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benthic cover
about 2.1 Reformat raw benthic cover data 2.2 Cross-reference benthic codes 2.3 percentage cover of main benthic categories 2.4 percentage cover of coral genera 2.5 percentage cover of coral species 2.6 percentage cover of coral trait groups 2.7 percentage cover of algae taxa 2.8 percentage cover of sponge morphs 2.9 percentage cover of zoanthids and gorgonians 2.10 percentage cover of other benthic categories
internal drivers
3.1 coral health 3.2 coral interactions 3.3 fish biodiversity 3.3b fish biodiversity using NOAA data 3.4 herbivory 3.5 coral biodiversity 3.6 diadema 3.7 nuisance species
external drivers
4.1 site rugosity 4.2 site depth 4.3 site distances from shore 4.4 daily temperatures 4.5 monthly and annual DHW and temperature 4.6 currents 4.7 management 4.8 wave benthic orbital velocities pt 1 4.9 wave benthic orbital velocities pt 2 4.10 water quality 4.11 sedimentation 4.12 connectivity
resilience values
5.1 absolute and relative coral cover and diversity 5.2 resilience classification based on coral cover 5.3 benthic community: benthic organisms 5.4 NMDS of coral genera during recovery period
Reproductive Mode & RarityOlinger et al., In Review
Overview TCRMP VINPS CSUN Octocorals Interactive
2023, 2024 Bleaching EventOlinger et al. In Prep
VI Reef Resilience StudyOlinger et al. Coming Soon
(Any Others?)co-author suggestions welcome

2.5 Percentage cover of coral species

Last updated

July 6, 2026

2.5 Percentage cover of coral species

On this page

This page aggregates surveyed benthic points to the coral-species level and calculates annual percent cover of each species at every monitoring site. It reads the reformatted survey data and the corrected benthic-code cross-reference produced by parts 1 and 2 of the benthic pipeline, groups points by species, and averages cover per site and year. The two figures show cover over time: Figure 1 stacks every species by site (shallow to deep), and Figure 2 shows the proportional make-up of the ten highest-cover species. The page writes the site-by-year coral-species cover product (s2pt5_benthicCoverCoralSpecies) that downstream publication and resilience pages read.

This is a multi-program page. TCRMP and VINPS have transect replicates, so their site means carry standard error. CSUN has no transect replicate at the site level, so its cover is computed cumulatively and shows no error bar. The programs also start in different years (VINPS around 1999, CSUN in 1992), so the site panels do not all begin at the same point on the x-axis.

Data sources

This page reads the reformatted survey data and corrected benthic-code cross-reference (benthicCoverXrefBenthicCodes.RData) written by parts 1 and 2 of the benthic pipeline (sections 2.1 and 2.2), which combine the TCRMP, VINPS, and CSUN programs. The cross-reference carries the UAGA correction that restores VINPS Agaricia agaricites, and the date range respects the site embargo (data through 2023). The derived site-by-year coral-species cover product this page computes is available in the Downloads section below.

Load workspace and define variables

We load the reformatted survey data and cross-referenced benthic codes written by parts 1 and 2 of the pipeline (sections 2.1 and 2.2).

Show code
load("benthicCoverXrefBenthicCodes.RData")

We set groupingvar to coralSpecies so the pipeline groups taxa at the species level on this page, and section to s2pt5 for output naming.

Show code
groupingvar <- "coralSpecies"
section <- "s2pt5" # for naming

Reorganize benthicDat into coral species

We restrict benthicCodes to the coral category so the grouping covers coral species only.

Show code
benthicCodes$tcrmp_trait[which(benthicCodes$csun_random_code == "Millepora")] <-
  NA
benthicCodes <-
  benthicCodes[which(benthicCodes$tcrmp_Category == "Coral"), ]
NoteWhat happens here

The include below runs the shared cross-referencing pipeline (_middlexref.qmd). It maps each program’s benthic-code columns to coral species, merges TCRMP, VINPS, and CSUN into one long table, and produces groupedBenthicDat, the per-record coral-species cover table used for the rest of the page. The UAGA guard in that include warns if any data column carries a taxon code missing from the cross-reference.

getBenthicDatColInds()

this is one of two functions that connect benthicDat to benthicCodes for all three programs.

getBenthicDatColInds() makes _columnInds that stores column indices of benthic codes corresponding to each item of coralSpecies arguments:

  • benthicDat is benthic dataset of interest

  • codecolumn is the name of the column of benthic code containing the code for benthicdat

  • groupingcolumn is the name of the column of benthic codes where the grouping variable of interest is stored.

Show code
getBenthicDatColInds <-
  function(benthicDat, codecolumn, groupingcolumn) {
    codecolind <-
      which(colnames(benthicCodes) == codecolumn)
    groupingcolind <-
      which(colnames(benthicCodes) == groupingcolumn)
    genusdf <-
      data.frame(group =
                   unique(benthicCodes[, groupingcolind]),
                 colinds =
                   rep(0, length(unique(benthicCodes[, groupingcolind]))))
    for (i in 1:nrow(genusdf)) {
      codei <-
        benthicCodes[which(benthicCodes[, groupingcolind] == genusdf$group[i]), ]
      genusdf$colinds[i] <-
        list(which(colnames(benthicDat) %in% codei[, codecolind]))
    }
    # UAGA-guard: warn on benthicDat data columns whose code is absent from benthicCodes and is
    # therefore silently dropped (the footgun that lost VINPS Agaricia agaricites, code UAGA).
    .known <- benthicCodes[[codecolind]]; .known <- .known[nchar(.known) > 0]
    .meta_cols <- c("program","date","site","period","replicate","replicatetype","nopts",
                    "Year","Date","SiteFullName","year","month","percentCover_allCoral",
                    "PC","Check","Notes","Transect")
    .dropped <- setdiff(colnames(benthicDat), c(.known, .meta_cols))
    if (length(.dropped) > 0)
      warning("getBenthicDatColInds(", codecolumn, "): ", length(.dropped),
              " data column(s) have codes absent from benthicCodes and are DROPPED: ",
              paste(.dropped, collapse = ", "), " -- add them to benthicCodes if they are taxa.")
    return(genusdf)
  }

apply getBenthicDatColInds() to _benthicDat dataframes . preview tcrmp_columnInds

Show code
tcrmp_columnInds <-
  getBenthicDatColInds(tcrmp_benthicDat, "tcrmp_Code", "tcrmp_Meaning")
vinps_columnInds <-
  getBenthicDatColInds(vinps_benthicDat, "vinps_TaxonCode", "tcrmp_Meaning")

kbl(tcrmp_columnInds) |>
  kable_paper(full_width = F) |>
  kable_styling(
    fixed_thead = T,
    bootstrap_options = c("hover", "condensed"),
    font_size = 8
  ) |>
  scroll_box(width = "100%", height = "250px")
group colinds
Millepora alcicornis 38
Millepora complanata 39
Millepora squarrosa 40
Dendrogyra cylindrus 22
Acropora cervicornis 9
Acropora palmata 15
Acropora prolifera 16
Orbicella faveolata 48
Orbicella franksii 49
Orbicella species complex 50
Scolymia lacera 65
Scolymia cubensis 61
Scolymia spp. 62
Porites branneri 52
Mycetophyllia aliciae 31
Mycetophyllia daniana 36
Mycetophyllia lamarckiana 41
Mycetophyllia spp. 45
Mussa angulosa 32
Madracis mirabilis 42
Isopyhyllastrea rigida 28
Coral spp. 20
Juvenile coral spp. 21
Helioseris cucullata 27
Oculina diffusa 47
Solenastrea bournoni 60
Solenastrea hyades 63
Tubastraea coccinea 69
Stephanocoenia intercepta 64
Siderastrea spp. 68
Siderastrea siderea 67
Orbicella annularis 46
Montastraea cavernosa 34
Montastraea spp. 44
Meandrina meandrites 43
Favia fragum 26
Eusmilia fastigiata 25
Diploria labyrinthiformis 23
Dichocoenia stokesii 24
Colpophyllia natans 19
Pseudodiploria clivosa 55
Pseudodiploria strigosa 59
Siderastrea radians 66
Porites divaricata 56
Porites astreoides 51
Porites furcata 57
Branching Porites spp. 53
Porites porites 58
Mycetophyllia ferox 37
Manicina areolata 33
Madracis decactis 35
Madracis formosa 30
Agaricia fragilis 10
Agaricia agaricites 8
Agaricia grahamae 11
Agaricia humilis 13
Agaricia spp. 12
Agaricia tenuifolia 17
Agaricia undata 18
Agaricia lamarcki 14
Isophyllia sinuosa 29

makeGroupedBenthicDat()

this is the second function that connects benthicDat to benthicCodes for all three programs.

makeGroupedBenthicDat() extracts the column indices (colinds) from each row of *_columnInds.

If only one index in colinds , assigns the corresponding column from benthicdat to the i-th column of a data frame _groupedBenthicDat.

if more than one index in colinds, stores row sum of benthicDat[,colinds] in the i-th column of _groupedBenthicDat.

arguments:

  • benthicDat is benthic dataset of interest

  • columnInds is the output of getBenthicDatColInds() above that contains column indices of benthic codes for each benthic subgroup of interest

Show code
makeGroupedBenthicDat <- function(benthicDat, columnInds) {
  groupeddat <- data.frame(matrix(nrow = nrow(benthicDat),
                                  ncol = nrow(columnInds)))
  colnames(groupeddat) <- columnInds$group
   benthicDat <- benthicDat %>% dplyr::mutate(dplyr::across(dplyr::where(is.numeric), ~replace(.x, is.na(.x), 0))) 
  for (i in 1:nrow(columnInds)) {
    geni <- columnInds[i,]
    datcoli <- unlist(geni$colinds)
    if (length(datcoli) == 1) {
      groupeddat[, i] <- benthicDat[, datcoli]
    } else {
      groupeddat[, i] <- rowSums(benthicDat[, datcoli])
    }
  }
  groupeddat <- cbind(benthicDat[, 1:6], groupeddat)
  return(groupeddat)
}

apply makeGroupedBenthicDat() to _benthicDat and _columnInds . preview tcrmp_groupedBenthicDat.

Show code
tcrmp_groupedBenthicDat <-
  makeGroupedBenthicDat(tcrmp_benthicDat, tcrmp_columnInds)
vinps_groupedBenthicDat <-
  makeGroupedBenthicDat(vinps_benthicDat, vinps_columnInds)

kbl(head(tcrmp_groupedBenthicDat)) |>
  kable_paper(full_width = F) |>
  kable_styling(
    fixed_thead = T,
    bootstrap_options = c("hover", "condensed"),
    font_size = 8
  ) |>
  scroll_box(width = "100%", height = "250px")
program date site period replicate replicatetype Millepora alcicornis Millepora complanata Millepora squarrosa Dendrogyra cylindrus Acropora cervicornis Acropora palmata Acropora prolifera Orbicella faveolata Orbicella franksii Orbicella species complex Scolymia lacera Scolymia cubensis Scolymia spp. Porites branneri Mycetophyllia aliciae Mycetophyllia daniana Mycetophyllia lamarckiana Mycetophyllia spp. Mussa angulosa Madracis mirabilis Isopyhyllastrea rigida Coral spp. Juvenile coral spp. Helioseris cucullata Oculina diffusa Solenastrea bournoni Solenastrea hyades Tubastraea coccinea Stephanocoenia intercepta Siderastrea spp. Siderastrea siderea Orbicella annularis Montastraea cavernosa Montastraea spp. Meandrina meandrites Favia fragum Eusmilia fastigiata Diploria labyrinthiformis Dichocoenia stokesii Colpophyllia natans Pseudodiploria clivosa Pseudodiploria strigosa Siderastrea radians Porites divaricata Porites astreoides Porites furcata Branching Porites spp. Porites porites Mycetophyllia ferox Manicina areolata Madracis decactis Madracis formosa Agaricia fragilis Agaricia agaricites Agaricia grahamae Agaricia humilis Agaricia spp. Agaricia tenuifolia Agaricia undata Agaricia lamarcki Isophyllia sinuosa
TCRMP 2001-04-25 Cane Bay Annual 1 transect 0.00 0 0 0 0 0 0 0 0 1.15 0 0 0 0 0.00 0 0 0 0 0 0 0.00 0 0 0 0 0 0 0.00 0 0.00 0 1.53 0 0 0 0 0.00 0 0.00 0 0.00 0 0.00 2.30 0 0 0.00 0.00 0 0 0 0 0.00 0 0 0 0 0 0 0.00
TCRMP 2001-04-25 Cane Bay Annual 2 transect 0.00 0 0 0 0 0 0 0 0 12.11 0 0 0 0 0.00 0 0 0 0 0 0 0.78 0 0 0 0 0 0 0.00 0 0.00 0 5.86 0 0 0 0 2.34 0 0.39 0 0.00 0 0.00 2.73 0 0 1.56 0.00 0 0 0 0 1.17 0 0 0 0 0 0 0.00
TCRMP 2001-04-25 Cane Bay Annual 3 transect 0.00 0 0 0 0 0 0 0 0 22.09 0 0 0 0 0.39 0 0 0 0 0 0 0.78 0 0 0 0 0 0 0.00 0 1.94 0 2.71 0 0 0 0 0.00 0 0.78 0 0.39 0 0.78 2.33 0 0 3.10 0.00 0 0 0 0 1.55 0 0 0 0 0 0 0.39
TCRMP 2001-04-25 Cane Bay Annual 4 transect 0.00 0 0 0 0 0 0 0 0 21.38 0 0 0 0 0.00 0 0 0 0 0 0 0.36 0 0 0 0 0 0 0.00 0 0.00 0 5.07 0 0 0 0 0.00 0 0.00 0 0.00 0 0.00 1.45 0 0 0.36 1.45 0 0 0 0 0.72 0 0 0 0 0 0 0.00
TCRMP 2001-04-25 Cane Bay Annual 5 transect 0.00 0 0 0 0 0 0 0 0 17.72 0 0 0 0 0.00 0 0 0 0 0 0 0.39 0 0 0 0 0 0 0.00 0 3.54 0 0.00 0 0 0 0 0.00 0 0.00 0 0.00 0 0.00 0.39 0 0 0.00 0.00 0 0 0 0 1.18 0 0 0 0 0 0 0.00
TCRMP 2001-04-25 Cane Bay Annual 6 transect 0.38 0 0 0 0 0 0 0 0 9.02 0 0 0 0 0.00 0 0 0 0 0 0 0.38 0 0 0 0 0 0 0.38 0 0.38 0 1.13 0 0 0 0 0.00 0 0.00 0 0.38 0 0.00 1.88 0 0 2.26 0.38 0 0 0 0 0.38 0 0 0 0 0 0 0.00

merge the benthicDat from three programs

now have three _groupedBenthicDat, need to merge them, start by comparing the column names, because some are missing from csun.

make sure each _groupedBenthicDat has the same column names

  • get unique column names across all data frames
Show code
all_columns <-
  unique(c(
    colnames(tcrmp_groupedBenthicDat),
    colnames(vinps_groupedBenthicDat)
    ))
  • identify missing columns in each data frame
Show code
missing_columns_tcrmp <-
  setdiff(all_columns, colnames(tcrmp_groupedBenthicDat))
missing_columns_vinps <-
  setdiff(all_columns, colnames(vinps_groupedBenthicDat))
  • add missing column with NAs.
Show code
for (col in missing_columns_tcrmp) {
  tcrmp_groupedBenthicDat[[col]] <- NA
}
for (col in missing_columns_vinps) {
  vinps_groupedBenthicDat[[col]] <- NA
}
  • reorder columns to match unique column order
Show code
tcrmp_groupedBenthicDat <- tcrmp_groupedBenthicDat[, all_columns]
vinps_groupedBenthicDat <- vinps_groupedBenthicDat[, all_columns]
  • combine the modified data frames using rbind to make groupedBenthicDat.
Show code
groupedBenthicDat <-
  rbind(tcrmp_groupedBenthicDat,
        vinps_groupedBenthicDat)
  • add column year
Show code
groupedBenthicDat <-
  cbind(year = lubridate::year(groupedBenthicDat$date),
        groupedBenthicDat)
  • melt groupedBenthicDat into long format.
Show code
groupedBenthicDat <- groupedBenthicDat |>
  tidyr::gather(!!groupingvar, "perccover", 8:ncol(groupedBenthicDat))

groupedBenthicDat <- groupedBenthicDat |>
  filter(!is.na(perccover))
  • make column pres to indicate whether coralSpecies was present or absent
Show code
groupedBenthicDat$pres <- rep(0, nrow(groupedBenthicDat))
groupedBenthicDat$pres[groupedBenthicDat$perccover > 0] <- 1

Summarize and plot

For TCRMP and VINPS we calculate annual transect-averaged percent cover of coral species at each site, so the site means carry standard error from the transect replicates. For CSUN we calculate cumulative percent cover at each site because CSUN has no transect replicate.

Annual total coral cover

We sum cover across species within each replicate, then average across replicates to get total coral cover per site and year with its standard error.

Show code
totaldatCovSum <- groupedBenthicDat |>
  dplyr::group_by(year, date, program, site, period, replicate) |>
  dplyr::summarise(perccover = sum(perccover))

totaldatCovSum <- totaldatCovSum |>
  dplyr::group_by(year, date, program, site) |>
  dplyr::summarise(
    meancov = mean(perccover),
    sdcov = sd(perccover),
    secov = sd(perccover) / (sqrt(length(perccover))),
    n = length(perccover)
  )

totaldatCovSum <- totaldatCovSum %>% dplyr::mutate(dplyr::across(dplyr::where(is.numeric), ~replace(.x, is.na(.x), 0)))

Annual cover of each coral species

We average cover per species across replicates to get species-level cover per site and year.

Show code
groupedBenthicDatCovSum <- groupedBenthicDat |>
  dplyr::group_by(year, date, program, site, period, coralSpecies) |>
  dplyr::summarise(
    meancov = mean(perccover),
    sdcov = sd(perccover),
    secov = sd(perccover) / (sqrt(length(perccover))),
    n = length(perccover)
  )
groupedBenthicDatCovSum <- groupedBenthicDatCovSum %>% dplyr::mutate(dplyr::across(dplyr::where(is.numeric), ~replace(.x, is.na(.x), 0)))

Coral cover over time, by site and species

We add site depth and order the sites shallow to deep so the facets read in depth order.

Show code
#add site info so can plot according to increasing depth
groupedBenthicDatCovSum <-
  merge(groupedBenthicDatCovSum, sitedat, by = "site")
groupedBenthicDatCovSum <-
  groupedBenthicDatCovSum[order(groupedBenthicDatCovSum$depth), ]
groupedBenthicDatCovSum$site <- factor(groupedBenthicDatCovSum$site,
                                       levels = unique(groupedBenthicDatCovSum$site))

#add site info so can plot according to increasing depth
totaldatCovSum <-
  merge(totaldatCovSum, sitedat, by = "site")
totaldatCovSum <-
  totaldatCovSum[order(totaldatCovSum$depth), ]
totaldatCovSum$site <- factor(totaldatCovSum$site,
                              levels = unique(totaldatCovSum$site))

create_stacked_area_plot() builds the site-faceted stacked-area figure: each site panel stacks species cover over time, overlays total coral cover as black points with standard-error linerange, and marks the 2005 bleaching year with a dashed line.

Show code
create_stacked_area_plot <- function(data_stacked,
                                     data_total,
                                     x_var = "year",
                                     y_var = "meancov",
                                     fill_var = "coralSpecies",
                                     facet_var = "site",
                                     error_var = "secov",
                                     min_year = NULL,
                                     max_year = NULL,
                                     highlight_year = NULL,
                                     max_facets_per_page = 35) {
  # Determine x-axis limits if not provided
  if (is.null(min_year))
    min_year <- floor(min(data_stacked[[x_var]]))
  if (is.null(max_year))
    max_year <- ceiling(max(data_stacked[[x_var]]))
  
  # Calculate optimal number of rows and columns
  n_facets <- length(unique(data_stacked[[facet_var]]))
  n_facets <- min(n_facets, max_facets_per_page)  # Limit to max_facets_per_page
  n_col <- ceiling(sqrt(n_facets))
  n_row <- ceiling(n_facets / n_col)
  
  # Create base plot
  p <- ggplot() +
    geom_area(data = data_stacked,
              aes(x = .data[[x_var]], y = .data[[y_var]], fill = .data[[fill_var]]),
              position = 'stack') +
    geom_point(
      data = data_total,
      aes(x = .data[[x_var]], y = .data[[y_var]]),
      color = "black",
      size = 0.5
    ) +
    geom_linerange(
      data = data_total,
      aes(
        x = .data[[x_var]],
        ymin = .data[[y_var]] - .data[[error_var]],
        ymax = .data[[y_var]] + .data[[error_var]]
      ),
      color = "black",
      linewidth = 0.5
    ) +
    facet_wrap(vars(.data[[facet_var]]), nrow = n_row, ncol = n_col, scales = "free_x") +
    labs(y = "Percent cover", x = toupper(x_var)) +
    scale_y_continuous(expand = c(0, 0)) +
    scale_x_continuous(
      expand = expansion(mult = 0.04),
      breaks = seq(min_year, max_year, by = 1),
      labels = function(x)
        ifelse(x %% 5 == 0, paste0("'", substr(x, 3, 4)), "")
    ) +
    theme_bw() +
    theme(
      strip.text = element_text(size = 10),
      legend.position = "bottom",
      panel.grid = element_blank()
    )
  
  # Add highlight line if specified
  if (!is.null(highlight_year)) {
    p <- p + geom_vline(
      xintercept = highlight_year,
      color = "gray50",
      alpha = 0.5,
      linetype = "dashed"
    )
  }
  
  return(p)
}

Figure 1 shows total coral cover falling at most sites after the 2005 bleaching year, with the species make-up of that cover visible in each panel. Sites read shallow to deep, and TCRMP and VINPS panels carry standard-error lineranges while CSUN panels do not.

Show code
# Usage:
stack <- create_stacked_area_plot(
  data_stacked = groupedBenthicDatCovSum,
  data_total = totaldatCovSum,
  min_year = minyear,
  max_year = maxyear,
  highlight_year = 2005,
  fill_var = "coralSpecies",
  max_facets_per_page = length(unique(groupedBenthicDatCovSum$site))
)

# Display the plot
print(stack)
Sec 2.5 Figure 1: Percent cover of coral over time at each site, separated into species. Sites are ordered from shallow to deep. Black points show average total coral cover +/- SE (TCRMP and VINPS only; CSUN has no transect replicate). The dashed line marks the 2005 bleaching event.
TipKey result

Total coral cover declined at most sites after the 2005 bleaching event, and a handful of species account for most of the remaining cover. The proportional view below (Figure 2) isolates the ten highest-cover species so the dominant species stand out.

Top ten coral species by cover

We rank species by overall mean cover and keep the ten highest for the proportional plot.

Show code
# Calculate the overall mean coverage for each coralSpecies
top_species <- groupedBenthicDatCovSum |>
  group_by(coralSpecies) |>
  summarise(overall_meancov = mean(meancov, na.rm = TRUE)) |>
  arrange(-overall_meancov) |>
  head(10) |>
  pull(coralSpecies)

# Filter the original dataframe to only include the top 10 species
groupedBenthicDatCovSumTop <- groupedBenthicDatCovSum |>
  filter(coralSpecies %in% top_species)

groupedBenthicDatCovSumTop now holds only the records for the ten coral species with the highest overall mean cover. The ten species retained are Agaricia agaricites, Agaricia lamarcki, Montastraea cavernosa, Orbicella annularis, Orbicella faveolata, Orbicella franksii, Orbicella species complex, Porites astreoides, Porites porites, Siderastrea siderea.

create_proportional_stacked_area_plot() builds the site-faceted proportional plot: within each site panel it rescales the ten species to fill the panel height, so the panels show relative make-up rather than absolute cover.

Show code
create_proportional_stacked_area_plot <-
  function(data_stacked,
           x_var = "year",
           y_var = "meancov",
           fill_var = "coralSpecies",
           facet_var = "site",
           min_year = NULL,
           max_year = NULL,
           highlight_year = NULL,
           max_facets_per_page = 35) {
    # Determine x-axis limits if not provided
    if (is.null(min_year))
      min_year <- floor(min(data_stacked[[x_var]]))
    if (is.null(max_year))
      max_year <- ceiling(max(data_stacked[[x_var]]))
    
    # Calculate optimal number of rows and columns
    n_facets <- length(unique(data_stacked[[facet_var]]))
    n_facets <- min(n_facets, max_facets_per_page)  # Limit to max_facets_per_page
    n_col <- ceiling(sqrt(n_facets))
    n_row <- ceiling(n_facets / n_col)
    
    # Create base plot
    p <- ggplot(data_stacked, aes(x = .data[[x_var]], y = .data[[y_var]], fill = .data[[fill_var]])) +
      geom_area(position = 'fill') +
      facet_wrap(vars(.data[[facet_var]]), nrow = n_row, ncol = n_col, scales = "free_x") +
      labs(y = "Proportion", x = toupper(x_var)) +
      scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
      scale_x_continuous(
        expand = expansion(mult = 0.04),
        breaks = seq(min_year, max_year, by = 1),
        labels = function(x)
          ifelse(x %% 5 == 0, paste0("'", substr(x, 3, 4)), "")
      ) +
      theme_bw() +
      theme(
        strip.text = element_text(size = 10),
        legend.position = "bottom",
        panel.grid = element_blank()
      )
    
    # Add highlight line if specified
    if (!is.null(highlight_year)) {
      p <- p + geom_vline(
        xintercept = highlight_year,
        color = "gray50",
        alpha = 0.5,
        linetype = "dashed"
      )
    }
    
    return(p)
  }

Figure 2 shows the proportional make-up of the ten highest-cover species at each site. Reading the panels shallow to deep shows how the dominant species shift with depth and over time.

Show code
# Usage:
stack <- create_proportional_stacked_area_plot(
  data_stacked = groupedBenthicDatCovSumTop,
  min_year = 2000,
  max_year = maxyear,
  highlight_year = 2005,
  fill_var = "coralSpecies",
  max_facets_per_page = length(unique(groupedBenthicDatCovSum$site))
)

# Display the plot
print(stack)
Sec 2.5 Figure 2: Proportional cover of the ten highest-cover coral species at each site over time. Sites are ordered from shallow to deep. Each panel is rescaled to fill height, so the values show relative make-up rather than absolute cover. The dashed line marks the 2005 bleaching event.

Downloads

This page writes the coral-species cover product s2pt5_benthicCoverCoralSpecies (one row per site, year, and species) plus its metadata. The download links below are generated from the same object the page computes. The metadata file carries the UAGA correction note so provenance travels with the data.

version 1.0.0 • in-review • data ≤ 2023