Abstract Effective management in the marine environment requires a thorough understanding of the distribution of natural resources, including that of the benthos, the animals living in and on the seabed. Hitherto, it has been difficult to identify broadscale patterns in the benthos as the faunal clusters identified from individual surveys are not directly comparable. As a result, much reliance has been placed on one-off broadscale spatial surveys or matching samples to a common set of biotopes. In this study, new benthic macrofaunal data from discrete surveys are matched to existing broadscale cluster groups identified using unsupervised machine learning (k-means). This objective approach allows for continual improvements in our understanding of macrofaunal distribution patterns, thereby supporting ongoing conservation and marine spatial planning efforts. Other benefits are discussed. Finally, an R shiny web application is presented, allowing users to biotope match their own data.

中文翻译：

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一种用于海底生物群落分类的新机器学习方法

摘要 海洋环境的有效管理需要对自然资源的分布有透彻的了解，包括底栖动物、生活在海床上和海底的动物的分布。迄今为止，由于从个别调查中确定的动物群不能直接比较，因此很难确定底栖生物的大尺度模式。因此，人们非常依赖一次性的大规模空间调查或将样本与一组常见的生物群落进行匹配。在这项研究中，来自离散调查的新底栖大型动物数据与使用无监督机器学习 (k-means) 识别的现有大规模集群组进行匹配。这种客观的方法可以不断改进我们对大型动物分布模式的理解，从而支持正在进行的保护和海洋空间规划工作。讨论了其他好处。最后，展示了一个 R 闪亮的 Web 应用程序，允许用户匹配他们自己的数据。