| Type: | Package |
| Title: | Simulating Forest Growth using the 3-PG Model |
| Description: | Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>. |
| Date: | 2023-09-18 |
| Version: | 0.1.6 |
| License: | GPL-3 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | knitr (≥ 1.15.1), rmarkdown (≥ 1.3), R.rsp (≥ 0.40.0), testthat (≥ 1.0.2), roxygen2, BayesianTools, sensitivity, dplyr, ggplot2 |
| VignetteBuilder: | R.rsp |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.2 |
| URL: | https://github.com/trotsiuk/r3PG |
| BugReports: | https://github.com/trotsiuk/r3PG/issues |
| NeedsCompilation: | yes |
| Packaged: | 2023-09-19 08:09:36 UTC; trotsiuk |
| Author: | Volodymyr Trotsiuk
 [aut, cre],
Florian Hartig
[aut],
David Forrester [aut],
Ramiro Silveyra Gonzalez
[aut] |
| Maintainer: | Volodymyr Trotsiuk <volodymyr.trotsiuk@wsl.ch> |
| Repository: | CRAN |
| Date/Publication: | 2023-09-19 08:50:04 UTC |
Simulating Forest Growth using the 3-PG Process-Based Vegetation Model
Description
The r3PG package provides a flexible and easy-to-use interface for Fortran implementations of the 3-PGpjs (monospecific, evenaged and evergreen forests) or 3-PGmix (deciduous, uneven-aged or mixed-species forests) forest growth models. 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. The core function to run the model is run_3PG. For more background, please consult the vignette via vignette(package = "r3PG")
Value
None
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
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
Climate input
Description
Table containing the information about monthly values for climatic data.
Usage
d_climate
Format
A data frame with 156 rows and 7 variables:
- year
calendar year
- month
month
- tmp_min
monthly mean daily minimum temperature (C)
- tmp_max
monthly mean daily maximum temperature (C)
- tmp_ave
monthly mean daily average temperature (C). (optional)
- prcp
monthly rainfall (mm month-1)
- srad
monthly mean daily solar radiation (MJ m-2 d-1)
- frost_days
frost days per month (d month-1)
- co2
monthly mean atmospheric co2 (ppm), required if calculate_d13c=1 (optional)
- d13catm
Monthly mean isotopic composition of air (‰), required if calculate_d13c=1 (optional)
Parameters input
Description
Table containing the information about parameters.
Usage
d_parameters
Format
A data frame with 65 rows and x variables:
- parameter
name of the parameter, must be consistent in naming with
i_parameters- Fagus sylvatica
parameter values for species 1
- Pinus sylvestris
parameter values for species 2
Site input
Description
Table containing the information about site conditions.
Usage
d_site
Format
A data frame with 1 rows and 8 variables:
- latitude
site latitude in the WGS84 coordinate system
- altitude
site altitude, m a.s.l.
- soil_class
soil class, according to table 2 user manual of 3PGpjs. 1 - Sandy; 2 - Sandy loam; 3 - Clay loam; 4 - Clay; 0 - No effect of available soil water on production
- asw_i
initial available soil water (mm)
- asw_max
minimum available soil water (mm)
- asw_min
maximum available soil water (mm)
- from
year and month indicating the start of simulation. Provided in form of year-month. E.g. "2000-01"
- to
year and month indicating the end of simulation. Provided in form of year-month. E.g. "2009-12", will include December 2009 as last simulation month
sizeDist input
Description
Table containing the information about size distribution.
Usage
d_sizeDist
Format
A data frame with 47 rows and x variables:
- parameter
name of the parameter, must be consistent in naming with
i_sizeDist- Fagus sylvatica
parameter values for species 1
- Pinus sylvestris
parameter values for species 2
Species input
Description
Table containing the information about species level data. Each row corresponds to one species/cohort.
Usage
d_species
Format
A data frame with number of rows corresponding to each species/cohort and 8 variables:
- species
species or cohort id/name. It must be consistent with species names in
d_thinning,d_parametersandd_sizeDisttables.- planted
year and month indicating when the species was planted. Provided in form of year-month. E.g. "2000-01"
- fertility
soil fertility for a given species. Range from 0 to 1
- stems_n
number of trees per ha
- biom_stem
stem biomass for a given species (Mg/ha)
- biom_root
root biomass for a given species (Mg/ha)
- biom_foliage
initial foliage biomass (Mg/ha). If this is a leafless period, provide the spring foliage biomass.
