kkougiou/bioregion

bioregion

This R package is for mapping biodiversity and for conservation. It is simple, fast, and particularly tailored for handling large datasets.

Authors

Barnabas Daru

Klaus Schliep

Citation

If you find bioregion helpful, please cite as:

Daru B. H. & Schliep, K. bioregion: an R package for mapping biodiversity and conservation (R package version 0.1.0. https://github.com/darunabas/bioregion, 2019).

Introduction

This tutorial is an introduction to using R for analysing geographic data in biodiversity science and conservation. Specifically, you will be testing my new R package called bioregion which is still in a beta-testing phase. The bioregion package is a tool for mapping various facets of biodiversity ranging from local (alpha-) to between community (beta-) diversity.

The bioregion package will introduce the basics of mapping various facets of spatial data ranging from species richness, endemism, to threat as evaluated by the International Union for the Conservation of Nature as well as beta diversity metrics. More advanced implementations of bioregion is the addition of phylogenetic information to quantify evolutionary diversity including phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment.

A major feature of bioregion is its ability to handle large datasets spanning 1000s to hundreds of thousands of taxa and spanning large geographic extents.

Installation

The bioregion package is available from github. First, you will need to install the devtools package. In R, type:

#install.packages("devtools") # uncommenting this will install the package

Next, load the devtools package.

library(devtools)

Then install the bioregion package from github:

#install_github("darunabas/bioregion") # uncommenting this will install bioregion package

Load the bioregion package:

library(bioregion)

Although the package's strong focus is for mapping biodiversity patterns, we will draw from other packages including: raster, Matrix, ape, data.table and rgeos.

z <- c("raster", "Matrix", "ape", "colorRamps", "data.table", "rgeos")
# install.packages(z) # uncommenting this will install the packages
lapply(z, library, character.only = TRUE) # load the required packages