Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change.

Leishmaniasis is a zoonotic disease caused by protozoan parasites of the genus Leishmania, transmitted by phlebotomine sandflies. While domestic dogs are the main hosts in the Mediterranean basin, wild carnivores have also been proposed as potential reservoirs. This study assessed the presence of Leishmania spp. in 250 animals from the Valencian Community, eastern Spain—an endemic region—using TaqMan qPCR on spleen samples from 216 wild carnivores and sera from 34 feral cats, and ELISA serology on 174 wild carnivores. DNA of Leishmania spp. was detected in 14 out of 250 individuals (5.6%), with red foxes representing most positive cases (10/102; 9.8%). Seropositivity was observed only in red foxes, with 5 out of 174 individuals testing positive (2.9%). Most qPCR-positive animals had high Ct values, consistent with low parasite loads. One fox, positive by both methods, showed advanced skin lesions and was later diagnosed with sarcoptic mange, suggesting possible interaction with Leishmania infection. The overall low prevalence and parasite burden suggest limited circulation in the surveyed wildlife. These findings contribute to understanding the epidemiological role of wild mesocarnivores and highlight the relevance of wildlife monitoring within a One Health approach.

Canine distemper virus (CDV) is a highly infectious morbillivirus posing a significant threat to global carnivore biodiversity. While domestic dogs are primary reservoirs, CDV circulates among solitary wildlife, suggesting complex maintenance mechanisms. We assessed CDV prevalence via RT-qPCR and serology in 106 free-ranging wild carnivores (families Canidae, Mustelidae, and Viverridae) collected opportunistically in the Valencian Community, Spain, between 2022 and 2025. Brain and lung tissues were screened for viral RNA, and 84 individuals were tested for antibodies. The overall molecular prevalence was 11.3% (n=12), with most positives identified in red foxes (Vulpes vulpes). Ct values (10.16–34.79) suggested active or recent infections. Seropositivity was observed in 9.5% (8/84) of the animals. Notably, viral RNA was detected in the brain of a pregnant common genet (Genetta genetta), though fetuses and placental tissues remained negative. These findings confirm ongoing CDV circulation in eastern Spain and underscore the necessity of long-term surveillance in regions considered for future conservation initiatives, such as the reintroduction of the endangered Iberian lynx (Lynx pardinus).

The limited availability of labelled visual data remains a major constraint for training reliable species classifiers in ecological applications. Although synthetic data generation using generative artificial intelligence represents a promising alternative, robust methodological frameworks are still lacking to evaluate its effectiveness and its ability to generalise to real-world data.In this work, we present a reproducible framework based on biologically informed prompt engineering to generate ecologically plausible synthetic data and systematically assess synthetic-to-real generalisation. Two widely used generative paradigms, Text-to-Video (T2V) and Image-to-Video (I2V), were employed to construct training datasets. These datasets were used to train five convolutional neural network architectures, ranging from lightweight models to pre-trained deep networks, using invasive psittacines as a case study.Evaluation on an independent set of unseen real-world images showed that both paradigms achieve comparable overall performance across architectures, with no statistically significant differences in F1-score. However, clear differences emerged in error profiles: models trained with I2V exhibited higher precision and fewer false positives, whereas those trained with T2V achieved higher recall at the cost of increased false positives. These patterns were consistent across runs, indicating that they reflect systematic differences between generative paradigms rather than random variability.The results demonstrate that synthetic video data can serve as a robust and cost-effective complement to real-world data in ecological classification tasks, provided that its generation and evaluation are embedded within a rigorous and reproducible methodological framework.The code and data used in this study are available for peer review in an anonymous private repository: