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Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales
Author(s) -
Ovaskainen Otso,
Abrego Nerea,
Halme Panu,
Dunson David
Publication year - 2016
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12501
Subject(s) - covariate , leverage (statistics) , latent variable , beech , spatial ecology , bayesian probability , variable (mathematics) , ecology , statistics , mathematics , biology , mathematical analysis
Summary We present a hierarchical latent variable model that partitions variation in species occurrences and co‐occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. We leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. We exemplify the framework with a community of 98 fungal species sampled in c . 22 500 dead wood units in 230 plots in 29 beech forests. The networks identified by correlations and partial correlations were consistent, as were networks for natural and managed forests, but networks at different spatial scales were dissimilar. Accounting for the occurrences of the other species roughly doubled the predictive powers of the models compared to accounting for environmental covariates only .