Estimating wood density and associated uncertainties

The function estimates the wood density (WD) and the associated standard deviation of the trees from their taxonomy or from their congeners using the global wood density database V2 (Fischer et al. 2026) or any additional dataset if the sd is also provided. The WD can either be attributed to an individual at a species, genus, family or stand level.

Usage

getWoodDensity(
  genus,
  species,
  family = NULL,
  stand = NULL,
  addWoodDensityData = NULL,
  verbose = TRUE
)

Arguments

genus: Vector of genus names.
species: Vector of species names.
family: (optional) Vector of families. If set, the missing wood densities at the genus level will be attributed at family level if available.
stand: (optional) Vector with the corresponding stands of your data. If set, the missing wood densities at the genus level will be attributed at stand level. If not, the value attributed will be the mean of the whole tree dataset.
addWoodDensityData: A dataframe containing additional wood density data to be combined with the global wood density database (see Details).
verbose: A logical, give some statistic with the database

Value

Returns a dataframe containing the following information:

family: Family
genus: Genus
species: Species
meanWD (g/cm^3): Mean wood density estimates
sdWD (g/cm^3): Standard deviation estimates of the wood density
levelWD: Level at which wood density has been calculated. Can be species, genus, family, dataset (mean of the entire dataset) or, if stand is set, the name of the stand (mean of the current stand)

Details

The function assigns wood density estimates (WD) and uncertainty (sigma) at species, genus or family level to each taxon, using the results of Bayesian hierarchical modelling on the Global Wood Density Database V2, with the following brms formula: WD ~ 1 + (1 | family / genus / species) + (1 | source_short) sigma ~ 1 + ind + (1 | species) The uncertainties related to the genus and family are then estimated by simulating WD values for all the species.

If a taxon is unidentified or absent from the database, the estimated WD and uncertainty of the stand (if set) is given.

When supplying addWoodDensityData, the dataframe should be organized as follow:

four (or five) columns: "genus","species","meanWD","sdWD" (the fifth column "family" is optional)
one row per species (not per individual measurement) The taxa present in addWoodDensityData will replace the GWDD V2 estimates.

References

Fischer, F. J., et al. (2026). Beyond species means - the intraspecific contribution to global wood density variation. New Phytol. https://doi.org/10.1111/nph.70860 Fischer, F. J., et al. (2026). Global Wood Density Database v.2 (GWDD v.2) (Data set). Zenodo. https://doi.org/10.5281/zenodo.18262736

Author

Arthur BAILLY, Maxime REJOU-MECHAIN, Fabian FISCHER, Dominique LAMONICA

Examples

# Load a data set
data(NouraguesTrees)

# Compute the Wood Density up to the genus level and give the mean wood density of the dataset
# \donttest{
WD <- getWoodDensity(
  genus = NouraguesTrees$Genus,
  species = NouraguesTrees$Species
)
#> Your taxonomic table contains 409 taxa
#> Warning: 142 taxa don't match the Global Wood Density Database V2. You may provide 'family' to match wood density estimates at family level.
# }

# Compute the Wood Density up to the genus level and then give the mean wood density per stand
# \donttest{
WD <- getWoodDensity(
  genus = NouraguesTrees$Genus,
  species = NouraguesTrees$Species,
  stand = NouraguesTrees$plotId
)
#> Your taxonomic table contains 409 taxa
#> Warning: 142 taxa don't match the Global Wood Density Database V2. You may provide 'family' to match wood density estimates at family level.
# }