Criterion A Reduction in distribution

Context

The Criterion A Reduction in geographic distribution is based on the theory that the decline of a geographic distribution influences the risk of collapse by:

• reducing the ability of an ecosystem to sustain its characteristic native biota and
• predisposing it to additional threats.

This criterion aims to link the change in geographic distribution to the risk of collapse.

Approach

As coral reef ecosystems are typically characterised by discrete patches that are distributed across a region, the evaluation of this criterion uses the percent cover of Hard Coral to characterise sites and evaluates the proportion of sites that are within a given threshold of coral loss over a standardised time period.

The script for creating the data object for this criterion can be found here:

 ## -- create data object -- ##
  # point to creation locale
    creation_locale <- "creation_code/criteria/criterion_a_reduction_geographic_distribution/"

  # generate object
    source(paste0(creation_locale, "create_criterion_a_reduction_geographic_distribution.R"))

The evaluation of this criterion includes a weighted and un-weighted evaluation, based on the coral reef area present in the region of analysis. The reef area present in each ecoregion is loaded from the *.rda. Summary data for this criterion (i.e. site and dates from 2013 to most recent year) are loaded directly from the *.rda from the pre-processing step:

##
## 1. Set up
##
 ## -- call to relative reef areas -- ##
  # point to data locale
    data_locale <- "data_intermediate/spatial/coral_reefs/"

  # set data file name
    data_file <- "reef_area_ecoregions.rda"

  # call to data
    load(paste0(data_locale, data_file))

 ## -- call to percent cover data -- ##
  # point to data locale
    data_locale <- "data_intermediate/criteria/"

  # set data file name
    data_file <- "criterion_a_data_table.rda"

  # call to data
    load(paste0(data_locale, data_file))

Evaluation

After some simple grooming steps (e.g. standardising Ecoregion names), Criterion A is evaluated by setting the thresholds for percent cover. In this example, the analysis evaluates the start of threshold cover from 1 to 10. Users can modify the s_cover and f_cover values to suit their particular region. For each percent cover value in the loop, results are combined in a single criterion_a_reduction_geographic_distribution data object and linked to the coral reef area to calculate weighted and un-weighted values for the proportion of collapse

##
## 3. Evaluate criterion
##
  # set start threshold percent cover
    s_cover <- 1

  # set final threshold cover
    f_cover <- 10

  # set test interval
    t_interval <- 1

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

  # loop to calculate   # p=10  ## -- for testing -- ##
    for (p in seq(from = s_cover,
                  to   = f_cover,
                  by   = t_interval)){

      # set threshold
        dat <-
          criterion_a_data_table %>%
            mutate(threshold = p)

      # create ratio
        dat %<>%
          mutate(ratio = current_coral_cover / threshold)

     ## -- set to assign a 1 if it has collapsed -- ##
      # set colapse
        dat %<>%
          mutate(collapse = ifelse(ratio <= 1, 1, 0))

      # for each eco-region caculate proportion of sites which collapsed
        dat_collapse_raw <-
          dat %>%
            group_by(Ecoregion) %>%
            summarise(prop_collapse = (sum(collapse) / length(collapse)) %>% round(2))

     ## -- calculate weighted -- ##
      # link data
        dat_collapse_weighted <-
          dat_collapse_raw %>%
            left_join(reef_area_ecoregions %>%
                        dplyr::select(Ecoregion,
                                      prop_area))

      # calculate weighting
        dat_collapse_weighted %<>%
          mutate(weighted_prop = (prop_collapse * prop_area) %>% round(5))

Next Steps

The resulting data object is then evaluated for Status and visualised in separate steps.