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Project Status: Active – The project has reached a stable, usable state and is being actively developed. Build Status CRAN_Status_Badge License: GPL v3

Purpose

r3PG provides an implementation of the Physiological Processes Predicting Growth (3-PG) model (Landsberg & Waring, 1997), which simulate forest growth and productivity. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (Sands, 2010) and the 3-PGmix (Forrester & Tang, 2016) model written in Fortran. The package enables fast and easy interaction with the model, and the Fortran re-implementation facilitates computationally intensive sensitivity analysis and calibration. The user can flexibly switch between various options and submodules, to use the original 3-PGpjs model version for monospecific, even-aged and evergreen forests and the 3-PGmix model, which can also simulate multi-cohort stands (e.g. mixtures, uneven-aged) that contain deciduous species.

Usage

Below is a basic example, for more extended examples please visit package vignette.

The main function is run_3PG() which returns all 108 simulated variables for each species at a monthly time-step, either as a 4-dimentional array or a long format data frame.

library(r3PG)
out_3PG <- run_3PG(
  site        = d_site, 
  species     = d_species, 
  climate     = d_climate, 
  thinning    = d_thinning,
  parameters  = d_parameters, 
  size_dist   = d_sizeDist,
  settings    = list(light_model = 2, transp_model = 2, phys_model = 2, 
                height_model = 1, correct_bias = 0, calculate_d13c = 0),
  check_input = TRUE, df_out = TRUE)

head( out_3PG )

To visualize the output:

library(dplyr)
library(ggplot2)

sel_var <- c('biom_stem', 'biom_foliage', 'biom_root')

out_3PG %>%
  filter( variable %in% sel_var ) %>%
  ggplot( aes(date, value, color = species) ) +
  geom_line() +
  facet_wrap(~variable, scales = 'free') +
  theme_classic()

If you prefer to use data stored in Excell, you can use the following example. Data to reproduce this example are stored in data-raw/internal_data/data.input.xlsx.

library(readxl)

f_loc <- 'data.input.xlsx'

run_3PG(
  site        = read_xlsx(f_loc, 'site'),
  species     = read_xlsx(f_loc, 'species'),
  climate     = read_xlsx(f_loc, 'climate'),
  thinning    = read_xlsx(f_loc, 'thinning'),
  parameters  = read_xlsx(f_loc, 'parameters'), 
  size_dist   = read_xlsx(f_loc, 'sizeDist'),
  settings    = list(light_model = 2, transp_model = 2, phys_model = 2, 
                height_model = 1, correct_bias = 0, calculate_d13c = 0),
  check_input = TRUE, df_out = TRUE)

Installation

Stable release

r3PG is available for instalation from CRAN

install.packages("r3PG")

Development release

To install the current (development) version from the repository, run the following command:

if(!require(devtools)){install.packages(devtools)}
devtools::install_github(repo = "trotsiuk/r3PG", subdir = "pkg", build_vignettes = T)

The unit test status of the master (development) branch is Build Status

Other 3-PG implementations in R

We would like to acknowledge that r3PG is not the only 3-PG implementations in R. We are aware of the following other packages:

Maintainer Source
Daniel M. Griffith https://github.com/griffithdan/r3PG
Georgios Xenakis https://github.com/drGeorgeXenakis/fr3PGD
Francesco Minunno https://github.com/checcomi/threePGN-package
Quinn Thomas https://github.com/EcoDynForecast/DAPPER

We explain in a recent publication (Trotsiuk et al, submitted) how this r3PG packages differs and / or improves over these.

Issues, suggestions, contributions

Please submit issues, bugs and suggestions in the dedicated page. Contribution and improvements are always welcome!

Author and contact

Volodymyr Trotsiuk; Florian Hartig; David I. Forrester

Citation

Trotsiuk, V., Hartig, F., Forrester, D.I., n.d. r3PG – an R package for simulating forest growth using the 3-PG process-based model. Methods Ecol. Evol. n/a. https://doi.org/10.1111/2041-210X.13474

References

Forrester, D. I., & Tang, X. (2016). Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model. Ecological Modelling, 319, 233–254. https://doi.org/10.1016/j.ecolmodel.2015.07.010

Landsberg, J. J., & Waring, R. H. (1997). A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management, 95(3), 209–228. https://doi.org/10.1016/S0378-1127(97)00026-1

Sands, P. J. (2010). 3PGpjs user manual. Retrieved from https://3pg.sites.olt.ubc.ca/files/2014/04/3PGpjs_UserManual.pdf

Trotsiuk, V., Hartig, F., Cailleret, M., Babst, F., Forrester, D. I., Baltensweiler, A., … Schaub, M. (2020). Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion. Global Change Biology, 26(4), 2463–2476. https://doi.org/10.1111/gcb.15011