How are species interactions structured in species-rich communities? A new method for analysing time-series data.
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.
['Ovaskainen|Otso|O|http://orcid.org/0000-0001-9750-4421', 'Tikhonov|Gleb|G|', 'Dunson|David|D|', 'Grøtan|Vidar|V|', 'Engen|Steinar|S|', 'Sæther|Bernt-Erik|BE|', 'Abrego|Nerea|N|']
D058448:Biota D003198:Computer Simulation D004463:Ecology D008954:Models, Biological D014871:Water Microbiology
Gompertz model community dynamics density dependence interaction network joint species distribution model temporal analysis