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Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Widespread tree mortality events occur during periods of severe drought in temperate conifer forests and are expected to become more frequent in many areas due to climate change. Improved mapping of individual tree mortality is needed to identify risk factors and design effective conservation strategies. In this study, we used National Ecological Observatory Network (NEON) lidar and multispectral reflectance airborne observations to map individual tree mortality over a 160 km2 area during and after the 2012–2016 drought for two sites in California's Sierra National Forest. We used NEON lidar to derive tree locations and crown perimeters and multispectral data to map tree mortality for more than 1 million trees. We found that 25.4% of the trees in our study area died between 2013 and 2017, with considerably higher mortality at the lower-elevation Soaproot Saddle site. Between 2017 and 2019, an additional 2.0%–2.8% of the trees died each year. Two wildfires in 2020 and 2021 increased tree mortality within burned area perimeters by 49%–89% between 2019 and 2021. Consistent with previous work, we found that tree mortality risk increased as a function of tree height. Tree mortality was positively associated with distance from rivers, trees per hectare, and decreasing slope at the lower elevation site. In contrast, increasing slope was positively associated with tree mortality at the higher elevation site. Our approach and dataset provide a means to study the combined effects of drought and wildfire on tree mortality and may improve projections of forest resilience under a changing climate.

Global change experiments often observe shifts in bacterial community composition based on 16S rRNA gene sequences. However, this genetic region can mask a large amount of genetic and phenotypic variation among bacterial strains sharing even identical 16S regions. As such, it remains largely unknown whether variation at the sub-16S level, sometimes termed microdiversity, responds to environmental perturbations and whether such changes are relevant to ecosystem processes. Here, we investigated microdiversity within Curtobacterium, the dominant bacterium found in the leaf litter layer of soil, to simulated drought and nitrogen addition in a field experiment. We first developed and validated Curtobacterium-specific primers of the groEL gene to assess microdiversity within this lineage. We then tracked the response of this microdiversity to simulated global change in two adjacent plant communities, grassland and coastal sage scrub (CSS). Curtobacterium microdiversity responded to drought but not nitrogen addition, indicating variation within the genus of drought tolerance but not nitrogen response. Further, the response of microdiversity to drought depended on the ecosystem, suggesting that litter substrate selects for a distinct composition of microdiversity that is constrained in its response, perhaps related to tradeoffs in resource acquisition traits. Supporting this interpretation, a metagenomic analysis revealed that the composition of Curtobacterium-encoded carbohydrate-active enzymes (CAZymes) varied distinctly across the two ecosystems. Identifying the degree to which relevant traits are phylogenetically conserved may help to predict when the aggregated response of a 16S-defined taxon masks differential responses of finer-scale bacterial diversity to global change. IMPORTANCE Microbial communities play an integral role in global biogeochemical cycling, but our understanding of how global change will affect microbial community structure and functioning remains l

It is critical to understand the consequences of environmental change for the microbial regulation of carbon and nutrient cycling. Specifically, understanding microbial community traits, such as extracellular enzyme activity, can help inform nutrient cycling models and address knowledge gaps. We analyzed data on extracellular enzyme activities and litter decomposition from an 18-month experiment in which microbial communities were reciprocally transplanted along a climate gradient in Southern California. Communities were from desert, scrubland, grassland, pine–oak, and subalpine ecosystems. We aimed to test how enzyme activities responded to climate change following transplantation and how those responses related to decomposition rates. We hypothesized that microbial communities would specialize on their native climate conditions, resulting in higher enzyme activities when transplanted back into their native climate. We investigated the relationship between extracellular enzyme Vmax values, substrate mass loss, and microbial biomass as well as variation in these variables across the climate gradient. We found little evidence for climate specialization, and there was rarely a reduction in enzyme functioning after microbial communities were transplanted into new climate conditions. Moreover, observed differences in decomposition were not related to changes in extracellular enzyme potential, although there were significant differences in enzyme activities and decomposition rates across sites. These results suggest that direct, physiological impacts of climate are likely to be important for enzyme-mediated decomposition, but climate specialization will not constrain the microbial response to climate change in our system.

Nearly all ecosystems host diverse microbiomes that support vital ecosystem processes. At the same time, these ecosystems and their microbiomes are increasingly altered by human activities, particularly in highly managed urban environments. While microbial ecologists are beginning to understand the drivers of microbial assembly and the link between community structure and function in many ecosystems, few of these advances have been applied to urban ecosystems. In this synthesis, we review research on the urban soil microbiome and develop a framework to integrate soil microbial communities with urban ecosystem function. We identify disturbance, altered resources, and heterogeneity as key drivers through which human activities including urban development affect soils and their resident microorganisms. Steep environmental gradients in many urban systems present a unique opportunity to address fundamental questions in microbial ecology, such as how microbes respond to stress and how biogeochemical rates relate to microbial diversity and composition. Soil microbiomes in cities also provide ecosystem services and harms, making it crucial to understand how human activity drives those functions and the consequences for environmental and human health. We argue that much-needed integration across disturbance ecology, urban ecology, and microbial ecology will help generate practical and equitable strategies for managing ecosystem benefits in cities where most humans now live.

