Virgin Islands National Park Service, species level, 2001 to present
Olinger LK, Edmunds PJ, Levitan D, Smith TB, Lasker H, Feeley M, Mahoney L, Dahl A. Reproductive mode, but not rarity, influences population trajectories in corals. In Review 2026.
This page reruns the published VINPS analysis on the full monitoring record rather than stopping at the published window. The method, the code, and the site and taxon scoping are identical to the published page; only the year range differs. As new monitoring years are added to the bundled cover file, this page picks them up automatically and shows how the trajectories continue.
The coral-cover data for this analysis comes from the Reef Code benthic cover section: coral species cover download.
The Virgin Islands National Park Service program records coral cover at the species level across 7 sites around St. John and Buck Island. The published analysis covered 2001 through 2023. This page runs the same analysis over the full bundled record, from 2001 through the most recent year available. The full analysis runs below, from the coral-cover file to the figure (Figure 1) and the statistics table (Table 1). Every step is folded. Open any block to read the code and the plain-language notes.
cover_files <- sort(list.files("../../data/cover", pattern = "s2pt5.*\\.csv$", full.names = TRUE))
max_year <- max(readr::read_csv(tail(cover_files, 1), show_col_types = FALSE)$year)
pub_range <- c(2001, max_year); program <- "VINPS"; taxon_level <- "species"; version <- "updated"suppressWarnings(suppressPackageStartupMessages({
library(tidyverse)
library(sandwich)
library(lmtest)
library(broom)
library(patchwork)
library(kableExtra)
}))
palette_grp <- c(
"Common.Broadcaster" = "#0072B2", "Rare.Broadcaster" = "#56B4E9",
"Common.Brooder" = "#D55E00", "Rare.Brooder" = "#E69F00"
)
label_grp <- c(
"Common.Broadcaster" = "Common broadcaster", "Rare.Broadcaster" = "Rare broadcaster",
"Common.Brooder" = "Common brooder", "Rare.Brooder" = "Rare brooder"
)The analysis begins from the species-level coral-cover file and the site master table. The cover file holds one row per transect observation: a site, a year, a survey period, a coral species, and its percent cover. The site master holds each site’s program, depth, and the year monitoring began. The cover file is matched by pattern in data/cover/, so this step reads whichever species-level file is bundled, and the analysis grows as that file grows.
cover_files <- sort(list.files("../../data/cover", pattern = "s2pt5.*\\.csv$", full.names = TRUE))
cover_file <- tail(cover_files, 1)
benthiccover <- read.csv(cover_file) |>
select(year, program, site, period, coralSpecies, perccover)
sitedat <- read.csv("../../data/site/00_RRS_siteMaster_allSites_data.csv")
c(cover_file = basename(cover_file), cover_rows = nrow(benthiccover), sites_listed = nrow(sitedat)) cover_file
"s2pt5_benthicCoverCoralSpecies_41sites_1999_2024.csv"
cover_rows
"481351"
sites_listed
"50" The first move is the explicit publication-range clip: every downstream step runs only on years inside pub_range. At the published range this clip is harmless, since the bundled file does not extend beyond it; as the cover file grows in later monitoring years, the same clip is what keeps this page reproducing the published analysis exactly. After the clip, the analysis holds the rest of the sampling design constant so that trends reflect coral change rather than changes in which reefs or taxa were surveyed: the VINPS sites established before 2006, and the coral species identified to species. It drops the fire corals (Millepora), the generic catch-all entries, the Orbicella complex placeholder, and any taxon left at the genus level. The shallow-site rule is applied where the cover joins the site depths, the same order as the original analysis.
# Publication-range clip: the explicit first step of scope.
benthiccover <- benthiccover |> filter(year >= pub_range[1], year <= pub_range[2])
# Sites: VINPS reefs established before 2006, with a depth category.
sitedat <- sitedat |>
filter(program == "VINPS", yearadded < 2006) |>
mutate(depth_cat = ifelse(depth < 21, "Shallow", "Deep"))
# Cover: VINPS surveys.
benthiccover <- benthiccover |>
filter(program == "VINPS")
# Taxa: fix one spelling, drop non-species and non-coral entries, keep shallow scoped sites.
benthiccover <- benthiccover |>
mutate(coralSpecies = ifelse(coralSpecies == "Orbicella franksii", "Orbicella franksi", coralSpecies)) |>
filter(
!coralSpecies %in% c("Millepora alcicornis", "Millepora complanata", "Millepora squarrosa",
"Coral spp.", "Branching Porites spp.", "Juvenile coral spp.", "Orbicella species complex"),
!grepl(" spp\\.$", coralSpecies),
site %in% sitedat$site
) |>
left_join(select(sitedat, site, depth_cat), by = "site") |>
filter(depth_cat == "Shallow")
c(cover_rows = nrow(benthiccover), sites_kept = n_distinct(benthiccover$site),
species = n_distinct(benthiccover$coralSpecies))cover_rows sites_kept species
144893 7 49 Validation note: the scoped dataset spans 2001 through 2024, holds 144,893 transect records across 7 VINPS sites and 49 coral species.
The analysis compares a baseline period to the full record. It bins the survey years into five-year groups and treats the first group as the baseline. Binning smooths the year-to-year noise in the baseline abundance ranking without changing the annual data used later in the model. The helper below builds the five-year groups from the data range and labels each year with its group.
bin_years <- function(df, interval = 5) {
min_yr <- min(df$year, na.rm = TRUE); max_yr <- max(df$year, na.rm = TRUE)
breaks <- seq(min_yr, max_yr, by = interval)
if (tail(breaks, 1) > max_yr - (interval - 1)) breaks <- head(breaks, -1)
labels <- paste(breaks, pmin(breaks + (interval - 1), max_yr), sep = "-")
map <- data.frame(year = seq(min_yr, max_yr)) |>
mutate(year_group = cut(year, breaks = c(breaks, max_yr + 1), labels = labels,
right = FALSE, include.lowest = TRUE))
df |> left_join(map, by = "year") |> mutate(year_group = as.factor(year_group))
}
benthiccover <- bin_years(benthiccover, 5)
earliest_year_group <- levels(benthiccover$year_group)[1]
# earliest_year_groupThe baseline period is the first five-year group, 2001-2005. Two presence rules keep the analysis on taxa that were part of the community at baseline and at sites within their range. The first keeps species recorded during the baseline period. The second drops species-site pairs where the species was never seen, since a site outside a species range would enter the model as a structural zero rather than a decline.
species_yeargroup_observed <- benthiccover |>
group_by(coralSpecies, year_group) |>
summarize(seen = max(perccover, na.rm = TRUE) > 0, .groups = "drop") |>
filter(seen)
spp_in_baseline <- species_yeargroup_observed |>
filter(year_group == earliest_year_group) |>
pull(coralSpecies) |> unique()
benthiccover <- benthiccover |> filter(coralSpecies %in% spp_in_baseline)
in_range <- benthiccover |>
group_by(coralSpecies, site) |>
summarize(seen = max(perccover, na.rm = TRUE) > 0, .groups = "drop") |>
filter(seen) |> mutate(key = paste(coralSpecies, site))
benthiccover <- benthiccover |>
filter(paste(coralSpecies, site) %in% in_range$key)
benthiccover <- benthiccover |>
group_by(year_group, coralSpecies, year, site) |>
summarise(perccover = mean(perccover, na.rm = TRUE), .groups = "drop")
c(rows = nrow(benthiccover), species = n_distinct(benthiccover$coralSpecies)) rows species
3006 28 Each species receives its mean percent cover during the baseline period. Ranking the species by that mean sets the order used in the figure and sets up the commonness split. The cumulative proportion of cover, summed down the ranked list, measures how much of the reef’s baseline cover the top species account for.
