Measuring the relative effect of factors affecting species distribution model predictions
Article first published online: 19 JUN 2014
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society
Methods in Ecology and Evolution
Volume 5, Issue 9, pages 947–955, September 2014
How to Cite
Thibaud, E., Petitpierre, B., Broennimann, O., Davison, A. C., Guisan, A. (2014), Measuring the relative effect of factors affecting species distribution model predictions. Methods in Ecology and Evolution, 5: 947–955. doi: 10.1111/2041-210X.12203
- Issue published online: 20 SEP 2014
- Article first published online: 19 JUN 2014
- Accepted manuscript online: 5 MAY 2014 08:45AM EST
- Manuscript Accepted: 24 APR 2014
- Manuscript Received: 5 MAR 2014
- SwissNational Science Foundation
- linear mixed-effects model;
- relative importance;
- spatial autocorrelation;
- virtual ecologist
- 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.
- We introduce a general ’virtual ecologist’ framework to study the relative importance of factors involved in the construction of species distribution models.
- 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 modelling technique – in a real study framework based on virtual 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 modelling technique. Contrary to repeatedly reported concerns, spatial autocorrelation has only comparatively small effects.
- This study shows the importance of using a nested statistical framework to evaluate the relative effects of factors that may affect species distribution models.