Abstract
AbstractAgencies 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.
Highlights
INTRODUCTION AND BACKGROUNDKrebs (1991) recognized that monitoring programs must advance our knowledge of the underlying dynamics of animal populations if they are to improve either science or conservation
(2002) identify two major sources of uncertainty, which must necessarily be addressed by any monitoring program aiming to generate strong inferences about animal population dynamics
If considered at face value, these numbers with their reported error bounds indicate spurts of increases in tiger numbers in India during the period between 2006 and 2018. Considering only those specific areas that were surveyed in all the first three surveys (2006, 2010, 2014; summarized from Jhala et al, 2015), these tiger population trends translate to a 17.3% increase in tiger abundance and a corresponding increase of 34.6% in local tiger density, implying that local tiger density D rose at twice the rate of tiger abundance N (ΔD/ΔN = 2 > 1) between 2006 and 2010
Summary
Krebs (1991) recognized that monitoring programs must advance our knowledge of the underlying dynamics of animal populations if they are to improve either science or conservation. One unit increase in the tiger sign index results in a corresponding exponential increase in tiger density of (a) 10.5% from the ShivalikGangetic Plains samples (b) 29.4% from the Central Indian and Eastern Ghats landscape, and (c) a large 174.6% from the samples of Western Ghats These results immediately contradict the linear regression model results of Jhala, Qureshi, and Gopal (2011) in that there is a nearly threefold difference in the compounded rate parameter beta between (a) and (b), which was not the case in Jhala, Qureshi, and Gopal (2011). These results imply that the predictive ability of the Jhala, Qureshi, and Gopal (2011) linear regression model collapses due to high variability (SOD) between these samples These estimates of beta indicate that the nonlinear nature of the relationship between tiger sign index and tiger density is pronounced, perhaps indicating a strong interaction between p and/or α and N as discussed in Appendix 1. In line with implications of Darimont et al (2018), the timing of Qureshi et al (2019a) assumed precedence over the need to achieve scientific coherence, in this example
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