Package 'r3PG' reference manual

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>.
Authors:	Volodymyr Trotsiuk [aut, cre] , Florian Hartig [aut] , David Forrester [aut], Ramiro Silveyra Gonzalez [aut]
Maintainer:	Volodymyr Trotsiuk <[email protected]>
License:	GPL-3
Version:	0.1.6
Built:	2024-11-12 06:55:21 UTC
Source:	CRAN

Climate input

Description

Table containing the information about monthly values for climatic data.

Usage

d_climate
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
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
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
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
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_parameters and d_sizeDist tables.
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
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_parameters and d_sizeDist tables.
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)
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 `mode = overview` with `sp_names = NULL`. See details below for more information.

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.

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' ) )


# 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
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
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
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
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")
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 - to. Otherwise, it will be subsetted to the selected time period. If this is required, year and month columns must be included in the climate table. The minimum required columns are listed below, but additionally you can include: tmp_ave, c02, d13catm. Please refer to d_climate for example.

year: year of observation (only required for subsetting) (numeric).
month: months of observation (only required for subsetting) (numeric).
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).

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

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')

# 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)
prepare_input(site, species, climate, thinning = NULL, parameters = NULL,
  size_dist = NULL, settings = NULL)

Arguments

`site`	table containing the information about site conditions. latitude: site latitude in the WGS84 coordinate system. altitude: site altitude, m a.s.l. soil_class: 1 - Sandy; 2 - Sandy loam; 3 - Clay loam; 4 - Clay; 0 - No effect of asw on production. asw_i: initial available soil water (mm). asw_min: minimum available soil water (mm). asw_max: 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
`species`	table containing the information about species level data. Each row corresponds to one species/cohort. species: species or cohort id/name. It must be consistent with species names in `thinning`, `parameters` and `sizeDist` tables. planted: year and month indicating when 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.
`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 `from` - `to`. Otherwise, it will be subsetted to the selected time period. More details about preparing climate data are at `prepare_climate`. year: year of observation (only required for subsetting) (optional). month: months of observation (only required for subsetting) (optional). 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). vpd_day: water pressure deficit (mbar) (optional). 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)
`thinning`	table containing the information about thinnings. If there is no thinning, it must be `NULL`. species: species or cohort id/name. It must be consistent with species names in `species`, `parameters` and `sizeDist` tables. age: age when thinning is performed. stems_n: number of trees remaining after thinning foliage: type of thinning (above/below). Default is 1. root: type of thinning (above/below). Default is 1. stem: type of thinning (above/below). Default is 1.
`parameters`	table containing the information about parameters to be modified. Values that are not provided are replaced by defaults. parameter: name of the parameter, must be consistent in naming with `i_parameters` species: each column must correspond to species/cohort id/name, as defined in `species` table
`size_dist`	table containing the information about size distribution to be modified. Values that are not provided are replaced by defaults. parameter: name of the parameter, must be consistent in naming with `i_sizeDist` species: each column must correspond to species/cohort id/name, as defined in `species` table
`settings`	a list with settings for the model. Values that are not provided are replaced by defaults. light_model: '1' - 3-PGpjs (default); '2' - 3-PGmix transp_model: '1' - 3-PGpjs (default); '2' - 3-PGmix phys_model: '1' - 3-PGpjs (default); '2' - 3-PGmix height_model: '1' - linear (default); '2' - non-linear correct_bias: '0' - no (default); '1' - yes calculate_d13c: '0' - no (default); '1' - yes

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

Examples

prepare_input( site = d_site, species = d_species, climate = d_climate, d_thinning)
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"))
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.

parameter: name of the parameter, must be consistent in naming with i_parameters
species: each column must correspond to species/cohort id/name, as defined in species table

sp_names

names of the species / cohorts used for the simulations.The 'sp_names' must be identical to those from species table.

Details

This function prepares the parameter table for run_3PG

Value

a data.frame with 47 rows and columns corresponding to each species.

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'))

# 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)
prepare_site(site)

Arguments

site

table containing the information about site data. It shall contain exactly one row.

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.

Details

This function check the site table for run_3PG.

Value

a data.frame with one row

Examples

# check site data
prepare_site( site = d_site)
# 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"))
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.

parameter: name of the parameter, must be consistent in naming with i_sizeDist.
species: each column must correspond to species/cohort id/name, as defined in species table.

sp_names

names of the species / cohorts used for the simulations.The 'sp_names' must be identical to those from species table.

Details

This function prepares the parameter table for run_3PG.

Value

a data.frame with 47 rows and columns corresponding to each species.

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'))

# 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)
prepare_species(species)

Arguments

species

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

species: species or cohort id/name. It must be consistent with species names in thinning, parameters and sizeDist tables.
planted: year and month indicating when 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.

Details

This function check the species table for run_3PG.

Value

a data.frame with one row

Examples

# check species data
prepare_species( species = d_species)
# 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"))
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 NULL. The following columns are required:

species: species or cohort id/name.
age: age at which thinning is done.
stems_n: number of trees remaining after thinning
stem: type of thinning (above/below). Default is 1.
foliage: type of thinning (above/below). Default is 1.
root: type of thinning (above/below). Default is 1.

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 species table.

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.

Examples

prepare_thinning( thinning = NULL, sp_names = c('Quercus', 'Abies'))

prepare_thinning( thinning = d_thinning, sp_names = c('Fagus sylvatica', 'Pinus sylvestris'))
prepare_thinning( thinning = NULL, sp_names = c('Quercus', 'Abies'))

prepare_thinning( thinning = d_thinning, sp_names = c('Fagus sylvatica', 'Pinus sylvestris'))

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., & 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

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
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

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)
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

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

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
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

Package 'r3PG'

Help Index

Climate input

Description

Usage

Format

Parameters input

Description

Usage

Format

Site input

Description

Usage

Format

sizeDist input

Description

Usage

Format

Species input

Description

Usage

Format

Thinning input

Description

Usage

Format

Get parameter sets

Description

Usage

Arguments

Details

Value

See Also

Examples

Information about model outputs

Description

Usage

Format

Information about parameters

Description

Usage

Format

Information about literature parameters

Description

Usage

Format

Details

Information about size distribution parameters

Description

Usage

Format

Subsets or replicate a climate data

Description

Usage

Arguments

Details

Value

See Also

Examples

Check and prepare input for running 3-PG model

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Prepare parameters table

Description

Usage

Arguments

Details

Value

See Also

Examples

Check the site data for consistency

Description

Usage

Arguments