南极半岛周边海域南极磷虾栖息地适应性
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S 932.5+1

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国家自然科学基金(41776185);国家重点研发计划国际科技创新合作重点专项(2023YFE0104500)


Habitat suitability of Antarctic krill (Euphausia superba) in the Antarctic Peninsula
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National Natural Science Foundation of China (41776185); National key R & D Project (2018YFC1406801)

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    摘要:

    南极磷虾作为南极生态系统中的关键物种,其栖息地适应性研究对可持续利用磷虾资源和了解南大洋生态系统均有重要作用。然而,不同的模型算法会导致估算的磷虾栖息地适宜性出现较大的偏差。为了探索构建磷虾栖息地指数模型的合适方法,实验利用海表温度 (sea surface temperature,SST)、海平面高度 (sea surface height,SSH)、海表面叶绿素 (sea surface chlorophyll,SSC)、海冰密集度(sea ice concentration,SIC)等环境因子,分别采用神经网络拟合和一元非线性拟合方法,并结合最小值法、最大值法、连乘法、算术平均法、几何平均法、加权算术平均法等算法构建磷虾栖息地适宜性指数 (habitat suitability index,HSI)模型。结果显示,神经网络模型预报结果更符合磷虾实际栖息分布情况,而一元非线性拟合预测结果较为连续。最大值法和最小值法计算结果差异较大,容易引进较大的误差。连乘法的预测效果较好,算术平均法、几何平均法和加权算术平均法的预测结果相似,且较为稳定。研究表明,神经网络模型是构建南极磷虾HSI模型的合适方法。此外,使用连乘法和加权算术平均法等算法能够提高模型预测结果的准确性和稳定性,而最大值法和最小值法要慎重使用。本研究的方法和结论有助于评估类似物种在栖息地方面的适宜性,对未来磷虾资源的评估和南极生态系统管理具有启示意义。同时,该研究也为其他生态学领域中栖息地适宜性研究提供参考。

    Abstract:

    Antarctic krill (Euphausia superba) is a key species in the Antarctic ecosystem. Investigating its habitat suitability can help the sustainable use of this resource and enhance our understanding of the Southern Ocean ecosystem. Such work is also useful for assessing krill population and exploring main fishing ground. However, different model algorithms may result in large deviations in calculating habitat suitability index (HSI) for krill resource. Therefore, this study aimed to explore appropriate methods for constructing HSI models for E. superba by using environmental factors such as sea surface temperature (SST), sea surface height (SSH), sea surface chlorophyll (SSC), and sea ice concentration (SIC). Two fitting methods, Neural Network (NN) model and Univariate Nonlinear (UN) model, were used to fit the environmental factors. Six algorithms, including minimum value, maximum value, product, arithmetic mean, geometric mean, and weighted arithmetic mean, were combined to construct HSI models. The results showed that the neural network model better predicted the actual distribution of habitats, while the univariate nonlinear regression method yielded more consistent results. The maximum and minimum value methods showed greater differences in their calculated results, introducing larger errors compared to other approaches. The continued multiplication method produced good predictive performance, while the arithmetic, geometric, and weighted arithmetic mean methods had similar results and were more stable. The proposed methods and conclusions in this study are of guiding significance for the assessment of similar species and the prediction of their habitats in the future.

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王嘉龙,刘慧,朱国平.南极半岛周边海域南极磷虾栖息地适应性[J].水产学报,2024,48(6):069307

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  • 收稿日期:2021-12-21
  • 最后修改日期:2022-01-10
  • 录用日期:2022-01-28
  • 在线发布日期: 2024-06-11
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