Joint Species Distribution Modeling of Percentage Cover Data with Exclusive Competition for Space

TL;DR

This paper introduces a hierarchical joint species distribution model that accounts for mutual exclusion due to space competition, improving ecological predictions and understanding of species interactions in plant communities.

Contribution

It develops a novel JSDM incorporating Dirichlet-Multinomial distribution and Gaussian processes to model exclusive competition for space among species.

Findings

Ignoring interspecific interactions reduces predictive accuracy.

The model accurately infers interspecific correlations and competition.

Model comparison methods influence predictive performance assessment.

Abstract

Joint species distribution models (JSDM) are among the most important statistical tools in community ecology. They are routinely used for inference and various prediction tasks, such as to build species distribution maps or biomass estimation over spatial areas. Existing JSDM's cannot, however, model mutual exclusion between species, which may happen in some species groups, such as mosses in the bottom layer of a peatland site. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where multivariate latent Gaussian variable model describes species' niche preferences and Dirichlet-Multinomial distribution models the observation process and exclusive competition for space between species. We use both stationary and non-stationary multivariate Gaussian processes to model residual phenomena. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition for space significantly reduces models' predictive performance and leads to biased estimates for total percentage cover both for individual species and over all species combined. A model's relative predictive performance also depends on the model comparison methods highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed joint species distribution model can be used to simultaneously infer interspecific correlations in niche preference as well as mutual exclusive competition for space and through that provide novel insight into ecological research.