Open AccessHow sampling‐based overdispersion reveals India's tiger monitoring orthodoxy
Author(s) -
Gopalaswamy Arjun M.,
Karanth K. Ullas,
Delampady Mohan,
Stenseth Nils C.
Publication year - 2019
Publication title -
conservation science and practice
Language(s) - English
Resource type - Journals
ISSN - 2578-4854
DOI - 10.1111/csp2.128
Subject(s) - tiger , population , geography , wildlife , abundance (ecology) , sampling (signal processing) , extrapolation , citizen science , overdispersion , ecology , statistics , mathematics , sociology , computer science , biology , demography , filter (signal processing) , computer vision , poisson regression , botany , algorithm
Agencies responsible for recovering populations of iconic mammals may exaggerate population trends without adequate scientific evidence. Recently, such populations were termed as “political populations” in the conservation literature. We surmise such cases are manifested when agencies are pressured to estimate population parameters at large spatial scales for elusive species. For example, India's tiger conservation agencies depend on an extrapolation method using index‐calibration models for estimating population size. A recent study demonstrated mathematically the unreliability of this approach in practical situations. However, it continues to be applied by official agencies in Asia and promoted further by global organizations working on tiger conservation. In this article, we aim to: (a) discuss the ecological oddities in the results of India's national tiger surveys, (b) contrast these survey approaches to known statistical approaches for large scale wildlife abundance estimation, (c) demystify the mathematics underlying the problems with the survey methodology, and (d) substantiate these arguments with results from India's national tiger survey of 2014. Our analyses show that the predictions of tiger abundance reported by the 2014 survey, and consequently on tiger population trends, are misleading because of the presence of high sampling‐based overdispersion and parameter covariance due to unexplained heterogeneity in detection probabilities. We plead for designing monitoring programs to answer clearly defined scientific or management questions rather than attempt to meet extraneous social or funding related expectations.