Calculate PFG traits values based on determinant species traits values

This script is designed to calculate PFG traits values based on determinant species traits values. Either the mean or the median is used depending on the trait class (i.e. numeric or categorical).

PRE_FATE.speciesClustering_step3(mat.traits, opt.mat.PA = NULL)

Arguments

mat.traits

a data.frame with at least 3 columns :

species: the ID of each determinant species (see PRE_FATE.speciesClustering_step2)
PFG: the corresponding Plant Functional Group (see PRE_FATE.speciesClustering_step2)
...: one column for each functional trait (see Details)

opt.mat.PA

(optional) default NULL.
a data.frame with sites in rows and species in columns, containing either NA, 0 or 1 (see PRE_FATE.selectDominant)

Value

A list containing one or two data.frame with the following columns, and one list with as many ggplot2 objects as functional traits given in mat.traits :

tab.PFG.traits

PFG: the concerned plant functional group
no.species: the number of species contained in this PFG
...: one column for each functional trait, computed as the mean (for numeric traits) or the median (for categorical traits) of the values of the determinant species of this PFG

tab.PFG.PA

table containing counts of presences for all PFG (sites in rows, PFG in columns)

plot

...: one for each functional trait, 'specific' cases excepted (see Details)

The information is written in PRE_FATE_PFG_TABLE_traits.csv, PRE_FATE_PFG_TABLE_sitesXPFG_PA.csv and PRE_FATE_CLUSTERING_STEP_3_PFGtraitsValues.pdf files.

This .csv file can be used to build parameter files to run a FATE simulation (e.g. PRE_FATE.params_PFGsuccession).

Details

This function allows to obtain 'average' functional trait values for each Plant Functional Group, based on values at the determinant species level.

A graphic is automatically produced for each functional trait given, with boxplot representing the values of determinant species, and colored points the values calculated for each PFG.
However, some traits can have 'specific' representation, as long as their names within mat.traits match one of the configuration detailed below :

maturity, longevity: to visualize the difference between these two values, for the maturity time has an impact on the fecundity of the PFG within FATE (see CORE module)
If there is NO values for longevity within one PFG, and some maturity values are available, some values might be inferred as \(\text{maturity} * 2\). If there is NO values for maturity within one PFG, and some longevity values are available, some values might be inferred as \(\text{longevity} / 2\).
height, light: to visualize the PFG light preference, and help decide and understand the choice of the height limits of strata in FATE (see LIGHT interaction module)
soil_contrib, soil_tol_min, soil_tol_max: to visualize the PFG soil preference, and help parameterize the global parameters of the soil interaction module within FATE (see SOIL interaction module)
soil_contrib, soil_tolerance: same as the previous one, but soil_tol_min and soil_tol_max values are obtained by adding or removing soil_tolerance to soil_contrib

If a sites x species table is provided (opt.mat.PA), a sites x PFG table (containing NA, 0 or 1) is also returned.

Author

Maya Guéguen

Examples


## Load example data
Champsaur_PFG = .loadData('Champsaur_PFG', 'RData')

## Species traits
tab.traits = Champsaur_PFG$sp.traits
str(tab.traits)

## Determinant species
tab.PFG = Champsaur_PFG$PFG.species
str(tab.PFG)

## Merge traits and PFG informations
mat.traits = merge(tab.PFG[which(tab.PFG$DETERMINANT==TRUE), c('species','PFG')]
                   , tab.traits
                   , by = 'species', all.x = TRUE)
str(mat.traits)

## Keep only traits of interest
mat.traits = mat.traits[, c('PFG', 'species', 'MATURITY', 'LONGEVITY'
                           , 'HEIGHT', 'LIGHT', 'DISPERSAL'
                           , 'NITROGEN', 'NITROGEN_TOLERANCE', 'LDMC', 'LNC')]
colnames(mat.traits) = c('PFG', 'species', 'maturity', 'longevity'
                         , 'height', 'light', 'dispersal'
                         , 'soil_contrib', 'soil_tolerance', 'LDMC', 'LNC')
mat.traits$soil_contrib = as.numeric(mat.traits$soil_contrib)
mat.traits$soil_tolerance = ifelse(mat.traits$soil_tolerance == 1, 0.5, 1)

## Compute traits per PFG : with one specific graphic ------------------------
PFG.traits = PRE_FATE.speciesClustering_step3(mat.traits = mat.traits)

names(PFG.traits)
str(PFG.traits$tab.PFG.traits)
str(PFG.traits$tab.PFG.PA)
names(PFG.traits$plot)
plot(PFG.traits$plot$maturity_longevity)
plot(PFG.traits$plot$height_light)
plot(PFG.traits$plot$soil)

Arguments

Value

Details

See also

Author

Examples