Advertisement

Community Ecology

, Volume 14, Issue 2, pp 180–188 | Cite as

Combinatorial functional diversity: an information theoretical approach

  • J. PodaniEmail author
  • C. Ricotta
  • J. G. Pausas
  • D. Schmera
Article

Abstract

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.

Keywords

Evenness Fire Functional traits Invertebrates Species richness Vegetation 

Abbreviations

CFD

Combinatorial functional diversity

CFE

Combinatorial functional evenness

CFR

Combinatorial functional richness

FA

Functional associatum

FD

Functional diversity

FH

Functional heterogeneity

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackerly, D.D. 2004. Functional strategies of chaparral shrubs in relation to seasonal water deficit and disturbance. Ecol. Monogr. 75: 25–44.CrossRefGoogle Scholar
  2. Cornwell, W.K., Schwilk, D.W. and Ackerly, D.D. 2006. A trait-based test for habitat filtering: convex hull volume. Ecology 87: 1465–1471.CrossRefGoogle Scholar
  3. de Bello, F., Lavergne, S., Meynard, C.N., Lepš, J. and Thuiller, W. 2010. The partitioning of diversity: showing Theseus a way out of the labyrinth. J. Veg. Sci. 21: 992–1000.CrossRefGoogle Scholar
  4. Erős, T., Heino, J., Schmera, D. and Rask, M. 2009. Characterising functional trait diversity and trait-environment relationships in fish assemblages of boreal lakes. Freshw. Biol. 54: 1788–1803.CrossRefGoogle Scholar
  5. Gross, N., Suding, K.N. and Lavorel, S. 2007. Leaf dry matter content and lateral spread predict response to land use change for six subalpine grassland species. J. Veg. Sci. 18: 289–300.CrossRefGoogle Scholar
  6. Heino, J. 2005. Functional biodiversity of macroinvertebrate assemblages along major ecological gradients of boreal headwater streams. Freshw. Biol. 50: 1578–1587.CrossRefGoogle Scholar
  7. 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
  8. Juhász-Nagy, P. 1993. Notes on compositional diversity. Hydrobiologia 249: 173–182.CrossRefGoogle Scholar
  9. Juhász-Nagy, P. and Podani, J. 1983. Information theory methods for the study of spatial processes and succession. Vegetatio 51:129–140.CrossRefGoogle Scholar
  10. Kattge, J. et al. 2011. TRY – a global database of plant traits. Global Change Biol. 17: 2905–2935.CrossRefGoogle Scholar
  11. Kühner, A. and Kleyer, M. 2008. A parsimonious combination of functional traits predicting plant response to disturbance and soil fertility. J. Veg. Sci. 19: 681–692.CrossRefGoogle Scholar
  12. 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
  13. Magurran, A.E. 1988. Ecological Diversity and its Measurement. Princeton University Press, Princeton.CrossRefGoogle Scholar
  14. 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
  15. Mason, N.W.H., Mouillot, D., Lee, W.G. and Wilson, J.B. 2005. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111: 112–118.CrossRefGoogle Scholar
  16. McGill, B.J., Enquist, B.J., Weiher, E. and Westoby, M. 2006. Rebuilding community ecology from functional traits. TREE 21: 178–185.PubMedGoogle Scholar
  17. Moog, O. 1995. Fauna Aquatica Austriaca. Lieferung Mai/95. Wasserwirtschaftskataster, Bundesministerium für Land- und Forstwirtschaft, Wien.Google Scholar
  18. Mouchet, M., Villéger, S., Mason, N.W.H. and Mouillot, D. 2010. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24: 867–876.CrossRefGoogle Scholar
  19. Mouillot, D., Culioli, J.M., Pelletier, D. and Tomasini, J.A. 2008. Do we protetct biological originality in protected areas? A new index and an application to the Bonifacio Strait Natural Reserve. Biol. Cons. 141: 1569–1580.CrossRefGoogle Scholar
  20. 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
  21. Orlóci, L. 1991. On character-based plant community analysis: choice, arrangement, comparison. In: Feoli, E. and Orlóci, L. (eds.), Computer Assisted Vegetation Analysis. Kluwer, Dordrecht. pp. 81– 86.CrossRefGoogle Scholar
  22. Pausas, J.G. and Verdú, M. 2008. Fire reduces morphospace occupation in plant communities. Ecology 89: 2181–2186.CrossRefGoogle Scholar
