Reef Code

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

5.1 Absolute and relative coral cover and diversity

This page derives coral resilience metrics from coral-genera percent cover across all sites and years. It computes absolute coral cover, coral cover relative to pre-disturbance values, relative recovery from the start of the recovery interval, and the diversity of coral genera as an inverse Simpson index. These metrics guide the multivariate analyses in section 5.2 and the choice of response variables in later analyses.

Data sources

This page draws on the reef monitoring programs described on the Data Sources page: TCRMP, VINPS, and CSUN. It reads the site master table and the derived coral-genera percent cover product (s2pt4). The resilience metrics computed here are saved to a workspace and offered for download in the next section (see Downloads below).

Import data

import sitedat

Show code

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

import coralcover

Show code

# file_coralcover <- "s2pt3_benthicCoverMajorBenthicCategories_35sites_2003_2022"
file_coralcover <- latest_output("s2pt4_benthicCoverCoralGenera")

data_coralcover <- read.csv(paste("../../outputs/",file_coralcover,".csv",sep=""))
metadata_coralcover <- readLines(paste("../../outputs/",file_coralcover,".txt",sep=""))

metadata

###############################
filename: s2pt4_benthicCoverCoralGenera_49sites_1987_2023.csv
date: 2026-07-06 01:33:05.158472
description: aggregated surveyed coral species by genera and annual percent cover at 35 sites. The idea to use coral genera is based on coral genera groupings in `csun_random_benthicDat` , resilience of coral genera scored by @vanwoesik2012 , and analyses of coral genera frequency by @longterm_moritz_2021
###############################

column names:
year program date site period replicate replicatetype coralGenera perccover pres

column: year
description: survey year
Data Type: numeric
Mean: 2012.58813260352
Min: 1987
Max: 2023

column: program
description: monitoring program
Data Type: character
Unique Values: (showing all 3 entries)
   TCRMP
   VINPS
   CSUN

column: date
description: date of survey
Data Type: Date
Date Range: 1987-06-01 to 2023-12-07

column: site
description: survey site
Data Type: character
Unique Values: (showing all 49 entries)
   Cane Bay
   Eagle Ray
   Lang Bank Red Hind FSA
   Jacks Bay
   Buck Island STX
   Salt River West
   Coculus Rock
   Magens Bay
   Sprat Hole
   Fish Bay
   Brewers Bay
   Botany Bay
   Hind Bank East FSA
   Castle
   Grammanik Tiger FSA
   College Shoal East
   Mutton Snapper FSA
   Flat Cay
   Seahorse Cottage Shoal
   Great Pond
   Black Point
   Savana
   South Capella
   Buck Island STT
   South Water
   St James
   Meri Shoal
   Kings Corner
   Salt River Deep
   Lang Bank EEMP
   Cane Bay Deep
   Ginsburg Fringe
   Coral Bay
   Buck Island STX Deep
   VIIS-Newfound
   VIIS-Yawzi
   VIIS-Mennebeck
   VIIS-Haulover
   VIIS-Tektite
   BUIS-Western Spur and Groove
   BUIS-South Fore Reef
   Cabritte Horn
   East Tektite
   Europa Bay
   West Little Lameshur
   West Tektite
   White Point
   Yawzi
   Tektite

column: period
description: monitoring period
Data Type: character
Unique Values: (showing all 6 entries)
   Annual
   PeakBL
   WS
   SCTLD
   PostBL
   NA

column: replicate
description: replicate number/identifier
Data Type: character
Unique Values: (showing all 20 entries)
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20

column: replicatetype
description: type of replicate
Data Type: character
Unique Values: (showing all 2 entries)
   transect
   site

column: coralGenera
description: coral genus
Data Type: character
Unique Values: (showing all 29 entries)
   Millepora
   Dendrogyra
   Acropora
   Orbicella
   Scolymia
   Porites
   Mycetophyllia
   Mussa
   Madracis
   Isopyhyllastrea
   unknown
   unknown juv.
   Helioseris
   Oculina
   Solenastrea
   Tubastraea
   Stephanocoenia
   Siderastrea
   Montastraea
   Meandrina
   Favia
   Eusmilia
   Diploria
   Dichocoenia
   Colpophyllia
   Pseudodiploria
   Manicina
   Agaricia
   Isophyllia

column: perccover
description: percentage cover of that coral genus
Data Type: numeric
Mean: 0.414024306680286
Min: 0
Max: 74.23

column: pres
description: presence/absence of that coral genus
Data Type: numeric
Mean: 0.160589914713281
Min: 0
Max: 1

