About

Steven D. Allison is a Professor of Ecology & Evolutionary Biology at UC Irvine, with a joint appointment in the Earth System Science School of Physical Sciences and the Center for Environmental Biology. He earned his Ph.D. in Biological Sciences from Stanford University in 2005. His research explores the functional roles of microbes in ecosystems, focusing on bacteria and fungi that contribute to global carbon balance and nutrient cycling essential for plant growth. Using theory, experiments, and mathematical models, he analyzes how microbial communities respond to environmental changes and the subsequent effects on Earth's systems. Much of his work concentrates on the soil microbiome and its feedbacks to global climate change, aiming to provide a scientific basis for solving environmental problems at local to global scales. Allison's lab promotes diversity and inclusivity in all efforts.

Research topics

• Ecology
• Biology
• Agronomy
• Atmospheric sciences
• Soil science
• Environmental science
• Physical geography
• Oceanography
• Geology
• Geography

Selected publications

• Gene duplication, horizontal gene transfer, and trait trade-offs drive evolution of postfire resource acquisition in pyrophilous fungi

  Proceedings of the National Academy of Sciences · 2026-01-02 · 2 citations

  articleOpen access

  Wildfires significantly alter soil carbon (C) and nitrogen (N), reducing microbial richness and biomass, while selecting for "fire-loving" pyrophilous microbes that drive postfire nutrient cycling. However, the genomic strategies and functional trade-offs (balancing gains in one trait with costs in another) underlying the traits that enable pyrophilous microbes to survive and thrive postfire are virtually unknown. We hypothesized that pyrophilous fungi employ specialized genomic adaptations for C and N cycling, with evolutionary trade-offs between traits governing aromatic C degradation, N acquisition pathways, and rapid growth. To test these hypotheses, we performed complementary comparative genomics, transcriptomics after pyrogenic organic matter amendment, and growth rate bioassays for 18 pyrophilous fungi from five Ascomycota (Eurotiales, Pleosporales, Sordariales, Coniochaetales, and Pezizales) and three Basidiomycota (Agaricales, Holtermanniales, and Geminibasidiales) orders isolated from burned soils. We found a dramatic trait trade-off between fast growth and number of genes responsible for aromatic C degradation, implying burned environments select for metabolically costly genes despite their evolutionary cost. We used the comparative genomics framework to evaluate genomic signatures of evolution and found that either gene duplication and somatic mutation, or recombination via sexual reproduction, were the primary drivers of fungal genomic variation in aromatic C degradation and N acquisition genes. Finally, we identified cross-kingdom bacterial to fungal horizontal gene transfer (HGT) as a secondary strategy producing novel aromatic C degradation genes. Overall, we found that trait trade-offs and genome evolutionary strategies are key drivers that may predict the persistence and contribution of pyrophilous fungi to global C and N cycling.

  PublisherOA PDFDOI

• The response of leaf litter bacterial communities to simulated drought depends on temperature

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-06

  articleOpen access

  Abstract Microbial communities regulate carbon and nitrogen (N) cycling, yet their long-term responses to chronic global changes remain unclear. Using 12 years of grassland litter samples from the Loma Ridge Global Change Experiment in Irvine, California, we tested whether interactions between experimental drought and N deposition, and previously observed temporal variability are driven by background climatic conditions, including precipitation and temperature. Consistent with short-term studies, drought and N addition had relatively small effects on bacterial community composition compared to pronounced seasonal and interannual variability, with drought-by-year interactions explaining more variation than drought alone. Seasonal shifts were largely driven by short-term fluctuations in rainfall and temperature, whereas the substantial interannual variability in community composition was not captured by site-level climate metrics. Contrary to expectations, drought effects were influenced more by background temperature than precipitation, with the strongest effects observed in cooler years. Lastly, a bacterial taxon’s sensitivity to climate variability under ambient conditions did not predict its response to chronic drought. Together, our findings show that bacterial responses to drought are temporally dynamic and influenced by background temperature, underscoring the need for long-term longitudinal studies of soil microbial communities to better predict microbial responses under future global change. Importance Microbial responses to global change, particularly drought and nitrogen addition, are often inferred from short-term studies (< 2 years), yet natural temporal variability may overshadow experimental effects. Using a 12-year dataset of grassland leaf litter communities, we show that temporal variability, both seasonal and interannual, exert a stronger influence on bacterial community composition than chronic drought or nitrogen deposition. These findings challenge assumptions about the magnitude of drought effects, particularly in naturally drought-affected ecosystem such as California grasslands and highlight the importance of long-term datasets for predicting microbial responses to climate change. By demonstrating that bacterial communities are strongly shaped by background climatic variability (baseline precipitation and temperature independent of imposed chronic treatments) and may be buffered to sustained drought, this work improves forecasts of ecosystem responses and informs the design of global change experiments and restoration strategies in future research studies.

