ecorisk (version 0.2.1)

vulnerability: Calculate Vulnerability Scores Using Expert-Based or Model-Derived Overall Exposure and Sensitivity (Including Adaptive Capacity) Scores

Description

This function calculates the state indicator ~ pressures ~ type specific vulnerability, from exposure scores and sensitivity scores. The function can either be used with the output from calc_exposure or model_exposure and calc_sensitivity or model_sensitivity.

Usage

vulnerability(
  exposure_results,
  sensitivity_results,
  method_vulnerability = "mean",
  method_uncertainty = "mean"
)

Value

a data frame containing state indicator, pressure, type and the vulnerability and associated uncertainty score.

Arguments

exposure_results

a data frame containing the output from calc_exposure or model_exposure.

sensitivity_results

a data frame containing the output from calc_sensitivity or model_sensitivity.

method_vulnerability

a character string specifying the method for aggregating the trait based vulnerabilities, available are mean (default), median, maximum, and minimum.

method_uncertainty

a character string specifying the method for the aggregation of the uncertainty scores from exposure and sensitivity. Available are mean (default), median, maximum, and minimum.

Details

For expert scores the following equation is applied

• (sensitivity + adaptive capacity) + exposure,

or in case of negative sensitivity values:

• (sensitivity + adaptive capacity) - exposure.

Trait based sensitivity and adaptive capacity scores will be assessed individually and then aggregated to one vulnerability score per state indicator and pressure combination. The aggregation method can be chosen with the method_vulnerability argument. For modelling scores sensitivity and exposure scores are summed up. If the exposure trend and the sensitivity score have the same direction, e.g. a decreasing trend in exposure and a negative sensitivity score, then the vulnerability effect is assigned as positive. If they have opposing directions, e.g. an increasing exposure, while sensitivity is negative, then the vulnerability is negative. Vulnerability scores can range only from -10 to 10, aligning with the ecorisk framework.

See Also

calc_exposure, calc_sensitivity, model_exposure, model_sensitivity, status, risk

Examples

# Using demo output data from the calc_exposure() and calc_sensitivity()
# functions:
vulnerability(
  exposure_results = ex_output_calc_exposure,
  sensitivity_results = ex_output_calc_sensitivity
)

# \donttest{
  ### Demo Expert-Based Pathway
  # - using the example scoring datasets 'ex_expert_exposure',
  #   and 'ex_expert_sensitivity'

  # Calculate (mean) exposure score:
  exp_expert <- calc_exposure(
    pressures = ex_expert_exposure$pressure,
    components = ex_expert_exposure[ ,2:5],
    uncertainty = ex_expert_exposure[ ,6:9],
    method = "mean" # default
  )
  # Calculate (mean) sensitivity (and adaptive capacity) score:
  sens_ac_expert <- calc_sensitivity(
    indicators = ex_expert_sensitivity$indicator,
    pressures = ex_expert_sensitivity$pressure,
    type = ex_expert_sensitivity$type,
    sensitivity_traits = ex_expert_sensitivity[ ,4:8],
    adaptive_capacities = ex_expert_sensitivity[ ,9:13],
    uncertainty_sens = ex_expert_sensitivity[ ,14:18],
    uncertainty_ac = ex_expert_sensitivity[ ,19:23],
    method = "mean"
  )
  # Calculate vulnerability using the mean (default):
  vulnerability(
    exposure_results = exp_expert,
    sensitivity_results = sens_ac_expert
  )
  # Calculate vulnerability using the median and maximum:
  vulnerability(
    exposure_results = exp_expert,
    sensitivity_results = sens_ac_expert,
    method_vulnerability = "median",
    method_uncertainty = "maximum"
  )


  ### Demo Model-Based Pathway
  # - using the demo time series 'pressure_ts_baltic' and 'indicator_ts_baltic'

  # Model exposure score:
  exp_model <- model_exposure(
    pressure_time_series = pressure_ts_baltic,
    base_years = c(start = 1984, end = 1994),
    current_years = c(start = 2010, end = 2016)
  )

  # Model sensitivity score:
  sens_ac_model <- model_sensitivity(
    indicator_time_series = indicator_ts_baltic,
    pressure_time_series = pressure_ts_baltic,
    current_years = c(start = 2010, end = 2016)
  )
  # Add manually adaptive capacity scores (otherwise zero):
  sens_ac_model$adaptive_capacity <- c(rep(1, 8), rep(-1, 8))

  # Calculate vulnerability using the mean (default):
  vulnerability(
    exposure_results = exp_model,
    sensitivity_results = sens_ac_model
  )

# }