baseline <- benthiccover |>
filter(year_group == earliest_year_group) |>
group_by(coralSpecies) |>
summarise(meancov = mean(perccover, na.rm = TRUE), .groups = "drop") |>
filter(meancov > 0) |>
arrange(desc(meancov)) |>
mutate(rank = row_number(),
normcov = meancov / sum(meancov, na.rm = TRUE),
cumsumcov = cumsum(normcov))
# head(baseline, 6)The commonness split follows one rule: the species that together hold the top 90 percent of baseline cumulative cover are common, and the rest are rare. The manuscript identified 0.90 through a sliding-window analysis as the strictest rarity definition that leaves the temporal slopes stable.
baseline <- baseline |>
mutate(commonness = ifelse(cumsumcov <= 0.900001, "Common", "Rare"))
table(baseline$commonness)
Common Rare
7 21 Each species carries a reproductive mode, brooder or broadcaster. The assignment reads a compiled trait table, drops unknown entries, and joins the mode onto the baseline species. A second trait table fills any species the first leaves unassigned. Species that still lack a mode after both sources leave the analysis, because the model compares brooders to broadcasters and cannot place an unassigned species.
repro1 <- read.csv("../../data/collab/reproMode_coralSpecies_Mahoney_20250108.csv") |>
rename(coralSpecies = Species) |>
filter(Reproductive_mode %in% c("Brooder", "Broadcaster")) |>
distinct(coralSpecies, Reproductive_mode)
repro2 <- read.csv("../../data/collab/reproMode_coralSpecies_Olinger_20250108.csv") |>
filter(Reproductive_mode %in% c("Brooder", "Broadcaster")) |>
distinct(coralSpecies, Reproductive_mode)
baseline <- baseline |>
left_join(repro1, by = "coralSpecies") |>
left_join(repro2, by = "coralSpecies", suffix = c("", ".2")) |>
mutate(Reproductive_mode = coalesce(Reproductive_mode, Reproductive_mode.2)) |>
select(-Reproductive_mode.2) |>
filter(Reproductive_mode %in% c("Brooder", "Broadcaster"))
# with(baseline, table(Commonness = commonness, `Reproductive mode` = Reproductive_mode))The model works on group means rather than individual species, so that a single abundant species cannot dominate the trend. This step joins the commonness and reproductive-mode labels onto the full cover series, averages cover within each group at each site and year, adds a year index that starts at zero, and takes the log10 of cover. Log10 cover is the response, because coral loss acts proportionally, and a proportional change is a straight line on the log scale.
analysis_data <- benthiccover |>
inner_join(baseline |> select(coralSpecies, commonness, Reproductive_mode), by = "coralSpecies") |>
group_by(category = commonness, Reproductive_mode, year, site) |>
summarise(perccover = mean(perccover, na.rm = TRUE), .groups = "drop") |>
mutate(yearind = year - min(year), perccoverLog = log10(perccover)) |>
filter(perccover > 0) |>
mutate(category = factor(category, levels = c("Common", "Rare")),
Reproductive_mode = factor(Reproductive_mode, levels = c("Broadcaster", "Brooder")))
c(model_rows = nrow(analysis_data), groups = nlevels(interaction(analysis_data$category, analysis_data$Reproductive_mode)))model_rows groups
589 4 The model predicts log10 percent cover from year, commonness, reproductive mode, and every interaction among them. Common and broadcaster are the reference levels, so the year term is the trend for common broadcasters, and the interactions measure how the other groups differ from it.
model_full <- lm(perccoverLog ~ yearind * category * Reproductive_mode, data = analysis_data)The residual variance is larger for the abundant groups than for the sparse groups, so ordinary standard errors would misstate the uncertainty. The analysis replaces them with HC3 heteroscedasticity-consistent standard errors, computed from a sandwich covariance matrix. Every standard error, p-value, and confidence interval below uses this robust covariance.
vcov_hc3 <- sandwich::vcovHC(model_full, type = "HC3")
coef_robust <- broom::tidy(lmtest::coeftest(model_full, vcov = vcov_hc3))The four group trends are linear combinations of the model coefficients. The common-broadcaster slope is the year term. Each other group adds the interactions that apply to it. The standard error of each slope comes from the robust covariance, so the uncertainty carries through. Annual percent change back-transforms the log10 slope into a yearly rate.