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Decomposer fungi play a fundamental role in terrestrial ecosystem dynamics. In the southwestern United States, climate change is causing more frequent and severe droughts, which may alter fungal community composition and activity. Investigating relationships between fungal traits may improve the prediction of fungal responses to drought. In this dual field and laboratory experiment, we examine whether trade-offs occur between traits associated with drought. Specifically, we test the hypothesis that fungi sort into lifestyles specializing in growth yield, resource acquisition, and drought stress tolerance (“YAS” framework). For the field experiment, we constructed microbial “cages” containing sterilized litter and 1 of 10 fungal isolates. These cages were placed in long-term drought and control plots in a southern Californian grassland for 6 and 12 months. We measured fungal hyphal length per unit litter mass loss for growth yield, the potential activities of four extracellular enzymes for resource acquisition, and the ability to grow in the drought versus control plots for drought stress tolerance. We compared these results with a laboratory microcosm experiment constructed with the same fungal isolates and that measured the same fungal traits. The field experiment corroborated our laboratory results, in that no trade-offs were observed between growth yield and resource acquisition traits. However, in contrast to the laboratory experiment, drought tolerance was negatively related to extracellular enzyme activity and growth yield in the field, implying a trade-off. Despite this observed trade-off in the field, growth yield was not hindered by drought. We propose a modification to the YAS framework, by combining the growth yield and resource acquisition lifestyles, which may be more appropriate for this arid system. This joint laboratory and field approach contextualizes a theoretical framework in microbial ecology and improves understanding of fungal community respon

Bacterial communities in the organic leaf litter layer and bulk (mineral and organic) soil are sensitive to environmental change. However, despite close interactions between these communities, the leaf litter layer has historically been studied in isolation from the bulk soil. Whether bacterial response to environmental change is uniform throughout the surface soil remains unclear. Here, we simultaneously characterized how bacterial community composition in three surface soil layers (the leaf litter layer, 0–2 cm of bulk soil, and 0–10 cm of bulk soil) responded to a wildfire burning through a 13-year drought simulation in two adjacent ecosystems, a grassland and coastal sage scrubland. We found that bacterial communities in all three surface soil layers were distinct in composition and varied with drought, ecosystem type, and temporal variation. Moreover, the impact of these environmental changes on bacterial community composition decreased with depth in the surface soil. Bacterial response to drought was three-fold higher in the leaf litter layer than in the top 10 cm of bulk soil, with the drought treatment explaining 4.8% and 1.6% of the compositional variation, respectively. Wildfire altered bacterial composition in the leaf litter layer but not within the top 10 cm of bulk soil. Further, previous exposure of the bacterial communities in the leaf litter layer to drought did not influence its response to the wildfire. Thus, considering soil depth when assessing the impact of environmental conditions on the surface soil microbiome may improve predictions about the degree to which microbial communities, and therefore soil carbon, will respond to future environmental change.

Most of the carbon (C) that enters the soil is broken down by the microbial community and either respired or stored in soil depending on the microbial allocation strategy. Changes in how the microbial community uses C can significantly affect soil C pool sizes, so new models have begun to explicitly represent microbial allocation. Most models use a parameter called carbon use efficiency (CUE) to represent microbial allocation, which partitions consumed C between respiration and growth. Here we compare a “Typical Microbial Model” with this representation of microbial allocation to two other models. One is the “Microbial Allocation Model” that represents CUE as an emergent property of the microbial community, explicitly modeling multiple processes involved in CUE. The second is the “Two-Pool Biomass Model” that similarly accounts for CUE as an emergent property but also represents the biomass using two C pools with different turnover times. We assessed the models’ relative ability to track a14C-glucose tracer pulse over three weeks through the extractable C pool, the microbial biomass, and respiration. We also used the 14C data to test how estimates of microbial CUE change during the incubation. Our results suggest that CUE estimates in soil are sensitive to incubation timing and are at no point stable. Isotopic data can best parameterize models when a time course of measurements is used. Our model comparison showed that the Two-Pool Biomass Model best fit our data. Using the Two-Pool Biomass model to represent microbial allocation is more biologically realistic and better matches the dynamics observed in our microbial C partitioning data. Mechanistic biogeochemical models should consider using a dynamic allocation scheme to represent CUE, rather than assuming a static value.

It is critical to understand the consequences of environmental change for the microbial regulation of carbon and nutrient cycling. Specifically, understanding microbial community traits, such as extracellular enzyme activity, can help inform nutrient cycling models and address knowledge gaps. We analyzed data on extracellular enzyme activities and litter decomposition from an 18-month experiment in which microbial communities were reciprocally transplanted along a climate gradient in Southern California. Communities were from desert, scrubland, grassland, pine–oak, and subalpine ecosystems. We aimed to test how enzyme activities responded to climate change following transplantation and how those responses related to decomposition rates. We hypothesized that microbial communities would specialize on their native climate conditions, resulting in higher enzyme activities when transplanted back into their native climate. We investigated the relationship between extracellular enzyme Vmax values, substrate mass loss, and microbial biomass as well as variation in these variables across the climate gradient. We found little evidence for climate specialization, and there was rarely a reduction in enzyme functioning after microbial communities were transplanted into new climate conditions. Moreover, observed differences in decomposition were not related to changes in extracellular enzyme potential, although there were significant differences in enzyme activities and decomposition rates across sites. These results suggest that direct, physiological impacts of climate are likely to be important for enzyme-mediated decomposition, but climate specialization will not constrain the microbial response to climate change in our system.

