Low spatial autocorrelation in mountain biodiversity data and model residuals

Details

Serval ID
serval:BIB_0ECB2B6BC896
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Low spatial autocorrelation in mountain biodiversity data and model residuals
Journal
Ecosphere
Author(s)
Chevalier M., Mod H., Broennimann O., Di Cola V., Schmid S., Niculita-Hirzel H., Pradervand J.-N., Schmidt B.R., Ursenbacher S., Pellissier L., Guisan A.
ISSN
2150-8925
2150-8925
Publication state
Published
Issued date
2021
Peer-reviewed
Oui
Volume
12
Number
3
Pages
e03403
Language
english
Abstract
Spatial autocorrelation (SAC) is a common feature of ecological data where observations tend to be more similar at some geographic distance(s) than expected by chance. Despite the implications of SAC for data dependencies, its impact on the performance of species distribution models (SDMs) remains controversial, with reports of both strong and negligible impacts on inference. Yet, no study has comprehensively assessed the prevalence and the strength of SAC in the residuals of SDMs over entire geographic areas. Here, we used a large‐scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of SAC for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from SDMs fitted with two algorithms with the six predictors included as covariates. We used various statistical tools to evaluate (1) the global level of SAC, (2) the spatial pattern and spatial extent of SAC, and (3) whether local clusters of SAC can be detected. We further investigated the effect of the sampling design on SAC levels. Overall, while environmental predictors expectedly displayed high SAC levels, SAC in biodiversity data was rather low overall and vanished rapidly at a distance of ~5–10 km. We found low evidence for the existence of local clusters of SAC. Most importantly, model residuals were not spatially autocorrelated, suggesting that inferences derived from SDMs are unlikely to be affected by SAC. Further, our results suggest that the influence of SAC can be reduced by a careful sampling design. Overall, our results suggest that SAC is not a major concern for rugged mountain landscapes.
Funding(s)
European Commission
Create date
09/03/2021 23:10
Last modification date
10/03/2021 7:28
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