Spatial confounding in Bayesian species distribution modeling
نویسندگان
چکیده
1) Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for geographical distribution. However, unobserved environmental conditions ecological processes may confound model if they have direct impact on and, at same time, correlated with observed covariates. This, so-called spatial confounding, is a general property of it has not been studied in context SDMs before. 2) We examine how estimation accuracy depends type confounding. construct two simulation studies where we alter structures covariates level dependence between them. fit generalized linear without random effects applying Bayesian inference recording bias induced by After this confounding also real vegetation data from northern Norway. 3) Our results show that coarse scale covariates, such as climate likely be biased an covariate operating finer scale. Pushing higher probability relatively weak smoothly varying effect compared improved model's accuracy. The improvement was independent actual structure covariate. 4) study addresses major factors provides list recommendations pre-inference assessment inference-based methods decrease chance estimates.
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ژورنال
عنوان ژورنال: Ecography
سال: 2022
ISSN: ['0906-7590', '1600-0587']
DOI: https://doi.org/10.1111/ecog.06183