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Chinese Journal of Applied Ecology ›› 2020, Vol. 31 ›› Issue (6): 2076-2086.doi: 10.13287/j.1001-9332.202006.034

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Overwintering distribution and its environmental determinants of small yellow croaker based on ensemble habitat suitability modeling

LIU Zun-lei1,2, YANG Lin-lin1,2, YUAN Xing-wei1,2, JIN Yan1,2, YAN Li-ping1,2, CHENG Jia-hua1,2*   

  1. 1East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;
    2Key Laboratory of East China Sea Fishery Resources Exploitation and Utilization, Ministry of Agriculture and Rural Affairs, Shanghai 200090, China
  • Received:2019-11-08 Online:2020-06-15 Published:2020-06-15
  • Contact: * E-mail: ziyuan@sh163.net
  • Supported by:
    This work was supported by the Ministry of Agriculture and Rural Affairs Fisheries Resource Survey in China Sea and Fisheries Resource Survey In Provisional Measures Zone between China and Japan (2015-2017).

Abstract: Small yellow croaker is a trans-boundary fish resource shared by China and South Korea. Information on the distribution and preferred habitats of overwintering populations is lacking, parti-cularly in southern waters of Yellow Sea where the species is regulated together by China and South Korea. We simulated the geographic distribution under current condition with eight species distribution models (SDM) based on the presence-absence data and five environmental variables. The performance of model’s prediction was evaluated using the area under the receiver operating characteris-tic curve (AUC) based on 5-fold cross-validation. Ensemble SDMs were constructed using a weighted average of eight habitat suitability model types to identify core areas with high probability of small yellow croaker occurrence. The results suggested that predictions based on presence-absence data generally perform better than those based on presence-only data and classical regression models under-performed compared to machine learning approaches. Among all the approaches that supported presence-absence data, support vector machine was the best performing technique and GLM was the worst. The ensemble model outperformed individual SDM models, demonstrating higher effectiveness of ensemble modelling approaches than individual models in reducing the predictive uncertainty. Salinity and temperature were important factors in predicting the overwintering distribution of small yellow croaker. The core areas with high probability of occurrence were concentrated in three areas, the open waters of southern Yellow Sea, the open waters of northern East China Sea, and the coastal sea of Zhejiang Province. Coastal waters in southern Yellow Sea and open waters in central and southern East China Sea were not suitable for overwintering of small yellow croaker. Our results provided a basis for predicting the potential overwintering distribution to guide spatial planning in support of sustainable utilization of small yellow croaker.