Home

Awesome

plant: A package for modelling forest trait ecology and evolution

Build Status Build status Coverage Status

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.

Citation

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

Documentation

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.

Package structure

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.

Installation

Requirements

install.packages("remotes")

remotes::install_github("smbache/loggr", dependencies=TRUE)
remotes::install_github("richfitz/RcppR6", dependencies=TRUE)


install.packages(c("Rcpp", "R6", "crayon", "nleqslv", "BB" ,"BH"))

Option 1, using remotes::install_github

remotes::install_github("traitecoevo/plant", dependencies=TRUE)

or if you have already installed the plant dependencies

remotes::install_github("traitecoevo/plant")

Option 2, download and install locally

If installing locally you will still need to install the loggr and 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")

where path_to_package is the folder for each package, e.g. ~/Downloads/plant-master

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)

with "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 plant repository

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 devtools

devtools::load_all('path/to/plant')

Usage

Plant has been used in the following publications: