Package 'matreex' reference manual

Title:	matreex: Simulation IPM for forest dynamic depending on climatic variables from Kunstler et al (2020)
Description:	The goal of this package is to run integrated projection models of tree species in single or multi-specific density dependence context. The simulations return the size distribution dynamics along time. These models can be completed with different harvest and disturbance models and be runned untile equilibrium.
Authors:	Maxime Jaunatre [aut, cre, trl] (ORCID: <https://orcid.org/0009-0002-2816-1677>), Julien Barrere [aut] (ORCID: <https://orcid.org/0000-0002-6686-726X>), Thomas Cordonnier [aut] (ORCID: <https://orcid.org/0000-0003-3684-4662>), Arnaud Guyennon [aut] (ORCID: <https://orcid.org/0000-0003-2178-3801>), Bjoern Reineking [aut] (ORCID: <https://orcid.org/0000-0001-5277-9181>), Laura Touzot [aut] (ORCID: <https://orcid.org/0000-0003-0445-554X>), INRAE [cph], Georges Kunstler [aut] (ORCID: <https://orcid.org/0000-0002-2544-1940>)
Maintainer:	Maxime Jaunatre <[email protected]>
License:	MIT + file LICENSE
Version:	0.4.0
Built:	2026-05-29 09:18:05 UTC
Source:	https://forge.inrae.fr/lessem/rpackages/matreex

Species climate optimum dataset.

Description

Climate defined per species from Kunstler et al. 2021. Each species had its IPM integrated for 3 climates :

1: hot edge
2: optimum climate for the species
3: cold edge

Usage

climate_species
climate_species

Format

An object of class data.frame with 81 rows and 11 columns.

Details

Climate was defined from sgdd and wai variables.

Source

https://doi.org/10.1111/1365-2745.13533

Get climatic values in matreex class object

Description

Get climatic values in matreex class object

Usage

climatic(x)
climatic(x)

Arguments

x

matreex class object. Used on ipm and species at this time.

Add correction states to a system

Description

Apply correction to a matrix by setting to 0 all right and bottom values

Usage

correction(x, correction = "none")
correction(x, correction = "none")

Arguments

x

an object that require a correction addition

correction

Type of correction, match between cut and none (default). chr.

Default disturbance function

Description

Default disturbance function

Usage

def_disturb(x, species, disturb = NULL, ...)
def_disturb(x, species, disturb = NULL, ...)

Arguments

x

population state distribution at time t

species

The species class object of interest to get mesh and RDIcoef values from. RDIcoef is a one line dataframe with RDI coefficient for one species.

disturb

Disturbance parameters. Highly depend on the disturbance impact parameters given to the species.

...

Default disturbance function does not require

Default population harvest

Description

Constant rate harvest of 0.06 percent per year (check if harvest frequence is 1 in forest object).

Usage

def_harv(x, species, ...)
def_harv(x, species, ...)

Arguments

x

population state at time t

species

ignored

...

Variables used in this case of Uneven harvest

ct: is the vector to compute BA with x (ct = Buildct(mesh, SurfEch))

Details

This function is the default function for harvest_fun function of a species and always takes arguments x, species plus specific argument from different harvest models..

Value

Distribution of population to harvest. Values are between 0 (null harvest) and Xi.

Default population initialization

Description

The population will initiate with a random distribution to match a basal area of 1.

Usage

def_init(mesh, SurfEch = 0.03)
def_init(mesh, SurfEch = 0.03)

Arguments

mesh

all possible states of a population, based on an IPM. Minimal and maximal values are respectively U and L, for a total number of m states.

SurfEch

Value of plot size surface in $m^2$

Details

This function is the default function for init_pop function of a species and always takes arguments mesh and SurfEch.

Default even population initialization

Description

The population will initiate with only individual in the 5 first cells of the mesh.

Usage

def_init_even(mesh, SurfEch = 0.03)
def_init_even(mesh, SurfEch = 0.03)

Arguments

mesh

all possible states of a population, based on an IPM. Minimal and maximal values are respectively U and L, for a total number of m states.

SurfEch

Value of plot size surface in $m^2$

Init population with precise distribution

Description

Init population with precise distribution

Usage

def_init_k(x)
def_init_k(x)

Arguments

x

Distribution to draw systematically. This distribution should be composed of values in [0, Inf] values with a sum superior to 0. This is the distribution per hectare and not for the sampled plot.

Details

The resulting function will check if the provided vector is the same length as mesh.

Value

Function similar def_init but with no random effect anymore.

Init population at BA

Description

This function modify the def_init function to start at a given BA with the same process of random distribution.

Usage

def_initBA(BA = 1, fun = c("def_init", "def_init_even"))
def_initBA(BA = 1, fun = c("def_init", "def_init_even"))

Arguments

BA

Basal area targeted. This single value must be above 0 but can be very close (minimal accepted value is 1e-10)

fun

Function to modify, single chr in choices.

Value

Function similar to def_init

Add delay states to a system

Description

Add delay states to an existing pop_state class object or an ipm class object.

Usage

delay(x, delay = 0)
delay(x, delay = 0)

Arguments

x

an object that require a delay addition

delay

the number of time delay to add. single int. Its possible to give negative values to remove delay from previously delayed objects. However final delay must be positive (0 is acceptable).

Details

This function is a method that call delay.ipm internal function.

Delay dgCMatrix

Description

Adding a topleft corner to a matrix filled with 0.

Usage

## S3 method for class 'dgCMatrix'
delay(x, delay = 0)
## S3 method for class 'dgCMatrix'
delay(x, delay = 0)

Arguments

x

an object that require a delay addition

delay

the number of time delay to add. single int. Its possible to give negative values to remove delay from previously delayed objects. However final delay must be positive (0 is acceptable).

Examples

x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)
x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)

Delay dsCMatrix

Description

Adding a topleft corner to a matrix filled with 0.

Usage

## S3 method for class 'dsCMatrix'
delay(x, delay = 0)
## S3 method for class 'dsCMatrix'
delay(x, delay = 0)

Arguments

x

an object that require a delay addition

delay

the number of time delay to add. single int. Its possible to give negative values to remove delay from previously delayed objects. However final delay must be positive (0 is acceptable).

Examples

x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)
x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)

Delay dtCMatrix

Description

Adding a topleft corner to a matrix filled with 0.

Usage

## S3 method for class 'dtCMatrix'
delay(x, delay = 0)
## S3 method for class 'dtCMatrix'
delay(x, delay = 0)

Arguments

x

an object that require a delay addition

delay

the number of time delay to add. single int. Its possible to give negative values to remove delay from previously delayed objects. However final delay must be positive (0 is acceptable).

Examples

x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)
x <- new("dtCMatrix", i = c(0L, 1L, 2L, 1L, 2L, 2L), p = c(0L, 3L,  5L, 6L),
         Dim = c(3L, 3L), x = c(0.1, 0.2, 0.3, 0.1, 0.2, 0.1), uplo = "L", diag = "N")
x <- delay(x, 2)
delay(x, -1)

Species disturbance parameters.

Description

Species disturbance parameters.

Usage

disturb_coef
disturb_coef

Format

An object of class data.frame with 64 rows and 10 columns.

Source

https://doi.org/10.1111/gcb.16630

Disturbance function

Description

Disturbance function

Usage

disturb_fun(x, species, disturb = NULL, ...)
disturb_fun(x, species, disturb = NULL, ...)

Arguments

x

population state distribution at time t

species

The species class object of interest to get mesh and RDIcoef values from. RDIcoef is a one line dataframe with RDI coefficient for one species.

disturb

Disturbance parameters. Highly depend on the disturbance impact parameters given to the species.

...

Not used in this case.

qmd: Forest Quadratic Mean Diameter

Details

Delayed mesh cells takes the value of the minimal mesh size.

Disturbance function

Description

Disturbance function

Usage

disturb_fun_mixt(x, species, disturb = NULL, ...)
disturb_fun_mixt(x, species, disturb = NULL, ...)

Arguments

x

population state distribution at time t

species

The species class object of interest to get mesh and RDIcoef values from. RDIcoef is a one line dataframe with RDI coefficient for one species.

disturb

Disturbance parameters. Highly depend on the disturbance impact parameters given to the species.

