Package 'LandR'

a Raster, SpatRaster, sf, SpatVector, or Spatial object

additional Raster or SpatRaster objects, and arguments passed to terra::compareGeom().

a data.table or data.frame with all covariates and the response variable. Note that incomplete lines are removed.

The non-linear equation as a call (quoted expression) passed to bbmle::mle2(minuslog1). See ?mle. Accepts equations with three parameters 'A', 'p' and 'k'.

A list of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover.

the output of a previous fitNLMwCovariates run which is used to extract last best AIC and maximum biomass estimate and judge if new iterations are better.

character. Non-linear model form used to estimate average maximum biomass. One of "CR" (Chapman-Richards) or "Logistic". In both cases, maximum biomass is equivalent to the 'A' asymptote parameter, which is estimated using observed mean values of predictors entering its linear equation and cover == maxCover, if this predictor is included (as it should). Passed to extractMaxB

numeric. Value indicating maximum cover/dominance.

data.table or data.frame of parameter starting values. Will be coerced to named list with names being parameter names.

passed to bbmle::mle2

passed to bbmle::mle2

integer. If > 1, the number of workers to use in parallelly::makeClusterPSOCK(nbWorkers = .), otherwise no parallellisation is done.

the output of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted

character. Non-linear model form used to estimate average maximum biomass. One of "CR" (Chapman-Richards) or "Logistic".

the species shade tolerance trait value for which we want to find the interpolated probability.

the floor of x corresponding to a class of shade tolerance in the sufficientLight table.

the ceiling of x corresponding to a class of shade tolerance in the sufficientLight table.

the probability of germination in the sufficientLight table corresponding to x0.

the probability of germination in the sufficientLight table corresponding to x1.

outputs of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted.

The non-linear equation as a call (quoted expression) passed to mle2(minuslog1). See ?mle. Accepts equations with three parameters 'A', 'p' and 'k'.

A list of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover.

the covariate for which variation in maxB values will be shown. Defaults to showing how maxB values change with "cover". All other covariates except "age" are averaged. Age values are generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0). When targetCovar != "cover", "cover" will be fixed at maxCover. See fixMaxCover.

logical. If TRUE and targetCovar != "cover", cover is not averaged and is fixed to maxCover.

numeric. Value indicating maximum cover/dominance.

data for estimation of maximum biomass. Should contain at least an 'age' column. Note that other covariates will be averaged and 'cover' values will be replaced with the maximum cover value (maxCover).

The function to apply when summarizing other variables. By default, the all other variables except age are averaged ("mean"). Other options are: "median", "min", "max".

should confidence intervals be calculated and plotted?

outputs of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted.

The non-linear equation as a call (quoted expression) passed to mle2(minuslog1). See ?mle. Accepts equations with three parameters 'A', 'p' and 'k'.

A list of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover.

numeric. Value indicating maximum cover/dominance.

data for estimation of maximum biomass. Should contain at least an 'age' column. Note that other covariates will be averaged and 'cover' values will be replaced with the maximum cover value (maxCover).

should covariates other than age/cover be averaged for biomass predictions? If not, for each age (at maximum cover) there will be as many predictions as other covariate values. If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.

should observed age values be used, or should these be generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0)? If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.

should confidence intervals be calculated and plotted?

a Raster, or SpatRaster object

the value to use as NA

a list of Raster or SpatRaster objects

passed to raster::projectRaster(..., crs = crs) and terra::project(..., y = crs)

matrix of values to reclassify. See terra::classify and raster::reclassify.

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

the columns in cohortData that define unique cohorts (default: c("pixelGroup", "speciesCode", "age")).

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

the columns in cohortData that define unique cohorts (default: c("pixelGroup", "speciesCode", "age")).

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

RasterLayer to aggregate

RasterLayer to match

function to use to aggregate pixel values

A numeric vector

A cohortData data.table with only the pixels what were LCC 34:36

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

An expression to evaluate, in the form of quote(B>0), used to select cohorts with biomass.

Integer vector of classes that are are to be replaced, e.g., 34, 35, 36 on LCC2005, which are burned young, burned 10 year, and cities.

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

A cohortData data.table with only the pixels what were LCC 34:36

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

Vector of column names on which to test for unique cohortData

The ecoregionMap, a raster of all the unique ecoregion groupings, which group together pixels of similar biophysical characteristics.

a data.frame with the cut points to classify stand shadiness.

A data.frame- or data.table-like object

A named vector of column classes, where the names are the column names

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

A numeric, length 1, indicating by how many they can diverge. Default 1.

An optional message to print. This may help identify where this function was called.

Controls message output. Defaults to getOption("LandR.verbose")

the columns in cohortData that define unique cohorts (default: c("pixelGroup", "speciesCode", "age")).

The full cohortData data.table

A vector of species names for which the labels will be checked

an table with a column containing species names

An expanded cohortData data.table with pixel-level cohort information on burnt pixels and the following (optional) columns: severity - fire severity in that pixel calculated based on fire behaviour properties; firetolerance - species-level fire tolerance; severityToleranceDif - the difference between severity and firetolerance.

A RasterStack or RasterLayer that should contain species cover data in the study area

the minimum number of pixels where the species must have biomass > 0 to be considered present in the study area. Defaults to 1.

a raster layer indicating pixels were land-use change occurred as 1s

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

A data.table with simulated and observed stand data for validation

A data.table with all simulated cohortData to use for validation

repetition ids

years

A rasterLayer of stand ages with attribute "imputedPixID"

A character vector of species to use. These species must all be from one naming convention, i.e., from one column in the sppEquiv.

