Jeth G. Walkup

Nitrogen (N) fertilization decreases carbon (C) decomposition, but there remain competing hypotheses underlying this response including reduced belowground C allocation, shifts in microbial diversity, and altered microbial function. Further, current soil organic matter (SOM) decomposition models lack the ability to test these hypothesized mechanisms. To theoretically test these mechanisms, we integrated the Carbon Acquisition Ecological Strategies (CAES) framework into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model. CAES represents a pathway for integrating functional diversity into microbial explicit soil decomposition models, with three microbial functional groups that specialize in decomposing and utilizing a different SOM pool: primary decomposers (1° decomposers) target complex SOM, secondary decomposers (2° decomposers) target microbial necromass, and passive consumers target simple SOM. We then used the refined model (Functional Group-CORPSE) to theoretically test hypotheses by leveraging long term data from a 30 year, watershed scale N fertilization experiment at the Fernow Experimental Forest in Parsons, WV. We performed modeling experiments to evaluate the microbial community response to varying C and N inputs, and to simulate hypothesized mechanisms of microbial response to N deposition. Varying substrate composition led to distinct microbial communities, reflecting substrate preferences between functional groups. However, only when both the function and abundance of complex C decomposers was reduced could FG-CORPSE capture the SOM dynamics observed at the Fernow. Collectively, our results show that FG-CORPSE enables the simulation of both functional and compositional shifts in microbial communities to facilitate the testing of microbe centric hypotheses for decomposition and C storage under N enrichment.

Nitrogen (N) fertilization decreases carbon (C) decomposition, but there remain competing hypotheses underlying this response including reduced belowground C allocation, shifts in microbial diversity, and altered microbial function. Further, current soil organic matter (SOM) decomposition models lack the ability to test these hypothesized mechanisms. To theoretically test these mechanisms, we integrated the Carbon Acquisition Ecological Strategies (CAES) framework into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model. CAES represents a pathway for integrating functional diversity into microbial explicit soil decomposition models, with three microbial functional groups that specialize in decomposing and utilizing a different SOM pool: primary decomposers (1° decomposers) target complex SOM, secondary decomposers (2° decomposers) target microbial necromass, and passive consumers target simple SOM. We then used the refined model (Functional Group-CORPSE) to theoretically test hypotheses by leveraging long term data from a 30 year, watershed scale N fertilization experiment at the Fernow Experimental Forest in Parsons, WV. We performed modeling experiments to evaluate the microbial community response to varying C and N inputs, and to simulate hypothesized mechanisms of microbial response to N deposition. Varying substrate composition led to distinct microbial communities, reflecting substrate preferences between functional groups. However, only when both the function and abundance of complex C decomposers was reduced could FG-CORPSE capture the SOM dynamics observed at the Fernow. Collectively, our results show that FG-CORPSE enables the simulation of both functional and compositional shifts in microbial communities to facilitate the testing of microbe centric hypotheses for decomposition and C storage under N enrichment.

Nitrogen (N) fertilization decreases carbon (C) decomposition, but there remain competing hypotheses underlying this response including reduced belowground C allocation, shifts in microbial diversity, and altered microbial function. Further, current soil organic matter (SOM) decomposition models lack the ability to test these hypothesized mechanisms. To theoretically test these mechanisms, we integrated the Carbon Acquisition Ecological Strategies (CAES) framework into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model. CAES represents a pathway for integrating functional diversity into microbial explicit soil decomposition models, with three microbial functional groups that specialize in decomposing and utilizing a different SOM pool: primary decomposers (1° decomposers) target complex SOM, secondary decomposers (2° decomposers) target microbial necromass, and passive consumers target simple SOM. We then used the refined model (Functional Group-CORPSE) to theoretically test hypotheses by leveraging long term data from a 30 year, watershed scale N fertilization experiment at the Fernow Experimental Forest in Parsons, WV. We performed modeling experiments to evaluate the microbial community response to varying C and N inputs, and to simulate hypothesized mechanisms of microbial response to N deposition. Varying substrate composition led to distinct microbial communities, reflecting substrate preferences between functional groups. However, only when both the function and abundance of complex C decomposers was reduced could FG-CORPSE capture the SOM dynamics observed at the Fernow. Collectively, our results show that FG-CORPSE enables the simulation of both functional and compositional shifts in microbial communities to facilitate the testing of microbe centric hypotheses for decomposition and C storage under N enrichment.

Nitrogen (N) fertilization decreases carbon (C) decomposition, but there remain competing hypotheses underlying this response including reduced belowground C allocation, shifts in microbial diversity, and altered microbial function. Further, current soil organic matter (SOM) decomposition models lack the ability to test these hypothesized mechanisms. To theoretically test these mechanisms, we integrated the Carbon Acquisition Ecological Strategies (CAES) framework into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model. CAES represents a pathway for integrating functional diversity into microbial explicit soil decomposition models, with three microbial functional groups that specialize in decomposing and utilizing a different SOM pool: primary decomposers (1° decomposers) target complex SOM, secondary decomposers (2° decomposers) target microbial necromass, and passive consumers target simple SOM. We then used the refined model (Functional Group-CORPSE) to theoretically test hypotheses by leveraging long term data from a 30 year, watershed scale N fertilization experiment at the Fernow Experimental Forest in Parsons, WV. We performed modeling experiments to evaluate the microbial community response to varying C and N inputs, and to simulate hypothesized mechanisms of microbial response to N deposition. Varying substrate composition led to distinct microbial communities, reflecting substrate preferences between functional groups. However, only when both the function and abundance of complex C decomposers was reduced could FG-CORPSE capture the SOM dynamics observed at the Fernow. Collectively, our results show that FG-CORPSE enables the simulation of both functional and compositional shifts in microbial communities to facilitate the testing of microbe centric hypotheses for decomposition and C storage under N enrichment.

