
Calculate Observed Species Richness Over Space or Time
Source:R/indicator_wrappers.R
obs_richness_map.Rd
This function calculates observed species richness over a gridded map or as a time series (see 'Details' for more information).
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)
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.
shapefile_crs
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
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)
include_ocean
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.
buffer_dist_km
The distance to buffer around the land if include_ocean is set to "buffered_coast".
force_grid
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.
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.