data, as well as variables
collected, inevitably varied. To minimize potential error
introduced by these differences, we re-analyzed data within each

dataset using a multimodel inference approach with the ‘MuMIn’
package (Barton, 2011) in *R* software (R Core Team, 2014) and
subsequently compared results from our analyses. Multimodel
inference is a broad regression-based, model-averaging statistical
approach designed to reduce errors in model selection, and this
method has the advantage of standardizing our approach across
studies while accounting for a lack of *a priori* system-specific
knowledge (Burnham and Anderson, 2002). This statistical
approach has been used in other small scale studies with similar
objectives (Powell et al., 2015). We validated the accuracy
of MuMIn in our dataset by comparing models for several
datasets with expert-built regression models, which yielded
comparable results (more details available in Supplementary
Methods).

Using the 'dredge' command in the 'MuMIn' package to fit
and evaluate the explanatory power of all possible combinations
of variables within a predictor set on a process rate, we selected
a set of best fit models for each predictor set consisting of all
models with a delta AICc value no more than four greater than
the model with the lowest AICc value (Burnham and Anderson,
2004). We generated an averaged final model from this model
set using Akaike's weights, implemented with the 'model.avg'
command in *R* (Supplementary Methods). Final models from
different predictor sets were compared for statistically significant
differences at a delta AICc value of four to provide conservative
estimates of model improvements (Burnham and Anderson,
2004). Models were also evaluated for ecologically relevant
improvement, defined by an increase in adjusted R² value greater
than 10% of the environmental model adjusted R² value. This
criterion was implemented to remove artifacts from datasets
in which statistically different models, based on AICc values,
yielded similar adjusted R² values. Only models that showed
both statistical and ecological improvement were considered to
be improved.

Finally, model results were synthesized across studies within
ecologically relevant subsets of data. We examined results
within the full dataset and within biogeochemical process [C
mineralization (referred to here as ‘respiration’), nitrification,
denitrification, N mineralization], microbial data (PLFA,
tRFLP, ARISA, DGGE, qPCR, next generation sequencing)
and environment types (natural soil, sediment, agricultural
soil) with sufficient replication (n ≥ 12). We report the mean
adjusted R² of models and the increase in adjusted R² value as
measures of effect size as well as the percent of models statistically
improved by the addition of microbial data relative to models
constructed with only environmental parameters. Differences
among the explanatory power of models with environmental,
microbial, or both environmental and microbial data were
assessed by comparing the distribution of model adjusted R²
values within each predictor set using unpaired one-sided Mann-
Whitney *U*-tests for non-parametric data. We also examined
correlations between predictor sets to determine the extent to
which environmental variables explained variation in microbial
community structure and biomass and to which biomass
explained variation in microbial community structure. We
analyzed these relationships with redundancy analysis (RDA),
utilizing the 'ordistep' function in the 'vegan' package (Oksanen
et al., 2013) in R to automate forward model selection, and we report the average $R^2$ values of correlations between predictor sets as a measure of effect size (Supplementary Methods).

## RESULTS

Overall, models based on environmental variables explained significantly more variation in processes than models based on microbial community structure (n = 82, average adjusted $R^2$ 0.56 vs. 0.31, Mann-Whitney *U*, *p* < 0.0001, **Figure 1**). On average, models with both environmental and microbial predictors explained more variation in processes than environmental models (n = 82, average adjusted $R^2$ 0.65 vs. 0.56, Mann-Whitney *U*, *p* = 0.046); however, only 29% of datasets were significantly improved by adding information on microbial community structure, by an average of 0.08 increase in adjusted $R^2$ within all models. Data on microbial community structure from ARISA, tRFLP, qPCR, and next generation technology did not differ in their explanatory power of process rates, but DGGE and PLFA had significantly lower explanatory power than other metrics (Supplementary Figure S2A). All microbial data types showed weak correlations to environmental variables but gene abundance data displayed a trend for higher correlation (n = 22, average adjusted $R^2$ 0.36) than community (n = 55, average adjusted $R^2$ 0.20) or functional diversity (n = 5, average adjusted $R^2$ 0.21) metrics (Supplementary Figure S2B).

