Welcome to the R package novelforestSG! It
contains the raw forest community data used in Lai et al. (2021), and
also comprised part of the raw data used in Neo et al. (2017).
In addition, it provides a download_model() function to
download the brms model fitted in Lai et al. (2021). Note
that the model object also contains input data that include the
environmental variables of forest plots (see below).
From CRAN:
install.packages("novelforestSG")Or install the development version (especially if the devel version > CRAN version as stated above):
install.packages("remotes") # prerequisite
remotes::install_github("hrlai/novelforestSG")To access the raw data:
library(novelforestSG)
novelforest_dataFor more information, see ?novelforest_data.
To access the summarised data and environmental variables, first
download the model object. The model object is too large (16.5 MB) to
come with the package, but the download_model function will
download the model from our GitHub development
website:
mod <- download_model()Then, extract the input data from the model object:
in_dat <- mod$dataIn the input/summarised data, you will find the environmental
variables as certain columns. These plot-level measurements can be
matched to the stem-level raw data via plot names. See
?download_model for more details.
Because the predictor variables were log-transformed and then scale to zero mean and unit SD prior to modelling, you may wish to backtransform them to their original scales, simply by:
backtransform(in_dat)Because we analysed the data using the brms v2.10.0
package in R, it is highly recommended that you install
brms to squeeze the most out of the model output:
install.packages("brms")You may also need RTools or Xcode,
depending on your operating system; see the
brms homepage. This will take a few minutes so have a
cup of hot beverage handy.
Please feel free to report any issues to our GitHub Issue page.
While releasing additional data between v1.2.1 to v2.0.0, we realised that some observations were removed by mistake in the original dataset used in Lai et al. (2021). These omitted data are now included in >v2.0.0. We repeated the analysis using the corrected data in v2.0.0 and obtained extremely similar findings as reported Lai et al. (2021), which is reassuring! Please feel free to contact us if you have any concern.
We believe that the sharing of datasets is important for advancing ecology. When you use the data or model output in your original research or meta-analysis, we appreciate if the following paper is cited:
Lai, H. R., Tan, G. S. Y., Neo, L., Kee, C. Y., Yee, A. T. K., Tan, H. T. W., & Chong, K. Y. (2021). Decoupled responses of native and exotic tree diversities to distance from old-growth forest and soil phosphorus in novel secondary forests. Applied Vegetation Science, 24, e12548. doi: 10.1111/avsc.12548
See the LICENSE file for license rights.
You may also be interested in a companion not-just-trees paper using presence–absence data:
Neo, L., Yee, A. T. K., Chong, K. Y., Kee, C. Y., & Tan, H. T. W. (2017). Vascular plant species richness and composition in two types of post-cultivation tropical secondary forest. Applied Vegetation Science, 20(4), 692–701. doi: 10.1111/avsc.12322
Hao Ran Lai
hrlai.ecology@gmail.com
Kwek Yan Chong
kwek@nus.edu.sg
Alex Thiam Koon Yee
alex_yee@nparks.gov.sg