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Calculate Cumulative Species Richness

Source: R/indicator_wrappers.R
cum_richness_ts.Rd

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

cell_size

(Optional) Length of grid cell sides, in km or degrees. Only used for maps and for time series that require a grid.

  • "grid" (default): use the native resolution of the cube. If this would produce more than about 1 million grid cells over the study area (for degree-based cubes: if the resolution is finer than 1 degree for 'world' or 'continent', or finer than 0.1 degrees otherwise), you are asked to confirm in an interactive session, and the function stops with an error in a non-interactive session.

  • "auto": determined automatically. For km-based grids it depends on the area of the study region: 100 km for areas of at least 1 million sq km, 10 km for at least 10,000 sq km, 1 km for at least 100 sq km, and 0.1 km for smaller areas. For degree-based grids it is 1 degree for 'world' or 'continent' and 0.1 degrees otherwise. The automatic size is never smaller than the cube's resolution.

  • A number (in the units of the cube's resolution, i.e. km or degrees), or for km-based grids a string such as "10km" or "500m".

A manually selected cell size must be a whole number multiple of the cube's resolution.

level

(Optional) Spatial level: 'cube', 'continent', 'country', 'world', 'sovereignty', or 'geounit'. (Default: 'cube')

region

(Optional) The region of interest (e.g., "Denmark"). Ignored if level is 'cube' or 'world'. (Default: "Europe")

ne_type

(Optional) The type of Natural Earth data to download: 'countries', 'map_units', 'sovereignty', or 'tiny_countries'. This parameter is ignored if level is set to 'cube' or 'world'. (Default: "countries")

ne_scale

(Optional) The scale of Natural Earth data to download: 'small' - 110m, 'medium' - 50m, or 'large' - 10m. (Default: "medium")

output_crs

(Optional) The CRS you want for your calculated indicator. (Leave blank to let the function choose a default based on grid reference system.)

first_year

(Optional) Exclude data before this year. (Uses all data in the cube by default.)

last_year

(Optional) Exclude data after this year. (Uses all data in the cube by default.)

spherical_geometry

(Optional) 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

(Optional) Calls st_make_valid() from the sf package after creating the grid. Increases processing time but may help if you are getting polygon errors. (Default is FALSE).

shapefile_path

(optional) Path of an external shapefile to merge into the workflow. For example, if you want to calculate your indicator for particular features such as protected areas or wetlands.

shapefile_crs

(Optional) CRS of a .wkt shapefile. If your shapefile is .wkt and you do NOT use this parameter, the CRS will be assumed to be EPSG:4326 and the coordinates will be read in as lat/long. If your shape is NOT a .wkt the CRS will be determined automatically.

invert

(optional) 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 within your cube). Default is FALSE.

include_land

(Optional) Include occurrences which fall within the land area. Default is TRUE. Note that this is purely a geographic filter, and does not filter based on whether the occurrence is actually terrestrial. Grid cells which fall partially on land and partially on ocean will be included even if include_land is FALSE. To exclude terrestrial and/or freshwater taxa, you must manually filter your data cube before calculating your indicator.

include_ocean

(Optional) Include occurrences which fall outside the land area. Default is TRUE. Set as "buffered_coast" to include a set buffer size around the land area rather than the entire ocean area. Note that this is purely a geographic filter, and does not filter based on whether the occurrence is actually marine. Grid cells which fall partially on land and partially on ocean will be included even if include_ocean is FALSE. To exclude marine taxa, you must manually filter your data cube before calculating your indicator.

buffer_dist_km

(Optional) The distance to buffer around the land if include_ocean is set to "buffered_coast". Default is 50 km.

force_grid

(Optional) Forces the calculation of a grid even if this would not normally be part of the pipeline, i.e. for time series. A grid is needed for time series of area-based rarity, Hill diversity and relative occupancy (and for completeness with gridded_average = TRUE). This is switched on automatically for these indicators: the wrappers area_rarity_ts(), hill0_ts(), hill1_ts() and hill2_ts() already set force_grid = TRUE, so do not pass it to them. (Default: FALSE)

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 is likely an indication that the dataset is not comprehensive and therefore observed richness will provide an underestimate of species richness.

References

Magurran, A. E. (1988). Ecological Diversity and Its Measurement. Princeton University Press.

See also

compute_indicator_workflow(), add_ci()

Examples

# \donttest{
cr_ts <- cum_richness_ts(example_cube_1, first_year = 1985)
plot(cr_ts)

# }