Spatial confounding in Bayesian species distribution modeling
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 a direct imp...
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ftzenodo:oai:zenodo.org:7055023 2024-09-15T18:25:55+00:00 Spatial confounding in Bayesian species distribution modeling Mäkinen, Jussi Numminen, Elina Niittynen, Pekka Luoto, Miska Vanhatalo, Jarno 2022-08-15 https://doi.org/10.5281/zenodo.7055023 unknown Zenodo https://doi.org/10.1038/s41558-018-0311-x https://doi.org/10.5061/dryad.hdr7sqvm5 https://zenodo.org/communities/dryad https://doi.org/10.5281/zenodo.7055022 https://doi.org/10.5281/zenodo.7055023 oai:zenodo.org:7055023 info:eu-repo/semantics/openAccess MIT License https://opensource.org/licenses/MIT estimation bias Gaussian process spatial confounfing spatial random effect Species distribution model info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5281/zenodo.705502310.1038/s41558-018-0311-x10.5061/dryad.hdr7sqvm510.5281/zenodo.7055022 2024-07-27T06:53:04Z 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 a 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 but it has not been studied in the context of SDMs before. Here 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 record the bias induced to model estimates by spatial confounding. After this, we examine spatial confounding also with real vegetation data from northern Norway. 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 spatially smoothly varying spatial random effect compared to the observed covariates improved estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate. 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. Matlab Funding provided by: Academy of Finland Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341 Award Number: 317255 Funding provided by: Jane ja Aatos Erkon Säätiö Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004012 Award ... Other/Unknown Material Northern Norway Zenodo |
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estimation bias Gaussian process spatial confounfing spatial random effect Species distribution model |
spellingShingle |
estimation bias Gaussian process spatial confounfing spatial random effect Species distribution model Mäkinen, Jussi Numminen, Elina Niittynen, Pekka Luoto, Miska Vanhatalo, Jarno Spatial confounding in Bayesian species distribution modeling |
topic_facet |
estimation bias Gaussian process spatial confounfing spatial random effect Species distribution model |
description |
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 a 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 but it has not been studied in the context of SDMs before. Here 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 record the bias induced to model estimates by spatial confounding. After this, we examine spatial confounding also with real vegetation data from northern Norway. 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 spatially smoothly varying spatial random effect compared to the observed covariates improved estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate. 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. Matlab Funding provided by: Academy of Finland Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341 Award Number: 317255 Funding provided by: Jane ja Aatos Erkon Säätiö Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004012 Award ... |
format |
Other/Unknown Material |
author |
Mäkinen, Jussi Numminen, Elina Niittynen, Pekka Luoto, Miska Vanhatalo, Jarno |
author_facet |
Mäkinen, Jussi Numminen, Elina Niittynen, Pekka Luoto, Miska Vanhatalo, Jarno |
author_sort |
Mäkinen, Jussi |
title |
Spatial confounding in Bayesian species distribution modeling |
title_short |
Spatial confounding in Bayesian species distribution modeling |
title_full |
Spatial confounding in Bayesian species distribution modeling |
title_fullStr |
Spatial confounding in Bayesian species distribution modeling |
title_full_unstemmed |
Spatial confounding in Bayesian species distribution modeling |
title_sort |
spatial confounding in bayesian species distribution modeling |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5281/zenodo.7055023 |
genre |
Northern Norway |
genre_facet |
Northern Norway |
op_relation |
https://doi.org/10.1038/s41558-018-0311-x https://doi.org/10.5061/dryad.hdr7sqvm5 https://zenodo.org/communities/dryad https://doi.org/10.5281/zenodo.7055022 https://doi.org/10.5281/zenodo.7055023 oai:zenodo.org:7055023 |
op_rights |
info:eu-repo/semantics/openAccess MIT License https://opensource.org/licenses/MIT |
op_doi |
https://doi.org/10.5281/zenodo.705502310.1038/s41558-018-0311-x10.5061/dryad.hdr7sqvm510.5281/zenodo.7055022 |
_version_ |
1810466381724909568 |