应用物种分布模型预测海洋大型底栖生物的分布。

Q3 Environmental Science
Jia-Yi Cong, Xin-Zheng Li, Yong Xu
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引用次数: 0

摘要

物种分布模型(SDM)是根据环境条件和物种分布数据预测物种分布范围和适宜生境的重要工具。这些模型包括相关模型、机理模型和机理-相关模型。在海洋科学领域,SDM 已被广泛用于预测各种海洋生物(包括鱼类、哺乳动物、藻类等)的空间分布模式。大型底栖生物是海洋生态系统不可或缺的组成部分,了解大型底栖生物的分布对生态保护和资源管理具有重要意义。我们回顾了数据和元数据管理中使用的常见方法,并介绍了使用不同模型预测海洋大型底栖生物分布模式的案例研究。此外,我们还强调使用相关模型和机理模型来分析气候变化对海洋大型底栖生物空间分布的影响。最后,我们讨论了与数据和元数据相关的挑战和前景。随着遥感技术和建模技术的进步,SDM 在海洋生态研究中的作用日益重要,可为应对气候变化和保护海洋生物多样性提供坚实的科学基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of species distribution models in predicting the distribution of marine macrobenthos.

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.

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应用生态学报
应用生态学报 Environmental Science-Ecology
CiteScore
2.50
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0.00%
发文量
11393
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