Optimization, Design, and Implementation of Biodiversity-Focused Fisheries Survey Stations: A Case Study of the Coastal Waters of Maoming

Abstract

Biodiversity assessment in fisheries plays a crucial role in conserving marine ecosystem diversity. Accurate survey data serve as the foundation for ensuring the precision of quantitative analysis and the effectiveness of resource conservation measures. However, conducting fishery resource surveys in the ocean is costly and constrained by on-site conditions. Therefore, meticulous sampling design is key to maximizing data quality and survey efficiency. While increasing the number of sampling stations can enhance result accuracy, it escalates survey costs and may adversely affect the marine environment and ecosystems. Hence, sampling design must balance sample size and precision to meet management objectives and budget constraints. Optimizing fishery resource survey sampling station design is widely recognized as an effective approach to enhance survey precision, and numerous studies have been conducted on this topic both domestically and internationally. Recent studies have indicated that stratified sampling offers high precision, making it a preferred choice for optimizing fishery resource survey site selection. However, most research primarily focuses on optimizing either stratification or sample allocation and pays less attention to methods that simultaneously optimize both stratification and sample allocation. This study aims to provide valuable insights into the simultaneous optimization of stratification and sample allocation for biodiversity-focused fisheries surveys.In this study, we take Maoming as a case study and employ the R package "SamplingStrata" to optimize the sampling design through stratified sampling under a multivariate scenario. This package, based on a genetic algorithm, can determine the optimal stratification, sample size, and sample allocation to meet precision constraints in the presence of multiple stratification variables and multiple target variables. When using this package, it is essential to clearly define the stratification and target variables while predefining the precision requirements for the target variables. To maximize the efficiency of the samples, the selection of stratification variables should be based on their correlation with the target variables. Choosing stratification variables that are best correlated with the target variables can enhance the representativeness of the samples. Additionally, the precision requirements are expressed using the coefficient of variation (CV) for each target variable. The CV value reflects the magnitude of the sample estimate variance relative to its mean for each target variable.In this study, we use environmental data (dissolved oxygen, water temperature, pH, and salinity) and water depth as stratification variables and define the Shannon–Wiener diversity index (H′), Margalef richness index (D), Pielou evenness index (J′), and species number as target variables. A maximum CV value is set for the target variables at 0.2, 0.15, 0.1, 0.05, 0.04, 0.03, 0.02, and 0.01. Subsequently, samples are selected from the optimized stratification design, and the relative errors are calculated.The results show that biodiversity in the coastal waters of Maoming exhibits seasonal differences, with diversity indices being higher in autumn than in spring. Additionally, the number of required sampling stations increases as the maximum CV decreases. However, when the maximum CV decreases below 0.05, the number of sampling stations increases significantly with each reduction of 0.01 CV, requiring one or more additional stations. At CV = 0.05, when stratified by environmental factors, spring conditions require eight sampling stations. In contrast, autumn and non-seasonal conditions require 7 and 11 sampling stations, respectively. When stratified by water depth, spring requires 12 sampling stations, whereas autumn and non-seasonal conditions require 14 and 21 sampling stations, respectively. According to relative error analysis, when stratified by environmental factors and water depth, the mean relative errors for H′, D, and J′ are 2.38, 2.02, 2.85, and 0.43, 3.14, and 1.74, respectively, with stratification by water depth resulting in smaller mean relative errors for all indices except J′.Through stratified sampling and appropriate sample allocation, reducing the number of required sampling stations while maintaining data accuracy, thus minimizing adverse impacts on marine ecosystems, is possible. In the coastal waters of Maoming, setting the maximum CV equal to 0.05 for the data precision requirement and using depth as the stratified variable are shown to be suitable considerations. The number of sampling stations for spring, autumn, and non-season-specific surveys is 12, 14, and 21, respectively. This study optimizes fisheries resource survey station selection in the coastal waters of Maoming, offering an effective sampling station optimization method for fisheries resource surveys and providing guidance for future fisheries resource survey station design.

Keywords

• stratified random sampling
• sampling design
• optimization
• biodiversity
• maoming coastal area