Forecasting model to the anchoveta (<i>Engraulis ringens</i>) biomass in the southeastern Pacific Ocean

Chen Peng; Chen Xinjun; Lei Lin; Hu Feifei

doi:10.3969/j.issn.0253-4193.2018.01.009

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Chen Peng, Chen Xinjun, Lei Lin, Hu Feifei. Forecasting model to the anchoveta (Engraulis ringens) biomass in the southeastern Pacific Ocean[J]. Haiyang Xuebao, 2018, 40(1): 77-86. doi: 10.3969/j.issn.0253-4193.2018.01.009

Citation:

Chen Peng, Chen Xinjun, Lei Lin, Hu Feifei. Forecasting model to the anchoveta (Engraulis ringens) biomass in the southeastern Pacific Ocean[J]. Haiyang Xuebao, 2018, 40(1): 77-86. doi: 10.3969/j.issn.0253-4193.2018.01.009

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Forecasting model to the anchoveta (Engraulis ringens) biomass in the southeastern Pacific Ocean

doi: 10.3969/j.issn.0253-4193.2018.01.009

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
3.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2017-04-19

Abstract

Abstract

Anchoveta (Engraulis ringens) is a small pelagic fish living in the coastal area at the Southeast Pacific Ocean. Forecasting anchoveta biomass effectively could provide decision supports for China's fishmeal import enterprises. Therefore, in this study, combining the biological factors (percentage of juvenile and previous biomass), oceanic environmental factors (temperature status in the fishing ground and climatic variations) and catch, we built the anchoveta biomass forecasting model in the seventeen fishing season (from 2006 to the first fishing season of 2014) combining the methods of principal component analysis (PCA) and multiple linear regression. Besides, we evaluated the factors affecting the changes of anchoveta biomass by the result of PCA. With the increase of sample size, the mean of relative error to the predicting sequences and actual sequences became smaller, and the correlation coefficient decreased. Finally, model 5 (modeling the data from 2006 to 2013 and validating the data in the first fishing season of 2014) could effectively fit out the changing trends of avchoveta biomass:the correlation coefficient of predicting sequences and actual sequences was 0.86 and the mean of relative error for these two sequences was 12%. What's more, the relative error was only 1% for the predicting value in the first fishing season at 2014. The results of correlation coefficients between the anchoveta biomass sequence and the scores of principal components sequence indicated that environmental factor could have huge impacts to the anchoveta biomass variations.
- anchoveta (Engraulis ringens),
- biomass,
- forecasting model,
- southeastern Pacific Ocean

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References(34)

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