Tutorial for bioRgeo
Maxime Lenormand, Boris Leroy and Pierre Denelle
This tutorial aims at describing the different features of the R package
bioRgeo. The main purpose of the
bioRgeo‘s package is to propose a transparent methodological framework to compare bioregionalization methods. Below is the typical flow chart of bioregions’ identification based on a site-species bipartite network or co-occurrence matrix with
bioRgeo (Figure 1). This workflow can be divided into four main steps:
- Preprocess the data (matrix or network formats)
- Compute similarity/dissimilarity metrics between sites based on species composition
- Run the different algorithms to identify different set of bioregions
- Evaluate and visualize the results
bioRgeo’s package takes as input site-species information stored in a bipartite network or a co-occurrence matrix. Relying on the function mat_to_net and net_to_mat , it handles both the matrix and network formats throughout the workflow.
Please have a look at this tutorial page to better understand how these two functions work.
The functions similarity and dissimilarity compute respectively pairwise similarity and dissimilarity metrics based on a (site-species) co-occurence matrix. The resulting
data.frame is stored in a
bioRgeo.pairwise.metric object containing all requested metrics between each pair of sites.
Please have a look at this tutorial page to better understand how these functions work.
Some functions or at least part of them (listed below) require executable binary files to run.
Please check this tutorial page to get instructions regarding the installation of the executable binary files.
bioRgeo’s package contains 8 network clustering functions:
Please check this tutorial page to get more information regarding the network clustering functions.