Bamboo comprises over 99 % of the diet of giant pandas (Ailuropoda melanoleuca). Giant pandas face a complex nutrient landscape. They eat more than one species of bamboo and various parts of the plant, and they move seasonally to find optimal forage. Though the seasonal habitat preferences of giant pandas have long been known, the spatial and temporal nutrient gradient of bamboo between seasonal habitats remains unclear. Few studies detail the nutrient content of bamboo in relation to the seasonal habitat selection of giant pandas in the wild. In this study, we collected bamboo samples from 57 plots considering four factors (seasons, elevations, species, and plant parts). We evaluated the effect of these factors on the contents of seven bamboo mineral elements (Cu, Zn, Fe, Mn, K, Ca, and Mg) and used a non–parametric ensemble tree model to model giant pandas’ presence and absence based on bamboo mineral content. Our results showed strong correlations between pairs of mineral contents (up to r = 0.69) with specific mineral elements such as Mn, consistently showing great importance in the models for differentiating the habitat selection. We also observed significant variation in mineral concentrations between seasons, bamboo species, and plant parts. Our results suggest that the studied bamboo mineral content strongly associates giant pandas’ habitat preferences. Our research may be useful for the development of conservation and reserve management strategies by providing guidelines to increase giant pandas’ opportunities to obtain sufficient nutrient within the Qinling region.
Giant pandas, Habitat selection, Machine learning, Mineral elements, Nutrient content, Qinling Mountains
Reception date: 28 II 17 | Acceptation date: 12 IX 17 | Publication date: 22 XII 17
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