  23. Pausas, J.G. and Verdú, M. 2010. The jungle of methods for evaluating phenotypic and phylogenetic structure of communities. BioScience 60: 614–625.CrossRefGoogle Scholar
  24. Pavoine, S., Gasc, A., Bonsall, M.B. and Mason, N.W.M. 2013. Correlation between phylogenetic and functional diversity: mathematical artefacts or true ecological and evolutionary process? J. Veg. Sci, 24: 781–793.CrossRefGoogle Scholar
  25. Peet, R.K. 1974. The measurement of species diversity. Ann. Rev. Ecol. Syst. 5: 285–307.CrossRefGoogle Scholar
  26. Petchey, O.L. and Gaston, K.J. 2006. Functional diversity: back to basics and looking forward. Ecol. Lett. 9: 741–758.CrossRefGoogle Scholar
  27. 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.
  28. Podani, J. 1993. SYN/TAX 5 User’s Manual. Scientia, BudapestGoogle Scholar
  29. Podani J. 2001. SYN-TAX 2000. Computer programs for data analysis in ecology and systematics. User’s Manual. Scientia, Budapest.Google Scholar
  30. Podani, J. 2009. Convex hulls, habitat filtering, and functional diversity: mathematical elegance versus ecological interpretability. Community Ecol. 10: 244–250.CrossRefGoogle Scholar
  31. Podani, J. and Schmera, D. 2006. On dendrogram-based measures of functional diversity. Oikos 115: 179–185.CrossRefGoogle Scholar
  32. Poff, N.L., Olden, J.D., Vieira, N.K.M., Finn, D.S., Simmons, M.P. and Kondratieff, B.C. 2006. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. J. North Amer. Benthological Soc. 25: 730–755.CrossRefGoogle Scholar
  33. Ricotta, C. 2003. On parametric evenness measures. J. Theor. Biol. 222: 189–197.Google Scholar
  34. Ricotta, C. and Moretti, M. 2011. CWM and Rao’s quadratic diversity: a unified framework for functional ecology. Oecologia 167: 181–188.Google Scholar
  35. Roscher, C., Schumacher, J., Gubsch, M., Lipowsky, A., Weigelt, A., Buchmann, N., Schmid, B. and Schulze, E.D. 2012. Using plant functional traits to explain diversity–productivity relationships. PLoS ONE 7(5): e36760. doi:10.1371/journal.pone. 0036760CrossRefGoogle Scholar
  36. Schmera, D. and Baur, B. 2011. Testing a typology system of running waters for conservation planning in Hungary. Hydrobiologia 665: 183–194.CrossRefGoogle Scholar
  37. Schmera, D., Erős, T. and Podani, J. 2009. A measure for assessing functional diversity in ecological communities. Aquat. Ecol. 43: 157–167.CrossRefGoogle Scholar
  38. Smith, B. and Wilson, J.B. 1996. A consumer’s guide to evenness indices. Oikos 76: 70–82.CrossRefGoogle Scholar
  39. 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
  40. Troussellier, M. and Legendre, P. 1981. A functional diversity index for microbial ecology. Microbial. Ecol. 7: 283–292.CrossRefGoogle Scholar
  41. Tuomisto, A. 2012. An updated consumer’s guide to evenness and related indices. Oikos 121: 1203–1218.CrossRefGoogle Scholar
  42. Verdú, M. and Pausas, J.G. 2007. Fire drives phylogenetic clustering in Mediterranean Basin woody plant communities. J. Ecol. 95: 1316–323CrossRefGoogle Scholar
  43. Villéger, S., Mason, N.W.H. and Mouillot, D. 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89: 2290–2301.CrossRefGoogle Scholar
  44. Whittaker, R.H. 1965. Dominance and diversity in land plant communities. Science 147: 250–260.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2013

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • J. Podani
    • 1
    Email author
  • C. Ricotta
    • 2
  • J. G. Pausas
    • 3
  • D. Schmera
    • 4
    • 5
  1. 1.Department of Plant Systematics, Ecology and Theoretical Biology, and Ecology Research Group of HAS, Institute of BiologyL. Eötvös UniversityBudapestHungary
  2. 2.Department of Environmental BiologyUniversity of Rome “La Sapienza”RomeItaly
  3. 3.Centro de Investigaciones sobre DesertificaciónConsejo Superior de Investigaciones Cientificas (CIDE-CSIC)MontcadaSpain
  4. 4.Section of Conservation BiologyUniversity of BaselBaselSwitzerland
  5. 5.Centre for Ecological Research, Hungarian Academy of SciencesBalaton Limnological InstituteTihanyHungary

Personalised recommendations