We also examined each process individually for those functions for which we had the most data: nitrification (n = 14),

denitrification (n = 17), N mineralization (n = 12), and respiration (n = 26). Overall, variation in N mineralization and denitrification rates was less well-explained than other processes by any predictor set (**Figure 2**). Only 17% of datasets examining N mineralization were improved by microbial community data – an average increase in adjusted $R^2$ of 0.008 within all N mineralization datasets – and the average explanatory power of microbial community structure alone on N mineralization rates was 0.21, compared to 0.31 in the full dataset. Within N mineralization studies that assayed microbial biomass, we found that microbial biomass correlated with environmental variables (n = 5, average adjusted $R^2$ 0.58) but not with process rates; none of the N mineralization models were improved with the addition of data on biomass. No denitrification studies examined microbial biomass or PLFAs. However, despite low replication, it is notable that in three of four studies, models of denitrification were improved with the addition of 16S rRNA gene diversity data (0.13 average increase in adjusted $R^2$), while only three of eleven models were improved with data on functional gene abundance (0.04 average increase in adjusted $R^2$).

Rates of nitrification were well-explained by all predictor sets (**Figure 2**). Nitrification models based on microbial data yielded the highest average adjusted $R^2$ of any process (0.38 across all data types and 0.50 if PLFA data are removed) with gene abundance (n = 4) data showing higher adjusted $R^2$ than community diversity data (n = 8; 0.61 vs. 0.21, Mann-Whitney *U*, *p* = 0.008). Moreover, 50% of environmental models of nitrification were improved with data on functional gene abundance or functional diversity (n = 6), while only 30% of models were improved with data on 16S rRNA gene diversity (n = 6), and PLFA data only improved 20% of models (n = 2). As with N mineralization, biomass was correlated with environmental variables (n = 3, average adjusted $R^2$ 0.62) and did not add explanatory power to nitrification rates.

Respiration rates were well-explained by the environment, with an average adjusted $R^2$ of 0.66 (**Figure 3**). Similar to the full dataset, microbial-only respiration models showed an average adjusted $R^2$ of 0.29 and only 23% of respiration models were improved with the addition of 16S rRNA gene diversity or PLFA data (no functional gene abundance datasets existed), by an average increase in 0.06 in adjusted $R^2$ across all respiration datasets. Interestingly, microbial biomass, when measured, improved 35% of models of respiration rates (n = 17, 0.09 average increase in adjusted $R^2$), while 53% of models were improved with data on biomass and community structure in combination (0.15 average increase in adjusted $R^2$). Microbial biomass was correlated to environmental variables with an average adjusted $R^2$ of 0.56 but not to microbial community structure (average adjusted $R^2$ 0.11).

Although most of our datasets were from natural soil studies (n = 47), we had sufficient replication of sediment (n = 12) and agricultural soil (n = 12) datasets to examine these independently; other environments (n < 12) were excluded from this analysis. While statistically significant differences were not found, possibly due to the large variation in our datasets, we observed some notable trends across environments. Datasets from sediment samples were less well-explained

<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Within the full dataset (n = 82), environmental variables explained more variation in ecosystem process rates than microbial community structure. Microbial predictors alone had low explanatory power on processes but improved model explanatory power on average. Error bars denote standard errors, and letters represent significantly different groups (Mann-Whitney *U*, *p* < 0.05).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Average explanatory power varied by process and microbial data type, and we present the explanatory power of functional genes (abundance and diversity) and community diversity for each N cycling process. Environmental model bars slightly differ within each figure as they represent only the environmental models for each data type, and functional genes are not depicted for N mineralization due to low sample size (n = 2). Error bars represent standard errors. (A) N mineralization (n = 12) and (B) denitrification (n = 17) models had relatively low explanatory by all predictor sets. However, community diversity metrics (n = 4) provided more added value for denitrification rates than for other processes. (C) By contrast, rates of nitrification (n = 14) were well-explained by both environmental and functional gene predictor sets (n = 6) and were more likely to be improved by functional data.</figcaption></figure>

by environmental or microbial predictor sets than other datasets (Figure 4). Relationships between community diversity and environmental conditions were also weak in sediment systems (n = 8, average adjusted R² 0.17), and community diversity metrics provided more added value (0.12 average increase in adjusted R²) in sediments than other systems. Agricultural systems had high explanatory power of process by both environmental (average adjusted R² 0.59) and microbial predictors (average adjusted R² 0.38) relative to other systems, and metrics of community diversity were more strongly correlated with the environment (n = 7, average adjusted R² 0.24) and had lower added value (average adjusted R² 0.06) than in other ecosystem types.