A named vector of colours to use for plotting. The names must conform with species name convention used (see sppEquivalencies_CA for possible naming conventions) and should also contain a colour for 'Mixed', when a mixed forest type is supported (see vegLeadingProportion parameter in vegTypeGenerator() for details on mixed forest types).

the column name to use from sppEquiv.

raw species traits data.table (see getSpeciesTable())

species traits data.table (see prepSpeciesTable())

original cohortData (prior to any modification)

original pixelGroupMap (prior to any modification)

modified cohortData output from updateCohortData()

modified pixelGroupMap output from updateCohortData()

modified cohortData output from updateCohortData()

cohortData-like object containing all dead, surviving and new cohorts (i.e. activated by serotiny/resprouting)

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

a data.frame (not data.table!) containing probability of establishment, given a site's light conditions (X0-X5) for each level of a species shade tolerance (1-5).

a modified version of cohortData that contains new cohorts.

Logical. Activates interpolation of probabilities of establishment between any two values of shade tolerance in the sufficient light table, allowing species shade tolerance trait values to take any decimal value between 1 and 5 (inclusively). If FALSE, species shade tolerances can only take integer values between 1 and 5 (inclusively).

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

output of biomod2::BIOMOD_EnsembleModeling

output of biomod2::BIOMOD_Projection

passed to biomod2::BIOMOD_Projection

passed to biomod2::BIOMOD_EnsembleForecasting

passed to biomod2::BIOMOD_EnsembleForecasting

passed to biomod2::BIOMOD_EnsembleForecasting

passed to reproducible::Cache

output of biomod2::BIOMOD_EnsembleForecasting(). Note that x and ycoordinates extracted from bm.em.proj@coord must be in the same projection as rasTemplate.

character. Which model predictions should be used. Choose one of [email protected].

a template RasterLayer or SpatRaster that can be used to map the projections

character. The original CRS projection of bm.em.proj@coord.

output of biomod2::BIOMOD_Modeling

passed biomod2::BIOMOD_EnsembleModeling. By default no thresholding is applied.

passed to reproducible::Cache

character of species name to subset responseVarData table

character vector, or list of character vectors, of response variable (column) to subset responseVarData table. If a list, it should be named according to sp, for subsetting.

a data.table or list of data.tables with environmental data. If a list, it should be named according to sp, for subsetting.#'

character vector, or list of character vectors, of environmental variables (columns) to subset predictorVarsData table. If a list, it should be named according to sp, for subsetting.

environmental data.

passed to biomod2::BIOMOD_FormatingData

a named list of arguments passed to biomod2::BIOMOD_Modeling

further arguments passed to reproducible::Cache

passed to biomod2::BIOMOD_Projection. If not supplied the data used to fit the model will be used.

passed to biomod2::BIOMOD_Projection

passed to biomod2::BIOMOD_Projection

passed to biomod2::BIOMOD_Projection

passed to reproducible::Cache

A digital elevation model (DEM) RasterLayer.

RasterLayer of elevation data (m).

numeric vector corresponding to the years to retrieve.

character. Specifies which climate suitability index to extract. Currently, one of "S", "L", "R", or "G", corresponding to Safranyik-P3P4, Logan-2b, Régnière Cold Tolerance Survival, or their Geometric product (S\*L\*R), respectively.

climate model to use. one of "GCM4" or "RCM4".

RCP scenario to use. one of "RCP45" or "RCP85".

RasterLayer of elevation data (m).

numeric vector corresponding to the years to retrieve.

climate model to use. one of "GCM4" or "RCM4".

RCP scenario to use. one of "RCP45" or "RCP85".

RasterLayer of elevation data (m).

numeric vector corresponding to the years to retrieve.

numeric vector corresponding to the months to retrieve (e.g., 6:8 for June through August).

climate model to use. one of "GCM4" or "RCM4".

RCP scenario to use. one of "RCP45" or "RCP85".

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

An expanded cohortData data.table with pixel-level cohort information on burnt pixels and the following (optional) columns: severity - fire severity in that pixel calculated based on fire behaviour properties; firetolerance - species-level fire tolerance; severityToleranceDif - the difference between severity and firetolerance.

The current simulation time e.g., time(sim).

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

a data.frame with the cut points to classify stand shadiness.

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

additional arguments that are passed to LandR.CS

TODO: description needed

TODO: description needed

TODO: description needed

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

path to data directory where objects will be downloaded or saved to

A RasterStack or RasterLayer that should contain species cover data in the study area

a data.table with species traits such as longevity, shade tolerance, etc. Must have column speciesCode, with species names/IDs. The following is a list of default trait columns:

• "species" same as "speciesCode" – species ID name

• "speciesCode"

• "Area" – inherited from LANDIS-II default table, the Canadian ecoregion from which traits where derived. Not used during the simulation

• "firetolerance" – modulerelative (to other species) fire tolerance

• "growthcurve" and "mortalityshape" – growth curve shape parameters.

• "longevity" – maximum species age

• "postfireregen" – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• "resproutprob" – probability of resprouting

• "resproutage_min" – minimum age at which species is capable of resprouting

• "resproutage_max" – maximum age at which species is capable of resprouting

• "seeddistance_eff" – effective dispersal distance

• "seeddistance_max" – maximum dispersal distance

• "shadetolerance" – relative (to other species) shade tolerance

• "sexualmature" – age at sexual maturity Known optional parameters added/needed by some modules (the user may add others for their own modules):

• "inflationFactor" – Biomass_speciesParameters module: inflation factor for maxB

• "growthCurveSource" – Biomass_speciesParameters module: how "growthcurve" was estimated

• "mANPPproportion" – Biomass_speciesParameters module: multiplication factor to calculate maxANPP from maxB

• "thermokarsttol" – Biomass_disturbances module: proportion of biomass surviving after thermokarst (i.e. permafrost thaw). Applied equally across cohorts. Parameters inherited from LANDIS-II default table, but not used in LandR at the moment:

• "leaflongevity"

• "wooddecayrate"

• "leafLignin"

• "hardsoft" Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

A named vector of colours to use for plotting. The names must conform with species name convention used (see sppEquivalencies_CA for possible naming conventions) and should also contain a colour for 'Mixed', when a mixed forest type is supported (see vegLeadingProportion parameter in vegTypeGenerator() for details on mixed forest types).

