将组合结构和栖息地测绘数据整合到多物种珊瑚鱼调查设计中

IF 1.8 3区 农林科学 Q2 FISHERIES
Theodore S. Switzer, Sean F. Keenan, Kevin A. Thompson, Colin P. Shea, Anthony R. Knapp, Matthew D. Campbell, Brandi Noble, Chris Gardner, Mary C. Christman
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引用次数: 0

摘要

自2010年以来,三次空间不相交的暗礁鱼类视频调查为墨西哥湾暗礁鱼类的评估和管理提供了重要的渔业独立数据。虽然最近已发展出分析方法,将这些调查的数据整合为相对丰度和大小组成的单一衡量标准,但更节省的方法是将调查工作整合到单一的海湾范围的调查设计下。因此,我们对历史视频和栖息地测绘数据进行了回顾性分析,以开发一种新的分层随机抽样设计,用于进行自然和人工珊瑚礁栖息地的调查。方法通过分类和回归树分析,划分空间和生境层,并进行模拟,评估优化后的调查设计效果。结果在空间上,分类和回归树结果识别出3个深度层(10-25 m、25-50 m、50-180 m)和3个区域层(Gulf中北部、Big Bend、Florida西南部)。对于天然和人工鱼礁,栖息地地层的划分是基于相对起伏度(低、中、高)和单个鱼礁特征大小的组合,尽管天然(100 m2、100 - 1000 m2、1000 m2)和人工(25 m2、25 - 100 m2、100 m2)的鱼礁尺度存在显著差异。为了优化各采样层间的努力,努力是基于生境可利用性和管理物种丰富度的组合按比例分配的。模拟结果表明,在优化设计下,除大琥珀黄(Greater Amberjack Seriola dumerili)外,大多数天然珊瑚礁物种的丰度估计相对中值偏差为10%,相对中值绝对偏差为30%。使用简单随机和分层随机调查设计模拟的大多数物种的这些偏差和不精确程度相似或更高。人工礁调查的估计相对中位偏差和相对中位绝对偏差明显更高。在此基础上,将调查工作整合为2020年海湾渔业生境和生态系统资源独立调查(G-FISHER)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrating assemblage structure and habitat mapping data into the design of a multispecies reef fish survey

Integrating assemblage structure and habitat mapping data into the design of a multispecies reef fish survey

Objective

Since 2010, three spatially disjunct reef fish video surveys have provided fishery-independent data critical to the assessment and management of reef fishes in the Gulf of Mexico. Although analytical approaches have recently been developed to integrate data from these surveys into a single measure of relative abundance and size composition, a more parsimonious approach would be to integrate survey efforts under a single Gulf-wide survey design. Accordingly, we conducted a retrospective analysis of historical video- and habitat-mapping data to develop a novel stratified random sampling design for conducting surveys of natural and artificial reef habitats.

Methods

We conducted a series of classification and regression tree analyses to delineate both spatial and habitat strata, and conducted simulations to assess the performance of an optimized survey design.

Result

Spatially, classification and regression tree results identified three depth strata (10–25 m, >25–50 m, >50–180 m) and three regional strata (north-central Gulf, Big Bend, southwest Florida) in the eastern Gulf. For both natural and artificial reefs, habitat strata were delineated based on a combination of relative relief (low, medium, high) and size of the individual reef feature, although reef scale differed markedly between natural (<100 m2, 100–1000 m2, >1000 m2) and artificial habitats (<25 m2, 25–100 m2, >100 m2). To optimize effort among sampling strata, effort was allocated proportionally based on a combination of habitat availability and managed-species richness for each stratum. Simulation results indicated that relative median biases were <10% and relative median absolute deviations <30% on estimates of abundance for most species examined on natural reefs under the optimal design, except Greater Amberjack Seriola dumerili. These measures of bias and imprecision were similar or higher for most species simulated using simple random and stratified random survey designs. Estimated relative median bias and relative median absolute deviations were notably higher for artificial reef surveys.

Conclusion

Based on these results, survey efforts were integrated as the Gulf Fishery Independent Survey of Habitat and Ecosystem Resources (G-FISHER) in 2020.

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来源期刊
Marine and Coastal Fisheries
Marine and Coastal Fisheries FISHERIES-MARINE & FRESHWATER BIOLOGY
CiteScore
3.40
自引率
5.90%
发文量
40
审稿时长
>12 weeks
期刊介绍: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science publishes original and innovative research that synthesizes information on biological organization across spatial and temporal scales to promote ecologically sound fisheries science and management. This open-access, online journal published by the American Fisheries Society provides an international venue for studies of marine, coastal, and estuarine fisheries, with emphasis on species'' performance and responses to perturbations in their environment, and promotes the development of ecosystem-based fisheries science and management.
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