Surplus-production models are widely used in fish stock assessment and fisheries management due to their simplicity and lower data demands than age-structured models such as Virtual Population Analysis. The CEDA (catc...Surplus-production models are widely used in fish stock assessment and fisheries management due to their simplicity and lower data demands than age-structured models such as Virtual Population Analysis. The CEDA (catch-effort data analysis) and ASPIC (a surplus-production model incorporating covariates) computer packages are data-fitting or parameter estimation tools that have been developed to analyze catch-and-effort data using non-equilibrium surplus production models. We applied CEDA and ASPIC to the hairtail (Trichiurus japonicus) fishery in the East China Sea. Both packages produced robust results and yielded similar estimates. In CEDA, the Schaefer surplus production model with log-normal error assumption produced results close to those of ASPIC. CEDA is sensitive to the choice of initial proportion, while ASPIC is not. However, CEDA produced higher R 2 values than ASPIC.展开更多
Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This pa...Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.展开更多
Reproduction in fish is controlled by the brain-pituitary-gonad reproductive axis. Although genes of the reproductive axis are conserved from fish to humans, their in vivo functions are less clear in fish. Mutant line...Reproduction in fish is controlled by the brain-pituitary-gonad reproductive axis. Although genes of the reproductive axis are conserved from fish to humans, their in vivo functions are less clear in fish. Mutant lines of the reproductive axis have been systematically investigated in zebrafish and medaka using recently developed genome-editing nucleases. Here, we review recent progress in the genetic analysis of the reproductive axis in fish as well as the opportunities and challenges of applying genome-editing nucleases in fisheries.展开更多
基金Supported by the Special Research Fund of Ocean University of China(No. 201022001)
文摘Surplus-production models are widely used in fish stock assessment and fisheries management due to their simplicity and lower data demands than age-structured models such as Virtual Population Analysis. The CEDA (catch-effort data analysis) and ASPIC (a surplus-production model incorporating covariates) computer packages are data-fitting or parameter estimation tools that have been developed to analyze catch-and-effort data using non-equilibrium surplus production models. We applied CEDA and ASPIC to the hairtail (Trichiurus japonicus) fishery in the East China Sea. Both packages produced robust results and yielded similar estimates. In CEDA, the Schaefer surplus production model with log-normal error assumption produced results close to those of ASPIC. CEDA is sensitive to the choice of initial proportion, while ASPIC is not. However, CEDA produced higher R 2 values than ASPIC.
文摘Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.
文摘Reproduction in fish is controlled by the brain-pituitary-gonad reproductive axis. Although genes of the reproductive axis are conserved from fish to humans, their in vivo functions are less clear in fish. Mutant lines of the reproductive axis have been systematically investigated in zebrafish and medaka using recently developed genome-editing nucleases. Here, we review recent progress in the genetic analysis of the reproductive axis in fish as well as the opportunities and challenges of applying genome-editing nucleases in fisheries.