A ⁠Raster*⁠ or SpatRaster class object.

a character vector of colours. See examples. Can also be a data.frame, see terra::coltab

A numeric scalar giving the number of colours to create. Passed to ⁠quickPlot::setColors(ras, n = n) <- ⁠. If missing, then n will be length(cols)

Integer vector of classes that are are to be replaced, e.g., 34, 35, 36 on LCC2005, which are burned young, burned 10 year, and cities.

LCC raster, e.g., LCC2005

A data.table or data.frame with 3 columns: speciesCode, initialEcoregionCode and pixelIndex. pixelIndex is the pixel id for each line in the data.table; speciesCode is the species name in the pixel (can have more than one species per pixel, so multiple rows per pixel); and, initialEcoregionCode is the unique codes that are "available" to be used as a replacement for classesToReplace. initialEcoregionCode must be a character vector, with one or no "" used as a separator, with the last component being the Land Cover Class that matches classesToReplace, e.g., "242_18". If there is no "" in this code, then the codes must match the classesToReplace exactly, e.g., "11". If pixelIndex is missing, the function will fill it with seq(ncell(rstLCC)). If speciesCode is missing, the function will replace it with a dummy value ("allSpecies").

An optional vector of pixel IDs that need to be changed. If not provided, then pixels to change will be taken from the match between availableERC_by_Sp and classesToReplace. Supplying this allows the user to only replace some of the pixels with a given class.

Deprecated. Use availableERC_by_Sp

Deprecated. Use availableERC_by_Sp

Deprecated. Use classesToReplace

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

Integer vector of classes that are are to be replaced, e.g., 34, 35, 36 on LCC2005, which are burned young, burned 10 year, and cities.

raster of land-cover class, e.g., LCC2005

the number of iterations to perform

the value to assign a pixel in classesToReplace if no valid pixel is closer after nIterations

classes that are not valid options

A Raster that represents land cover (e.g., Land Cover Classified map from 2005 or 2010 from the Canadian Forest Service).

numeric vector defining which classes in LandCoverClassifiedMap.

A raster to use as a mask (see raster::mask()).

Passed to postProcessTo(..., to = to) and to the mask arg here, if mask is not supplied.

See reproducible::postProcess(). Default NULL.

additional args (not used)

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

An expanded cohortData data.table with pixel-level cohort information on burnt pixels and the following (optional) columns: severity - fire severity in that pixel calculated based on fire behaviour properties; firetolerance - species-level fire tolerance; severityToleranceDif - the difference between severity and firetolerance.

an empty cohortData-like table with columns: age, B, mortality, aNPPAct, and sumB removed; and pixelIndex added.

a data.table summarizing for which species serotiny/resprouting were activated and in how many pixels, for each year. Only necessary if calibrate = TRUE.

a vector of pixels where serotiny was activated;

a vector of pixels that burnt and were forested in the previous time step.

integer. The current simulation time obtained with time(sim)

a data.table with species traits such as longevity, shade tolerance, etc. Must have column speciesCode, with species names/IDs. The following is a list of default trait columns:

• "species" same as "speciesCode" – species ID name

• "speciesCode"

• "Area" – inherited from LANDIS-II default table, the Canadian ecoregion from which traits where derived. Not used during the simulation

• "firetolerance" – modulerelative (to other species) fire tolerance

• "growthcurve" and "mortalityshape" – growth curve shape parameters.

• "longevity" – maximum species age

• "postfireregen" – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• "resproutprob" – probability of resprouting

• "resproutage_min" – minimum age at which species is capable of resprouting

• "resproutage_max" – maximum age at which species is capable of resprouting

• "seeddistance_eff" – effective dispersal distance

• "seeddistance_max" – maximum dispersal distance

• "shadetolerance" – relative (to other species) shade tolerance

• "sexualmature" – age at sexual maturity Known optional parameters added/needed by some modules (the user may add others for their own modules):

• "inflationFactor" – Biomass_speciesParameters module: inflation factor for maxB

• "growthCurveSource" – Biomass_speciesParameters module: how "growthcurve" was estimated

• "mANPPproportion" – Biomass_speciesParameters module: multiplication factor to calculate maxANPP from maxB

• "thermokarsttol" – Biomass_disturbances module: proportion of biomass surviving after thermokarst (i.e. permafrost thaw). Applied equally across cohorts. Parameters inherited from LANDIS-II default table, but not used in LandR at the moment:

• "leaflongevity"

• "wooddecayrate"

• "leafLignin"

• "hardsoft" Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

a data.frame (not data.table!) containing probability of establishment, given a site's light conditions (X0-X5) for each level of a species shade tolerance (1-5).

logical. Determines whether to output postFirePixelCohortData. Defaults to FALSE

An expanded cohortData data.table with pixel-level cohort information on burnt pixels and the following (optional) columns: severity - fire severity in that pixel calculated based on fire behaviour properties; firetolerance - species-level fire tolerance; severityToleranceDif - the difference between severity and firetolerance.

an empty cohortData-like table with columns: age, B, mortality, aNPPAct, and sumB removed; and pixelIndex added.

a data.table summarizing for which species serotiny/resprouting were activated and in how many pixels, for each year. Only necessary if calibrate = TRUE.

a data.table with species traits such as longevity, shade tolerance, etc. Must have column speciesCode, with species names/IDs. The following is a list of default trait columns:

• "species" same as "speciesCode" – species ID name

• "speciesCode"

• "Area" – inherited from LANDIS-II default table, the Canadian ecoregion from which traits where derived. Not used during the simulation

• "firetolerance" – modulerelative (to other species) fire tolerance

• "growthcurve" and "mortalityshape" – growth curve shape parameters.

