The assignment of relevés to pre-existing vegetation units: a comparison of approaches using species fidelity

Abstract

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. 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. 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. 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. 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. 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.