vulnerability {ecorisk} | R Documentation |
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"
)
Arguments
exposure_results |
a data frame containing the output from |
sensitivity_results |
a data frame containing the output from |
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.
Value
a data frame containing state indicator, pressure, type and the vulnerability and associated uncertainty score.
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
)
### 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
)