• "longevity" – maximum species age

• "postfireregen" – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• "resproutprob" – probability of resprouting

• "resproutage_min" – minimum age at which species is capable of resprouting

• "resproutage_max" – maximum age at which species is capable of resprouting

• "seeddistance_eff" – effective dispersal distance

• "seeddistance_max" – maximum dispersal distance

• "shadetolerance" – relative (to other species) shade tolerance

• "sexualmature" – age at sexual maturity Known optional parameters added/needed by some modules (the user may add others for their own modules):

• "inflationFactor" – Biomass_speciesParameters module: inflation factor for maxB

• "growthCurveSource" – Biomass_speciesParameters module: how "growthcurve" was estimated

• "mANPPproportion" – Biomass_speciesParameters module: multiplication factor to calculate maxANPP from maxB

• "thermokarsttol" – Biomass_disturbances module: proportion of biomass surviving after thermokarst (i.e. permafrost thaw). Applied equally across cohorts. Parameters inherited from LANDIS-II default table, but not used in LandR at the moment:

• "leaflongevity"

• "wooddecayrate"

• "leafLignin"

• "hardsoft" Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

a data.frame (not data.table!) containing probability of establishment, given a site's light conditions (X0-X5) for each level of a species shade tolerance (1-5).

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

integer. The current simulation time obtained with time(sim)

a vector of pixels that burnt and were forested in the previous time step.

logical. Determines whether to output postFirePixelCohortData. Defaults to FALSE

A model formula.

Character vector giving the name of the term to drop.

a list with two rasters, one with ecoregions (e.g. ecodistricts) and another with land cover (e.g. LCC).

the name describing the type of ecoregions in first map (e.g. "ecoDistrict") if passing a polygon file.

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

Vector of values to match in df.

A data.frame where every row is a set of equivalent names.

A character string or numeric of length 1, indicating the column in df to return names from.

Logical. If TRUE, then all matches will be returned. Default FALSE for backwards compatibility. This may result in more elements than were in value, as each value may be matched by more than one entry, returning more than one result each from column. If a species is found in the df, but there is no corresponding value in column, it will return NA.

Optionally, provide the name of a column in df that results must be found in. The return value will still be from column

the output of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted

data for estimation of 'A'

should 'A' be estimated for average values of its predictors.

character. Non-linear model form used to estimate average maximum biomass. One of "CR" (Chapman-Richards) or "Logistic".

a shapefile to rasterize

the template raster to use

the field to use (will be ignored if the shapefile has no fields)

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

the columns in cohortData that define unique cohorts (default: c("pixelGroup", "speciesCode", "age")).

logical. Determines whether to output postFirePixelCohortData. Defaults to FALSE

a data.table summarizing for which species serotiny/resprouting were activated and in how many pixels, for each year. Only necessary if calibrate = TRUE.

data.table with 3 columns: pixelIndex, pixelGroup, and burnTime. Each row represents a forested pixel that was burned up to and including this year, since last dispersal event, with its corresponding pixelGroup and time it occurred Pixel group IDs correspond to the last year's pixelGroupMap and not necessarily the pixelGroupMap of the burnTime year.

raster layer with burnt pixels in currentTime. Usually generated by a fire spread module (or collection of modules) like fireSense. Values in the raster should contain 0's for unburnt pixels and values > 0 for burn pixels. NA's are assumed to be outside of the study area.

an internal object to Biomass_core that track of inactive pixels.

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

numeric. Current simulation time step (as in time(sim))

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

a data.table with species traits such as longevity, shade tolerance, etc. Must have column speciesCode, with species names/IDs. The following is a list of default trait columns:

• "species" same as "speciesCode" – species ID name

• "speciesCode"

• "Area" – inherited from LANDIS-II default table, the Canadian ecoregion from which traits where derived. Not used during the simulation

• "firetolerance" – modulerelative (to other species) fire tolerance

• "growthcurve" and "mortalityshape" – growth curve shape parameters.

• "longevity" – maximum species age

• "postfireregen" – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• "resproutprob" – probability of resprouting

• "resproutage_min" – minimum age at which species is capable of resprouting

• "resproutage_max" – maximum age at which species is capable of resprouting

• "seeddistance_eff" – effective dispersal distance

• "seeddistance_max" – maximum dispersal distance

• "shadetolerance" – relative (to other species) shade tolerance

• "sexualmature" – age at sexual maturity Known optional parameters added/needed by some modules (the user may add others for their own modules):

• "inflationFactor" – Biomass_speciesParameters module: inflation factor for maxB

• "growthCurveSource" – Biomass_speciesParameters module: how "growthcurve" was estimated

• "mANPPproportion" – Biomass_speciesParameters module: multiplication factor to calculate maxANPP from maxB

• "thermokarsttol" – Biomass_disturbances module: proportion of biomass surviving after thermokarst (i.e. permafrost thaw). Applied equally across cohorts. Parameters inherited from LANDIS-II default table, but not used in LandR at the moment:

• "leaflongevity"

• "wooddecayrate"

• "leafLignin"

• "hardsoft" Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

a data.frame (not data.table!) containing probability of establishment, given a site's light conditions (X0-X5) for each level of a species shade tolerance (1-5).

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

the initial biomass of new cohorts. Defaults to 10. If NULL or NA, initial cohort biomass is calculated as in LANDIS-II Biomass Succession Extension v3.2.1 (Scheller & Miranda, 2015):

initialB = asInteger(pmin(maxANPP, asInteger(pmax(1, maxANPP \* exp(-1.6 \* sumB / maxB_eco)))))

where maxANPP and maxB_eco are the maximum ANPP and B parameters of the species in question within the pixel's ecolocation, and sumB is the total stand biomass excluding cohorts with ages less than successionTimestep.

The time between successive seed dispersal events. In LANDIS-II, this is called "Succession Timestep".