Interactive dashboard

Explore the complete coral-genera percent-cover dataset used in this section. The plot shows mean percent cover through time for the most abundant genera; the table is the entire dataset, searchable and filterable by any column.

Table shows a random sample of 15,000 of 219,964 rows. The plot above uses every row.

site/year replication

site	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2000	2001	1992	1993	1994	1995	1996	1997	1998	1999	1987	1988	1989	1990	1991
BUIS-South Fore Reef	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
BUIS-Western Spur and Groove	19	20	20	20	20	20	20	20	20	20	20	20	20	NA	NA	NA	NA	NA	NA	NA	NA	NA	19	19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Black Point	NA	6	NA	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Botany Bay	6	6	6	6	6	6	6	6	6	5	NA	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Brewers Bay	6	6	NA	NA	NA	NA	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Buck Island STT	NA	NA	NA	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Buck Island STX	3	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Buck Island STX Deep	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Cabritte Horn	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
Cane Bay	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Cane Bay Deep	NA	NA	NA	NA	NA	NA	NA	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Castle	NA	6	NA	NA	NA	NA	NA	NA	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Coculus Rock	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
College Shoal East	NA	6	NA	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	5	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Coral Bay	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	6	NA	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eagle Ray	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
East Tektite	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
Europa Bay	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
Fish Bay	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	NA	6	6	6	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Flat Cay	NA	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Ginsburg Fringe	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	NA	6	6	6	NA	6	6	NA	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Grammanik Tiger FSA	NA	6	10	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Great Pond	NA	6	6	6	6	6	6	NA	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Hind Bank East FSA	NA	6	10	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Jacks Bay	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Kings Corner	NA	NA	NA	NA	NA	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Lang Bank EEMP	NA	NA	NA	NA	NA	NA	NA	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Lang Bank Red Hind FSA	5	NA	6	4	6	NA	NA	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Magens Bay	6	6	6	6	5	6	6	6	6	6	NA	6	6	6	6	6	6	6	6	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Meri Shoal	NA	NA	NA	6	6	6	5	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Mutton Snapper FSA	NA	6	6	6	6	6	6	6	6	5	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Neptunes Table	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
SARI-Salt River	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Salt River Deep	NA	NA	NA	NA	NA	NA	NA	6	6	5	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Salt River West	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Savana	NA	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Seahorse Cottage Shoal	NA	6	10	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
South Capella	NA	6	10	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
South Water	NA	NA	NA	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Sprat Hole	5	6	6	6	NA	6	6	6	6	6	6	6	6	6	6	NA	6	6	NA	6	6	6	NA	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
St James	NA	NA	NA	6	6	6	6	6	6	6	6	6	6	6	6	NA	6	6	6	6	6	6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Tektite	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
VIIS-Haulover	NA	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
VIIS-Mennebeck	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	19	20	20	20	20	NA	20	20	20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
VIIS-Newfound	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	NA	NA	NA	NA	NA	NA	NA	20	NA	NA	NA	NA	NA
VIIS-Tektite	NA	NA	NA	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
VIIS-Yawzi	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	20	NA	20	20	20	NA	NA	NA	NA	NA	NA	NA	20	NA	NA	NA	NA	NA
West Little Lameshur	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
West Tektite	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
White Point	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	NA	NA	NA	NA	NA
Yawzi	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1

Derive metrics from coral cover

1. Absolute coral cover

Show code

# 1: Group the data by 'program', 'site', 'replicate', 'year', and 'date'
# 2: Calculate the sum of 'perccover' for each group
# 3: Create a new column 'coralcover_date' and set it equal to the 'date' column
# 4: Remove the 'date' column from the dataset
# 5: Ungroup the dataset, removing the grouping structure
mod_coralcover <- data_coralcover |> 
  group_by(program, site, replicate, year, date) |>
  summarise(coralcover = sum(perccover)) |>
  mutate(coralcover_date = date) |>
  select(-date) |>
  ungroup()

