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5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years.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. Taxonomic reference: Conti et al. (2005). Abbreviations: CLM = Community-level models; FMM = Finite Mixture Model; NMDS = non‐metric multidimensional scaling.
Highlights
The analysis of the spatial distribution of assemblages of communities is receiving increasing attention by ecologists (Nieto-Lugilde et al 2017)
This paper aims to verify the applicability of Finite Mixture Modelling (FMM) as classification method of vegetation plots using a complex case study and a large dataset, comparing the classification results with (1) those obtained by the TWINSPAN algorithm and (2) with current syntaxonomic classification schemes
Group 8 is potentially widespread at the highest altitude along the Apennine chain and on the Etna volcano, while at a lower altitude, group 2 is mainly found in the southern part of the peninsula, and group 23 in the central-north (Figure 2)
Summary
The analysis of the spatial distribution of assemblages of communities is receiving increasing attention by ecologists (Nieto-Lugilde et al 2017). Much of these are based on simple Euclidean distances between each plot and the group prototypes that do not consider the dependence, the association and the covariance between the variables (plant species abundance values) characterizing the plots In this respect, finite mixtures of multivariate Gaussian densities provide a simple, model-based, extension to the K-means method, allowing for overlapping clusters oriented according to the group-specific covariances and providing, a posteriori, for the classification of each plot to one of the groups. Finite mixtures of multivariate Gaussian densities provide a simple, model-based, extension to the K-means method, allowing for overlapping clusters oriented according to the group-specific covariances and providing, a posteriori, for the classification of each plot to one of the groups For this reason, among CLMs, Finite Mixture Modelling (FMM) is an emerging method and has already been used to identify marine bioregions on the Western Australian continental margin (Woolley et al 2013) and forest physiognomic types in Italy (Attorre et al 2014). Geo-pedological diversity, a variety of microclimates (Attorre et al 2007), and a long history of disturbance that dates thousands of years and includes logging, fire, grazing, and plantation activities (Médail and Quézel 1999; Scarascia-Mugnozza et al 2000; Vallejo et al 2005), make the identification and classification of vegetation types difficult
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