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

Controls message output. Defaults to getOption("LandR.verbose")

character. cohortData columns used to find identical pixels in terms of cohort composition, and group them into pixelGroups (default: c("ecoregionGroup", "speciesCode", "age", "B")).

logical. Should partial mortality be calculated as in LANDIS-II Dynamic Fire System v3.0. Must be TRUE for the time being.

data.table defining upper age limit of cohorts killed by fire. See LANDIS-II Dynamic Fire System v3.0 Manual for an example.

raster layer of critical spread rate for crowning (m/min) for each burned pixel. If not provided dummy values are used.

raster layer of equilibrium rate of spread (m/min) for each burned pixel. If not provided dummy values are used.

raster layer of crown fraction burnt for each burned pixel. If not provided dummy values are used.

a data.frame with the cut points to classify stand shadiness.

integer. Vector of pixel IDs than underwent thermokarst (and were converted to wetlands) in the current year (not the last thermokarst event).

data.table with 3 columns: pixelIndex, pixelGroup, and burnTime. Each row represents a forested pixel that was burned up to and including this year, since last dispersal event, with its corresponding pixelGroup and time it occurred Pixel group IDs correspond to the last year's pixelGroupMap and not necessarily the pixelGroupMap of the burnTime year.

binary SpatRaster with current wetland pixels.

species name – only used for messaging.

a data.table of predictor variables including those in predictorVars and age, as well as pixelIndex. Note that age should be in the original scale (e.g., not logged).

s data.table of species biomass (B) and pixelIndex.

character vector of predictor variables to be included in the linear component of the model affecting the asymptote (need to correspond to names(predictorVarsData[[sp]])) the same predictors will be considered for all species.

a list of sets of covariates in predictorVars to add to the fitted models. If this list has several entries with sets of covariates, each will be fitted as part of the model selection process.

how many covariates from predictorVars should be added to the linear component of the model affecting the asymptote? Note that the more covariates are added the longer the model takes to fit, as all combinations are attempted. For 2 or more covariates, only combinations with "cover" are attempted.

should covariates be added one at a time to the linear component of the model? If TRUE, and is.null(predictorVarsCombos), then each entry in predictorVarsCombos is used as the set of covariates to test. Otherwise, predictorVarsCombos will be created from combinations of predictorVars, with maxNoCoefs determining the maximum number of covariates to add. If FALSE the full model is fitted.

how many data points should be randomly sampled to fit the model? If NA the full dataset will be used. Note that this may result in long computation times. Biomass data will be binned into 10 regular bins before sampling points in number equal to sampleSize.

how many times should the models be fit with new randomly generated starting values? Only used if randomStarts == TRUE.

numeric. Value indicating maximum cover/dominance.

character vector of models to fit. Only Chapman-Richards ('CR') and 'Logistic' can be chosen at the moment.

previous outputs of fitNLMmodels. The model will try refitting and comparing AIC with the last results.

logical. Should random starting values of A, k and p non-linear parameters be picked from a range sensible values, or should all combinations of values within this range be used? If FALSE, the default, the starting values are spaced at regular intervals within an acceptable range for each parameter – 20 values for A, 10 for k and p – and all combinations are used (2000 starting values in total). Parameter ranges are estimated from data following Fekedulegn et al. (1999) as follows:

• range of A starting values (B0 parameter in Fekedulegn et al. 1999) varies between $ObsMaxB \times 0.3$ and $ObsMaxB \times 0.9$, where $$ObsMaxB$$ is the maximum observed B across the full dataset (not the sampled data for fitting)

• k (CR model) and p (Logistic model; both are B2 parameter in Fekedulegn et al. 1999) are estimated as a constant rate to get to $$ObsMaxB$$, calculated as $\frac{\frac{Bobs2 - Bobs1}{age2 - age1}}{ObsMaxB}$, where B1/B2 and age1/age2 are are observed values at two points in time. We draw 100 samples of two age values, and corresponding B, to calculate a sample of rates. After excluding rates <= 0, we take the minimum and maximum values as the range of k (CR model) or p (Logistic model) parameters of the growth model

• the p parameter (CR model) (related to B3 parameter in Fekedulegn et al. 1999) should be > 1. Here we use a range of values between 1.1 and 80 which provided suitable fitting using data from the Northwest Territories, Canada

• the k parameter (Logistic model; B1 parameter in Fekedulegn et al. 1999) is estimated as $B_0 = \frac{ObsMaxB}{1 + k}$, using a small positive number for $B_0$, e.g. 2. Here, we estimate k values for $B_0$ values 1 to 5, and use the minimum and maximum to determine the range from where to draw starting values.

a named vector of lower parameter boundaries. If FALSE, no lower boundaries are applied. If TRUE, coefficients of the linear model on the A parameter (intercept, cover, k and p) are bound (⁠intercept = observed maximum B * 0.5⁠, cover = 0, k = 0.05 and p = 1). Alternatively, pass a named vector of parameter boundaries.

a named vector of upper parameter boundaries. If FALSE, no lower boundaries are applied. If TRUE, coefficient of the linear model on the A parameter (intercept and k) are bound (⁠intercept = observed maximum B * 1.5⁠, k = 0.2). Alternatively, pass a named vector of parameter boundaries.

integer. If > 1, the number of workers to use in future.apply::future_apply, otherwise no parallellisation is done.

A data.table with column-based descriptions. Must have a column called pixelIndex, which allows for multiple rows to be associated with a single pixel.

A length 1 numeric indicating the current maximum pixelGroup value; the pixelGroup numbers returned will start at maxPixelGroup + 1.

A character vector of column names to use as part of the generation of unique combinations of features. Default is c("ecoregionGroup", "speciesCode", "age", "B")

a raster or polygon object (sp, raster or terra)

a points or polygons spatial object (sp, sf, or terra)

character. The field(s) to extract when x is a polygon. If NULL, all fields are extracted and returned. IDs of y are always returned (ID column).

passed to terra::extract()

data.table. The pre-disturbance cohortData table

SpatRaster. The pre-disturbance pixelGroupMap.

data.table. A table with at least the pixelIndex of all disturbed pixels. Additional columns are ignored.

a cohortData-like table with information of dead, and surviving, but NOT regenerating cohorts (cohorts for whom regeneration via, e.g., serotiny or resprouting was successfully activated), in disturbed pixels only. Dead cohorts should age B == 0, surviving cohorts B > 0.

character. cohortData columns used to find identical pixels in terms of cohort composition, and group them into pixelGroups (default: c("ecoregionGroup", "speciesCode", "age", "B")).

