Frontiers in Zoology最新文献

The investigation of the effects of artificial 50 Hz electric field (E-field) frequency on Apis mellifera is a relatively new field of research. Since the current literature focuses mainly on short-term effects, it is unknown whether E-fields have permanent effects on bees or whether their effects can be neutralized. In this study we assessed gene expression immediately after exposure to the E-field, as well as 7 days after exposure. The aim of this work was to identify potentially dysregulated gene transcripts in honey bees that correlate with exposure time and duration to E-fields.Newly emerged bees were marked daily with a permanent marker (one color for each group). Then bees were exposed to the 50 Hz E-field with an intensity of 5.0 kV/m or 10.0 kV/m for 1-3 h. After exposure, half of the bees were analyzed for gene expression changes. The other half were transferred to a colony kept in a mini-hive. After 7 days, marked bees were collected from the mini-hive for further analysis. Six regulated transcripts were selected of transcripts involved in oxidative phosphorylation (COX5a) and transcripts involved in endocrine functions (HBG-3, ILP-1), mitochondrial inner membrane transport (TIM10), and aging (mRPL18, mRPS30).Our study showed that in Apis mellifera the expression of selected genes is altered in different ways after exposure to 50 Hz electric fields -. Most of those expression changes in Cox5a, mRPL18, mRPS30, and HGB3, were measurable 7 days after a 1-3 h exposure. These results indicate that some E-field effects may be long-term effects on honey bees due to E-field exposure, and they can be observed 7 days after exposure.

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.