This paper records a new distribution of long-billed vulture in the Sindh Province of Pakistan. Existing literature on long-billed vultures provides the distribution and status of this vulture in Sindh Province but does not cover the western mountainous eco-region. IUCN Pakistan's team conducted a pre-monsoon baseline survey in the province in the dry season in June 2019 and another survey in the vulture breeding season, in February 2020. Methodology included direct field observations and interviews of local communities. Field Guide to the Birds of Pakistan, by the senior author and other related literature, as well as the use of a high resolution camera helped in the identification of the species. During the surveys, information on the use of non-steroid anti-inflammatory drugs, that is diclofenac and other drugs known to be harmful to vultures, were gathered through interviews. This paper suggests that the vultures and other raptors in the mountainous eco-region of Sindh are in relatively larger populations compared to the Eastern side of Sindh due to the abundance of wild ungulates in protected areas such as the Khirthar National Park.
The data on 14 morphological trait measurements were obtained from 291 indigenous sheep found in the southern Punjab of Pakistan. Ten various body indices were obtained from 14 morphological traits. Pearson correlations between live body weight and 13 other morphological traits obtained to analyze the nature and strength of the relationship. Simple regression models were fitted to predict live body weight from 13 other morphological traits as independent variables. Descriptive statistics of body indices and morphological traits were also reported. The variation pattern was observed by coefficient of variation in both body indices and morphological traits. In body indices less variation was observed in BeronCrevit index, having a smallest coefficient variation of 8.50% and high variation in Area Index having largest coefficient of variation 23.98%. Similarly, in morphological traits less variation was observed in ear length, which had the smallest coefficient variation of 3.3%. The high variation pattern was observed in head width and percent weight, having the two largest coefficients of variations being 28.5% and 28.2%, respectively. As there was high Pearson correlation between live weight and barrel depth, the best regression model was on barrel depth with a high R 2 of 0.938. The simple regression analysis shows that barrel depth is best predictor of live body weight.
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