The internal structure of metacommunities

Mathew A. Leibold, F. Javiera Rudolph, F. Guillaume Blanchet, Luc De Meester, Dominique Gravel, Florian Hartig, Pedro Peres-Neto, Lauren Shoemaker, Jonathan M. Chase

Oikos, 2022(1) (2022)
Cite this
@article{leibold2022internal,
  author = {Mathew A. Leibold and F. Javiera Rudolph and F. Guillaume Blanchet and Luc De Meester and Dominique Gravel and Florian Hartig and Pedro Peres-Neto and Lauren Shoemaker and Jonathan M. Chase},
  title = {The internal structure of metacommunities},
  journal = {Oikos},
  volume = {2022},
  number = {1},
  year = {2022},
  doi = {10.1111/oik.08618},
}

DOI: 10.1111/oik.08618
Cited by 115 (Google Scholar) · 89 (OpenAlex), as of 07 September 2026

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Abstract

Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity-wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites, which we call the “internal structure” of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process-based colonization-extinction metacommunity model, then use joint species distribution models to estimate how the contributions of space, environment, and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure, and although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory.

What the paper shows and why it matters (AI-generated)

Standard metacommunity analysis collapses an entire landscape into single averages — one number for how much space matters, one for environment — even though individual species and individual sites can differ enormously in what actually drives them. This paper proposes recovering that variation instead of averaging it away, demonstrating with simulated data that the “internal structure” it reveals is real, useful information rather than noise. The idea has been picked up broadly since, from river-floodplain networks to coastal wetland macrophyte communities to Patagonian fjords, suggesting species- and site-level variation is exactly the kind of information metacommunity ecology had been discarding.