Rapid and non-destructive differentiation of Shahtoosh from Pashmina/Cashmere wool using ATR FT-IR spectroscopy.

Shahtoosh, the most expensive and sought-after wool in the illegal wildlife trade is obtained from the underfur of a critically endangered species-the Tibetan Antelope (Pantholops hodgsonii). It is often adulterated or mixed with the wool of Pashmina goat (Capra aegagrus hircus) for making shawls, scarves and other woolen articles to maximize the profit. The comparable fineness, color and texture, makes it a challenging task in wildlife forensics to differentiate them. In this study, an attempt has been made to differentiate 50 reference unprocessed underfur hairs from five individuals of each species using ATR FT-IR spectroscopy in combination with chemometric tools such as PCA, and PLS-DA. Results of PCA model demonstrated slight overlap and thus failed to distinguish hairs of these two species. Subsequently, PLS-DA model was employed, and also validation tests (external and blind testing) were carried out to ensure the predictive ability of the model, which resulted in 100% accuracy. The results of PLS-DA model exhibited complete differentiation between Shahtoosh, Pashmina and Angora (Oryctolagus cuniculus domesticus) wool used for external validation study with highly significant predictive ability (R-square value 0.99). This proof-of-concept study illustrates the potential of ATR FT-IR spectroscopy to complement current forensic microscopic and DNA based technique to analyze hair evidence in wildlife investigations owing to its rapid and non-destructive nature with high degree of confidence, and its ease-of-use with minimal to no sample preparation.

Blue Sheep Resource Selection in Alpine Grasslands of a Western Himalayan Landscape - A Point Process Approach.

In-depth knowledge of distribution and factors influencing it is important for species conservation and management. Many forms of such data have led to the development of new analytical techniques for better interpretation. For mountainous terrains with certain limitations, species data are obtained in the presence-only form. The point process model is one of the recent approaches for modelling such data, taking care of pseudo-absences and spatial independence. For conservation in regions with limited resources and species with similar ecological requirements, it is important to properly assess the extent of competition extent between wild and domestic species. We attempted to use point process framework to estimate the function of resource selection in blue sheep (Pseudois nayaur) in areas influenced by pastoralism in a western Himalayan region. Our study is the first attempt to use this framework to estimate resource selection on a dataset not collected using radio-telemetry. Spatial locations of blue sheep and livestock and a background sample of random points with six topographic covariates were used to model resource selection probability via intensity function. Blue sheep showed its predicted presence in areas with open vegetation coinciding with alpine meadows, influenced by southern aspect keeping a threshold distance of 600-1000 m from cliffs (escape terrain). Livestock, also showed presence probability in open vegetation, but at lower altitudes, mainly on valley floors. Our results suggest that though blue sheep continued to use the same habitat type after livestock arrival, they selected different resources based on topographic factors. Livestock were in areas where it was convenient for pastoralists to establish campsites and where nutritious grasses were present, making it feasible to graze. Thus, we argue that the probable shift in habitat for blue sheep from optimal areas occurs due to livestock presence, which might disturb their nutritional balance. Our study provides helpful insights for managing rangelands, which when tied with dietary patterns will give a better idea for proper conservation measures in the future.