Zero-inflated binary Tree P\'olya splitting regression for multivariate count data

TL;DR

This paper introduces a novel zero-inflated Tree Pólya-splitting regression model for multivariate count data, improving ecological species distribution modeling by capturing inter-species dependencies and zero-inflation.

Contribution

It extends Tree Pólya-splitting distributions to include zero-inflation, providing a flexible, interpretable model suitable for ecological multivariate count data.

Findings

Model effectively captures zero-inflation in species counts.

Application to Congo Basin data demonstrates ecological relevance.

Efficient inference enabled by standard software.

Abstract

Species distribution models (SDMs) are widely used to assess the effects of environmental factors on species distributions. However, classical SDMs ignore inter-species dependencies. Multivariate SDMs (MSDMs), especially those based on latent Gaussian fields such as the multivariate Poisson log-normal (MPLN), address this limitation but face challenges related to computation, dimensionality, and interpretability. P\'olya-splitting (PS) distributions offer an alternative, combining a model for total abundance with a multivariate allocation structure, and have natural interpretations from ecological process models. Yet, they lack flexibility in modeling correlation structures. Tree P\'olya-splitting (TPS) distributions overcome this by introducing hierarchical structure such as a phylogenetic tree. In this paper, we extend TPS to account for zero-inflation, leading to the zero-inflated tree P\'olya-splitting (Z-TPS) family. We detail its statistical properties, show how standard software enables efficient inference, and illustrate its ecological relevance using tree abundance data from over 180 genera across the Congo Basin tropical rainforest.