2. Relative coral cover

want to scale coral cover relative to pre-disturbance values (2003-2005)

the equation

\[\frac{{\text{coral cover post-disturbance} - \text{coral cover pre-disturbance}}}{{\text{coral cover pre-disturbance}}}\]

In the context of measuring reef recovery represents the relative change in coral cover over the period of a disturbance event, expressed as a fraction or percentage of the original (pre-disturbance) coral cover.

A positive value indicates that the coral cover post-disturbance is greater than the coral cover pre-disturbance, meaning there has been an increase in coral cover.

A negative value indicates that the coral cover post-disturbance is less than the coral cover pre-disturbance, implying a loss in coral cover.

Interpretation

Fluctuating Between Positive and Negative: could indicate a dynamic environment where the reef experiences periods of recovery and decline.
Approaching Zero or remaining consistent might suggest stabilization and balance of disturbances and recovery are in balance. If remaining consistent at some negative value, could indicate a shift in the dominant benthic organism(s) on that site.

“Moreover, the slowing of recovery usually indicates that a system is deteriorating and may be approaching a critical threshold, beyond which the system switches to an alternate and often undesirable state19,20. Indeed,understanding the rates of change and the resilience of systems has become central to our understanding of modern ecology21,22” Woesik et al. (2018)

Show code

# 1: Create 'mod_coralcover_predist' dataset
# 2: Filter the data to include only records for the years in predisturbance interval
# 3: Group the data by 'program', 'site', 'replicate', 'year', and 'date'
# 4: Calculate the sum of 'perccover' for each group
# 5: Ungroup the dataset, removing the grouping structure
# 6: Group the data again by 'program', 'site', and 'replicate'
# 7: Calculate the mean 'coralcover' for the years predist
# 8: Ungroup the dataset, removing the grouping structure

mod_coralcover_predist <- data_coralcover |>
  # filter(site %in% sitetokeep) |>
  filter(date < "2005-08-01") |>
  # filter(year %in% c(2003, 2004, 2005)) |>
  group_by(program, site, replicate, year, date) |>
  summarise(coralcover = sum(perccover)) |>
  ungroup() |>
  group_by(program, site, replicate) |>
  summarise(coralCoverPreDisturbance = mean(coralcover)) |>
  ungroup()

# 1: Create 'mod_coralcover_relative' dataset:
# 2: Filter the data to include only records within a specified year range
# 3: Group the data by 'program', 'site', 'replicate', 'year', and 'date'
# 4: Calculate the sum of 'perccover' for each group
# 5: Create a new column 'coralcover_date' and set it equal to the 'date' column
# 6: Remove the 'date' column from the dataset
# 7: Left join with 'mod_coralcover_predist' dataset using 'site' and 'replicate' as the join keys
# 8: Ungroup the dataset, removing the grouping structure
# 9: Calculate 'coralcover_relative' by subtracting 'coralCoverPreDisturbance' and dividing by 'coralCoverPreDisturbance'
mod_coralcover_relative <- data_coralcover |>
  # filter(site %in% sitetokeep) |>
  filter(year >= set_minyear & year <= set_maxyear) |>
  group_by(program, site, replicate, year, date) |>
  summarise(coralcover = sum(perccover)) |>
  mutate(coralcover_date = date) |>
  select(-date) |>
  left_join(
    mod_coralcover_predist |> select(site, replicate, coralCoverPreDisturbance),
    by = c("site", "replicate")
  ) |>
  ungroup() |>
  mutate(coralcover_relative = ((coralcover - coralCoverPreDisturbance) / coralCoverPreDisturbance
  ))

make mod

Show code

mod <- mod_coralcover_relative

3. Relative recovery

want to scale coral cover relative to the year that recovery started (2007)

the equation

\[\frac{{\text{coral cover post-disturbance} - \text{coral cover at beginning of recovery interval}}}{{\text{coral cover at beginning of recovery interval}}}\]

