Stopping Rule Sampling to Monitor and Protect Endangered Species

Abstract Ecological science and management often require animal population abundance estimates to determine population status, set harvest limits on exploited populations, assess biodiversity, and evaluate the effects of management actions. However, sampling can harm animal populations. Motivated by trawl sampling of an endangered fish, we present a sequential adaptive sampling design focused on making population-level inferences while limiting harm to the target population. The design incorporates stopping rules such that multiple samples are collected at a site until one or more individuals from the target population are captured, conditional on the number of samples falling within a predetermined range. With this application in mind, we pair the stopping rules sampling design with a density model from which to base abundance indices. We use theoretical analyses and simulations to evaluate inference of population parameters and reduction in catch under the stopping rule sampling design compared to fixed sampling designs. Density point estimates based on stopping rules could theoretically be biased high, but simulations indicated that the stopping rules did not induce noticeable bias in practice. Retrospective analysis of the case study indicated that the stopping rules reduced catch by 60% compared to a fixed sampling design with maximum possible effort.Supplementary materials accompanying this paper appear online.