DISCUSSION

Despite the vast amount of variation in our datasets, our
results indicate that data on microbial community structure may
strengthen predictions of C and N cycling rates. Within our full
dataset, environmental variables alone had greater explanatory
power than either metrics of microbial community structure
or microbial biomass but left 44% of variation in process rates
unexplained on average (Figure 1). While we acknowledge that
factors such as unmeasured variables, macroinvertebrates (Ferris
et al., 2004), fungi (Talbot et al., 2008), plant communities
(Hanson et al., 2000), and non-linearity could reduce the
explanatory power of our models, a number of ecological
dynamics can disassociate environmental conditions from

microbial communities mediating C and N cycle processes and
alter the relative value of environmental and microbial data for
explaining rates of ecosystem processes (Knelman and Nemergut,
2014; Nemergut et al., 2014). When such factors strongly impact
microbial communities, data on microbial community structure
should enhance our predictions of ecosystem processes beyond
those based solely on environmental data.

We observed stronger trends in the explanatory power
of microbial predictors on ecosystem process rates when
we reduced noise by examining subsets of data containing
a single process or environment type. However, the lack of
consistent trends within the full dataset is not surprising
due to a variety of ecological and methodological factors.
For instance, microorganisms can enter a state of lowered
activity or dormancy in response to unfavorable environmental
conditions (Jones and Lennon, 2010; Lennon and Jones,
2011), and research suggests that the percent of soil microbial
communities catalyzing chemical reactions in soils at a given
point in time can vary widely with resource history and
disturbances (Blagodatskaya and Kuzyakov, 2013). These factors
also induce spatially and temporally variable relationships
between microbial communities and ecosystem processes,
and the same microbial community may display different
relationships to ecosystem function when sampled through
time. Moreover, the extent to which functional traits are
phylogenetically conserved varies with the trait of interest
(Martiny et al., 2013), and thus, some processes may exhibit
inherently stronger correlations with taxonomic metrics
than others. Although, rapidly improving transcriptomic and
<figure><img src="image_6.png" /></figure>

**FIGURE 3** | Rates of respiration (n = 26) were well-explained by all predictor sets relative to the full dataset. In this figure, we present microbial biomass in addition to microbial community structure, with standard errors represented as error bars. Microbial community structure and biomass independently had low explanatory power compared to environmental variables; however, they were largely uncorrelated with each other and provided greater model improvement when added to environmental variables in conjunction than when added independently.

<figure><img src="image_7.png" /></figure>

**FIGURE 4** | We analyzed natural soil (n = 47), sediment (n = 12), and agricultural soil (n = 12) environments separately. Bar height represents the average explanatory power of each predictor set, with error bars denoting standard errors. While predictor sets in natural soils nearly matched those within the full dataset, environmental and microbial predictor sets had lower explanatory power within sediments and higher explanatory power within agricultural soils. Microbial community diversity was less tightly coupled to the environment and provided more model improvement in sediments, with the opposite relationship in agricultural soils.

proteomic sequencing technology may enhance predictions of ecosystem processes by identifying active segments of microbial communities (Abram, 2015), too few datasets presently exist to conduct a robust analysis with these data. Studies investigating the extent to which these dynamics are masked within observations of microbial community structure and function are imperative to deciphering and improving the current ability of microbial data to predict ecosystem process rates.

As well, discrepancies in methodology may also confound the relationship we observed in the full dataset between microbial communities and biogeochemical processes (Sinsabaugh et al., 2015). Microbial DNA is typically extracted from a fraction of gram of soil at a single time point and methods detect both dormant and active organisms, while corresponding process data are measured in several grams of soil over longer timeframes. Further, process rate measures are either collected as net measurements that aggregate multiple chemical reactions, potential rates that represent microbial response to substrate addition, or gross rates that trace individual chemical transformations (Bier et al., 2015). These approaches inevitably introduce data aggregation issues (Clark et al., 2011) and rely on assumptions that microbial and biogeochemical data are spatially and temporally representative. Recently, studies have attempted to reduce uncertainty associated with scalar differences by pairing spatially and temporally explicit field studies with ecological modeling approaches that interpolate data across

scales (Lira-Noriega et al., 2013; Walker and Wardle, 2014). Leveraging these approaches in conjunction requires substantial resource investment but is increasingly feasible and presents a promising avenue for maximizing the value of microbial data.