Decomposer fungi play a fundamental role in terrestrial ecosystem dynamics. In the southwestern United States, climate change is causing more frequent and severe droughts, which may alter fungal community composition and activity. Investigating relationships between fungal traits may improve the prediction of fungal responses to drought. In this dual field and laboratory experiment, we examine whether trade-offs occur between traits associated with drought. Specifically, we test the hypothesis that fungi sort into lifestyles specializing in growth yield, resource acquisition, and drought stress tolerance (“YAS” framework). For the field experiment, we constructed microbial “cages” containing sterilized litter and 1 of 10 fungal isolates. These cages were placed in long-term drought and control plots in a southern Californian grassland for 6 and 12 months. We measured fungal hyphal length per unit litter mass loss for growth yield, the potential activities of four extracellular enzymes for resource acquisition, and the ability to grow in the drought versus control plots for drought stress tolerance. We compared these results with a laboratory microcosm experiment constructed with the same fungal isolates and that measured the same fungal traits. The field experiment corroborated our laboratory results, in that no trade-offs were observed between growth yield and resource acquisition traits. However, in contrast to the laboratory experiment, drought tolerance was negatively related to extracellular enzyme activity and growth yield in the field, implying a trade-off. Despite this observed trade-off in the field, growth yield was not hindered by drought. We propose a modification to the YAS framework, by combining the growth yield and resource acquisition lifestyles, which may be more appropriate for this arid system. This joint laboratory and field approach contextualizes a theoretical framework in microbial ecology and improves understanding of fungal community respon

Nearly all ecosystems host diverse microbiomes that support vital ecosystem processes. At the same time, these ecosystems and their microbiomes are increasingly altered by human activities, particularly in highly managed urban environments. While microbial ecologists are beginning to understand the drivers of microbial assembly and the link between community structure and function in many ecosystems, few of these advances have been applied to urban ecosystems. In this synthesis, we review research on the urban soil microbiome and develop a framework to integrate soil microbial communities with urban ecosystem function. We identify disturbance, altered resources, and heterogeneity as key drivers through which human activities including urban development affect soils and their resident microorganisms. Steep environmental gradients in many urban systems present a unique opportunity to address fundamental questions in microbial ecology, such as how microbes respond to stress and how biogeochemical rates relate to microbial diversity and composition. Soil microbiomes in cities also provide ecosystem services and harms, making it crucial to understand how human activity drives those functions and the consequences for environmental and human health. We argue that much-needed integration across disturbance ecology, urban ecology, and microbial ecology will help generate practical and equitable strategies for managing ecosystem benefits in cities where most humans now live.

Global change experiments often observe shifts in bacterial community composition based on 16S rRNA gene sequences. However, this genetic region can mask a large amount of genetic and phenotypic variation among bacterial strains sharing even identical 16S regions. As such, it remains largely unknown whether variation at the sub-16S level, sometimes termed microdiversity, responds to environmental perturbations and whether such changes are relevant to ecosystem processes. Here, we investigated microdiversity within Curtobacterium, the dominant bacterium found in the leaf litter layer of soil, to simulated drought and nitrogen addition in a field experiment. We first developed and validated Curtobacterium-specific primers of the groEL gene to assess microdiversity within this lineage. We then tracked the response of this microdiversity to simulated global change in two adjacent plant communities, grassland and coastal sage scrub (CSS). Curtobacterium microdiversity responded to drought but not nitrogen addition, indicating variation within the genus of drought tolerance but not nitrogen response. Further, the response of microdiversity to drought depended on the ecosystem, suggesting that litter substrate selects for a distinct composition of microdiversity that is constrained in its response, perhaps related to tradeoffs in resource acquisition traits. Supporting this interpretation, a metagenomic analysis revealed that the composition of Curtobacterium-encoded carbohydrate-active enzymes (CAZymes) varied distinctly across the two ecosystems. Identifying the degree to which relevant traits are phylogenetically conserved may help to predict when the aggregated response of a 16S-defined taxon masks differential responses of finer-scale bacterial diversity to global change. IMPORTANCE Microbial communities play an integral role in global biogeochemical cycling, but our understanding of how global change will affect microbial community structure and functioning remains l

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Bacterial communities in the organic leaf litter layer and bulk (mineral and organic) soil are sensitive to environmental change. However, despite close interactions between these communities, the leaf litter layer has historically been studied in isolation from the bulk soil. Whether bacterial response to environmental change is uniform throughout the surface soil remains unclear. Here, we simultaneously characterized how bacterial community composition in three surface soil layers (the leaf litter layer, 0–2 cm of bulk soil, and 0–10 cm of bulk soil) responded to a wildfire burning through a 13-year drought simulation in two adjacent ecosystems, a grassland and coastal sage scrubland. We found that bacterial communities in all three surface soil layers were distinct in composition and varied with drought, ecosystem type, and temporal variation. Moreover, the impact of these environmental changes on bacterial community composition decreased with depth in the surface soil. Bacterial response to drought was three-fold higher in the leaf litter layer than in the top 10 cm of bulk soil, with the drought treatment explaining 4.8% and 1.6% of the compositional variation, respectively. Wildfire altered bacterial composition in the leaf litter layer but not within the top 10 cm of bulk soil. Further, previous exposure of the bacterial communities in the leaf litter layer to drought did not influence its response to the wildfire. Thus, considering soil depth when assessing the impact of environmental conditions on the surface soil microbiome may improve predictions about the degree to which microbial communities, and therefore soil carbon, will respond to future environmental change.

