Institute of Marine Science, University of Auckland, Auckland, 1142, New Zealand.
Bioinformatics Institute, University of Auckland, Auckland, 1142, New Zealand.
Nat Commun. 2018 Nov 30;9(1):5085. doi: 10.1038/s41467-018-07252-4.
Recently, we classified the oceans into 30 biogeographic realms based on species' endemicity. Castro-Insua et al. criticize the choices of dissimilarity coefficients and clustering approaches used in our paper, and reanalyse the data using alternative techniques. Here, we explain how the approaches used in our original paper yield results in line with existing biogeographical knowledge and are robust to alternative methods of analysis. We also repeat the analysis using several similarity coefficients and clustering algorithms, and a neural network theory method. Although each combination of methods produces outputs differing in detail, the overall pattern of realms is similar. The coarse nature of the present boundaries of the realms reflects the limited field data but may be improved with additional data and mapping to environmental variables.
最近，我们根据物种特有性将海洋划分为 30 个生物地理区域。Castro-Insua 等人批评了我们论文中使用的不相似系数和聚类方法的选择，并使用替代技术重新分析了数据。在这里，我们解释了我们原始论文中使用的方法如何产生与现有生物地理知识一致的结果，并且对替代分析方法具有鲁棒性。我们还使用了几种相似系数和聚类算法以及神经网络理论方法重复了分析。虽然每种方法组合的输出都存在细节上的差异，但区域的整体模式是相似的。目前区域边界的粗略性质反映了有限的现场数据，但可以通过增加数据和与环境变量的映射来改进。
