Jay T. Lennon

Microbial seed banks: the ecological and evolutionary implications of dormancy

Dormancy is a bet-hedging strategy used by a wide range of taxa, including microorganisms. It refers to an organisms ability to enter a reversible state of low metabolic activity when faced with unfavourable environmental conditions. Dormant microorganisms generate a seed bank, which comprises individuals that are capable of being resuscitated following environmental change. In this Review, we highlight mechanisms that have evolved in microorganisms to allow them to successfully enter and exit a dormant state, and discuss the implications of microbial seed banks for evolutionary dynamics, population persistence, maintenance of biodiversity, and the stability of ecosystem processes.

Dormancy contributes to the maintenance of microbial diversity

Dormancy is a bet-hedging strategy used by a variety of organisms to overcome unfavorable environmental conditions. By entering a reversible state of low metabolic activity, dormant individuals become members of a seed bank, which can determine community dynamics in future generations. Although microbiologists have documented dormancy in both clinical and natural settings, the importance of seed banks for the diversity and functioning of microbial communities remains untested. Here, we develop a theoretical model demonstrating that microbial communities are structured by environmental cues that trigger dormancy. A molecular survey of lake ecosystems revealed that dormancy plays a more important role in shaping bacterial communities than eukaryotic microbial communities. The proportion of dormant bacteria was relatively low in productive ecosystems but accounted for up to 40% of taxon richness in nutrient-poor systems. Our simulations and empirical data suggest that regional environmental cues and dormancy synchronize the composition of active communities across the landscape while decoupling active microbes from the total community at local scales. Furthermore, we observed that rare bacterial taxa were disproportionately active relative to common bacterial taxa, suggesting that microbial rank-abundance curves are more dynamic than previously considered. We propose that repeated transitions to and from the seed bank may help maintain the high levels of microbial biodiversity that are observed in nearly all ecosystems.

Fundamentals of microbial community resistance and resilience.

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.

Knowing when to draw the line: designing more informative ecological experiments

Linear regression and analysis of variance (ANOVA) are two of the most widely used statistical techniques in ecology. Regression quantitatively describes the relationship between a response variable and one or more continuous independent variables, while ANOVA determines whether a response variable differs among discrete values of the independent variable(s). Designing experiments with discrete factors is straightforward because ANOVA is the only option, but what is the best way to design experiments involving continuous factors? Should ecologists prefer experiments with few treatments and many replicates analyzed with ANOVA, or experiments with many treatments and few replicates per treatment analyzed with regression? We recommend that ecologists choose regression, especially replicated regression, over ANOVA when dealing with continuous factors for two reasons: (1) regression is generally a more powerful approach than ANOVA and (2) regression provides quantitative output that can be incorporated into ecological models more effectively than ANOVA output.

Mapping the niche space of soil microorganisms using taxonomy and traits

The biodiversity of microbial communities has important implications for the stability and functioning of ecosystem processes. Yet, very little is known about the environmental factors that define the microbial niche and how this influences the composition and activity of microbial communities. In this study, we derived niche parameters from physiological response curves that quantified microbial respiration for a diverse collection of soil bacteria and fungi along a soil moisture gradient. On average, soil microorganisms had relatively dry optima (0.3 MPa) and were capable of respiring under low water potentials (-2.0 MPa). Within their limits of activity, microorganisms exhibited a wide range of responses, suggesting that some taxa may be able to coexist by partitioning the moisture niche axis. For example, we identified dry-adapted generalists that tolerated a broad range of water potentials, along with wet-adapted specialists with metabolism restricted to less-negative water potentials. These contrasting ecological strategies had a phylogenetic signal at a coarse taxonomic level (phylum), suggesting that the moisture niche of soil microorganisms is highly conserved. In addition, variation in microbial responses along the moisture gradient was linked to the distribution of several functional traits. In particular, strains that were capable of producing biofilms had drier moisture optima and wider niche breadths. However, biofilm production appeared to come at a cost that was reflected in a prolonged lag time prior to exponential growth, suggesting that there is a trade-off associated with traits that allow microorganisms to contend with moisture stress. Together, we have identified functional groups of microorganisms that will help predict the structure and functioning of microbial communities under contrasting soil moisture regimes.

