# Calculate Overall Sensitivity and Adaptive Capacity Scores from Trait-Specific Expert Ratings The function `calc_sensitivity` calculates aggregated sensitivity and adaptive capacity scores. Additionally it prepares the scoring data for the further usage in the `vulnerability` function. The scores for sensitivity and adaptive capacity can be trait-based or general (one score per state indicator and pressure combination). ```r calc_sensitivity( indicators, pressures, type = "direct", sensitivity_traits, adaptive_capacities = NULL, uncertainty_sens = NULL, uncertainty_ac = NULL, method = "mean" ) ``` ## Arguments - `indicators`: A character vector or column of a data frame containing the names of the state indicators. - `pressures`: A character vector or column of a data frame containing the names of the pressures. - `type`: A character vector or column of a data frame specifying the effect type. Effects could be direct or indirect or a combination of both. Default is `direct`. - `sensitivity_traits`: A data frame containing the numeric sensitivity values per species trait or as a general value. - `adaptive_capacities`: A data frame (or single vector) containing the numeric values for the adaptive capacity. Either per trait or as one general value. When values are given per trait, they have to be in the same order as the values for the sensitivity. The default is `NULL`. - `uncertainty_sens`: A data frame (or a vector) containing the numeric uncertainty values associated with the sensitivity; default is `NULL`. - `uncertainty_ac`: A data frame (or a vector) containing the numeric uncertainty values associated with the adaptive capacity; default is `NULL`. - `method`: A character string specifying the method of the aggregation of the traits. Available are `median`, `minimum`, `maximum` and `mean`, the `mean` is default. ## Returns a data frame containing the indicator, pressure and effect type, the aggregated sensitivity and adaptive capacity as well as their associated uncertainty scores. Additionally, the trait specific sensitivity and adaptive capacity scores are stored and used later as input for the `vulnerability` function. ## Details The function calculates per state indicator and pressure combination one aggregated sensitivity and adaptive capacity score, the aggregation method can be determined with the parameter `method`. The assessment of adaptive capacity is optionally, if no scores for adaptive capacity are provided the function calculates an aggregated sensitivity score and prepares only the sensitivity scores for the `vulnerability` function. Guidance for the scoring process can be found here: `create_template_sensitivity` or in the vignette or in Gutte et al., 2025. Using exposure and sensitivity scorings, vulnerability is calculated. ## Examples ```r ### Example using demo data with four indicators and five pressures with # scores for direct as well as combined direct-indirect effects based on # the template function create_template_sensitivity(). For two # indicators, sensitivity, adaptive capacity, and their uncertainties are # provided as general scores, while for the other two, they are based on # individual traits. ex_expert_sensitivity # Calculate only mean sensitivity scores: calc_sensitivity( indicators = ex_expert_sensitivity$indicator, pressures = ex_expert_sensitivity$pressure, sensitivity_traits = ex_expert_sensitivity[ ,4:8], adaptive_capacities = NULL, # (default) uncertainty_sens = NULL, # (default) uncertainty_ac = NULL, # (default) method = "mean" # (default) ) # Calculate mean scores for sensitivity, adaptive capacity and # associated uncertainties: calc_sensitivity( indicators = ex_expert_sensitivity$indicator, pressures = ex_expert_sensitivity$pressure, type = ex_expert_sensitivity$type, sensitivity_traits = ex_expert_sensitivity[ ,4:8], adaptive_capacities = ex_expert_sensitivity[ ,9:13], uncertainty_sens = ex_expert_sensitivity[ ,14:18], uncertainty_ac = ex_expert_sensitivity[ ,19:23] ) ### Example for one indicator and three pressures to evaluate direct # effects where sensitivity is scored for four individual traits: ind <- "herring" press <- c("fishing", "temperature increase", "salinity decrease") # Create scoring table using the template function: sens_ac_tbl <- create_template_sensitivity( indicators = ind, pressures = press, type = "direct", # (default) n_sensitivity_traits = 4, adaptive_capacity = TRUE, # (default) mode_adaptive_capacity = "general", # (default) uncertainty = TRUE, # (default) mode_uncertainty = "general" # (default) ) # Rename trait columns: trait_cols <- paste0("sens_", c("feeding", "behaviour", "reproduction", "habitat")) names(sens_ac_tbl)[4:7] <- trait_cols # Give trait-specific sensitivity scores: sens_ac_tbl$sens_feeding <- c(0,0,0) sens_ac_tbl$sens_behaviour <- c(-1,0,-4) sens_ac_tbl$sens_reproduction <- c(-2,-2,-5) sens_ac_tbl$sens_habitat <- c(-3,-2,0) # Give general adaptive capacity and uncertainty scores: sens_ac_tbl$ac_general <- c(0,0,-1) sens_ac_tbl$uncertainty_sens <- c(1,1,1) sens_ac_tbl$uncertainty_ac <- c(1,1,2) sens_ac_tbl # Calculate median sensitivity scores (adaptive capacities and # uncertainties cannot be aggregated further): calc_sensitivity( indicators = sens_ac_tbl$indicator, pressures = sens_ac_tbl$pressure, sensitivity_traits = sens_ac_tbl[, trait_cols], adaptive_capacities = sens_ac_tbl$ac_general, uncertainty_sens = sens_ac_tbl$uncertainty_sens, uncertainty_ac = sens_ac_tbl$uncertainty_ac, method = "median" ) ``` ## See Also `create_template_exposure`, `create_template_sensitivity`, `calc_exposure`, `vulnerability`

calc_sensitivity function