Thinning input
Description
Table containing the information about thinnings
Usage
d_thinning
Format
A data frame with 3 rows and 6 variables:
- species
species or cohort id/name. It must be consistent with species names in
d_species,d_parametersandd_sizeDisttables.- age
age when thinning is performed
- stems_n
number of trees remaining after thinning
- stem
type of thinning (above/below). Default is 1
- root
type of thinning (above/below). Default is 1
- foliage
type of thinning (above/below). Default is 1
Get parameter sets
Description
Gets parameter sets from published studies with 3PG
Usage
get_parameters(mode = "overview", sp_names = NULL)
Arguments
mode |
must be one of the following: full, overview, source, comments, parameters, sizeDist |
sp_names |
names of the species. The 'sp_names' must be either a scientific name
(Picea abies). If passing parameters or sizeDist to mode, it also possible
to use a scientific name with a integer, which is the value of parset_id, e.g.
Picea abies 37.
If unsure, consider using first the option |
Details
This function access the parameter database stored in the package,
which is named i_parameters_lit, and also accessible without this function.
If mode = overview, a simplified table with
all existing parameter sets will be returned. A extended version including information
about to the source and remarks can be obtained with mode = comments.
A complete description of the source is returned with mode = source.
The full version of this table (species,source, comments,
parameters) will be returned if mode = full. Alternatively, with mode = parameters
and mode = sizeDist, it is possible to obtain parameters in the format
required by run_3PG.
Passing species names with sp_names will selecting from the table
the desired species. Please note that one species might have more than one
available parameter sets, unless species names contain the parset_id value.
Please also note that mode = source is not compatible with sp_names.
The parameter sets were obtained from published studies. For basic information about this
dataset check in i_parameters_lit. Relevant information about
the parameter sets and the corresponding studies is provided in the table.
For further information, please consider checking the original publications.
Value
a data frame with parameter sets for all available species, or only the
requested species, if sp_names is not null.
See Also
Examples
# see an overview of the existing parameter sets
get_parameters(mode = 'overview')
get_parameters(mode = 'overview', sp_names = c('Eucalyptus globulus', 'Pinus sylvestris' ) )
# see existing parameter sets and comments
get_parameters(mode = 'comments')
get_parameters(mode = 'comments', sp_names = c('Eucalyptus globulus', 'Pinus sylvestris' ) )
# see parameters and source information
get_parameters(mode = 'source')
# obtain parameter sets in for some species in the format required by run_3PG
get_parameters(mode = 'parameters', sp_names = c('Fagus sylvatica', 'Picea abies'))
get_parameters(mode = 'parameters', sp_names = 'Fagus sylvatica 34' )
get_parameters(mode = 'sizeDist', sp_names = c('Fagus sylvatica 9', 'Picea abies 37'))
# see parameter sets with full information (species, source, parameters)
get_parameters(mode = 'full')
get_parameters(mode = 'full', sp_names = c('Fagus sylvatica', 'Pinus radiata' ) )
Information about model outputs
Description
A dataset containing the list of output variables and their description.
Usage
i_output
Format
A data frame with 150 rows and 7 variables:
- group_id
serial number of the group
- variable_id
serial number of the variable
- variable_group
group name to which variable belongs
- variable_name
variable name as named in output
- description
description of the variable
- unit
unit of the variable
- variable_vba
corresponding name of the variable as output from Excel version of 3-PGmix
Information about parameters
Description
A dataset containing the parameters order and description.
Usage
i_parameters
Format
A data frame with 82 rows and 3 variables:
- parameter
parameter name
- description
description of the parameter
- unit
unit
- default
default value for E.globulus from original 3-PG
Information about literature parameters
Description
A dataset containing parameter sets from published studies with 3PG
Usage
i_parameters_lit
Format
A data frame with 110 rows and 124 variables:
- parset_id
id of the parameter set
- species
species scientific name
- age
whether the parameter set was was used in even or uneven stands.
- type
whether the parameter set was used in monocultures or mixed stands.
- year
year of publication
- region
geographical region in which the parameters were tested. NAs values are allowed.
- country
country or countries in which the parameters were tested.
- notes
any relevant remark about how the parameters were processed. NAs are allowed.
- source
short reference to publication
- source_comments
any relevant comment about the parameters present in the publication. NAs are allowed.