Nitrogen (N) fertilization decreases carbon (C) decomposition, but there remain competing hypotheses underlying this response including reduced belowground C allocation, shifts in microbial diversity, and altered microbial function. Further, current soil organic matter (SOM) decomposition models lack the ability to test these hypothesized mechanisms. To theoretically test these mechanisms, we integrated the Carbon Acquisition Ecological Strategies (CAES) framework into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model. CAES represents a pathway for integrating functional diversity into microbial explicit soil decomposition models, with three microbial functional groups that specialize in decomposing and utilizing a different SOM pool: primary decomposers (1° decomposers) target complex SOM, secondary decomposers (2° decomposers) target microbial necromass, and passive consumers target simple SOM. We then used the refined model (Functional Group-CORPSE) to theoretically test hypotheses by leveraging long term data from a 30 year, watershed scale N fertilization experiment at the Fernow Experimental Forest in Parsons, WV. We performed modeling experiments to evaluate the microbial community response to varying C and N inputs, and to simulate hypothesized mechanisms of microbial response to N deposition. Varying substrate composition led to distinct microbial communities, reflecting substrate preferences between functional groups. However, only when both the function and abundance of complex C decomposers was reduced could FG-CORPSE capture the SOM dynamics observed at the Fernow. Collectively, our results show that FG-CORPSE enables the simulation of both functional and compositional shifts in microbial communities to facilitate the testing of microbe centric hypotheses for decomposition and C storage under N enrichment.

A major challenge in biogeochemistry is to reduce the uncertainty of projections made by soil carbon models. In the last two decades, carbon-use efficiency, the proportion of consumed carbon incorporated into microbial biomass, has become a central parameter to represent microbial control on carbon fluxes. For models that integrate other elements, like nitrogen, the adjustment of carbon-use efficiency in response to substrate stoichiometry has gained popularity as a mechanism to balance these fluxes, mostly due to its mathematical convenience. The reasoning behind this, is that microbes release the excess carbon as CO₂, a mechanism known as overflow respiration. This mechanism, however, causes a characteristic decrease of carbon-use efficiency when reaching nitrogen limitation. In this study we propose that the implementation of overflow respiration forces an unrealistic decrease of carbon-use efficiency for three reasons: 1) physiological mechanisms can minimize carbon excess, avoiding overflow; 2) carbon overflow has been reported in laboratory experiments mainly as dissolved organic carbon (organic acids), and not as CO₂; 3) functionally diverse microbial communities can exhibit higher-level dynamics, improving the recycling of nutrients and avoiding overflow. We use an individual-based microbial litter decomposition model to test the impact of these mechanisms on carbon-use efficiency. We found that physiological mechanisms such as flexible biomass stoichiometry can eliminate overflow, but nutrient allocation does not. When carbon overflow occurs as dissolved organic carbon, carbon-use efficiency increases under nitrogen limitation. Finally, a functionally diverse community can avoid carbon overflow, although carbon-use efficiency declines due to higher maintenance respiration. We demonstrate that the representation of overflow respiration in soil carbon models is more relevant than currently acknowledged. Redirecting carbon overflow to a dissolved organic carbon pool can lead to opposite trends in carbon losses. The soil carbon modeling community should thoroughly assess current implementations and explore more mechanistically grounded alternatives. This includes dissolved organic carbon pathways, more realistic microbial community representations, or other processes that better capture the complexity of microbial carbon use.

Nitrogen (N) deposition increases soil carbon (C) storage by reducing microbial activity. These effects vary in soil beneath trees that associate with arbuscular (AM) and ectomycorrhizal (ECM) fungi. Variation in carbon C and N uptake traits among microbes may explain differences in soil nutrient cycling between mycorrhizal associations in response to high N loads, a mechanism not previously examined due to methodological limitations. Here, we used quantitative Stable Isotope Probing (qSIP) to measure bacterial C and N assimilation rates from an added organic compound, which we conceptualize as functional traits. As such, we applied a trait‐based approach to explore whether variation in assimilation rates of bacterial taxa can inform shifts in soil function under chronic N deposition. We show taxon‐specific and community‐wide declines of bacterial C and N uptake under chronic N deposition in both AM and ECM soils. N deposition‐induced reductions in microbial activity were mirrored by declines in soil organic matter mineralization rates in AM but not ECM soils. Our findings suggest C and N uptake traits of bacterial communities can predict C cycling feedbacks to N deposition in AM soils, but additional data, for instance on the traits of fungi, may be needed to connect microbial traits with soil C and N cycling in ECM systems. Our study also highlights the potential of employing qSIP in conjunction with trait‐based analytical approaches to inform how ecological processes of microbial communities influence soil functioning.

Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.