Which is the optimal sampling strategy for habitat suitability modelling

Details

Serval ID
serval:BIB_81CEF22B8C19
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Which is the optimal sampling strategy for habitat suitability modelling
Journal
Ecological Modelling
Author(s)
Hirzel A., Guisan A.
ISSN
0304-3800
Publication state
Published
Issued date
2002
Peer-reviewed
Oui
Volume
157
Number
2-3
Pages
331-341
Language
english
Abstract
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'
Keywords
sampling design, logistic model, GLM, simulations, virtual species, bootstrap statistics
Web of science
Create date
24/01/2008 19:05
Last modification date
20/08/2019 14:42
Usage data