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Feedbacks between plants and soil microbial communities can play an important role in structuring plant communities. However, little is known about how soil legacies caused by environmental disturbances such as drought and extreme precipitation events may affect plant–soil feedback or whether plant–soil feedback operates within species as it does between species. If soil legacies alter plant–soil feedback among genotypes within a plant species, then soil legacies may alter the diversity within plant populations. We conducted a fully factorial pairwise plant–soil feedback experiment to test how precipitation legacies influenced intraspecific plant–soil feedbacks among three genotypes of a dominant grass species,Panicum virgatum.Panicum virgatumexperienced negative intraspecific plant–soil feedback, i.e., genotypes generally performed worse on soil from the same genotype than different genotypes. Soil precipitation legacies reversed the rank order of the strength of negative feedback among the genotypes. Feedback is often positively correlated with plant relative abundance. Therefore, our results suggest that soil precipitation legacies may alter the genotypic composition ofP. virgatumpopulations, favoring genotypes that develop less negative feedback. Changes in intraspecific diversity will likely further affect community structure and ecosystem functioning, and may constrain the ability of populations to respond to future changes in climate.

The spatial and temporal linkages between turnover of soil microbial communities and their associated functions remain largely unexplored in terrestrial ecosystems. Yet defining these relationships and how they vary across ecosystems and microbial lineages is key to incorporating microbial communities into ecological forecasts and ecosystem models. To define linkages between turnover of soil bacterial and fungal communities and their function we sampled fungal and bacterial composition, abundance, and enzyme activities across a 3‐ha area of wet tropical primary forest over 2 yr. We show that fungal and bacterial communities both exhibited temporal turnover, but turnover of both groups was much lower than in temperate ecosystems. Turnover over time was driven by gain and loss of microbial taxa and not changes in abundance of individual species present in multiple samples. Only fungi varied over space with idiosyncratic variation that did not increase linearly with distance among sampling locations. Only phosphorus‐acquiring enzyme activities were linked to shifts in septate, decomposer fungal abundance; no enzymes were affected by composition or diversity of fungi or bacteria. Although temporal and spatial variation in composition was appreciable, because turnover of microbial communities did not alter the functional repertoire of decomposing enzymes, functional redundancy among taxa may be high in this ecosystem. Slow temporal turnover of tropical soil microbial communities and large functional redundancy suggests that shifts in abundance of particular functional groups may capture ecosystem function more accurately than composition in these heterogeneous ecosystems.

This article is a Commentary onDavisonet al. (2021),231: 763–776.

Ecosystem models commonly use stable‐state assumptions to predict responses of soil microbial functions to environmental change. However, past climatic conditions can shape microbial functional responses resulting in a ‘legacy effect’. For instance, exposure to drier conditions in the field may shape how soil microbial communities respond to subsequent drought and drying and rewetting (DRW) events.

Soil moisture constrains the activity of decomposer soil microorganisms, and in turn the rate at which soil carbon returns to the atmosphere. While increases in soil moisture are generally associated with increased microbial activity, historical climate may constrain current microbial responses to moisture. However, it is not known if variation in the shape and magnitude of microbial functional responses to soil moisture can be predicted from historical climate at regional scales. To address this problem, we measured soil enzyme activity at 12 sites across a broad climate gradient spanning 442–887 mm mean annual precipitation. Measurements were made eight times over 21 months to maximize sampling during different moisture conditions. We then fit saturating functions of enzyme activity to soil moisture and extracted half saturation and maximum activity parameter values from model fits. We found that 50% of the variation in maximum activity parameters across sites could be predicted by 30‐year mean annual precipitation, an indicator of historical climate, and that the effect is independent of variation in temperature, soil texture, or soil carbon concentration. Based on this finding, we suggest that variation in the shape and magnitude of soil microbial response to soil moisture due to historical climate may be remarkably predictable at regional scales, and this approach may extend to other systems. If historical contingencies on microbial activities prove to be persistent in the face of environmental change, this approach also provides a framework for incorporating historical climate effects into biogeochemical models simulating future global change scenarios.

The complexity of processes and interactions that drive soil C dynamics necessitate the use of proxy variables to represent soil characteristics that cannot be directly measured (correlative proxies), or that aggregate information about multiple soil characteristics into one variable (integrative proxies). These proxies have proven useful for understanding the soil C cycle, which is highly variable in both space and time, and are now being used to make predictions of the fate and persistence of C under future climate scenarios. However, the C pools and processes that proxies represent must be thoughtfully considered in order to minimize uncertainties in empirical understanding. This is necessary to capture the full value of a proxy in model parameters and in model outcomes. Here, we provide specific examples of proxy variables that could improve decision‐making, and modeling skill, while also encouraging continued work on their mechanistic underpinnings. We explore the use of three common soil proxies used to study soil C cycling: metabolic quotient, clay content, and physical fractionation. We also consider how emerging data types, such as genome‐sequence data, can serve as proxies for microbial community activities. By examining some broad assumptions in soil C cycling with the proxies already in use, we can develop new hypotheses and specify criteria for new and needed proxies.