Habitat selection by breeding rock ptarmigan Lagopus muta helvetica males in the western Italian Alps

Knowledge of resource selection patterns can provide important information for species conservation. During spring 2010 and 2011, we investigated habitat selection by territorial rock ptarmigan Lagopus muta helvetica males in a protected area of the western Italian Alps. We located males from 30 randomly selected survey points, and we measured the proportions of cover-type categories found within a 37-ha area surrounding each observed bird using three classification maps of differing information and resolution. We also evaluated physical variables (altitude, slope and solar radiation) associated with the birds using a 75-m digital terrain model. We modelled land cover and physical attributes under these three alternative land-classification maps. The lowest resolution map, based on the Corine land-cover map, did not have high predictive value because only orographic variables described the presence of birds; in our case, we found a negative effect of slope and a positive effect of altitude on presence of ptarmigan. The next higher resolution map, a local forest resource map, showed that slope had a negative effect and rocky grasslands had a positive effect on ptarmigan presence. Finally, using the highest resolution map, a phytosociological map of our Natural Park study area, the best-ranked models were those having only cover-type variables, with alpenrose Rhododendron ferrugineum and blueberry Vaccinium myrtillus scrubland and pioneer vegetation negatively correlated with the presence of rock ptarmigan. We concluded that a staged approach that uses maps of differing detail was successful for obtaining useful information on rock ptarmigan habitat selection, but the most interesting results about rock ptarmigan habitat selection at the scale of breeding territories were obtained only using a very detailed vegetation map. Because such detailed information is difficult to obtain on larger scales, we suggest wildlife managers cooperate to build similar mapping tools that allow analyses similar to ours.