Co-ordinating Editor: Prof. Meelis Pärtel.
Incorporating functional dissimilarities into sample-based rarefaction curves: from taxon resampling to functional resampling
Version of Record online: 24 NOV 2009
© 2009 International Association for Vegetation Science
Journal of Vegetation Science
Volume 21, Issue 2, pages 280–286, April 2010
How to Cite
Ricotta, C., Burrascano, S. and Blasi, C. (2010), Incorporating functional dissimilarities into sample-based rarefaction curves: from taxon resampling to functional resampling. Journal of Vegetation Science, 21: 280–286. doi: 10.1111/j.1654-1103.2009.01142.x
- Issue online: 22 FEB 2010
- Version of Record online: 24 NOV 2009
- Received 19 March 2009; Accepted 15 October 2009.
- Expected number of species;
- Functional diversity;
- Pairwise species dissimilarities;
- Parametric diversity;
- Rao's quadratic diversity
Question: Indices of functional diversity have been seen as the key for integrating information on species richness with measures that focus on those components of community composition related to ecosystem functioning. For comparing species richness among habitats on an equal-effort basis, so-called sample-based rarefaction curves may be used. Given a study area that is sampled for species presence and absence in N plots, sample-based rarefaction generates the expected number of accumulated species as the number of sampled plots increases from 1 to N. Accordingly, the question for this study is: can we construct a ‘functional rarefaction curve’ that summarizes the expected functional dissimilarity between species when n plots are drawn at random from a larger pool of N plots?
Methods: In this paper, we propose a parametric measure of functional diversity that is obtained by combining sample-based rarefaction techniques that are usually applied to species richness with Rao's quadratic diversity. For a given set of N presence/absence plots, the resulting measure summarizes the expected functional dissimilarity at an increasingly larger cumulative number of plots n (n≤N).
Results and Conclusions: Due to its parametric nature, the proposed measure is progressively more sensitive to rare species with increasing plot number, thus rendering this measure adequate for comparing the functional diversity of species assemblages that have been sampled with variable effort.