Session D37: Ecological Dynamics I

Warmer temperatures favor slower-growing bacteria in natural marine communities

Earth's life-sustaining oceans harbor diverse bacterial communities that display varying composition across time and space. While particular patterns of variation have been linked to a range of factors, unifying rules are lacking, preventing the prediction of future changes. Here, analyzing the distribution of fast- and slow-growing bacteria in ocean datasets spanning seasons, latitude, and depth, we show that higher seawater temperatures universally favor slower-growing taxa, in agreement with theoretical predictions of how temperature-dependent growth rates differentially modulate the impact of mortality on species abundances. Changes in bacterial community structure promoted by temperature are independent of variations in nutrients along spatial and temporal gradients. Our results help explain why slow growers dominate at the ocean surface, during summer, and near the tropics and provide a framework to understand how bacterial communities will change in a warmer world.   

How realistic features effect steady state stability of an Arctic marine food web model

Rapid sea ice decline and warmer waters are threatening the stability of Arctic ecosystems and potentially forcing their restructuring. Mathematical models that support observational evidence are becoming increasingly important. Generalized modeling is applied to a food web model with species of high ecological significance for the Southern Beaufort Sea. Generalized modeling is a powerful method that provides a linear stability analysis for systems with uncertainty in data and underlying physical processes. We find that including realistic characteristics into the model, like predator-specific foraging traits or habitat constraints increases food-web stability. The absence of a fierce top predator (killer whale, polar bear) also significantly increases the portion of stable webs. Adding ecosystem background noise in terms of minor ecosystem members results in a peak in stability at an optimum value of background pressure. These results indicate that refining models with more realistic detail to account for the complexity of the ecological system may be key to bridge the gap between empirical observations and model predictions in ecosystem stability.   

Emergent simplicity in microbial ecosystems

Microbes carry out essential functions for global climate, human health, and industry. Investigations into natural microbial communities have uncovered a surprising amount of functionally-relevant diversity at all levels of taxonomic resolution, making predicting community-level function from composition difficult. Nevertheless, recent studies suggest that simple regularities, such as reproducible proportions of functional groups, can emerge from complexity itself. A deeper understanding of such "emergent simplicity" could enable new approaches for predicting the behaviors of the complex ecosystems in nature. However, most examples described so far afford limited predictive power, as identifying features that are approximately reproducible across examples of ecosystems does not necessarily entail an ability to predict functional properties of interest. Here, we propose an information-theoretic framework for quantifying emergent simplicity in empirical data based on the ability of simple models to predict community-level functional properties. Using this framework to analyze two published datasets of synthetic microbial communities, we reveal that as community diversity increases, simple models become more predictive rather than less.   

Selection for Coexistence May Help Explain Smooth Functional Landscapes of Microbial Ecosystems

Understanding how the function of microbial communities arises from the individual microbes has been an active and important area of research in ecology. Recent work from Skwara et al. (2023) found that the structure-function mapping of multiple empirical examples was surprisingly "smooth" (well fit by models as simple as linear or quadratic regression over presence/absence of species). Understanding what contributes to this smoothness is important because it might enable easier design of microbial communities and shed light on the significance of interactions to community function. One notable feature laboratory experiments of synthetic ecosystems share with natural ecosystems is that the set of species being assayed is non-random. Species in experiments are typically chosen to favor their ability to coexist, while in nature evolution selects for communities that coexist. Here, we adopt a simulation-based approach to test the likely impact of such species selection on the smoothness of community functional landscapes, and find that, at least in our model, filtering species for coexistence rendered the landscapes smoother. We discuss the implications for applications of community design and for understanding the species-function mapping.   

A universal geometry governs the response of ecosystems to environmental perturbations

A fundamental question in ecology is to understand how ecosystems respond to environmental perturbations. This is especially challenging due to the strong coupling between species and their environments. Here, we introduce a theoretical framework for calculating the linear response of ecosystems to environmental perturbations in generalized consumer resource models. Our construction is applicable to a wide class of systems including models with non-reciprocal interactions, cross-feeding, and non-linear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces and ecological interactions are represented by geometric transformations between vector spaces. We show that near a steady state, such geometric transformations directly map environmental perturbations—in resource availability and mortality rates—to shifts in niche structure. We illustrate these ideas in a variety of settings including a simple model for pH-induced toxicity in bacterial denitrification. We conclude by discussing the implications of this universal niche geometry for other problems in theoretical ecology including identifying collective modes and characterizing eco-evolutionary dynamics.   

