Package {funbiogeo}

Type:	Package
Title:	Streamlining Functional Biogeography Analyses
Version:	0.0.1
Description:	Helps users with analyses in functional biogeography by loading and combining data, computing trait coverage, as well as computing functional diversity indices, drawing maps, correlating them with the environment, and upscaling assemblages.
URL:	https://frbcesab.github.io/funbiogeo/, https://github.com/frbcesab/funbiogeo
BugReports:	https://github.com/frbcesab/funbiogeo/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5.0)
Imports:	ggplot2 (≥ 3.4.0), grid, rlang, rnaturalearth, scales, sf, stats, terra, tidyr, tidyselect, tools, utils, xfun
Suggests:	dplyr, fs, fundiversity, ggridges, knitr, mFD, mockery, patchwork, rmarkdown, rstudioapi, testthat (≥ 3.0.0), vdiffr, withr
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-07-20 10:22:19 UTC; nicolas
Author:	Nicolas Casajus [aut, cre, cph], Matthias Grenié [aut]
Maintainer:	Nicolas Casajus <nicolas.casajus@fondationbiodiversite.fr>
Repository:	CRAN
Date/Publication:	2026-07-29 16:40:19 UTC

funbiogeo: Streamlining Functional Biogeography Analyses

Description

Helps users with analyses in functional biogeography by loading and combining data, computing trait coverage, as well as computing functional diversity indices, drawing maps, correlating them with the environment, and upscaling assemblages.

Author(s)

Maintainer: Nicolas Casajus nicolas.casajus@fondationbiodiversite.fr (ORCID) [copyright holder]

Authors:

• Nicolas Casajus nicolas.casajus@fondationbiodiversite.fr (ORCID) [copyright holder]

• Matthias Grenié matthias.grenie@univ-grenoble-alpes.fr (ORCID)

See Also

Useful links:

• https://frbcesab.github.io/funbiogeo/

• https://github.com/frbcesab/funbiogeo

• Report bugs at https://github.com/frbcesab/funbiogeo/issues

Aggregate Site Data Along Coarser Spatial Grid

Description

This function helps aggregating site data along a coarser grid. The idea is that you have any type of data at the site scale (diversity metrics, environmental data, etc.) but you would like to work on it at a coarser scale, or you want to visualize it at that scale. This function helps you do exactly that.

Usage

fb_aggregate_site_data(site_locations, site_data, agg_geom, fun = mean, ...)

Arguments

Details

The agg_geom object will condition the type of object output by the function. It can be of any sort of a SpatRaster or a sf object. Depending on the need, it could be a regular square grid or hexagonal grid; it could also be irregular polygons like biomes or ecoregions, or points, and even lines (such as when aggregating across transects or trajectories).

Value

An object of the same type as the agg_geom input with as many layers (if SpatRaster) or columns (if sf) as columns provided in the input site_data.

Examples

library("sf")
## Raster grid
tavg <- system.file(
  "extdata", "annual_mean_temp.tif", package = "funbiogeo"
)
tavg <- terra::rast(tavg)

# Rasterize 3 first species counts
fb_aggregate_site_data(
    head(woodiv_locations), woodiv_site_species[, 1:4], tavg, fun = sum
)

## Irregular polygons
countries <- readRDS(system.file(
  "extdata", "countries_sf.rds", package = "funbiogeo"
))
# Aggregate occurrence per country
fb_aggregate_site_data(
    head(woodiv_locations, n = 20), woodiv_site_species[, 1:4], countries,
    fun = sum
)

Count Number of Sites Occupied by Species

Description

This function computes the number and proportion of sites occupied by each species (distribution value higher than 0 and non-NA).

Usage

fb_count_sites_by_species(site_species)

Arguments

Value

A three-column data.frame with:

• species: the name of the species;

• n_sites: the number of sites where the species is present;

• coverage: the percentage of sites where the species is present.

Examples

site_coverage_by_species <- fb_count_sites_by_species(woodiv_site_species)
head(site_coverage_by_species)

Count Number of Species per Site

Description

This function computes for each site the number and proportion of species present (distribution value higher than 0 and non-NA) compared to all species provided. For example, a site could contain only 20% of all species provided.