- source_full
full reference to publication
- link
a link to the publication, e.g. doi
- pFS2
Foliage:stem partitioning ratio - D = 2 cm
- pFS20
Foliage:stem partitioning ratio - D = 20 cm
- aWS
Constant in the stem mass v. diam. relationship
- nWS
Power in the stem mass v. diam. relationship
- pRx
Maximum fraction of NPP to roots
- pRn
Minimum fraction of NPP to roots
- gammaF1
Maximum litterfall rate
- gammaF0
Litterfall rate at t = 0
- tgammaF
Age at which litterfall rate has median value
- gammaR
Average monthly root turnover rate
- leafgrow
If deciduous, leaves are produced at end of this month
- leaffall
If deciduous, leaves all fall at start of this month
- Tmin
Minimum temperature for growth
- Topt
Optimum temperature for growth
- Tmax
Maximum temperature for growth
- kF
Days production lost per frost day
- SWconst
Moisture ratio deficit for fq = 0.5
- SWpower
Power of moisture ratio deficit
- fCalpha700
Assimilation enhancement factor at 700 ppm
- fCg700
Canopy conductance enhancement factor at 700 ppm
- m0
Value of m when FR = 0
- fN0
Value of fNutr when FR = 0
- fNn
Power of (1-FR) in fNutr
- MaxAge
Maximum stand age used in age modifier
- nAge
Power of relative age in function for fAge
- rAge
Relative age to give fAge = 0.5
- gammaN1
Mortality rate for large t
- gammaN0
Seedling mortality rate (t = 0)
- tgammaN
Age at which mortality rate has median value
- ngammaN
Shape of mortality response
- wSx1000
Max. stem mass per tree - 1000 trees/hectare
- thinPower
Power in self-thinning rule
- mF
Fraction mean single-tree foliage biomass lost per dead tree
- mR
Fraction mean single-tree root biomass lost per dead tree
- mS
Fraction mean single-tree stem biomass lost per dead tree
- SLA0
Specific leaf area at age 0
- SLA1
Specific leaf area for mature leaves
- tSLA
Age at which specific leaf area = (SLA0+SLA1)/2
- k
Extinction coefficient for absorption of PAR by canopy
- fullCanAge
Age at canopy closure
- MaxIntcptn
Maximum proportion of rainfall evaporated from canopy
- LAImaxIntcptn
LAI for maximum rainfall interception
- cVPD
LAI for 50% reduction of VPD in canopy
- alphaCx
Canopy quantum efficiency
- Y
Ratio NPP/GPP
- MinCond
Minimum canopy conductance
- MaxCond
Maximum canopy conductance
- LAIgcx
LAI for maximum canopy conductance
- CoeffCond
Defines stomatal response to VPD
- BLcond
Canopy boundary layer conductance
- RGcGw
The ratio of diffusivities of CO2 and water vapour in air
- D13CTissueDif
d13C difference of modelled tissue and new photosynthate
- aFracDiffu
Fractionation against 13C in diffusion
- bFracRubi
Enzymatic fractionation by Rubisco
- fracBB0
Branch and bark fraction at age 0
- fracBB1
Branch and bark fraction for mature stands
- tBB
Age at which fracBB = (fracBB0+fracBB1)/2
- rhoMin
Minimum basic density - for young trees
- rhoMax
Maximum basic density - for older trees
- tRho
Age at which rho = (rhoMin+rhoMax)/2
- aH
Constant in the stem height relationship
- nHB
Power of DBH in the stem height relationship
- nHC
Power of competition in the stem height relationship
- aV
Constant in the stem volume relationship
- nVB
Power of DBH in the stem volume relationship
- nVH
Power of height in the stem volume relationship
- nVBH
Power of DBH^2 x height in the stem volume relationship
- crownshape
Crown shape (1=cone, 2=ellipsoid, 3=half-ellipsoid, 4=rectangular)
- aK
Constant in the crown diameter relationship
- nKB
Power of DBH in the crown diameter relationship
- nKH
Power of height in the crown diameter relationship
- nKC
Power of competition in the crown diameter relationship
- nKrh
Power of relative height in the crown diameter relationship
- aHL
Constant in the LCL relationship
- nHLB
Power of DBH in the LCL relationship
- nHLL
Power of LAI in the LCL relationship
- nHLC
Power of competition in the LCL relationship
- nHLrh
Power of relative height in the LCL relationship
- Dscale0
Constant in the relationship for Weibull scale parameter of D distribution
- DscaleB
Slope of DBH in relationship for Weibull scale parameter of D distribution
- Dscalerh
Slope of relative height in relationship for Weibull scale parameter of D distribution
- Dscalet
Slope of age in relationship for Weibull scale parameter of D distribution
- DscaleC
Slope of competition in relationship for Weibull scale parameter of D distribution
- Dshape0
Constant in the relationship for Weibull shape parameter of D distribution
- DshapeB
Slope of DBH in relationship for Weibull shape parameter of D distribution
- Dshaperh
Slope of relative height in relationship for Weibull shape parameter of D distribution
- Dshapet
Slope of age in relationship for Weibull shape parameter of D distribution
- DshapeC
Slope of competition in relationship for Weibull shape parameter of D distribution
- Dlocation0
Constant in the relationship for Weibull location parameter of D distribution
- DlocationB
Slope of DBH in relationship for Weibull location parameter of D distribution
- Dlocationrh
Slope of relative height in relationship for Weibull location parameter of D distribution
- Dlocationt
