An Analysis of Mass Mortalities in Aquaculture Fish Farms on the Southern Coast in Korea

Lee, Deokchan; Won, Kyoung Mi; 박명애,; Sung, Choi Hye; 정승희,

doi:10.35372/kmiopr.2018.33.1.001

Cited by 8 publications

(4 citation statements)

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“…Short-term SST and MHW forecasts are particularly relevant in sectors such as aquaculture, a very important activity in the Mediterranean sea, where the acute impacts of short-duration extreme events can significantly impact operations (DeMott et al, 2021;Frölicher and Laufkötter, 2018). For instance, an extreme MHW lasting a week might impose sudden stresses on temperature-sensitive aquatic species (e.g., the early life stages of fish; Buttay et al, 2023), potentially causing immediate adverse effects, such as mass mortality (Lee et al, 2018;Buttay et al, 2023;Guinaldo et al, 2023), compared to a more prolonged, moderate MHW. Timely predictions of these short-term fluctuations enable proactive management strategies, aiding in disease prevention, optimizing feeding schedules, preventing potential coral bleaching events, and ensuring the wellbeing of farmed species and hence safeguarding economic interests and sustaining food security (Oidtmann et al, 2011;DeMott et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Bonino,

Galimberti,

Masina

et al. 2024

Ocean Sci.

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Abstract. Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict sea surface temperature (SST) time series and marine heatwaves in 16 regions of the Mediterranean Sea. ML algorithms, including the random forest (RForest), long short-term memory (LSTM), and convolutional neural network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 d ahead. For each region, we performed 15 different experiments for ML techniques, progressively sliding the training and the testing period window of 4 years from 1981 to 2017. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. For each area, the results show that all the ML methods performed with minimum root mean square errors (RMSEs) of about 0.1 °C at a 1 d lead time and maximum values of about 0.8 °C at a 7 d lead time. In all regions, both the RForest and LSTM consistently outperformed the CNN model across all lead times. LSTM has the highest predictive skill in 11 regions at all lead times. Importantly, the ML techniques show results similar to the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods compare favorably with MedFS up to 3 d lead time in 14 regions, while MedFS shows superior skill at 5 d lead time in 9 out of 16 regions. All methods predict the occurrence of MHWs with a confidence level greater than 50 % in each region. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself.

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Section: Discussionmentioning

confidence: 99%

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Bonino,

Galimberti,

Masina

et al. 2024

Ocean Sci.

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show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning methods to predict Sea Surface Temperature and Marine Heatwave occurrence: a case study of the Mediterranean Sea

Bonino,

Galimberti,

Masina

et al. 2023

Preprint

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Abstract. Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict Sea Surface Temperature (SST) time series and Marine Heatwaves (MHWs) in 16 regions of the Mediterranean Sea. ML algorithms, including Random Forest (RForest), Long short-term memory (LSTM), and Convolutional Neural Network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 days ahead. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. The results show that ML methods, particularly RForest and LSTM, performed well with minimum Root Mean Square Errors (RMSE) of about 0.1 °C at a 1-day lead time and maximum values of about 0.8 °C at a 7-day lead time. Importantly, the ML techniques outperform the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods outperform MedFS up to 3-day lead time in most regions, while MedFS shows superior skill at 5-day lead time in 9 out of 16 regions. All methods in all regions predict the occurrence of MHWs with a confidence level greater than 50 %. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself.

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“…Conversely, Korean aquaculture has experienced substantial growth over the past three decades [ 24 ]. However, emphasizing quantitative production has led to excessive inputs, elevated mortality rates, food safety challenges, and environmental degradation [ 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Fish Welfare-Related Issues and Their Relevance in Land-Based Olive Flounder (Paralichthys olivaceus) Farms in Korea

Oh,

Lee

2024

Animals

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Korean aquaculture has expanded considerably in recent decades; however, this growth has often prioritized quantity over fish welfare. Therefore, we analyzed the aquaculture practices of olive flounder, the predominant species in Korean consumption and production, within the framework of fish welfare. We conducted extensive interviews and surveys across olive flounder farms in Jeju-do and Wando to examine prevalent issues impacting fish welfare in aquaculture. These issues include stressors, mass mortality events, and disease outbreaks, all of which strain the welfare of farmed fish. Moreover, our survey revealed farmers’ varying perceptions of fish welfare, highlighting the necessity for a cohesive approach. Accordingly, we propose recommendations to enhance fish welfare and establish a more sustainable aquaculture model in Korea. Ensuring fish welfare in aquaculture operations requires a comprehensive approach that considers the physiological and behavioral needs of fish throughout the farming lifecycle. By prioritizing fish welfare, Korean aquaculture can strengthen its growth while maintaining ethical standards and ensuring the well-being of farmed fish. This welfare-centric approach is crucial for the long-term sustainability and resilience of the Korean aquaculture industry. By addressing welfare concerns and promoting responsible practices, Korean aquaculture can foster an ethically sound and sustainable future.

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An Analysis of Mass Mortalities in Aquaculture Fish Farms on the Southern Coast in Korea

Cited by 8 publications

References 0 publications

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Machine learning methods to predict Sea Surface Temperature and Marine Heatwave occurrence: a case study of the Mediterranean Sea

Fish Welfare-Related Issues and Their Relevance in Land-Based Olive Flounder (Paralichthys olivaceus) Farms in Korea

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