Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.

Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon–climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040 Pg C, compared to an estimate of 1260 Pg C (with a 95% confidence interval of 890–1660 Pg C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820 Pg C, compared to 500 Pg C (with a 95% confidence interval of 380–620 Pg C) for the NCSCD and 290 Pg C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model–data agreement was moderate at the biome level (R² values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1° scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8 kg C m⁻²). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1° spatial variation in soil carbon within most ESMs (R² values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model R² = 0.93), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes.

Climate warming is expected to have particularly strong effects on tundra and boreal ecosystems, yet relatively few studies have examined soil responses to temperature change in these systems. We used closed‐top greenhouses to examine the response of soil respiration, nutrient availability, microbial abundance, and active fungal communities to soil warming in an Alaskan boreal forest dominated by mature black spruce. This treatment raised soil temperature by 0.5 °C and also resulted in a 22% decline in soil water content. We hypothesized that microbial abundance and activity would increase with the greenhouse treatment. Instead, we found that bacterial and fungal abundance declined by over 50%, and there was a trend toward lower activity of the chitin‐degrading enzymeN‐acetyl‐glucosaminidase. Soil respiration also declined by up to 50%, but only late in the growing season. These changes were accompanied by significant shifts in the community structure of active fungi, with decreased relative abundance of a dominant Thelephoroid fungus and increased relative abundance of Ascomycetes and Zygomycetes in response to warming. In line with our hypothesis, we found that warming marginally increased soil ammonium and nitrate availability as well as the overall diversity of active fungi. Our results indicate that rising temperatures in northern‐latitude ecosystems may not always cause a positive feedback to the soil carbon cycle, particularly in boreal forests with drier soils. Models of carbon cycle‐climate feedbacks could increase their predictive power by incorporating heterogeneity in soil properties and microbial communities across the boreal zone.

Rates of ecosystem processes such as decomposition are likely to change as a result of human impacts on the environment. In southern California, climate change and nitrogen (N) deposition in particular may alter biological communities and ecosystem processes. These drivers may affect decomposition directly, through changes in abiotic conditions, and indirectly through changes in plant and decomposer communities. To assess indirect effects on litter decomposition, we reciprocally transplanted microbial communities and plant litter among control and treatment plots (either drought or N addition) in a grassland ecosystem. We hypothesized that drought would reduce decomposition rates through moisture limitation of decomposers and reductions in plant litter quality before and during decomposition. In contrast, we predicted that N deposition would stimulate decomposition by relieving N limitation of decomposers and improving plant litter quality. We also hypothesized that adaptive mechanisms would allow microbes to decompose litter more effectively in their native plot and litter environments. Consistent with our first hypothesis, we found that drought treatment reduced litter mass loss from 20.9% to 15.3% after six months. There was a similar decline in mass loss of litter inoculated with microbes transplanted from the drought treatment, suggesting a legacy effect of drought driven by declines in microbial abundance and possible changes in microbial community composition. Bacterial cell densities were up to 86% lower in drought plots and at least 50% lower on litter derived from the drought treatment, whereas fungal hyphal lengths increased by 13–14% in the drought treatment. Nitrogen effects on decomposition rates and microbial abundances were weaker than drought effects, although N addition significantly altered initial plant litter chemistry and litter chemistry during decomposition. However, we did find support for microbial adaptation to N addition with N‐derived microbes facilitating greater mass loss in N plots than in control plots. Our results show that environmental changes can affect rates of ecosystem processes directly through abiotic changes and indirectly through microbial abundances and communities. Therefore models of ecosystem response to global change may need to represent microbial biomass and community composition to make accurate predictions.