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Article

A Generalized Framework for Analyzing Taxonomic, Phylogenetic, and Functional Community Structure Based on Presence–Absence Data

1
Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, Eötvös University, H-1117 Budapest, Hungary
2
MTA-ELTE-MTM Ecology Research Group, Eötvös University, H-1117 Budapest, Hungary
3
Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, 75005 Paris, France
4
Department of Environmental Biology, University of Rome ‘La Sapienza’, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Mathematics 2018, 6(11), 250; https://doi.org/10.3390/math6110250
Received: 17 October 2018 / Revised: 5 November 2018 / Accepted: 6 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue New Paradigms and Trends in Quantitative Ecology)
Community structure as summarized by presence–absence data is often evaluated via diversity measures by incorporating taxonomic, phylogenetic and functional information on the constituting species. Most commonly, various dissimilarity coefficients are used to express these aspects simultaneously such that the results are not comparable due to the lack of common conceptual basis behind index definitions. A new framework is needed which allows such comparisons, thus facilitating evaluation of the importance of the three sources of extra information in relation to conventional species-based representations. We define taxonomic, phylogenetic and functional beta diversity of species assemblages based on the generalized Jaccard dissimilarity index. This coefficient does not give equal weight to species, because traditional site dissimilarities are lowered by taking into account the taxonomic, phylogenetic or functional similarity of differential species in one site to the species in the other. These, together with the traditional, taxon- (species-) based beta diversity are decomposed into two additive fractions, one due to taxonomic, phylogenetic or functional excess and the other to replacement. In addition to numerical results, taxonomic, phylogenetic and functional community structure is visualized by 2D simplex or ternary plots. Redundancy with respect to taxon-based structure is expressed in terms of centroid distances between point clouds in these diagrams. The approach is illustrated by examples coming from vegetation surveys representing different ecological conditions. We found that beta diversity decreases in the following order: taxon-based, taxonomic (Linnaean), phylogenetic and functional. Therefore, we put forward the beta-redundancy hypothesis suggesting that this ordering may be most often the case in ecological communities, and discuss potential reasons and possible exceptions to this supposed rule. Whereas the pattern of change in diversity may be indicative of fundamental features of the particular community being studied, the effect of the choice of functional traits—a more or less subjective element of the framework—remains to be investigated. View Full-Text
Keywords: beta diversity; diversity partitioning; Jaccard dissimilarity; redundancy; richness difference; simplex plot; species replacement beta diversity; diversity partitioning; Jaccard dissimilarity; redundancy; richness difference; simplex plot; species replacement
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MDPI and ACS Style

Podani, J.; Pavoine, S.; Ricotta, C. A Generalized Framework for Analyzing Taxonomic, Phylogenetic, and Functional Community Structure Based on Presence–Absence Data. Mathematics 2018, 6, 250. https://doi.org/10.3390/math6110250

AMA Style

Podani J, Pavoine S, Ricotta C. A Generalized Framework for Analyzing Taxonomic, Phylogenetic, and Functional Community Structure Based on Presence–Absence Data. Mathematics. 2018; 6(11):250. https://doi.org/10.3390/math6110250

Chicago/Turabian Style

Podani, János; Pavoine, Sandrine; Ricotta, Carlo. 2018. "A Generalized Framework for Analyzing Taxonomic, Phylogenetic, and Functional Community Structure Based on Presence–Absence Data" Mathematics 6, no. 11: 250. https://doi.org/10.3390/math6110250

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