Editorial: Fishing effort and the evolving nature of its efficiency

Demirel, Nazlı; Nauen, Cornelia E.; Palomares, Maria Lourdes D.

doi:10.3389/fmars.2023.1180174

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The oceans are currently experiencing a critical ecological deterioration, with an alarming number of species facing the risk of extinction (Bongaarts, 2019). This crisis can be attributed, in part, to the extensive and unsustainable fishing practices that have significantly depleted fishery resources, resulting in adverse effects on marine ecosystems and biodiversity (Demirel et al, 2023). To promote sustainable development, it is imperative for fishery management authorities to analyze the dynamic changes in fishing activities promptly y.…”

Section: Introductionmentioning

confidence: 99%

HyFish: hydrological factor fusion for prediction of fishing effort distribution with VMS dataset

Shi,

Hong,

Zhao

et al. 2024

Front. Mar. Sci.

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Predicting fishing effort distribution is crucial for guiding fisheries management in developing effective strategies and protecting marine ecosystems. This task requires a deep understanding of how various hydrological factors, such as water temperature, surface height, salinity, and currents influence fishing activities. However, there are significant challenges in designing the prediction model. Firstly, how hydrological factors affect fishing effort distributions remains unquantified. Secondly, the prediction model must effectively integrate the spatial and temporal dynamics of fishing behaviors, a task that shows analytical difficulties. In this study, we first quantify the correlation between hydrological factor fields and fishing effort distributions through spatiotemporal analysis. Building on the insights from this analysis, we develop a deep-learning model designed to forecast the daily distribution of fishing effort for the upcoming week. The proposed model incorporates residual networks to extract features from both the fishing effort distribution and the hydrological factor fields, thus addressing the spatial limits of fishing activity. It also employs Long Short-Term Memory (LSTM) networks to manage the temporal dynamics of fishing activity. Furthermore, an attention mechanism is included to capture the importance of various hydrological factors. We apply the approach to the VMS dataset from 1,899 trawling fishing vessels in the East China Sea from September 2015 to May 2017. The dataset from September 2015 to May 2016 is used for correlation analysis and training the prediction model, while the dataset from September 2016 to May 2017 is employed to evaluate the prediction accuracy. The prediction error ratio for each day of the upcoming week range is only 5.6% across all weeks from September 2016 to May 2017. HyFish, notable for its low prediction error ratio, will serve as a versatile tool in fisheries management for developing sustainable practices and in fisheries research for providing quantitative insights into fishing resource dynamics and assessing ecological risks related to fishing activities.

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Section: Introductionmentioning

confidence: 99%