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Abstract

There have been considerable advances in our understanding of the tolerance of species interaction networks to sequential extinctions of plants and animals. However, communities of species exist in a mosaic of habitats, and the vulnerability of habitats to anthropogenic change varies. Here, we model the cascading effects of habitat loss, driven by plant extinctions, on the robustness of multiple animal groups. Our network is constructed from empirical observations of 11 animal groups in 12 habitats on farmland. We simulated sequential habitat removal scenarios: randomly; according to prior information; and with a genetic algorithm to identify best- and worst-case permutations of habitat loss. We identified two semi-natural habitats (waste ground and hedgerows together comprising < 5% of the total area of the farm) as disproportionately important to the integrity of the overall network. Our approach provides a new tool for network ecologists and for directing the management and restoration of multiple-habitat sites. © 2013 John Wiley & Sons Ltd/CNRS.

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Network structure and biodiversity loss in food webs: robustness increases with connectance

Jennifer A. Dunne, Richard J. Williams, Neo Martinez (2002)

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The architecture of mutualistic networks minimizes competition and increases biodiversity.

Ugo Bastolla, Miguel A. Fortuna, Alberto Pascual-García … (2009)

The main theories of biodiversity either neglect species interactions or assume that species interact randomly with each other. However, recent empirical work has revealed that ecological networks are highly structured, and the lack of a theory that takes into account the structure of interactions precludes further assessment of the implications of such network patterns for biodiversity. Here we use a combination of analytical and empirical approaches to quantify the influence of network architecture on the number of coexisting species. As a case study we consider mutualistic networks between plants and their animal pollinators or seed dispersers. These networks have been found to be highly nested, with the more specialist species interacting only with proper subsets of the species that interact with the more generalist. We show that nestedness reduces effective interspecific competition and enhances the number of coexisting species. Furthermore, we show that a nested network will naturally emerge if new species are more likely to enter the community where they have minimal competitive load. Nested networks seem to occur in many biological and social contexts, suggesting that our results are relevant in a wide range of fields.