create_template_exposure: Create a Template for Expert-Based Exposure Scoring

Description

The function create_template_exposure generates a template for a semi-quantitative, expert-based scoring of individual exposure components. This template is designed for use within the ecorisk workflow. The user can define the number of exposure components (e.g., magnitude, frequency, trend, and spatial scale) to be included in the scoring process and whether uncertainty should be assessed.

Usage

create_template_exposure(
  pressures,
  n_components = 4,
  uncertainty = TRUE,
  mode_uncertainty = "general",
  probability = FALSE
)

Value

The function returns a data frame containing the names of the pressures and the specified number of exposure components. If uncertainty is assessed, the data frame also includes either one general uncertainty column or one uncertainty column per component, depending on the selected uncertainty assessment mode.

Arguments

pressures: A character vector specifying the names of the pressures to be assessed.
n_components: A positive integer or integer indicating the number of exposure components to be included in the assessment table. Within the ecorisk framework four components are usually scored:magnitude, frequency, trend and spatial scale. The default includes therefore four components.
uncertainty: logical; should uncertainty be assessed? Default is TRUE.
mode_uncertainty: character; if uncertainty is assessed, it can be scored for each exposure component and pressure individually ("component") or as a general uncertainty score for each pressure ("general", default). The default assigns one general uncertainty score per pressure.
probability: logical; for hazardous risks that may have a lower probability of occurring within the assessed future time period, probabilities between 0 and 1 can be assigned to each pressure and considered in the calc_exposure function.

Details

By default, the function creates a template with four exposure components and a general uncertainty scoring column. Within the ecorisk framework, exposure components should be scored on a scale from 1 to 5 (low to high impact), while uncertainty should be scored from 1 to 3 (low to high uncertainty).

The returned data frame can be exported as a CSV or Excel file. Components can be renamed and additional components can be added if necessary. The completed file can then be analyzed using the calc_exposure function.

The default scoring system is as follows:

Exposure components: Scale of 1 to 5 (low to high impact).
Uncertainty: Scale of 1 to 3 (low to high uncertainty).

Examples

Run this code

### Create a full template for three pressures, including all four components
#   and a general uncertainty column:
press <- c("fishing", "temperature increase", "salinity decrease")

exp_tbl <- create_template_exposure(pressures = press)
# --> Export table and re-import after completion or fill in directly in R.

# Rename exposure components
names(exp_tbl)[2:5] <- c("magnitude", "frequency", "trend", "spatial")

# Assign individual scores between 1 (low) and 5 (high impact):
exp_tbl$magnitude <- c(1,5,4)
exp_tbl$frequency <- c(1,5,3)
exp_tbl$trend <- c(1,4,2)
exp_tbl$spatial <- c(1,5,5)

# Assign uncertainty scores from 1 (low) to 3 (high uncertainty):
exp_tbl$uncertainty <- c(1,1,3)


### Create a template for two more hazardous risks with only two components
#   ('magnitude' and 'spatial'), including component-specific uncertainties,
#   and a probability column:
hazard <- c("heat waves", "hurricanes")

exp_tbl <- create_template_exposure(
  pressures = hazard,
  n_components = 2,
  mode_uncertainty = "component",
  probability = TRUE
)

# Rename components and uncertainties
names(exp_tbl)[2:5] <- c("magnitude", "spatial", "uncertainty_magnitude",
                         "uncertainty_spatial")
exp_tbl$magnitude <- c(5,4)
exp_tbl$spatial <- c(5,3)
exp_tbl$uncertainty_magnitude <- c(1,1)
exp_tbl$uncertainty_spatial <- c(3,3)

# Assign probabilities of their occurrence within the assessed future period:
exp_tbl$probability <- c(0.8,0.3)