U.S. Department of Commerce
Date of this Version
2021
Citation
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2021, VOL. 116, NO. 533, 107–115: Applications and Case Studies
doi:10.1080/01621459.2020.1764362
Abstract
Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows themethod to be negligibly biased (<5%) in scenarioswhere conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999– 2006) of spotted dolphins (Stenella attenuata) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Comments
U.S. government work