Usage

fb_count_species_by_site(site_species)

Arguments

Value

A three-column data.frame with:

• site: the name of the site;

• n_species: the number of present species;

• coverage: the percentage of present species.

Examples

species_coverage_by_site <- fb_count_species_by_site(woodiv_site_species)
head(species_coverage_by_site)

Count Number of Species for Each Trait

Description

This function computes, for each trait, the number and proportion of species without missing trait value (NA).

Usage

fb_count_species_by_trait(species_traits)

Arguments

Value

A three-column data.frame with:

• trait: the name of the trait;

• n_species: the number of species with non-missing value for the trait;

• coverage: the percentage of species with non-missing value for the trait.

Examples

species_coverage_by_trait <- fb_count_species_by_trait(woodiv_traits)
head(species_coverage_by_trait)

Compute Number of Known Trait(s) per Species

Description

For each species, this function computes the number and proportion of traits without NA (missing trait values).

Usage

fb_count_traits_by_species(species_traits)

Arguments

Value

A three-column data.frame with:

• species: the name of the species;

• n_traits: the number of traits with non-missing value for the species;

• coverage: the percentage of traits with non-missing value for the species.

Examples

trait_coverage_by_species <- fb_count_traits_by_species(woodiv_traits)
head(trait_coverage_by_species)

Compute community-weighted means (CWM) of trait values

Description

This function returns the community-weighted mean of provided trait values. It only works with quantitative traits and will warn you otherwise. It will remove species that either have NA values in the site_species input or NA values as their trait in the provided trait object.

Usage

fb_cwm(site_species, species_traits)

Arguments

Details

The community-weighted mean is a site-based trait mean weighted by the abundance of the species. It can be written with the following equation:

\text{CWM}_k = \sum_{i = 1}^S p_{ik} \times t_{ik}

with \text{CWM}_k the CWM of site k, p_{ik} the relative abundance of species i in site k, and t_{ik} the trait of species i in site k.

Value

A data.frame with sites in rows and the following variables:

• site, the site label,

• trait, the trait label as provided in species_traits,

• and cwm, the community-weighted means of quantitative traits values.

Examples

site_cwm <- fb_cwm(head(woodiv_site_species), woodiv_traits)
head(site_cwm)

Filter sites with a given species coverage threshold

Description

Selects sites (rows) for which the proportion of species present (distribution value higher than 0 and non-NA) is higher than the user-defined threshold.

Usage

fb_filter_sites_by_species_coverage(
  site_species,
  threshold_species_proportion = 0
)

Arguments

Value

A subset of site_species with sites with at least threshold_species_proportion% species present.

Examples

# Get sites with more than 40% of the species
new_site_species <- fb_filter_sites_by_species_coverage(
  woodiv_site_species,
  threshold_species_proportion = 0.4
)
# There are now only 148 sites
dim(new_site_species)
new_site_species[1:3, 1:4]

Filter sites with a given trait coverage threshold

Description

Select sites (rows of the site_species object) with all given traits available for at least the user-defined proportion of species threshold_traits_proportion. If a single trait is given, then the threshold applies to a single trait, if more than one trait is provided, then the function considers a threshold across all traits taken together.

Usage

fb_filter_sites_by_trait_coverage(
  site_species,
  species_traits,
  threshold_traits_proportion = 1
)

Arguments

Value

A subset of site_species with sites covered by X% of abundance/coverage considering all provided traits.

Examples

# Filter all the sites where all species have known traits
new_site_species <- fb_filter_sites_by_trait_coverage(
  woodiv_site_species, woodiv_traits
)

# There is only one such site
nrow(new_site_species)

# Filter sites where at least 80% of species have known traits
new_site_species_2 <- fb_filter_sites_by_trait_coverage(
  woodiv_site_species, woodiv_traits, threshold_traits_proportion = 0.8
)

# There are now four sites
nrow(new_site_species_2)

Filter species with a given sites coverage threshold

Description

Selects species (columns) for which the percentage of sites where the species is present (distribution value higher than 0 and non-NA) is higher than a user-defined threshold.

Usage

fb_filter_species_by_site_coverage(
  site_species,
  threshold_sites_proportion = 0
)

Arguments

Value

A subset of site_species with species with a prevalence higher than threshold_sites_proportion.

Examples

# Filter species present in at least 10% of the sites
new_site_species <- fb_filter_species_by_site_coverage(
  woodiv_site_species,
  threshold_sites_proportion = 0.1
)

new_site_species[1:3, 1:4]

# There are now only 84 species (instead of 149)
ncol(new_site_species)

Filter species with a given traits coverage threshold

Description

Selects species (rows) for which the proportion of traits without NA (missing trait values) is higher than the user-defined threshold. It considers as many traits as the ones provided to filter given the threshold.

Usage

fb_filter_species_by_trait_coverage(
  species_traits,
  threshold_traits_proportion = 0
)

Arguments

Value

A subset of species_traits with species covered by X% of traits.

Examples

# Filter species that have at least 60% of the traits described
new_species_traits <- fb_filter_species_by_trait_coverage(
  woodiv_traits,
  threshold_traits_proportion = 0.6
)

# There are now only 93 species
nrow(new_species_traits)

Filter traits with a given species coverage threshold

Description

Selects traits (columns) for which the percentage of species without NA (missing trait values) is higher than a user-defined threshold.

Usage

fb_filter_traits_by_species_coverage(
  species_traits,
  threshold_species_proportion = 0
)

Arguments

Value

A subset of species_traits with traits for the specified proportion of species.

Examples

# Filter traits that have at least 60% non-missing values
new_species_traits <- fb_filter_traits_by_species_coverage(
  woodiv_traits,
  threshold_species_proportion = 0.6
)

# There are now only 2 traits
ncol(new_species_traits)

Extract site x locations information from long format data

Description

Convert a flat data.frame with site coordinates into a proper sf object that can then be used by other functions. This function assumes by default that the coordinates are given in WGS84 (longitude vs. latitude), but this can be changed through the crs argument. The function automatically removes repeated coordinates from the input dataset.

Usage

fb_format_site_locations(
  data,
  site,
  longitude,
  latitude,
  crs = sf::st_crs(4326),
  na_rm = FALSE
)

Arguments

Value

An sf object with a site column specifying site coordinates.

Examples

filename <- system.file(
    "extdata", "woodiv_raw_data.csv",
    package = "funbiogeo"
)
all_data <- read.csv(filename)

head(all_data)

site_locations <- fb_format_site_locations(
    all_data, "site", "longitude",
    "latitude"
)
head(site_locations)

Extract site x species information from long format data

Description

Convert a flat data.frame with species occurrence/abundance at site level into a proper data.frame object that can then be used by other functions. The final output contains sites in rows and species in columns.

Usage

fb_format_site_species(data, site, species, value, na_to_zero = TRUE)

Arguments

Value

A data.frame with sites in rows and species in columns. The first column is named "site" and contains the name of the sites.

Examples

filename <- system.file(
  "extdata", "woodiv_raw_data.csv",
  package = "funbiogeo"
)
all_data <- read.csv(filename)

head(all_data)

site_species <- fb_format_site_species(all_data, "site", "species", "count")
site_species[1:3, 1:4]

Extract species x categories information from long format data

Description

Convert a flat data.frame with species names and species (supra-)category (e.g. family, order, endemism status, etc.) into a proper species_categories object that can then be used by other functions. The final output contains species in rows and two columns (species name and species category).

Usage

fb_format_species_categories(data, species, category)

Arguments

Value

A data.frame with species in rows and two columns (species name and species category).

Examples

filename <- system.file(
  "extdata", "woodiv_raw_data.csv",
  package = "funbiogeo"
)
all_data <- read.csv(filename)

head(all_data)

species_categories <- fb_format_species_categories(
  all_data, "species", "genus"
)
species_categories[1:6, ]

Extract species x traits information from long format data

Description

Convert a flat data.frame with traits values for different species into a proper species_traits object that can then be used by other functions. The final output contains species in rows and traits in columns.

