The Internal Structure of Metacommunities

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>Metacommunity ecology has become an important subdiscipline of ecology, but it is increasingly evident that its foundational theoretical and analytical frameworks do not adequately incorporate a realistic continuum of environmental and biotic process at play. We propose an approach that develops stronger links between theoretical and statistical frameworks to shift the focus towards the study of the ‘internal structure’ of metacommunities by dissecting how different species and different sites contribute to overall metacommunity structure. To illustrate this, we simulate data from a model that includes environmental variation, dispersal, biotic interactions, and stochasticity as the basic ecological processes that influence species’ (co)-distributions. We analyze the simulated data with hierarchical community models and propose a new method to visualize and analyze the simultaneous role of species co-distribution, environment, space, and stochasticity in this emerging statistical approach. We focus in particular on quantifying how species affect the overall structure of the metacommunity via differences in their dispersal and niche traits, and how environmental filtering, dispersal and species interactions varies from site to site in relation to environmental conditions and connectivity. Although there are still challenges ahead, this framework provides a roadmap for a more comprehensive approach by jointly developing more mechanistic theory based on community assembly processes and the analytical tools needed to map these concepts onto data in diverse landscapes and systems.</jats:p>

A Landscape of Opportunities for Microbial Ecology Research

“…To a large degree, this is because metacommunity ecology has focused more on species attributes and how they contribute to community assembly than to site attributes. A promising step toward reconciling the two involves the modification of joint species distribution models and related methods (primarily focused on the distribution of species) to address landscape distributions (e.g., Fournier et al, 2017;Leibold et al, 2020).…”

A new joint species distribution model for faster and more accurate inference of species associations from big community data

“…To additionally explore the ability of sjSDM to infer community assembly processes from more realistic ecological data, we simulated communities from the process-based ecological model used by Leibold et al (2021) and compared the inferred species-species association structures with the true structures for sjSDM, BayesComm, Hmsc and gllvm. For details, see Supporting Information S1.…”

“…These could be used, for example, to test if the strength or structure of species associations varies with space or environmental predictors; or if spatial species associations correlate with local trophic or competitive interactions or traits (see generally Poisot et al, 2015). For regional studies, there is the prospect of extending the traditional variation partitioning (environment and space; Cottenie, 2005) to include biotic associations by using JSDMs (Leibold et al, 2021). Our results regarding the moderate, but significantly better than random accuracy of inferred covariance structures, even on datasets with hundreds of species, are encouraging for such a research program.…”

Synthesising the multiple impacts of climatic variability on community responses to climate change

“…Understanding which aspects of variability are most influential will be key to building models of minimal necessary complexity. Determination of the relative contributions of dispersal, interspecific interactions and environmental dependence has been identified as the key challenge to understanding the dynamics of whole communities (Leibold et al 2020). There is evidence that biotic resistance to invasive species is widespread, but the global contribution of biotic resistance to climate refugee species is challenging to measure (Levine & Rees 2004;Alexander et al 2015Alexander et al , 2016Louthan et al 2015;Godsoe et al 2017Godsoe et al , 2018Beaury et al 2020).…”