Package 'funbiogeo' reference manual

Title:	Functional Biogeography Analyses
Description:	Helps users with analyses in functional biogeogeography 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.
Authors:	Nicolas Casajus [aut, cre, cph] , Matthias Grenié [aut]
Maintainer:	Nicolas Casajus <[email protected]>
License:	GPL (>= 2)
Version:	0.0.0.9000
Built:	2025-02-14 15:29:14 UTC
Source:	https://github.com/FRBCesab/funbiogeo

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_grid, fun = mean)
fb_aggregate_site_data(site_locations, site_data, agg_grid, fun = mean)

Arguments

`site_locations`	an `sf` object with the spatial geometries of sites. NOTE: the first column should be named `"site"` and indicate site names.
`site_data`	a `matrix` or `data.frame` containing values per sites to aggregate along the provided grid. Can have one or several columns (variables to aggregate). The first column must contain sites names as provided in the example dataset `site_locations`.
`agg_grid`	a `SpatRaster` object (package `terra`). A raster of one single layer, that defines the grid along which to aggregate.
`fun`	the function used to aggregate points values when there are multiple points in one cell. Default is `mean`.

Value

A SpatRaster object with as many layers as columns in site_data.

Examples

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

## Rasterize 3 first species counts ----
fb_aggregate_site_data(
    woodiv_locations, woodiv_site_species[, 1:4], tavg, fun = sum
)
## Import grid ----
tavg <- system.file("extdata", "annual_mean_temp.tif", package = "funbiogeo")
tavg <- terra::rast(tavg)

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

Count Number of Sites Occupied by Species

Description

For each species computes the percentage of sites where the species is present (distribution value higher than 0 and non-NA).

Usage

fb_count_sites_by_species(site_species)
fb_count_sites_by_species(site_species)

Arguments

site_species

a data.frame with sites in rows and species in columns. NOTE: the first column should be named "site" and indicate site names. The other columns should be named according to species names.

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)
site_coverage_by_species <- fb_count_sites_by_species(woodiv_site_species)
head(site_coverage_by_species)

Count Number of Species per Site

Description

For each site computes the 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)
fb_count_species_by_site(site_species)

Arguments

site_species

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)
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

For each trait computes the percentage of species without NA (missing trait values).

Usage

fb_count_species_by_trait(species_traits)
fb_count_species_by_trait(species_traits)

Arguments

species_traits

a data.frame with species in rows and traits as columns. NOTE: The first column should be named "species" and contain species names. The other columns should be named according to trait names.

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)
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 computes the percentage of traits without NA (missing trait values).

Usage

fb_count_traits_by_species(species_traits)
fb_count_traits_by_species(species_traits)

Arguments

species_traits

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)
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.

Usage

fb_cwm(site_species, species_traits)
fb_cwm(site_species, species_traits)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.

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)
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 percentage of present species (distribution value higher than 0 and non-NA) is higher than a threshold.

Usage

fb_filter_sites_by_species_coverage(
  site_species,
  threshold_species_proportion = 0
)
fb_filter_sites_by_species_coverage(
  site_species,
  threshold_species_proportion = 0
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`threshold_species_proportion`	a numeric of length 1 between 0 and 1. The percentage of species coverage threshold.

Value

A subset of site_species with sites covered by X% of species.

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]
# 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

...

Usage

fb_filter_sites_by_trait_coverage(
  site_species,
  species_traits,
  threshold_traits_proportion = 1
)
fb_filter_sites_by_trait_coverage(
  site_species,
  species_traits,
  threshold_traits_proportion = 1
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`threshold_traits_proportion`	a numeric of length 1 between 0 and 1. The percentage trait coverage threshold

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 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 threshold.

Usage

fb_filter_species_by_site_coverage(
  site_species,
  threshold_sites_proportion = 0
)
fb_filter_species_by_site_coverage(
  site_species,
  threshold_sites_proportion = 0
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`threshold_sites_proportion`	a numeric of length 1 between 0 and 1. The percentage of sites coverage threshold.

