The common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ and K), Pakistan, is increasingly threatened by habitat fragmentation and climate change. This study employs a dual-model approach, integrating Maximum Entropy (MaxEnt) and Random Forest algorithms with multi-source remote sensing data to evaluate leopard habitat suitability. Our analysis identifies land cover (LC), fractional vegetation cover (FVC), elevation, temperature seasonality (bio4), and distance to roads (Dist_road) as the most influential habitat predictors. Leopards exhibit a strong preference for mixed forests at elevations between 1000 and 3000 m, with a suitability index of 0.83. The study identifies several unsuitable conditions including: road proximity (<0.08 km), low elevation zones ( 8 °C), and non-forested land cover types. MaxEnt demonstrated superior habitat prediction accuracy over Random Forest (AUC = 0.912 vs. 0.827). The results highlight a distinct north-to-south suitability gradient, with optimal habitats concentrated in the northern districts (Muzaffarabad, Hattian, Neelum, Bagh, Haveli, Poonch, Sudhnutti) and declining suitability in human-dominated southern areas. Based on these findings, this study underscores the urgency of targeted conservation efforts in the northern districts of AJ and K, where optimal leopard habitats are identified. The findings emphasize the need for habitat connectivity and protection measures to mitigate the impacts of habitat fragmentation and climate change. Future conservation strategies should prioritize the preservation of mixed forests and the establishment of buffer zones around roads to ensure the long-term survival of the common leopard in this region.

Illegal wildlife trade is a major driver of biodiversity loss, and online platforms are increasingly used to advertise protected wildlife. In Pakistan, raptor trade is increasingly mediated by online marketplaces, reflecting a wider global transition. As a case study, we assessed the online trade in raptors in Pakistan from January 2021 to December 2023 by monitoring groups on the social media platforms Facebook, Instagram, YouTube, TikTok and WhatsApp, and local e-commerce websites, using keyword-based searches, with cross-checking to reduce duplication and misidentification. We recorded 310 raptors advertised for sale from 92 seller accounts, representing 24 species across four families. Falconidae comprised 61% of individuals and Accipitridae 37%, with peregrine falcon Falco peregrinus and saker falcon Falco cherrug the most frequently advertised species. A substantial proportion of listings involved species protected under provincial wildlife legislation, as well as species listed under CITES and categorized as threatened on the IUCN Red List. Using a generalized linear model, we found that asking prices were higher for threatened and visitor species. Seller activity was concentrated in Punjab and Khyber Pakhtunkhwa, and Facebook accounted for the largest share of records, indicating that improved monitoring and enforcement on this platform is a priority. Our findings provide baseline evidence to support demand-reduction outreach, community reporting and capacity building within wildlife agencies, alongside improved online detection tools and updated assessments of raptor population status in Pakistan, particularly for migratory species using the Central Asian Flyway.

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems.