Landscape -scale habitat associations in an urban Stock Dove Columba oenas population

Abstract

While some species are known to thrive in urban areas, urban expansion is a major driver of biodiversity loss. Columbids such as feral Rock Doves Columba livia domestica and Common Woodpigeon Columba palumbus have adapted extremely well to the urban environment in Europe and beyond, but the Stock Dove Columba oenas, a bird of farmland and woodland edge in the UK and of national conservation concern, is encountered far more rarely in urban areas. Here we explore the multi-scale landscape associations of the little-studied Stock Dove within the urban matrix of Greater Manchester, UK, as a step towards identifying its long-term conservation needs. We built a pilot model from historical citizen science records to identify potentially occupied patches within the city, and then surveyed these patches for Stock Dove during Spring 2019. We combined the survey results with citizen science records from the same period. We described the habitat and landscape characteristics of these patches using four variables at different scales and twelve unscaled variables. We used a three-stage random forest approach to identify a subset of these variables for interpretation and a subset for prediction for the presence of Stock Dove within these patches. Key variables for predicting Stock Dove presence were their relative abundance in the region immediately beyond the core urban area, the greenness (NDVI) of the environment around patches, and the canopy cover of individual trees over 20m high within patches. Stock Doves tended to be associated with habitats with more surface water during the non-breeding season than the breeding season. Our results highlight the importance of large trees within urban greenspace and the importance of softer boundaries around urban patches for Stock Doves. While Stock Dove share many traits with species that are successful in the urban environment, they remain relatively poor urban adapters.

Avian diversity and function across the world's most populous cities

“…We used a variable selection technique based on random forests (Breiman, 2001) originally developed by Genuer et al (2010). This method performs well in an ecological setting (Fox et al, 2017;Richardson et al, 2022). It is also robust to a high predictor-to-data point ratio and to autocorrelation among predictors (Genuer et al, 2010).…”