Calculating functional diversity metrics using neighbor-joining trees

The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g., Rao entropy, functional dendrograms) or multidimensional spaces (e.g., convex hulls, kernel-density hypervolumes), each with their own strengths and limits. Frameworks based on dissimilarity matrices either do not enable the measurement of all components of FD (i.e., richness, divergence, and regularity), or result in the distortion of the functional space. Frameworks based on multidimensional spaces do not allow for comparisons with phylogenetic diversity (PD) measures and can be sensitive to outliers.
We propose the use of neighbor-joining trees (NJ) to represent and quantify FD in a way that combines the strengths of current frameworks without many of their weaknesses. Importantly, our approach is uniquely suited for stud..., , , # Calculating functional diversity metrics using neighbor-joining trees

[Access this dataset on Dryad] https://doi.org/10.5061/dryad.c866t1gdw

This includes R code and data used in the production of the paper by Cardoso et al. (subm.) Calculating functional diversity metrics using neighbor-joining trees. All code and data needed to reproduce the analyses and create the graphics is included. Also included is the AVONET data to reproduce the empirical example and the related Fig. 3.

## Description of the data and file structure

Data is a RDS file for use in R.

## Sharing/Access information

Data was derived from the following sources:
- Tobias et al (2022) https://doi.org/10.1111/ele.13898.

## Code/Software

Code was run in R version 4.3.2 and R Studio 2023.12.0 build 369