The ecological consequences of microbial metabolic strategies in fluctuating environments

Microbes adopt a variety of metabolic strategies to consume resources in fluctuating environments, but most work has focused on understanding these strategies in the context of isolated species, rather than diverse natural communities. We systematically measure the feasibility, dynamical and structural stability of multispecies microbial communities adopting different metabolic strategies. Our results reveal key distinctions between the ecological properties of different metabolic strategies, showing that communities containing sequential utilizers are more resilient to resource fluctuations, but are less feasible than co-utilizing communities.   

Timescale of environmental fluctuations determines dimensionality of microbial community response.

Microbial communities display a wide range of collective properties, from driving global nutrient cycles in the biosphere to supporting the immune function of human hosts. It is critical, therefore, to understand the way that the collective properties of microbial communities are driven by their composition. However, natural microbial communities are high-dimensional at the species level, complicating the relationship between species present and collective properties. Here, we ask whether a description at the level of the phenotypic traits of species can provide a lower-dimensional description, simplifying this relationship. We use a consumer-resource model framework to demonstrate that communities assembled in rapidly fluctuating environments exhibit low-dimensional community structure in which species with similar phenotypes have correlated abundances, whereas in slowly fluctuating environments, abundances of similar phenotypes are anti-correlated. As a corollary, we show that the transient response to environmental perturbation reveals coarse-grained similarities in species traits, whereas long-time responses reveal fine-grained differences. Our results relate the timescale of environmental fluctuations to extant structure in natural microbial communities, suggest experimental procedures to measure this structure, and provide a path forward for elucidating the relationship between community composition and collective properties.   

Growing phylometabolic trees from metabolic networks

Comparative genomics has been essential to inferring the evolutionary trajectories of species and producing classifications on the basis of DNA sequence. However, these genomic classifications present an incomplete characterization of the functional role of species in an ecological environment. For example, changes to nutrient availabilities lead to changes in the metabolic fluxes of an organisms which requires a classification scheme that uses metabolic information. Previous work has shown the potential for classification methods using metabolic networks, but these do not directly utilize the full topological information contained in the networks. Here we introduce and apply a computational framework for a classification scheme of species that compares metabolic networks using distance metrics on linear subspaces of stoichiometric matrices. We find that for the AGORA2 human gut microbiome, the underlying genetic information is insufficient to match species to their metabolic classifications. We further demonstrate that (2+1)-dimensional phylometabolic trees constructed from the distances more faithfully capture relationships between species compared to standard planar representations.   

Macroecological patterns in coarse-grained microbial systems

The structure of microbial communities is intrinsically hierarchical due to the shared evolutionary history of community members. This history is primarily captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. However, the macroecological consequences of this shared history are rarely examined. This omission is not simply a methodological detail, as the shared history of community members provides an opportunity to investigate the dependence of microbial patterns of diversity and abundance across scales of organization. Here, we evaluate the extent that macroecological laws endure across taxonomic and phylogenetic scales among disparate environments using data from the Earth Microbiome Project. We find that measures of biodiversity at a given scale can be consistently predicted using a minimal model containing zero free parameters, the Stochastic Logistic Model of growth (SLM). Extending these within-scale results, we examine the relationship between measures of biodiversity calculated at different scales (e.g., genus vs. family), an empirical prediction known as the Diversity Begets Diversity (DBD) hypothesis. We find that the relationship between richness estimates at different scales can be quantitatively predicted using the SLM, a stark contrast to the results we obtaned using the Unified Neutral Theory of Biodiversity. Contrastingly, only by including correlations between species (i.e., interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of evolutionary organization and establish a sharp demarcation between macroecological patterns that can and cannot be captured by a minimal model of biodiversity.