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dc.contributor.authorDubos, Nicolas
dc.contributor.authorPréau, Clémentine
dc.contributor.authorLenormand, Maxime
dc.contributor.authorPapuga, Guillaume
dc.contributor.authorMonsarrat, Sophie
dc.contributor.authorDenelle, Pierre
dc.contributor.authorLouarn, Marine Le
dc.contributor.authorHeremans, Stien
dc.contributor.authorMay, Roelof Frans
dc.contributor.authorRoche, Philip
dc.contributor.authorLuque, Sandra
dc.identifier.citationEcological Indicators. 2022, 145 .en_US
dc.description.abstract1. Open-source biodiversity databases contain a large number of species occurrence records but are often spatially biased; which affects the reliability of species distribution models based on these records. Sample bias correction techniques require data filtering which comes at the cost of record numbers, or require considerable additional sampling effort. Since independent data is rarely available, assessment of the correction technique often relies solely on performance metrics computed using subsets of the available – biased – data, which may prove misleading. 2. Here, we assess the extent to which an acknowledged sample bias correction technique is likely to improve models’ ability to predict species distributions in the absence of independent data. We assessed variation in model predictions induced by the aforementioned correction and model stochasticity; the variability between model replicates related to a random component (pseudo-absences sets and cross-validation subsets). We present, then, an index of the effect of correction relative to model stochasticity; the Relative Overlap Index (ROI). We investigated whether the ROI better represented the effect of correction than classic performance metrics (Boyce index, cAUC, AUC and TSS) and absolute overlap metrics (Schoener’s D, Pearson’s and Spearman’s correlation coefficients) when considering data related to 64 vertebrate species and 21 virtual species with a generated sample bias. 3. When based on absolute overlaps and cross-validation performance metrics, we found that correction produced no significant effects. When considering its effect relative to model stochasticity, the effect of correction was strong for most species at one of the three sites. The use of virtual species enabled us to verify that the correction technique improved both distribution predictions and the biological relevance of the selected variables at the specific site, when these were not correlated with sample bias patterns. 4. In the absence of additional independent data, the assessment of sample bias correction based on subsample data may be misleading. We propose to investigate both the biological relevance of environmental variables selected, and, the effect of sample bias correction based on its effect relative to model stochasticity. Accessibility maps Cross-validation Performance metrics Overlap Pseudo-absence selection Terrestrial vertebrates Variable selection Virtual speciesen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleAssessing the effect of sample bias correction in species distribution modelsen_US
dc.title.alternativeAssessing the effect of sample bias correction in species distribution modelsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Authorsen_US
dc.subject.nsiVDP::Matematikk og naturvitenskap: 400en_US
dc.subject.nsiVDP::Mathematics and natural scienses: 400en_US
dc.subject.nsiVDP::Matematikk og naturvitenskap: 400en_US
dc.subject.nsiVDP::Mathematics and natural scienses: 400en_US
dc.source.journalEcological Indicatorsen_US
dc.relation.projectAndre: IMAGINE project (ERANET BIODIVERSA)en_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal