A data-driven approach supporting the 50 Reefs Portfolio
Identifying climate-resilient reefs is crucial for the conservation and management of coral reef ecosystems in the face of climate change. These reefs have the potential to withstand and recover from environmental stressors, maintaining their ecological functions and biodiversity. Focusing conservation efforts on these climate-resilient reefs would increase the overall survival of coral reef biodiversity and also support the livelihood of communities dependent on these ecosystems (McClanahan et al. 2024).
Refugia are defined as locations where biodiversity retreats to, persists in, and potentially expands from once environmental conditions change. Three categories of refugia for reefs have been defined based on coral life history strategies (Darling et al. 2012): avoidance, resistance and recovery (ARR). These locations represent diverse responses that facilitate strategic conservation interventions.
The 50 Reefs portfolio, a global study that was conducted by Beyer et al. (2018), identifies potential climate refugia locations anticipated to avoid future coral losses and is founded on excess heat metrics, such as degree heating waves. To extend the 50 Reefs portfolio, there is a need to evaluate and validate the predictions of climate refugia with long-term field data on coral abundance and diversity.
Turning data into action
To expand and refine the 50 Reefs portfolio within the limited timeframe leading up to 2030, we are undertaking a pilot analysis using a decade's worth of benthic data from WCS-Fiji (see map below). This effort aims to advance innovative science by incorporating coral life history strategies—specifically avoidance, resistance, and recovery reefs—into the 50 Reefs study.
Getting data from MERMAID
This article serves to provide a guideline for using MERMAID benthic data to identify climate-resilient reefs as described by Darling et al. (2012) and McClanahan et al. (2024). Identifying locations of resilient reefs means locating areas of Avoidance, Resistance and Recovery reefs that can be used for planning purposes. Using the mermaidr package, you can map out refugia sites with your data. If you would like to fully replicate the climate resilient strategy code, please refer to the full resource at the bottom of this article.
fijiProjects <- mermaid_get_my_projects(include_test_projects = F) %>%
filter(countries == "Fiji")
wcsFijiProjects <- fijiProjects %>%
filter(grepl(pattern = "WCS Fiji", x = tags))
fijiBenthicPitObsTBL <- mermaid_get_project_data(
project = wcsFijiProjects,
method = "benthicpit",
data = "observations"
)Filtering MERMAID data
The first step of identifying climate-resilient reefs involves creating subsets from your projects to remove any benthic PIT transects that have >20% of the hard corals unassigned to a life history (i.e. weedy, stress-tolerant, competitive and generalist categories).
fijiBenthicPitSuUnassignedTBL <- fijiBenthicPitObsTBL %>%
filter(benthic_category == "Hard coral") %>%
group_by(sample_unit_id) %>%
summarise(NumUnassignedHardCoral = sum(life_histories_competitive == 0 &
life_histories_generalist == 0 &
life_histories_stress_tolerant == 0 &
life_histories_weedy == 0)) %>%
ungroup() %>%
right_join(fijiBenthicPitObsTBL %>%
group_by(sample_unit_id) %>%
summarise(NumPts = length(benthic_category)) %>%
ungroup,
by = "sample_unit_id") %>%
mutate(NumUnassignedHardCoral = replace_na(NumUnassignedHardCoral, 0),
PercUnassignedHardCoral = NumUnassignedHardCoral/NumPts*100) %>%
select(sample_unit_id, PercUnassignedHardCoral)
#Get the sample unit level data and remove >20% unassigned
fijiBenthicPitSuTBL <- mermaid_get_project_data(
project = wcsFijiProjects,
method = "benthicpit",
data = "sampleunits") %>%
left_join(fijiBenthicPitSuUnassignedTBL %>%
rename(sample_unit_ids = sample_unit_id),
by = "sample_unit_ids") %>%
filter(PercUnassignedHardCoral <= 20)Calculate averages for life histories and coral richness
Once you have assigned filtered the data, we then calculate the averages for life history categories and the total unique coral richness per sample event.
fijiBenthicPitSeTBL <- fijiBenthicPitSuTBL %>%
group_by(project, tags, country, site, latitude, longitude,
management, management_rules, sample_date, sample_event_id) %>%
summarise(mean_percent_cover_life_histories_weedy = mean(percent_cover_life_histories_weedy),
mean_percent_cover_life_histories_generalist = mean(percent_cover_life_histories_generalist),
mean_percent_cover_life_histories_competitive = mean(percent_cover_life_histories_competitive),
mean_percent_cover_life_histories_stress_tolerant = mean(percent_cover_life_histories_stress_tolerant),
mean_percent_hard_coral = mean(percent_cover_benthic_category_hard_coral),
.groups = "keep")
fijiBenthicPitSeTotalCoralRichTBL <- fijiBenthicPitObsTBL %>%
filter(sample_unit_id %in% fijiBenthicPitSuTBL$sample_unit_ids &
benthic_category == "Hard coral") %>%
group_by(sample_event_id) %>%
summarise(NumUniqueHardCoralBAs = length(unique(benthic_attribute)))
fijiBenthicPitSeTBL <- fijiBenthicPitSeTBL %>%
left_join(fijiBenthicPitSeTotalCoralRichTBL,
by = "sample_event_id")Map locations based on climate resilience strategies
The next step is to assign each site to a climate strategy (i.e. Avoidance, Resistance or Recovery) based on the dominant life histories found there. We then use existing shapefiles and the 50 Reefs geojson file to map locations of dominant climate-resilient reefs in Fiji. Note that the full resource creates an interactive map. The image below is a saved static image of the interactive map.
