渔业生物多样性评估为目标的站位优化设计实现——以茂名海域为例

Optimization, design, and implementation of biodiversity-focused fisheries survey stations: A case study of the coastal waters of Maoming

中文关键词: 分层抽样  站位优化  采样设计  生物多样性  茂名海域

英文关键词: Stratified random sampling  Sampling design  Optimization  Biodiversity  Maoming coastal area

中文摘要:

      渔业资源调查对于渔业资源评估和管理至关重要，也对生物多样性的保护具有重要影响。然而，在海洋中进行以生物多样性评估为目标的渔业资源调查成本高，且受到现场条件制约。因此，周密的采样站点设计是确保数据质量和调查效益最大化的关键。增加采样站点数量可以提高生物多样性数据的准确性，但也增加了调查成本，并可能对海洋环境和生态系统造成负面影响。因此，采样设计需要在样本量和准确性之间取得平衡，以满足预定目标和预算。本研究以广东省茂名海域为例，采用了R语言数据包SamplingStrata中提供的可在多变量情境下通过分层抽样进而优化采样设计的方法，通过综合考虑分层和样本分配优化，评估了渔业生物多样性调查所需最佳站位数量。结果表明，通过分层抽样和适当的样本分配，可以在保证生物多样性数据精确度的同时降低采样站点数量，减少对海洋生态系统的不利影响。在茂名海域调查中，将最大变异数等于0.05作为数据精确度要求以及水深作为分层变量更适宜，春季、秋季以及不区分季节采样站点数量分别取12、14和21为宜。本研究优化了茂名海域以渔业生物多样性评估为目标的渔业资源调查站位设计，提供了一种有效的站位优化方法，并可为后期渔业资源调查站位设计提供参考。

英文摘要:

      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.