group_slope <- function(terms) {
L <- setNames(rep(0, length(coef(model_full))), names(coef(model_full)))
L[terms] <- 1
est <- sum(L * coef(model_full))
se <- sqrt(as.numeric(t(L) %*% vcov_hc3 %*% L))
tibble(Slope = est, SE = se, `Annual % change` = 100 * (10^est - 1),
`P-value` = 2 * pnorm(-abs(est / se)))
}
group_slopes <- bind_rows(
"Common broadcaster" = group_slope("yearind"),
"Rare broadcaster" = group_slope(c("yearind", "yearind:categoryRare")),
"Common brooder" = group_slope(c("yearind", "yearind:Reproductive_modeBrooder")),
"Rare brooder" = group_slope(c("yearind", "yearind:categoryRare",
"yearind:Reproductive_modeBrooder",
"yearind:categoryRare:Reproductive_modeBrooder")),
.id = "Group"
)The figure pairs the baseline abundance distribution with the temporal trends. The left panel ranks species by baseline cover and colors each by group. The right panel shows site-level group mean cover over time on a log scale, with a linear fit per group.
sad_df <- baseline |>
filter(meancov > 0) |>
mutate(coralSpecies = factor(coralSpecies, levels = rev(baseline$coralSpecies[order(baseline$rank)])),
grp = interaction(commonness, Reproductive_mode))
p_sad <- ggplot(sad_df, aes(coralSpecies, meancov, fill = grp)) +
geom_col(width = 0.8) +
scale_fill_manual(values = palette_grp, labels = label_grp, name = NULL, drop = FALSE) +
coord_flip() +
labs(x = NULL, y = "Mean baseline percent cover", title = "Baseline abundance") +
theme_classic(base_size = 11) +
theme(legend.position = "top", axis.text.y = element_text(face = "italic", size = 8))
trend_df <- analysis_data |> mutate(grp = interaction(category, Reproductive_mode))
p_trend <- ggplot(trend_df, aes(year, perccover, color = grp, fill = grp)) +
geom_jitter(shape = 21, color = "black", alpha = 0.2, width = 0.25, height = 0, show.legend = FALSE) +
geom_smooth(method = "lm", formula = y ~ x, se = TRUE, alpha = 0.15, linewidth = 0.8) +
scale_color_manual(values = palette_grp, labels = label_grp, name = NULL, drop = FALSE) +
scale_fill_manual(values = palette_grp, labels = label_grp, name = NULL, drop = FALSE) +
scale_y_log10() + annotation_logticks(sides = "l", color = "grey70") +
labs(x = "Year", y = "Percent cover (log scale)", title = "Temporal trends") +
theme_classic(base_size = 11) +
theme(legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1))
p_sad + p_trend
The table reports the model coefficients with HC3 robust standard errors and p-values. Bold marks terms significant at p less than 0.05.