fijiBenthicPitSeTBL <- fijiBenthicPitSeTBL %>%
mutate(ClimateStrategy =
case_when(mean_percent_cover_life_histories_competitive >
mean_percent_cover_life_histories_weedy &
mean_percent_cover_life_histories_competitive >
mean_percent_cover_life_histories_generalist &
mean_percent_cover_life_histories_competitive >
mean_percent_cover_life_histories_stress_tolerant ~
"avoidance",
mean_percent_cover_life_histories_stress_tolerant >
mean_percent_cover_life_histories_weedy &
mean_percent_cover_life_histories_stress_tolerant >
mean_percent_cover_life_histories_generalist &
mean_percent_cover_life_histories_stress_tolerant >
mean_percent_cover_life_histories_competitive ~
"resistance",
mean_percent_cover_life_histories_weedy >
mean_percent_cover_life_histories_generalist &
mean_percent_cover_life_histories_weedy >
mean_percent_cover_life_histories_competitive &
mean_percent_cover_life_histories_weedy >
mean_percent_cover_life_histories_stress_tolerant ~
"recovery",
.default = "none"))
fijiClimateResTBL <- fijiClimateResTBL %>%
mutate(ClimateStrategy = factor(ClimateStrategy,
levels = c("avoidance", "resistance",
"recovery", "none")))
#Open the 50 reefs fiji Geojson file
fiftyReefsGeojson <- read_sf("FiftyReefsFiji.geojson")
#Colors for 50 reefs
fiftyReefsPal <- colorFactor("Dark2", domain = levels(fiftyReefsGeojson$BCU_nam))
# Legend size values and labels
size_values <- scale_size(c(10, 20, 30, 40)) # Ensure alignment with scaling logic
size_labels <- c("10%", "20%", "30%", "40%")
# Define color legend
color_pal <- colorFactor(
palette = c("#FF9999", "lightyellow", "#6699FF", "#66CC66"),
domain = levels(fijiClimateResTBL$ClimateStrategy)
)
# Create a leaflet map with circle markers
coralResilience50reefsMap <- leaflet() %>%
addTiles(group = "StreetMap") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "ESRI World Imagery") %>%
setView(lng = 179, lat = -17.5, zoom = 8) %>%
addPolygons(data = fiftyReefsGeojson,
color = fiftyReefsPal(fiftyReefsGeojson$BCU_nam),
stroke = 1,
opacity = 0.8,
group = "50 Reefs") %>%
addLegendSize(
position = "bottomright",
size_values = size_values,
size_labels = size_labels,
title = "Circle Size: Hard Coral %",
colors = "black"
) %>%
addLegend(
position = "bottomright",
pal = color_pal,
values = levels(fijiClimateResTBL$ClimateStrategy), # Use factor levels
title = "Climate Strategy",
opacity = 1
) %>%
addLegend(data = fiftyReefsGeojson,
position = "bottomright",
pal = fiftyReefsPal,
values = ~BCU_nam,
title = "50 Reefs Name",
opacity = 1) %>%
addLayersControl(baseGroups = c("StreetMap", "ESRI World Imagery"),
overlayGroups = c("avoidance", "resistance",
"recovery", "none", "50 Reefs"),
position = "topright")
References
Beyer, H. L., Kennedy, E. V., Beger, M., Chen, C. A., Cinner, J. E., Darling, E. S., Eakin, C. M., Gates, R. D., Heron, S. F., & Knowlton, N. (2018). Risk‐sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, 11(6), e12587. https://doi.org/10.1111/conl.12587.
Darling, E. S., Alvarez‐Filip, L., Oliver, T. A., McClanahan, T. R., & Côté, I. M. (2012). Evaluating life‐history strategies of reef corals from species traits. Ecology Letters, 15(12), 1378–1386. https://doi.org/10.1111/j.1461-0248.2012.01861.x.
McClanahan, T. R., Darling, E. S., Beger, M., Fox, H. E., Grantham, H. S., Jupiter, S. D., Logan, C. A., Mcleod, E., McManus, L. C., & Oddenyo, R. M. (2024). Diversification of refugia types needed to secure the future of coral reefs subject to climate change. Conservation Biology, 38(1), e14108. https://doi.org/10.1111/cobi.14108.