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Abstract:Understanding feeding habits and responses to habitat changes can be a critical step toward the conservation of threatened species. Pressured by hunting, habitat loss, and competition from livestock, the dwarf blue sheep ( Pseudois schaeferi ) of the Yangtze River gorge in the Eastern Himalaya is an IUCN-listed endangered species with a diminished range and population, and yet little is known of its basic biological requirements. Diet composition was quantified and compared between male and female adults, and juveniles of P. schaeferi on Rini Mountain, Yunnan, China from 10-min scan samples from October 2006 to February 2007. In total, 17 food species were identified though only six species ( Opuntia ficus-indica , Themeda triandra , Festuca durata , Polygonum thunbergii , Elsholtzia cypriani , and Excoecaria acerifolia ) made up nearly 90 % of the diet. Although feeding niches of both adults and juveniles highly overlapped, significant quantitative differences in their food composition were found. Adult male and juvenile diets were the most dissimilar; adult sheep fed more frequently on the introduced succulent cactus, O. ficus-indica , whereas juvenile sheep fed more frequently on the herbs, P. thunbergii and E. cypriani , and the woody shrub, Buddleja caryopteridifolia . Field observations showed that P. schaeferi frequently used its broad curved horns to remove the spines of O. ficusindica , presumably to gain access to the fleshy leaves. We suggest differences in the diets are the result of differential access to the cactus, but may also be influenced by nutritional requirements.

Camera traps are widely used in wildlife surveys because they are non‐invasive, low‐cost, and highly efficient. Camera traps deployed in the wild often produce large datasets, making it increasingly difficult to manually classify images. Deep learning is a machine learning method that provides a tool to automatically identify images, but it requires labeled training samples and high‐performance servers with multiple Graphics Processing Units (GPUs). However, manually preparing large‐scale training images for training deep learning models is labor intensive, and the high‐performance servers with multiple GPUs are often not available for wildlife management agencies and field researchers. Our study explores an adaptive deep learning method to use small‐scale training sets and a commonly‐available, desktop personal computer (PC) to achieve automatic filtering of empty camera images. Our results showed that by using 29,192 training samples, the overall error, commission error, and omission error of the proposed method on a PC were 2.69%, 6.82%, and 6.45%, respectively. Moreover, the accuracy of our method can be adaptively improved on PCs in actual ecological monitoring projects, which would benefit researchers in field settings when only a PC is available. © 2021 The Wildlife Society.

The elevational range where montane species live is a key factor of spatial niche partitioning, because the limits of such ranges are influenced by interspecies interaction, abiotic stress, and dispersal barriers. At the regional scale, unimodal distributions of single species along the elevation gradient have often been reported, while discontinuous patterns, such as bimodal distributions, and potential ecological implications have been rarely discussed. Here, we used extensive camera trap records to reveal the elevation distribution of Himalaya blue sheep (Pseudois nayaur) and its co-existence with other ground animal communities along a slope of Baima Snow Mountain, southwest China. The results show that Himalaya blue sheep exhibited a distinctive bimodal distribution along the elevation gradient contrasting the unimodal distributions found for the other ungulates in Baima snow mountain. A first distributional peak was represented by a population habituating in scree habitat around 4100 m, and a second peak was found in the dry-hot valley around 2600 m. The two distinct populations co-existed with disparate animal communities and these assemblages were similar both in the dry and rainy seasons. The extremely low abundance of blue sheep observed in the densely forested belt at mid-elevation indicates that vegetation rather than temperature is responsible for such segregation. The low-elevation population relied highly on Opuntia ficus-indica, an invasive cactus species that colonized the region six hundred years ago, as food resource. Being the only animal that developed a strategy to feed on this spiky plant, we suggest invasive species may have formed new foraging niche to support blue sheep population in lower elevation hot-dry river valleys, resulting in the geographic separation from the original population and a potential morphological differentiation, as recorded. These findings emphasize the important conservation values of role of ecological functions to identify different taxa, and conservation values of apparent similar species of different ecological functions.

Farmland birds are of conservation concerns around the world. In China, conservation management has focused primarily on natural habitats, whereas little attention has been given to agricultural landscapes. Although agricultural land use is intensive in China, environmental heterogeneity can be highly variable in some regions due to variations in crop and noncrop elements within a landscape. We examined how noncrop heterogeneity, crop heterogeneity, and noncrop features (noncrop vegetation and water body such as open water) influenced species richness and abundance of all birds as well as three functional groups (woodland species, agricultural land species, and agricultural wetland species) in the paddy‐dominated landscapes of Erhai water basin situated in northwest Yunnan, China. Birds, crop, and noncrop vegetation surveys in twenty 1 km × 1 km landscape plots were conducted during the winter season (from 2014 to 2015). The results revealed that bird community compositions were best explained by amounts of noncrop vegetation and compositional heterogeneity of noncrop habitat (Shannon–Wiener index). Both variables also had a positive effect on richness and abundance of woodland species. Richness of agricultural wetland species increased with increasing areas of water bodies within the landscape plot. Richness of total species was also greater in the landscapes characterized by larger areas of water bodies, high proportion of noncrop vegetation, high compositional heterogeneity of noncrop habitat, or small field patches (high crop configurational heterogeneity). Crop compositional heterogeneity did not show significant effects neither on the whole community (all birds) nor on any of the three functional groups considered. These findings suggest that total bird diversity and some functional groups, especially woodland species, would benefit from increases in the proportion of noncrop features such as woody vegetation and water bodies as well as compositional heterogeneity of noncrop features within landscape.