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 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 percentage of traits without NA (missing trait values) is higher than a threshold.

Usage

fb_filter_species_by_trait_coverage(
  species_traits,
  threshold_traits_proportion = 0
)
fb_filter_species_by_trait_coverage(
  species_traits,
  threshold_traits_proportion = 0
)

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`threshold_traits_proportion`	a numeric of length 1 between 0 and 1. The percentage of traits coverage threshold.

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 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 threshold.

Usage

fb_filter_traits_by_species_coverage(
  species_traits,
  threshold_species_proportion = 0
)
fb_filter_traits_by_species_coverage(
  species_traits,
  threshold_species_proportion = 0
)

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`threshold_species_proportion`	`numeric(1)` [default = `NULL`] between 0 and 1. The percentage of species coverage threshold.

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)
# 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 that the coordinates are given in WGS84 (longitude vs. latitude). 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
)
fb_format_site_locations(
  data,
  site,
  longitude,
  latitude,
  crs = sf::st_crs(4326),
  na_rm = FALSE
)

Arguments

`data`	a `data.frame` in a long format (see example).
`site`	a `character` of length 1. Name of the column with site labels.
`longitude`	a `character` of length 1. Name of the column with longitude. The function assumes coordinates are WGS84 (EPSG:4326).
`latitude`	a `character` of length 1. Name of the column with latitude. The function assumes coordinates are WGS84 (EPSG:4326).
`crs`	a `character` of length 1 or an object of class `crs`. Coordinate Reference System (CRS) of the specified coordinates. The CRS should be a valid CRS in R. It can either be a character like `"+proj=longlat +datum=WGS84 +no_defs"` or as specified using `sf::st_crs()` like `sf::st_crs(4326)` the default value.
`na_rm`	a logical value. If `TRUE` remove sites with incomplete coordinates. Default is `FALSE`.

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

Arguments

`data`	a `data.frame` in a long format (see example).
`site`	a `character` of length 1. Name of the column with site labels.
`species`	a `character` of length 1. Name of the column with species names.
`value`	a `character` of length 1. Name of the column with species occurrence/abundance.
`na_to_zero`	a logical value. If `TRUE` (default) `NA` are replaced by `0`.

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]
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 data.frame 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)
fb_format_species_categories(data, species, category)

Arguments

`data`	a `data.frame` in a long format (see example).
`species`	a `character` of length 1. Name of the column with species names.
`category`	a `character` of length 1. Name of the column with species category.

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, ]
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 data.frame 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)
fb_format_species_traits(data, species, traits)

Arguments

`data`	a `data.frame` in a long format (see example).
`species`	a `character` of length 1. Name of the column with species names.
`traits`	a `character` of length >= 1. Name(s) of trait column(s).

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)
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 percentage 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
)
fb_get_all_trait_coverages_by_site(
  site_species,
  species_traits,
  all_traits = TRUE
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

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)
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

Extract Raster Values at Location of Sites

Usage

fb_get_environment(site_locations, environment_raster)
fb_get_environment(site_locations, environment_raster)

Arguments

`site_locations`	an `sf` object with the spatial geometries of sites. NOTE: the first column should be named `"site"` and indicate site names.
`environment_raster`	a `SpatRaster` object (package `terra`). A single or multi-layers environmental raster.

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)
## 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 combinations. If not provided, consider all possible trait combinations. 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
)
fb_get_trait_combination_coverage(
  site_species,
  species_traits,
  comb_size = NULL
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`comb_size`	an integer vector defining one or more sizes of combinations (default: `NULL`)

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 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 percentage 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). 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)
fb_get_trait_coverage_by_site(site_species, species_traits)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.

