run_3PG {r3PG}

Runs a 3-PG model simulation

Description

Runs the 3-PGpjs (monospecific, evenaged and evergreen forests) or 3-PGmix (deciduous, uneven-aged or mixed-species forests) model. For more details on parameters and structure of input visit prepare_input.

Usage

run_3PG(site, species, climate, thinning = NULL, parameters = NULL,
  size_dist = NULL, settings = NULL, check_input = TRUE, df_out = TRUE)

Arguments

site

table as described in prepare_input containing the information about site conditions.

species

table as described in prepare_input containing the information about species level data. Each row corresponds to one species/cohort.

climate

table as described in prepare_input containing the information about monthly values for climatic data. See also prepare_climate

thinning

table as described in prepare_input containing the information about thinnings. See also prepare_thinning

parameters

table as described in prepare_input containing the information about parameters to be modified. See also prepare_parameters

size_dist

table as described in prepare_input containing the information about size distributions. See also prepare_sizeDist

settings

a list as described in prepare_input with settings for the model.

check_input

logical if the input shall be checked for consistency. It will call prepare_input function.

df_out

logical if the output shall be long data.frame (TRUE) the 4-dimensional array (FALSE).

Details

'r3PG' provides an implementation of the Physiological Processes Predicting Growth 3-PG model, which simulates forest growth and productivity. The 'r3PG' serves as a flexible and easy-to-use interface for the '3-PGpjs' (monospecific, evenaged and evergreen forests) and the '3-PGmix' (deciduous, uneven-aged or mixed-species forests) model written in 'Fortran'. The package, allows for fast and easy interaction with the model, and '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.

This implementation of 3-PG includes several major variants / modifications of the model in particular the ability to switch between 3-PGpjs (the more classic model version for monospecific stands) vs. 3-PGmix (a version for mixed stands), as well as options for bias corrections and \delta^13 C calculations (see parameters).

Value

either a 4-dimentional array or a data.frame, depending on the parameter df_out. More details on the output is i_output

Note

The run_3PG also checks the quality of input data. When names, or structures are not consistent with requirements it will return an error. Turn this off to optimize for speed.

References

Forrester, D. I., 2020. 3-PG User Manual. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland. 70 p. Available at the following web site: http://sites.google.com/site/davidforresterssite/home/projects/3PGmix/3pgmixdownload

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. doi: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. doi:10.1016/S0378-1127(97)00026-1

Sands, P. J., 2010. 3PGpjs user manual. Available at the following web site: https://3pg.sites.olt.ubc.ca/files/2014/04/3PGpjs_UserManual.pdf

See Also

prepare_input, prepare_parameters, prepare_sizeDist, prepare_thinning, prepare_climate

Examples

out <- 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,
                  correct_bias = 1, calculate_d13c = 0),
  check_input = TRUE, df_out = TRUE) # note that default is TRUE

str(out) # List output format