Emergence of structured communities through evolutionary dynamics

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Species-rich communities, in which many competing species coexist in a single trophic level, are quite frequent in nature, but pose a formidable theoretical challenge. In particular, it is known that complex competitive systems become unstable and unfeasible when the number of species is large. Recently, many studies have attributed the stability of natural communities to the structure of the interspecific interaction network, yet the nature of such structures and the underlying mechanisms responsible for them remain open questions. Here we introduce an evolutionary model, based on the generic Lotka-Volterra competitive framework, from which a stable, structured, diverse community emerges spontaneously. The modular structure of the competition matrix reflects the phylogeny of the community, in agreement with the hierarchial taxonomic classification. Closely related species tend to have stronger niche overlap and weaker fitness differences, as opposed to pairs of species from different modules. The competitive-relatedness hypothesis and the idea of emergent neutrality are discussed in the context of this evolutionary model.

Original languageEnglish
Pages (from-to)138-144
Number of pages7
JournalJournal of Theoretical Biology
Volume383
DOIs
StatePublished - 21 Oct 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Ltd.

Funding

This research was supported by the Porter School of Environmental Studies at Tel Aviv University (E.S.) and by the ISF, grant no. 376/12 (D.A.K) and grant no. 1026/11(N.S.).

FundersFunder number
Israel Science Foundation376/12, 1026/11
Tel Aviv University

    Keywords

    • Competition
    • Generalized lotka-volterra dynamics
    • Modularity
    • Speciation
    • Species coexistence

    Fingerprint

    Dive into the research topics of 'Emergence of structured communities through evolutionary dynamics'. Together they form a unique fingerprint.

    Cite this