Eltonian Niche Modelling: Applying Joint Hierarchical Niche Models to Ecological Networks

There is currently a dichotomy in the modelling of Grinnellian and Eltonian niches. Despite similar underlying data, Grinnellian niches are modelled with species-distribution models (SDMs), whereas Eltonian niches are modelled with ecological-network analysis, mainly because the sparsity of species-interaction data prevents the application of SDMs to Eltonian-niche modelling. Here, we propose to adapt recently developed joint species distribution models (JSDMs) to data on ecological networks, functional traits, and phylogenies to model species' Eltonian niches. JSDMs overcome sparsity and improve predictions for individual species by considering non-independent relationships among co-occurring species; this unique ability makes them particularly suited for sparse datasets such as ecological networks. Our Eltonian JSDMs reveal strong relationships between species' Eltonian niches and their functional traits and phylogeny. Moreover, we demonstrate that JSDMs can accurately predict the interactions of species for which no empirical interaction data are available, based solely on their functional traits. This facilitates prediction of new interactions in communities with altered composition, for example, following climate-change induced local extinctions or species introductions. The high interpretability of Eltonian JSDMs will provide unique insights into mechanisms underlying species interactions and the potential impacts of environmental changes and invasive species on species interactions in ecological communities.

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Publication Details

Journal
Ecology Letters
Published
2025-05-29
DOI
https://doi.org/10.1111/ele.70120
Citations
4
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
5.51

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article

Eltonian Niche Modelling: Applying Joint Hierarchical Niche Models to Ecological Networks

Daniel B. Stouffer, D. Matthias Dehling, Hao Ran Lai
4 citations
Ecology Letters
Species Distribution and Climate Change
5.51
article

Eltonian Niche Modelling: Applying Joint Hierarchical Niche Models to Ecological Networks

Daniel B. Stouffer, D. Matthias Dehling, Hao Ran Lai
article en
4 citations

Abstract

There is currently a dichotomy in the modelling of Grinnellian and Eltonian niches. Despite similar underlying data, Grinnellian niches are modelled with species-distribution models (SDMs), whereas Eltonian niches are modelled with ecological-network analysis, mainly because the sparsity of species-interaction data prevents the application of SDMs to Eltonian-niche modelling. Here, we propose to adapt recently developed joint species distribution models (JSDMs) to data on ecological networks, functional traits, and phylogenies to model species' Eltonian niches. JSDMs overcome sparsity and improve predictions for individual species by considering non-independent relationships among co-occurring species; this unique ability makes them particularly suited for sparse datasets such as ecological networks. Our Eltonian JSDMs reveal strong relationships between species' Eltonian niches and their functional traits and phylogeny. Moreover, we demonstrate that JSDMs can accurately predict the interactions of species for which no empirical interaction data are available, based solely on their functional traits. This facilitates prediction of new interactions in communities with altered composition, for example, following climate-change induced local extinctions or species introductions. The high interpretability of Eltonian JSDMs will provide unique insights into mechanisms underlying species interactions and the potential impacts of environmental changes and invasive species on species interactions in ecological communities.

Ecology LettersVol. 28(6)
University of Canterbury (NZ), Leibniz Institute of Freshwater Ecology and Inland Fisheries (DE), Monash University (AU)
Royal Society Te Apārangi, Monash University, Australian Research Council, Marsden Fund
Openalex Percentile: Top 5%
Species Distribution and Climate Change
5.51
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