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0. Brief introduction

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:

  1. Preprocess the data (matrix or network formats)
  2. Compute similarity/dissimilarity metrics between sites based on species composition
  3. Run the different algorithms to identify different set of bioregions
  4. Evaluate and visualize the results


Figure 1: Workflow of the bioRgeo’s package.


1. Matrix or Network formats

The 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.

2. Pairwise similarity/dissimilarity metrics

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.

The functions dissimilarity_to_similarity and similarity_to_dissimilarity can be used to transform a similarity object into a dissimilarity object and vice versa.

Please have a look at this tutorial page to better understand how these functions work.

3. Clustering algorithms

3.1 Install executable binary files

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.

3.2 The bioRgeo.clusters class

bioRgeo.clusters

3.3 Hierarchical clustering

3.4 Non-hierarchical clustering

3.5 Network clustering

The bioRgeo’s package contains 8 network clustering functions:

Please check this tutorial page to get more information regarding the network clustering functions.

4. Visualization and evaluation of the results