Usage

fb_format_species_traits(data, species, traits)

Arguments

Value

A data.frame with species in rows and traits in columns, with the first column names "species" containing the species names.

Examples

filename <- system.file(
  "extdata", "woodiv_raw_data.csv", package = "funbiogeo"
)
all_data <- read.csv(filename)

head(all_data)

traits <- c("plant_height", "seed_mass", "sla", "wood_density")

species_traits <- fb_format_species_traits(all_data, "species", traits)
head(species_traits)

Compute Trait Coverage per Site for Each Trait

Description

Compute trait coverage for all sites, i.e., the proportion of total abundance/presence of species that have traits data compared to total species. This function assumes that all species provided in the traits dataset have all their traits specified (meaning that all species have either known or NA values reported as their traits). The coverage of each trait separately is returned as well as all traits taken together if wanted.

Usage

fb_get_all_trait_coverages_by_site(
  site_species,
  species_traits,
  all_traits = TRUE
)

Arguments

Value

a data.frame with a column with sites and one column per provided trait giving its coverage (percent species per site, weighted by abundance that have trait data), and, when argument all_traits = TRUE, an additional column named all_traits considering the coverage of all traits taken together.

Examples

site_trait_cov <- fb_get_all_trait_coverages_by_site(
  woodiv_site_species, woodiv_traits
)

head(site_trait_cov)

Extract Raster Values at Location of Sites

Description

This function uses a site_locations object and a given SpatRaster. It extracts the mean value of the provided raster for each site.

Usage

fb_get_environment(site_locations, environment_raster)

Arguments

Value

A data.frame with average environmental values (columns) per site (rows), with the first column being "site", indicating site names.

Examples

## Import climate rasters ----
prec <- system.file("extdata", "annual_tot_prec.tif", package = "funbiogeo")
tavg <- system.file("extdata", "annual_mean_temp.tif", package = "funbiogeo")

layers <- terra::rast(c(tavg, prec))

fb_get_environment(head(woodiv_locations), layers)

Compute site trait coverage for each trait combination

Description

This function computes trait coverage for each site for different trait The function will not run if the total number of combinations given is over 10,000.

Usage

fb_get_trait_combination_coverage(
  site_species,
  species_traits,
  comb_size = NULL
)

Arguments

Value

a data.frame with the following columns:

• site with the site indices from site_species,

• combination_length with the number of traits in given combinations,

• combination_name with the name of the trait combination (concatenated trait names with ⁠__⁠),

• trait_coverage the corresponding trait coverage for the given trait combination and site.

Examples

# Compute Coverages using All Trait Combinations
all_combinations <- fb_get_trait_combination_coverage(
    woodiv_site_species, woodiv_traits
)

# Get only combinations of 3 traits
three_traits <- fb_get_trait_combination_coverage(
    woodiv_site_species, woodiv_traits, 3
)

# Combinations of 2, 3, and 4 traits
two_to_four <- fb_get_trait_combination_coverage(
    woodiv_site_species, woodiv_traits, 2:4
)

Compute Trait Coverage For Each Site Weighted by Abundance

Description

Compute trait coverage for all sites, i.e., the proportion of total abundance/presence of species that have traits data compared to all species. This function assumes that all species provided in the trait dataset have all their traits specified (meaning that all species have either known or NA values reported as their traits). NB: this function returns trait coverage using all traits provided in the input species_traits data.frame.

Usage

fb_get_trait_coverage_by_site(site_species, species_traits)

Arguments

Value

A data.frame with n rows (where n is the number of sites) and two columns: site, the site label, and trait_coverage, the percent of total abundance/presence of species that have traits data.

Examples

site_trait_cov <- fb_get_trait_coverage_by_site(
    woodiv_site_species, woodiv_traits
)

head(site_trait_cov)

Create an Rmarkdown Report to Explore User Data

Description

Creates an R Markdown (.Rmd) report from a template to explore and summarize user data in (functional) biogeography through the use of the site-species, the species-traits, and the site-locations objects. Users can modify this report and use the function rmarkdown::render() (or click the Render of the RStudio IDE) to convert this .Rmd in different formats:

• HTML document (output_format = "bookdown::html_document2");

• PDF document (output_format = "bookdown::pdf_document2");

• Word document (output_format = "bookdown::word_document2");

• HTML, PDF and Word documents (output_format = "all").

