Calculate Observed Species Richness Over Space or Time

This function calculates observed species richness over a gridded map or as a time series (see 'Details' for more information).

Usage

obs_richness_map(data, ...)

obs_richness_ts(data, ...)

Arguments

data

A data cube object (class 'processed_cube').

...

Arguments passed on to compute_indicator_workflow

ci_type: (Optional) Type of bootstrap confidence intervals to calculate. (Default: "norm"). Select "none" to avoid calculating bootstrap CIs.
cell_size: (Optional) Length of grid cell sides, in km or degrees. If NULL, this will be automatically determined according to the geographical level selected. This is 100 km or 1 degree for 'continent' or 'world', 10 km or (for a degree-based CRS) the native resolution of the cube for 'country', 'sovereignty' or 'geounit'. If level is set to 'cube', cell size will be the native resolution of the cube for a degree-based CRS, or for a km-based CRS, the cell size will be determined by the area of the cube: 100 km for cubes larger than 1 million sq km, 10 km for cubes between 10 thousand and 1 million sq km, 1 km for cubes between 100 and 10 thousand sq km, and 0.1 km for cubes smaller than 100 sq km. (Default: NULL)
level: (Optional) Spatial level: 'cube', 'continent', 'country', 'world', 'sovereignty', or 'geounit'. (Default: 'cube')
region: (Optional) The region of interest (e.g., "Europe"). This parameter is ignored if level is set to 'cube' or 'world'. (Default: NULL)
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).
num_bootstrap: (Optional) Set the number of bootstraps to calculate for generating confidence intervals. (Default: 100)
shapefile_path: (optional) 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.
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 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, e.g. for time series. This setting is required for the calculation of rarity, and is turned on by the ab_rarity_ts and area_rarity_ts wrappers. (Default: FALSE)

Value

An S3 object with the classes 'indicator_map' or 'indicator_ts' and 'obs_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.

Observed richness

Observed richness is calculated by summing the number of unique species observed for each year or each cell. Observed richness is highly dependent on the comprehensiveness of the dataset it is being applied to. If some regions are more intensively, carefully or systematically sampled than others, this will likely reflect as higher observed richness. Observed richness also depends on the relative abundance and spatial aggregation of each species, with less abundant and less aggregated species less likely to be discovered during surveys (Hillebrand et al., 2018), as well as the detectability of each species.

Functions

obs_richness_map():
obs_richness_ts():

References

Hillebrand, H., Blasius, B., Borer, E. T., Chase, J. M., Downing, J. A., Eriksson, B. K., Filstrup, C. T., Harpole, W. S., Hodapp, D., Larsen, S., Lewandowska, A. M., Seabloom, E. W., Van de Waal, D. B., & Ryabov, A. B. (2018). Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. Journal of Applied Ecology, 55(1), 169-184.

Examples

if (FALSE) { # \dontrun{
or_map <- obs_richness_map(example_cube_1, level = "country",
                           region = "Denmark")
plot(or_map)
} # }
if (FALSE) { # \dontrun{
or_ts <- obs_richness_ts(example_cube_1, first_year = 1985)
plot(or_ts)
} # }