NDLI: Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology

Content Provider	Springer Nature Link
Author	Niku, Jenni Warton, David I. Hui, Francis K. C. Taskinen, Sara
Copyright Year	2017
Abstract	In this paper we consider generalized linear latent variable models that can handle overdispersed counts and continuous but non-negative data. Such data are common in ecological studies when modelling multivariate abundances or biomass. By extending the standard generalized linear modelling framework to include latent variables, we can account for any covariation between species not accounted for by the predictors, notably species interactions and correlations driven by missing covariates. We show how estimation and inference for the considered models can be performed efficiently using the Laplace approximation method and use simulations to study the finite-sample properties of the resulting estimates. In the overdispersed count data case, the Laplace-approximated estimates perform similarly to the estimates based on variational approximation method, which is another method that provides a closed form approximation of the likelihood. In the biomass data case, we show that ignoring the correlation between taxa affects the regression estimates unfavourably. To illustrate how our methods can be used in unconstrained ordination and in making inference on environmental variables, we apply them to two ecological datasets: abundances of bacterial species in three arctic locations in Europe and abundances of coral reef species in Indonesia.Supplementary materials accompanying this paper appear on-line.
Starting Page	498
Ending Page	522
Page Count	25
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	22
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer US
Publisher Date	2017-08-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biomass Laplace approximation Ordination Overdispersed count Species interactions Statistics for Life Sciences, Medicine, Health Sciences Agriculture Monitoring/Environmental Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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