Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys
Article first published online: 11 DEC 2007
© 2007 The Authors. Journal compilation © 2007 British Ecological Society
Journal of Applied Ecology
Volume 45, Issue 2, pages 589–598, April 2008
How to Cite
Kéry, M. and Royle, J. A. (2008), Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology, 45: 589–598. doi: 10.1111/j.1365-2664.2007.01441.x
- Issue published online: 11 DEC 2007
- Article first published online: 11 DEC 2007
- Received 23 March 2007; accepted 29 October 2007Handling Editor: Robert Freckleton
- breeding bird survey;
- multisite estimation;
- 1Species richness is the most widely used biodiversity metric, but cannot be observed directly as, typically, some species are overlooked. Imperfect detectability must therefore be accounted for to obtain unbiased species-richness estimates. When richness is assessed at multiple sites, two approaches can be used to estimate species richness: either estimating for each site separately, or pooling all samples. The first approach produces imprecise estimates, while the second loses site-specific information.
- 2In contrast, a hierarchical Bayes (HB) multispecies site-occupancy model benefits from the combination of information across sites without losing site-specific information and also yields occupancy estimates for each species. The heart of the model is an estimate of the incompletely observed presence–absence matrix, a centrepiece of biogeography and monitoring studies. We illustrate the model using Swiss breeding bird survey data, and compare its estimates with the widely used jackknife species-richness estimator and raw species counts.
- 3Two independent observers each conducted three surveys in 26 1-km2 quadrats, and detected 27–56 (total 103) species. The average estimated proportion of species detected after three surveys was 0·87 under the HB model. Jackknife estimates were less precise (less repeatable between observers) than raw counts, but HB estimates were as repeatable as raw counts. The combination of information in the HB model thus resulted in species-richness estimates presumably at least as unbiased as previous approaches that correct for detectability, but without costs in precision relative to uncorrected, biased species counts.
- 4Total species richness in the entire region sampled was estimated at 113·1 (CI 106–123); species detectability ranged from 0·08 to 0·99, illustrating very heterogeneous species detectability; and species occupancy was 0·06–0·96. Even after six surveys, absolute bias in observed occupancy was estimated at up to 0·40.
- 5Synthesis and applications. The HB model for species-richness estimation combines information across sites and enjoys more precise, and presumably less biased, estimates than previous approaches. It also yields estimates of several measures of community size and composition. Covariates for occupancy and detectability can be included. We believe it has considerable potential for monitoring programmes as well as in biogeography and community ecology.