Abstract

ABSTRACTDuring recent years, predictive modelling techniques have been increasingly used to identify regional patterns of species spatial occurrence, to explore species–habitat relationships and to aid in biodiversity conservation. In the case of birds, predictive modelling has been mainly applied to the study of species with little variable interannual patterns of spatial occurrence (e.g. year‐round resident species or migratory species in their breeding grounds showing territorial behaviour). We used predictive models to analyse the factors that determine broad‐scale patterns of occurrence and abundance of wintering Swainson's hawks (Buteo swainsoni). This species has been the focus of field monitoring in its wintering ground in Argentina due to massive pesticide poisoning of thousands of individuals during the 1990s, but its unpredictable pattern of spatial distribution and the uncertainty about the current wintering area occupied by hawks led to discontinuing such field monitoring. Data on the presence and abundance of hawks were recorded in 30 × 30 km squares (n = 115) surveyed during three austral summers (2001–03). Sixteen land‐use/land‐cover, topography, and Normalized Difference Vegetation Index (NDVI) variables were used as predictors to build generalized additive models (GAMs). Both occurrence and abundance models showed a good predictive ability. Land use, altitude, and NDVI during spring previous to the arrival of hawks to wintering areas were good predictors of the distribution of Swainson's hawks in the Argentine pampas, but only land use and NDVI were entered into the model of abundance of the species in the region. The predictive cartography developed from the models allowed us to identify the current wintering area of Swainson's hawks in the Argentine pampas. The highest occurrence probability and relative abundances for the species were predicted for a broad area of south‐eastern pampas that has been overlooked so far and where neither field research nor conservation efforts aiming to prevent massive mortalities has been established.

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