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 physiology models 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++14 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.
Requirements
-
You must be using R 4.1.0 or newer. At this stage the package is not on CRAN. You're options for installing are described below.
-
Installation requires a c++14 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
Yes!
Option 1, using remotes::install_github
The plant package can be installed direct from github using the remotes package:
remotes::install_github("traitecoevo/plant", dependencies=TRUE)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/[email protected]", 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 released. plant follows semantic versioning meaning that major version indicate a potential break in backward compatibility.
Option 2, 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
Open an R session in the folder, then to install dependencies run
devtools::install_deps()
Then to compile the project
devtools::install()
or
devtools::load_all()
Here are some example publications using plant:
- 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 code: github
- Falster DS, Duursma RA, FitzJohn RG (2018) How functional traits influence plant growth and shade tolerance across the life cycle. Proceedings of the National Academy of Sciences 115: E6789–E6798. DOI: 10.1073/pnas.1714044115 code: github
- Falster DS, Kunstler GK, FitzJohn RG, Westoby M (2021) Emergent shapes of trait-based competition functions from resource-based models: a Gaussian is not normal in plant communities. The American Naturalist 198: 256–267. DOI: 10.1086/714868 code: github