Human activities negatively impact distribution of ungulates in the Mongolian Gobi

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• 作者：Bayarbaatar Buuveibaatar^a ; ^b ; ^{buuveibaatar@wcs.org" class="auth_mail" title="E-mail the corresponding author} ; Thomas Mueller^c ; Samantha Strindberg^d ; Peter Leimgruber^e ; Petra Kaczensky^f ; Todd K. Fuller^b
• 关键词：Spatial modelling ; Equus hemionus ; Mining development ; Gobi desert ; Mongolia ; Gazella subgutturosa
• 刊名：Biological Conservation
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：203
• 期：Complete
• 页码：168-175
• 全文大小：1332 K

文摘

The Southern Gobi of Mongolia is an iconic ungulate stronghold that supports the world's largest populations of Asiatic wild ass (or khulan – Equus hemionus) and goitered gazelle (Gazella subgutturosa). A growing human population, intensifying exploitation of natural resources, and the development of infrastructure in the region place increasing pressure on these species and their habitats. During 2012–2015, we studied factors influencing the distribution of these two ungulate species in the Southern Gobi to better inform management. We built Generalized Linear Mixed Models (GLMMs) to predict the location of suitable habitat for the two species using environmental and human-associated factors. These models were validated using independent telemetry data for each species. The GLMMs suggest that the probability of ungulate presence decreased with increasing human influence and increased in areas with intermediate values of elevation and Normalized Difference Vegetation Index (except for goitered gazelle). Notably, human-associated factors were more important than environmental variables in explaining the distribution of the two species. Habitat models predicted between 45 and 55% of the study area to be suitable for khulan and between 50 and 55% suitable for goitered gazelles during 2012–2015. Models for both species had good predictive power, as nearly 90% of khulan and 100% of goitered gazelle telemetry locations from separate data sets were found within the predicted preferred areas. Our approach quantifies the key drivers of their distribution and our findings are useful for policy makers, managers, and industry to plan mitigation measures to reduce the impacts of development.