Beyond pairwise mechanisms of species coexistence in complex communities

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The tremendous diversity of species in ecological communities has motivated a century of research into the mechanisms that maintain biodiversity. However, much of this work examines the coexistence of just pairs of competitors. This approach ignores those mechanisms of coexistence that emerge only in diverse competitive networks. Despite the potential for these mechanisms to create conditions under which the loss of one competitor triggers the loss of others, we lack the knowledge needed to judge their importance for coexistence in nature. Progress requires borrowing insight from the study of multitrophic interaction networks, and coupling empirical data to models of competition.

At a glance


  1. Coexistence mechanisms that emerge only with more than two competitors.
    Figure 1: Coexistence mechanisms that emerge only with more than two competitors.

    a, Strictly pairwise competition between a forb and a grass, showing both interspecific and intraspecific interactions. b, An interaction chain in which a shrub provides indirect benefits (red arrow) to a grass through the suppression of a forb. Grey arrows indicate pairwise interactions that are not directly involved in the interaction chain or higher-order interactions. c, A higher-order interaction in which a shrub alters the per capita effect of a forb on a grass. In this case, the shrub induces a plastic change in the forb that leads it to root at more shallow depths of soil, bringing it into greater competitive contact with the grass. Arrow widths in a–c indicate the strength of the per capita competitive effect.

  2. A competitive network and an extinction cascade.
    Figure 2: A competitive network and an extinction cascade.

    a, An intransitive competitive network or 'tournament', in which arrows point from the winner to the loser in pairwise competition. The system is composed of a number of smaller intransitive loops (for example, between the light blue, the dark blue and the pink species) that are nested in larger loops that include all seven species (see ref. 3 for examples of competitive network structure). b, Simulation of the dynamics of the system following the methods of ref. 6. Owing to intransitive competitive relationships, the seven species shown would coexist indefinitely, cycling around an equilibrium in which the red and dark-green species have a proportional abundance (the proportion of individuals that belong to each species) of 1/3, the light and dark blue species have a proportional abundance of 1/9 and the light green, orange and pink species have a proportional abundance of 1/27. At year 50, the dark blue species is sent to extinction, which causes a further 3 species to become extinct owing to the disruption of the intransitivity that stabilized their dynamics. The remaining three species oscillate in a rock–paper–scissors fashion around a proportional abundance of 1/3. The y-axis is presented on a square-root scale to improve the visibility of species with low abundance.

  3. A data-driven approach to modelling species dynamics.
    Figure 3: A data-driven approach to modelling species dynamics.

    Observational or experimental data can be fitted to models, which are then used to analyse the effects of interaction chains or higher-order interactions on dynamics. Between-year patterns of abundance (top) are converted into demographic transitions as a function of size (middle). For a size-based model, all individuals may be considered as circles of a given area. The change in size of species i (Si) from one year to the next can be modelled as a function of the abundance or collective size of other individuals within a given radius or neighbourhood, assuming pairwise or higher-order interactions. Similar models can be built for survival and reproduction. The fitted functions can then be used to inform individual-based or integral projection models of community dynamics (bottom). These models and their parameters can be manipulated to add or remove particular mechanisms of coexistence, which enables their contribution to diversity maintenance to be evaluated.


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  1. Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, 8092 Zürich, Switzerland.

    • Jonathan M. Levine
  2. Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland.

    • Jordi Bascompte
  3. Department of Wildland Resources and the Ecology Center, Utah State University, Logan, Utah 84322, USA.

    • Peter B. Adler
  4. Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA.

    • Stefano Allesina

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Author Contributions All authors researched the literature to assemble the review. J.M.L. assembled the first draft of the paper, with all authors contributing individual sections and revisions.

Reviewer Information Nature thanks A. Golubski, E. Thebault and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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