Acciaro Marco, Pittarello Marco, Decandia Mauro, Sitzia Maria, Giovanetti Valeria, Lombardi Giampiero, Clark Patrick E
AGRIS Sardegna, Sassari, Italy.
Department of Veterinary Sciences, University of Torino, Grugliasco, Italy.
Front Vet Sci. 2024 Mar 7;11:1348736. doi: 10.3389/fvets.2024.1348736. eCollection 2024.
Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), 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 five 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 the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation 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 opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful 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 behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.
关于放牧的牛如何利用地中海农牧交错区的异质景观的知识十分匮乏。用于追踪动物的全球定位系统（GPS），连同地理信息系统（GIS），可以将动物分布与景观特征联系起来。为了开发一个能准确预测地中海山区农牧交错区内牛的资源选择模式的通用空间模型，给自由放养的萨尔达牛佩戴了GPS项圈来追踪它们的空间行为。开发了资源选择函数模型（RSF）来估计作为环境变量函数的资源利用概率。将一组由多达五个环境预测变量组成的500多个候选RSF模型与数据进行拟合。为了确定一个能对不同季节的牛资源选择模式进行稳健预测的最终模型，将10个最佳模型（根据AIC分数排名）与季节数据进行拟合。使用先前预留用于模型验证的GPS位置数据集，通过Spearman相关性分析评估模型的预测性能。最终模型强调，饮水点、海拔和到围栏的距离是影响牛资源选择模式的重要因素。最终模型在拟合每个季节时的预测性能（作为Spearman等级相关分数）在0.7到0.94之间。与夏季相比，冬季的牛更倾向于选择海拔较低且离饮水点较远的区域（分别为693±1米和847±13米，而夏季为707±1米和635±21米），春季则选择离水源最远的区域（963±12）。尽管在推广到其他农牧交错区时应谨慎，但应用于不同季节的最终RSF模型令人满意的Spearman相关性分数表明，资源选择函数是一个强大的预测模型。模型中各个预测变量的相对重要性在不同季节有所不同，这表明RSF模型能够解释一年中不同时间动物行为的变化。RSF模型已被证明是解释在地中海农牧交错区放牧的牛的空间行为的有用工具，因此有助于管理和保护这些地区的生态系统服务。
