4.7 Article

Spatial confounding in Bayesian species distribution modeling

Journal

ECOGRAPHY
Volume 2022, Issue 11, Pages -

Publisher

WILEY
DOI: 10.1111/ecog.06183

Keywords

estimation bias; Gaussian process; spatial confounding; spatial random effect; species distribution model

Funding

  1. Academy of Finland [317255]
  2. Jane and Aatos Erkko foundation
  3. Kone foundation
  4. Societas pro Fauna et Flora Fennica
  5. Academy of Finland (AKA) [317255, 317255] Funding Source: Academy of Finland (AKA)

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

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