...

Not used in this case.

qmd: Forest Quadratic Mean Diameter

Details

Delayed mesh cells takes the value of the minimal mesh size.

Perform the harvest Even

Description

Perform the harvest Even

Usage

Even_harv(x, species, ...)
Even_harv(x, species, ...)

Arguments

x

population state distribution at time t

species

The species class object of interest to get mesh and RDIcoef values from. RDIcoef is a one line dataframe with RDI coefficient for one species.

...

Variables used in this case of Uneven harvest

targetKg: is the target Kg
targetRDI: is the target RDI
ct: is the vector to compute BA with x (ct = Buildct(mesh, SurfEch))
SurfEch: Value of plot size surface in ha

Details

Even harvest is done at the stand level with the getPcutEven function to acomodate with the multispecific case.

Reformated fitted models from Kunstler et al. 2021. Each species is fitted for growth, survival and recruitment along climatic conditions based on sgdd and wai. All objects were modified with the oldfit2fit function from this package.

Usage

fit_species

lag_species

fit_Abies_alba

fit_Acer_campestre

fit_Acer_pseudoplatanus

fit_Alnus_glutinosa

fit_Betula

fit_Carpinus_betulus

fit_Fagus_sylvatica

fit_Fraxinus_excelsior

fit_Juniperus_thurifera

fit_Larix_decidua

fit_Picea_abies

fit_Pinus_halepensis

fit_Pinus_nigra

fit_Pinus_pinaster

fit_Pinus_pinea

fit_Pinus_sylvestris

fit_Pinus_uncinata

fit_Populus_tremula

fit_Prunus_padus

fit_Quercus_faginea

fit_Quercus_ilex

fit_Quercus_petraea

fit_Quercus_pubescens

fit_Quercus_pyrenaica

fit_Quercus_robur

fit_Quercus_suber

fit_Salix_caprea
fit_species

lag_species

fit_Abies_alba

fit_Acer_campestre

fit_Acer_pseudoplatanus

fit_Alnus_glutinosa

fit_Betula

fit_Carpinus_betulus

fit_Fagus_sylvatica

fit_Fraxinus_excelsior

fit_Juniperus_thurifera

fit_Larix_decidua

fit_Picea_abies

fit_Pinus_halepensis

fit_Pinus_nigra

fit_Pinus_pinaster

fit_Pinus_pinea

fit_Pinus_sylvestris

fit_Pinus_uncinata

fit_Populus_tremula

fit_Prunus_padus

fit_Quercus_faginea

fit_Quercus_ilex

fit_Quercus_petraea

fit_Quercus_pubescens

fit_Quercus_pyrenaica

fit_Quercus_robur

fit_Quercus_suber

fit_Salix_caprea

Format

An object of class character of length 27.

An object of class numeric of length 27.

An object of class fit_sgr of length 4.

Details

The fit_species dataset contains the vector of species stored with the package. The lag_species dataset contains the vector of default delay for each species.

The lag was computed by Kunstler and missing values for Betula, Juniperus_thurifera, Prunus_padus, Quercus_faginea and Quercus_pyrenaica are set with the mean of other species. More details is given in https://github.com/gowachin/matreex/issues/10

Source

https://doi.org/10.1111/1365-2745.13533

Create a new fitted models from

Description

Species are defined by an IPM which is a transition matrix from size between t and t+1, recruitment and harvest functions. Each species has these items defined for a given climate. An additionnal vector of harvest parameers is required with minimal size to harvest (dth), size above wich harvest is constant (dha).

Usage

fit_sgr(
  sv_params,
  sv_family,
  gr_params,
  gr_sigma,
  rec_params,
  species,
  max_dbh,
  delay
)
fit_sgr(
  sv_params,
  sv_family,
  gr_params,
  gr_sigma,
  rec_params,
  species,
  max_dbh,
  delay
)

Arguments

sv_params

Named vector of survival parameters fitted for this species and climatic condition. Minimal parameters are intercept and size.

sv_family

Family list contain the details of the models used when fitting by functions such as glm. Only the inverse of the link function (linkinv) is used in the model for now. list.

gr_params

Named vector of growth parameters fitted for this species and climatic condition. Minimal parameters are intercept and size.

gr_sigma

Standard deviation of the residuals for the growth fitted model.

rec_params

Named vector of growth parameters fitted for this species and climatic condition. Minimal parameters are intercept, BATOTSP and BATOTNonSP.

species

Name of the species to run simulation on. Single char.

max_dbh

Maximum diameter of the fitted dataset. Single dbl.

delay

Delay for the species. Single dbl.

