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应用生态学报 ›› 2024, Vol. 35 ›› Issue (9): 2392-2400.doi: 10.13287/j.1001-9332.202409.029

• 专家见解 • 上一篇    下一篇

物种分布模型在海洋大型底栖动物分布预测中的应用

丛佳仪1,2, 李新正1,2,3,4, 徐勇1,2,3,4*   

  1. 1中国科学院海洋研究所海洋生物分类与系统演化实验室, 山东青岛 266071;
    2中国科学院大学, 北京 100049;
    3中国科学院海洋大科学研究中心, 山东青岛 266071;
    4青岛海洋科学与技术国家实验室海洋生物与生物技术功能实验室, 山东青岛 266237
  • 收稿日期:2024-01-02 接受日期:2024-05-22 出版日期:2024-09-18 发布日期:2025-03-18
  • 通讯作者: * E-mail: xuyong@qdio.ac.cn
  • 作者简介:丛佳仪, 男, 2000年生, 硕士研究生。主要从事大型底栖动物生态学。E-mail: congjiayi@qdio.ac.cn
  • 基金资助:
    国家重点研发计划项目(2022YFF0802202)和国家自然科学基金项目(42006078, 42176114)

Application of species distribution models in predicting the distribution of marine macrobenthos

CONG Jiayi1,2, LI Xinzheng1,2,3,4, XU Yong1,2,3,4*   

  1. 1Department of Marine Organism Taxonomy and Phylogeny, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, Shandong, China;
    2University of Chinese Academy of Sciences, Beijing 100049, China;
    3Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, Shandong, China;
    4Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, Shandong, China
  • Received:2024-01-02 Accepted:2024-05-22 Online:2024-09-18 Published:2025-03-18

摘要: 物种分布模型是一种基于环境条件和物种分布数据预测物种在不同环境中的分布范围和适宜生境的模型工具,主要包括关联模型、机理模型和机理-关联混合模型。在海洋领域,物种分布模型已被广泛应用于预测鱼类、哺乳类和藻类等海洋生物的分布,但在大型底栖动物中的应用还比较匮乏。大型底栖动物作为海洋生态系统的重要组成部分,研究其分布规律对生态保护和资源管理具有重要意义。本文对物种分布模型的常用方法进行了总结,介绍了不同模型在预测海洋大型底栖动物分布中的研究案例,重点介绍了关联模型和机理模型在分析气候变化对海洋大型底栖动物分布影响中的应用,阐述了物种分布模型面临的挑战以及发展前景。随着遥感技术和建模方法的不断进步,物种分布模型将在海洋生态学研究中发挥更加重要的作用,为应对气候变化和保护海洋生物多样性提供科学依据。

关键词: 物种分布模型, 大型底栖动物, 气候变化, 关联模型, 机理模型

Abstract: Species distribution models (SDMs) are valuable tools in predicting species distribution ranges and the suitable habitats, which are based on environmental conditions and species distribution data. These models encompass correlative models, mechanistic models, and mechanistic-correlative models. In the field of marine science, SDMs have been extensively used for predicting the spatial distribution patterns of various marine organisms including fish, mammals, algae, et al. However, the application of SDMs in predicting the distribution of macrobenthos remains scarce. Understanding the distribution of macrobenthos, the integral components of marine ecosystems, has significant implications for ecological conservation and resource management. We reviewed common methodologies employed in SDMs and presented case studies using different models to predict the distribution patterns of marine macrobenthos. Further, we emphasized the use of correlative and mechanistic models to analyze the impact of climate change on the spatial distribution of marine macrobenthos. Finally, we discussed the challenges and prospects associated with SDMs. With the advances in remote sensing technology and modeling techniques, SDMs are becoming increasingly pivotal in marine ecological research, which could offer a robust scientific foundation for addressing climate change and preserving marine biodiversity.

Key words: species distribution models, macrobenthos, climate change, correlative models, mechanistic models