Usage

fb_make_report(
  path,
  filename = NULL,
  title = NULL,
  author = NULL,
  species_traits,
  site_species,
  site_locations,
  species_categories = NULL,
  overwrite = FALSE,
  open = TRUE
)

Arguments

Details

Note that a copy of user data will be saved as .rds files in ⁠path/funbiogeo/data/⁠ (where path is the directory defined by the user).

Value

No return value.

Examples

if (interactive()) {

# Create temporary folder (optional) ----
temp_path <- tempdir()

# Create report ----
fb_make_report(
  path           = temp_path,
  author         = "Casajus N. and Grenié M.",
  species_traits = woodiv_traits,
  site_species   = woodiv_site_species,
  site_locations = woodiv_locations,
  open           = FALSE
)

# Open Rmd file ----
utils::file.edit(file.path(temp_path, "funbiogeo", "funbiogeo_report.Rmd"))

# Render Rmd file ----
rmarkdown::render(
  input         = file.path(temp_path, "funbiogeo", "funbiogeo_report.Rmd"),
  output_format = "all"
)
}

Map a Single Raster Layer

Description

This is a helper function to plot a map of an environmental raster. The raster is plotted as is, with its given coordinate reference system. The function can provide a background map if the background argument is toggled.

Usage

fb_map_raster(x, background = FALSE, ...)

Arguments

Value

A ggplot object.

Examples

library(ggplot2)

## Load raster ----
tavg <- system.file("extdata", "annual_mean_temp.tif", package = "funbiogeo")
tavg <- terra::rast(tavg)

## Default map ----
fb_map_raster(tavg)

## Map with a background ----
fb_map_raster(tavg, background = TRUE)

## Map with custom theme ----
fb_map_raster(tavg, legend.position = "bottom")

## Advanced customization ----
my_map <- fb_map_raster(tavg) +
  scale_fill_distiller("Temperature", palette = "Spectral") +
  theme(legend.position = "bottom") +
  ggtitle("Mean annual temperature in Western Europe")

my_map

## Map w/o annotation ----
fb_map_raster(tavg) +
  theme_void() +
  theme(legend.position = "none")

Map Arbitrary Site Data

Description

This function helps to map arbitrary site data using the site-locations object and a dataset organized by site. The returned plot is as little customized as possible to let the user choose. The function can provide a basic background map if the background argument is toggled.

Usage

fb_map_site_data(site_locations, site_data, selected_col, background = FALSE)

Arguments

Value

a ggplot object.

Examples

site_rich <- fb_count_species_by_site(woodiv_site_species)

# Map of Species Richness
rich_map <- fb_map_site_data(woodiv_locations, site_rich, "n_species")
rich_map

# Customize the map
rich_map +
  ggplot2::scale_fill_viridis_c("Species Richness")

# Map w/ a background
fb_map_site_data(
  woodiv_locations, site_rich, "n_species", background = TRUE
)

Map Trait Coverage Per Site

Description

Returns a ggplot2 map of sites colored by trait coverage (proportion of species having a known trait value). By default shows one plot for each trait and add an additional facet named "all_traits" considering the trait coverage with all provided traits taken together.

Usage

fb_map_site_traits_completeness(
  site_locations,
  site_species,
  species_traits,
  all_traits = TRUE,
  background = FALSE
)

Arguments

Value

a 'ggplot2' object

Examples

# Map without a background
fb_map_site_traits_completeness(
  woodiv_locations, woodiv_site_species, woodiv_traits
)

# Map with a background
fb_map_site_traits_completeness(
  woodiv_locations, woodiv_site_species, woodiv_traits, background = TRUE
)

Plot Distribution of Trait Coverages across all Sites

Description

Plots the distributions of trait coverage across site, i.e. the proportion of species weighted by abundance with known trait values, for each trait separately and all traits taken together. The trait distributions are ordered from the lowest to the highest average trait coverage (top to bottom). The top always displays a distribution named "all_traits" that contains the distribution of coverage all traits taken together.