Create a new forest for simulation

Description

A forest is a group of one of multiple species to silumate along time using the IPM defined for each species and harvest rules.

Usage

forest(
  species = list(),
  harv_rules = c(Pmax = 0.25, dBAmin = 3, freq = 1, alpha = 1),
  favoured_sp = c()
)
forest(
  species = list(),
  harv_rules = c(Pmax = 0.25, dBAmin = 3, freq = 1, alpha = 1),
  favoured_sp = c()
)

Arguments

species

List of species created with matreex package.

harv_rules

Vector for harvest rules at the scale of the forest.

Pmax: maximum proportion of BAcut / BA
dBAmin: the minimum BA to perform cut
freq: Frequence at which the harvest will be executed.
alpha: parameter for uneven harvest, allowing to target more cuts for abundant species or not. See Harvesting vignette.

favoured_sp

Logical named vector to tell if species are favoured during Uneven harvesting or not. If not NULL, the species names should be the same as in the species list.

Get the max_dbh of a fitted species

Description

Get the max_dbh of a fitted species

Usage

get_maxdbh(x)
get_maxdbh(x)

Arguments

x

matreex class object. Used on fit_sgr for now.

Extract an IPM matrix

Description

Extract the IPM matrix needed during simulation for a given BA and optionally climate.

Usage

get_step_IPM(x, ...)
get_step_IPM(x, ...)

Arguments

x

IPM or mu_gr class object.

...

Variables used depending on the class of x.

BA: Total basal area to get the IPM for during simulation.
climate: Climate for which the IPM is needed.
sim_corr: Simulation correction applied to the IPM. "cut" or "none"
IsSurv: Does this step IPM require survival. If missing or NULL, the value will be taken from x$info object

Details

Methods are set for IPM objects and mu_gr. Only the second one require to give the climate and simulation correction to apply to the matrix.

Build IPM for a given species and climate

Description

Integrate IPM for growth and survival function at a specific climate for a species on a basal area variation.

Usage

make_IPM(
  species,
  climate,
  clim_lab,
  fit,
  mesh = c(m = 700, L = 90, U = 1500),
  BA = 0:100,
  correction = c("none", "constant", "ceiling", "sizeExtremes"),
  level = 420,
  diag_tresh = 50,
  midbin_tresh = 25,
  mid_level = 5,
  year_delta = 1,
  IsSurv = TRUE,
  delay = NULL,
  verbose = FALSE
)
make_IPM(
  species,
  climate,
  clim_lab,
  fit,
  mesh = c(m = 700, L = 90, U = 1500),
  BA = 0:100,
  correction = c("none", "constant", "ceiling", "sizeExtremes"),
  level = 420,
  diag_tresh = 50,
  midbin_tresh = 25,
  mid_level = 5,
  year_delta = 1,
  IsSurv = TRUE,
  delay = NULL,
  verbose = FALSE
)

Arguments

species

The species names to be registered in the object

climate

Named vector of the environmental variables used in the fitted model. Was data_plot_pred before.

clim_lab

Label for climatic used. This values will be matched when simulating multiple species together.

fit

Fitted model for growth and survival of the species and climate given. Functions will depend on size and basal area.

mesh

vector of mesh variables. m is the number of bins, L is the minimum size and U the maximum size. h will be defined in the function as $h <- (U - L) / m$ .

