Which is the optimal sampling strategy for habitat suitability modelling

Détails

ID Serval
serval:BIB_81CEF22B8C19
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Which is the optimal sampling strategy for habitat suitability modelling
Périodique
Ecological Modelling
Auteur(s)
Hirzel A., Guisan A.
ISSN
0304-3800
Statut éditorial
Publié
Date de publication
2002
Peer-reviewed
Oui
Volume
157
Numéro
2-3
Pages
331-341
Langue
anglais
Résumé
Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, 'random', 'regular', 'proportional-stratified' and 'equal -stratified'- to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/ absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearson's correlation coefficient and presence/absence predictions by Cohen's K agreement coefficient. The results show the 'regular' and 'equal-stratified' sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design'
Mots-clé
sampling design, logistic model, GLM, simulations, virtual species, bootstrap statistics
Web of science
Création de la notice
24/01/2008 20:05
Dernière modification de la notice
03/03/2018 18:48
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