Please use this identifier to cite or link to this item: http://umt-ir.umt.edu.my:8080/handle/123456789/21567
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dc.contributor.authorXuehai Liu-
dc.contributor.authorMingliang Zhang-
dc.contributor.authorZongling Wang-
dc.date.accessioned2025-03-09T12:00:19Z-
dc.date.available2025-03-09T12:00:19Z-
dc.date.issued2021-
dc.identifier.urihttp://umt-ir.umt.edu.my:8080/handle/123456789/21567-
dc.description.abstractThe increasing growth of the bivalve-culture industry necessitates a scientific assessment of the carrying capacity (CC) of this aquaculture system, in order to ensure sustainability. Here, using a marine plankton ecological model and the food balance, bivalve CCs were estimated for aquaculture zones in Jiaozhou Bay (JZB), an important aquaculture center in China. The results indicated that CCs mainly depended on bivalve filtration rates, which were in turn regulated by temperature. The CCs of Manila clams, razor clams, and scallops were lowest in August–September, while the CCs of oysters and mussels were lowest in October. For all taxa, CCs were higher in areas with higher primary production. Further evaluation indicated that present cultures were saturated, with high mortality rates. To improve culture quality and efficiency, the seeding density of the Manila clam, the most widely farmed species in the JZB, should be reduced to 700 individuals (ind.)/m2. We also recommend reducing the seeding densities of razor clams, oysters, and mussels to 345, 60, and 165 ind./m2, and reducing scallop seeding density to 280 ind./cage. Our methods and results provide a basis for further assessments of stocking capacities and the promotion of sustainable, healthy aquaculture in other bays.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.subjectaquacultureen_US
dc.subjectbivalveen_US
dc.subjectcarrying capacityen_US
dc.subjectJiaozhou Bayen_US
dc.subjectmodel estimationen_US
dc.subjectseeding densitiesen_US
dc.titleAssessment of bivalve carrying capacities and seeding densities in aquaculture areas of Jiaozhou Bay, China, using ecological modeling and the food balanceen_US
dc.typeArticleen_US
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