文章摘要
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
投稿时间:2023-09-18  修订日期:2023-11-01
DOI:
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
基金项目:广东省基础与应用基础研究基金(2022A1515110957)、广东海洋大学科研启动经费资助项目(060302022302,060302022301)、中国与印度尼西亚海洋近海海洋生态牧场技术合作研究 (12500101200021002)和广东海洋大学本科生创新团队项目(CCTD201803)
作者单位邮编
邓越秀 广东海洋大学水产学院 524088
黄永恒 广东海洋大学水产学院 
董建宇 广东海洋大学水产学院 
陈宁 广东海洋大学水产学院 
张静 广东海洋大学水产学院 
王学锋* 广东海洋大学水产学院 524088
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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,同时对生物多样性的保护产生重要影响。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,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,以满足管理目标和预算。本文以茂名海域为例,采用了R语言数据包SamplingStrata中提供的一种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。研究结果表明,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为12、14和21。本文优化了茂名海域渔业生物多样性评估为目标的渔业资源调查站位设计,为渔业资源调查提供了一种有效的站位优化方法,并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 the conservation of 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 a key factor in maximizing data quality and survey efficiency. While increasing the number of sampling stations can enhance result accuracy, it also escalates survey costs and may have adverse effects on the marine environment and ecosystems. Hence, sampling design needs to strike a balance between sample size and precision to meet management objectives and budget constraints. Optimizing fishery resource survey sampling stations design is widely recognized as an effective approach to enhance survey precision, and there have been numerous studies conducted on this topic both domestically and internationally. And recent studies have indicated that stratified sampling offers higher precision, making it a preferred choice for optimizing fishery resource survey site selection. However, most current research primarily focuses on optimizing stratification or sample allocation and pays less attention to methods that simultaneously optimize both stratification and sample allocation. This paper aims to provide valuable insights into the simultaneous optimization of stratification and sample allocation for biodiversity-focused fisheries surveys. In this study, we take the Maoming as a case study and employs a method provided by the R package "SamplingStrata" to optimize the sampling design through stratified sampling under a multivariate scenario. This package, based on the use of a genetic algorithm, can determine the optimal stratification, sample size, and sample allocation that 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 variables 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 most 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 paper, we use environmental data (water depth, dissolved oxygen, water temperature, pH, salinity) and water depth as stratification variables, and Shannon-Wiener diversity index (H′), Margalef richness index (D), Pielou evenness index (J′), and species number as target variables. Sequentially, the same maximum coefficient of variation (CV) is set for the target variables at 0.2, 0.15, 0.1, 0.05, 0.04, 0.03, 0.02, 0.01. Subsequently, samples were selected from the optimized stratification design, and the relative errors were calculated. Results showed 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 sampling sites increases as the maximum coefficient of variation (CV) decreases. However, when the maximum coefficient of variation continues to decrease from 0.05, the number of sampling sites increases more significantly with each reduction of 0.01 CV, requiring one or more additional stations. At CV = 0.05, when stratified by environmental factors, spring requires 8 sampling points, while autumn and non-seasonal conditions require 7 and 11 sampling points, respectively. When stratified by water depth, spring requires 12 sampling points, while autumn and non-seasonal conditions require 14 and 21 sampling points, respectively. According to relative error analysis, when stratified by environmental factors and water depth, the mean relative errors for each index are 2.38, 2.02, 2.85, and 0.43, 3.14, 1.74, respectively, with stratification by water depth resulting in smaller mean relative errors for all indices except evenness. In general, through stratified sampling and appropriate sample allocation, it is possible to reduce the number of sampling sites while maintaining data accuracy, thus minimizing the adverse impact on marine ecosystems. In the coastal waters of Maoming survey, setting a maximum coefficient of variation equal to 0.05 as the data precision requirement and using depth as the stratified variable prove to be suitable. The number of sampling sites for spring, autumn, and non-season-specific surveys is found to be 12, 14, and 21, respectively. This paper optimizes the fishery resource survey stations selection in the coastal waters of Maoming, offering an effective sampling stations optimization method for fishery resource surveys and providing guidance for future fishery resource survey stations design.
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