Frontiers in Zoology最新文献

Background: Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification.

Results: In this study, we investigated the capability of deep learning models to classify 7 horseshoe bat taxa (CHIROPTERA: Rhinolophus) from Southern China. We constructed an image dataset of 879 front, oblique, and lateral targeted facial images of live individuals collected during surveys between 2012 and 2021. All images were taken using a standard photograph protocol and setting aimed at enhancing the effectiveness of the DCNNs classification. The results demonstrated that our customized VGG16-CBAM model achieved up to 92.15% classification accuracy with better performance than other mainstream models. Furthermore, the Grad-CAM visualization reveals that the model pays more attention to the taxonomic key regions in the decision-making process, and these regions are often preferred by bat taxonomists for the classification of horseshoe bats, corroborating the validity of our methods.

Conclusion: Our finding will inspire further research on image-based automatic classification of chiropteran species for early detection and potential application in taxonomy.

A comprehensive understanding of the dietary habits of carnivores is essential to get ecological insights into their role in the ecosystem, potential competition with other carnivorous species, and their effect on prey populations. Genetic analysis of non-invasive samples, such as scats, can supplement behavioural or microscopic diet investigations. The objective of this study was to employ DNA metabarcoding to accurately determine the prey species in grey wolf (Canis lupus) and Eurasian lynx (Lynx lynx) scat samples collected in the Julian Alps and the Dinaric Mountains, Slovenia. The primary prey of wolves were red deer (Cervus elaphus) (detected in 96% scat samples), European roe deer (Capreolus capreolus) (68%), and wild boar (Sus scrofa) (45%). A smaller portion of their diet consisted of mesocarnivores, small mammals, and domestic animals. In contrast, the lynx diet mostly consisted of European roe deer (82%) and red deer (64%). However, small mammals and domestic animals were also present in lynx diet, albeit to a lesser extent. Our findings indicate that the dietary habits of wolves and lynx are influenced by geographical location. Snapshot dietary analyses using metabarcoding are valuable for comprehending the behaviour and ecology of predators, and for devising conservation measures aimed at sustainable management of both their natural habitats and prey populations. However, to gain a more detailed understanding of wolf and lynx dietary habits and ecological impact, it would be essential to conduct long-term genetic monitoring of their diet.

Background: Recent climate changes have produced extreme climate events. This study focused on extreme snowfall and intended to discuss the vulnerability of temperate mammals against it through interspecies comparisons of spatial niches in northern Japan. We constructed niche models for seven non-hibernating species through wide-scaled snow tracking on skis, whose total survey length was 1144 km.

Results: We detected a low correlation (r_s < 0.4) between most pairs of species niches, indicating that most species possessed different overwintering tactics. A morphological advantage in locomotion cost on snow did not always expand niche breadth. In contrast, a spatial niche could respond to (1) drastic landscape change by a diminishing understory due to snow, possibly leading to changes in predator-prey interactions, and (2) the mass of cold air, affecting thermoregulatory cost and food accessibility. When extraordinary snowfall occurred, the nonarboreal species with larger body sizes could niche shift, whereas the smaller-sized or semi-arboreal mammals did not. In addition, compared to omnivores, herbivores were prone to severe restriction of niche breadth due to a reduction in food accessibility under extreme climates.

Conclusions: Dietary habits and body size could determine the redundancy of niche width, which may govern robustness/vulnerability to extreme snowfall events.