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Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future. [ABSTRACT FROM AUTHOR]

Peatland vegetation takes up mercury (Hg) from the atmosphere, typically contributing to net production and export of neurotoxic methyl-Hg to downstream ecosystems. Chemical reduction processes can slow down methyl-Hg production by releasing Hg from peat back to the atmosphere. The extent of these processes remains, however, unclear. Here we present results from a comprehensive study covering concentrations and isotopic signatures of Hg in an open boreal peatland system to identify post-depositional Hg redox transformation processes. Isotope mass balances suggest photoreduction of HgII is the predominant process by which 30% of annually deposited Hg is emitted back to the atmosphere. Isotopic analyses indicate that above the water table, dark abiotic oxidation decreases peat soil gaseous Hg0 concentrations. Below the water table, supersaturation of gaseous Hg is likely created more by direct photoreduction of rainfall rather than by reduction and release of Hg from the peat soil. Identification and quantification of these light-driven and dark redox processes advance our understanding of the fate of Hg in peatlands, including the potential for mobilization and methylation of HgII. Mercury isotope signatures in groundwater, soil gas, solid peat, and atmosphere suggest that dark abiotic reduction of peat soil HgII to volatile Hg0 does not play a significant role in mobilizing Hg during peat mass loss [ABSTRACT FROM AUTHOR]

Purpose: The environmental safety of both nano-TiO particles and mercury (Hg) pollution has been of widespread concern. Soils and sediments were the main receiving environments of nano-TiO and Hg. There was a strong association between nano-TiO with Hg compounds. Very limited research has been carried out on the impact of nano-TiO on the environmental behavior of Hg in sediments. The objective of this study was to investigate the effects of nano-TiO on the release and transformation of Hg in sediments. Materials and methods: Two types of TiO nanoparticles were employed. Sediment samples were collected in the water level fluctuating zone of the Three Gorges Reservoir, China. Sediment samples spiked with HgCl solution were taken as Hg-contaminated sediments. The flooding experiments were conducted using capped borosilicate glass bottles containing 300 ml TiO suspensions (0.2 g L and 0.4 g L) and 30 g original or Hg-contaminated sediment samples outdoors. All experiments were conducted in triplicate. Water samples were collected at determined interval times over a 60-day period. Sediments were collected on the 60th day. Total Hg (THg) and methylmercury (MHg) in both water and sediment samples and Hg speciation just in sediment samples were determined. A series of control samples without TiO addition were also prepared. Results and discussion: The results showed that nano-TiO particles at high content (4 g kg) promoted the release of Hg in sediments, and the anatase particles showed a stronger effect. Methyl Hg concentrations in waters of nano-TiO treatments significantly decreased by the 10th day. The effect of nano-TiO greatly depended on anatase and rutile particles. After that, methyl Hg concentrations had no significant difference between nano-TiO treatments and the control, except 2 g kg rutile treatment by the 20th day. The changes of Hg speciation in sediments demonstrated that nano-TiO inhibited the transformation of Hg-bound organic matter into the oxidated state, especially upon 2 g kg rutile treatment. This was consistent with the lower MHg concentrations in the 2 g kg rutile treatment. Conclusions: Both anatase and rutile nano-TiO were found to affect the release of Hg in sediment. Nano-TiO promoted THg release in sediments and posed an increased risk of Hg contamination in waters, especially for anatase particles. Moreover, nano-TiO inhibited the methylation process of Hg in sediments at the initial stage of flooding and could affect the transformation of Hg speciation in sediments, especially for low-content rutile particles. [ABSTRACT FROM AUTHOR]