International Journal of Marine Science, 2025, Vol.15, No.4, 186-198 http://www.aquapublisher.com/index.php/ijms 195 Secondly, the sampling layout in the research design is crucial to whether the structure can be discovered. If the sampling points are unevenly distributed or the sample size is insufficient, the key genetic pattern is easily missed. For example, many early studies only took two or three location samples and came to the conclusion that "no significant structure". However, Feutry and others discovered the isolation mode with distance through six samples. If the sampling point is too thin, it is likely that the demarcation will be missed. The selection of data analysis methodology will also affect the conclusion. For example, the commonly used STRUCTURE clustering is not sensitive to detecting weak differentiation. If you rely too much on it, you may misjudgment the same group. Some early studies did not find that the structure was partly limited by the method used. To avoid method deviation, it is best to combine multiple analyses, such as F_ST, AMOVA, principal component analysis, and cluster analysis to obtain a consensus. In particular, it should be noted that population differentiation may be extremely low and sufficient statistical power must be used to identify it (Yan et al., 2015). 7 Conclusion 7.1 Problems and research gaps in current research Although important progress has been made in the research on the genetic diversity and population structure of the genus Mackerel in recent years, there are still several problems and gaps that need to be resolved. First, the area coverage is unbalanced. Currently, research on the inheritance of mackerel populations is mostly concentrated in a few areas and species. For example, there have been some results in the coastal areas of China, the narrowband mackerel of the Indian Ocean, and the Western Atlantic Sera mackerel of the West, but data is lacking for many other distribution areas and species. Second, marking and method limitations. Looking back at previous literature, it can be found that most studies use limited markers (mtDNA fragments, microsatellites at several sites, etc.), and lack resolution. High-resolution multigenomic means are just in the beginning and are only used in individual cases. Therefore, some early conclusions need to be re-evaluated. Third, there is a lack of long-term monitoring. The genetic structure of the population is not static, and may change with changes in resources, climate, etc. However, most of the current studies are one-time sampling analysis and lack cross-year comparisons. This prevents us from understanding the genetic structural dynamics of mackerel. Fourth, adaptive genetic research is missing. Almost all existing studies focus on the structure of neutral markers, and there is no systematic study on local adaptation or functional gene differentiation of mackerel. Mackerels are widely distributed in many environments, but the problems of which genes help them adapt to different temperature and salts and why some groups have different growth speeds and slow growth have not been analyzed at the gene level. 7.2 Potential of multigenome integration in future research The development of multigenomic data and comprehensive analysis methods will bring breakthrough progress to future research on mackerel population structure. Applying genome-wide scanning based on global sampling will allow us to build a comprehensive genetic map of the genus Mackerel. Through international cooperation, samples from major species and key sea areas were obtained and genome-wide association analysis was conducted, which can simultaneously examine the differences in neutral marker differentiation and adaptive marker. The introduction of ancient DNA and population history models will enrich our understanding of the evolutionary history of mackerel. If the DNA of mackerels from fishery archives or sediments can be extracted decades ago or even earlier, compared with contemporary populations, the genetic structure can be directly observed to change over time. Population dynamic models (such as Coalescent modeling, DIYABC, etc.) can be used to infer the expansion and bottleneck situations of populations during the Ice Age, Holocene and other periods based on modern data. This will extend our understanding of the current structure to the historical dimension and explain more accurately the causes of the current pattern (Wang et al., 2024). New sequencing strategies such as environmental DNA macrobarcoding technology also have potential. By collecting water samples at different time and sequencing mackerel DNA fragments, their distribution changes and rough genetic diversity can be monitored at high frequency. If this non-invasive method is combined with traditional fishing investigations and genomic analysis, it will form a new monitoring system. Multidisciplinary intersections such as combining telemetry,
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