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An individual‐based model for the eco‐evolutionary emergence of bipartite interaction networks

Odile Maliet

Corresponding Author

E-mail address: odile.maliet@orange.fr

Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, 75005 France

Correspondence: E‐mail: odile.maliet@orange.fr

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Nicolas Loeuille

Sorbonne Université, UPEC, CNRS, IRD, INRA, Institut d'Ecologie et des Sciences de l'Environnement, IEES, Paris, F‐75005 France

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Hélène Morlon

Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, 75005 France

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First published: 04 September 2020

Abstract

How ecological interaction networks emerge on evolutionary time scales remains unclear. Here we build an individual‐based eco‐evolutionary model for the emergence of mutualistic, antagonistic and neutral bipartite interaction networks. Exploring networks evolved under these scenarios, we find three main results. First, antagonistic interactions tend to foster species and trait diversity, while mutualistic interactions reduce diversity. Second, antagonistic interactors evolve higher specialisation, which results in networks that are often more modular than neutral ones; resource species in these networks often display phylogenetic conservatism in interaction partners. Third, mutualistic interactions lead to networks that are more nested than neutral ones, with low phylogenetic conservatism in interaction partners. These results tend to match overall empirical trends, demonstrating that structures of empirical networks that have most often been explained by ecological processes can result from an evolutionary emergence. Our model contributes to the ongoing effort of better integrating ecological interactions and macroevolution.

Peer Review

The peer review history for this article is available at https://publons.com/publon/10.1111/ele.13592.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no new data were generated during the current study. The functions for running the simulations are available in the Rpackage RPANDA (Morlon et al., 2016).

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.