The Western Capercaillie (Tetrao urogallus), hereafter capercaillie, is the largest galliform bird present in the boreal and montane forests of the Western Palearctic. Precise and accurate methods for estimating the number of individuals and/or their densities are crucial for the proper management of its free-ranging populations. However, obtaining reliable estimates of the abundance of populations of wild species and, particularly, of birds is not a simple task. In the case of lek-mating birds such as capercaillie, surveys are traditionally based on lek counts, that is, counts of calling males present in their mating areas: the leks. This study was carried out on the Pyrenees at six capercaillie leks where two different lek counting approaches were performed: hide-based and walk-based. The results were compared with those obtained from an estimate of minimum population size (MPE) derived from genotyping all faeces samples found in the lek area, and with a population size estimate derived from a genetic mark-recapture model (N ̂) of each capercaillie lek. The results of N ̂ were used to estimate the detection rate (D) of both lek count approaches. Our results show that traditional lek counts do not detect all male capercaillies since the detection rate was 0.34 (95% CI: 0.26-0.43) for hide- and 0.56 (95% CI: 0.43-0.68) for walk-based lek counts. Our results suggest that the walk-based lek counts were more efficient than the hide-based ones, providing more accurate results compared to the N ̂ estimate. The combination of non-invasive sampling with the genetic mark-recapture model was found to be the most reliable method for obtaining the N ̂ of leks given that traditional lek counts underestimate the number of capercaillies and, furthermore, can cause disturbance to the species at these sites.

To estimate the population size (N) of each lek, we use only the male samples that have been correctly analysed i.e., with a QI>0.6.

We used the Bayesian approach of this classical M₀ model, in which the model is implemented in JAGS code (Plummer 2003) using data augmentation (Kéry and Schaub 2012, Chapter 6.2.1). This model estimated the detection probability on all three sampling occasions based on the assumption that all individuals in each sampling session have equal detection probabilities (see Otis et al., 1978, Kéry and Schaub, 2012 and Supplementary Material S1 for more details and see the Otis Mo model code in R).

References:

Kéry, M. and Schaub, M. 2012. Bayesian population analysis using WinBUGS. A hierarchical perspective. First edition. - Academic Press.

Otis, D.L., Burnham, K. P., White, G. C. and Anderson, D. R. 1978. Statistical inference from capture data on closed animal populations. – Wildl. Monogr. 62:3–135.

Plummer, M. 2003. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the International Workshop on Distributed Stat. Comput. 3:124.