Hemmer-Hansen Jakob, Hüssy Karin, Baktoft Henrik, Huwer Bastian, Bekkevold Dorte, Haslob Holger, Herrmann Jens-Peter, Hinrichsen Hans-Harald, Krumme Uwe, Mosegaard Henrik, Nielsen Einar Eg, Reusch Thorsten B H, Storr-Paulsen Marie, Velasco Andres, von Dewitz Burkhard, Dierking Jan, Eero Margit
National Institute of Aquatic Resources Technical University of Denmark Silkeborg Denmark.
National Institute of Aquatic Resources Technical University of Denmark Kgs. Lyngby Denmark.
Evol Appl. 2019 Jan 20;12(4):830-844. doi: 10.1111/eva.12760. eCollection 2019 Apr.
Genetic data have great potential for improving fisheries management by identifying the fundamental management units-that is, the biological populations-and their mixing. However, so far, the number of practical cases of marine fisheries management using genetics has been limited. Here, we used Atlantic cod in the Baltic Sea to demonstrate the applicability of genetics to a complex management scenario involving mixing of two genetically divergent populations. Specifically, we addressed several assumptions used in the current assessment of the two populations. Through analysis of 483 single nucleotide polymorphisms (SNPs) distributed across the Atlantic cod genome, we confirmed that a model of mechanical mixing, rather than hybridization and introgression, best explained the pattern of genetic differentiation. Thus, the fishery is best monitored as a mixed-stock fishery. Next, we developed a targeted panel of 39 SNPs with high statistical power for identifying population of origin and analyzed more than 2,000 tissue samples collected between 2011 and 2015 as well as 260 otoliths collected in 2003/2004. These data provided high spatial resolution and allowed us to investigate geographical trends in mixing, to compare patterns for different life stages and to investigate temporal trends in mixing. We found similar geographical trends for the two time points represented by tissue and otolith samples and that a recently implemented geographical management separation of the two populations provided a relatively close match to their distributions. In contrast to the current assumption, we found that patterns of mixing differed between juveniles and adults, a signal likely linked to the different reproductive dynamics of the two populations. Collectively, our data confirm that genetics is an operational tool for complex fisheries management applications. We recommend focussing on developing population assessment models and fisheries management frameworks to capitalize fully on the additional information offered by genetically assisted fisheries monitoring.
遗传数据在通过识别基本管理单元（即生物种群）及其混合情况来改善渔业管理方面具有巨大潜力。然而，到目前为止，利用遗传学进行海洋渔业管理的实际案例数量有限。在这里，我们以波罗的海的大西洋鳕鱼为例，展示遗传学在涉及两个基因不同种群混合的复杂管理场景中的适用性。具体而言，我们探讨了当前对这两个种群评估中所使用的几个假设。通过分析分布在大西洋鳕鱼基因组中的483个单核苷酸多态性（SNP），我们证实机械混合模型而非杂交和基因渗入能最好地解释遗传分化模式。因此，该渔业最好作为混合种群渔业进行监测。接下来，我们开发了一个由39个具有高统计效力的SNP组成的靶向面板，用于识别来源种群，并分析了2011年至2015年期间收集的2000多个组织样本以及2003/2004年收集的260个耳石。这些数据提供了高空间分辨率，使我们能够研究混合的地理趋势，比较不同生命阶段的模式，并研究混合的时间趋势。我们发现组织样本和耳石样本所代表的两个时间点具有相似的地理趋势，并且最近实施的两个种群的地理管理分离与它们的分布相对匹配。与当前假设相反，我们发现幼鱼和成鱼的混合模式不同，这一信号可能与两个种群不同的繁殖动态有关。总体而言，我们的数据证实遗传学是复杂渔业管理应用中的一种实用工具。我们建议专注于开发种群评估模型和渔业管理框架，以充分利用遗传辅助渔业监测提供的额外信息。
