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How do species and data characteristics affect species distribution models and when to use environmental filtering?

Gábor Lukáš, Moudrý Vítězslav, Barták Vojtěch et Lecours Vincent. (2020). How do species and data characteristics affect species distribution models and when to use environmental filtering? International Journal of Geographical Information Science, 34, (8), p. 1567-1584.

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URL officielle: https://doi.org/10.1080/13658816.2019.1615070

Résumé

Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:1365-8816
Volume:34
Numéro:8
Pages:p. 1567-1584
Version évaluée par les pairs:Oui
Date:2 Août 2020
Nombre de pages:18
Identifiant unique:10.1080/13658816.2019.1615070
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Sciences naturelles et génie > Sciences naturelles > Biologie et autres sciences connexes
Département, module, service et unité de recherche:Unités de recherche > Centre de recherche sur la Boréalie (CREB)
Départements et modules > Département des sciences humaines
Mots-clés:MaxEnt, Schoener’s D, spatial data filtering, species rarity, virtual species, distribution models
Déposé le:23 mai 2024 19:16
Dernière modification:31 juill. 2024 15:41
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