Multiple Assemblage Dissimilarity for Orders q = 0-N

Calculate multiple or pairwise dissimilarity for orders q = 0-N (CqN; Chao et al. 2008 ) for a set of species assemblages or interaction networks.

dissCqN

dissCqN is a small package designed to make the process of calculating multiple or pairwise assemblage dissimilarity via the C_qN generalisation of similarity indices (Chao et al. 2008, Jost et al. 2011) relatively straightforward and fast. Although C_qN can also be calculated using the SpadeR package (e.g. SpadeR::SimilarityMult() and SpadeR::SimilarityPair()) – which generates a more comprehensive set of measures and also standard errors/confidence intervals – the main advantage of dissCqN is it’s simplicity and speed, when only the original empirical C_qN measures are required (and also if dissimilarity is preferred to similarity). Everything can be accomplished with a single function, dissCqN(), which takes a matrix of assemblages x species as it’s first argument (or a list of species interaction matrices, for network dissimilarity).

Installation

You can install the released version of dissCqN from CRAN with:

install.packages("dissCqN")

And the development version from GitHub with:

devtools::install_github("murphymv/dissCqN@dev")

Examples

See the following vignette for a demonstration:

• Calculating multiple and pairwise assemblage dissimilarity for tree species in a tropical forest

References

Chao, A., Jost, L., Chiang, S. C., Jiang, Y.-H., & Chazdon, R. L. (2008). A Two-Stage Probabilistic Approach to Multiple-Community Similarity Indices. Biometrics, 64(4), 1178–1186. https://doi.org/10/fcvn63

Jost, L., Chao, A., & Chazdon, R. L. (2011). Compositional similarity and beta diversity. In A. E. Magurran & B. J. McGill (Eds.), Biological Diversity: Frontiers in Measurement and Assessment (pp. 66–84). Oxford University Press.