term_labels <- c(
"(Intercept)" = "Intercept", "yearind" = "Year",
"categoryRare" = "Rarity", "Reproductive_modeBrooder" = "Repro. mode (brooder)",
"yearind:categoryRare" = "Year x Rarity", "yearind:Reproductive_modeBrooder" = "Year x Repro. mode",
"categoryRare:Reproductive_modeBrooder" = "Rarity x Repro. mode",
"yearind:categoryRare:Reproductive_modeBrooder" = "Year x Rarity x Repro. mode"
)
coef_tbl <- coef_robust |>
transmute(Predictor = term_labels[term],
`Estimate (SE)` = sprintf("%.3f (%.3f)", estimate, std.error),
`P-value` = ifelse(p.value < 0.001, "< 0.001", sprintf("%.3f", p.value)),
.sig = p.value < 0.05)
kbl(coef_tbl |> select(-.sig), align = c("l", "r", "r"),
caption = "Table 1 (VINPS). Linear-model coefficients with HC3 robust standard errors.") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE) |>
row_spec(which(coef_tbl$.sig), bold = TRUE)| Predictor | Estimate (SE) | P-value |
|---|---|---|
| Intercept | 0.082 (0.053) | 0.119 |
| Year | -0.012 (0.004) | 0.002 |
| Rarity | -1.063 (0.076) | < 0.001 |
| Repro. mode (brooder) | -0.414 (0.077) | < 0.001 |
| Year x Rarity | 0.002 (0.006) | 0.686 |
| Year x Repro. mode | 0.027 (0.005) | < 0.001 |
| Rarity x Repro. mode | -0.021 (0.113) | 0.854 |
| Year x Rarity x Repro. mode | -0.025 (0.008) | 0.003 |
group_slopes |>
mutate(Slope = sprintf("%.4f", Slope), SE = sprintf("%.4f", SE),
`Annual % change` = sprintf("%.2f", `Annual % change`),
`P-value` = ifelse(`P-value` < 0.001, "< 0.001", sprintf("%.3f", `P-value`))) |>
kbl(align = c("l", "r", "r", "r", "r"),
caption = "Group-specific temporal slopes estimated by Wald tests on linear combinations of model coefficients. The annual percent change is calculated as (10^slope - 1) x 100. P-values test the null hypothesis that the total temporal slope equals zero (H0: slope = 0). Common Broadcasters are the reference group; their slope and p-value come directly from the Year coefficient.") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| Group | Slope | SE | Annual % change | P-value |
|---|---|---|---|---|
| Common broadcaster | -0.0115 | 0.0038 | -2.62 | 0.002 |
| Rare broadcaster | -0.0093 | 0.0041 | -2.12 | 0.022 |
| Common brooder | 0.0157 | 0.0035 | 3.67 | < 0.001 |
| Rare brooder | -0.0069 | 0.0051 | -1.58 | 0.177 |
The coefficient table reports the fitted trends. This table steps back to the species themselves and reports, for every taxon in the baseline community, how abundant and how widespread it was at the start of the record and how that changed by the end. Three complementary measures separate the different ways a species can decline. Cover is the mean percent cover across the species range. Occupancy is the proportion of surveyed sites where the species was present, so it tracks range contraction independent of local abundance. Extent is the mean cover at the sites where the species was actually present, so it tracks thinning within the occupied range. A species can hold its occupancy while its extent falls, or contract its range while staying dense where it persists; reporting both separates the two. Start values average over the baseline five-year group, and end values average over the last three years of the record. Occupancy counts a site only from the year it entered monitoring, so the denominator follows the site-by-site survey history rather than assuming every reef was watched from the first year.
source("../../_includes/_analysis_helpers.R")
species_table <- compute_change_metrics(benthiccover, baseline, sitedat, earliest_year_group, "coralSpecies")
