An R package to simulate species abundances (counts) along gradients
One of the key ways quantitative ecologists attempt to understand the properties and behaviour of the methods they use or dream up is through the use of simulated data. There are a number of computer programmes for simulating ecological data along gradients, such as Peter Minchin’s COMPAS, but none (that I am aware of) that are available for R on CRAN. Dave Robert’s coenoflex package for R is a useful alternative but at the time I started writing this, coenoflex was archived on CRAN (it is now back on CRAN).
Rather than have to reinvent the wheel each time I wanted to simulate some new data for a paper or to work on a new approach, I decided to start my own R package to contain a range of simulators encapsulating different response models, numbers of gradients, etc.
At the moment, coenocliner is limited in what it can do practically. There are two response models, the Gaussian response, which is a symmetric model of the parameters (the optimum, tolerance and height of the response curve) and the generalized beta response model. Count data can be generated from this model from several distributions, including the Poisson or negative binomial distributions, using the parameterised response model as the expectation or mean of the distribution. The complete list of distributions supported are:
A further feature of coenocliner that I hope to
develop is to include simulation wrapper functions that replicate the
simulation methods used in research papers. A working example is
simJamil, which produces simulations from a Gaussian logit
response following the scheme described in Jamil & ter Braak
(2013).
I would like to see coenocliner be as inclusive as possible; if you have code to simulate ecological species or community data that is just sitting around, consider adding it to coenocliner. In the meantime, I’m happy just having something tangible for my own use without having to remember the expressions for some of the response models.
Currently coenocliner is licensed under the GPL v2, but I’m happy to reconsider this if you want to contribute code under a more permissive licence.
You can install the released version directly from CRAN using
install.packages("coenocliner")If you use the remotes package then you can install coenocliner directly from GitHub using functions that remotes provides. To do this, install remotes from CRAN via
install.packages("remotes")then run
remotes::install_github("gavinsimpson/coenocliner")Jamil and ter Braak (2013) Generalized linear mixed models can detect unimodal species-environment relationships. PeerJ 1:e95; DOI 10.7717/peerj.95