Show code

mod_coralcover_beginningRecovery <- data_coralcover |>
  # filter(site %in% sitetokeep) |>
  filter(year == toString(recovery_interval[1])) |>
  # filter(year %in% c(2003, 2004, 2005)) |>
  group_by(program, site, replicate, year, date) |>
  summarise(coralcover = sum(perccover)) |>
  ungroup() |>
  group_by(program, site, replicate) |>
  summarise(coralCoverRecoveryStart = mean(coralcover)) |>
  ungroup()

mod_coralcover_relativeRecovery <- data_coralcover |>
  # filter(site %in% sitetokeep) |>
  filter(year >= recovery_interval[1] & year <= recovery_interval[2]) |>
  group_by(program, site, replicate, year, date) |>
  summarise(coralcover = sum(perccover)) |>
  mutate(coralcover_date = date) |>
  select(-date) |>
  left_join(
    mod_coralcover_beginningRecovery |> select(site, replicate, coralCoverRecoveryStart),
    by = c("site", "replicate")
  ) |>
  ungroup() |>
  mutate(coralcover_relative = ((coralcover - coralCoverRecoveryStart) / coralCoverRecoveryStart
  ))

make mod

Show code

mod <- mod_coralcover_relativeRecovery

4. Diversity of coral genera

The Inverse Simpson Diversity Index is calculated for each combination of year, site, and replicate. This index provides insights into the diversity of coral genera observed during each survey, with higher values indicating greater diversity.

the Simpson Diversity Index (\(D\)):

\[D = \sum_{i=1}^{S} p_i^2\]

where:

\(p_i\) would be the proportion of each coralGenera in each year, site, and replicate, which can be calculated as the perccover of that coralGenera divided by the total perccover for that group.
\(S\) would be the number of unique coralGenera in that group.

This index calculates the probability that two randomly selected individuals (here, points) from the dataset will belong to the same species. A higher value of \(D\) implies lower diversity, as it indicates a higher probability of two individuals being of the same species.

And the inverse Simpson Diversity Index is:

\[D_{\text{inv}} = \frac{1}{D}\]

This value will increase as \(D\) decreases, meaning that higher diversity (more equal distribution among species) will result in a higher \(D_{\text{inv}}\). Conversely, lower diversity (one or few species dominating) will result in a lower \(D_{\text{inv}}\).

Show code

# 1: Calculate total coral cover for each combination of 'site', 'year', and 'replicate'
# 2: Left join 'simpson_calculation' with the original 'data_coralcover' dataset
# 3: Calculate the proportion of coral cover for each observation
# 4: Group by 'site', 'year', and 'replicate' again
# 5: Calculate the Simpson index for each group
# 6: Calculate Simpson diversity by taking the reciprocal of the Simpson index
# 7: Calculate the mean and standard deviation of Simpson diversity for each combination of 'site' and 'year'
simpson_calculation <- data_coralcover |>
  group_by(site, year, replicate) |>
  summarise(total = sum(perccover))  |>
  left_join(data_coralcover)  |>
  mutate(proportion = perccover / total) |>
  group_by(site, year, replicate)  |>
  summarise(simpson_index = sum(proportion ^ 2))  |>
  mutate(simpson_diversity = 1 / simpson_index)

# Calculate the mean and standard deviation of Simpson diversity for each combination of 'site' and 'year'
simpson_calculation_sum <- simpson_calculation |>
  group_by(site, year) |>
  summarise(meandiv = mean(simpson_diversity),
            sddiv = sd(simpson_diversity))

left join with mod

Show code

mod_coralcover_relative <- mod_coralcover_relative |>
  left_join(
    simpson_calculation |> select(site, year, replicate, simpson_diversity),
    by = c("site", "year", "replicate")
  )

Average across transects

calculating summary df, creating a new ‘status’ column, setting specific values for ‘status’ based on year, joining with another dataset, converting columns to factors, and reordering factor levels.

