How are species interactions structured in species-rich communities? A new method for analysing time-series data

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Ovaskainen , O , Tikhonov , G , Dunson , D , Grotan , V , Engen , S , Saether , B-E & Abrego , N 2017 , ' How are species interactions structured in species-rich communities? A new method for analysing time-series data ' , Proceedings of the Royal Society B. Biological Sciences , vol. 284 , no. 1855 , 20170768 . https://doi.org/10.1098/rspb.2017.0768

Title: How are species interactions structured in species-rich communities? A new method for analysing time-series data
Author: Ovaskainen, Otso; Tikhonov, Gleb; Dunson, David; Grotan, Vidar; Engen, Steinar; Saether, Bernt-Erik; Abrego, Nerea
Contributor: University of Helsinki, Biosciences
University of Helsinki, Biosciences
University of Helsinki, Department of Agricultural Sciences
Date: 2017-05-31
Language: eng
Number of pages: 7
Belongs to series: Proceedings of the Royal Society B. Biological Sciences
ISSN: 0962-8452
URI: http://hdl.handle.net/10138/307607
Abstract: Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.
Subject: community dynamics
density dependence
Gompertz model
interaction network
joint species distribution model
temporal analysis
DENSITY-DEPENDENCE
SEABIRD COMMUNITY
MODELS
DYNAMICS
BIODIVERSITY
COMPETITION
POPULATIONS
STABILITY
PREDATION
ECOLOGY
1181 Ecology, evolutionary biology
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