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Compute species impact indicator

Source: R/species_impact.R
compute_impact_per_species.Rd

Combines occurrences cube and impact data using the given method (e.g., mean, max) to compute the impact indicator per species. Optionally computes bootstrap confidence intervals for the indicator grouped by year and species.

Interval calculation is currently not implemented!

Usage

compute_impact_per_species(
  cube,
  impact_data = NULL,
  method = NULL,
  trans = 1,
  ci_type = "none",
  confidence_level = 0.95,
  boot_args = list(samples = 1000, seed = NA),
  ci_args = list(no_bias = TRUE),
  col_category = NULL,
  col_species = NULL,
  col_mechanism = NULL,
  region = NULL
)

Arguments

cube

A data cube object (class 'processed_cube' or 'sim_cube', processed from b3gbi::process_cube()) or a dataframe (cf. $data slot of 'processed_cube' or 'sim_cube') or an impact cube (class 'impact_cube' from create_impact_cube_data()).

impact_data

A dataframe of species impact which contains columns of impact_category, scientific_name and impact_mechanism. Ignored if cube is already an 'impact_cube'.

method

A method of computing the indicator. The method used in the aggregation of within-species impact. The method can be:

  • "max": The maximum method assigns a species the maximum impact across all records of the species. It is best for precautionary approaches. However, it can overestimate the impact of a species if the highest impact requires rare or specific conditions.

  • "mean": Assigns a species the mean impact across all its impact records. This method computes the expected impact of the species and is adequate when many impact records are available.

  • "max_mech": Assigns a species the summation of the maximum impact per mechanism. This assumes species with multiple mechanisms of impact have higher potential to cause impact.

trans

Numeric: 1 (default), 2 or 3. The method of transformation to convert the EICAT categories c("MC", "MN", "MO", "MR", "MV") to numerical values:

  • 1: converts the categories to c(0, 1, 2, 3, 4)

  • 2: converts the categories to c(1, 2, 3, 4, 5)

  • 3: converts the categories to c(1, 10, 100, 1000, 10000)

ci_type

A character string specifying the type of confidence intervals to compute. Options include:

  • "perc": Percentile intervals (default).

  • "bca": Bias-corrected and accelerated intervals.

  • "norm": Normal approximation intervals.

  • "basic": Basic bootstrap intervals.

  • "none": No confidence intervals calculated.

confidence_level

The confidence level for the calculated intervals. Default is 0.95 (95% confidence level).

boot_args

(Optional) Named list of additional arguments passed to dubicube::bootstrap_cube(). Default: list(samples = 1000, seed = NA).

ci_args

(Optional) Named list of additional arguments passed to dubicube::calculate_bootstrap_ci(). Default: list(no_bias = TRUE).

col_category

The name of the column containing the impact categories. The first two letters of each category must be an EICAT short name (e.g., "MC - Minimal concern").

col_species

The name of the column containing species names.

col_mechanism

The name of the column containing mechanisms of impact.

region

The shape file of the specific region to calculate the indicator on. If NULL (default), the indicator is calculated for all cells in the cube.

Value

A list of class 'species_impact', with the following components:

  • method: Method used in computing the indicator.

  • num_species: Number of species in the indicator.

  • names_species: Names of species in the indicator.

  • species_impact: A dataframe containing impact per species and year.

See also

Other Indicator function: compute_impact_indicator(), compute_impact_per_site()

Examples

acacia_cube <- taxa_cube(
  taxa = taxa_Acacia,
  region = southAfrica_sf,
  res = 0.25,
  first_year = 2010
)

# Without confidence intervals
speciesImpact <- compute_impact_per_species(
  cube = acacia_cube,
  impact_data = eicat_acacia,
  method = "mean",
  trans = 1
)