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In this article, we present the case for Generalized Linear Latent Variable Models (GLLVMs) as a go-to choice of statistical method for any community ecologist wanting to tackle a range of present-day ecological research questions. GLLVMs bring tools and capabilities from classic (mixed-effects) regression models to multivariate community analysis, providing a number of novel ways to tailor models specifically to one’s study questions and data properties not available when using non-model-based multivariate methods. In order to facilitate further adoption of these methods by community ecologists, we provide 1) a practitioner-focused and practical overview of the advantages the GLLVM framework brings to the table when addressing different core ecological questions, 2) a number of concrete suggestions for how GLLVMs best can be incorporated into the analytical workflow of community ecologists, and 3) two illustrative worked examples of this workflow in action on real-world data.
https://doi.org/10.32942/X2KM2V
Ecology and Evolutionary Biology, Multivariate Analysis, Research Methods in Life Sciences, Statistical Methodology, Statistical Models
Community ecology, Ordination, Data exploration, Model selection, Model-based workflow, Invasive species, Ecological restoration, Latent variable models, Multispecies models, Community modelling
Published: 2026-02-05 15:53
CC-BY Attribution-NonCommercial-ShareAlike 4.0 International
Data and Code Availability Statement:
The data that support the findings of this study are openly available on Zenodo, at https://doi.org/10.5281/zenodo.18391448.
Language:
English
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