Model-based indicators for co-clustered environments and species communities
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909934617100288 |
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| author | Scherting, Braden Ovaskainen, Otso Roslin, Tomas Dunson, David B. |
| author_facet | Scherting, Braden Ovaskainen, Otso Roslin, Tomas Dunson, David B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00678 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-based indicators for co-clustered environments and species communities Scherting, Braden Ovaskainen, Otso Roslin, Tomas Dunson, David B. Applications 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. |
| title | Model-based indicators for co-clustered environments and species communities |
| topic | Applications |
| url | https://arxiv.org/abs/2512.00678 |