A manual and software for common statistical methods for ecological and biodiversity studies

Effective data analysis requires familiarity with basic concepts and an ability to use a set of standard tools, as well as creativity and imagination. Tree diversity analysis provides a solid practical foundation for training in statistical methods for ecological and biodiversity studies.

This manual arose from training researchers to analyse tree diversity data collected on African farms, yet the statistical methods can be used for a wider range of organisms, for different hierarchical levels of biodiversity and for a variety of environments — making it an invaluable tool for scientists and students alike.

Focusing on the analysis of species survey data, Tree diversity analysis provides a comprehensive review of the methods that are most often used in recent diversity and community ecology literature including:

• Species accumulation curves for site-based and individual-based species accumulation, including a new technique for exact calculation of site-based species accumulation.
• Description of appropriate methods for investigating differences in diversity and evenness such as Rényi diversity profiles, including methods of rarefaction to the same sample size for different subsets of the data.
• Modern regression methods of generalized linear models and generalized additive models that are often appropriate for investigating patterns of species occurrence and species counts.
• Methods of ordination for investigating community structure and the influence of environmental characteristics, including recent methods such as distance-based redundancy analysis and constrained analysis of principal coordinates.

The BiodiversityR software was initially developed for the R 2.1.1 statistical environment. Since then, installation procedures have changed:

Windows installation

1. Download R base package (URL http://cran.r-project.org/bin/windows/base/)
2. Install R with the default options, except for:
   1. Do you want to customize the startup options? Yes (customized startup)
   2. Do you prefer the MDI or SDI interface? SDI (separate windows)
3. Launch R
4. Paste the following command in the R GUI:

   install.packages(pkgs=c("BiodiversityR", "vegan","Rcmdr", "MASS", "mgcv","cluster", "RODBC", "rpart", "effects", "multcomp","ellipse", "maptree", "sp", "splancs", "spatial","akima", "nnet", "dismo", "raster", "rgdal",
   "bootstrap", "PresenceAbsence","maxlike", "gbm", "randomForest", "gam", "earth", "mda","kernlab", "e1071", "glmnet", "sem", "rgl", "relimp","lmtest", "leaps", "Hmisc", "colorspace", "aplpack","abind", "XLConnect", "car", "markdown", "knitr","geosphere", "maptools", "rgeos", "ENMeval", "red"),dependencies=c("Depends", "Imports"))

5. Launch BiodiversityR and the Graphical User Interface by pasting the following commands
   (note that R is case-sensitive): library(BiodiversityR)
BiodiversityRGUI()

Linux/Unix or Mac Installation

1. Install the base package (URL https://cran.r-project.org/bin/) 
2. Follow the instructions provided for the R-Commander (URL http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/installation-notes.html )
3. Repeat steps 4 and 5 of the Windows instructions

Useful links

• Tree diversity analysis manual
• Changes since R 2.1.1
• BiodiversityR software
  (http://cran.r-project.org/web/packages/BiodiversityR/index.html )
• Installation of BiodiversityR software on Windows
• BiodiversityR changeLog
• Data
• Scripts
• Ensemble suitability modelling with BiodiversityR
• Ensemble suitability modelling training manual