Search

Simple Summary: Environmental factors varying widely across altitudinal gradients contribute significantly to the intricate and diverse distribution patterns observed in insect communities. Investigating the diversity patterns in the grasshopper community along altitudinal gradients in Xizang is crucial for understanding broader trends in insect diversity. This study revealed a strong effect of altitude on grasshopper community diversity distribution in Xizang, showing that grasshopper species richness, Margalef richness index, Shannon–Wiener index, and Simpson dominance index decreased with an increase in altitude. The results of Pearson correlation analysis and hierarchical partitioning showed that temperature, moisture, and soil properties are closely related to the altitude distribution patterns of grasshopper communities. The key factors driving changes in grasshopper community diversity along altitudinal gradients include the mean annual temperature range, precipitation in the coldest season, and precipitation in the driest month. To summarize, the interplay between elevation and environmental variables significantly influences grasshopper community structure, distribution patterns, and diversity. To determine the grasshopper species composition, altitudinal distribution patterns, and their main drivers, we conducted a study in Xizang using 33 sample plots ranging from 600 to 4100 m. Grasshoppers were collected from August to October during 2020–2022 using sweep nets. A total of 1159 grasshoppers from six families, 28 genera, and 44 species were identified, with Omocestus cuonaensis and Aserratus eminifrontus as the dominant species, comprising 30.03% and 10.26% of total grasshoppers, respectively. The results showed that species richness and the Margalef richness index of grasshopper communities decreased significantly (p < 0.05) with increasing altitude, peaking at 1100–1600 m and lowest values at 2600–3100 m. Similarly, the Shannon–Wiener index and Simpson dominance index also decreased significantly (p < 0.05) with an increase in altitude, showing the highest and lowest values at 600–1100 m and 3100–3600 m, respectively. The Jaccard similarity coefficients among grasshopper communities varied from 0 to 0.40 across altitudinal gradients, indicating different degrees of dissimilarity. The results of Pearson correlation analyses showed that the Shannon–Wiener index, species richness, Margalef richness index, and Simpson dominance index of grasshopper communities were significantly negatively correlated with the temperature factors and soil pH, but they were significantly positively correlated with the moisture factors. Hierarchical partitioning identified annual mean temperature–daily difference, precipitation in the coldest season, and driest month precipitation as the primary factors explaining variance in grasshopper community diversity in Xizang. These findings provided greater insights into the mechanisms underlying insect community structure, distribution patterns, and diversity in Xizang ecosystems, including implications for the effects of global warming on insect communities. [ABSTRACT FROM AUTHOR]

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP. [ABSTRACT FROM AUTHOR]

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model. [ABSTRACT FROM AUTHOR]

Simple Summary: The Amur tiger is an endangered species in the world, and effective statistics on its individuals and population through re-identification will contribute to ecological diversity investigation and assessment. Due to the fact that the fur texture features of the Amur tiger contain genetic information, the main method of identifying Amur tigers is to distinguish their fur and facial features. In summary, this paper proposes a serial multi-scale feature fusion and enhancement network for Amur tiger re-identification, and designs a global inverted pyramid multi-scale feature fusion module and a local dual-domain attention feature enhancement module. We aim to enhance the learning of fine-grained features and differences in fur texture by better fusing and enhancing global and local features. Our proposed network and module have achieved excellent results on the public dataset of the ATRW. The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training. [ABSTRACT FROM AUTHOR]

Magnesium slag-based porous materials (MSBPM) were successfully synthesized using alkali activation and foaming methods as an effective adsorbent for Pb2+ in solution. The effects of foaming agent type, foaming agent dosage, alkali dosage, and water glass modulus on the properties of the MSBPM were studied, and the micromorphology and porosity of the MSBPM were observed using microscopy. The influence of pH value, initial concentration, and adsorbent dosage on the Pb2+ adsorption was investigated. The results showed that a porous material (MSBPM-H2O2) with high compressive strength (8.46 MPa) and excellent Pb2+ adsorption capacity (396.11 mg·g−1) was obtained under the optimal conditions: a H2O2 dosage of 3%, an alkali dosage of 9%, a water glass modulus of 1.3, and a liquid–solid ratio of 0.5. Another porous material (MSBPM-Al) with a compressive strength of 5.27 MPa and the Pb2+ adsorption capacity of 424.89 mg·g−1 was obtained under the optimal conditions: an aluminum powder dosage of 1.5‰, an alkali dosage of 8%, a water glass modulus of 1.0, and a liquid–solid ratio of 0.5. When the pH of the aqueous solution is 6 and the initial Pb2+ concentrations are 200~500 mg·L−1, the MSBPM-H2O2 and MSBPM-Al can remove more than 99% of Pb2+ in the solution. The adsorption process of both materials followed the Langmuir isotherm model and pseudo-second-order kinetic model, indicating that the adsorption process was a single-molecule layer chemical adsorption. [ABSTRACT FROM AUTHOR]

The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability. [ABSTRACT FROM AUTHOR]

Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features. [ABSTRACT FROM AUTHOR]