Global changes such as increased drought and atmospheric nitrogen deposition perturb both the microbial and plant communities that mediate terrestrial ecosystem functioning. However, few studies consider how microbial responses to global changes may be influenced by interactions with plant communities. To begin to address the role of microbial–plant interactions, we tested the hypothesis that the response of microbial communities to global change depends on the plant community. We characterized bacterial and fungal communities from 395 plant litter samples taken from the Loma Ridge Global Change Experiment, a decade-long global change experiment in Southern California that manipulates rainfall and nitrogen levels across two adjacent ecosystems, a grassland and a coastal sage scrubland. The differences in bacterial and fungal composition between ecosystems paralleled distinctions in plant community composition. In addition to the direct main effects, the global change treatments altered microbial composition in an ecosystem-dependent manner, in support of our hypothesis. The interaction between the drought treatment and ecosystem explained nearly 5% of the variation in bacterial community composition, similar to the variation explained by the ecosystem-independent effects of drought. Unexpectedly, we found that the main effect of drought was approximately four times as strong on bacterial composition as that of nitrogen addition, which did not alter fungal or plant composition. Overall, the findings underscore the importance of considering plant–microbe interactions when considering the transferability of the results of global change experiments across ecosystems.

Termites are important ecosystem engineers in tropical habitats, with different feeding groups able to decompose wood, grass, litter, and soil organic matter. In most tropical regions, termite abundance and species diversity are assumed to increase with rainfall, with highest levels found in rainforests. However, in the Australian tropics, this pattern is thought to be reversed, with lower species richness and termite abundance found in rainforest than drier habitats. The potential mechanisms underlying this pattern remain unclear. We compared termite assemblages (abundance, activity, diversity, and feeding group composition) across five sites along a precipitation gradient (ranging from ∼800 to 4,000 mm annual rainfall), spanning dry and wet savanna habitats, wet sclerophyll, and lowland and upland rainforests in tropical North Queensland. Moving from dry to wet habitats, we observed dramatic decreases in termite abundance in both mounds and dead wood occupancy, with greater abundance and activity at savanna sites (low precipitation) compared with rainforest or sclerophyll sites (high precipitation). We also observed a turnover in termite species and feeding group diversity across sites that were close together, but in different habitats. Termite species and feeding group richness were highest in savanna sites, with 13 termite species from wood-, litter-, grass-, dung-, and soil-feeding groups, while only five termite species were encountered in rainforest and wet sclerophyll sites—all wood feeders. These results suggest that the Australian termite diversity anomaly may be partly driven by how specific feeding groups colonized habitats across Australia. Consequently, termites in Australian rainforests may be less important in ecosystem processes, such as carbon and nutrient cycling during decomposition, compared with termites in other tropical rainforests.

Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait-based microbiome model (DEMENTpy) to implement trade-off-mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade-offs were assumed to follow the Y-A-S framework that defines three primary life-history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade-offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community-level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade-off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait-based modeling study motivates additional empirical measurements to quantify YAS traits and trade-offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach f

Global changes such as increased drought and atmospheric nitrogen deposition perturb both the microbial and plant communities that mediate terrestrial ecosystem functioning. However, few studies consider how microbial responses to global changes may be influenced by interactions with plant communities. To begin to address the role of microbial–plant interactions, we tested the hypothesis that the response of microbial communities to global change depends on the plant community. We characterized bacterial and fungal communities from 395 plant litter samples taken from the Loma Ridge Global Change Experiment, a decade-long global change experiment in Southern California that manipulates rainfall and nitrogen levels across two adjacent ecosystems, a grassland and a coastal sage scrubland. The differences in bacterial and fungal composition between ecosystems paralleled distinctions in plant community composition. In addition to the direct main effects, the global change treatments altered microbial composition in an ecosystem-dependent manner, in support of our hypothesis. The interaction between the drought treatment and ecosystem explained nearly 5% of the variation in bacterial community composition, similar to the variation explained by the ecosystem-independent effects of drought. Unexpectedly, we found that the main effect of drought was approximately four times as strong on bacterial composition as that of nitrogen addition, which did not alter fungal or plant composition. Overall, the findings underscore the importance of considering plant–microbe interactions when considering the transferability of the results of global change experiments across ecosystems.

Termites are important ecosystem engineers in tropical habitats, with different feeding groups able to decompose wood, grass, litter, and soil organic matter. In most tropical regions, termite abundance and species diversity are assumed to increase with rainfall, with highest levels found in rainforests. However, in the Australian tropics, this pattern is thought to be reversed, with lower species richness and termite abundance found in rainforest than drier habitats. The potential mechanisms underlying this pattern remain unclear. We compared termite assemblages (abundance, activity, diversity, and feeding group composition) across five sites along a precipitation gradient (ranging from ∼800 to 4,000 mm annual rainfall), spanning dry and wet savanna habitats, wet sclerophyll, and lowland and upland rainforests in tropical North Queensland. Moving from dry to wet habitats, we observed dramatic decreases in termite abundance in both mounds and dead wood occupancy, with greater abundance and activity at savanna sites (low precipitation) compared with rainforest or sclerophyll sites (high precipitation). We also observed a turnover in termite species and feeding group diversity across sites that were close together, but in different habitats. Termite species and feeding group richness were highest in savanna sites, with 13 termite species from wood-, litter-, grass-, dung-, and soil-feeding groups, while only five termite species were encountered in rainforest and wet sclerophyll sites—all wood feeders. These results suggest that the Australian termite diversity anomaly may be partly driven by how specific feeding groups colonized habitats across Australia. Consequently, termites in Australian rainforests may be less important in ecosystem processes, such as carbon and nutrient cycling during decomposition, compared with termites in other tropical rainforests.

Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait-based microbiome model (DEMENTpy) to implement trade-off-mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade-offs were assumed to follow the Y-A-S framework that defines three primary life-history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade-offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community-level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade-off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait-based modeling study motivates additional empirical measurements to quantify YAS traits and trade-offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach f

Fungal metabolic rates could increase under climate warming but may be counteracted by mechanisms of physiological acclimation and evolutionary adaptation. We hypothesized that Vmax and Km parameters of Neurospora discreta extracellular enzymes would acclimate to warmer temperatures through compensatory mechanisms. We also predicted that evolution under warmer temperatures would alter enzyme parameters and fungal respiration through adaptive mechanisms. In contrast to these predictions, growth at higher temperature (22 °C versus 16 °C) increased the temperature-corrected Vmax of three enzymes. The carbon substrate used for fungal growth (lignin versus sucrose) had a much greater impact on enzyme Vmax than temperature. Following experimental evolution, the enzymatic parameters of Neurospora strains did not adapt to higher temperatures as hypothesized; rather, enzyme Vmax values were unaffected, and respiration rates increased. Together, these results suggest that physiological and evolutionary mechanisms are unlikely to counteract soil carbon losses driven by saprotrophic fungi under climate warming.

Fungal metabolic rates could increase under climate warming but may be counteracted by mechanisms of physiological acclimation and evolutionary adaptation. We hypothesized that Vmax and Km parameters of Neurospora discreta extracellular enzymes would acclimate to warmer temperatures through compensatory mechanisms. We also predicted that evolution under warmer temperatures would alter enzyme parameters and fungal respiration through adaptive mechanisms. In contrast to these predictions, growth at higher temperature (22 °C versus 16 °C) increased the temperature-corrected Vmax of three enzymes. The carbon substrate used for fungal growth (lignin versus sucrose) had a much greater impact on enzyme Vmax than temperature. Following experimental evolution, the enzymatic parameters of Neurospora strains did not adapt to higher temperatures as hypothesized; rather, enzyme Vmax values were unaffected, and respiration rates increased. Together, these results suggest that physiological and evolutionary mechanisms are unlikely to counteract soil carbon losses driven by saprotrophic fungi under climate warming.

The potential consequences of global warming for ecosystem carbon stocks are a major concern, particularly in high-latitude regions where soil carbon pools are especially large. Research on soil and plant carbon responses to warming are often based on short-term (' 10 year) warming experiments. Furthermore, carbon budgets from boreal forests, which contain at least 10–20% of the global soil carbon pool, have shown mixed responses to warming. In this study, we measured carbon and nitrogen budgets (i.e., soil and understory vegetation carbon and nitrogen stocks) from a 13-year greenhouse warming experiment in an Alaskan boreal forest. Although there were no differences in total aboveground + belowground pools, the carbon in the moss biomass and in the soil organic layer significantly decreased with the warming treatment (− 88.3% and − 19.1%, respectively). Declines in moss biomass carbon may be a consequence of warming-associated drying, while shifts in the soil microbial community could be responsible for the decrease in carbon in the soil organic layer. Moreover, in response to warming, aboveground plant biomass carbon tended to increase while root biomass carbon tended to decrease, so carbon allocation may shift aboveground with warming. Overall these results suggest that permafrost-free boreal forests are susceptible to soil carbon loss with warming.

To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

The potential consequences of global warming for ecosystem carbon stocks are a major concern, particularly in high-latitude regions where soil carbon pools are especially large. Research on soil and plant carbon responses to warming are often based on short-term (' 10 year) warming experiments. Furthermore, carbon budgets from boreal forests, which contain at least 10–20% of the global soil carbon pool, have shown mixed responses to warming. In this study, we measured carbon and nitrogen budgets (i.e., soil and understory vegetation carbon and nitrogen stocks) from a 13-year greenhouse warming experiment in an Alaskan boreal forest. Although there were no differences in total aboveground + belowground pools, the carbon in the moss biomass and in the soil organic layer significantly decreased with the warming treatment (− 88.3% and − 19.1%, respectively). Declines in moss biomass carbon may be a consequence of warming-associated drying, while shifts in the soil microbial community could be responsible for the decrease in carbon in the soil organic layer. Moreover, in response to warming, aboveground plant biomass carbon tended to increase while root biomass carbon tended to decrease, so carbon allocation may shift aboveground with warming. Overall these results suggest that permafrost-free boreal forests are susceptible to soil carbon loss with warming.

