Modelling the national breeding distribution and population size of an elusive forest bird, the Eurasian Woodcock ( Scolopax rusticola )

Understanding the distribution of species is central to conservation biology. Species distribution modelling (SDM) is a standard method used for this purpose, especially for elusive species for which limited occurrence data exist. The Eurasian Woodcock Scolopax rusticola (hereafter Woodcock) is an elusive, woodland‐dwelling wader that is declining in parts of its range but is poorly covered by general bird monitoring schemes. Hence, we used SDM to estimate the distribution and abundance of Woodcock at a national scale in the Republic of Ireland. Quantitative occurrence data were collected using a species‐specific protocol to survey displaying males, which were analysed via a two‐step conditional modelling approach using bioclimatic and land‐cover variables. Geographical distribution was most strongly influenced by weather: probability of occurrence was higher where mean winter temperature and seasonal differences in temperature were lower, and peaked where mean summer temperature was 14–14.6°C, while abundance increased with mean summer temperature. Abundance also peaked where the local landscape comprised 40–50% forest cover, and where forest was more diverse in tree species and age. The selected abundance model was projected across the defined distribution to generate estimates of national densities and total population size. While this model projection lends support to an apparent national range contraction since 1970, breeding Woodcock are still more widely distributed than recently reported. The national population was estimated at 27 434 males (95% CL 16 947–36 288), an important baseline for future monitoring efforts. We discuss the potential future effects of climate change on Woodcock, and the need to create more diverse forested landscapes and to implement sustainable hunting practices. Our study demonstrates the importance of species‐specific surveys and the suitability of the two‐step conditional model method for monitoring populations of elusive species at large spatial scales.