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

If NULL (the default), uses one from D. Cyr's LANDIS-II files: https://github.com/dcyr/LANDIS-II_IA_generalUseFiles/master/speciesTraits.csv).

The destination path.

User tags to pass to Cache.

A species traits table, with at least the following columns:

• speciesCode an character representation of species;

• Area a character of geographical area within a species range for which trait values are relevant;

• longevity species longevity in years, sexualmature age in years at sexual maturity;

• shadetolerance numeric value between 1-5 of relative shade tolerance (with respect to other species);

• seeddistance_eff the "effective" seed dispersal distance;

• seeddistance_max a numeric with the maximum seed dispersal distance;

• mortalityshape and growthcurve: growth curve shape parameters. Other columns (e.g. fire-related traits) can also be included depending on LandR modules in use. Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

Deprecated.

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

A character vector of areas to use. Can be one or more of c("Acadian", "AM", "NorthShore", "BP", "BSE", "BSW", "LSJ", "MC", "PM", "WestON"). If it is more than one, this function will take the minimum value, within a species. These are short versions of the Canada Ecoprovinces. Currently defaults to c("BSW", "BP", "MC") for historical reasons.

destination path for archive

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

location from which to download the wildfire raster layer(s)

a named list with outputs of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted. If several model outputs are provided all fitted models will be plotted, with plot labels corresponding to list names.

data for estimation of maximum biomass. Should contain at least an 'age' column. Note that other covariates will be averaged and 'cover' values will be replaced with the maximum cover value (maxCover). If mll is a list data is assumed to be the same for the two models.

numeric. Value indicating maximum cover/dominance.

the variable shown in the x axis. Defaults to age.

character. Passed to title in ggplot2::labs().

a named list of non-linear equations as a call (quoted expression) passed to mle2(minuslog1). See bbmle::mle2(). Accepts equations with three parameters 'A', 'p' and 'k'. List names and length must the same as in mll.

A named list of lists of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover. List names and length must the same as in mll.

should covariates other than age/cover be averaged for biomass predictions? If not, for each age (at maximum cover) there will be as many predictions as other covariate values. If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.

should observed age values be used, or should these be generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0)? If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.

should confidence intervals be calculated and plotted?

character vector giving the library directories where to install the packages. Recycled as needed. If missing, defaults to the first element of .libPaths().

data.table

data.table

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

A species traits table, with at least the following columns:

• speciesCode an character representation of species;

• Area a character of geographical area within a species range for which trait values are relevant;

• longevity species longevity in years, sexualmature age in years at sexual maturity;

• shadetolerance numeric value between 1-5 of relative shade tolerance (with respect to other species);

• seeddistance_eff the "effective" seed dispersal distance;

• seeddistance_max a numeric with the maximum seed dispersal distance;

• mortalityshape and growthcurve: growth curve shape parameters. Other columns (e.g. fire-related traits) can also be included depending on LandR modules in use. Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

An expression that can take a "dis" argument. See details. Default is "Ward" (temporarily unused, as it is hard coded inside Rcpp function)

LANDIS Ward seed dispersal calibration coefficient (set to 0.01 in LANDIS)

LANDIS Ward seed dispersal the probability that seed will disperse within the effective distance (e.g., 0.95)

Deprecated. If TRUE, then plot the raster at every interaction, so one can watch the LANDISDisp event grow.

The time between successive seed dispersal events. In LANDIS-II, this is called "Succession Timestep".

Controls message output. Defaults to getOption("LandR.verbose")

Additional parameters. Currently none.

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

data.table (with columns leading and LCC) defining the mappings between leading species and landcover classes.

numeric between 0 and 1 that translates %% cover to %% biomass. It assumes all hardwoods are equivalent; all softwoods are equivalent; and that %% cover of hardwoods will be an overesimate of the %%% biomass of hardwoods. Hardwoods in Canada have a much wider canopy than softwoods. E.g., 30%% cover of hardwoods might translate to 20%% biomass of hardwoods. NB: the default deciduousCoverDiscount value was estimated from NWT data in March 2020, and may not be useful for other study areas.

Numeric between 0-1, determining the relative biomass threshold a species needs to pass to be considered "leading".

character vector of deciduous species names. If not supplied, will attempt to match deciduous species names in sppEquivalencies_CA to those in cohortData.

landcover raster

TODO: description needed

TODO: description needed

TODO: description needed

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

Character string. Either "cover" or "age".

path to the data directory

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

A ⁠SpatialPolygons*⁠ object used as the principle study region, passed to reproducible::prepInputs().

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

which year's layers should be retrieved? One of 2001 (default) or 2011.

character string indicating the column in sppEquiv containing kNN species names. Default "KNN" for when sppEquivalencies_CA is used.

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

the minimum percent cover a species must have (per pixel) to be considered present in the study area. Defaults to 10.

the source url for the data, default is KNN 2011 dataset (https://ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/canada-forests-attributes_attributs-forests-canada/2011-attributes_attributs-2011/)

Additional arguments passed to reproducible::Cache() and equivalentName(). Also valid: outputPath, and studyAreaName.

A data.table with columns described above.

A vector of the names of the columns in inputDataTable that represent percent cover by species, rescaled to sum up to 100%%.

statistical model used to impute ages in pixels with missing data or with cover == 0. If set to NULL no imputation will be attempted, and pixels with missing age are excluded.

minimum total cover percentage necessary to consider the pixel vegetated, or a cohort present in a pixel.

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

Turns on/off subsetting. Defaults to TRUE.

The full cohortData data.table

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

The full cohortData data.table

Default columns that define pixel groups

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

Integer. When assigning pixelGroup membership, this defines the resolution of biomass that will be considered 'the same pixelGroup', e.g., if it is 100, then 5160 and 5240 will be the same

Integer. When assigning pixelGroup membership, this defines the resolution of ages that will be considered 'the same pixelGroup', e.g., if it is 10, then 6 and 14 will be the same.

