The confiscation and release of animals into the wild are common conservation responses to the illegal wildlife trade, yet their effectiveness remains uncertain, especially for cryptic and territorial species. We assessed the post-release survival and spatial ecology of nine confiscated Bengal slow lorises (Nycticebus bengalensis) translocated to a fragmented forest patch in northeastern Bangladesh. Using radio telemetry, we tracked individuals for 138 nights across eight months. Only two lorises (22.2 %) survived beyond six months, while the others died within days to weeks, including four individuals to fatal intraspecific aggression. Survivors exhibited significantly larger home ranges (95 % KDE: male = 187.7 ha, female = 57.5 ha) than deceased individuals. Activity budgets revealed higher alertness and reduced locomotion in lorises that died, suggesting chronic stress and poor habitat adaptation. Survival was negatively correlated with duration in captivity prior to release. Our findings highlight critical challenges in translocating highly territorial nocturnal animals, including territorial conflict and inadequate release protocols. We recommend rigorous site assessments, long-term monitoring, and species-specific rehabilitation guidelines to improve outcomes of welfare-based translocations for slow lorises and similar species.
The present study examined the morpho-cultural and molecular characterisation of Colletotrichum gloeosporioides, as well as the disease incidence and severity % under field conditions and the bioefficacy of native biogents in in vitro and in field conditions. The field experiment included a control and treatment with Trichoderma viride (2%), Bacillus subtilis (2%), Pseudomonas fluorescens (2%) and copper oxychloride (3%) applied under mulch and non-mulch conditions and replicated thrice. The pathogen was confirmed as C. gloeosporioides, and the sequenced DNA of the pathogen was submitted to NCBI under accession No. OR101922. The maximum disease incidence (34.16%) and severity (20.33%) were under non-mulched conditions, while T. viride resulted in the lowest incidence (23.09%) and severity (14.16%). Similarly, under in vitro, T. viride showed the highest inhibition (72.44%) and the lowest mycelium growth (3.01 cm) in biculture. Additionally, scanning electron microscope analysis revealed lysis and distortion effects of T. viride on the mycelium of C. gloeosporioides.
Air quality in India faces significant risk from agricultural residue burning, especially in Punjab and Haryana, which are pivotal to the world's second-largest agrarian economy. This study quantifies emissions from post-monsoon biomass burning (10 October-30 November 2022) in these states using VIIRS fire detection data and Sentinel-2-derived burnt areas. Ground validation via district-level surveys aligns with the findings of our study. Results show 51% of the total crop area was burned (14,700 km2 in Punjab; 8,300 km2 in Haryana), leading to substantial emissions of PM2.5 (54.28 Gg; 7.94 Gg), CH4 (25.63 Gg; 3.75 Gg), CO2 (1,100.3 Gg; 195.7 Gg), NH3 (0.83 Gg; 0.15 Gg), SO2 (0.68 Gg; 0.12 Gg), and CO (62.1 Gg; 11.04 Gg). Emissions in Punjab are about 6.5 times higher than in Haryana attributable to greater burnt area (similar to 14,700 km2), higher crop yield, and elevated residue-to-crop ratios. Compared to VIIRS, Sentinel-2 data provides approximately 3.6 times higher emission estimates, reflecting improved burnt area detection. District-level emission variations underscore the influence of diverse farming practices, weather, and residue management. An uncertainty analysis, derived from multiple emissions estimates and methodologies, highlights regional disparities: SO2 exhibits the highest uncertainty in both states with PM2.5 and CO, respectively, showing the least. Understanding these emissions and uncertainties is vital for forecasting air pollution in downwind cities such as New Delhi and for formulating targeted mitigation strategies.
The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations.
Solar energy is a renewable source that is suitable for local applications due to its low operating costs, its environmentally friendly structure, its simpler technology infrastructure, and its clean and inexhaustible supply. In T & uuml;rkiye, the installation of solar power plants (SPP) is increasing in direct proportion to the demand for renewable energy. Within this scope, the site selection problem is vital in order to effectively solve the energy problems, minimize the existing costs, and ensure sustainability. The final decision of the SPP location selection involves numerous subjective and objective evaluation factors, making it a challenging and complex decision-making process. In this study, a new integrated model is introduced that combines fuzzy set theory, analytic hierarchy process (AHP), geographic information systems (GIS), and information axiom methods to address the SPP site selection problems. In this paper, an application has been presented to illustrate the effectiveness of the proposed model. In the context of this study, the most suitable location for a solar power plant (SPP) was identified among six different provinces in T & uuml;rkiye (Erzurum, Kayseri, Mersin, Mugla, Sanliurfa, and Van) by considering both quantitative and qualitative criteria. These provinces have different characteristics and are located in different regions of T & uuml;rkiye. The result of this investigation was that Mersin was determined to be the most appropriate location for the SPP. Furthermore, a sensitivity analysis has been performed in order to examine the impact of the weights of the evaluation criteria on the results of the application.
