This function wraps some features of BIOMASS::getWoodDensity(), which you
may see for more details and options. It estimates the wood density of the
trees from their taxonomy or from their congeners using the global wood
density database (Chave et al. 2009, Zanne et al. 2009) or any additional
dataset. The resulting values of wood density can either be attributed to an
individual at the level of a species, genus, or family.
add_wood_density(data, species, family = TRUE, region = "World")
| data | A ForestGEO-like census-dataframe. |
|---|---|
| species | A ForestGEO-like species-dataframe. |
| family | (optional) Vector of families. If set, the missing wood densities at the genus level will be attributed at family level if available. |
| region | Region (or vector of region) of interest of your sample. By default, Region is set to 'World', but you can restrict the WD estimates to a single region :
|
A dataframe as that passed to data, but with additional columns giving
taxonomic information, and the following wood density information (oven dry
mass/fresh volume in g/cm^3):
wd_mean: Mean wood density.
wd_sd: Standard deviation of the wood density that can be used in error propagation (see sd_10 and AGBmonteCarlo()).
wd_level: 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).
This function assigns to each taxon a species- or genus- level average if at
least one wood density value at the genus level is available for that taxon
in the reference database. If not, the mean wood density of the family (if
family = TRUE).
As an estimate of the error associated with the wood density estimate, this function also provides the mean standard deviation value at the appropriate taxonomic level.
Rejou-Mechain M, Tanguy A, Piponiot C, Chave J, Herault B (2017). “BIOMASS : an R package for estimating above-ground biomass and its uncertainty in tropical forests.” Methods in Ecology and Evolution, 8(9). ISSN 2041210X, doi: 10.1111/2041-210X.12753 (URL: http://doi.org/10.1111/2041-210X.12753), <URL: http://doi.wiley.com/10.1111/2041-210X.12753>.
BIOMASS::getWoodDensity
tree <- fgeo.biomass::scbi_stem_tiny_tree species <- fgeo.biomass::scbi_species add_wood_density(tree, species)#> ℹ Wood density given in [g/cm^3].#> # A tibble: 11 x 26 #> treeID stemID tag StemTag sp quadrat gx gy DBHID CensusID dbh #> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 3175 3175 22146 1 caca 0221 29 404. 6103 1 107 #> 2 3175 32574 22146 2 caca 0221 29 404. 6104 1 31.4 #> 3 3996 3996 30363 1 litu 0307 50.3 139. 7096 1 755. #> 4 3996 32682 30363 2 litu 0307 50.3 139. 7097 1 127. #> 5 5570 5570 42082 1 caca 0419 71.3 363. 9047 1 98.3 #> 6 5570 32894 42082 2 caca 0419 71.3 363. 9048 1 49.3 #> 7 5570 37222 42082 3 caca 0419 71.3 363. 9049 1 44.3 #> 8 10060 10060 82218 1 litu 0822 159. 436 14579 1 614. #> 9 10060 33449 82218 2 litu 0822 159. 436 14580 1 222. #> 10 26760 26760 180969 1 ulru 1812 350. 227. 34958 1 343. #> 11 26760 35964 180969 2 ulru 1812 350. 227. 34959 1 50.3 #> # … with 15 more variables: pom <dbl>, hom <dbl>, ExactDate <date>, #> # DFstatus <chr>, codes <chr>, countPOM <dbl>, date <dbl>, status <chr>, #> # agb <dbl>, family <chr>, genus <chr>, species <chr>, wd_level <chr>, #> # wd_mean <dbl>, wd_sd <dbl>