Resource acquisition and growth yield are fundamental microbial traits that affect biogeochemical processes and have consequences for ecosystem functioning. However, there is a lack of empirical observations linking these traits. Using a landscape-scale survey of temperate near-neutral pH soils, we show tradeoffs in key community-level parameters linked to these traits. Increased investment into extracellular enzymes estimated using specific potential enzyme activity was associated with reduced growth yield obtained using carbon use efficiency measures from stable isotope tracing. Reduction in growth yield was linked more to carbon than nitrogen acquisition highlighting smaller stoichiometric than energetic constraints on community metabolism in examined soils.

Plant residues and soil organic matter are predominantly decomposed by exoenzymes. Many soil carbon models now represent enzymatic decomposition, but the mathematical formulation of this process has been debated over the last 15 years. Some models apply the traditional “forward” Michaelis-Menten equation to represent enzyme kinetics, whereas others apply the “reverse” Michaelis-Menten equation, which assumes that kinetic rates saturate at high enzyme concentrations. Recently the equilibrium chemistry approximation (ECA) has been proposed as an alternative to both Michaelis-Menten formulations. However, because of methodological limitations, in-situ enzyme kinetics—especially in the context of soil system heterogeneity—have been difficult to verify experimentally. Therefore, the overarching goal of our study was to evaluate different enzyme kinetic formulations using model-based evidence at microbial to ecosystem scales. We used a spatially explicit individual- and trait-based microbial model, DEMENT, to circumvent methodological challenges. Although DEMENT assumes forward Michaelis-Menten kinetics at local scales, at the grid scale we found saturating relationships between degradation rate and both substrate concentrations and enzyme concentrations that fit the forward and reverse Michaelis-Menten equations, respectively, at specific successional stages during decomposition. Although forward and reverse Michaelis-Menten equations emerged under some conditions, only the ECA adequately represented decay rates emerging from the spatial-temporal variation in substrate and enzyme concentrations throughout the decomposition process. Our results support a more widespread adoption of the ECA equation in soil biogeochemical modelling at ecosystem scales.

Resource acquisition and growth yield are fundamental microbial traits that affect biogeochemical processes and have consequences for ecosystem functioning. However, there is a lack of empirical observations linking these traits. Using a landscape-scale survey of temperate near-neutral pH soils, we show tradeoffs in key community-level parameters linked to these traits. Increased investment into extracellular enzymes estimated using specific potential enzyme activity was associated with reduced growth yield obtained using carbon use efficiency measures from stable isotope tracing. Reduction in growth yield was linked more to carbon than nitrogen acquisition highlighting smaller stoichiometric than energetic constraints on community metabolism in examined soils.

Plant residues and soil organic matter are predominantly decomposed by exoenzymes. Many soil carbon models now represent enzymatic decomposition, but the mathematical formulation of this process has been debated over the last 15 years. Some models apply the traditional “forward” Michaelis-Menten equation to represent enzyme kinetics, whereas others apply the “reverse” Michaelis-Menten equation, which assumes that kinetic rates saturate at high enzyme concentrations. Recently the equilibrium chemistry approximation (ECA) has been proposed as an alternative to both Michaelis-Menten formulations. However, because of methodological limitations, in-situ enzyme kinetics—especially in the context of soil system heterogeneity—have been difficult to verify experimentally. Therefore, the overarching goal of our study was to evaluate different enzyme kinetic formulations using model-based evidence at microbial to ecosystem scales. We used a spatially explicit individual- and trait-based microbial model, DEMENT, to circumvent methodological challenges. Although DEMENT assumes forward Michaelis-Menten kinetics at local scales, at the grid scale we found saturating relationships between degradation rate and both substrate concentrations and enzyme concentrations that fit the forward and reverse Michaelis-Menten equations, respectively, at specific successional stages during decomposition. Although forward and reverse Michaelis-Menten equations emerged under some conditions, only the ECA adequately represented decay rates emerging from the spatial-temporal variation in substrate and enzyme concentrations throughout the decomposition process. Our results support a more widespread adoption of the ECA equation in soil biogeochemical modelling at ecosystem scales.

Carbon use efficiency (CUE), the proportion of carbon (C) consumed by microbes that is converted into biomass, is an important parameter for soil C models with explicit microbial controls. While often considered as a single parameter, CUE is an emergent property of multiple microbial processes, including assimilation efficiency, biomass-specific respiration, enzyme production, and respiratory costs of enzyme production. These processes occur over variable time scales and imply different fates for C, and the same emergent CUE value can result when C is allocated in fundamentally different ways (e.g. a high investment in enzyme production vs. a high assimilation cost). We developed a model that represents the individual processes underlying emergent CUE to test how shifts in microbial allocation alter equilibrium soil C pool sizes. We found that an increase in emergent CUE that results from a change in assimilation efficiency, biomass specific respiration, or respiration costs from enzyme production causes soil organic C (SOC) to decline, while the same change in emergent CUE resulting from a change in enzyme production causes SOC to increase. We also used the model to test the sensitivity of CUE from isotopic C tracer estimates to changes in microbial allocation processes. We found that these estimates do not account for the same microbial processes represented by emergent CUE in models. We propose that considering microbial processes explicitly rather than representing CUE as a single parameter can improve data-model integration. In addition, modeling microbial processes explicitly will account for a wider range of possible outcomes from shifts in microbial C allocation, such as when increased SOC results from increasing CUE.

