Identification of Major Threats of Climate Change, Hazards, and Anthropogenic Activities on Biodiversity Conservation in the Buxa Tiger Reserve, India.

The Himalayan forests are facing a range of threats, which are making conservation efforts challenging. Using a mixed-method approach, this study investigated the threats to biodiversity conservation in Buxa Tiger Reserve (BTR), a fragile ecosystem in the Eastern Himalayan foothills. The study found that between 1990 and 2021, BTR experienced rising summer temperatures and decreasing annual precipitation, contributing to forest dryness, water scarcity, and forest fires. Natural disasters such as floods, flash floods, earthquakes, and landslides also caused significant damage to wildlife habitats. Changes in land use and land cover, including encroachment, infrastructure development, fuelwood collection, and grazing practices, were also identified as significant drivers of ecosystem alteration. Besides, hunting and poaching also emerged as threats to wildlife populations in the reserve. By employing the Analytic Hierarchy Process (AHP), the study determined that land use change, infrastructure development, climate change, livestock grazing, and fuelwood collection pose significant threats to flora conservation outcomes in BTR, while infrastructure development, climate change, livestock grazing, and forest fires are the primary threats to wildlife conservation outcomes in the reserve. The study recommends the regulation of land use practices, promotion of sustainable livelihoods for local communities, effective conservation strategies, and public awareness and education programs to promote the value of biodiversity conservation.

Factors Influencing the Feasibility, Effectiveness, and Sustainability of Farmers' Adaptation Strategies to Climate Change in The Indian Eastern Himalayan Foothills.

The rapidly changing climatic conditions are adversely impacting the Indian agricultural sector. Farmers are frequently seen adopting several adaptation measures, which are neither equally efficient nor mutually exclusive. Based on the primary data collected from 300 farming households of the Indian Eastern Himalayan foothills, the present study attempts to examine the efficiency of local farmers' adaptation by developing indices combining the feasibility, effectiveness, and sustainability of the adaptation measures with the scale of actual adoption. Further, by employing multiple linear regression, the study analyzes the internal (psychological) and external (physical and socio-economic) factors influencing higher scores of these indices. Results show that local farmers are well aware of climate change and are responding through implementing at least one and up to seven adaptation measures. Farmers preferred agroforestry, a shift from cereals to low water-intensive commercials, irrigation, and intensification of winter crops as the most efficient. There was, however, a misalignment between the perceived efficiency of adaptation measures and their scale of adoption. Farmers' perceptions of pest infestation, satisfaction with farming, soil characteristics, farm size, remittances, and access to credit were found to be positively and significantly influencing the adaptation indices, while open-mindedness toward changing farming practices and crop-raiding by elephants were found to be negatively and significantly associated with adaptation indices. Lastly, the study made relevant recommendations for improving farmers' efficiency in adopting appropriate adaptation measures and strengthening the "State Action Plan on Climate Change".

Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis.

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.