Calculate Cumulative Species Richness

This function calculates cumulative species richness as a time series (see 'Details' for more information).

Usage

cum_richness_ts(data, ...)

Arguments

data

A data cube object (class 'processed_cube').

...

Arguments passed on to compute_indicator_workflow

ci_type: Type of bootstrap confidence intervals to calculate. (Default: "norm". Select "none" to avoid calculating bootstrap CIs.)
cell_size: Length of grid cell sides, in km. (Default: 10 for country, 100 for continent or world)
level: Spatial level: 'cube', 'continent', 'country', 'world', 'sovereignty', or 'geounit'. (Default: 'cube')
region: The region of interest (e.g., "Europe"). (Default: "Europe")
ne_type: The type of Natural Earth data to download: 'countries', 'map_units', 'sovereignty', or 'tiny_countries'. (Default: "countries")
ne_scale: The scale of Natural Earth data to download: 'small' - 110m, 'medium' - 50m, or 'large' - 10m. (Default: "medium")
output_crs: The CRS you want for your calculated indicator. (Leave blank to let the function choose a default based on grid reference system)
first_year: Exclude data before this year. (Uses all data in the cube by default.)
last_year: Exclude data after this year. (Uses all data in the cube by default.)
spherical_geometry: If set to FALSE, will temporarily disable spherical geometry while the function runs. Should only be used to solve specific issues. (Default is TRUE)
make_valid: Calls st_make_valid() from the sf package. Increases processing time but may help if you are getting polygon errors. (Default is FALSE).
num_bootstrap: Set the number of bootstraps to calculate for generating confidence intervals. (Default: 1000)
crs_unit_convert: Force a particular output CRS even when it has different units than the input CRS. (Default: FALSE)
shapefile_path: Path of an external shapefile to merge into the workflow. For example, if you want to calculate your indicator particular features such as protected areas or wetlands.
invert: Calculate an indicator over the inverse of the shapefile (e.g. if you have a protected areas shapefile this would calculate an indicator over all non protected areas)

Value

An S3 object with the classes 'indicator_ts' and 'cum_richness' containing the calculated indicator values and metadata.

Details

Species richness

Species richness is the total number of species present in a sample (Magurran, 1988). It is a fundamental and commonly used measure of biodiversity, providing a simple and intuitive overview of the status of biodiversity. However, richness is not well suited to measuring biodiversity change over time, as it only decreases when local extinctions occur and thus lags behind abundance for negative trends. While it may act as a leading indicator of alien species invasions, it will not indicate establishment because it ignores abundance. Nor will it necessarily indicate changes in local species composition, which can occur without any change in richness. Although richness is conceptually simple, it can be measured in different ways.

Cumulative richness

Cumulative richness is calculated by adding the newly observed unique species each year to a cumulative sum. This indicator provides an estimation of whether and how many new species are still being discovered in a region. While an influx of alien species could cause an increase in cumulative richness, a fast-rising trend as shown in Fig. 2 is likely an indication that the dataset is not comprehensive and therefore observed richness will provide an underestimate of species richness.

Examples

if (FALSE) { # \dontrun{
cr_ts <- cum_richness_ts(example_cube_1, first_year = 1985)
plot(cr_ts)
} # }