Towards causal relationships for modelling species distribution

Abstract

AimUnderstanding 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 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 the species under investigation. This might result in an oversimplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism.LocationNot applicable.Time PeriodNot applicable.Major Taxa StudiedVirtual species.Materials and MethodsWe believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop more ecologically 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.ResultsOur findings suggest that building a model relying on a strong conceptual basis improves the stability of the estimated model's coefficients, without necessarily increasing the predictive accuracy metrics of the model.Main ConclusionsIncluding causal processes underlying the spatial distribution of species into an inferential formal system highlights the methodological steps where uncertainty can arise and results in model outputs which are tightly linked to the ecology of the target species.

Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios

“…Species distribution models (SDMs), also called environmental niche models, are a widely used tool for understanding the processes underlying the distribution of species (Da Re et al, 2023;Franklin, 2023). The basis of the whole diversity of SDMs (e.g., generalised linear models, random forest, maxent, etc.)…”