Show code

# 1: Group 'mod_coralcover_relative' dataset by 'site' and 'year'
# 2: Calculate the mean and standard deviation of 'coralcover_relative', 'coralcover', and "simpson_diversity' for each group
# 3: Calculate the mean of 'coralCoverPreDisturbance' (same as original coralCoverPreDisturbance, which is already an average)
# 4: Create a new column 'status' based on a conditional statement, whther relative is 
summary_coralcover_absreldiv <- mod_coralcover_relative |>
  group_by(site, year) |>
  summarise(
    mean_coralcover = mean(coralcover),
    sd_coralcover = sd(coralcover),
    mean_coralcover_relative = mean(coralcover_relative),
    sd_coralcover_relative = sd(coralcover_relative),
    mean_coraldiversity = mean(simpson_diversity),
    sd_coraldiversity = sd(simpson_diversity),
    coralCoverPreDisturbance = mean(coralCoverPreDisturbance)
  ) |>
  mutate(status = ifelse(mean_coralcover_relative > 0, "recovered", "not recovered"))

# Set 'status' to "pre-disturbance" for certain years
summary_coralcover_absreldiv$status[which(
  summary_coralcover_absreldiv$year %in% c(predisturbance_interval[1]:predisturbance_interval[2])
)] <-
  "pre-disturbance"

# Left join 'summary_coralcover_absreldiv' with selected columns from 'sitedat' based on 'site'
summary_coralcover_absreldiv <- summary_coralcover_absreldiv |>
  left_join(sitedat |>
              select(c(site, depth)), by = "site")

# Convert 'site' column to a factor
summary_coralcover_absreldiv$site <-
  factor(summary_coralcover_absreldiv$site)

# Reorder levels of 'site' based on 'depth'
summary_coralcover_absreldiv$site <-
  reorder(summary_coralcover_absreldiv$site,
          summary_coralcover_absreldiv$depth,
          FUN = mean)

# Reorder levels of 'site' again based on 'coralCoverPreDisturbance'
summary_coralcover_absreldiv$site <-
  reorder(
    summary_coralcover_absreldiv$site,
    summary_coralcover_absreldiv$coralCoverPreDisturbance,
    FUN = mean
  )

# Convert 'status' column to a factor with specific levels
summary_coralcover_absreldiv$status <-
  factor(
    summary_coralcover_absreldiv$status,
    levels =  c("pre-disturbance", "not recovered", "recovered")
  )

Alternate plot: sites not grouped

numeric(0)

Sec 5.1 Figure 1: The relative percent coral cover over time. Values are relative to average of pre-disturbance values. Sites are ordered from lowest to highest coral cover pre-disturbance. Points show average relative coral cover +/- sd, and are colored by the absolute coral cover at that year. Dashed horizontal line indicates y= 0, above which sites showed gain of coral cover since the disturbance, possibly indicating recovery or growth, and below which sites showed loss of coral cover since the disturbance, reflecting ongoing losses post disturbance and no recovery. In this plot, a value of 1 indicates a doubling of coral cover relative to pre-disturbance, and avalue of 0 indicates complete loss of coral cover relative to pre disturbance. The grey region and ribbon in the background shows coral diversity, calculated as the inverse simpsons D of coral genera.

Save workspace

this will allow to load in next steps

Show code

rm(list = setdiff(ls(),c("summary_coralcover_absreldiv","mod_coralcover_relative","set_minyear","set_maxyear","predisturbance_interval","resistance_year","recovery_interval","sitedat","mod_coralcover_relativeRecovery")))

save(list = ls(), file = "coralCov.Rdata")
rm(list = ls())

Downloads

The resilience metrics derived on this page are saved to the shared workspace and offered for download in the next section.

References

Woesik, Robert van, Semen Köksal, Arzu Ünal, Chris W. Cacciapaglia, and Carly J. Randall. 2018. “Predicting Coral Dynamics Through Climate Change.” Scientific Reports 8 (1). https://doi.org/10.1038/s41598-018-36169-7.

5.1 Absolute and relative coral cover and diversity

On this page

Data sources

Import data

Interactive dashboard

Derive metrics from coral cover

1. Absolute coral cover

2. Relative coral cover

3. Relative recovery

4. Diversity of coral genera

Average across transects

Alternate plot: sites not grouped

Save workspace

Downloads

References