Testing the Utility of an Adaptive Cluster Sampling Method for Monitoring a Rare and Imperiled Darter

Abstract

Adaptive cluster sampling (ACS) designs were tested against simple random sampling (SRS) designs to determine whether ACS designs were more suitable sampling protocols for monitoring rare fish species. To test the utility of these designs, baseline data on the tuxedo darter Etheostoma lemniscatum (a rare, federally endangered fish) were collected at three sites on the Big South Fork of the Cumberland River and used in computer simulation of ACS designs. Based on the simulation models, five ACS designs were chosen and tested at 13 potential monitoring sites. In terms of efficiency, ACS designs performed better than SRS designs, providing estimates with smaller standard errors. Adaptive cluster sampling design efficiency increased with effort; therefore, the two goals—minimizing effort and maximizing accuracy—are incompatible. Because it had less error, an inverse ACS design was recommended, although it required more sampling effort than other designs. Factors possibly affecting overall design performance include the sample size, neighborhood configuration, and habitat complexity of sampling sites. This study was the first to implement ACS for rare and endangered stream-dwelling fishes and provided methodologies for pilot testing and selecting appropriate ACS designs for an imperiled fish species. Adaptive cluster sampling designs are best suited for monitoring imperiled fishes that exhibit a more-clustered spatial distribution. Population estimates from ACS designs using underwater observation provide quantitative data from standardized protocols that are applicable to long-term monitoring of imperiled fishes.

Received May 21, 2010; accepted June 21, 2011

ACKNOWLEDGMENTS

We thank the National Park Service and the Tennessee Technological University Center for the Management, Utilization, and Protection of Water Resources for financial support for this study. We also thank Steve Bakaletz, biologist for the BSFNRRA, for his expertise and assistance. We are also grateful to the number of volunteers and “rock turners” who assisted in collection of data, especially Darrin Bergen, Shane Billings, Tyler Black, V. Malissa Davis, Keith Gibbs, Ben Hutton, Tomas Ivasauskas, Jason Miller, Drew Russell, Jason Throneberry, and Jade Young. Comments from anonymous reviewers are appreciated as well.