.end_yrs <- (max(benthiccover$year) - 2):max(benthiccover$year)
.desc_tbl <- species_table |>
arrange(rank_start) |>
transmute(
Species = taxon,
`Repro. mode` = mode,
Commonness = commonness,
`Start rank` = rank_start,
`End rank` = ifelse(is.na(rank_end), "-", as.character(rank_end)),
`Start cover (%)` = sprintf("%.2f", cover_start),
`End cover (%)` = ifelse(is.na(cover_end), "-", sprintf("%.2f", cover_end)),
`Start occ. (%)` = sprintf("%.1f", occ_start),
`End occ. (%)` = sprintf("%.1f", occ_end),
`Start extent (%)` = ifelse(is.na(ext_start), "-", sprintf("%.2f", ext_start)),
`End extent (%)` = ifelse(is.na(ext_end), "-", sprintf("%.2f", ext_end)),
.common = commonness == "Common"
)
kbl(.desc_tbl |> select(-.common), align = c("l", "l", "l", rep("r", 8)),
caption = sprintf(
"Descriptive statistics for coral species in the VINPS monitoring program. Species are ordered by their rank in the baseline period. Reproductive Mode ('Repro. Mode') indicates reproductive strategy. Rows in bold signify common species, determined by the top 90%% cumulative cover threshold during the baseline period (%s). Start Cover (%%), Start Occupancy (Start Occup. (%%)), Start Extent (%%), and Start Rank are metrics from the baseline period. End Cover (%%), End Occupancy (End Occup. (%%)), End Extent (%%), and End Rank are metrics for the recent period (%d-%d).",
earliest_year_group, min(.end_yrs), max(.end_yrs))) |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE) |>
column_spec(1, italic = TRUE) |>
row_spec(which(.desc_tbl$.common), bold = TRUE)| Species | Repro. mode | Commonness | Start rank | End rank | Start cover (%) | End cover (%) | Start occ. (%) | End occ. (%) | Start extent (%) | End extent (%) | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Orbicella annularis | Orbicella annularis | Broadcaster | Common | 1 | 1 | 6.76 | 1.35 | 100.0 | 71.4 | 6.76 | 1.89 |
| Orbicella franksi | Orbicella franksi | Broadcaster | Common | 2 | 3 | 1.44 | 0.60 | 73.1 | 66.7 | 1.58 | 0.90 |
| Porites porites | Porites porites | Brooder | Common | 3 | 4 | 0.91 | 0.53 | 84.8 | 71.4 | 1.06 | 0.75 |
| Montastraea cavernosa | Montastraea cavernosa | Broadcaster | Common | 4 | 9 | 0.55 | 0.04 | 100.0 | 52.4 | 0.55 | 0.07 |
| Porites astreoides | Porites astreoides | Brooder | Common | 5 | 2 | 0.54 | 0.83 | 100.0 | 71.4 | 0.54 | 1.17 |
| Siderastrea siderea | Siderastrea siderea | Broadcaster | Common | 6 | 5 | 0.46 | 0.16 | 100.0 | 71.4 | 0.46 | 0.23 |
| Pseudodiploria strigosa | Pseudodiploria strigosa | Broadcaster | Common | 7 | 10 | 0.36 | 0.02 | 75.5 | 28.6 | 0.48 | 0.09 |
| Agaricia agaricites | Agaricia agaricites | Brooder | Rare | 8 | 7 | 0.33 | 0.13 | 100.0 | 61.9 | 0.33 | 0.22 |
| Orbicella faveolata | Orbicella faveolata | Broadcaster | Rare | 9 | 6 | 0.24 | 0.15 | 60.3 | 71.4 | 0.40 | 0.21 |
| Colpophyllia natans | Colpophyllia natans | Broadcaster | Rare | 10 | 11 | 0.21 | 0.02 | 81.5 | 38.1 | 0.26 | 0.05 |
| Diploria labyrinthiformis | Diploria labyrinthiformis | Broadcaster | Rare | 11 | 14 | 0.15 | 0.01 | 95.0 | 23.8 | 0.15 | 0.03 |
| Dichocoenia stokesii | Dichocoenia stokesii | Broadcaster | Rare | 12 | - | 0.11 | 0.00 | 22.5 | 0.0 | 0.16 | - |
| Dendrogyra cylindrus | Dendrogyra cylindrus | Broadcaster | Rare | 13 | - | 0.09 | 0.00 | 28.7 | 0.0 | 0.12 | - |
| Madracis mirabilis | Madracis mirabilis | Brooder | Rare | 14 | 13 | 0.05 | 0.01 | 31.4 | 23.8 | 0.07 | 0.05 |
| Acropora cervicornis | Acropora cervicornis | Broadcaster | Rare | 15 | - | 0.03 | 0.00 | 15.7 | 0.0 | 0.04 | - |