Despite inherent limitations in microbial data, our data analysis suggests that microbial data types differ in their ability to explain processes that are phylogenetically broad vs. narrow and that are products of facultative vs. obligate metabolisms (Figure 2). Nitrification is the product of an obligate chemolithotrophic metabolism, and functional markers for this process are narrowly constrained within ammonia-oxidizing bacteria and archaea that catalyze the rate-limiting step in nitrification, putatively representing a likely dataset for detecting relationships between microbial data and process rates (Schimel, 1995; Schimel and Gulledge, 1998). While both functional gene abundance and community diversity metrics had similar average explanatory power within the full dataset (an effect that also did not vary among microbial technologies, Supplementary Figure S2A), models based on functional gene abundances or functional diversity explained more variation in process rates than community diversity within datasets measuring rates of nitrification (Figure 2C). Although these metrics were more strongly correlated with environmental factors than community diversity, they also provided greater explanatory power in combination with environmental variables
than community diversity metrics, which include variation
in many microbial guilds external to nitrification. Notably,
models based on environmental variables alone also had higher
explanatory power for nitrification than for denitrification or
N mineralization, suggesting that the environment may also be
more strongly coupled to nitrification rates than other N cycle
processes. Taken together, these results suggest that despite a
relatively tight connection between the environment, microbial
community structure, and process data, the microbial guilds
driving nitrification rates are partially decoupled at a functional
level from environmental drivers of nitrification. Importantly,
although nitrification was the only narrow process in our
dataset with sufficient replication to examine independently, the
high explanatory power of functional genes, both alone and
in combination with environmental variables, for describing
nitrification rates relative to other processes also conveys the
potential for the inclusion of functional metrics in improving
predictions of ecosystem processes that are mediated by
phylogenetically narrow functional guilds.

By contrast, denitrification is a broad process, catalyzed by
diverse facultative anaerobic microorganisms, and community
diversity metrics provided more added value to denitrification
rates than functional gene diversity or abundances (Figure 2B).
While we acknowledge that genetic markers may not denote
all functional genes involved in a process (Jones et al.,
2014; Verbaendert et al., 2014), we observed more added
value from community diversity metrics than functional genes
despite several studies assaying up to three genes to explain
denitrification rates, as opposed to one or two functional
markers measured for nitrification. For broad processes such
as denitrification, representations of niche complementarity or
resource availability reflected in diversity metrics (Cadotte et al.,
2011) rather than functional gene abundances that do not
account for interactions between individuals (Barberán et al.,
2012; Gagic et al., 2015) or functional redundancy (Allison and
Martiny, 2008), may be important for explaining process rates.
Thus, it is plausible that denitrifying communities are decoupled
from environmental variables when measured at a broad
taxonomic level and that such taxonomic metrics encompass
more variation in within community interactions affecting
denitrification rates than functional approaches. Although the
sample size of datasets measuring denitrification was small, our
analysis suggests that measurements of community diversity may
be more likely than functional metrics to improve predictions of
ecosystem processes that are catalyzed by facultative metabolisms
and/or phylogenetically broad suites of organisms.

Similarly, the ability to respire carbon is widely distributed
among microorganisms, and community diversity metrics
(no datasets measured functional markers) described rates of
respiration better than all other processes except denitrification
(Figure 3). In fact, environmental and microbial variables, both
alone and in combination, yielded higher average explanatory
power of respiration rates than all other processes we examined
independently with the exception of nitrification. Importantly,
while recent work has suggested that microbial biomass (Schimel
and Weintraub, 2003) or physiological properties such as drought
tolerance (Manzoni et al., 2014) and growth efficiency (Wieder

et al., 2013) can improve C cycling models, our results indicate
that information on microbial community structure may further
enhance our understanding of ecosystem C cycling. Microbial
community structure and biomass appeared to explain differing
portions of variation in process, as they exhibited extremely weak
correlations with each other and, together, provided additive
value for explaining rates of respiration. Thus, biomass and
structure may jointly contribute to explaining rates of respiration
as biomass may serve as a proxy for unmeasured environmental
variables that regulate activity, such as soil structure (Gupta
and Germida, 1988), while community structure may relate
to the genetic capability of a community to respire carbon.
Regardless, our results suggest that these data are at least
partially independent and that measurements of microbial
community structure could reduce uncertainty within Earth
Systems models.