Nitramines are potentially carcinogenic by-products of amines used in post-combustion CO2-capture. The influence of monoethanol (MEA)-, monomethyl (MMA)-, and dimethyl (DMA)-nitramines on the growth of environmental strains of bacteria, Pseudomonas fluorescens (P. fluorescens) and Rhodococcus spp. (R. spp.), was investigated in the laboratory. Additionally, the persistence of the nitramines in the presence of bacteria was determined. Growth of R. spp. was found to be sensitive to MMA-nitramine (EC50 = 157 mg L−1), while P. fluorescens growth was insensitive to all nitramines tested. Moreover, P. fluorescens was capable of degrading 8–10% of the nitramines during the 33 h experiments. Results from this study provide insight into important processes of bacterial response to nitramines that merit further investigation considering the ongoing implementation of CO2 capture technology.

Shift in precipitation regime could greatly alter plant and microbial activity, and thus the contemporary and future ecosystem dynamics in grasslands. We investigated how changes in snow depth affect plants, microbes and their relationships after 10 consecutive years of snow treatments in different steppes. We selected 8 snow fences along a mean annual precipitation (MAP) gradient from 225 to 375 mm in Inner Mongolia. For each snow fence, study plots were set up at 7 transects with different levels of snow depth. We found that ecosystem properties, including soil moisture, the biomass and nitrogen (N) pools of microbes and plants, the fungi: bacteria ratio and the grass: forb ratio, increased with increasing snow depth at the drier sites with lower MAP, but not at the wetter sites with higher MAP. At any given site, the sensitivity of these ecosystem properties to changes in snow depth was determined by the slopes of these variables against snow depth. The results showed that the sensitivity of these ecosystem properties to changes in snow depth decreased linearly with increase in MAP levels. In addition, we also found that increased snow depth shifted the relationship between microbial and plant biomass from positive to negative. Our work reveals the importance of snow water in regulating plant and microbial processes in temperate steppes, especially under lower MAP conditions. The greater plant and microbial biomass and the shift of community toward greater fungi: bacteria and grass: forb ratio imply that increased snowmelt input alleviated water limitation in temperate steppes and altered plant and microbial communities. Our study helps to better predict that how changes in winter precipitation could affect the biomass and composition of plants and soil microbes in grasslands.

To better understand mechanisms of carbon (C) and nitrogen (N) dynamics under anthropogenic N enrichment, we examined frequencies of C- and N-targeting genes in litter fungi. In particular, we tested the hypothesis that N enrichment selects for C-targeting genes but against N-targeting genes, if fungi preferentially invest resources in acquisition of growth-limiting nutrients. We conducted a fully-factorial litter and microbial transplant in a N fertilization experiment in Southern California grassland. The transplant design enabled us to contrast direct effects of N fertilization in the environment, indirect effects of N-induced shifts in the microbial community, and indirect effects of N-induced changes in plant litter chemistry. For each treatment, we assessed frequencies of select well-annotated fungal functional genes: cellulose-targeting AA9 genes (for C acquisition) versus ammonium transporter genes and amino acid permease genes (for N acquisition). We found that our hypothesis was upheld only with regard to shifts in the microbial community. Specifically, when grown in the same environment and litter, fungi from the N-fertilized plots displayed greater frequencies of cellulose-targeting AA9 genes from basidiomycetes, but smaller frequencies of ammonium transporter genes and amino acid permease genes, when compared to fungi from the control plots. In contrast, N fertilization in the plot environment was associated with higher frequencies of amino acid permease genes and ammonium transporter genes. Likewise, plant litter from the N-fertilized plots selected for higher frequencies of ammonium transporter genes. Altogether, we found fairly inconsistent effects of N enrichment on fungal functional genes related to C and N acquisition. Even if the genetic capacity of the fungal community to acquire C versus N changes owing to shifts in the microbial community, direct effects of N fertilization and indirect effects of litter chemistry may offset the response.

In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as 'Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA' instead of 'Department of Biology, Boston University, Boston, Massachusetts 02215, USA'. This has been corrected online.

Carbon use efficiency (CUE), the proportion of carbon (C) consumed by microbes that is converted into biomass, is an important parameter for soil C models with explicit microbial controls. While often considered as a single parameter, CUE is an emergent property of multiple microbial processes, including assimilation efficiency, biomass-specific respiration, enzyme production, and respiratory costs of enzyme production. These processes occur over variable time scales and imply different fates for C, and the same emergent CUE value can result when C is allocated in fundamentally different ways (e.g. a high investment in enzyme production vs. a high assimilation cost). We developed a model that represents the individual processes underlying emergent CUE to test how shifts in microbial allocation alter equilibrium soil C pool sizes. We found that an increase in emergent CUE that results from a change in assimilation efficiency, biomass specific respiration, or respiration costs from enzyme production causes soil organic C (SOC) to decline, while the same change in emergent CUE resulting from a change in enzyme production causes SOC to increase. We also used the model to test the sensitivity of CUE from isotopic C tracer estimates to changes in microbial allocation processes. We found that these estimates do not account for the same microbial processes represented by emergent CUE in models. We propose that considering microbial processes explicitly rather than representing CUE as a single parameter can improve data-model integration. In addition, modeling microbial processes explicitly will account for a wider range of possible outcomes from shifts in microbial C allocation, such as when increased SOC results from increasing CUE.

