Finite Mixture Model-based classification of a complex vegetation system

By

Attorre, Fabio
Cambria, Vito E.
Agrillo, Emiliano
Alessi, Nicola
Alfò, Marco
De Sanctis, Michele
Malatesta, Luca
Sitzia, Tommaso
Guarino, Riccardo
Marcenò, Corrado
Massimi, Marco
Spada, Francesco
Fanelli, Giuliano

Type

Article

Date of Publication

2020-5-4

Original Publication

Vegetation classification and survey

Volume

1

Pages

77--86

Subjects

Cluster analysis , finite mixture model , forest vegetation , Italian Peninsula , Vegetation plots

Contributed by

Pensoft Publishers

Language

English

Published by

Sofia Pensoft Publishers 2020-

Identifiers

DOI: https://doi.org/10.3897/VCS/2020/48518

 

Abstract

Aim: To propose a Finite Mixture Model (FMM) as an additional approach for classifying large datasets of georeferenced vegetation plots from complex vegetation systems. Study area: The Italian peninsula including the two main islands (Sicily and Sardinia), but excluding the Alps and the Po plain. Methods: We used a database of 5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years. The plots were classified according to species composition and environmental variables using a FMM. Classification results were compared with those obtained by TWINSPAN algorithm. Groups were characterized in terms of ecological parameters, dominant and diagnostic species using the fidelity coefficient. Interpretation of resulting forest vegetation types was supported by a predictive map, produced using discriminant functions on environmental predictors, and by a non‐metric multidimensional scaling ordination. Results: FMM clustering obtained 24 groups that were compared with those from TWINSPAN, and similarities were found only at a higher classification level corresponding to the main orders of the Italian broadleaf forest vegetation: Fagetalia sylvaticae, Carpinetalia betuli, Quercetalia pubescenti-petraeae and Quercetalia ilicis. At lower syntaxonomic level, these 24 groups were referred to alliances and sub-alliances. Conclusions: Despite a greater computational complexity, FMM appears to be an effective alternative to the traditional classification methods through the incorporation of modelling in the classificatory process. This allows classification of both the co-occurrence of species and environmental factors so that groups are identified not only on their species composition, as in the case of TWINSPAN, but also on their specific environmental niche.<br> Taxonomic reference: Conti et al. (2005).<br> Abbreviations: CLM = Community-level models; FMM = Finite Mixture Model; NMDS = non‐metric multidimensional scaling.