Institut for Biologi

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J.-C. Svenning

A network approach for inferring species associations from co-occurrence data

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

DOI

  • Naia Morueta-Holme, Univ Calif Berkeley, University of California Berkeley, University of California System, Dept Integrat Biol
  • ,
  • Benjamin Blonder, Aarhus Univ, Aarhus University, Dept Biosci, Sect Ecoinformat & Biodivers, University of Oxford
  • ,
  • Brody Steven Sandel
  • Brian J. McGill, Univ Main, Sustainabil Solut Initiat, Sch Biol & Ecol
  • ,
  • Robert K. Peet, Univ N Carolina, University of North Carolina, University of North Carolina Chapel Hill, Dept Biol
  • ,
  • Jeffrey E. Ott, Univ N Carolina, University of North Carolina, University of North Carolina Chapel Hill, Dept Biol
  • ,
  • Cyrille Violle, Univ Paul Valery Montpellier, Centre National de la Recherche Scientifique (CNRS), Universite de Montpellier, Languedoc-Roussillon Universites (ComUE), Univ Montpellier, EPHE, CEFE UMR 5175,CNRS
  • ,
  • Brian J. Enquist, Univ Arizona, University of Arizona, Dept Ecol & Evolut Biol
  • ,
  • Peter M. Jorgensen, Missouri Bot Garden, Missouri Botanical Gardens
  • ,
  • Jens-Christian Svenning

Positive and negative associations between species are a key outcome of community assembly from regional species pools. These associations are difficult to detect and can be caused by a range of processes such as species interactions, local environmental constraints and dispersal. We integrate new ideas around species distribution modeling, covariance matrix estimation, and network analysis to provide an approach to inferring non-random species associations from local-and regional-scale occurrence data. Specifically, we provide a novel framework for identifying species associations that overcomes three challenges: 1) correcting for indirect effects from other species, 2) avoiding spurious associations driven by regional-scale distributions, and 3) describing these associations in a multi-species context. We highlight a range of research questions and analyses that this framework is able to address. We show that the approach is statistically robust using simulated data. In addition, we present an empirical analysis of >1000 North American tree communities that gives evidence for weak positive associations among small groups of species. Finally, we discuss several possible extensions for identifying drivers of associations, predicting community assembly, and better linking biogeography and community ecology.

OriginalsprogEngelsk
TidsskriftEcography
Vol/bind39
Nummer12
Sider (fra-til)1139-1150
Antal sider12
ISSN0906-7590
DOI
StatusUdgivet - dec. 2016

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