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Get coral habitat suitability data

get_coral_habitat.Rd

This function extracts coral habitat suitability and creates presence (1) or absence (0) grids for three groups of deep-water coral species: antipatharia, cold water corals, and octocorals.

Usage

get_coral_habitat(
  spatial_grid = NULL,
  raw = FALSE,
  antipatharia_threshold = 22,
  octocoral_threshold = 2,
  antimeridian = NULL
)

Arguments

spatial_grid

`sf` or `terra::rast()` grid, e.g. created using `get_grid()`. Alternatively, if raw data is required, an `sf` polygon can be provided, e.g. created using `get_boundary()`, and set `raw = TRUE`.

raw

`logical` if TRUE, `spatial_grid` should be an `sf` polygon, and the raw data in that polygon(s) will be returned

antipatharia_threshold

`numeric` between 0 and 100; the threshold value for habitat suitability for antipatharia corals to be considered present (default is 22, as defined in Yesson et al., 2017)

octocoral_threshold

`integer` between 0 and 7; the threshold value for how many species (out of 7) should be predicted present in an area for octocorals to be considered present (default is 2)

antimeridian

Does `spatial_grid` span the antimeridian? If so, this should be set to `TRUE`, otherwise set to `FALSE`. If set to `NULL` (default) the function will try to check if `spatial_grid` spans the antimeridian and set this appropriately.

Value

If an `area_polygon` is supplied, a raster stack of coral habitat suitability data is returned; note this is the raw habitat suitability/ no. of species values. If a `spatial_grid` is supplied, a raster stack or `sf` of gridded coral habitat presence/ absence data is returned, depending on `spatial_grid` format.

Details

Habitat suitability data are from global Maxent species distribution models for the following species groups:

  • Antipatharia: The global extent of black coral habiatat suitability modelled using Maxent. The antipatharia habitat suitability is converted to a presence/absence map "by choosing a threshold value of habitat suitability based on the maximum sum of sensitivity and specificity (threshold mss = 0.23)" (Yesson et al. 2017). This threshold can be altered via the function input. Data from [Yesson et al. 2017](https://doi.org/10.1016/j.dsr2.2015.12.004).

  • Cold water corals: The global habitat suitability for five species of Scleractinia modelled using Maxent. Presence defined using Maxent values above the lowest 10 percent of values. Data from [Davies and Guinotte 2011](https://doi.org/10.1371/journal.pone.0018483).

  • Octocorals: The global habitat suitability modelled for 7 species of cold-water octocoral found deeper than 50m. Data from and described in [Yesson et al. 2012](https://doi.org/10.1111/j.1365-2699.2011.02681.x): "A consensus/summary map incorporating all seven octocoral suborders was constructed by generating binary presence/absence maps from the model outputs indicating areas of high suitability using a score threshold that maximized the sum of the specificity and sensitivity based on the validation data (Carroll, 2010). These binary layers were summed to generate a layer containing the number of octocoral suborders predicted to be present per cell."

Examples

# Get EEZ data first
bermuda_eez <- get_boundary(name = "Bermuda")
# Get raw coral habitat data
coral_habitat <- get_coral_habitat(spatial_grid = bermuda_eez, raw = TRUE)
terra::plot(coral_habitat)


# Get gridded coral habitat data
bermuda_grid <- get_grid(boundary = bermuda_eez, 
  crs = '+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs',
  resolution = 10000)
  
bermuda_coral_gridded <- get_coral_habitat(spatial_grid = bermuda_grid)

terra::plot(bermuda_coral_gridded)