Sub-issue 5. Acquisition of data related to aquaculture of marine products and analysis with AI (Hidehiro Kondo, Ikuo Hirono, Keiichiro Koiwai) Sub-issue 2. Establishment of AI analysis methods to solve other issues in the marine field Theme a. Spatial-temporal distribution modeling using GaussianMarkovRandomField and its application (Toshihide Kitakado) Metagenome analysis was performed on the bacterial flora and phytoplankton flora in the breeding water of a tiger prawn farm and the bacterial flora in the stomach of five tiger prawns from each pond.Both the microbiota and the phytoplankton flora in the culture pond differed depending on the time of sampling, and it was found that they were not stable during the culture period.It was also found that the microbiota and phytoplankton flora of different pond waters within the same farm were not the same.It was found that the bacterial flora in the prawn stomach was similar among prawns from the same pond, but differed depending on the time of sampling.The diversity of bacterial flora tended to be maintained at a high level in farms that were able to produce stably. (Plan/Method) (Results and Future Prospects) It is important to know in advance the occurrence of fish and shellfish infectious diseases that occur every year at aquaculture sites and cause great economic loss to related industries, and to develop methods to reduce the damage. The purpose is to develop a method that utilizes big data of DNA sequence information to understand seasonal changes in environmental DNA and microbiota around fish farms.Since it is also important to develop technology to overcome microbial infections in farmed fish and shellfish, we will build and update the genome sequence data base for farmed species. (Plan/Method) This fiscal year, we will collect gene sequence big data that can identify species or genera of bacterial flora in shrimp pond water and shrimp at prawn farms.Sampling is done once a month.We will ask the farmer to provide us with the mortality and water quality data of kuruma shrimp in the farm, and analyze the relationship between the pond water and the bacterial flora in the shrimp body.We will also collect gene sequence information to upgrade the prawn genome sequence information and gene sequence information database that we have constructed in our research so far. (Results and future prospects) Since there were no serious deaths of kuruma prawns due to the outbreak of infectious diseases in the prawn farms during the period of this research survey, the bacterial flora and phytoplankton in ponds with and without infectious diseases Although we were not able to perform a comparative analysis of the flora, we thought that by continuing to investigate the flora and fauna of the phytoplankton, it would be possible to evaluate the ponds with good and bad productivity for each season.The purpose of this project is to build a software base for statistical estimation of spatiotemporal distribution using GaussianMarkovRandomField (GMRF) using spatiotemporal data on fish resources.If necessary, we will conduct a trial analysis of the density survey data borrowed from the institution with which the person in charge of the project has been conducting joint research, together with the environmental data.Targets for initial analysis include the Sendai sand eel, Indian Ocean tuna, freshwater fish from Southeast Asia, and microplastics in the waters surrounding Japan, but selections will be made during the preparation process.For tuna data, we have developed spatio-temporal fishery data and environmental data obtained from satellites.In the Indian Ocean, for yellowfin and bigeye tropical tuna, we preliminarily estimated the relationship between organism density information derived from fisheries data and environmental variables such as water temperature using an additive model.As for albacore, we obtained data from three oceans and made similar assumptions.In addition, we experimentally predicted how the spatio-temporal distribution of tuna species will change with future environmental changes based on climate change scenarios.On the other hand, we also tried to analyze the spatial distribution of freshwater fish in Southeast Asia and microplastics in the waters around Japan, but it was difficult to derive reasonable results due to the relationship between data resolution and accuracy.Currently, tuna species are being analyzed by GMRF based on TemplateModelBuilder (TMB). We are planning to improve the data analysis method. 3.Acquisition of marine big data and promotion of AI analysis research (XNUMX) Development of a fishery integrated support system that realizes resilient and sustainable fisheries using marine organism big data and training of marine AI human resources
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