The Daba Mountain area is recognized as one of the global biodiversity hotspots. In recent years, human–wildlife conflicts (HWCs) in this region have intensified significantly. Understanding and mitigating the damage caused by wildlife is crucial for maintaining ecological balance and enhancing public welfare. We analyzed the current status of HWCs in the southern foothills of the Daba Mountains through semi-structured interviews and field surveys. Additionally, using the collected data on HWC occurrences, we predicted HWC hotspots using the maximum entropy (MaxEnt) model. The results: (1) A total of 449 cases of actual HWC incidents were investigated, primarily involving wild boars (Sus scrofa; 81.96%) and Asiatic black bears (Ursus thibetanus; 18.04%). The main types of HWC included crop destruction and attacks on beehives. (2) Incidents of damage caused by wild boars and Asiatic black bears were concentrated in the summer months, particularly from June to August. (3) In the study area, 21 out of 25 townships reported HWC cases. Notably, the townships of Fuxing, Gaoyan and Gaoguan were identified as the most affected, with Gaoyan and Fuxing experiencing the highest amounts of loss. (4) The variable of DTP (Distance to Protected Area) was found to have the greatest influence on the hotspot distributions of HWCs. The hotspot zone exhibited a strip-like distribution along the northwest–southeast axis in the central part of the study area. The total area of the HWC hotspots was 1352.56 km². The largest hotspot areas were located in Xiuqi Town, Gaoyan Town, and Houping Township, accounting for 13.26%, 12.01%, and 7.83% of the total hotspot area, respectively. HWC hotspots require robust management measures to mitigate wildlife damage to local communities and enhance indigenous people’s engagement in wildlife conservation. Implementation of physical measures and compensation schemes may be necessary in these hotspots to alleviate the financial burden on indigenous populations. Our findings may provide valuable insights for the future formulation of biodiversity conservation policies and HWC mitigation strategies in the Daba Mountains.

The decline in biodiversity poses a serious threat to natural ecosystems and has become one of the most pressing global environmental issues. Establishing conservation priorities for protected areas (PAs) is one of the most direct and effective biodiversity conservation measures. However, conservation gaps arise as a result of existing problems in spatial layout of PAs, including overlapping protection scopes, artificial fragmentation of natural ecological regions, as well as “over‐protection” and “over‐exploitation.” To optimize the spatial layout of PAs and improve the efficiency of biodiversity conservation, we employed the Habitat Quality module of the Integrated Assessment of Ecosystem Services and Tradeoffs (InVEST) model and the Maximum Entropy (MaxEnt) model to assess the PAs in the Songnen Plain, China. The combined model (MaxEnt‐InVEST) revealed that the conservation priorities for PAs in the Songnen Plain occupied a total area of 14,764.14 km ² (10.24% of the total area of the Songnen Plain). The conservation priorities outside PAs occupied a total area of 7858.45 km ² (5.45% of the total area of the Songnen Plain) and were primarily distributed in the northeastern, central, and southwestern regions of the Songnen Plain. This indicated that existing PAs did not offer adequate protection for local biodiversity. The consistency of our combined modeling framework was 72.11%, which enabled a more accurate assessment of biodiversity hotspots and respects the land uses of the Songnen Plain. In addition, the modeling framework successfully created maps of conservation gaps of biodiversity hotspots based on actual species distribution data and considers current land uses. Our study was aimed at optimizing the spatial conservation efficiency of the Songnen Plain by assessing the conservation gaps in the Songnen Plain. It could provide a reference for the future development of a PA system centering on national parks.

