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      Mapping the potential for offshore aquaculture of salmonids in the Yellow Sea


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          Mariculture has been one of the fastest-growing global food production sectors over the past three decades. With the congestion of space and deterioration of the environment in coastal regions, offshore aquaculture has gained increasing attention. Atlantic salmon ( Salmo salar) and rainbow trout ( Oncorhynchus mykiss) are two important aquaculture species and contribute to 6.1% of world aquaculture production of finfish. In the present study, we established species distribution models (SDMs) to identify the potential areas for offshore aquaculture of these two cold-water fish species considering the mesoscale spatio-temporal thermal heterogeneity of the Yellow Sea. The values of the area under the curve (AUC) and the true skill statistic (TSS) showed good model performance. The suitability index (SI), which was used in this study to quantitatively assess potential offshore aquaculture sites, was highly dynamic at the surface water layer. However, high SI values occurred throughout the year at deeper water layers. The potential aquaculture areas for S. salar and O. mykiss in the Yellow Sea were estimated as 52,270 ± 3275 (95% confidence interval, CI) and 146,831 ± 15,023 km 2, respectively. Our results highlighted the use of SDMs in identifying potential aquaculture areas based on environmental variables. Considering the thermal heterogeneity of the environment, this study suggested that offshore aquaculture for Atlantic salmon and rainbow trout was feasible in the Yellow Sea by adopting new technologies (e.g., sinking cages into deep water) to avoid damage from high temperatures in summer.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s42995-022-00141-2.

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          Most cited references112

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          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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                Author and article information

                Mar Life Sci Technol
                Mar Life Sci Technol
                Marine Life Science & Technology
                Springer Nature Singapore (Singapore )
                18 August 2022
                18 August 2022
                August 2022
                : 4
                : 3
                : 329-342
                [1 ]GRID grid.4422.0, ISNI 0000 0001 2152 3263, Key Laboratory of Mariculture of Ministry of Education, College of Fisheries, , Ocean University of China, ; Qingdao, 266003 China
                [2 ]GRID grid.484590.4, ISNI 0000 0004 5998 3072, Function Laboratory for Marine Fisheries Science and Food Production Processes, , Pilot National Laboratory for Marine Science and Technology (Qingdao), ; Qingdao, 266235 China
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, , Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, ; Guangzhou, 510301 China
                [4 ]GRID grid.10776.37, ISNI 0000 0004 1762 5517, Laboratory of Ecology, Department of Earth and Marine Sciences, , University of Palermo, ; 90128 Palermo, Italy
                Author information
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                : 9 December 2021
                : 17 June 2022
                Research Paper
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                © Ocean University of China 2022

                aquaculture potential,offshore aquaculture,oncorhynchus mykiss,salmo salar,species distribution models,the yellow sea cold water mass


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