Formalized classification of species‐poor vegetation: a proposal of a consistent protocol for aquatic vegetation

Abstract

Aims: Most vegetation classification systems developed for large areas include various inconsistencies. Therefore, we (1) propose a new consistent Cocktailbased approach to redefine the traditional phytosociological classification of species-poor vegetation; (2) apply it to create a classification protocol for aquatic vegetation;(3) implement this protocol in a computer expert system; and (4) test it with a data set previously classified using an older version of the Cocktail method. Methods:The new approach uses formal logic to provide formal definitions of vegetation units. In the classification protocol for aquatic vegetation we defined consistent criteria for delimitation of associations according to the concepts that are predominantly used in phytosociology, based on species cover, dominance patterns and functional species groups. We applied these criteria in a computer expert system running in the JUICE 7.0 program, and applied them to a test data set of 12 171 vegetation plots from the Czech Republic containing at least one aquatic species. The new classification was compared with (1) the previous national Cocktail classification based on species cover values and in few cases on sociological species groups, and (2) a non-formalized expert-based classification.Results: Thirteen functional species groups were created to build logical formulas of 64 aquatic associations and 5297 (44% of the total data set) vegetation plots were assigned to these associations, i.e. by 4% and 12% more than in the previous Cocktail and expert-based classifications, respectively. There was 94% and 83% classification agreement with the previous Cocktail and expert-based classification. Conclusions:The new approach produces a formal, consistent and unequivocal classification of species-poor vegetation with several advantages over similar approaches. It provides not only a set of formal definitions of vegetation units, but also a set of rules for building such definitions. All associations with common characteristics are defined by structurally identical formulas, ensuring consistency of the classification. While similar approaches for species-rich vegetation use sociological species groups, which are not applicable to species-poor vegetation, the new approach introduces the use of functional species groups, which reflect vegetation physiognomy and spatial structure and, in combination with species dominance, enable the classification of species-poor vegetation in a similar manner as in traditional phytosociology.

Artificial ponds in Central Europe do not fall behind the natural ponds in terms of macrophyte diversity

“…The first matrix was composed of helophyte species, the second of hydrophytes (true aquatic species), the third of both species categories and the fourth represented red list species. Classification of helophyte and hydrophyte species groups was based on previous studies (Alahuhta et al, 2012;Landucci et al, 2015;Kolada, 2016); 15 recorded non-wetland or non-aquatic species were excluded from the dataset (Appendix A). Next, we conducted analyses for comparison of diversity between artificial and natural ponds.…”

Cocktail clustering – a new hierarchical agglomerative algorithm for extracting species groups in vegetation databases

“…In general, pseudospecies offer a solution for the problem of using species groups to define communities based on dominant species (e.g., Landucci et al. ).…”

Variation in Mountain Vegetation Composition between the East and the West Sides of Southern Taiwan

“…The cocktail determination key is a crucial basis for establishing different species groups. The species group comprises different species that have a similar geographical area and habitat [23,32]. The species groups were based on the species established, and selected from the National Vegetation Database of Taiwan (AS-TW-001) by fidelity (phi coefficient).…”

“…An example is the cocktail determination key [18][19][20][21], which describes each vegetation type using a logical formula that combines statements concerning the presence or absence of typical or dominant species [1]. The cocktail determination key was developed by combining the presence or absence of specific ecologically meaningful species groups [18] that provide unequivocal rules for assigning relevés to vegetation types [20][21][22][23]. We used the vegetation dataset from the National Vegetation Database of Taiwan and used the cocktail determination key to defining unequivocal assignment rules for each vegetation type in this study.…”