Usage

fb_plot_distribution_site_trait_coverage(
  site_species,
  species_traits,
  species_categories = NULL,
  all_traits = TRUE
)

Arguments

Details

Note: this function requires the installation of the ggridges package. If it isn't installed, the function will give an error suggesting installing it through install.packages("ggrdiges").

Value

a 'ggplot2' object

Examples

fb_plot_distribution_site_trait_coverage(woodiv_site_species, woodiv_traits)

Plot Number of Sites by Species

Description

Represent all species in each function of the number of sites they occupy. The species are ordered from the ones that occupy the least number of sites from the ones that occupy the most. The number of site is indicated at the bottom x-axis, while the top x-axis represents the proportion of occupied sites. The left y-axis label species names and their rank by increasing prevalence. The user can supplied a threshold of sites to see how many species occupy more or less than the given proportion of sites.

Usage

fb_plot_number_sites_by_species(
  site_species,
  threshold_sites_proportion = NULL
)

Arguments

Value

a ggplot2 object

Examples

fb_plot_number_sites_by_species(woodiv_site_species)

# Add a vertical cutoff line (40% of sites)
fb_plot_number_sites_by_species(woodiv_site_species, 0.4)

Plot Number of Species per Trait

Description

Display a lollipop graph showing the number and proportion of species with non-NA trait for each trait ranked in decreasing order.

Usage

fb_plot_number_species_by_trait(
  species_traits,
  species_categories = NULL,
  threshold_species_proportion = NULL
)

Arguments

Value

a ggplot2 object

Examples

fb_plot_number_species_by_trait(woodiv_traits)

# Add a vertical cutoff line (12.5% of species)
fb_plot_number_species_by_trait(woodiv_traits, NULL, 1/8)

Plot Number of Traits per Species

Description

Display a graph showing the number (and proportion) of species having at least 0, 1, etc. number of traits. It provides a lollipop graph to examine which number of trait cover a certain proportion of the species. This plot doesn't show which traits are concerned.

Usage

fb_plot_number_traits_by_species(
  species_traits,
  species_categories = NULL,
  threshold_species_proportion = NULL
)

Arguments

Value

a ggplot2 object

Examples

fb_plot_number_traits_by_species(woodiv_traits)

# Add a vertical cutoff line (33% of the species)
fb_plot_number_traits_by_species(
    woodiv_traits, threshold_species_proportion = 1/3
)

Plot Position of Sites in Environmental Space

Description

Plot a figure showing the average environmental space of given sites compared to a full environmental vector. For the sake of simplicity only represents the figure along two environmental axes. The average environmental value are extracted for each site.

Usage

fb_plot_site_environment(
  site_locations,
  environment_raster,
  first_layer = names(environment_raster)[1],
  second_layer = names(environment_raster)[2]
)

Arguments

Value

a ggplot object

Examples

# Import climate rasters
prec <- system.file("extdata", "annual_tot_prec.tif", package = "funbiogeo")
tavg <- system.file("extdata", "annual_mean_temp.tif", package = "funbiogeo")

layers <- terra::rast(c(tavg, prec))

# Make plot (show environmental position of 6 first sites)
fb_plot_site_environment(head(woodiv_locations), layers)

Plot Trait Coverage per Site for each Trait

Description

Display a binary heatmap visualizing the site x traits matrix with colors displaying the proportion of occurring species with known trait values. Traits are ordered from the most to the least known (left to right). Sites are ordered from the ones with highest to lowest overall trait coverage (bottom to top). The site average proportion of species with known trait for each trait (across all sites) is shown in the x-axis labels. An additional column at the very right of the plot named "all_traits" shows a summary considering traits together.

