Performance evaluation of spatially balanced sampling designs in fishery-independent surveys

Abstract

Cost-effective fishery-independent surveys are critical for obtaining high-quality data to support stock assessment and fisheries management. Classical probability-based sampling designs may exhibit limitations in spatial coverage and flexibility. Spatially balanced sampling (SBS) designs offer the ability to efficiently distribute sampling stations across a survey area. This study evaluates the performance of SBS designs in multispecies fishery-independent surveys through simulation studies, involving two widely used classical sampling designs (Simple Random Sampling and Stratified Random Sampling) and two SBS designs (Generalized Random Tessellation Stratified Model and Balanced Acceptance Sampling). The aim of the simulation study was to assess the performance of different sampling designs in estimating the abundance index for four target fish species across different seasons. The results showed that SBS designs had relatively high accuracy and precision in terms of relative estimation error, relative bias, and coefficient of variation. Among the four sampling designs, the Generalized Random Tessellation Stratified Model performed optimally at the same sample size. In conclusion, this study suggests that SBS designs can enhance the precision of abundance index estimation for different fish species. Given its ability to effectively distribute sampling stations and its flexibility, a better understanding and application of SBS designs in fishery-independent surveys are warranted.