A collaborative approach for estimating terrestrial wildlife abundance

Product Type: Journal Article

Year: 2012

Author(s): Ransom, J.I., P. Kaczensky, B.C. Lubow, O. Ganbaatar, and N. Altansukh

Suggested Citation:
Ransom, J.I., P. Kaczensky, B.C. Lubow, O. Ganbaatar, and N. Altansukh. 2012. A collaborative approach for estimating terrestrial wildlife abundance. Biological Conservation 153: 219-226.

Abstract

Accurately estimating abundance of wildlife is critical for establishing effective conservation and management strategies. Aerial methodologies for estimating abundance are common in developed countries, but they are often impractical for remote areas of developing countries where many of the world’s endangered and threatened fauna exist. The alternative terrestrial methodologies can be constrained by limitations on access, technology, and human resources, and have rarely been comprehensively conducted for large terrestrial mammals at landscape scales. We attempted to overcome these problems by incorporating local peoples into a simultaneous point count of Asiatic wild ass (Equus hemionus) and goitered gazelle (Gazella subgutturosa) across the Great Gobi B Strictly Protected Area, Mongolia. Paired observers collected abundance and covariate metrics at 50 observation points and we estimated population sizes using distance sampling theory, but also assessed individual observer error to examine potential bias introduced by the large number of minimally trained observers. We estimated 5671 (95% CI = 3611–8907) wild asses and 5909 (95% CI = 3762–9279) gazelle inhabited the 11,027 km2 study area at the time of our survey and found that the methodology developed was robust at absorbing the logistical challenges and wide range of observer abilities. This initiative serves as a functional model for estimating terrestrial wildlife abundance while integrating local people into scientific and conservation projects. This, in turn, creates vested interest in conservation by the people who are most influential in, and most affected by, the outcomes.