create_template_sensitivity: Create a Template for Expert-Based Sensitivity and Adaptive Capacity Scoring

Description

The function crt_sensitivity creates a template for semi-quantitative, expert-based sensitivity and, optionally, adaptive capacity scoring. The template allows for assessing sensitivity and adaptive capacity using either a general score for each state indicator-pressure combination or a trait-based approach using life history traits. The latter is particularly useful when state indicators represent individual species and detailed biological information is available.

Usage

create_template_sensitivity(
  indicators,
  pressures,
  type = "direct",
  n_sensitivity_traits = 1,
  adaptive_capacity = TRUE,
  mode_adaptive_capacity = "general",
  uncertainty = TRUE,
  mode_uncertainty = "general"
)

Value

A data frame where each row represents a state indicator-pressure-type combination, containing the specified sensitivity traits, adaptive capacity, and uncertainty columns (if selected).

If adaptive capacity and uncertainty are assessed, the data frame includes either one general column or one column per trait, depending on the settings.
If using trait-based scoring, the data frame includes trait-specific sensitivity, adaptive capacity, and uncertainty columns, which can be renamed as needed.

Arguments

indicators: A character vector specifying the names of the state indicators to assess.
pressures: A character vector specifying the names of the pressures to assess.
type: A character vector defining the type(s) of influence, such as "direct", "indirect" or "direct_indirect". The default is "direct".
n_sensitivity_traits: A positive integer specifying the number of traits used to assess sensitivity. The default is 1, meaning that one general sensitivity score per state indicator-pressure-type combination is provided.
adaptive_capacity: logical; should adaptive capacity be assessed? Default is TRUE.
mode_adaptive_capacity: A character vector specifying whether adaptive capacity should be assessed for each trait individually ("trait") or as a general score ("general", default). Note: This parameter is only relevant when n_sensitivity_traits > 1.
uncertainty: logical; should uncertainty be assessed? Default is TRUE. Note: Uncertainty is only added for components included in the assessment. If adaptive capacity (adaptive_capacity = FALSE) is not included, no uncertainty scoring will be applied to it.
mode_uncertainty: A character vector specifying whether uncertainty should be assessed for each trait individually ("trait") or as a general score ("general").

Details

For each state indicator-pressure combination, different types of influence can be assessed. The type of influence describes whether the pressure acts directly, indirectly, or as a combination of both, which is important for identifying impact pathways and potential management measures.

Within the ecorisk framework, it is recommended to use negative scores (-1 to -5) to indicate negative impacts (low to high severity) and positive scores (1 to 5) for positive effects of a pressure on an indicator. If an indicator is not sensitive to a pressure, the score should be 0. Adaptive capacity is scored from -1 (no adaptive capacity) to 1 (high adaptive capacity).

To improve the reliability of the scoring, uncertainty should also be assessed. Uncertainty can be scored for each trait individually or as a general score and should be rated on a scale from 1 to 3 (low to high uncertainty).

The returned data frame can be exported as a CSV or Excel file. Column names can be modified as needed. The completed file can be analyzed using the calc_sensitivity function.

Depending on the settings, the data frame will include:

A single "sensitivity" column, if using general scoring.
Multiple trait-specific sensitivity columns (e.g., "sensitivity_trait_1", "sensitivity_trait_2", etc.), which can be renamed as necessary.

Within this data frame, trait-based and general scoring can be mixed. It is therefore recommended to set n_sensitivity_traits to the maximum number of traits to be assessed for any state indicator. The calc_sensitivity function automatically distinguishes between general and trait-based scoring.

Examples

Run this code

### Create a table for two state indicators and two pressures to evaluate direct
#   effects (default). Return a general sensitivity and adaptive capacity
#   column as well as uncertainty columns for both components:
ind <- c("seabirds", "seals")
press <- c("plastic pollution", "temperature increase")

sens_ac_tbl <- create_template_sensitivity(
  indicators = ind,
  pressures = press
)
# --> Export table and re-import after completion or fill in directly in R.

# Assign sensitivity scores from -5 (strong negative response to pressure)
# to +5 (strong positive response) (0 = no sensitivity):
sens_ac_tbl$sens_general <- c(-5,3,-4,4)

# Assign adaptive capacity scores from -1 (none) to +1 (good adaptive capacity):
sens_ac_tbl$ac_general <- c(-1,1,-1,1)

# Assign uncertainty scores from 1 (low) to 3 (high uncertainty):
sens_ac_tbl$uncertainty_sens <- c(1,2,1,1)
sens_ac_tbl$uncertainty_ac <- c(3,2,3,2)


### Create a table for four indicators and three pressures to evaluate both direct
#   and indirect effects. Return columns for five trait-specific sensitivities
#   and their respective uncertainties, but no adaptive capacity:
ind <- c("cod", "herring", "seabirds", "seals")
press <- c("fishing", "temperature increase", "salinity decrease")
sens_ac_tbl <- create_template_sensitivity(
  indicators = ind,
  pressures = press,
  type = c("direct", "direct_indirect"),
  n_sensitivity_traits = 5,
  adaptive_capacity = FALSE,
  uncertainty = TRUE,
  mode_uncertainty = "trait"
)
sens_ac_tbl
# --> You might want to rename the generic trait columns with specific traits.
# --> Export table as e.g. CSV-file and re-import again after completion.

### Create a mixed table for two indicators and two pressures, where for one
#   indicator sensitivity is scored overall and for one sensitivity is scored
#   by individual traits:
ind <- c("phytoplankton", "herring")
press <- c("temperature", "salinity")

sens_ac_tbl <- create_template_sensitivity(
  indicators = ind,
  pressures = press,
  n_sensitivity_traits = 4,
  adaptive_capacity = FALSE,
  uncertainty = TRUE,
  mode_uncertainty = "general"
)
# Rename trait columns:
names(sens_ac_tbl)[4:7] <- paste0("sens_",
  c("feeding", "behaviour", "reproduction", "general"))

# Give overall sensitivity score for phytoplankton
# (keep NAs for herring):
sens_ac_tbl$sens_general[1:2] <- c(-3,0)

# Give trait-specific sensitivity scores for herring
# (keep NAs for phytoplankton):
sens_ac_tbl$sens_feeding[3:4] <- c(0,0)
sens_ac_tbl$sens_behaviour[3:4] <- c(-1,0)
sens_ac_tbl$sens_reproduction[3:4] <- c(-2,-2)

# Give overall uncertainty score:
sens_ac_tbl$uncertainty_sens <- c(1,2,1,1)