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Fishermen catch, sell sawfish carcass.

Sri Lanka pushes for protection of sea cucumbers amid overexploitation

Brisbane, Fast, First: Angler catches rare sawfish off Queensland coast.

Iranian Held By Somali Pirates Since 2015 freed.

Hilsa: The fish that is being loved to death.

Rare 700-kg sawfish chokes to death after Maharashtra fisherman nets it.

Chinese Vessels' Overfishing Affect Iranian Fishermen's Livelihoods.

Tide turned: surveys show the public has lost its appetite for shark culls.

Zebra Sharks Have A New Color.

Shark attack kills surfer off France's Reunion.

A new study shows small juvenile sea turtles have a very high occurrence of ingested plastic

Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling - regarded as the largest in the WIO and the fifth most important upwelling system globally - takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

Small-scale fisheries (SSFs) are often undervalued and unmanaged as a result of a lack of data. A study of SSFs in Menabe, western Madagascar in 1991 found diverse catches and a productive fishery with some evidence of declining catches. Here we compare data collected at the same landing site in 1991 and 2011. 2011 had seven times greater total monthly landings due to more people fishing and higher individual catches. Catch composition showed a lower mean trophic level in 2011 indicating overfishing, the true extent of which may be masked due to changes in technology and fishing behaviours. Limited management action since 2011 means these trends have likely continued and an urgent need for both greater understanding, and management of these fisheries remains if they are to continue providing food and income for fishing communities.

To better develop and protect the pelagic fishery in the northwest Indian Ocean, China's fishing enterprises have been producing pelagic fisheries in the said area for a long time. Based on the fishing log data of light falling gear in the northwest Indian Ocean from 2016 to 2020, this study analyzed the impact of different time scales on the catch rate and fishing ground center of gravity of light falling gear fishing grounds. We also explored the relationship between different time scales and catch per unit effort (CPUE) by using the fishing ground center of gravity, the Random Forest model (RF), and the generalized additive model (GAM). The results were shown as follows: (1) From 2016 to 2020, 76,576 t were captured, and 16,496 nets were operated; (2) The gravity center of fishing ground in the Northwest Indian Ocean moved to the northeast as a whole, and the monthly fishing ground gravity center changed first to the Southern and then to the northern; (3) RF model (R² = 0.709, RMSE = 0.2034, and prediction accuracy is 55.8%), which is better than the GAM model (R² = 0.632, RMSE = 0.2242, and prediction accuracy is 37.3%). In the RF model, the importance of time variables on CPUE was in the order of week, year, operation time, and lunar phase; in the GAM model, it was week, year, lunar phase, and operation time. On the whole, the importance of the long time scale (year, week) is greater than that of the short time scale (lunar phase and operation time). (4) The RF model and GAM model show that the most critical environmental variables were SST, DO, SSS, and Chla, and the least important were SSH, Δ50, and CV50. SST, Chla, and DO significantly impact pelagic fishing and CPUE and are critical reference indexes for predicting the Northwest Indian Ocean light falling gear fishing ground. (5) The 95% confidence interval showed that the suitable interval of time, space, and environmental variables in the RF model was much smaller than in the GAM model.