Usage

fb_plot_site_traits_completeness(
  site_species,
  species_traits,
  species_categories = NULL,
  all_traits = TRUE
)

Arguments

Value

a ggplot2 object

Examples

fb_plot_site_traits_completeness(woodiv_site_species, woodiv_traits)

Plot Trait Coverage per Species for each Trait

Description

Display a binary heatmap visualizing the species x traits matrix with colors displaying present and missing traits. Traits are ordered from the most to the least known (left to right). Species are ordered from the ones with most to the ones with least traits (bottom to top). The proportion of species with non-missing traits is shown on the x-axis labels. An additional column at the very right of the plot named "all_traits" shows a summary considering if all other traits are known.

Usage

fb_plot_species_traits_completeness(
  species_traits,
  species_categories = NULL,
  all_traits = TRUE
)

Arguments

Value

a ggplot2 object

Examples

fb_plot_species_traits_completeness(woodiv_traits)

Plot Trait Coverage per Species for each Trait

Description

Display a binary heatmap visualizing the species x traits matrix with colors displaying present and missing traits. Traits are ordered from the most to the least known (left to right). Species are ordered from the ones with most to the ones with least traits (bottom to top). The proportion of species with non-missing traits is shown on the x-axis labels. An additional column at the very right of the plot named "all_traits" shows a summary considering if all other traits are known.

Usage

fb_plot_species_traits_missingness(
  species_traits,
  species_categories = NULL,
  all_traits = TRUE
)

Arguments

Value

a ggplot2 object

Examples

data(woodiv_traits)
fb_plot_species_traits_missingness(woodiv_traits)

Plot Present and Missing Trait Combinations Frequencies

Description

Display a figure of Present/Missing Trait Combinations where one square is represents one trait. The y-axis gives the frequency of the row (as well as its proportions compared to the total number of rows).

Usage

fb_plot_trait_combination_frequencies(
  species_traits,
  species_categories = NULL,
  order_by = c("number", "complete")
)

Arguments

Value

a ggplot2 object

Examples

# Ordered by number by default
fb_plot_trait_combination_frequencies(woodiv_traits)

# Order by present traits
fb_plot_trait_combination_frequencies(woodiv_traits, NULL, "complete")

Plot Trait Correlation Matrix

Description

This functions outputs a ggplot2 figure that outputs a trait correlation matrix. It only works on numerical traits and will output messages if non-numerical traits are found and errors if no numerical traits are found. Internally it uses the stats::cor() function and only works on complete observation (it removes any row that contains any NA). Use the ... argument to pass options to the cor() function.

Usage

fb_plot_trait_correlation(species_traits, species_categories = NULL, ...)

Arguments

Value

a ggplot object

Examples

fb_plot_trait_correlation(woodiv_traits)

# Plot Spearman's correlation
fb_plot_trait_correlation(woodiv_traits, method = "spearman")

Summary Table on Traits (Missingness, Range, etc.)

Description

This function outputs a data.frame that summarises the species by trait table to have many information in a glance. This can then return a data.frame or a nicely formatted knitr::kable() for inclusion in an Rmarkdown document.

Usage

fb_table_trait_summary(species_traits, kable = FALSE)

Arguments

Value

a data.frame with the following columns:

• trait_name: a character column with the trait name as indicated in species_traits

• trait_type: the nature of the trait (numeric, categorical, or ordered)

• number_non_missing: the total number of non-NA trait values

• proportion_non_missing: the proportion of non-NA trait values

• trait_range: for numerical traits, the range of values

• trait_mean_sd: for numerical traits, the mean plus-minus the standard deviation

• number_distinct: for non-numerical traits, the number of categories

• list_distinct: for non-numerical traits, the list of categories

Examples

# Get a data.frame back
fb_table_trait_summary(woodiv_traits)

# Get a kable (to use in Rmd documents)
fb_table_trait_summary(woodiv_traits, TRUE)

Species x Categories of some Mediterranean Conifers

Description

This dataset is derived from the WOODIV database (available at: https://www.nature.com/articles/s41597-021-00873-3). It contains information (classification, endemism, etc.) of the 24 Conifer tree species occurring in Portugal, Spain, France, and Italy (Mediterranean part). This information will be used as a species grouping factor.

