Hunting bamboo: Foraging patch selection and utilization by giant pandas and implications for conservation

Abstract

Food resources are patchily distributed in the environment and carnivores and herbivores have adopted different foraging strategies to maximize feeding efficiency. One interesting strategy is that of the giant panda, a member of the Carnivora that has evolved into a bamboo specialist. Giant pandas forage discriminately, but it remains unknown how nutritional hierarchical levels and landscape configuration heterogeneity affect foraging patch selection. Here, we used global positioning system collars to track wild giant pandas at high resolution (<10m) and sampled foraging patches for nutritional hierarchical level analysis. We predicted that giant pandas select foraging patches with microhabitat characteristics that decrease energy expenditure during foraging according to optimal search theory. We introduce the concept of nutrient load as the product of local patch nutrient concentration and predicted that relatively efficient nutrients in fluctuating nutritional environments may determine foraging patch selection in giant pandas. This is the first time that microhabitat characteristics, key nutrients and foraging behavior have been studied in combination in giant pandas. We used random forest (RF) and generalized linear mixed-effects models (GLMMs) to infer habitat and nutritional factors that may influence foraging patch selection and utilization. Our results reveal that giant pandas select foraging patches with a topography that likely decreases energy expenditure. Giant pandas also favor protein-rich foraging patches, probably because protein can be digested and assimilated faster than cellulose and this maximizes net energy gains. These data provide a new perspective on foraging patch selection strategies in heterogeneous habitats of diet-specialized species under constant nutritional challenge. Improved conservation planning can be undertaken according to our findings.