Calculate Occupancy Turnover

This function calculates occupancy turnover as a time series (see 'Details' for more information).

Usage

occ_turnover_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_ts' and 'occ_turnover' containing the calculated indicator values and metadata.

Details

Occupancy turnover measures the change in species composition over time, reflecting the rate at which species appear or disappear from a given area. It provides insights into the dynamic nature of ecological communities, highlighting shifts in species distributions and potential environmental changes. High turnover rates may indicate rapid community restructuring, potentially driven by factors such as habitat alteration, climate change, or invasive species. Analyzing occupancy turnover can be crucial for understanding ecosystem stability, identifying areas of conservation concern, and assessing the effectiveness of management strategies.

Occupancy turnover can be calculated in different ways, but here we use the Jaccard dissimilarity index (Jaccard, 1901) to measure the similarity between two sets of species occurrences. The Jaccard index is calculated as:

$$ J = (b + c) / (a + b + c) $$

where a is the number of species present in both time periods, b is the number of species present only in the first time period, and c is the number of species present only in the second time period. The index ranges from 0 (no turnover) to 1 (complete turnover).

References

Jaccard, P. (1901). Étude de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Société Vaudoise des Science Naturelles, 37(142), 547-579.

Examples

ot_ts <- occ_turnover_ts(example_cube_1, first_year = 1985)
#> Warning: Bootstrapped confidence intervals cannot be calculated for the chosen indicator.
plot(ot_ts)
#> Warning: Removed 1 row containing non-finite outside the scale range (`stat_smooth()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).