Lastly, the relationship between microbial community
structure and ecosystem processes in our dataset varied by
environment type (Figure 4). Sediment systems had weaker
explanatory power of process rates by both environmental
variables and microbial community structure than natural or
agricultural soils. Sediment processes are influenced by both
sediment and porewater chemistry (Middelburg and Levin,
2009), and these datasets may be more likely to have unmeasured
variables or errors due to spatial discrepancies than other
systems. In particular, dissolved oxygen concentration is a strong
determinant of redox potential and ecosystem processes in
sediments (Abell et al., 2011) but was only measured in three
of 12 datasets. Despite the low explanatory power of sediment
processes by all predictor sets, microbial community diversity
added more value to explaining process rates and was more
decoupled from environmental variables in sediments than in
natural soil or agricultural systems, indicating that measurements
of microbial community structure may improve predictions of
sediment processes. Conversely, in agricultural soils, microbial
community structure explained more variation in ecosystem
processes but provided less improvement over environmental
variables than other systems. Environmental variables were also
better predictors of process in agricultural soils, and microbial
community diversity was more tightly correlated with the
environment than in sediments or natural soils. Agricultural
systems homogenize soil structure and decrease soil organic
matter (Dick, 1992; Chan and Heenan, 1996), reducing the
number and complexity of microbial niches in agricultural
soils. Thus, relatively coarse-scale measures of environmental
conditions may more directly correlate with both community
structure and ecosystem process rates than in more variable
systems. Overall, these results indicate that ecosystem-specific
dynamics may be crucial to understanding the value of microbial
community structure for explaining ecosystem processes
and emphasize the importance of future investigations into
understudied biomes in enhancing our understanding of global
relationships between microbial communities and ecosystem
processes.

Here, we present the first extensive investigation into
the relationship between environmental conditions, microbial
community structure, and ecosystem function by re-analyzing 82
datasets collected from an international team of collaborators, and we demonstrate that nuanced relationships between the environment and microbial communities can influence the importance of microbial community structure for explaining ecosystem-level processes. Our analysis provides an empirical basis for future hypothesis-testing on the roles of assembly, dormancy, redundancy and phenotypic plasticity in microbial community structure and function, and despite complexity in our datasets, the trends we observed represent an encouraging step for linking microbial community data to ecosystem function. As a whole, our results indicate that a greater understanding of microbial community structure informed by ecological principles may further our ability to accurately predict rates of ecosystem processes beyond environmental variables and bulk physiological characterizations of microbial communities.

## AUTHOR CONTRIBUTIONS

All authors collected and contributed datasets for analysis as well as participated in the conceptual drafting and revision of this manuscript. EG conducted all data analysis and was the primary author in writing and revising the manuscript. DN

and JK contributed significantly in manuscript development and revision, and DN provided financial support for this work.

## FUNDING

This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1.

## ACKNOWLEDGMENT

We thank the USGS Powell Center 'Next Generation Microbes' working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.00214

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Graham, Knelman, Schindlbacher, Siciliano, Breulmann, Yannarell, Beman, Abell, Philippot, Prosser, Foulquier, Yuste, Glanville, Jones, Angel, Salminen, Newton, Bürgmann, Ingram, Hamer, Siljanen, Peltoniemi, Potthast, Bañeras, Hartmann, Banerjee, Yu, Nogaro, Richter, Koranda, Castle, Goberna, Song, Chatterjee, Nunes, Lopes, Cao, Kaisermann, Hallin, Strickland, Garcia-Pausas, Barba, Kang, Isobe, Papaspyrou, Pastorelli, Lagomarsino, Lindström, Basiliko and Nemergut. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.