Towards causal relationships for modelling species distribution

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Daniele Da Re, Enrico Tordoni, Jonathan Lenoir , Sergio Rubin, Sophie O. Vanwambeke


1. Understanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models (SDMs) involve popular statistical tools to infer species geographical distribution thanks to spatiotemporally explicit observations of species occurrences coupled with a set of environmental predictors.
2. So-called SDMs rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g., temperature, precipitation), without clear causal links to the biology of species under investigation. This might result in an over-simplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism.
3. We believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop ecologically more realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling (SEM).
4. Implementing the modelling relation into SDMs would improve the inclusion of the causal processes underlying the spatial distribution of species into an inferential formal system, potentially highlighting the methodological steps where uncertainty arises and eventually resulting in model outputs which are tightly linked to the ecology of the target species.



Biodiversity, Bioinformatics, Life Sciences, Natural Resources and Conservation, Statistical Models


Directed acyclic graph, environmental niche models, habitat suitability models, Path Analyses, Process-based Models, Robert Rosen, Statistical models, virtual species


Published: 2023-10-14 16:13


CC BY Attribution 4.0 International

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