The plant package for R is an extensible framework for modelling size- and trait-structured demography, ecology and evolution in simulated forests. At its core, plant is an individual-based model where plant physiology and demography are mediated by traits. Individual plants from multiple species can be grown in isolation, in patches of competing plants or in metapopulations under a disturbance regime. These dynamics can be integrated into metapopulation-level estimates of invasion fitness and vegetation structure. Accessed from R, the core routines in plant are written in C++. The package provides for alternative physiologies and for capturing trade-offs among parameters. A detailed test suite is provided to ensure correct behaviour of the code.
Falster DS, FitzJohn RG, Brännström Å, Dieckmann U, Westoby M (2016) plant: A package for modelling forest trait ecology & evolution. Methods in Ecology and Evolution 7: 136-146. doi: 10.1111/2041-210X.12525
An overview of the plant package is given by the above publication. Further background on the default
FF16 growth model is available in Falster et al 2011 (10.1111/j.1365-2745.2010.01735.x) and Falster et al 2017 (10.1101/083451).
plant comes with a lot of documentation, available at https://traitecoevo.github.io/plant/. Initial versions for some of the material there was also included as supplementary material with the publication about plant, which can be accessed here.
Plant is a complex package, using C++11 behind the scenes for speed with R6 classes (via the Rcpp and RcppR6 packages). In this blog post, Rich FitzJohn and I describe the key technologies used to build the plant package.
If you are interested in developing plant you should read the Developer Notes.
You must be using R 3.3.0 or newer. At this stage the package is not on CRAN. You're options for installing are described below.
Installation requires a C++11 compatible C compiler (OSX >= 10.10/Yosemite satisfies this, as do standard linux Ubuntu 12.04 and 14.04). On Windows machines you will need to install Rtools. When I tried this in Rstudio, the program automagically sensed the absence of a compiler and asked if I wanted to install Rtools. Click
plant package can be installed direct from github using the
plant also requires the packages
RcppR6 packages. Install those with
install.packages("remotes") remotes::install_github("smbache/loggr", dependencies=TRUE) remotes::install_github("richfitz/RcppR6", dependencies=TRUE)
plantalso depends on several packages available from CRAN. You can either install these yourself or let
remoteshandle if following installation Option 1. Package management in R can sometimes be messy, if you run into errors please try installing these pacakges one at a time.
install.packages(c("Rcpp", "R6", "crayon", "nleqslv", "BB" ,"BH"))
Option 1, using
or if you have already installed the
Option 2, download and install locally
If installing locally you will still need to install the
RcppR6 packages. Install using
remotes::install_github as above or, alternatively, download a zip file from github:
Unzip these archives and then for each package run the command
install.packages("path_to_package", repos = NULL, type="source")
path_to_package is the folder for each package, e.g.
Option 3, installing different versions
To install a specific (older) release, decide for the version number that you want to install in https://github.com/traitecoevo/plant/releases e.g.
remotes::install_github("traitecoevo/plant", ref = "v1.0.0", dependencies=TRUE)
"v1.0.0" replaced by the appropriate version number. Note, the latest version of
plant resides on the
develop branch, which is sporadically relased.
plant follows semantic versioning meaning that major version indicate a potential break in backward compatibility.
Option 4, building from source
If familiar with git you might find it easiest to build
plant directly from the source code. This is most useful if developing new models or strategies, or to contribute new features.
First, clone the
git clone https://github.com/traitecoevo/plant
then in the terminal or command line
cd plant make
If using Rstudio, you might like to use
Plant has been used in the following publications: