Visualisation of results

Context

The final assessment of the IUCN RLE criteria involves numerical and visualisation of the resulting threat classifications. For each of the different measures, there are different ways to visualise the results.

As this project was largely based on the WIO analysis from Obura, et al. 2022, we have followed the examples from that study.

This wiki page provides an example of the general processing for visualising and reporting the RLE evaluation results.

Analysis code

In general, the visualisation of the RLE evaluation results can be found in the analysis_code folder and found in the sequence from integrate.R. For example, for Criterion A Reduction in geographic distribution:

  # point to analysis locale
    analysis_locale <- "analysis_code/criteria/criterion_a_reduction_geographic_distribution/"

  # plot number of collapsed sites
    source(paste0(analysis_locale, "plot_number_of_collapsed_sites.R"))

The visualisation for each criterion follows a similar sequence in the code, where the criterion data object (i.e. *.rda) is imported and visualised:

##
## 1. Set up
##
 ## -- call to sensitivity analyses-- ##
  # point to data locale
    data_locale <- "data_intermediate/criteria/"

  # point to data file
    data_file <- "criterion_a_reduction_geographic_distribution.rda"

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

Visualisations are largely based on the ggplot2 library. Users are encouraged to modify dimensions and other aesthetics to suit their applications:

Ecoregion and Regional visualisations

For some metrics, the spatial visualisations for criteria include Ecoregion and region-wide visualisations. These plots include additional elements such as coastline, ecoregions and coral reef areas. For example, for Criterion B1 Extent of Occurrence (EOO):

 ## -- plot regional data -- ##
  # set region name
    region_name <- "Western Indian Ocean"

  # open window
    # quartz("criterion b1 eoo", 7, 7)

       # create figure
         ggplot() +
           geom_sf(fill   = r_colour,
                   colour = r_colour,
                   size   = 0.1,
                   alpha  = 0.5,
                   data   = regional_coral_reefs %>%
                              st_transform(32737))  +
           geom_sf(fill   = "grey75",
                   colour = "grey75",
                   size   = 0.1,
                   data   = regional_coastline %>%
                              st_transform(32737) %>%
                              st_crop(e_zoom)) +
           geom_sf(aes(colour = Ecoregion),
                   fill   = NA,
                   size   = 0.5,
                   alpha  = 0.4,
                   data   = criterion_b1_polygons %>%
                              st_transform(32737)) +
      geom_sf_text(aes(colour = Ecoregion,
                       label  = paste0(Ecoregion, "\n",
                                       "EOO = ", Area, " ",
                                       expression(km^2))),
               data  = area_centroid %>%
                         st_as_sf(coords = c("easting", "northing"),
                                   crs   = 32737)) +
      theme_void() +
      coord_sf(xlim = c(e_zoom[1], e_zoom[3]),
               ylim = c(e_zoom[2], e_zoom[4]),
               datum = st_crs(32737)) +
      ggspatial::annotation_scale() +
      scale_colour_manual(values = c_palette) +
      scale_fill_manual(values = c_palette) +
      labs(title    = region_name,
           subtitle = "Criterion B1: Extent of Occurrence") +
      theme(legend.position = "none",
            plot.title      = element_text(hjust = 0.5),
            plot.subtitle   = element_text(hjust = 0.5,
                                           face  = "italic"))

Next steps

Visualisation outputs are saved to the figures folder and can be found in subfolders for each criterion. Most outputs are in *.png format for single panels or *.pdf format for multiple panels. Users are able to modify the dimensions and format of the outputs in the analysis_code files.

For example, for the regional plot for B1 EOO:

  # set save locale
    figure_locale <- "figures/criteria/criterion_b_restricted_geographic_distribution/"

  # save to file
    ggsave(paste0(figure_locale, "criterion_b1_extent_of_occurrence_regional.png"),
      width  = 7,
      height = 7)