In the development of modern urban gardens, delving into visitor experience perception to address homogenization issues is a crucial means to enhance garden space characteristics and meet diverse demands. This study selected nine representative modern gardens in the Taihu Lake region as samples. Utilizing machine learning techniques combined with image semantic segmentation and principal component analysis, it systematically assessed various microscopic features of gardens and explored their relationship with actual visitor experience ratings. The aim is to reveal emotional resonance and aesthetic preferences during the touring process. The results indicate: (1) Comfort holds a primary position in influencing visitor touring experiences; (2) Microscopic features such as water features, tall vegetation, architecture, and ornametal elements significantly contribute to visitor comfort; (3) The random forest model unveils complex interactions between microscopic features and the overall visitor experience, indicating limitations in traditional linear model evaluations for such relationships. Through analyzing the relationship between microscopic garden features and visitor experiences in the selected cases, this study highlights the importance of adopting non-linear models to optimize garden landscape design and enhance the visitor touring experience.

Climate change poses a significant threat to biodiversity. Predicting the impacts of climate change on species distribution and dispersal through computational models and big data analysis can provide valuable insights. These predictions are crucial for developing effective strategies to mitigate the threats that climate change poses to biodiversity. Our study investigated the potential impact of climate change on an umbrella species (Ursus arctos pruinosus) in Western Sichuan Province, China. We employed the MaxEnt and Circuit Theory to assess both the current and potential future shifts in the distribution and migration corridors. The results indicated that climate and environmental factors had the greatest influence on species distribution, with bioclimatic variables bio12, bio3, and elevation contributing 22.1%, 21.5%, and 19.3%, respectively. Under current climatic conditions, the total suitable habitat area for the species was 70,969.78 km², with the largest suitable habitats located in Shiqu and Litang, accounting for 24.39% and 15.86% of the total area, respectively. However, under future climate scenarios, predictions for RCP 2.6, RCP 4.5, and RCP 8.5 showed a significant reduction in suitable habitat area, ranging from 7789.26 km² to 16,678.85 km². The Yajiang and Xinlong counties experienced the most severe habitat reductions, with declines exceeding 50%. Additionally, the altitudinal distribution of suitable habitats shifted, with suitable habitats gradually moving to higher elevations under future climate scenarios. Our study also analyzed the species’ dispersal paths. Under current climatic conditions, the dispersal paths predominantly followed a northwest-to-southeast orientation. However, by the 2070s, under all three RCPs, dispersal resistance is projected to significantly increase, the density of dispersal paths will decrease, and the connectivity of these paths will be reduced. In the most extreme RCP 8.5 scenario, southern dispersal paths nearly disappeared, and the dispersal paths contracted towards the northwest. These findings highlight potential threats posed by climate change to the species’ habitats and dispersal corridors, emphasizing the importance of considering both current and future climate change in conservation strategies to protect this vulnerable species and its ecosystem.

Invasive alien species often undergo shifts in their ecological niches when they establish themselves in environments that differ from their native habitats. Leptocybe invasa Fisher et LaSalle (Hymenoptera: Eulophidae), specifically, has caused huge economic losses to Eucalyptus trees in Australia. The global spread of eucalyptus cultivation has allowed L. invasa to threaten plantations beyond its native habitat. It is, therefore, urgent to implement effective control measures to mitigate the impact of this pest. The optimized MaxEnt model was used to predict the potential global distribution of L. invasa based on occurrence data and environmental variables. The centroid shift, overlap, unfilling, and expansion (COUE) framework was employed to evaluate niche dynamics during the global invasion process by comparing the ecological niches of L. invasa in both native regions and regions affected by invasions (hereafter referred to as “invaded”). The results indicated that the distribution of L. invasa is primarily influenced by temperature, precipitation, and the human influence index variables. Its ecological niche was shown to have considerably expanded from native to invaded regions. Under future climate scenarios, the potential geographical distribution of L. invasa is projected to be concentrated primarily in East Asia, Southeast Asia, Western Europe, and Southern Oceania. In the future, the potentially suitable areas for the establishment of L. invasa are expected to further expand. This study provides a unified framework for exploring the niche dynamics of invasive alien species globally. Emphasizing early warning and control in uninvaded areas is crucial for minimizing L. invasa ecological and economic threats.