Criterion D Biotic disruption

Context

Criterion D Biotic disruption is based on a similar ecological theory to Criterion C, where the risk of collapse is based on symptoms of the disruption of biological processes. There are a number of variables that are potentially useful for quantifying biotic processes and associated declines (IUCN 2024), including:

• Species richness
• Species composition and dominance
• Abundance of ecosystem engineers or keystone species
• Functional diversity, functional redundancy and functional complexity
• Trophic diversity
• and others...

Following the example from Obura, et al. 2022, Criterion D1 was evaluated representing historical decline during the past 50 years for a number of metrics related to different elements of the ecosystem:

These include the percentage of hard coral cover, the algae-coral ratio, parrotfish abundance and grouper abundance. These measures represent key elements of the ecosystem, including the abundance of key ecosystem taxa (hard coral cover), the status based on the relative proportion of algae to coral, key grazers (i.e. parrotfish abundance) and key consumers (i.e. grouper abundance).

This wiki page outlines the steps for evaluating Criterion D for these different metrics, using the Algal-coral ratio as an example. Details for the analysis of fish consumers can be found in the individual scripts noted below. As different regions may have different key fish consumers and other ecological ratios to represent coral reef status, the analysis steps can be modified to suit other taxa and ecosystem ratios to adequately represent coral reef status.

Approach

The creation of the Algal-coral ratio is conducted in a separate pre-processing script and the creation of individual objects for coral cover, algal-coral ratio and key fish consumers are executed in separate scripts in integrate.R:

 # -- create algal coral ratio -- ##
  # point to creation locale
    creation_locale <- "creation_code/biological/algal_coral_ratio/"
 
  # create gcrmn benthic cover data
    source(paste0(creation_locale, "create_algal_coral_ratio.R"))
 
 
 ## -- evaluate criterion -- ##
  # point to creation locale
    creation_locale <- "creation_code/criteria/criterion_d_biotic_disruption/"
 
  # evaluate with coral cover method b
    source(paste0(creation_locale, "create_criterion_d_biotic_disruption_coral_cover_method_b.R"))
 
  # evaluate with algal-coral ratio
    source(paste0(creation_locale, "create_criterion_d_biotic_disruption_algal_coral_ratio.R"))
 
  # evaluate with key fish consumers
    source(paste0(creation_locale, "create_criterion_d_biotic_disruption_key_fish_consumers.R"))

Evaluation

Each script to evaluate Criterion D1 follow a similar process for each measure, including the loading of the data object for each measure and iterations to calculate declines from baseline measures. Example simulated data files from the WIO region are provided for users to explore the analysis steps:

##
## 1. Set up
##
 ## -- call to criterion d data table -- ##
  # point to data locale
    data_locale <- "data_raw/biological/percent_cover/"

  # point to data file
    data_file <- "Crit_D_HC_data_table.xlsx"

  # call to data
    criterion_d_data_table <-
      paste0(data_locale, data_file) %>%
      read_xlsx()

 ## -- call to hard coral baseline data -- ##
  # point to data locale
    data_locale <- "data_raw/biological/percent_cover/"

  # point to data file
    data_file <- "Criterion_D_hard_coral_baseline.xlsx"

  # call to data
    hard_coral_baseline <-
      paste0(data_locale, data_file) %>%
      read_xlsx()

The evaluation of Criterion D1 for each measure begins with setting the number of iterations and looping through each to calculate the relative_severity of decline. For example, for Hard coral cover, the decline is measured relative to the baseline for each site x Ecoregion combination:

##
## 3. Evaluate criterion
##
 # set iteration levels
    i_min <- 10
    i_max <- 1e3

  # set iteration interval
    i_interval <- 10

  # create empty object to hold results
    criterion_d_biotic_disruption_coral_cover_method_b <- tibble()

  # loop through iterations # i=10  ## -- for testing -- ##
    for(i in seq(from = i_min,
                 to   = i_max,
                 by   = i_interval)){
  
     ## -- calculate severity -- ##
      # set seed for reproducibility
        set.seed(i + 81)

      # randomly assign baseline values
        dat <-
          criterion_d_data_table %>%
              rename(Ecoregion = eco_rgn) %>%
            left_join(hard_coral_baseline %>%
                        dplyr::select(Ecoregion = eco_rgn,
                                      baseline_mean,
                                      baseline_sd)) %>%
           group_by(Ecoregion) %>%
           mutate(baseline_cover = rnorm(1, mean = baseline_mean,
                                               sd = baseline_sd))

      # calculate relative severity
        dat %<>%
          mutate(relative_severity = 100 * (baseline_cover - current_cover) /
                                           (baseline_cover - threshold_cover))

      # bound by 0 and 100
        dat %<>%
          mutate(relative_severity = relative_severity %>% scales::rescale(to = c(0, 100)))

Next Steps

The resulting data objects are then evaluated as a sequential combination to determine the overall Status.