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Abstract Long-term climate history can influence rates of soil carbon cycling but the microbial traits underlying these legacy effects are not well understood. Legacies may result if historical climate differences alter the traits of soil microbial communities, particularly those associated with carbon cycling and stress tolerance. However, it is also possible that contemporary conditions can overcome the influence of historical climate, particularly under extreme conditions. Using shotgun metagenomics, we assessed the composition of soil microbial functional genes across a mean annual precipitation gradient that previously showed evidence of strong climate legacies in soil carbon flux and extracellular enzyme activity. Sampling coincided with recovery from a regional, multi-year severe drought, allowing us to document how the strength of climate legacies varied with contemporary conditions. We found increased investment in genes associated with resource cycling with historically higher precipitation across the gradient, particularly in traits related to resource transport and complex carbon degradation. This legacy effect was strongest in seasons with the lowest soil moisture, suggesting that contemporary conditions—particularly, resource stress under water limitation—influences the strength of legacy effects. In contrast, investment in stress tolerance did not vary with historical precipitation, likely due to frequent periodic drought throughout the gradient. Differences in the relative abundance of functional genes explained over half of variation in microbial functional capacity—potential enzyme activity—more so than historical precipitation or current moisture conditions. Together, these results suggest that long-term climate can alter the functional potential of soil microbial communities, leading to legacies in carbon cycling. 

Abstract Soil moisture is a major driver of microbial activity and thus, of the release of carbon (C) into the Earth's atmosphere. Yet, there is no consensus on the relationship between soil moisture and microbial respiration, and as a result, moisture response functions are a poorly constrained aspect of C models. In addition, models assume that the response of microbial respiration to moisture is the same for all ecosystems, regardless of climate history, an assumption that many empirical studies have challenged. These gaps in understanding of the microbial respiration response to moisture contribute to uncertainty in model predictions.We review our understanding of what drives microbial moisture response, highlighting evidence that historical precipitation can influence both responses to moisture and sensitivity to drought. We present two hypotheses, the ‘climate history hypothesis’, where we predict that baseline moisture response functions change as a function of precipitation history, and the ‘drought legacy hypothesis’, in which we suggest that the intensity and frequency of historical drought have shaped microbial communities in ways that will control moisture responses to contemporary drought. Underlying mechanisms include biological selection and filtering of the microbial community by rainfall regimes, which result in microbial traits and trade‐offs that shape function.We present an integrated modelling and empirical approach for understanding microbial moisture responses and improving models. Standardized measures of moisture response (respiration rate across a range of moistures) and accompanying microbial properties are needed across sites. These data can be incorporated into trait‐based models to produce generalized moisture response functions, which can then be validated and incorporated into conventional and microbially explicit ecosystem models of soil C cycling. Future studies should strive to analyse realistic moisture conditions and consider the role of environmental factors and soil structure in microbial response.Microbes are the engines that drive C storage and are sensitive to changes in rainfall. A greater understanding of the factors that govern this sensitivity could be a key part of improving predictions of soil C dynamics, climate change and C‐climate feedbacks. Read the freePlain Language Summaryfor this article on the Journal blog. 

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.