BA

Vector of basal area to integrate on. Integrating on 0 is important so use it. Integrating above 200 is absurd.

correction

Correction to apply to the IPM matrix for eviction. Choices none (default), constant, ceiling and sizeExtremes.

level

Number of point to use for integration in a cell during Gauss-Legendre integration. This value will be divided by 3 since size t is integrated at level = 3 and size t+1 at level = level/3. single int (default 420).

diag_tresh

Threshold for Gauss-Legendre integration, which a distance to the diagonal. Number of cell integrated is the number of cell for which size t+1 - size t is inferior to this threshold. single dbl (default 50).

midbin_tresh

Number of cells external to the GL integration to integrate with the mid bin method.

mid_level

Number of point to use for integration in a cell during mid bin integration.

year_delta

Number of year between 2 obersavtion when using this model. default 1, single int. NOTE : value for dev usage only !

IsSurv

Adding survival to the IPM. Set to FALSE is useful to test for eviction of the model. TRUE by default.

delay

the number of time delay to add. By default, it will use the delay stored in the fit object provided, but can be provided.

verbose

Print message. FALSE by default

Details

The check between climate variables and fitted variable will assert if all variables in the model are provided expect variables derived from "size" (size, size2, logsize), "intercept" and "BATOTcomp". An error will be triggered if the climate variable is missing.

One can desactivate each kind of integration with some treshold values. A negative value in diag_tresh (ex: -1) will cancel the Gauss-Legendre integration and a midbin_tresh null value (ex: 0) will cancel the midbin integration.

Build IPM for a given species and climate

Description

Integrate IPM for growth and survival function at a specific climate for a species on a basal area variation.

Usage

make_mu_gr(
  species,
  fit,
  climate = NULL,
  mesh = c(m = 700, L = 90, U = 1500),
  BA = 0:200,
  correction = c("none", "constant", "ceiling", "sizeExtremes"),
  stepMu = 0.001,
  level = c(3, 140),
  diag_tresh = 50,
  midbin_tresh = 25,
  mid_level = 5,
  year_delta = 1,
  IsSurv = TRUE,
  verbose = FALSE
)
make_mu_gr(
  species,
  fit,
  climate = NULL,
  mesh = c(m = 700, L = 90, U = 1500),
  BA = 0:200,
  correction = c("none", "constant", "ceiling", "sizeExtremes"),
  stepMu = 0.001,
  level = c(3, 140),
  diag_tresh = 50,
  midbin_tresh = 25,
  mid_level = 5,
  year_delta = 1,
  IsSurv = TRUE,
  verbose = FALSE
)

Arguments

species

The species names to be registered in the object

fit

Fitted model for growth and survival of the species and climate given. Functions will depend on size and basal area.

climate

Climate table for the species. Optionnal if the species is defined in the package. The format is climatic variable in column and different climate by row. An example is in the package with cliamte_species.

mesh

vector of mesh variables. m is the number of bins, L is the minimum size and U the maximum size. h will be defined in the function as $h <- (U - L) / m$ .

BA

Vector of basal area to integrate on. Integrating on 0 is important so use it. Integrating above 200 is absurd.

correction

Correction to apply to the IPM matrix for eviction. Choices constant (default), ceiling, sizeExtremes and none.

stepMu

Step between each mu in the species range. This value has effect simulation. Default is 1e-3. Values below 1e-5 takes longer than classical matrix integration.

level

diag_tresh

midbin_tresh

Number of cells external to the GL integration to integrate with the mid bin method.

mid_level

Number of point to use for integration in a cell during mid bin integration.

year_delta

Number of year between 2 obersavtion when using this model. default 1, single int. NOTE : value for dev usage only !

IsSurv

Adding survival to the IPM. Set to FALSE is useful to test for eviction of the model. TRUE by default.

verbose

Print message. FALSE by defaul

Details

This is a working function to test faster integration but it integrate a ba value

matreex

Description

This package is developped for LESSEM internal users only. It allow to run simulation from IPM and NFI datasets.

Details

Fill the package description !

load old fitted models

Description

load old fitted models

Usage

old_fit2fit(species, path = here(), replicat = 42, mean = FALSE)
old_fit2fit(species, path = here(), replicat = 42, mean = FALSE)

Arguments

species

Name of the species to run simulation on. Single char.

path

Place to save the resulting file. Single Char.

replicat

Numeric for the simulation to select. By default, the 42th.

mean

Should the return element be a mean of all models or a single value. FALSE by default, TRUE will ignore replicat argument

Dev function to read old ipm

Description

Read old ipm and put them in the new class format.

