Presence-only and presence-absence data for comparing species distribution modeling methods

Détails

ID Serval
serval:BIB_583ED014AA5D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Presence-only and presence-absence data for comparing species distribution modeling methods
Périodique
Journal of Biodiversity Informatics
Auteur⸱e⸱s
Elith J., Graham C.H., Valavi R., Abegg M., Bruce C., Ford A., Guisan A., Hijmans R.J., Huettmann F., Lohmann L., Loiselle B., Moritz C., Overton J., Peterson A.T., Phillips S., Richardson K., Williams S.E., Wiser S.K., Wohlgemuth T., Zimmermann N.E.
Statut éditorial
Publié
Date de publication
2020
Peer-reviewed
Oui
Volume
15
Pages
69-80
Langue
anglais
Résumé
Species distribution models (SDMs) are widely used to predict and study distributions of species. Many different modeling methods and associated algorithms are used and continue to emerge. It is important to understand how different approaches perform, particularly when applied to species occurrence records that were not gathered in structured surveys (e.g. opportunistic records). This need motivated a large-scale, collaborative effort, published in 2006, that aimed to create objective comparisons of algorithm performance. As a benchmark, and to facilitate future comparisons of approaches, here we publish that dataset: point location records for 226 anonymised species from six regions of the world, with accompanying predictor variables in raster (grid) and point formats. A particularly interesting characteristic of this dataset is that independent presence-absence survey data are available for evaluation alongside the presence-only species occurrence data intended for modeling. The dataset is available on Open Science Framework and as an R package and can be used as a benchmark for modeling approaches and for testing new ways to evaluate the accuracy of SDMs.
Création de la notice
03/04/2020 22:59
Dernière modification de la notice
22/01/2021 6:24
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