  PublisherOA PDFDOI

• A Framework for Variational Inference and Data Assimilation of Soil Biogeochemical Models Using Normalizing Flows

  Journal of Advances in Modeling Earth Systems · 2025-08-01

  articleOpen accessSenior author

  Abstract Soil biogeochemical models (SBMs) represent soil variables and their responses to global change. Data assimilation approaches help determine whether SBMs accurately represent soil processes consistent with soil pool and flux measurements. Bayesian inference is commonly used in data assimilation procedures that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. The ability to account for data and parameter uncertainty is a strength of MCMC inference, but the computational inefficiency of MCMC methods remains a barrier to their wider application, especially with large data sets. Given the limitations of MCMC approaches, we developed an alternative variational inference framework that uses a method called normalizing flows from the field of machine learning. Normalizing flows rely on deep learning to map probability distributions and approximate SBMs that have been discretized into state space models. As a test of our method, we fit approximated SBMs to synthetic data sourced from known data‐generating processes to identify discrepancies between the inference results and true parameter values. Our approach compares favorably with established MCMC methods and could be a viable alternative for SBM data assimilation that reduces computational time and resource needs. However, our method has some limitations, including challenges assimilating data with irregular measurement intervals, underestimation of posterior parameter uncertainty, and limited goodness‐of‐fit metrics for comparison to MCMC inference methods. Many of these limitations could be overcome with additional algorithm development based on the approaches we report here.

  PublisherDOI

• Gene Duplication, Horizontal Gene Transfer, and Trait Trade-offs Drive Evolution of Post-Fire Resource Acquisition in Pyrophilous Fungi

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-21 · 2 citations

  preprintOpen access

  Wildfires significantly alter soil carbon (C) and nitrogen (N), reducing microbial richness and biomass, while selecting for fire-loving pyrophilous microbes that drive post-fire nutrient cycling. However, the genomic strategies and functional trade-offs (balancing gains in one trait with costs in another) underlying the traits that enable pyrophilous microbes to survive and thrive post-fire are virtually unknown. We hypothesized that pyrophilous fungi employ specialized genomic adaptations for C and N cycling, with evolutionary trade-offs between traits governing aromatic C degradation, N acquisition pathways, and rapid growth. To test these hypotheses, we assessed hyphal extension rates and constructed a comparative genomics framework for 18 pyrophilous fungi from five Ascomycota (Eurotiales, Pleosporales, Sordariales, Coniochaetales, and Pezizales) and three Basidiomycota (Agaricales, Holtermaniales, and Geminibasidiales) orders isolated from burned soils. We found a dramatic trait trade-off between fast growth and number of genes responsible for aromatic C degradation, implying that these metabolically costly genes are selected for in burned environments despite their evolutionary cost. We used the comparative genomics framework to evaluate the genomic signatures of evolution and found that either gene duplication and somatic mutation, or recombination via sexual reproduction, were the primary drivers of fungal genomic variation in aromatic C degradation and N acquisition genes. Finally, we identified rare cross-kingdom bacterial to fungal horizontal gene transfer as a third strategy producing novel aromatic C degradation genes. Overall, we found that trait trade-offs and genome evolutionary strategies are key drivers of the rapid colonization and persistence of pyrophilous fungi in post-fire ecosystems.

  PublisherOA PDFDOI

• Integrating the effect of microbial legacy and adaptation on soil biogeochemistry in the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. 