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)
site_trait_cov <- fb_get_trait_coverage_by_site(
    woodiv_site_species, woodiv_traits
)

head(site_trait_cov)

Create an R Markdown Report on Site/Species/Trait Coverage with Plots

Description

Creates an R Markdown (.Rmd) report from a template to explore and summarize users data. User can modify this report and use the function rmarkdown::render() 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_name,
  site_species_name,
  site_locations_name,
  overwrite = FALSE,
  open = TRUE
)
fb_make_report(
  path = ".",
  filename = NULL,
  title = NULL,
  author = NULL,
  species_traits_name,
  site_species_name,
  site_locations_name,
  overwrite = FALSE,
  open = TRUE
)

Arguments

`path`	a `character` of length 1. The directory in which the `.Rmd` file will be created. This directory must exist.
`filename`	a `character` of length 1. The name of the `.Rmd` file to be created. If `NULL` (default) the `.Rmd` file will be named `funbiogeo_report.Rmd`.
`title`	a `character` of length 1. The title of the report. If `NULL` (default) the title will be `⁠funbiogeo Report⁠`.
`author`	a `character` of length 1. The author(s) of the report. If `NULL` (default) no author will be added.
`species_traits_name`	a `character` of length 1. The name of the species x traits dataset (not the object). Note that before rendering the report this dataset must be loaded.
`site_species_name`	a `character` of length 1. The name of the sites x species dataset (not the object). Note that before rendering the report this dataset must be loaded.
`site_locations_name`	a `character` of length 1. The name of the sites x locations dataset (not the object). Note that before rendering the report this dataset must be loaded.
`overwrite`	a logical. If this file is already present and `overwrite = TRUE`, it will be erased and replaced by the template. Default is `FALSE`.
`open`	a logical. If `TRUE` (default), this file will be opened on the text editor.

Value

No return value.

Examples

# Create temporary folder
temp_path <- tempdir()

# Create report
fb_make_report(
  path                = temp_path, 
  author              = "Casajus N. and Grenié M.",
  species_traits_name = "woodiv_traits",
  site_species_name   = "woodiv_site_species",
  site_locations_name = "woodiv_locations",
  open = FALSE
)

## Not run: 
# Open Rmd file ----
utils::file.edit(file.path(temp_path, "funbiogeo_report.Rmd"))

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

## End(Not run)
# Create temporary folder
temp_path <- tempdir()

# Create report
fb_make_report(
  path                = temp_path, 
  author              = "Casajus N. and Grenié M.",
  species_traits_name = "woodiv_traits",
  site_species_name   = "woodiv_site_species",
  site_locations_name = "woodiv_locations",
  open = FALSE
)

## Not run: 
# Open Rmd file ----
utils::file.edit(file.path(temp_path, "funbiogeo_report.Rmd"))

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

## End(Not run)

Map a Single Raster Layer

Description

Map a Single Raster Layer

Usage

fb_map_raster(x, ...)
fb_map_raster(x, ...)

Arguments

`x`	a `SpatRaster` object (package `terra`). A raster of one single layer
`...`	other parameters passed to `theme()`

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 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")
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 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

From the site-locations data and a dataset organized by site, plot a map of this information. The returned plot is as little customized as possible to let the user do the customization.

Usage

fb_map_site_data(site_locations, site_data, selected_col)
fb_map_site_data(site_locations, site_data, selected_col)

Arguments

`site_locations`	an `sf` object with the spatial geometries of sites. NOTE: the first column should be named `"site"` and indicate site names.
`site_data`	`data.frame()` of additional site information containing the column `"site"` to merge with the `site_locations` argument
`selected_col`	`character(1)` name of the column to plot

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")
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 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 traits taken together.

Usage

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

Arguments

`site_locations`	an `sf` object with the spatial geometries of sites. NOTE: the first column should be named `"site"` and indicate site names.
`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

Value

a 'ggplot2' object

Examples

fb_map_site_traits_completeness(
    woodiv_locations, woodiv_site_species, woodiv_traits
)
fb_map_site_traits_completeness(
    woodiv_locations, woodiv_site_species, woodiv_traits
)

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

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

Value

a 'ggplot2' object

Examples

fb_plot_distribution_site_trait_coverage(woodiv_site_species, woodiv_traits)

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
)
fb_plot_number_sites_by_species(
  site_species,
  threshold_sites_proportion = NULL
)

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`threshold_sites_proportion`	a numeric of length 1 between 0 and 1. The percentage of sites coverage threshold.

