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

2025-11-29

AI Summary

Current biodiversity monitoring relies on subjectively defined ecosystems and communities, along with empirically selected indicator species, resulting in poor reproducibility and low cost-effectiveness. To address this, we propose a Bayesian decoupling framework that jointly infers ecological subcommunities, their associated habitat characteristics, and indicator species. First, Poisson factorization enables simultaneous co-clustering of environmental variables and species occurrences. Second, a latent hierarchical regression model links inferred subcommunities to habitat covariates. Third, a model-driven algorithm ranks species by their indicator strength for each subcommunity. Our approach eliminates dependence on prior ecological classifications and manual species selection, enabling automated, scalable analysis of large-scale arthropod abundance data. Experiments demonstrate strong scalability, reproducibility, and biological interpretability. The framework establishes a novel, data-driven paradigm for ecosystem classification and evidence-based conservation decision-making.

Technology Category

• Machine Learning: Bayesian Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Identifies ecological sub-communities and indicators from species survey data
• Co-clusters environments and species using Bayesian Poisson factorization
• Provides model-based ranking of indicator species for scalable biodiversity monitoring

Innovation

• Bayesian decoupling for Poisson factorization co-clusters environments and species
• Latent hierarchical regression links habitat features to subcommunities
• Model-based ranking of indicator species generalizes classical approaches