Criterion C Environmental degradation

Context

Criterion C Environmental degradation provides a means for assessing declines in ecosystem function based on symptoms of abiotic (environmental) degradation. For coral reef ecosystems, there can be numerous abiotic variables that influence coral growth and survival, including Sea Surface Temperature (SST), Degree Heating Weeks (DHW), sedimentation and the influence of severe storms.

This project follows the regional analysis of the Western Indian Ocean (WIO) by Obura, et al. 2022. This analysis used Criterion C2a referring to future declines over a 50 year time period, measured by the exceeding Degree Heating Weeks (DHW) of 12 more than 2 years per decade based on future projections from climate change forecasts (i.e. RCP 6.0).

This wiki provides an overview of the analysis of Criterion C based on DHW from each Ecoregion in the regional Area of Assessment

Approach

The analysis for Environmental degradation focuses on prolonged periods of elevated Sea Surface Temperature (SST), represented in the Degree Heating Weeks (DHW) measure. The analysis requires the creation of the projected DHW values for the region of interest and then evaluating the criterion based on threshold values (i.e. number of DHW episodes per decade).

Scripts for creating data objects for RCP data and the evaluation of Criterion C can be found in integrate.R:

 ## -- create degree heating weeks from rcp projections -- ##
  # point to creation locale
    creation_locale <- "creation_code/geophysical/representative_concentration_pathways/"
 
  # create degree heating weeks
    source(paste0(creation_locale, "create_representative_concentration_pathways.R"))
 
 ## -- evaluate criterion -- ##
  # point to creation locale
    creation_locale <- "creation_code/criteria/criterion_c_environmental_degradation/"
 
  # evaluate criterion
    source(paste0(creation_locale, "create_criterion_c_environmental_degradation.R"))

In the first step, Representative Concentration Pathways (RCP) data from different models are combined into a single data object from each Ecoregion in the Area of Assessment region. Example data provided in the repository illustrate how the core RCP data are compiled for evaluation.

##
## 1. Set up
##
  # set data locale
    data_locale <-
      "data_raw/geophysical/representative_concentration_pathways/"

  # get data files
    data_files <-
      paste0(# project_locale, 
             data_locale) %>%
      list.files(pattern    = "RCP",
                 full.names = FALSE)

 ## -- import data -- ##
  # create empty object to hold results
    representative_concentration_pathways <- tibble()

  # loop
    for(i in 1:length(data_files)){

      # import data
        dat <-
          paste0(# project_locale, 
                 data_locale,
                 data_files[i]) %>%
          read_csv()

      # add origin file
        dat %<>%
          mutate(file_name = data_files[i] %>% str_remove(".csv"))

      # set to long
        dat %<>%
          gather(Ecoregion, DHW,
                 -file_name,
                 -Year)

      # harvest results
        representative_concentration_pathways %<>%
          bind_rows(dat)

    }

Evaluation

The evaluation of the RCP data then involves the setting of lower and higher thresholds to calculate the relative risk of ecosystem collapse for each Ecoregion:

 ## -- calculate number of exceedences of bleaching threshold -- ##
  # set thresholds
    low_thresh  <-  8
    high_thresh <- 12

 ## -- loop creates two new columns dhw_bl_low and dhw_bl_high          ##
 ##    for each of the bleaching thresholds. The loop assigns a 1       ## 
 ##    if the respective thresholds are met or exceeded and 0 if not -- ##

  # set thresholds
    rcp_thresholds <-
      representative_concentration_pathways %>%
        mutate(dhw_bl_low  = ifelse(DHW <  low_thresh, 0, NA),
               dhw_bl_high = ifelse(DHW > high_thresh, 1, NA))

 ## -- sum exceedences per decade -- ##
  # set decade 5 years either side of 2020
    rcp_thresholds %<>%
      mutate(decade = Year - (Year + 5) %% 10)

  # summarise number of exceedences
    rcp_thresholds_per_decade <-
      rcp_thresholds %>%
        group_by(Ecoregion,
                 file_name,
                 decade) %>%
        summarise(dhw_exc_low  = dhw_bl_low %>% sum(na.rm = TRUE),
                  dhw_exc_high = dhw_bl_high %>% sum(na.rm = TRUE))

Next Steps

The resulting data object is then evaluated for Status based on the relative severity of environmental degradation (measured by DHW projections).