Rapid responses of soil microorganisms improve plant fitness in novel environments

Global change is challenging plant and animal populations with novel environmental conditions, including increased atmospheric CO2 concentrations, warmer temperatures, and altered precipitation regimes. In some cases, contemporary or “rapid” evolution can ameliorate the effects of global change. However, the direction and magnitude of evolutionary responses may be contingent upon interactions with other community members that also are experiencing novel environmental conditions. Here, we examine plant adaptation to drought stress in a multigeneration experiment that manipulated aboveground–belowground feedbacks between plants and soil microbial communities. Although drought stress reduced plant growth and accelerated plant phenologies, surprisingly, plant evolutionary responses to drought were relatively weak. In contrast, plant fitness in both drought and nondrought environments was linked strongly to the rapid responses of soil microbial community structure to moisture manipulations. Specifically, plants were most fit when their contemporary environmental conditions (wet vs. dry soil) matched the historical environmental conditions (wet vs. dry soil) of their associated microbial community. Together, our findings suggest that, when faced with environmental change, plants may not be limited to “adapt or migrate” strategies; instead, they also may benefit from association with interacting species, especially diverse soil microbial communities, that respond rapidly to environmental change.

Scaling laws predict global microbial diversity

Significance Ecological scaling laws are intensively studied for their predictive power and universal nature but often fail to unify biodiversity across domains of life. Using a global-scale compilation of microbial and macrobial data, we uncover relationships of commonness and rarity that scale with abundance at similar rates for microorganisms and macroscopic plants and animals. We then show a unified scaling law that predicts the abundance of dominant species across 30 orders of magnitude to the scale of all microorganisms on Earth. Using this scaling law combined with the lognormal model of biodiversity, we predict that Earth is home to as many as 1 trillion (1012) microbial species. Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ∼35,000 sites and ∼5.6⋅106 species, including the largest ever inventory of high-throughput molecular data and one of the largest compilations of plant and animal community data, we show similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upward of 1 trillion (1012) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.

Temporal variability in soil microbial communities across land-use types

Although numerous studies have investigated changes in soil microbial communities across space, questions about the temporal variability in these communities and how this variability compares across soils have received far less attention. We collected soils on a monthly basis (May to November) from replicated plots representing three land-use types (conventional and reduced-input row crop agricultural plots and early successional grasslands) maintained at a research site in Michigan, USA. Using barcoded pyrosequencing of the 16S rRNA gene, we found that the agricultural and early successional land uses harbored unique soil bacterial communities that exhibited distinct temporal patterns. α-Diversity, the numbers of taxa or lineages, was significantly influenced by the sampling month with the temporal variability in α-diversity exceeding the variability between land-use types. In contrast, differences in community composition across land-use types were reasonably constant across the 7-month period, suggesting that the time of sampling is less important when assessing β-diversity patterns. Communities in the agricultural soils were most variable over time and the changes were significantly correlated with soil moisture and temperature. Temporal shifts in bacterial community composition within the successional grassland plots were less predictable and are likely a product of complex interactions between the soil environment and the more diverse plant community. Temporal variability needs to be carefully assessed when comparing microbial diversity across soil types and the temporal patterns in microbial community structure can not necessarily be generalized across land uses, even if those soils are exposed to the same climatic conditions.

The generation and maintenance of diversity in microbial communities

Microorganisms play a central role in the regulation of ecosystem processes, and they comprise the vast majority of species on Earth. With recent developments in molecular methods, it has become tractable to quantify the extent of microbial diversity in natural environments. Here we examine this revolution in our understanding of microbial diversity, and we explore the factors that contribute to the seemingly astounding numbers of microbial taxa found within individual environmental samples. We conducted a meta-analysis of bacterial richness estimates from a variety of ecosystems. Nearly all environments contained hundreds to thousands of bacterial taxa, and richness levels increased with the number of individuals in a sample, a pattern consistent with those reported for nonmicrobial taxa. A cursory comparison might suggest that bacterial richness far exceeds the richness levels typically observed for plant and animal taxa. However, the apparent diversity of bacterial communities is influenced by phylogenetic breadth and allometric scaling issues. When these features are taken into consideration, the levels of microbial diversity may appear less astounding. Although the fields of ecology and biogeography have traditionally ignored microorganisms, there are no longer valid excuses for neglecting microorganisms in surveys of biodiversity. Many of the concepts developed to explain plant and animal diversity patterns can also be applied to microorganisms once we reconcile the scale of our analyses to the scale of the organisms being observed. Furthermore, knowledge from microbial systems may provide insight into the mechanisms that generate and maintain species richness in nonmicrobial systems.

Integrating microbial ecology into ecosystem models: challenges and priorities

Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ—sometimes substantially—from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.