Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: A simulation study

Abstract

We compared the performance of two traditional sampling designs with three adaptive sampling designs using simulated data based on fishery-independent surveys for yellow perch in Lake Erie. Traditionally, the fishery-independent survey has been conducted with a stratified random sampling design based on basin and depth strata; however, adaptive sampling designs are thought to be more suitable for surveying heterogeneous populations. A simulation study was conducted to compare these designs by examining the accuracy and precision of the estimators. Initially in the simulation study, we used bias, variance of the mean, and mean squared error (MSE) of the estimators to compare simple random sampling (SRS), stratified random sampling (StRS), and adaptive two-phase sampling (ATS). ATS was the best design according to these measurements. We then compared ATS, adaptive cluster sampling (ACS), adaptive two-stage sequential sampling (ATSS), and the currently used stratified random sampling design. ATS performed better than the other two approaches and the current stratified random sampling design. We concluded that ATS is preferable for yellow perch fishery-independent surveys in Lake Erie. Simulation study is a preferred approach when we seek an appropriate sampling design or evaluate the current sampling design.