Successful management practices for declining bird species depend often on long-term surveys acquired by point counts. Despite high standardization of field protocols, uncertain detection probability remains an important source of variability and bias in point-count data. This effect is of main importance in low-responsive species as the Red-legged partridge (Alectoris rufa), but it can be counterbalanced, increasing detection probability. In this 2-year study, we sampled using traditional point-count methods, followed by playback sessions for each repetition. We measured detection probability and the efficiency of playback for detectability in the context of a feasibility study on long-term point-count surveys for a harvested game bird, the Red-legged partridge. The results for both study years show a distinct increase in detection probability (23% and 45%, respectively) when using playback vs. the traditional point-count method. We also tested our results for heterogeneity, trap dependence, and time dependence, and no effect was detected. Thus, we suggest that the future design of long-term surveys on Red-legged partridges should consider abundance indices using playback sessions.

The reliability of long‐term population estimates is crucial for conservation and management purposes. Most game species population monitoring programs assume that count indices are proportionally related to abundance. However, this assumption is untenable when detection varies spatially and temporally. We assessed whether N‐mixture models, which allow detection modeling using spatially and temporally repeated count data, were relevant for monitoring the population of red‐legged partridge (Alectoris rufa). Unbiased estimates are needed for calculating hunting quotas for this game species. We used the simple aural playback point‐count method and a dataset of 121 point‐transects (n = 16,288 counts) collected from 1992 to 2010. Covariates such as date, hour, wind, rain, and vegetation density influenced detection probability. Estimated abundance ranged from 0 to 15 males per point and exhibited variable coefficients of variation depending on sites (range: 0.14–1.31, mean: 0.44). We found a positive and linear relationship between the abundance estimated by the N‐mixture model and the densities provided by 2 other counting methods, square sampling and kilometric abundance index, but not with blank beat. We established a maximum detection radius for the playback surveys, which enabled conversion of abundance at the points into density. The N‐mixture modeling approach is more cost‐effective for game species monitoring than capture–recapture methods or square sampling, and more reliable than indices of relative abundances. © 2014 The Wildlife Society, 2014.

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