Ruddy Shelduck Tadorna ferruginea home range and habitat use during the non-breeding season in Assam, India

India is an important non-breeding ground for migratory waterfowl in the Central Asian Flyway. Millions of birds visit wedands across the country, yet information on their distribution, abundance, and use of resources is rudimentary at best. Limited information suggests that populations of several species of migratory ducks are declining due to encroachment of wedand habitats largely by agriculture and industry. The development of conservation strategies is stymied by a lack of ecological information on these species. We conducted a preliminary assessment of the home range and habitat use of Ruddy Shelduck Tadornaferruginea in the northeast Indian state of Assam. Seven Ruddy Shelducks were fitted with solar-powered Global Positioning System (GPS) satellite transmitters, and were tracked on a daily basis during the winter of 2009-2010. Locations from all seven were used to describe habitat use, while locations from four were used to quantify their home range, as the other three had too few locations (<30) for home range estimation. A Brownian Bridge Movement Model (BBMM), used to estimate home ranges, found that the Ruddy Shelduck had an average core use area (i.e. the contour defining 50% of positions) of 40 km 2 (range = 22-87 km2) and an average home range (95% contour) of 610 km2 (range = 222-1,550 km2). Resource Selection Functions (RSF), used to describe habitat use, showed that the birds frequented riverine wetlands more than expected, occurred on grasslands and shrublands in proportion to their availability, and avoided woods and cropland habitats. The core use areas for three individuals (75%) were on the Brahmaputra River, indicating their preference for riverine habitats. Management and protection of riverine habitats and nearby grasslands may benefit conservation efforts for the Ruddy Shelduck and waterfowl species that share these habitats during the non-breeding seaso

Occurrence of Indian Wolf Canis Lupus Pallipes in the Pench Tiger Reserve, Madhya Pradesh

Volume: 101Start Page: 149End Page: 15

Spatio-temporal pattern of urban eco-environmental quality of Indian megacities using geo-spatial techniques

Rapid urbanization is often responsible for the degradation of urban eco-environmental quality (UEQ), which is comprised of ecological, environmental and anthropogenic components. Hence, frequently monitoring and tracking UEQ for sustainable cities and communities is recommendable. This study, attempted to compare UEQ of three rapidly growing Indian metros/megacities – Ahmedabad, Hyderabad and Bangalore. A remote sensing-based composite index, namely ‘urban eco-environmental quality index’ (UEQI), was constructed by utilizing Landsat 5 (TM), 8 (OLI-TIRS) satellite imageries and MODIS LST products of 1999/2000–2001 and 2018. Five vegetation indices (i.e. NDVI, SAVI, GVI, NDMI and TcWet) and four urban indices (i.e. BI, NDBI, UI and DTR) were categorized as per pressure-state-response (PSR) framework and integrated through spatial principal component analysis (SPCA) to construct the UEQI. The sensitivity and elasticity of UEQI had been tested with respect to population density (PD) and the percentage of impervious surface (IS). Subsequently, the spatio-temporal pattern (i.e. (non)sequential transition – from excellent to very poor) and spatial heterogeneity of UEQ were investigated using Moran’s I, and local indicator of spatial auto-correlation (LISA). Result indicated overall UEQI value for selected cities during studied periods ranged from 0.20 to 2.20, where lower and higher value referred very poor and excellent UEQ, respectively. The most degraded UEQ was found in Bangalore (average UEQI values were 1.08 and 0.80 in 2001 and 2019, respectively); and the degradation rate was also quite inflated than other cities (i.e. UEQI 1.64 per year). Additionally, the spatio-temporal pattern of UEQI demonstrated that very poor and poor UEQ were primarily clustered in the city’s centre and spilling out towards the outskirts of all cities during the studied periods. Overall, the proportional area under very poor UEQ category was increased i.e., 7.94%, 7.03% and 5.24% for Bangalore, Ahmedabad and Hyderabad, respectively. The non-sequential transition (i.e. excellent to poor or very poor) was prominent in Bangalore, which implied that rapid and abrupt degradation of UEQ. Whereas, Ahmedabad and Hyderabad followed mostly sequential transition. Besides, significant global (Moran’s I = > 0.80) and LISA confirmed the non-randomness pattern of UEQ for three cities. The analysis of sensitivity ensured both PD and IS were strongly influenced poor UEQI (as R2 ≥ 0.50). Elasticity of UEQI revealed 1% increase of IS would lead to declining of 0.64%, 0.46% and 0.21% of UEQI in Ahmedabad, Hyderabad and Bangalore, respectively. Moreover, the study’s observation and findings could also be used for possible area intervention for ‘area-based development’ (ADB) and ‘greenfield development’ (GFD) plan, as recommended by ‘climate-resilient smart city mission’

Debarking of Teak Tectona Grandis Linn. F. by Gaur Bos Gaurus H. Smith During Summer in a Tropical Dry Deciduous Habitat of Central India

Volume: 99Start Page: 238End Page: 24

Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier

In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namely Bagging-MLPC, Dagging-MLPC, Decorate-MLPC which are a combination Multi-layer Perceptron Neural Network Classifier (MLPC) and Bagging, Dagging, and Decorate ensemble methods, respectively. The proposed models were trained and validated with the help of 103 historical landslide events (divided into 2 samples: training (70%) and validation (30%)) and 12 landslide conditioning factors. The accuracy of the models was evaluated using different statistical methods including Area Under Curve (AUC) of Receiver Operating Characteristic (ROC). The results show that though performance of all the studied models is good (AUC > 0.80) but of the hybrid Bagging-MLPC model is the best (AUC:0.965). Therefore, this newly hybrid model (Bagging-MLPC) can be used for the accurate landslide susceptibility mapping and assessment of landslide prone areas for landslide prevention and management