Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks
Article first published online: 16 DEC 2013
© 2013 John Wiley & Sons Ltd
Diversity and Distributions
Volume 20, Issue 3, pages 309–321, March 2014
How to Cite
Meller, L., Cabeza, M., Pironon, S., Barbet-Massin, M., Maiorano, L., Georges, D., Thuiller, W. (2014), Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks. Diversity and Distributions, 20: 309–321. doi: 10.1111/ddi.12162
- Issue published online: 14 FEB 2014
- Article first published online: 16 DEC 2013
- European Research Council
- European Community's Seventh Framework Programme. Grant Number: FP7/2007-2013
- TEEMBIO. Grant Number: 281422
- Rhône-Alpes region. Grant Number: CPER07_13 CIRA
- EU FP7. Grant Number: 244092
- LUOVA Graduate School
- EU FP7-PEOPLE-2011-IOF
- Consensus predictions;
- rare species;
- systematic conservation planning;
Conservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection.
We used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset.
Networks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods.
Retaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted distribution data in conservation prioritization.