Species distribution modelling and imperfect detection: comparing occupancy versus consensus methods
Article first published online: 26 MAR 2013
© 2013 John Wiley & Sons Ltd
Diversity and Distributions
Volume 19, Issue 8, pages 996–1007, August 2013
How to Cite
Comte, L., Grenouillet, G. (2013), Species distribution modelling and imperfect detection: comparing occupancy versus consensus methods. Diversity and Distributions, 19: 996–1007. doi: 10.1111/ddi.12078
- Issue published online: 9 JUL 2013
- Article first published online: 26 MAR 2013
- Consensus method;
- false absence;
- global change;
- occupancy model;
- species distribution model
We assessed the influence of species non-detection in modelling species distributions with an ensemble consensus approach that did not account for imperfect detection, compared with an occupancy model that did.
The hydrographic network of France.
We compared range maps of 35 stream fish species with differing degrees of detectability predicted using a consensus approach combining eight species distribution models (SDMs) to maps produced using an occupancy model. Using a spatially and temporally extensive monitoring database of fish populations (France), we modelled the occurrence of species as a function of several climatic and habitat variables and projected species distributions across the whole of the French hydrographic network. The benefits of occupancy models were then assessed from the differences in both predictive performance and species distribution.
We found that although the occupancy models enhanced the performance for difficult to detect species, consensus models outperformed occupancy models for highly detectable species. In contrast to the minor differences observed in performance measures, estimates of species distributions were severely affected by whether or not imperfect detection was accounted for and varied linearly according to species detectability.
This study demonstrated that false absences could have major consequences in estimating species distribution ranges. However, accounting for imperfect detection may not be enough to improve conventional SDMs. These findings could have important implications for conservation, notably in developing large-scale distribution models and documenting species range shifts in the context of recent climate change.