The population of Atlantic cod significantly contributes to the prosperity of fishery production in the world. In this paper, we quantitatively investigate the global abundance variation in Atlantic cod from 1919 to 2016, in favor of spatio-temporal interactions over manifold impact factors at local observation sites, and propose to explore the predictive mechanism with the help of its periodicity, time-frequency co-movement and lead-lag effects, via Long Short Term Memory (LSTM). We first integrate evidences yielded from wavelet coefficients, to suggest that the abundance variation potentially follows a 36-year major cycle and 24-year secondary cycle at the time scales of 55 years and 37 years. We further evaluate the responses of Atlantic cod abundance to the external impact factors, including SST, catches, prey biomass, SSS, in aid of the wavelet coherence and phase difference, which allows to identify the dominantly correlative factors and capture the leading roles along the time domain, and then divide the responses around recent 60 years into three stages: before 1985, 1985-1995, after 1995. At the first stage, the reason for the decline in abundance could be mainly attributed to the rapid rise of fish catches. At the second stage, the impact of SST and SSS also constitute significant indices, besides over-fishing, meanwhile, the mortality of primary producers and forced migration of fish species, indirectly cause the decline. At the third stage, warming SST and growing SSS, directly led to the decrease of abundance. Finally, we establish one ensemble of LSTM-SAE architecture, to comprehensively reflect the predictive patterns at each stage. It has been demonstrated from experiment results that the models behaved better when intentionally feeding with the dominantly correlative multivariate inputs, instead of either all factors or only the abundance. The proposed scheme provides opportunities to symmetrically identify the underlying predictive attributes of Atlantic cod abundance, potentially perform as the quantitative references in reasonably making fishing decision. With the rapid development in deep learning capabilities, it is hopeful to expect better predictions of the responses to global changes, not only for Atlantic cod but also for other fish species and the ecosystem as a whole.

中文翻译：

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1919年至2016年大西洋鳕鱼的周期性变化，时频共运动和超前滞后效应在整个海表温度，海表盐度，渔获量和猎物生物量中的长期短期记忆，对大西洋鳕鱼的丰度变化进行识别和预测。

大西洋鳕鱼的数量极大地促进了世界渔业生产的繁荣。在本文中，我们定量研究了1919年至2016年大西洋鳕鱼的全球丰度变化，支持时空相互作用而不是当地观察点的多种影响因素，并建议借助其周期性，时间探索预测机制长期记忆（LSTM）来实现频率共运动和超前滞后效应。我们首先整合从小波系数得出的证据，以表明丰度变化可能在55年和37年的时间尺度上遵循36年的主要周期和24年的次要周期。我们进一步评估了大西洋鳕鱼丰度对外部影响因素（包括SST，渔获物，猎物生物量，SSS，借助小波相干和相位差，可以识别主导性相关因素并捕获时域上的主导作用，然后将近60年的响应分为三个阶段：1985年之前，1985-1995年，1995年之后在第一阶段，丰度下降的原因可能主要归因于渔获量的快速上升。在第二阶段，SST和SSS的影响不仅构成过度捕捞的重要指标，同时，初级生产者的死亡率和鱼类的强迫迁徙也间接导致了下降。在第三阶段，SST的变暖和SSS的增长直接导致了丰度的下降。最后，我们建立一个LSTM-SAE体系结构的集合，以全面反映每个阶段的预测模式。从实验结果已经证明，当有意使用显性相关的多元输入而不是所有因素或仅是丰度时，模型的表现会更好。拟议的方案提供了机会，可以对称地识别大西洋鳕鱼丰度的潜在预测属性，并有可能在合理地进行捕捞决策时作为定量参考。随着深度学习能力的飞速发展，人们希望对全球变化做出更好的预测，不仅是对大西洋鳕鱼，而且对于其他鱼类和整个生态系统也是如此。拟议的方案提供了机会，可以对称地识别大西洋鳕鱼丰度的潜在预测属性，并有可能在合理地进行捕捞决策时作为定量参考。随着深度学习能力的飞速发展，人们希望对全球变化做出更好的预测，不仅是对大西洋鳕鱼，而且对于其他鱼类和整个生态系统也是如此。拟议的方案提供了机会，可以对称地识别大西洋鳕鱼丰度的潜在预测属性，并有可能在合理地进行捕捞决策时作为定量参考。随着深度学习能力的飞速发展，人们希望对全球变化做出更好的预测，不仅是对大西洋鳕鱼，而且对于其他鱼类和整个生态系统也是如此。