  2025-03-14

  preprintOpen accessCorresponding

  As an essential process in the global carbon cycle, litter decomposition functions both as a means of carbon loss from soil through microbial respiration and as a contributor to the soil carbon pool. While traditional controls such as climate and litter quality are well-documented, recent research has revealed that microbial legacy and adaptation also significantly impact litter decomposition. However, this new understanding has yet to be incorporated into ecosystem-scale models. To address this gap, we leveraged a large experiment involving the decomposition of 200 types of litter across Europe, placed in a common garden to be decomposed under the same climate and by the same microbes. This setup allowed us to isolate the effect of litter chemistry on litter decomposition. We compared the observations with the predictions of two microbial models: the Microbial-Mineral Carbon Stabilization (MIMICS) model, widely used at the ecosystem scale but not accounting for microbial legacy and adaptation, and the DEMENT model, which does account for these factors but is not yet used at the ecosystem scale. Our findings indicated that MIMICS could not accurately represent the sensitivity of litter decay to carbon and nitrogen ratios. Consequently, it overestimated litter decay in low nutrient conditions and underestimated it in high nutrient conditions. In contrast, DEMENT successfully predicted this sensitivity due to its explicit representation of both the variation (adaptation) and delayed response (legacy) of microbial community composition to litter chemistry. We proposed a method to integrate the emergent properties of microbial legacy and adaptation from DEMENT into MIMICS, thereby enhancing its predictive accuracy. Importantly, these results challenge the assumption that microbial legacy and adaptation can be neglected when predicting litter decay rates.

  PublisherDOI

• Y-A+S is the new Y-A-S: Updating microbial life history tradeoffs with comparative genomics

  Research Square · 2025-05-02 · 2 citations

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Decadal recovery of fungal but not termite deadwood decay in tropical rainforest

  Journal of Applied Ecology · 2025-05-06

  articleOpen access

  Abstract Deadwood represents ~11% of carbon stocks in tropical rainforest ecosystems and its decay is driven largely by fungi and termites, which contribute to the cycling of carbon and nutrients. Due to land use change, such as forest clearing, secondary growth tropical rainforests are increasingly prevalent around the globe. In secondary growth rainforest, studies found lower decay rates of leaf litter; however, little is known about how deadwood decays in these forests. Here, we tested whether termite and fungal species richness, composition and functions in decaying deadwood were similar in secondary and old‐growth tropical rainforests. We assessed termite ability to discover and consume deadwood, as well as fungi community composition and contributions to wood decay. We placed non‐native pine blocks, half of which were accessible to termites, in an old‐growth rainforest site as a reference and two secondary growth rainforest sites that were restored 4 and 8 years before the start of the experiment. Blocks were harvested every 6 months for 4 years (eight harvests). Using fungal ITS amplicon sequencing of sawdust samples from the decaying deadwood blocks at the seventh harvest, we determined wood‐dwelling fungal community composition. We found that termites discovered similar proportions of deadwood across the secondary and old growth rainforest sites, although the decay rates of the discovered deadwood were lower in the secondary growth rainforest. Further, fungal decay was similar to old growth rainforest levels in the older but not younger secondary growth rainforest, where it was slower; although differences among sites were small. Wood‐dwelling fungal communities were similar between secondary and old growth rainforests. Synthesis and applications . Contrary to common assumptions, fungal communities and their wood decay functions were resilient and recovered relatively quickly within secondary growth rainforests; however, those of termites did not, which could reduce carbon and nutrient cycling in secondary growth rainforests. Active management methods such as the local transplant of termite‐ and fungi‐occupied logs could accelerate the recovery of these ecosystems.

  PublisherOA PDFDOI

• Functional Consequences of Solving Elemental Imbalances

  2025-03-14

  preprintOpen accessSenior authorCorresponding

  Currently, most microbially-explicit biogeochemical models use flexible carbon-use efficiency (i.e., overflow respiration) to balance the mismatch between microbial biomass and litter stoichiometry (e.g. carbon : nitrogen, C:N). However, other known mechanisms might lead to different biogeochemical outcomes. Here we perform a rigorous test of the functional consequences of several mechanisms that aid in solving this mismatch. We used an individual-based, trait-based leaf litter decomposition model that represents microbial functional groups by uptake and extracellular enzyme genes. The original model incorporates overflow respiration and flexible biomass stoichiometry as mechanisms to solve elemental imbalance. We further introduce a novel mechanism of enzyme allocation. We established 4 simulation treatments: overflow, overflow + flexible stoichiometry, overflow + enzyme allocation, and overflow + flexible stoichiometry + enzyme allocation. In each treatment we manipulate initial litter C:N from 10 to 90. We also manipulate the initial community to yield scenarios with high and low functional redundancy based on the number of polymers each “taxon” can degrade. We found that biomass production was greatest when all mechanisms were in operation, followed by enzyme allocation, flexible stoichiometry, and overflow being the lowest. This pattern inverted in the low redundancy scenario. Total respiration decreased with higher litter C:N but was greater for flexible stoichiometry and lowest for enzyme allocation. When enzyme allocation was present, mass loss and nutrient mineralization were consistently decreased. As suggested by other studies, carbon-use efficiency remained high when having alternatives to overflow. This, however, occurs only in the low redundancy scenario. We conclude that current microbially-explicit biogeochemical models might be overestimating carbon losses for high C:N substrates due to an unrealistic increase in respiration rates by overflow. We urge for the quantification of these mechanisms in natural systems.

  PublisherDOI

• Scaling the impact of microbial ecophysiology on ecosystem-level decomposition rates under drought

  2025-03-15

  preprintOpen accessSenior authorCorresponding

  Quantifying the influence of drought on microbial processes in soil and its consequences for carbon cycling is hindered by the lack of underlying mechanistic understanding. Drought affects soil microbes directly by causing physiological stress but also affects indirectly by influencing substrate transport and diffusion. Another indirect effect is through changes in plant litter chemistry which impacts microbial resource acquisition strategies. Here we present a theoretical framework to study the effects of drought as well as the ecosystem feedbacks that are generated due to the complex interactions of above-ground and below-ground processes. We classify microbial life history strategies into high yield (Y), resource acquisition (A) and stress tolerance (S), or Y-A-S along two main axes of environmental variation: resources and abiotic stress. We propose the use of this framework that incorporates trait-based ecology to link drought-impacted microbial processes to rates of soil carbon decomposition and stabilisation. We also present empirical evidence in plant litter microbial communities from a decade-long precipitation manipulation experiment in the field in Mediterranean grass and shrub ecosystems in Southern California. Using metagenome-assembled genomes (MAGs), we demonstrate trade-offs in stress tolerance and resource acquisition traits in bacterial populations in grass litter which arise due to selection of certain taxa by drought as the environmental filter. Through taxonomic and MAGs analyses across four time points over 18 months, we observed the dominance of fungi at the start of the litter decomposition process. These fungal pioneers by secreting extracellular enzymes likely enable the survival of drought tolerating bacteria with reduced decomposition capabilities. The indirect effect of drought on plant litter chemistry was examined by FTIR analysis of litter linked to microbial Carbohydrate-Active Enzyme (CAZyme) gene abundance for different substrates which shows subtle shifts in plant litter chemistry and associated changes in microbial resource acquisition traits that were linked to community succession during the decomposition process. We also observed signatures of recycling of fungal and bacterial necromass. Litter decomposition rates measured as mass loss using litter bags were unaffected by drought in shrub ecosystems but showed trends of reduction in grass ecosystems. The integrated knowledge from these studies demonstrates the various mechanisms by which microbial ecophysiology influences decomposition rates under drought and highlights the need for such scaling up of microbial response to climate change factors from individual soil microbes to collective communities to ecosystems.

  PublisherDOI

• Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming

  Global Change Biology · 2025-06-01 · 5 citations

  articleOpen access

  Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)-is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.

  PublisherDOI

Recent grants

• Collaborative Research: Microbes, memory, and moisture: leveraging DroughtNet to predict how microbial moisture responses will impact carbon cycling

  NSF · $412k · 2020–2023

• NRT: A training incubator for addressing urban environmental change from Ridge to Reef (R2R)

  NSF · $3.0M · 2017–2023

• Applying social evolution theory in microbial ecosystems

  NSF · $369k · 2009–2013

• Collaborative research: Controls over decomposition by microbial communities under climate change

  NSF · $840k · 2015–2019

• Collaborative Research: NSFDEB-NERC: Tropical deadwood carbon fluxes: Improving carbon models by incorporating termites and microbes

  NSF · $246k · 2017–2023

Frequent coauthors

• Kathleen K. Treseder

  University of California, Irvine

  64 shared

• Adam C. Martiny

  University of California, Irvine

  56 shared

• Jennifer B. H. Martiny

  University of California, Irvine

  52 shared

• Eoin Brodie

  University of California, Berkeley

  32 shared

• Amy E. Zanne

  University of Miami

  24 shared

• Ashish A. Malik

  University of California, Irvine

  22 shared

• Claudia Weihe

  University of California, Irvine

  22 shared

• Michael L. Goulden

  22 shared

Labs

• The Allison LabPI

Awards & honors

• Highly Cited Researcher, Clarivate Analytics (2017)
• UC Irvine Climate Action Champion (2016)
• Selected as one of UCI’s top 50 graduate and postdoctoral al…
• Chancellor's Award for Excellence in Fostering Undergraduate…
• Faculty Mentor of the Month (May 2015)