Twisted tale of the tiger: the case of inappropriate data and deficient science

• Main Authors: Qamar Qureshi, Rajesh Gopal, Yadvendradev Jhala
• Format: Article
• Language: English
• Published: PeerJ Inc. 2019-08-01
• Series: PeerJ
• Subjects: Double sampling; Index calibration; Large-scale surveys; Wildlife surveys; Tigers status
• Online Access: https://peerj.com/articles/7482.pdf

Publications in peer-reviewed journals are often looked upon as tenets on which future scientific thought is built. Published information is not always flawless and errors in published research should be expediently reported, preferably by a peer-review process. We review a recent publication by Gopalaswamy et al. (10.1111/2041-210X.12351) that challenges the use of “double sampling” in large-scale animal surveys. Double sampling is often resorted to as an established economical and practical approach for large-scale surveys since it calibrates abundance indices against absolute abundance, thereby potentially addressing the statistical shortfalls of indices. Empirical data used by Gopalaswamy et al. (10.1111/2041-210X.12351) to test their theoretical model, relate to tiger sign and tiger abundance referred to as an Index-Calibration experiment (IC-Karanth). These data on tiger abundance and signs should be paired in time and space to qualify as a calibration experiment for double sampling, but original data of IC-Karanth show lags of (up to) several years. Further, data points used in the paper do not match the original sources. We show that by use of inappropriate and incorrect data collected through a faulty experimental design, poor parameterization of their theoretical model, and selectively picked estimates from literature on detection probability, the inferences of this paper are highly questionable. We highlight how the results of Gopalaswamy et al. were further distorted in popular media. If left unaddressed, the paper of Gopalaswamy et al. could have serious implications on statistical design of large-scale animal surveys by propagating unreliable inferences.