This dataset shows the format of the argument species_categories used in several functions of funbiogeo. Note that species names must be the first column and must be named species.

Usage

woodiv_categories

Format

A data.frame with 24 rows (species) and 6 columns (species, family, genus, binomial, endemism, and cultivated).

References

Monnet AC, Cilleros K, Médail F et al. (2021) WOODIV, a database of occurrences, functional traits, and phylogenetic data for all Euro-Mediterranean trees. Scientific Data, 8, 89. DOI: doi:10.1038/s41597-021-00873-3.

Examples

data("woodiv_categories")
class(woodiv_categories)
dim(woodiv_categories)
woodiv_categories[1:6, ]

Sites x Locations of some Mediterranean Conifers

Description

This dataset is derived from the WOODIV database (available at: https://www.nature.com/articles/s41597-021-00873-3). It contains the grid cells of sites (10 km x 10 km horizontal resolution) sampled in Portugal, Spain, France, and Italy (Mediterranean part) for which at least one of the 24 conifer tree species occurs.

This dataset exemplifies the argument site_locations used in several functions of funbiogeo. The variable site corresponds to the site labels (must match the same column in site_species) and the variable country will be used as a site grouping factor.

Usage

woodiv_locations

Format

An sf POLYGON object with 5,366 rows (grid cells) defined in the ETRS89-extended / LAEA Europe Coordinate Reference System (EPSG:3035).

References

Monnet AC, Cilleros K, Médail F et al. (2021) WOODIV, a database of occurrences, functional traits, and phylogenetic data for all Euro-Mediterranean trees. Scientific Data, 8, 89. DOI: doi:10.1038/s41597-021-00873-3.

Examples

data("woodiv_locations")
class(woodiv_locations)
dim(woodiv_locations)
head(woodiv_locations)

Sites x Species of some Mediterranean Conifers

Description

This dataset is derived from the WOODIV database (available at: https://www.nature.com/articles/s41597-021-00873-3). It contains the presence/absence of the 24 Conifer tree species occurring in Portugal, Spain, France, and Italy (Mediterranean part). The presence/absence is available at 5,366 sites (grid cells of 10 x 10 km horizontal resolution). Sites coordinates are provided in site_locations.

This dataset shows the format of the argument site_species used in several functions of funbiogeo. Note that site labels must be the first column and must be named site.

Usage

woodiv_site_species

Format

A data.frame with 5,366 rows (sites) and 25 columns (1 column for site label and 24 for species occurrence).

References

Monnet AC, Cilleros K, Médail F et al. (2021) WOODIV, a database of occurrences, functional traits, and phylogenetic data for all Euro-Mediterranean trees. Scientific Data, 8, 89. DOI: doi:10.1038/s41597-021-00873-3.

Examples

data("woodiv_site_species")
class(woodiv_site_species)
dim(woodiv_site_species)
woodiv_site_species[1:6, 1:6]

Species x Traits of some Mediterranean Conifers

Description

This dataset is derived from the WOODIV database (available at: https://www.nature.com/articles/s41597-021-00873-3). It contains the values of four functional traits for 24 Conifer tree species occurring in Portugal, Spain, France, and Italy (Mediterranean part).

This dataset shows the format of the argument species_traits used in several functions of funbiogeo. Note that species names must be the first column and must be named species.

Usage

woodiv_traits

Format

A data.frame with 24 rows (species) and the following five columns:

species

species name corresponding to the columns of site_species

plant_height

adult plant height (in m)

seed_mass

seed mass (in g)

sla

specific leaf area, i.e. the ratio between leaf area and dry mass (in m^{2}\cdot kg^{-1})

wood_density

wood density, i.e. stem specific density (in kg\cdot dm^{–3})

References

Monnet AC, Cilleros K, Médail F et al. (2021) WOODIV, a database of occurrences, functional traits, and phylogenetic data for all Euro-Mediterranean trees. Scientific Data, 8, 89. DOI: doi:10.1038/s41597-021-00873-3.

Examples

data("woodiv_traits")
class(woodiv_traits)
dim(woodiv_traits)
head(woodiv_traits)