PoisotStoufferGravel2015

Référence

Poisot, T., Stouffer, D.B. and Gravel, D. (2015) Beyond species: Why ecological interaction networks vary through space and time. Oikos, 124(3):243-251. (Scopus )

Résumé

Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism-based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of inter-specific interactions. Here we argue that the current conceptualization of species interaction networks is ill-suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra-specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population-based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales. Synthesis Although species interactions are the backbone of ecological communities, we have little insights on how (and why) they vary through space and time. In this article, we build on existing empirical literature to show that the same species may happen to interact in different ways when their local abundances vary, their trait distribution changes, or when the environment affects either of these factors. We discuss how these findings can be integrated in existing frameworks for the analysis and simulation of species interactions. © 2014 The Authors.

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@ARTICLE { PoisotStoufferGravel2015,
    AUTHOR = { Poisot, T. and Stouffer, D.B. and Gravel, D. },
    TITLE = { Beyond species: Why ecological interaction networks vary through space and time },
    JOURNAL = { Oikos },
    YEAR = { 2015 },
    VOLUME = { 124 },
    PAGES = { 243-251 },
    NUMBER = { 3 },
    NOTE = { cited By 4 },
    ABSTRACT = { Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism-based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of inter-specific interactions. Here we argue that the current conceptualization of species interaction networks is ill-suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra-specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population-based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales. Synthesis Although species interactions are the backbone of ecological communities, we have little insights on how (and why) they vary through space and time. In this article, we build on existing empirical literature to show that the same species may happen to interact in different ways when their local abundances vary, their trait distribution changes, or when the environment affects either of these factors. We discuss how these findings can be integrated in existing frameworks for the analysis and simulation of species interactions. © 2014 The Authors. },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/oik.01719 },
    KEYWORDS = { abundance; community ecology; ecological modeling; interspecific interaction; intraspecific variation; numerical model; population distribution; population structure; prediction },
    SOURCE = { Scopus },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84923362158&partnerID=40&md5=601e2d4547877de2e0ed69364f1da35e },
}

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