Reef Code
About this site
Data Sources
more about
study sites making of the logo
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

4.7 Management

Last updated

July 6, 2026

4.7 Management

On this page

This page attaches marine-management attributes to each VIRRS monitoring site. We assigned every site to its overlapping managed area by hand: we opened the USA Dataset of Marine Managed Areas in the online map viewer, imported the VIRRS study sites, and read off the managed area each site falls within. The page reads three tables (the site master, the managed-area catalog, and the manual site-to-area assignments), joins them into one site-management table, and classifies each site into four levels of fishing protection, following the fishing-gear-restriction scheme of Gove et al. (2023). It produces one downloadable product, siteManagementStatus, with its metadata sidecar, plus a figure (Figure 1) that maps the protection levels across the study region.

Data sources

This page uses the management datasets: the site master (program, site, latitude, longitude), the managed-area catalog (managed-area attributes from the USA Dataset of Marine Managed Areas), and the manual site-to-area assignments. Each links to its entry on the Data Sources page. The derived site-management table produced here is available in the Downloads section below.

Site data

This table holds the program, site name, and coordinates for every monitoring site.

Show code
sitedat <-
  read.csv(
    "../../../RRSdata/00_RRS_dataCatalogStatus/00_RRS_siteMaster_allSites_data.csv"
  )

List of managed areas

This table catalogs the marine managed areas and their attributes.

Show code
managedAreas <-
  read.csv(
    "../../../RRSdata/data_adhoc/management/marineManagedAreas_data.csv"
  )

Manually assigned management categories

This table records the managed area we hand-assigned to each site.

Show code
managedAreaAssignments <-
  read.csv(
    "../../../RRSdata/data_adhoc/management/marineManagedAreas_siteAssignments_allSites_data.csv"
  )

Join to build the site-management table

We join the manual assignments to the managed-area catalog and to the site master, then rename the columns to a compact, descriptive set.

Show code
siteManagement <- managedAreaAssignments |>
  left_join(managedAreas, by = "Site.Name") |>
  select(-c(OBJECTID, Site.ID, Country, State))

siteManagement <- siteManagement[, c(1:24)]

siteManagement <- siteManagement |>
  select(
    -c(
      Effective.From,
      Effective.To,
      Season,
      Report.Violations,
      S.57.Category,
      IUCN.Category,
      WDPA.ID
    )
  )

siteManagement <- siteManagement |>
  left_join(sitedat |> select(c(program, site, lat, lon)), by = "site") |>
  select(program, site, lat, lon, everything())

colnames(siteManagement) <-
  c(
    "program",
    "site",
    "lat",
    "lon",
    "managedAreaName",
    "website",
    "managingAuthority",
    "levelOfGovernment",
    "designation",
    "categories",
    "yearEstablished",
    "purpose",
    "restrictions",
    "allowed",
    "regulationName",
    "regulationWebsite",
    "latestUpdates",
    "protectionFocus",
    "speciesOfConcern",
    "levelFishingProtection"
  )

Map sites by level of fishing protection

We classify each site into one of four levels of fishing protection, then map those levels across the study region. Figure 1 shows where the more- and less-protected sites sit, which lets downstream analyses group sites by protection level.

Show code
siteManagement$LFP <- "Least Restrictive"
siteManagement$LFP[grep("Heavily", siteManagement$levelFishingProtection)] <-
  "Heavily Restrictive"
siteManagement$LFP[grep("Moderately", siteManagement$levelFishingProtection)] <-
  "Moderately Restrictive"
siteManagement$LFP[grep("Most ", siteManagement$levelFishingProtection)] <-
  "Most Restrictive"

siteManagement$LFP <-
  factor(
    siteManagement$LFP,
    levels = c(
      "Least Restrictive",
      "Moderately Restrictive",
      "Heavily Restrictive",
      "Most Restrictive"
    )
  )

# Define the bounding box
bbox <- make_bbox(siteManagement$lon, siteManagement$lat, f = 0.1)

# Fetch the map
# map <-
#   get_map(location = bbox,
#           source = "stamen",
#           maptype = "toner-lite")  # You can change the source if you prefer a different map style

background_map <-
  st_read(dsn = "../shapefiles/pvishrpl/", layer = "pvishrpl", quiet = TRUE)
lon_range <- range(sitedat$lon)
lat_range <- range(sitedat$lat)


p <- ggplot() +
  # Plot the background map
  geom_sf(data = background_map, fill = "grey50", color = "white") +
  geom_point(
    data = siteManagement,
    aes(x = lon, y = lat, fill = LFP),
    shape = 21,
    col = "black",
    size = 4,
    alpha = 0.7
  ) +
  geom_point() +
  labs(x = "Longitude", y = "Latitude") +
  scale_fill_manual(values = c("blue", "green", "orange", "red")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(legend.position = "bottom") +
  coord_sf(xlim = lon_range, ylim = lat_range) +
  # labs(x = "Longitude", y = "Latitude") +
  theme_minimal() +
  theme(legend.position = "bottom")

p
# svg(filename="management.svg", width = 13, height =13)
# p
# dev.off()
Sec 4.7 Figure 1: Level of fishing protection at each VIRRS monitoring site, mapped over the study region. Point color encodes the protection level: least restrictive (no known fishing restrictions), moderately restrictive (several species- or gear-specific restrictions apply, or either commercial or recreational fishing is entirely prohibited), heavily restrictive (fishing is mostly prohibited, with few exceptions), and most restrictive (fishing is prohibited).

Filter to the analysis site set

We keep only sites added by the maxyearadded cutoff, so the management table matches the site set used across the rest of the analysis.

Show code
sitekp <-
  sitedat |>
  filter(yearadded <= maxyearadded) |>
  select(site)

siteManagement <-
  siteManagement |>
  filter(site %in% sitekp$site)

Downloads

The buttons below download the site-management table and its metadata sidecar, both generated by the code on this page.

version 1.0.0 • in-review • data ≤ 2023-12-31

References

Gove, Jamison M., Gareth J. Williams, Joey Lecky, Eric Brown, Eric Conklin, Chelsie Counsell, Gerald Davis, et al. 2023. “Coral Reefs Benefit from Reduced Landsea Impacts Under Ocean Warming.” Nature, August. https://doi.org/10.1038/s41586-023-06394-w.