Shift in precipitation regime could greatly alter plant and microbial activity, and thus the contemporary and future ecosystem dynamics in grasslands. We investigated how changes in snow depth affect plants, microbes and their relationships after 10 consecutive years of snow treatments in different steppes. We selected 8 snow fences along a mean annual precipitation (MAP) gradient from 225 to 375 mm in Inner Mongolia. For each snow fence, study plots were set up at 7 transects with different levels of snow depth. We found that ecosystem properties, including soil moisture, the biomass and nitrogen (N) pools of microbes and plants, the fungi: bacteria ratio and the grass: forb ratio, increased with increasing snow depth at the drier sites with lower MAP, but not at the wetter sites with higher MAP. At any given site, the sensitivity of these ecosystem properties to changes in snow depth was determined by the slopes of these variables against snow depth. The results showed that the sensitivity of these ecosystem properties to changes in snow depth decreased linearly with increase in MAP levels. In addition, we also found that increased snow depth shifted the relationship between microbial and plant biomass from positive to negative. Our work reveals the importance of snow water in regulating plant and microbial processes in temperate steppes, especially under lower MAP conditions. The greater plant and microbial biomass and the shift of community toward greater fungi: bacteria and grass: forb ratio imply that increased snowmelt input alleviated water limitation in temperate steppes and altered plant and microbial communities. Our study helps to better predict that how changes in winter precipitation could affect the biomass and composition of plants and soil microbes in grasslands.

In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as 'Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA' instead of 'Department of Biology, Boston University, Boston, Massachusetts 02215, USA'. This has been corrected online.

Nitramines are potentially carcinogenic by-products of amines used in post-combustion CO2-capture. The influence of monoethanol (MEA)-, monomethyl (MMA)-, and dimethyl (DMA)-nitramines on the growth of environmental strains of bacteria, Pseudomonas fluorescens (P. fluorescens) and Rhodococcus spp. (R. spp.), was investigated in the laboratory. Additionally, the persistence of the nitramines in the presence of bacteria was determined. Growth of R. spp. was found to be sensitive to MMA-nitramine (EC50 = 157 mg L−1), while P. fluorescens growth was insensitive to all nitramines tested. Moreover, P. fluorescens was capable of degrading 8–10% of the nitramines during the 33 h experiments. Results from this study provide insight into important processes of bacterial response to nitramines that merit further investigation considering the ongoing implementation of CO2 capture technology.

To better understand mechanisms of carbon (C) and nitrogen (N) dynamics under anthropogenic N enrichment, we examined frequencies of C- and N-targeting genes in litter fungi. In particular, we tested the hypothesis that N enrichment selects for C-targeting genes but against N-targeting genes, if fungi preferentially invest resources in acquisition of growth-limiting nutrients. We conducted a fully-factorial litter and microbial transplant in a N fertilization experiment in Southern California grassland. The transplant design enabled us to contrast direct effects of N fertilization in the environment, indirect effects of N-induced shifts in the microbial community, and indirect effects of N-induced changes in plant litter chemistry. For each treatment, we assessed frequencies of select well-annotated fungal functional genes: cellulose-targeting AA9 genes (for C acquisition) versus ammonium transporter genes and amino acid permease genes (for N acquisition). We found that our hypothesis was upheld only with regard to shifts in the microbial community. Specifically, when grown in the same environment and litter, fungi from the N-fertilized plots displayed greater frequencies of cellulose-targeting AA9 genes from basidiomycetes, but smaller frequencies of ammonium transporter genes and amino acid permease genes, when compared to fungi from the control plots. In contrast, N fertilization in the plot environment was associated with higher frequencies of amino acid permease genes and ammonium transporter genes. Likewise, plant litter from the N-fertilized plots selected for higher frequencies of ammonium transporter genes. Altogether, we found fairly inconsistent effects of N enrichment on fungal functional genes related to C and N acquisition. Even if the genetic capacity of the fungal community to acquire C versus N changes owing to shifts in the microbial community, direct effects of N fertilization and indirect effects of litter chemistry may offset the response.

Many regions on Earth are expected to become drier with climate change, which may impact nitrogen (N) cycling rates and availability. We used a meta-analytical approach on the results of field experiments that reduced precipitation and measured N supply (i.e., indices of N mineralization), soil microbial biomass, inorganic N pools (ammonium (NH4+) and nitrate (NO3−)), and nitrous oxide (N2O) emissions. We hypothesized that N supply and N2O emissions would be relatively insensitive to precipitation reduction and that reducing precipitation would increase extractable NH4+ and NO3− concentrations because microbial processes continue, whereas plant N uptake diminishes with drought. In support of this hypothesis, extractable NH4+ increased by 25% overall with precipitation reduction; NH4+ also increased significantly with increasing magnitude of precipitation reduction. In contrast, N supply and extractable NO3− did not change and N2O emissions decreased with reduced precipitation. Across studies microbial biomass appeared unchanged, yet from the diversity of studies, it was clear that proportionally smaller precipitation reductions increased microbial biomass, whereas larger proportional reductions in rainfall reduced microbial biomass; there was a positive intercept (P = 0.005) and a significant negative slope (P = 0.0002) for the regression of microbial biomass versus % precipitation reduction (LnR = −0.009 × (% precipitation reduction) + 0.4021). Our analyses imply that relative to other N variables, N supply is less sensitive to reduced precipitation, whereas processes producing N2O decline. Drought intensity and duration, through sustained N supply, may control how much N becomes vulnerable to loss via hydrologic and gaseous pathways upon rewetting dry soils.