Anthropogenic landscape changes such as land use change and habitat fragmentation are known to alter wildlife diversity. Since host and parasite diversities are strongly connected, landscape changes are also likely to change wildlife parasite diversity with implication for wildlife health. However, research linking anthropogenic landscape change and wildlife parasite diversity is limited, especially comparing effects of land use change and habitat fragmentation, which often cooccur but may affect parasite diversity substantially differently. Here, we assessed how anthropogenic land use change (presence of plantation, livestock foraging and human settlement) and habitat fragmentation may change the gastrointestinal parasite diversity of wild mammalian host species (n = 23) in Anamalai hills, India. We found that presence of plantations, and potentially livestock, significantly increased parasite diversity due possibly to spillover of parasites from livestock to wildlife. However, effect of habitat fragmentation on parasite diversity was not significant. Together, our results showed how human activities may increase wildlife parasite diversity within human-dominated landscape and highlighted the complex pattern of parasite diversity distribution as a result of cooccurrence of multiple anthropogenic landscape changes.
Tigers continue to face unprecedented threats to their existence due to poaching, habitat loss, habitat fragmentation and anthropogenic disturbances. The present study examines the physiological stress response of tigers due to anthropogenic activities including wildlife tourism in Bandhavgarh Tiger Reserve and Kanha Tiger Reserve using faecal glucocorticoid metabolite (fGCM) measurement. We collected a total of 341 faecal samples from both reserves during tourism and non-tourism periods. Data on various anthropogenic disturbances including tourism activities like number of vehicles and visitors were also collected. We ascertained the species identity and sex of all the samples collected using genetic markers. fGCMs were extracted using a previously reported procedure, and fGCM concentrations were subsequently determined using an established enzyme immunoassay. There was no significant difference in overall mean fGCM concentrations between the two tiger reserves, but within each reserve, concentrations were significantly higher in tigers during the tourism period as compared to the non-tourism period. We also found that the number of tourist vehicles and disturbance level significantly correlated with fGCM concentrations. This study further supports the assumption that unbridled tourism associated with high anthropogenic disturbance can be related to perceived stress and consequently may have an impact on the reproductive fitness of tigers and long-term survival of isolated populations.
IRS-LISS-III satellite imagery covering Nongkhyllem Wildlife Sanctuary area located within the Ri-Bhoi District of Meghalaya State, northeast India, was used for analysis of the landcover pattern and vegetation types occurring there. A maximum likelihood classifier was used to generate a supervised classification into land-cover types and the vegetation types within the forested area. The preparation of training data sets used thematic maps of the area, and knowledge accruing from extensive personal field visits. Sample field plots were located at 30 different places in the Sanctuary for classification accuracy assessment. The Normalized Difference Vegetation Index (NDVI) was also computed from LISS-III satellite imagery. A digital elevation model (DEM) of the Sanctuary was generated using a GIS. The DEM was used to test the hypothesis that its joint use with the satellite data would increase classification accuracy. This proved to be the case. Bivariate correlation analysis was performed between spectral and DEM variables to cross-check the results. In the example used, that of the rugged terrain in mountainous parts of northeast India, such integration of satellite land-cover data and DEM information appears to be a necessity in improved land-cover mapping for resource planning and utilization.
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