Usage

old_ipm2forest(sp_name, climatic = 1, path = here(), replicat = 42)
old_ipm2forest(sp_name, climatic = 1, path = here(), replicat = 42)

Arguments

sp_name

Name of the species to add in a single forest object. char.

climatic

Vector of climatic situations to run on. IPM must exist for each one or else this climatic value will be skipped. int.

path

Place to save the resulting file. Single Char.

replicat

Numeric for the simulation to select. By default, the 42th.

Dev function to read old ipm

Description

Read old ipm and put them in the new species class format.

Usage

old_ipm2species(
  species,
  climatic = 1,
  path = here(),
  replicat = 42,
  harvest = def_harv,
  disturb = def_disturb,
  init_pop = def_init,
  delay = 0
)
old_ipm2species(
  species,
  climatic = 1,
  path = here(),
  replicat = 42,
  harvest = def_harv,
  disturb = def_disturb,
  init_pop = def_init,
  delay = 0
)

Arguments

species

Name of the species to load. Single char.

climatic

Numeric that coded old climatic state. int.

path

Place to load the previous IPM file. Single Char.

replicat

Numeric for the model to select. By default, the 42th.

harvest

Function to impact the population with harvest rule. Argument must be pop. Should return a population state as it's take it in input, with less population than before. Unless you want zombie trees.

disturb

Function to impact the population with possibles disturbances. Extra care is needed to give this function all needed parameters/ Default is def_disturb.

init_pop

Function to initiate the population at simulation start. Arguments must be mesh and SurfEch. Using the arguments is not mandatory, it's most usefull when creating random population.

delay

Number of year delay between the recruitment of an individual and it's inclusion in the IPM. This will enlarge the IPM and add sub diagonal values of 1. See delay.

get Quadratic Mean Diameter

Description

Compute the quadratic mean diameter for a given size distribution.

Usage

QMD(size, n)
QMD(size, n)

Arguments

size

Size class vector of the following distribution. dbl vector

n

population state distribution at time t. dbl vector.

Details

Both input share the same length.

Species Rdi intercept and slope.

Description

Species Rdi intercept and slope.

Usage

rdi_coef
rdi_coef

Format

An object of class data.frame with 28 rows and 4 columns.

Details

Missing values for Quercus faginea from french NFI

Source

Personnal contact with Patrick Vallet

Deterministic Tree population simulation

Description

Simulate a population size state during $[1, t_{lim}]$ times. The state at time $t+1$ is dependent on state at time $t$ and the projection matrix (IPM). The simulation will run until $t_{lim}$ and if the equilibrium is not reached, it will continue. Only simulation in $[1, t_{lim}]$ , and equilibrium state are returned.

Usage

sim_deter_forest(
  Forest,
  tlim = 3000,
  equil_dist = 250,
  equil_diff = 1,
  equil_time = 10000,
  harvest = c("default", "Uneven", "Even", "Favoured_Uneven"),
  targetBA = 20,
  targetRDI = 0.9,
  targetKg = 0.9,
  final_harv = 100,
  climate = NULL,
  disturbance = NULL,
  correction = "none",
  SurfEch = 0.03,
  verbose = FALSE
)
sim_deter_forest(
  Forest,
  tlim = 3000,
  equil_dist = 250,
  equil_diff = 1,
  equil_time = 10000,
  harvest = c("default", "Uneven", "Even", "Favoured_Uneven"),
  targetBA = 20,
  targetRDI = 0.9,
  targetKg = 0.9,
  final_harv = 100,
  climate = NULL,
  disturbance = NULL,
  correction = "none",
  SurfEch = 0.03,
  verbose = FALSE
)

Arguments

Forest

Group of species that each contains IPM for deterministic transition for $Z_{t}$ state in a population to $Z_{t+1}$ state. A species is also defined with recruitment and harvest functions, please see species for more information.

tlim

Number of simulation iterations (years) in the future. single int.

equil_dist

Number of last n time for which the range difference should not exceed equil_diff during the equilibrium research. single int.

