apa_ndiv: Calculate the index for neighborhood diversity NDiv for an...

In JonasGlatthorn/APAtree: Computation of the 'Area Potentially Available' (APA) to Trees

Description

NDiv assesses the average dissimilarity between trees and their neighbors using APA-maps. Dissimilarity can be assessed using a species- or a trait-based approach. Upscaling of the tree-level NDiv to the species- or stand-level are provided.

Usage

apa_ndiv(
  apa_list,
  dis_trait_column,
  dis_method = "gowdis",
  dis_transform = sqrt,
  scope = "global",
  edge_correction = "none",
  pdiv = TRUE
)

Arguments

apa_list	A apa_list-object that was created with the [apa_list]-function.
dis_trait_column	A list containing combinations of traits that will be used to estimate dissimilarity between trees when calculating apa-properties. Refers to column names of tree_dat.
dis_method	Which method should be used to estimate dissimilarity between trees. If dis_method is gowdis, FD::gowdis() will be used to calculate Gower dissimilarity. Alternatively, dis_method may be any function that calculates a dissimilarity matrix out of a data.frame with trait values.
dis_transform	A function to transform dissimilarities. Defaults to sqrt.
scope	Should scaling of the dissimilarity be done at "global" or at "local" level? If dis_method scales dissimilarity between trees according to the range of occurring values in the dataset (as done by "gowdis"), a "global" scope will use the range of all values in tree_dat. Any other scope will use the range of values at plot-level to scale dissimilarity.
edge_correction	which of the implemented edge correction method should be applied when calculating apa-properties ("none", "critical" or "border_tree_exclusion".
pdiv	If TRUE (default), pdiv will be calculated (proportion-based diversity, average dissimilarity between a tree and all other trees in a stand, irrespective of the spatial configuration.)

Details

See Glatthorn (2021) for details.

References

Glatthorn, Jonas (2021). A spatially explicit index for tree species or trait diversity at neighborhood and stand level. Ecological Indicators, 130, 108073. https://doi.org/10.1016/j.ecolind.2021.108073.

Examples

library(APAtree)
data(tree_enrico, package = "APAtree")
data(plot_enrico, package = "APAtree")

tree_enrico$height_class <- tree_enrico$height > 20

# only calculate an apa_list for two plots and with a coarse resolution of 1 m
# to save time.
apa_list_enrico <- 
  apa_list(plot_dat = subset(plot_enrico, id_plot %in% c("5.2", "8.2")), 
           tree_dat = tree_enrico,
           buffer_column = "buffer_geometry",
           core_column = "border_geometry",
           plot_id_column = "id_plot",
           tree_id_column = "id_tree",
           agg_class_column = "species",
           weight_column = "crown_radius_95",
           res = 1,
           apa_polygon = FALSE)

# Calculate SpeciesNDiv
apa_list_enrico <-
  apa_ndiv(apa_list_enrico,
           dis_trait_column = "species")

# tree-level SpeciesNDiv:
head(apa_list_enrico$tree_dat[, c("id_tree", "species_ndiv")])

# stand-level SpeciesNDiv:
apa_list_enrico$plot_dat[, c("id_plot", "species_ndiv")]

# species-level SpeciesNDiv:
head(apa_list_enrico$species[, c("id_plot", "species", "species_ndiv")])

JonasGlatthorn/APAtree documentation built on Dec. 18, 2021, 1:41 a.m.