Combinatorial functional diversity: an information theoretical approach
A new approach to the measurement of functional diversity based on two-state nominal traits is developed from the florula diversity concept of P. Juhász-Nagy. For evaluating functional diversity of an assemblage, first a traits by species matrix is compiled. Various information theory functions are used to examine structural properties in this matrix, including the frequency distribution of trait combinations. The method is illustrated by actual examples, the first from plant communities prone to fire in Spain, and the second from running water invertebrate assemblages in Hungary. The results suggest that of the various functions used the standardized joint entropy, termed combinatorial functional evenness supplies most meaningful results. In plant communities, high fire recurrence decreased combinatorial functional evenness, while this measure for freshwater assemblages was uncorrelated with stream width and negatively correlated with the degree of human impact. Stream width is negatively correlated with the number of manifested functional combinations. In both case studies, combinatorial functional evenness has an inverse relationship to species richness – i.e., fewer species have a larger chance to produce equiprobable functional combinations.
KeywordsEvenness Fire Functional traits Invertebrates Species richness Vegetation
Combinatorial functional diversity
Combinatorial functional evenness
Combinatorial functional richness
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- Juhász-Nagy, P. 1984. Spatial dependence of plant populations. Part 2. A family of new models. Acta Bot. Acad. Sci. Hung. 30: 363–402.Google Scholar
- Lavorel, S., Grigulis, K., McIntyre, S., Williams, N.S.G., Garden, D., Dorrough, J., Berman, S., Quetier, F., Thebault, A. and Bonis, A. 2008. Assessing functional diversity in the field - methodology matters! Funct. Ecol. 22: 134–147.Google Scholar
- Maire, V., Gross, N., Wirth, C., Pontes, L.D.S., Proulx, R., Börger, L., Soussana, J-F. and Loualt, F. 2012. Habitat-filtering and niche differentiation jointly determine species relative abundance within grassland communities along fertility and disturbance gradients. New Phytol. 196: 497–509.CrossRefGoogle Scholar
- Moog, O. 1995. Fauna Aquatica Austriaca. Lieferung Mai/95. Wasserwirtschaftskataster, Bundesministerium für Land- und Forstwirtschaft, Wien.Google Scholar
- Orlóci, L. 1969. Information theory models for hierarchic and non-hierarchic classifications. In: Cole, A.D. (ed.), Numerical Taxonomy. Academic, London. pp. 148–164.Google Scholar
- Pillar, V.D. and Orlóci, L. 2004. Character-Based Community Analysis: The Theory and an Application Program. Electronic Edition available at https://doi.org/ecoqua.ecologia.ufrgs.br.
- Podani, J. 1993. SYN/TAX 5 User’s Manual. Scientia, BudapestGoogle Scholar
- Podani J. 2001. SYN-TAX 2000. Computer programs for data analysis in ecology and systematics. User’s Manual. Scientia, Budapest.Google Scholar
- Ricotta, C. 2003. On parametric evenness measures. J. Theor. Biol. 222: 189–197.Google Scholar
- Ricotta, C. and Moretti, M. 2011. CWM and Rao’s quadratic diversity: a unified framework for functional ecology. Oecologia 167: 181–188.Google Scholar
- Taillie, C. 1979. Species equitability: a comparative approach. In: Grassle, J.F., Patil, G.P., Smith, W.K. and Taillie, C. (eds.), Ecological Diversity in Theory and Practice. International Cooperative Publishing House, Fairland, MD, pp. 51–62.Google Scholar
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