Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks. Issue 3 (1st October 2018)

• Record Type: Journal Article
• Authors: Gravel, Dominique; Baiser, Benjamin; Dunne, Jennifer A.; Kopelke, Jens‐Peter; Martinez, Neo D.; Nyman, Tommi; Poisot, Timothée; Stouffer, Daniel B.; Tylianakis, Jason M.; Wood, Spencer A.; Roslin, Tomas
• Is Part Of: Ecography. Volume 42:Issue 3(2019)
• Page Start: 401
• Page End: 415
• Publication Date: 2018-10-01
• Subjects: networks -- spatial ecology -- co-occurrence; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.04006
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time – i.e. a novel approach to interaction distribution modeling. We apply this framework to host–parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co‐occurrence, but not on species interactions. Even where species co‐occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host‐parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.