Measuring the relative effect of factors affecting species distribution model predictions


Autoria(s): Thibaud E.; Petitpierre B.; Broennimann O.; Davison A.C.; Guisan A.
Data(s)

2014

Resumo

1. Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although some factors probably have negligible effects compared to others, their relative effects are largely unknown. 2. We introduce a general "virtual ecologist" framework to study the relative importance of factors involved in the construction of species distribution models. 3. We illustrate the framework by examining the relative importance of five key factors-a missing covariate, spatial autocorrelation due to a dispersal process in presences/absences, sample size, sampling design and modeling technique-in a real study framework based on plants in a mountain landscape at regional scale, and show that, for the parameter values considered here, most of the variation in prediction accuracy is due to sample size and modeling technique. Contrary to repeatedly reported concerns, spatial autocorrelation has only comparatively small effects. 4. This study shows the importance of using a nested statistical framework to evaluate the relative effects of factors that may affect species distribution models.

Identificador

https://serval.unil.ch/?id=serval:BIB_FD78C2B7C0FE

isbn:2041-210X; 2041-2096 (electronic)

isiid:000342722100012

doi:10.1111/2041-210X.12203

Idioma(s)

en

Fonte

Methods in Ecology and Evolution, vol. 5, no. 9, pp. 947-955

Palavras-Chave #linear mixed-effects model; relative importance; spatial autocorrelation; virtual ecologist
Tipo

info:eu-repo/semantics/article

article