This study used time series remote sensing data from 1973, 1990 and 2004 to assess spatial forest cover change patterns in the Kalakad-Mundanthurai Tiger Reserve (KMTR), South Western Ghats (India). Analysis of forest cover changes and its causes are the most challenging areas of landscape ecology, especially due to the absence of temporal ground data and comparable space platform based data. Comparing remotely sensed data from three different sources with sensors having different spatial and spectral resolution presented a technical challenge. Quantitative change analysis over a long period provided a valuable insight into forest cover dynamics in this area. Time-series maps were combined within a geographical information system (GIS) with biotic and abiotic factors for modelling its future change. The land-cover change has been modelled using GEOMOD and predicted for year 2020 using the current disturbance scenario. Comparison of the forest change maps over the 31-year period shows that evergreen forest being degraded (16%) primarily in the form of selective logging and clear felling to raise plantations of coffee, tea and cardamom. The natural disturbances such as forest fire, wildlife grazing, invasions after clearance and soil erosion induced by anthropogenic pressure over the decades are the reasons of forest cover change in KMTR. The study demonstrates the role of remote sensing and GIS in monitoring of large-coverage of forest area continuously for a given region over time more precisely and in cost-effective manner which will be ideal for conservation planning and prioritization.
Aglaia bourdillonii is a plant narrowly endemic to the southern portion of the Western Ghats (WG), in peninsular India. To understand its ecological and geographic distribution, we used ecological niche modeling (ENM) based on detailed distributional information recently gathered, in relation to detailed climatic data sets. The ENMs successfully reconstructed key features of the species' geographic distribution, focusing almost entirely on the southern WG. Much of the species' distributional potential is already under protection, but our analysis allows identification of key zones for additional protection, all of which are adjacent to existing protected areas. ENM provides a useful tool for understanding the natural history of such rare and endangered species.
The present study mapped the potential geographic distribution of subspecies of slender loris Loris lydekkerianus from peninsular India. We utilized occurrence records of more than 300 confirmed sightings of slender lorises to model the species' potential geographic distribution by applying an ecological niche modeling (ENM) framework using a desktop genetic algorithm for ruleset prediction (GARP) algorithm. Results indicate that the modeled potential distribution of a morphologically different and hitherto undescribed subspecies of slender loris is noticeably different in geographic space from the 2 known subspecies found within peninsular India. The potential geographic distribution of this subspecies appears to occupy a distinct and intermediate climate region running along the eastern fringe of the southern Western Ghats. Among the 2 known subspecies, the modeled potential distribution of L. l. lydekkerianus corresponds with a relatively drier climate, largely occupying deciduous and open-scrub forest types, whereas the modeled potential distribution of L. l. malabaricus corresponds with wetter climates, ranging from deciduous to evergreen forest types. The presence of an undescribed subspecies of slender loris demonstrates an urgent need for a detailed exploration within the range modeled by the present study.
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