Small to train, small to test: Dealing with low sample size in model evaluation

Détails

Ressource 1Demande d'une copie Sous embargo jusqu'au 24/04/2025.
Accès restreint UNIL
Etat: Public
Version: Final published version
Licence: Tous droits réservés
ID Serval
serval:BIB_DDA1566B06B4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Small to train, small to test: Dealing with low sample size in model evaluation
Périodique
Ecological Informatics
Auteur⸱e⸱s
Collart Flavien, Guisan Antoine
ISSN
1574-9541
Statut éditorial
Publié
Date de publication
07/2023
Peer-reviewed
Oui
Volume
75
Pages
102106
Langue
anglais
Résumé
Sample size is a key issue in species distribution modelling. While many studies focused on the relevance of sample size for model calibration, the importance of the size of the dataset used for model evaluation has received much less attention. Here, we highlight two previously published approaches to address the problem, and which are relatively simple to implement: the pooling evaluation and the implementation of null models. We discuss the importance of these or other potential approaches that are critical for model evaluation in rare species, which represent the bulk of biodiversity, and for which accurate models are most necessary in a conservation context.
Mots-clé
Species distribution model, Ecological niche, Evaluation, Sample size, Rare species, Conservation
Web of science
Financement(s)
Fonds national suisse / 197777
Création de la notice
19/04/2023 13:51
Dernière modification de la notice
26/08/2023 6:52
Données d'usage