• Key message

Total fidelity value index can be used for the assignment of new relevés to existing vegetation units and it can be used to refine classifications derived from unsupervised clustering.

• Context

Diagnostic species is an important concept in vegetation classification. Apart from its usefulness to characterize species niche preferences, the diagnostic species concept is used in vegetation classification: (1) for the assignment of new relevés to the vegetation units of an existing classification; (2) to refine vegetation classifications by reassigning relevés that sustain the definition of vegetation units.

• Aims

The main aims were to evaluate the relative predictive performance of different statistical fidelity measures for the reassignment of relevés to existing vegetation units, and in which cases reassignments improve the quality of the original classification.

• Methods

We took the classifications produced by three commonly used unsupervised classification methods, and all relevés were reassigned to the closest vegetation unit according to the total fidelity value index (TFVI), where fidelity value had been calculated using one of eight distinct statistical measures, and according to the frequency-positive fidelity index (FPFI). Classifications obtained after relevé reassignments were compared to the initial ones using the Adjusted Rand Index. The quality of all classification solutions, including the initial ones, was evaluated using thirteen different evaluator statistics.

• Results

The predictive performance of IndVal was the best among all eight fidelity indices in the TFVI framework, and also outperformed FPFI. The TFVI framework based on group-equalized fidelity indices produced better results than other assignment rules in terms of the chosen evaluator statistics. Re-assignments based on IndVal, r, or FPFI produced classifications with the best quality, when combining the results of all evaluators.

• Conclusion

We conclude that TFVI based on IndVal and r has the best quality for assigning of new relevés to existing vegetation units, and it also could be used to refine classifications derived from unsupervised clustering. Consequently, our results reiterate that TFVI, which is new in vegetation sciences, can be a good alternative for FPFI, as the most commonly used in the assignment of vegetation plots (relevés), to predefined vegetation types in large datasets.

中文翻译：

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将相关物分配给先前存在的植被单位：使用物种保真度的方法的比较

• 关键信息

总保真度值指数可用于将新的相关性分配给现有植被单元，并可用于细化从无监督聚类得出的分类。

• 上下文

诊断物种是植被分类中的重要概念。诊断物种概念除了可用于描述物种生态位偏好外，还用于植被分类：（1）为现有分类的植被单元分配新的相关性；（2）通过重新分配维持植被单位定义的相关性来完善植被分类。

• 目的

主要目的是评估针对将相关物重新分配给现有植被单位的不同统计保真度测量方法的相对预测性能，在这种情况下，重新分配可改善原始分类的质量。

• 方法

我们采用了三种常用的无监督分类方法进行的分类，并根据总保真度值指数（TFVI）将所有相关项重新分配给最近的植被单位，其中总保真度值是使用八种不同的统计方法之一计算的，并根据频率正保真度指数（FPFI）。使用调整后的兰德指数将相关性重新分配后获得的分类与初始分类进行比较。所有分类解决方案的质量，包括初始分类，均使用13种不同的评估者统计数据进行了评估。

• 结果

的预测性能IndVal是在TFVI框架所有八个保真度指标名列前茅，同时也跑赢FPFI。就选定的评估者统计数据而言，基于组均衡的保真度指标的TFVI框架产生了比其他分配规则更好的结果。当结合所有评估者的结果时，基于IndVal，r或FPFI进行的重新分配产生了质量最高的分类。

• 结论

我们得出的结论是，基于IndVal和r的TFVI具有最佳质量，可以为现有植被单元分配新的相关性，它也可以用于完善无监督聚类的分类。因此，我们的结果重申，植被科学中新出现的TFVI可以很好地替代FPFI，因为FPFI是在大型数据集中分配植被图（关联）到预定义植被类型时最常用的方法。