Work

Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales

Year: 2015

Type: article

Abstract: 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 .

Source: Methods in Ecology and Evolution

DOI: 10.1111/2041-210x.12501

Authors: Otso Ovaskainen, Nerea Abrego, Panu Halme, David Dunson

Institutions: University of Helsinki, Norwegian University of Science and Technology, University of the Basque Country, University of Jyväskylä, Duke University

Language: English

Topic: Forest Ecology and Biodiversity Studies

Subfield: Insect Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Life on land

Open Access status: bronze

Funders: Academy of Finland, Euskal Herriko Unibertsitatea, Maj ja Tor Nesslingin Säätiö, Norges Forskningsråd