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`threshold_species_proportion`	`numeric(1)` [default = `NULL`] between 0 and 1. The percentage of species coverage threshold.

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)

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`threshold_species_proportion`	`numeric(1)` [default = `NULL`] between 0 and 1. The percentage of species coverage threshold.

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
)

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

Arguments

`site_locations`	an `sf` object with the spatial geometries of sites. NOTE: the first column should be named `"site"` and indicate site names.
`environment_raster`	a `SpatRaster` object (package `terra`). A single or multi-layers environmental raster.
`first_layer`	`character(1)` the name of the first layer to use, by default uses the first layer of `environment_raster`
`second_layer`	`character(1)` the name of the second layer to use, by default uses the second layer of `environment_raster`

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

Arguments

`site_species`	a `data.frame` with sites in rows and species in columns. NOTE: the first column should be named `"site"` and indicate site names. The other columns should be named according to species names.
`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

Value

a ggplot2 object

Examples

fb_plot_site_traits_completeness(woodiv_site_species, woodiv_traits)

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

Value

a ggplot2 object

Examples

fb_plot_species_traits_completeness(woodiv_traits)

fb_plot_species_traits_completeness(woodiv_traits)

Plot Trait Coverage per Species for each Trait

Description

Usage

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	2-columns `data.frame` giving species categories `NULL` by default, with the first column describing the species name, and the second column giving their corresponding categories
`all_traits`	a logical (default = `TRUE`) which tell if the coverage considering all provided traits should be provided in an additional column `all_traits`

Value

a ggplot2 object

Examples

data(woodiv_traits)
fb_plot_species_traits_missingness(woodiv_traits)

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`order_by`	`character(1)` either `"number"` or `⁠"complete⁠` If `"number"` order rows by frequency so that most frequent rows are at the bottom. Otherwise order rows to order table by the number of non-missing traits then by the frequency of combinations

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")

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

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`species_categories`	(default = `NULL`) 2-columns `data.frame` giving species categories, with the first column describing the species name, and the second column giving their corresponding categories
`...`	Additional options passed to `stats::cor()`

Value

a ggplot object

Examples

fb_plot_trait_correlation(woodiv_traits)

# Plot Spearman's correlation
fb_plot_trait_correlation(woodiv_traits, method = "spearman")
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)
fb_table_trait_summary(species_traits, kable = FALSE)

Arguments

`species_traits`	a `data.frame` with species in rows and traits as columns. NOTE: The first column should be named `"species"` and contain species names. The other columns should be named according to trait names.
`kable`	`TRUE` or `FALSE` Should function returns a `knitr::kable()`? defaults `FALSE`

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)
# 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
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: 10.1038/s41597-021-00873-3

Examples

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

Examples

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

Examples

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

Examples

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

Package 'funbiogeo'

Help Index

Aggregate Site Data Along Coarser Spatial Grid

Description

Usage

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Value

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Count Number of Sites Occupied by Species

Description

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Value

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Count Number of Species per Site

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Value

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Count Number of Species for Each Trait

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Value

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Compute Number of Known Trait(s) per Species

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Value

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Compute community-weighted means (CWM) of trait values

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Value

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Filter sites with a given species coverage threshold

Description

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Value

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Filter sites with a given trait coverage threshold

Description

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Value

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Filter species with a given sites coverage threshold

Description

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Value

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Filter species with a given traits coverage threshold

Description

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Value

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Filter traits with a given species coverage threshold

Description

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Value

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Extract site x locations information from long format data

Description

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Value

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Extract site x species information from long format data

Description

Usage

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Value

Examples