Musculoskeletal Growth Modulation in Gilthead Sea Bream Juveniles Reared at High Water Temperature and Fed with Palm and Rapeseed Oils-Based Diets

The upward trend of seawater temperature has encouraged improving the knowledge of its consequences on fish, considering also the development of diets including vegetable ingredients as an approach to achieve a more sustainable aquaculture. This study aims to determine the effects on musculoskeletal growth of: (1) a high-water temperature of 28 °C (versus 21 °C) in gilthead sea bream juveniles (Sparus aurata) fed with a diet rich in palm oil and, (2) feeding the fish reared at 28 °C with two other diets containing rapeseed oil or an equilibrated combination of both vegetable oils. Somatic parameters and mRNA levels of growth hormone-insulin-like growth factors (GH-IGFs) axis-, osteogenic-, myogenic-, lipid metabolism- and oxidative stress-related genes in vertebra bone and/or white muscle are analyzed. Overall, the data indicate that high-water rearing temperature in this species leads to different adjustments through modulating the gene expression of members of the GH-IGFs axis (down-regulating igf-1, its receptors, and binding proteins) and also, to bone turnover (reducing the resorption-activity genes cathepsin K (ctsk) and matrix metalloproteinase-9 (mmp9)) to achieve harmonic musculoskeletal growth. Moreover, the combination of palm and rapeseed oils seems to be the most beneficial at high-water rearing temperature for both balanced somatic growth and muscular fatty acid uptake and oxidation.

Abnormally high water temperature prediction using LSTM deep learning model

Recently, abnormally high water temperature (AHWT) phenomena are occurring more often due to the global warming and its impact. These phenomena have damaged extensively to the maritime economy around the southern coast of Korea and caused an illness by exacerbating the propagation of Vibrio pathogens. To mitigate damages by AHWT phenomena, it is necessary to respond as quickly as possible or predict them in advance. In this study, therefore, we proposed a deep learning-based methodology to predict the occurrences of AHWT phenomena using the long short-term memory (LSTM) model. First, a LSTM model was trained using the satellite-derived water temperature data over the past ten years. Then, the water temperatures after a few days were estimated using the trained LSTM model. In a performance evaluation, when estimating water temperatures after one-day, the model achieved results of 1.865 and 0.412 in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. Second, we developed a decision algorithm based on the Markov state transition in order to predict the AHWT occurrence probability. As a result, we obtained 0.88 of F1 score for predicting AHWT phenomena after 1 day in case of the southern coast of Korea.

Analysis on the distribution of zooplankton in tropical shallow urban lakes

The distribution characteristics of zooplankton in shallow urban lakes of Lingnan that in the tropics and subtropics were studied. The results showed that Brachionus, Trichocerea, and Lecane were dominant species of composition and abundance. The abundance of large Cladocera was low, which was affected by high water temperature, food restriction, and predation pressure of fish. Rotifera, small Cladocera, and Nauplius were the main zooplankton. Combined with the analysis of typical tropical shallow urban lakes, the dominant zooplankton tended to have a small individual population. It was expected to provide a reference for the ecological restoration of tropical shallow-water urban lakes.

ANALYSIS OF THE RED TIDE OCCURRENCE PATTERN WHEN HIGH WATER TEMPERATURE

In 1982, the red tide caused by Cochlodimium polykrikodies occurred at first in Jinhae Bay in Korea. Since then, it causes serious fisheries damage every year. In 2018, red tide occurred when high water temperature. We analyzed red tide occurrence pattern when high water temperature. Red tide occurrence date, occurrence area caused by the Cochlodinium polykrikoides were used provided by National Institute of Fisheries Science. SST data were used the GHRSST Level 4 OSTIA data provided by NOAA. The red tide occurred July 14 to September 2 in 2013, July 29 to October 4 in 2014, August 5 to September 23 in 2015, and July 23 to August 8 in 2018 in Korea. The nearest SST data from the red tide occurrence area were extracted. When red tide occurred, SST was 22~28 °C in 2013, 23~25 °C in 2014, 21~27 °C in 2015, 26~28 °C in 2018. SST in 2013 was increasing trend and 2015 was downward trend. SST in 2018 occurred at high water temperatures above 25 °C. The spatial pattern by using the self-organizing map (3x3 map), node 4 was the highest frequency (16.9%). It is considered that it appears at the beginning and end of red tide occurrence. Node 1 was 16.3% frequency, it showed at the end of 2014 and 2015. SST of node 1 and 4 maintained 22~23 °C on the southern sea of Korea. Node 9 was 13.9%, which is the late 2013 and 2018. Node 9 showed high temperature pattern of more than 26 °C in the southern sea of Korea.