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

Naia Morueta-Holme, Benjamin Blonder, Brody Sandel, Brian J. McGill, Robert K. Peet, Jeffrey E. Ott, Cyrille Violle, Brian J. Enquist, Peter M. Jørgensen, Jens Christian Svenning

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1139-1150
Number of pages12
JournalEcography
Volume39
Issue number12
DOIs
StatePublished - Dec 1 2016
Externally publishedYes

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biogeography
community ecology
species pool
environmental constraint
network analysis
empirical analysis
matrix
modeling

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

Cite this

Morueta-Holme, N., Blonder, B., Sandel, B., McGill, B. J., Peet, R. K., Ott, J. E., ... Svenning, J. C. (2016). A network approach for inferring species associations from co-occurrence data. Ecography, 39(12), 1139-1150. https://doi.org/10.1111/ecog.01892

A network approach for inferring species associations from co-occurrence data. / Morueta-Holme, Naia; Blonder, Benjamin; Sandel, Brody; McGill, Brian J.; Peet, Robert K.; Ott, Jeffrey E.; Violle, Cyrille; Enquist, Brian J.; Jørgensen, Peter M.; Svenning, Jens Christian.

In: Ecography, Vol. 39, No. 12, 01.12.2016, p. 1139-1150.

Research output: Contribution to journalArticle

Morueta-Holme, N, Blonder, B, Sandel, B, McGill, BJ, Peet, RK, Ott, JE, Violle, C, Enquist, BJ, Jørgensen, PM & Svenning, JC 2016, 'A network approach for inferring species associations from co-occurrence data', Ecography, vol. 39, no. 12, pp. 1139-1150. https://doi.org/10.1111/ecog.01892
Morueta-Holme N, Blonder B, Sandel B, McGill BJ, Peet RK, Ott JE et al. A network approach for inferring species associations from co-occurrence data. Ecography. 2016 Dec 1;39(12):1139-1150. https://doi.org/10.1111/ecog.01892
Morueta-Holme, Naia ; Blonder, Benjamin ; Sandel, Brody ; McGill, Brian J. ; Peet, Robert K. ; Ott, Jeffrey E. ; Violle, Cyrille ; Enquist, Brian J. ; Jørgensen, Peter M. ; Svenning, Jens Christian. / A network approach for inferring species associations from co-occurrence data. In: Ecography. 2016 ; Vol. 39, No. 12. pp. 1139-1150.
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