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The New England Journal of Statistics in Data Science


Some Noteworthy Issues in Joint Species Distribution Modeling for Plant Data
Volume 1, Issue 1 (2023), pp. 102–109
Pub. online: 19 October 2022      Type: Commentary And/or Historical Perspective      Open accessOpen Access
Area: Spatial and Environmental Statistics
Accepted
21 July 2022
Published
19 October 2022

Abstract

Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables.
These issues address the following: (i) how the observed presence/absence data is linked to the latent normal variables as well as the resulting implications with regard to modeling the data sites as independent or spatially dependent, (ii) the incompatibility of point referenced and areal referenced presence/absence data in spatial modeling of species distribution, (iii) the effect of modeling species independently/marginally rather than jointly within site, with regard to assessing species distribution, (iv) the interpretation of species dependence under the use of latent multivariate normal specification, and (v) the interpretation of clustering of species associated with specific joint species distribution modeling specifications.
It is hoped that, by attempting to clarify these issues, ecological modelers and quantitative ecologists will be able to better appreciate some subtleties that are implicit in this growing collection of modeling ideas. In this regard, this paper can serve as a useful companion piece to the recent survey/comparison article by [33] in Methods in Ecology and Evolution.

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© 2023 New England Statistical Society
Open access article under the CC BY license.

Keywords
Dirichlet process Gaussian process Latent factor analysis Latent variables Model-based clustering Odds ratios Spatial dependence Species richness

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