Both authors contributed equally.
Do habitat suitability models reliably predict the recovery areas of threatened species?
Article first published online: 10 FEB 2010
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Journal of Applied Ecology
Volume 47, Issue 2, pages 421–430, April 2010
How to Cite
Cianfrani, C., Le Lay, G., Hirzel, A. H. and Loy, A. (2010), Do habitat suitability models reliably predict the recovery areas of threatened species?. Journal of Applied Ecology, 47: 421–430. doi: 10.1111/j.1365-2664.2010.01781.x
- Issue published online: 8 MAR 2010
- Article first published online: 10 FEB 2010
- Received 5 August 2009; accepted 12 January 2009 Handling Editor: Nathalie Pettorelli
- ecological niche;
- presence-absence data;
- spatial spread;
- species distribution model;
1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species’ potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.
2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models’ capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.
3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river’s suitability and badly describing the otter’s niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models’ fit to the recolonization observations.
4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.
5.Synthesis and applications. Conservation plans depend on reliable models of the species’ suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.