Model-based indicators for co-clustered environments and species communities

TL;DR

This paper introduces a Bayesian model-based framework for identifying ecological sub-communities and indicator species from survey data, enhancing biodiversity monitoring with scalable and reproducible methods.

Contribution

It develops a novel co-clustering approach using Bayesian decoupling for Poisson factorization and introduces a new ranking method for indicator species based on learned subcommunities.

Findings

Effective identification of ecological sub-communities

A new model-based ranking of indicator species

Scalable and reproducible biodiversity monitoring

Abstract

Accurate biodiversity monitoring is essential for effective environmental policy, yet current practices often rely on arbitrarily defined ecosystems, communities, and ad-hoc indicator species, limiting cost-efficiency and reproducibility. We present a model-based framework that infers ecological sub-communities and corresponding indicators in terms of habitat and species from species survey data, such as large-scale arthropod abundance data used here as example. Environments and species are co-clustered using Bayesian decoupling for Poisson factorization. Latent, hierarchical regression relates observable habitat features to each subcommunity. Additionally, we propose a novel, model-based ranking of indicator species based on the learned subcommunities, generalizing classical approaches. This integrated approach motivates model-based ecosystem classification and indicator species selection, offering a scalable, reproducible pathway for biodiversity monitoring and informed conservation.