equil_diff

Difference threshold of the basal area under which equilibrium is assumed. single real.

equil_time

Total maximum time simulation allowed in equilibrium research. Must be higher or equal to tlim and equil_dist. single int.

harvest

Choice of harvest rules between default, Uneven and Even. This indicate what settings will be used. See Details.

targetBA

BA value per ha that is targeted when using uneven harvesting. Single numeric in $m^2$ .

targetRDI

RDI value that is targeted when using even harvesting. RDI is the ratio between the number of trees and the maximum number of trees given the self-thinning boundary for the corresponding mean diameter and species.

targetKg

Kg value that is targeted when using even harvesting. Kg is the ratio between mean quadratic diameter of killed trees and mean quadratic diameter of trees before harvesting.

final_harv

Final harvest time used when harvest is set to "Even". This parameter drives the final cut time for even stands.

climate

Optional, climate matrix if climate variation along time is needed. Climate variation rely on species created with mu_gr class objects. This matrix require as many rows as time steps until equil_time. If the climate does not variate, a single row can given and will be reused.

disturbance

parameter.

correction

Choice of correction of the IPM between "none" (default) and "cut". The second option set the last column to 0 in the IPM so that no individual can grow outside of the defines classes.

SurfEch

Value of plot size surface in ha

verbose

Print message. FALSE by default

Details

At each iteration, the basal area is evaluated to select the corresponding IPM matrix.

Basic simulations input are illustrated in the main vignette. The harvesting scenario and theory is explained in the harvesting vignette.

Value

Data.frame with long tidyverse format : a row for each observation and a column per variable. Columns are listed below, some may contains NA values, as for example species when there is a non-specific variable (BA).

species: Name of the species.
var: Variable of interest
time: Time step of the simulation. If the equilibrium is the last time in tlim input, this time will occur twice in the table.
mesh: Mesh class number, from 1 to n class.
size: Size corresponding to the mesh class.
equil: Logical if this time step is the equilibrium or last step of simulation
value: Numeric values of the variables.

The variables are :

n: Distribution of density by mesh along time per ha.
N: Sum of density per ha. (colSums for n)
BAsp: Basal area of the population per ha and species
BAstand: Basal area of the population per ha and species when excluding size class below dth. See Harvesting vignette.
h: Distribution of harvest density by mesh along time per ha.
H: Sum of harvested density per ha. (colSums for h)

Compute RDI and Kg from simulations output

Description

Compute RDI and Kg from simulations output

Usage

sim_rdikg(sim, rdi_c = NULL)
sim_rdikg(sim, rdi_c = NULL)

Arguments

sim

simulation output table.

rdi_c

rdic coefficients for all species. Named vector. If NULL (default, the values are taken from matreex::rdi_coef)

List the species in matreex class object

Description

List the species in matreex class object

Usage

sp_name(x)
sp_name(x)

Arguments

x

matreex class object. Used on ipm and species at this time.

sp recruit

Description

Get species recruitment function

Usage

sp_rec(x, climatic, regional = FALSE)
sp_rec(x, climatic, regional = FALSE)

Arguments

x

Species to get the recruitment function from

climatic

Climate vector is needed for mu_gr object to build the corresponding recruitment function.

regional

TRUE/FALSE if we want to use a regional BA for fecundity.

Create a new species for simulation

Description

Species are defined by an IPM which is a transition matrix from size between t and t+1, recruitment and harvest functions (see Details). Each species has these items defined for a given climate. An additionnal vector of harvest parameters is required with minimal size to harvest (dth), size above wich harvest is constant (dha).

