create_template_sensitivity {ecorisk}R Documentation

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"
)

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:

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.

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).

See Also

create_template_exposure, calc_exposure, calc_sensitivity

Examples

### 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)

[Package ecorisk version 0.1.1 Index]