Resource selection by Sarda cattle in a Mediterranean silvopastoral system - Acciaro, Marco; Pittarello, Marco; Decandia, Mauro; Sitzia, Maria; Giovanetti, Valeria; Lombardi, Giampiero; Clark, Patrick E. (Frontiers in Veterinary Science, 2024)
Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning system (GPS) to track animals, together with geographic information systems (GISs) can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area free-roaming Sarda cows were fitted with GPS collars, to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to 5 of environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of final model, when fitted to each season, ranged between 0.7 and 0.94. The cows selected lower-elevation areas and farther from the watering point in winter than in summer (693±1 m and 847±13 m vs 707±1 m and 635±21 m, respectively), and in spring the areas furthest from the water (963±12). Although caution should be exercised in generalising to other silvopastoral areas, the satisfactory Spearman correlations scores from final RSF model applied to different season, indicate resource selection function is a powerfully predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model's ability to interpret changes in animal behaviour at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviours of cows grazing in Mediterranenan silvopastoral areas, and could therefore be helpful in managing and preserving ecosystem services of these areas.