Slope of age in relationship for Weibull location parameter of D distribution
- DlocationC
Slope of competition in relationship for Weibull location parameter of D distribution
- wsscale0
Constant in the relationship for Weibull scale parameter of ws distribution
- wsscaleB
Slope of DBH in relationship for Weibull scale parameter of ws distribution
- wsscalerh
Slope of relative height in relationship for Weibull scale parameter of ws distribution
- wsscalet
Slope of age in relationship for Weibull scale parameter of ws distribution
- wsscaleC
Slope of competition in relationship for Weibull scale parameter of ws distribution
- wsshape0
Constant in the relationship for Weibull shape parameter of ws distribution
- wsshapeB
Slope of DBH in relationship for Weibull shape parameter of ws distribution
- wsshaperh
Slope of relative height in relationship for Weibull shape parameter of ws distribution
- wsshapet
Slope of age in relationship for Weibull shape parameter of ws distribution
- wsshapeC
Slope of competition in relationship for Weibull shape parameter of ws distribution
- wslocation0
Constant in the relationship for Weibull location parameter of ws distribution
- wslocationB
Slope of DBH in relationship for Weibull location parameter of ws distribution
- wslocationrh
Slope of relative height in relationship for Weibull location parameter of ws distribution
- wslocationt
Slope of age in relationship for Weibull location parameter of ws distribution
- wslocationC
Slope of competition in relationship for Weibull location parameter of ws distribution
- Qa
Intercept of net v. solar radiation relationship
- Qb
Slope of net v. solar radiation relationship
- gDM_mol
Molecular weight of dry matter
- molPAR_MJ
Conversion of solar radiation to PAR
Details
Each row refers to an unique parameter set.
The function get_parameters eases the use to this dataset.
Information about size distribution parameters
Description
A dataset containing the parameters order and description.
Usage
i_sizeDist
Format
A data frame with 30 rows and 3 variables:
- parameter
parameter name
- description
description of the parameter
- unit
unit
- default
default value equal to 0
Subsets or replicate a climate data
Description
Prepares the climate table, by either replicating the average climate for the required number of years, or by sub-setting from a longer time-series of climate data.
Usage
prepare_climate(climate, from = "2000-04", to = "2010-11")
Arguments
climate |
table containing the information about monthly values for climatic data. If the climate table have exactly 12 rows it will be replicated for the number of years and months specified by
|
from |
year and month indicating the start of simulation. Provided in form of year-month. E.g. "2000-01". |
to |
year and month indicating the end of simulation. Provided in form of year-month. E.g. "2009-12", will include December 2009 as last simulation month. |
Details
This function prepares the climate table for run_3PG.
In case a user provides only average climate, this is replicated for the desired simulation period.
In case a larger climate file is provided, the simulation period is selected from this.
Value
a data.frame with number of rows corresponding to number of simulated month and 10 columns
See Also
run_3PG, prepare_input, prepare_parameters, prepare_sizeDist, prepare_thinning
Examples
# sub-setting climate data
prepare_climate( climate = d_climate, from = '2003-04', to = '2010-11')
# replicating climate data
set.seed(1)
climate = data.frame( tmp_min = rnorm(12, mean = 10),
tmp_max = rnorm(12, mean = 20),
prcp = sample(c(0:200), 12),
srad = sample(c(1:100), 12),
frost_days = sample(c(0:30), 12))
prepare_climate( climate = climate, from = '2000-04', to = '2010-11')
Check and prepare input for running 3-PG model
Description
Checks and prepares all input tables to be used in run_3PG. For detailed descriptions see Forrester (2020).
Usage
prepare_input(site, species, climate, thinning = NULL, parameters = NULL,
size_dist = NULL, settings = NULL)
Arguments
site |
table containing the information about site conditions.
|
species |
table containing the information about species level data. Each row corresponds to one species/cohort.
|
climate |
table containing the information about monthly values for climatic data. If the climate table has exactly 12 rows it will be replicated for the number of years and months specified by
|
thinning |
table containing the information about thinnings. If there is no thinning, it must be
|
parameters |
table containing the information about parameters to be modified. Values that are not provided are replaced by defaults.
|
size_dist |
table containing the information about size distribution to be modified. Values that are not provided are replaced by defaults.
|
settings |
a list with settings for the model. Values that are not provided are replaced by defaults.
|
Details
This function checks and prepares the input data for the run_3PG. The output is a list with 7 tables. Each of them corresponds to the one from input.
