Understanding how sympatric carnivores coexist in human-dominated landscapes is crucial for their conservation. This study investigated the mechanisms of niche partitioning among carnivores under human disturbance in the non-protected area surrounding Bujia Snow Mountain, eastern Qinghai-Tibet Plateau. We employed a multi-dimensional approach integrating dietary analysis, spatial occupancy modeling, and temporal activity rhythm analysis, based on camera-trapping data and scat samples collected from 2022 to 2023. The predator community included the snow leopard (Panthera uncia), gray wolf (Canis lupus), Eurasian lynx (Lynx lynx), red fox (Vulpes vulpes), leopard cat (Prionailurus bengalensis), and brown bear (Ursus arctos). Dietary niche overlap was high between snow leopard and wolf (Pianka's Overlap is 0.78, P<0.001) and between red fox and leopard cat (Pianka's Overlap is 0.74, P>0.05), but other pairs showed moderate to low overlap. Wolves heavily relied on domestic cattle (%PA=81.25%), which may intensify human–carnivore conflict. Spatial co-occurrence was common among carnivores (SIF>1 for multiple species pairs), with no statistically significant avoidance. Temporal activity patterns showed clear differentiation: snow leopards exhibited no distinct diel rhythm or slight nocturnality, wolves showed slight diurnally except in early spring, and lynx and red fox were predominantly nocturnal. Notably, species responded differently to human activity: snow leopard occupancy was negatively associated with proximity to roads during high-disturbance periods, whereas wolves selected areas closer to roads and showed a positive association with cattle abundance. Our findings demonstrate that in this human-influenced landscape, carnivores achieve coexistence through multidimensional niche partitioning in diet and time, coupled with divergent responses to anthropogenic pressures. This study highlights the need for tailored conservation strategies and supports the potential of the snow leopard as an umbrella species for broader community-level conservation.
The abandonment of farmland necessitates the implementation of intelligent monitoring platforms that are tailored to the specific requirements of relevant stakeholders. However, the prevailing solutions in this domain tend to prioritize technical performance over considerations of seasonality. The present study proposes a Reverse Quality Function Deployment (R-QFD) framework integrating interval type-2 fuzzy sets, Regret theory, and grey-entropy TOPSIS to analyze and prioritize user requirements for smart farmland abandonment governance. To address these challenges, the model incorporates interval type-2 fuzzy sets to manage linguistic uncertainty, regret theory to capture behavioral biases, and a feedback-driven R-QFD mechanism for adaptive design. Using data from 204 stakeholders in Jiangxi Province, China, this model revealed distinct seasonal demand fluctuations: land abandonment warnings predominate in spring; online customer service reaches its zenith in summer; land transfer information becomes paramount in autumn; and online service experiences a resurgence in winter. The recommendations made in this study include prioritizing seasonal features, incorporating behavioral feedback loops, and using modular architectures. The present study proposes a replicable, behavior-aware demand analysis model for smart agriculture and associated technology governance.
Private forests (PFs) are an important component of Nepal's forestry sector, providing social, ecological, and economic benefits. However, their development remains constrained by institutional, regulatory, and operational barriers. This study examined the status, management practices, and institutional challenges of private forests in Parsa District of Madhesh Province, Nepal, using a mixed-methods approach. Primary data were collected through a questionnaire survey of 34 registered private forest owners (20% of 168 registered PFs), eight key informant interviews, four focus group discussions, and direct field observation. Secondary data from institutional records, policies, regulations, and relevant literature were used to complement and triangulate the primary data. Results showed that registration peaked during 2005–2010, coinciding with external project support, but subsequently stagnated amid complex, multi-agency administrative procedures. This has created a registration gap in which policy recognizes private ownership but provides limited post-registration support. Management practices were largely market-oriented, with preferences for fast-growing, high-value species such as Eucalyptus camaldulensis, Tectona grandis, and Dalbergia sissoo, potentially limiting native species diversity. Private forests provided multiple livelihood and ecosystem benefits, including fuelwood (27.97%), timber, fodder, fruits, and contributions to soil and biodiversity conservation. However, their productivity and sustainability were constrained by pest and weed infestations, seedling mortality and theft, inadequate irrigation, and limited access to quality planting materials. A chi-square goodness-of-fit test indicated a significant concentration of responses around bureaucratic permitting procedures (χ2=11.65, P=0.009), which were the most frequently reported policy barrier. Value-added taxation, species-specific restrictions, and weak post-registration monitoring further constrained private forest development. Simplifying regulatory procedures, strengthening institutional and technical support, and improving policy coherence are therefore important for enhancing private forestry and promoting sustainable forest management in Nepal.
This study explores a methodology for evaluating the component elements of health tourism destinations from the perspective of “therapeutic landscapes”. An index system for evaluating suitable areas of health tourism was established, and an empirical analysis was conducted using Hainan Island as a case study. The findings revealed that the primary constituent elements of health tourism area are natural and cultural environments, health tourism resources, and health facilities. The evaluation indicators for developmental suitability include a healthy environment, health tourism resources, health facilities, and developmental conditions, with weights of 0.53, 0.27, 0.13, and 0.07, respectively. The evaluated areas were categorized into four zones, high suitability zone (24.47%), moderate suitability zone (43.99%), marginal suitability zone (23.24%), and unsuitable zone (8.3%), which can provide spatial references for optimizing the layout of health tourism products. This study advances the perspectives and content in health geography and health tourism studies, and it offers insights to support the sustainable development of health tourism destinations.
The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is rich in cultural heritage resources and has maintained strong momentum in tourism development. However, the region is also highly vulnerable to frequent, severe weather events such as typhoons and rainstorms, which pose significant challenges to heritage conservation and sustainable tourism development. This study aims to establish a targeted resilience evaluation system and clarify its underlying mechanism. Using the Delphi method and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, this study integrated expert opinions through multiple rounds of consultation. It then assessed the causal relationships among indicators and ultimately established an evaluation system comprising five dimensions and 19 specific indicators. The main results are threefold. First, the five dimensions are ranked by weight as follows: economic resilience, physical resilience, institutional resilience, social resilience, and cultural resilience. Second, the weight distributions for each dimension and specific indicator were clarified, and the weight allocations primarily reflect characteristics such as the outward-oriented nature of the GBA's tourism economy, the high frequency of compound disasters, and the need for cross-border coordination. Third, the DEMATEL method further identified causal networks among the dimensions, which revealed the driving factors for priority interventions. The findings can provide direct decision-making support and practical guidance for heritage tourism destinations in the GBA to optimize their disaster prevention and mitigation strategies and achieve coordinated development between heritage conservation and the tourism industry.