| Meandrina meandrites | Meandrina meandrites | Broadcaster | Rare | 16 | - | 0.02 | 0.00 | 34.2 | 0.0 | 0.06 | - |
| Stephanocoenia intercepta | Stephanocoenia intercepta | Broadcaster | Rare | 17 | 8 | 0.02 | 0.04 | 36.0 | 47.6 | 0.07 | 0.08 |
| Eusmilia fastigiata | Eusmilia fastigiata | Broadcaster | Rare | 18 | 16 | 0.02 | 0.00 | 48.2 | 14.3 | 0.04 | 0.02 |
| Mycetophyllia lamarckiana | Mycetophyllia lamarckiana | Brooder | Rare | 19 | - | 0.01 | 0.00 | 19.0 | 0.0 | 0.03 | - |
| Madracis decactis | Madracis decactis | Brooder | Rare | 20 | 12 | 0.01 | 0.02 | 23.7 | 38.1 | 0.04 | 0.04 |
| Mycetophyllia ferox | Mycetophyllia ferox | Brooder | Rare | 21 | - | 0.01 | 0.00 | 9.7 | 0.0 | 0.03 | - |
| Pseudodiploria clivosa | Pseudodiploria clivosa | Broadcaster | Rare | 22 | - | 0.01 | 0.00 | 3.3 | 0.0 | 0.04 | - |
| Favia fragum | Favia fragum | Brooder | Rare | 23 | - | 0.01 | 0.00 | 18.5 | 0.0 | 0.02 | - |
| Mycetophyllia aliciae | Mycetophyllia aliciae | Brooder | Rare | 24 | 15 | 0.00 | 0.00 | 9.0 | 9.5 | 0.02 | 0.05 |
| Isophyllia sinuosa | Isophyllia sinuosa | Brooder | Rare | 25 | - | 0.00 | 0.00 | 3.3 | 0.0 | 0.02 | - |
| Porites furcata | Porites furcata | Brooder | Rare | 26 | 17 | 0.00 | 0.00 | 8.0 | 9.5 | 0.02 | 0.02 |
| Helioseris cucullata | Helioseris cucullata | Brooder | Rare | 27 | 18 | 0.00 | 0.00 | 7.3 | 4.8 | 0.02 | 0.01 |
| Siderastrea radians | Siderastrea radians | Brooder | Rare | 28 | - | 0.00 | 0.00 | 2.9 | 0.0 | 0.02 | - |
The analysis objects are written to a timestamped RData file under data/rdata/, tagged with the program and the version (as published or updated) so the two ranges never overwrite each other. The manuscript-values page loads the most recent file for each program and version and reads every derived number from these saved objects, rather than repeating the computation. The derived CSVs written elsewhere are left unchanged.
The trend model runs only on species that were part of the baseline community, so that a modeled slope reflects a species present at the outset rather than one that arrived mid-record. Some species were observed at least once during the record but not during the baseline period, or could not be assigned a reproductive mode; those are held out of the trend analysis and listed here with the year each was first observed.
.excl <- excluded_taxa_table(species_yeargroup_observed, baseline$coralSpecies, "coralSpecies")
kbl(.excl, align = c("l", "r"),
caption = "Taxa excluded from trend analyses because they were not recorded during the baseline period (2001–2005 for TCRMP and VINPS; 1992–1996 for CSUN). For each taxon, the program and year of first observation at shallow monitoring sites are shown. CSUN taxa are at the genus level; TCRMP and VINPS taxa are at the species level.") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE) |>
column_spec(1, italic = TRUE)| Taxon | Year first observed |
|---|---|
| Acropora palmata | 2006 |
| Agaricia fragilis | 2016 |
| Agaricia grahamae | 2011 |
| Agaricia humilis | 2011 |
| Agaricia lamarcki | 2006 |
| Agaricia tenuifolia | 2011 |
| Agaricia undata | 2011 |
| Isopyhyllastrea rigida | 2006 |
| Scolymia cubensis | 2011 |
The coefficient table uses HC3 robust standard errors because the residual variance is not constant across groups. These diagnostic panels show why. Residuals versus fitted values reveal the unequal spread, the normal Q-Q plot shows the tails, and the residual distribution summarizes the whole. The robust standard errors used throughout the analysis are the response to exactly this structure.
model_diagnostics_plot(model_full)