Value
a list with seven tables. Each table corresponds to one of the input tables.
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
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
run_3PG, prepare_parameters, prepare_sizeDist, prepare_thinning, prepare_climate, prepare_site
Examples
prepare_input( site = d_site, species = d_species, climate = d_climate, d_thinning)
Prepare parameters table
Description
Prepares the parameters table, by either replicating the defaults or replicating defaults for each of the species.
Usage
prepare_parameters(parameters = NULL, sp_names = c("Fagus sylvatica",
"Pinus sylvestris"))
Arguments
parameters |
table containing the information about parameters to be modified. Values that are not provided are replaced by defaults.
|
sp_names |
names of the species / cohorts used for the simulations.The 'sp_names' must be identical to those from |
Details
This function prepares the parameter table for run_3PG
Value
a data.frame with 47 rows and columns corresponding to each species.
See Also
run_3PG, prepare_input, prepare_sizeDist, prepare_thinning, prepare_climate
Examples
# replace some
prepare_parameters( parameters = d_parameters[1:4,],
sp_names = c('Fagus sylvatica', 'Pinus sylvestris' ))
# Make default
prepare_parameters( parameters = NULL, sp_names = c('Quercus', 'Abies'))
Check the site data for consistency
Description
Prepares the site table, by checking whether the input information is consistent.
Usage
prepare_site(site)
Arguments
site |
table containing the information about site data. It shall contain exactly one row.
|
Details
This function check the site table for run_3PG.
Value
a data.frame with one row
See Also
run_3PG, prepare_input, prepare_parameters, prepare_sizeDist, prepare_thinning
Examples
# check site data
prepare_site( site = d_site)
Prepare parameters table
Description
Prepares the parameters table, by either replicating the defaults or replicating defaults for each of the species.
Usage
prepare_sizeDist(size_dist = NULL, sp_names = c("Fagus sylvatica",
"Pinus sylvestris"))
Arguments
size_dist |
table containing the information about size distribution to be modified. Values that are not provided are replaced by defaults.
|
sp_names |
names of the species / cohorts used for the simulations.The 'sp_names' must be identical to those from |
Details
This function prepares the parameter table for run_3PG.
Value
a data.frame with 47 rows and columns corresponding to each species.
See Also
run_3PG, prepare_input, prepare_parameters, prepare_thinning, prepare_climate
Examples
# replace some
prepare_sizeDist( size_dist = d_sizeDist[1:4,],
sp_names = c('Fagus sylvatica', 'Pinus sylvestris' ))
# Make default
prepare_sizeDist( size_dist = NULL, sp_names = c('Quercus', 'Abies'))
Check the species data for consistency
Description
Prepares the species table, by checking whether the input information is consistent.
Usage
prepare_species(species)
Arguments
species |
table containing the information about species level data. Each row corresponds to one species/cohort.
|
Details
This function check the species table for run_3PG.
Value
a data.frame with one row
See Also
run_3PG, prepare_input, prepare_parameters, prepare_sizeDist, prepare_thinning, prepare_site
Examples
# check species data
prepare_species( species = d_species)
Check and prepare management information.
Description
Prepares the management table and checks for consistency.
Usage
prepare_thinning(thinning = NULL, sp_names = c("Fagus sylvatica",
"Pinus sylvestris"))
Arguments
thinning |
table containing the information about thinnings. If there is no thinning, it must be
|
sp_names |
names of the species / cohorts used for the simulations. This is required whether 'thinning=NULL' or if not all species are indicated in the 'thinning' table. The 'sp_names' must be identical to those from |
Details
This function prepares the thinning table for run_3PG.
In case there is no thinning it will return empty 3-d array.
In case there will be thinning it will return 3-d array, where one dimension correspond to each species.
Value
a 3-dimentional array, where third dimention correspond to each species.
See Also
run_3PG, prepare_input, prepare_parameters, prepare_sizeDist, prepare_climate
Examples
prepare_thinning( thinning = NULL, sp_names = c('Quercus', 'Abies'))
prepare_thinning( thinning = d_thinning, sp_names = c('Fagus sylvatica', 'Pinus sylvestris'))
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 |
species |
table as described in |
climate |
table as described in |
thinning |
table as described in |
parameters |
table as described in |
size_dist |
table as described in |
settings |
a list as described in |
check_input |
|
df_out |
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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