The Identification and Prediction in Abundance Variation of Atlantic Cod via Long Short-Term Memory With Periodicity, Time–Frequency Co-movement, and Lead-Lag Effect Across Sea Surface Temperature, Sea Surface Salinity, Catches, and Prey Biomass From 1919 to 2016

Abstract

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 spatiotemporal 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 sea surface temperature (SST), catches, prey biomass, and sea surface salinity (SSS), in aid of the wavelet coherence and phase difference, which allows us to identify the dominantly correlative factors and capture the leading roles along the time domain and then divide the responses around the recent 60 years into three stages: before 1985, 1985–1995, and 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 provides significant indices, besides overfishing; 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 experimental 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 and 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.

Climate Variability, Coastal Livelihoods, and the Influence of Ocean Change on Fish Catch in the Coastal Savannah Zone of Ghana

“…In most fish species, SSS changes determine egg fertilization, incubation, yolk sac resorption, early embryogenesis, and larval growth, according to Boeuf and Payan [86]. Nian et al [87] found that increasing SSS and warming SST directly led to a decline in Atlantic cod abundance from 1919 to 2016. As a result, the fishermen in the zone demand greater information and explanation about SSS changes and their implications.…”

Toward Exploring Topographic Effects on Evolution and Propagation of Ocean Mesoscale Eddies Through Life Cycle Across Izu-Ogasawara Ridge in Northwestern Pacific Ocean

“…Recently, machine learning (ML), has become one of the most powerful tools in the field of multivariate multi-step time series prediction (Hochreiter and Schmidhuber, 1997;Geurts et al, 2006;Sapankevych and Sankar, 2009;Box et al, 2015;Hu and Zheng, 2020;Nian et al, 2021c). Deep learning could be regarded as one of the hottest topics in the context, and all kinds of most emerging and advanced algorithms have been put forward and made progresses (Hinton and Salakhutdinov, 2006;Krizhevsky et al, 2012;Goodfellow et al, 2014;He et al, 2016;Huang et al, 2017;Wan et al, 2019), such as Recurrent Neural Network (RNN) (Elman, 1990;Lipton et al, 2015;Braakmann-Folgmann et al, 2017;Qin et al, 2017) and Long Short Term Memory (LSTM) (Kalchbrenner et al, 2015;Shi et al, 2015;Greff et al, 2016;Zhang et al, 2017;Shi and Yeung, 2018;Wang et al, 2021;Gangopadhyay et al, 2021;Nian et al, 2021b). We expect to establish a comprehensive predictive model of mesoscale eddy trajectories toward meridional ridges on a global scale in the future, coupling with the topographic effects, via deep learning.…”

Ecological niche models for the assessment of site suitability of sea cucumbers and sea urchins in China

“…The development of remote sensed technology ensures the wide coverage, high accuracy, and high accessibility to the data of sea surface temperature (SST) 15 . As an important indicator of sea water temperature, the data of SST has been well used to predict the resource distribution of various marine organisms, including Thunnus albacares 16 , Dosidicus gigas 17 , and Gadus morhua 18 . Since aquaculture is primarily conducted in coastal areas, it is directly affected by the local sea surface temperature.…”