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首页> 外文期刊>ICES Journal of Marine Science >Robustness of fish assemblages derived from three hierarchical agglomerative clustering algorithms performed on Icelandic groundfish survey data
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Robustness of fish assemblages derived from three hierarchical agglomerative clustering algorithms performed on Icelandic groundfish survey data

机译：鱼群的稳健性来自对冰岛底层鱼调查数据执行的三种层次聚类聚类算法

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摘要

Heatmaps are used to identify species-area assemblages based on Icelandic groundfish survey data. Hierarchical agglomerative clustering algorithms are widely applied for species assemblage studies and form the basis for heatmaps. First, the robustness of fish assemblages derived from three clustering algorithms, Average, Complete, and Ward's linkage, was examined. For statistical reliability, the use of a bootstrap resampling technique to generate the confidence values for the clusters is emphasized. Two cluster validity indices were used to measure the efficiency and the quality of the clusters. To examine the stability of the results, clustering was carried out across different sample sizes and levels of data smoothing. Second, cluster analysis was carried out using a different combination of data standardization and dissimilarity measure. Ward's linkage gave the most robust fish assemblages for both modes of data analyses. Four fish assemblages were identified which could be characterized according to the depth and the geographic distribution. This algorithm was then used to generate a heatmap to determine the species-area relationships. Specific areas were characterized by the identified species groups.
机译：热图用于根据冰岛底栖鱼类调查数据来识别物种区域组合。层次聚类聚类算法被广泛应用于物种组装研究，并为热图奠定了基础。首先，研究了来自三种聚类算法（平均，完整和沃德关联）的鱼类组合的鲁棒性。对于统计可靠性，强调了使用自举重采样技术来生成聚类的置信度值。使用两个聚类有效性指标来衡量聚类的效率和质量。为了检查结果的稳定性，对不同样本大小和数据平滑级别进行了聚类。其次，使用数据标准化和相异性度量的不同组合进行聚类分析。沃德的联系为这两种数据分析提供了最强大的鱼类组合。确定了四个鱼类组合，可以根据深度和地理分布对其进行表征。然后，使用该算法生成热图，以确定物种-区域关系。通过确定的物种组来表征特定区域。

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