Minimum age for regrouping. This may be because there is a source of ages for young stands/trees that is very reliable, such as a fire database. Ages below this will not be grouped together. Defaults to -1, meaning treat all ages equally. If this is related to known ages from a high quality database, then use age of the oldest trees in that database.

Should imputed pixels be removed

a vector of IDs of pixels that suffered data imputation.

A data.table of pixelFateDT; if none provided, will make an empty one.

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

a rawBiomassMap (e.g. the one used throughout the simulation)

The ecoregionMap, a raster of all the unique ecoregion groupings, which group together pixels of similar biophysical characteristics.

raster of land-cover class, e.g., LCC2005

The full cohortData data.table

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

A list with two objects: the ecoregionMap and a table summarizing its information per pixelID.

The full cohortData data.table

The full cohortData data.table

TODO: description needed

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

path to data directory where objects will be downloaded or saved to

The full cohortData data.table

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

A RasterStack or RasterLayer that should contain species cover data in the study area

A rasterLayer of stand ages with attribute "imputedPixID"

A list with two objects: the ecoregionMap and a table summarizing its information per pixelID. See ecoregionProducer.

raster of total stand biomass in t/ha. Biomass units are converted to g/m^2.

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

raster of land-cover class, e.g., LCC2005

Logical. If TRUE, the default, a print out of the summary(pixelTable) will occur.

A logical indicating whether some internal tests should be run to ensure the function is running correctly. Default: getOption("LandR.assertions", TRUE).

a subset of cohortData object

a subset of cohortData

a subset of cohortData

a data.table with species traits such as longevity, shade tolerance, etc. Must have column speciesCode, with species names/IDs. The following is a list of default trait columns:

• "species" same as "speciesCode" – species ID name

• "speciesCode"

• "Area" – inherited from LANDIS-II default table, the Canadian ecoregion from which traits where derived. Not used during the simulation

• "firetolerance" – modulerelative (to other species) fire tolerance

• "growthcurve" and "mortalityshape" – growth curve shape parameters.

• "longevity" – maximum species age

• "postfireregen" – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• "resproutprob" – probability of resprouting

• "resproutage_min" – minimum age at which species is capable of resprouting

• "resproutage_max" – maximum age at which species is capable of resprouting

• "seeddistance_eff" – effective dispersal distance

• "seeddistance_max" – maximum dispersal distance

• "shadetolerance" – relative (to other species) shade tolerance

• "sexualmature" – age at sexual maturity Known optional parameters added/needed by some modules (the user may add others for their own modules):

• "inflationFactor" – Biomass_speciesParameters module: inflation factor for maxB

• "growthCurveSource" – Biomass_speciesParameters module: how "growthcurve" was estimated

• "mANPPproportion" – Biomass_speciesParameters module: multiplication factor to calculate maxANPP from maxB

• "thermokarsttol" – Biomass_disturbances module: proportion of biomass surviving after thermokarst (i.e. permafrost thaw). Applied equally across cohorts. Parameters inherited from LANDIS-II default table, but not used in LandR at the moment:

• "leaflongevity"

• "wooddecayrate"

• "leafLignin"

• "hardsoft" Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

statistical model of species presence/absence

statistical model of species biomass

The time between successive seed dispersal events. In LANDIS-II, this is called "Succession Timestep".

time(sim)

A RasterStack of species abundances. This must be one RasterLayer per species.

Numeric between 0-1, determining the relative biomass threshold a species needs to pass to be considered "leading".

Deprecated. See mixedType argument to vegTypeMapGenerator.

Additional arguments passed to reproducible::Cache() and equivalentName().

path to the data directory

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

A ⁠SpatialPolygons*⁠ object used as the principle study region, passed to reproducible::prepInputs().

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

character string indicating the column in sppEquiv containing kNN species names. Default "KNN" for when sppEquivalencies_CA is used.

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

the minimum number of pixels where the species must have biomass > 0 to be considered present in the study area. Defaults to 1.

the source url for the data, passed to reproducible::prepInputs()

path to data directory where objects will be downloaded or saved to

path to output directory

A data.table with species-ecoregion-specific species trait values. Ecoregion refers to "ecolocation", a categorical variable grouping sites with similar biophysical characteristics. The table should have at least the following columns: speciesCode and ecoregionGroup, character representation of species and ecoregion groups respectively, maxB the maximum biomass for the species in a given 'ecoregion', maxANPP the maximum aboveground net primary productivity and SEP the species establishment probability. May contain columns inflationFactor (used to adjust maxB) and mANPPproportion (used to calculate maxANPP).

A species traits table, with at least the following columns:

• speciesCode an character representation of species;

• Area a character of geographical area within a species range for which trait values are relevant;

• longevity species longevity in years, sexualmature age in years at sexual maturity;

• shadetolerance numeric value between 1-5 of relative shade tolerance (with respect to other species);

• seeddistance_eff the "effective" seed dispersal distance;

• seeddistance_max a numeric with the maximum seed dispersal distance;

• mortalityshape and growthcurve: growth curve shape parameters. Other columns (e.g. fire-related traits) can also be included depending on LandR modules in use. Please see the LANDIS-II Biomass Succession Extension v3.2.1 manual (Scheller and Miranda 2015) for further detail.

A RasterStack or RasterLayer that should contain species cover data in the study area

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

A RasterStack or RasterLayer that should contain species cover data in the study area

logical. Should pixels with classes in forestedLCCClasses be included as non-forested?

vector of non-forested land-cover classes in rstLCC

raster of land-cover class, e.g., LCC2005

A named list or named RasterStack of layers whose content is Land Cover Class.

A named list of same length and names as LCCs indicating which classes in each LCC raster are 'forested', either permanent or transient

A character string that matches one of the named elements in LCCs. This will be the classification system returned.

The condition when a pixel is deemed to be NA. Given as a character string of a vectorized logical statement that will be within the forestEquivalencies table. It should be a set of conditions with ⁠== 0⁠, i.e., non-forested. Examples:, e.g., "LCC2005 == 0" or "CC == 0 | LCC2005 == 0", where 0 is the non-forested pixels based on converting LCCs and forestedList to 1 and 0.

