Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño-Southern Oscillation events in northern Chile

IF 1.9 2区 农林科学 Q2 FISHERIES Fisheries Oceanography Pub Date : 2024-03-03 DOI:10.1111/fog.12672
Elier Armas, Hugo Arancibia, Sergio Neira, María Carla Marín
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Abstract

Engraulis ringens (anchovy) is a small pelagic fish of the Family Engraulidae that inhabits the neretic-coastal zone from northern Peru to south-central Chile. It is the main resource species of industrial fishing of northern Chile, representing 80% of the annual landings of the purse seine fleet. The history of this fishery (1985–2023) shows a strong decrease in annual industrial landings, especially during extreme El Niño–Southern Oscillation (ENSO) events. The greatest decrease in landings in two decades occurred in 2020, coinciding with a cold La Niña event, which had not been observed in previous La Niña events. We evaluated whether the current decrease in annual landings of E. ringens is associated with oceanographic changes in northern Chile during El Niño or La Niña events. We applied a neuronal network model to identify the spatial and temporal distribution of E. ringens using the catch probability of each boat of the industrial purse seine fleet. The selected oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height and currents) for the 2003–2020 period were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS program) and used as predictor variables of the monthly landings of E. ringens. The neural network model explained 97% of the monthly variability of catch probability of E. ringens by the industrial purse seine fleet. The spatial distribution of catch probability of E. ringens was analyzed independently for El Niño (2015), La Niña (2007, 2013, and 2020), and Neutral (2004) years. We found that catch probability extended further west during La Niña events (except for 2020), occupying a greater area, but were limited to a 10 nautical mile coastal strip during the El Niño event. The spatial distribution of catch probability in the Neutral condition was near the coast, although not as restricted as during the El Niño event. The higher catch probabilities in the La Niña event of 2020 were near the coast, in contrast to the previous La Niña events of 2007 and 2013, due to the restriction of the optimal habitat of E. ringens in response to changes in oceanographic conditions. The application of the results of this study will allow understanding and probably anticipating the consequences that extreme ENSO events could have on the catch probability of the industrial anchovy purse seine fleet in northern Chile.

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用神经网络方法检测智利北部厄尔尼诺-南方涛动事件期间 Engraulis ringens 捕获概率的空间变化
Engraulis ringens(凤尾鱼)是一种小型中上层鱼类,属于 Engraulidae 科,栖息于秘鲁北部至智利中南部的近海沿海地区。它是智利北部工业化捕捞的主要资源品种,占围网船队年上岸量的 80%。该渔业的历史(1985-2023 年)显示,年工业上岸量大幅下降,尤其是在厄尔尼诺-南方涛动(ENSO)极端事件期间。2020 年上岸量出现了 20 年来的最大降幅,当时正值寒冷的拉尼娜事件,而在之前的拉尼娜事件中并未观察到这一现象。我们评估了目前每年 E. ringens 上岸量的减少是否与厄尔尼诺或拉尼娜事件期间智利北部海洋变化有关。我们采用神经元网络模型,利用工业围网船队每艘船的捕获概率来确定 E. ringens 的时空分布。我们从哥白尼海洋环境监测服务(CMEMS 计划)中获取了 2003-2020 年期间选定的海洋变量(海面温度、盐度、混合层深度、海平面高度和海流),并将其用作 E. ringens 月上岸量的预测变量。神经网络模型解释了工业围网船队捕获 E. ringens 的月度变化概率的 97%。对厄尔尼诺年(2015 年)、拉尼娜年(2007 年、2013 年和 2020 年)和中性年(2004 年)E. ringens 捕获概率的空间分布进行了独立分析。我们发现,在拉尼娜事件期间(2020 年除外),捕获概率进一步向西扩展,占据了更大的区域,但在厄尔尼诺事件期间,捕获概率仅限于 10 海里的沿海地带。在中性条件下,捕获概率的空间分布靠近海岸,但不像厄尔尼诺事件期间那样受到限制。与 2007 年和 2013 年的拉尼娜事件不同,2020 年的拉尼娜事件中较高的捕获概率出现在海岸附近,这是因为 E. ringens 的最佳栖息地因海洋条件的变化而受到限制。应用该研究结果将有助于了解并预测极端厄尔尼诺/南方涛动事件可能对智利北部工业化围网捕捞凤尾鱼船队的捕获概率造成的后果。
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来源期刊
Fisheries Oceanography
Fisheries Oceanography 农林科学-海洋学
CiteScore
5.00
自引率
7.70%
发文量
50
审稿时长
>18 weeks
期刊介绍: The international journal of the Japanese Society for Fisheries Oceanography, Fisheries Oceanography is designed to present a forum for the exchange of information amongst fisheries scientists worldwide. Fisheries Oceanography: presents original research articles relating the production and dynamics of fish populations to the marine environment examines entire food chains - not just single species identifies mechanisms controlling abundance explores factors affecting the recruitment and abundance of fish species and all higher marine tropic levels
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