Usage

species(
  IPM,
  init_pop = def_init,
  harvest_fun = def_harv,
  disturb_fun = def_disturb,
  harv_lim = c(dth = 175, dha = 575, hmax = 1),
  rdi_coef = NULL,
  disturb_coef = NULL,
  type = c("Undefined", "Broadleaf", "Coniferous")
)
species(
  IPM,
  init_pop = def_init,
  harvest_fun = def_harv,
  disturb_fun = def_disturb,
  harv_lim = c(dth = 175, dha = 575, hmax = 1),
  rdi_coef = NULL,
  disturb_coef = NULL,
  type = c("Undefined", "Broadleaf", "Coniferous")
)

Arguments

IPM

ipm class object from the matreex package.

init_pop

Function to initiate the population at simulation start. Arguments must be mesh and SurfEch. Using the arguments is not mandatory, it's most useful when creating random population.

harvest_fun

Function to impact the population with harvest rule. Argument must be x, species, ...,

disturb_fun

Function to impact the population with possibles disturbances. Extra care is needed to give this function all needed parameters. Default is def_disturb.

harv_lim

Limits of harvest for a population size distribution.

dth: minimum diameter at which we cut the size distribution
dha: harvest diameter
hmax: maximum harvest rate for a size class

rdi_coef

Coefficient for RDI curve used in even harvest. The model require the intercept and slope.

disturb_coef

Species coefficient for disturbance reaction. These values and names are highly dependent on the disturbance function. BAtarget, ct and t. Should return a population state as it's take it in input, with less population than before. Unless you want zombie trees. It represent the distribution of the population to harvest

type

Type of the tree, choosing between "Broadleaf" and "Coniferous". This value is only used during biotic disturbance with a specific disturb_fun. This is experimental

Details

A species is defined by an IPM that is an integrated prediction matrix for growth and survival functions of the species. Since the species has other functions defined, they are accessible and editable.

init_pop: Function to initiate a new population. Default is def_init.
recruit_fun: Function that give a distribution for recruits. The default is defined from models associated with the IPM (x$IPM$rec) but it's possible to replace it. For example you can nullify the recruitment to simulate extinction.
harvest_fun: Function that give harvest density distribution when an harvest event occurs (this frequence is set at the forest scale.). The default function is def_harv with a constant harvest rate of 0.6 percent. Other functions are Uneven_harv and Even_harv.

IPM summary

Description

Additionnal argument :

fline: skip the first line. FALSE by default.

Usage

## S3 method for class 'ipm'
summary(object, ...)
## S3 method for class 'ipm'
summary(object, ...)

Arguments

object

Ipm object.

...

Dots are dots.

mu_gr summary

Description

This function is not written yet.

Usage

## S3 method for class 'mu_gr'
summary(object, ...)
## S3 method for class 'mu_gr'
summary(object, ...)

Arguments

object

Ipm object.

...

Dots are dots.

species summary

Description

Additionnal argument :

sum_ipm: Print IPM summary. TRUE by default.

Usage

## S3 method for class 'species'
summary(object, ...)
## S3 method for class 'species'
summary(object, ...)

Arguments

object

Ipm object.

...

Dots are dots.

Species type.

Description

Species type.

Usage

tree_type
tree_type

Format

An object of class data.frame with 28 rows and 2 columns.

Source

Maxime Jaunatre

Perform the harvest Uneven

Description

Perform the harvest Uneven

Usage

Uneven_harv(x, species, ...)
Uneven_harv(x, species, ...)

Arguments

x

population state distribution at time t

species

The species class object of interest to get mesh and harv_lim values from.

...

Variables used in this case of Uneven harvest

targetBAcut: Basal Area to cut.
ct: is the vector to compute BA with x (ct = Buildct(mesh, SurfEch))

Package 'matreex'

Help Index

Species climate optimum dataset.

Description

Usage

Format

Details

Source

Get climatic values in matreex class object

Description

Usage

Arguments

Add correction states to a system

Description

Usage

Arguments

Default disturbance function

Description

Usage

Arguments

Default population harvest

Description

Usage

Arguments

Details

Value

See Also

Default population initialization

Description

Usage

Arguments

Details

See Also

Default even population initialization

Description

Usage

Arguments

See Also

Init population with precise distribution

Description

Usage

Arguments

Details

Value

See Also

Init population at BA

Description

Usage

Arguments

Value

See Also

Add delay states to a system

Description

Usage

Arguments

Details

Delay dgCMatrix

Description

Usage

Arguments

Examples

Delay dsCMatrix

Description

Usage

Arguments

Examples

Delay dtCMatrix

Description

Usage

Arguments

Examples

Species disturbance parameters.

Description

Usage

Format

Source

Disturbance function

Description

Usage

Arguments