The 'nearest-neighbour' condition; i.e., the condition when a nearest-neighbour search is done to fill in the pixel with forested type. Given as a character string of a vectorized logical statement that will be parsed within the forestEquivalencies table. It should be a set of conditions with ⁠== 0⁠, i.e., non-forested. Examples:, e.g., "LCC2005 == 0" or "CC == 0 | LCC2005 == 0", where 0 is the non-forested pixels based on converting LCCs and forestedList to 1 and 0.

data.table. This would be for a situation where 2 LCC layers are provided, one has information in a pixel, but not the one which is outputLayer, so this needs a reclassify or remap.

Passed to convertUnwantedLCC(), for the pixels where NNcondition is TRUE

Passed to convertUnwantedLCC(), for the pixels where NNcondition is TRUE. If this is NULL, then it will be created internally with all pixels with: data.table(initialEcoregionCode = LCCs[[outputLayer]][])

A data.frame or NULL. If NULL, this function will derive this table automatically from the other arguments. Otherwise, the user must provide a data.frame with length(LCCs) + 1 columns, and 2 ^ length(LCCs) rows. Currently not used.

high quality list/stack of rasters (will be used preferentially)

low quality list/stack of rasters (will be used to fill NAs in highQualityStack)

file suffix to save raster if there was overlaying. Defaults to "overlay".

path to data directory where objects will be downloaded or saved to

a named list with outputs of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted. If several model outputs are provided all fitted models will be plotted, with plot labels corresponding to list names.

data for estimation of maximum biomass. Should contain at least an 'age' column. Note that other covariates will be averaged and 'cover' values will be replaced with the maximum cover value (maxCover). If mll is a list, data is assumed to be the same for the two models.

the covariate for which variation in maxB values will be shown per age value. Defaults to showing how maxB values change with "cover" at any given age. Age values are generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0). When targetCovar != "cover", "cover" may be fixed at maxCover. See fixMaxCover.

numeric. Value indicating maximum cover/dominance.

logical. If TRUE and targetCovar != "cover", cover is not averaged and is fixed to maxCover.

the variable shown in the x axis. Defaults to "age". When xCovar == "age" the output plots are not true "Partial Effects" plots. Instead, they show the variation is B values across values of targetCovar for each value of age.

controls whether quantile predictions will be shown. If "allQuantiles", quantile values (5%, 25%, 50%, 75%, 95%) of B will be plotted as blue lines, with their respective asymptote values (quantile values at maximum age) as dashed lines. If "maximum" the 100% quantile will be plotted. If "none", only the 50% quantile line (average prediction) is plotted. Quantiles are always calculated at max(age).

character. Passed to title in ggplot2::labs()

a named list of non-linear equations as a call (quoted expression) passed to mle2(minuslog1). See ?mle. Accepts equations with three parameters 'A', 'p' and 'k'. List names and length must the same as in mll.

A named list of lists of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover. List names and length must the same as in mll.

passed to .MLLMaxBPartialPlotData.

should confidence intervals be calculated and plotted?

The ratio for deciduous cover:biomass, where conifer cover:biomass = 1

A full pixelCohortData object (i.e., not cohortData)

A pixelFateDT data.table with 3 columns: fate, pixelsRemoted, and runningPixelTotal.

A character string (length 1) describing in words the change

A numeric indicating how many pixels were removed due to the fate.

an optional numeric with new, running total. If not supplied, it will be calculated from the last row of pixelFateDT runningTotal minus the pixelsRemoved

the cohorts that were harvested

A data.table with columns: pixelGroup, ecoregionGroup, speciesCode, and optionally age, B, mortality, aNPPAct, and sumB.

A RasterLayer with pixel values equal to a pixel group number that corresponds exactly to pixelGroup column in cohortData.

the initial biomass of new cohorts. Defaults to ten.

The current simulation time e.g., time(sim).

The time between successive seed dispersal events. In LANDIS-II, this is called "Succession Timestep".

adds column that tracks planted cohorts if TRUE

character string giving the study area name

character string specifying the name of a CIMP6 climate scenario, including SSP, formatted as in ClimateNA, using underscores as separator (e.g., 'CanESM5_SSP370').

the number of simulation replicates/run used to produce summary figures. NOTE: mclapply is used internally, so you should set options(mc.cores = nReps) to take advantage of parallel processing.

TODO

Path specifying the directory to which outputs figures/objects should be saved.

TODO

TODO

TODO

TODO

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

SpatRaster, RasterLayer, SpatVector or sf object

character. A title for the plot passed to ggplot::labs(title = plotTitle).

TODO

character. If x is sf or SpatVector, a field to plot.

A SpatRaster, RasterStack or RasterBrick of percent-cover-by-species layers.

An optional vegetation type map (RasterLayer or SpatRaster). If not supplied, will be produced internally by makeVegTypeMap.

Numeric between 0-1, determining the relative biomass threshold a species needs to pass to be considered "leading".

table with species name equivalencies between the kNN and final naming formats. See data("sppEquivalencies_CA", "LandR"). For functions that have mixedType, this only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use data("sppEquivalencies_CA", "LandR").

the column name to use from sppEquiv. For functions that have mixedType, only necessary if mixedType == 2. If not provided and mixedType == 2, will attempt to use "Boreal".

Named vector of colour codes, named using species names. NOTE: plot order will follow this order.

The title to use for the generated plots.

an optional raster object that could be passed to sim, representing ecoregions

an sf polygons object representing ecoregions

optional. The field in ecoregionLayer that represents ecoregions.

the rasterToMatchLarge object from sim

RasterLayer representing land cover adjusted for non-forest classes

a vector of pixels to remove

UserTags to pass to cache

character. ecozones shapefile url. Default: http://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip.

path to data directory where objects will be downloaded or saved to

A ⁠SpatialPolygons*⁠ object used as the principle study region, passed to reproducible::prepInputs().

A RasterLayer objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).