How can get the total beta diversity (nestedness and turnover too) in mfd package?

I use the dist.point to get the results pair to pair of the places, but I want to know the total contribution of nestedness and turnover

I'm not familiar with the mfd package. However, there are other resources.

To calculate the total contribution of nestedness and turnover in R, you can use the betapart package, which computes total dissimilarity as Sørensen or Jaccard indices, as well as their respective turnover and nestedness components[3]. You can install the package using install.packages("betapart") and then load it with library(betapart).

Here's an example of how to use the betapart package:

# Load the package

# Create a presence-absence matrix
presence_absence_matrix <- ... # Your data here

# Calculate pairwise dissimilarities
pairwise_dissim <- beta.pair(presence_absence_matrix, = "sor")

# Extract turnover and nestedness components
turnover <- pairwise_dissim$beta.sim
nestedness <- pairwise_dissim$beta.sne

In this example, turnover and nestedness will contain the turnover and nestedness components, respectively, for each pair of sites in your presence-absence matrix.

[1] jaccard_components: Nestedness and turnover components for presence/absence data in abdiv: Alpha and Beta Diversity Measures
[2] Is there an R function to compute turnover and nestedness components of Hill beta diversity? - Stack Overflow
[4] R: Nestedness and turnover components for presence/absence data

Yes, I'm familiar with this package, but it's for taxonomic beta diversity, not functional diversity. The package "FD" is an alternative, but it's very slow, and I'm not sure how much time I can devote to running the analyses.

(A little domain knowledge doesn't go very far. In my case.)

If any of these are what you're looking for see the {fundiversity} vignette

  • functional richness (FRic),
  • functional volume intersections (FRic_intersect),
  • functional divergence (FDiv),
  • functional evenness (FEve),
  • functional dispersion (FDis)
  • Rao’s quadratic entropy (Q)

Although I haven't tried it myself, it has parallelization for some operations that speed things up considerably. See this vignette


By default, when loading fundiversity , the functions to compute convex hulls are memoised through the memoise package if it is installed. It means that repeated calls to fd_fric() with similar arguments won’t be recomputed each time but recovered from memory.

That should speed up things, also.

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