This group project was completed as an assignment for my Master’s program course, Environmental Data Science 213: Metadata Standards, Data Modeling, and Data Semantics. The goal of the group project was to practice the re-use of data for a synthesis project merging different datasets to get new insights, as well as preserving scientific products on a data repository.
Environmental Question:
What is the effect of sea surface temperature on coral bleaching?
Our group discovered the Scott Reef and Rowley Shoals Coral Bleaching Data dataset while searching the DataOne repository for “coral bleaching.” This particular dataset focuses on long-term monitoring data from 1994 to 2017 at reef slope habitats off the coast of northwestern Australia. The metadata includes the reef system, date range, taxonomy, habitat type, coral coverage, and dataset methods.
Search terms: coral bleaching, El Nino effect data, sea surface temperature data, species diversity
Data Management and Analysis Workflow
Coral data was downloaded for the local coral reef ecosystems at Rowley Shoals (RS) and Scott Reef (SR) systems in Northwest Australia. Data on the monthly input to Ocean Nino Index was used to compute the 3 month mean of the temperature anomaly. Both datasets were combined to align the time periods. Then a Simpson’s Diversity Index (SDI) was computed using the functions available in the vegan R package to draw the potential relationships between the temperature anomaly and coral species diversity. The data will be preserved until the SDI methodology has been superseded by more significant research. Our group used a KNB project repository to preserve our data due to their reputation, reliability and commitment to the future in case of company changes.
Potential Legal Implications
Currently, there are no legal constraints associated with acquiring, using and sharing the project data that we are aware of.
Data Download Process
Read in coral data from DataOne
Show code
# assign data url to access coral data from DataOne then download
data_url <- "https://cn.dataone.org/cn/v2/resolve/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fedi%2F952%2F1%2Ff6212784c45d0a077f2c863868d22c4b"
# If data is already up to date, this will throw an error
# error=T in header will move past this issue
#download_d1_data(data_url, dir_name = "data", path = ".")Show code
# Read in data with metajam
coral_list <- read_d1_files(data_path)Read in Oceanic Niño Index (ONI) dataset
Show code
# Read in ONI to be joined to coral data
oni <- read.table(
"https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt",
header = T) %>%
dplyr::mutate(date = as.Date(ISOdate(YR, MON, 1)),
date_start = as.Date(ISOdate(YR, MON, 1)),
date_end = ceiling_date(date_start, "month")) %>%
dplyr::rename(oni_anomaly = ANOM,
month = MON,
year = YR) %>%
dplyr::select(year, month, oni_anomaly, date_start, date_end) %>%
dplyr::mutate(roll_3_month_mean = zoo::rollmean(x = oni_anomaly, k = 3, fill = NA, align = "right")) %>%
filter(date_start > ymd("1994-09-01"))Data Analysis
Joining the two datasets (ONI and coral reef) and calculating the Simpson’s Diversity Index
Simpson’s Diversity Index is a measure of diversity which takes into account the number of species present, as well as the relative abundance of each species. As species richness and evenness increase, diversity increases.
Show code
# join corals and ONI data
coral_oni <- corals %>%
pivot_longer(cols = 5:18, names_to = "species", values_to = "cover") %>%
dplyr::group_by(system, reef, year, month, location) %>%
dplyr::summarise(simpson_index = vegan::diversity(cover, index = "simpson")) %>%
dplyr::left_join(oni %>% select(-date_start, -date_end)) %>%
dplyr::mutate(date = lubridate::make_date(year = year, month = month, day = 1))Visualizing the data
Plotting coral diversity data with the ONI color bar
Show code
ggplot2::ggplot() +
ggplot2::geom_rect(
data = oni,
aes(xmin = date_start, xmax = date_end, ymin = -Inf, ymax = .3, fill = oni_anomaly)) +
ggplot2::scale_fill_viridis_c(
option = "plasma",
guide = guide_colorbar(direction = "horizontal", title.position = "top",
order = 2, barheight = unit(.2, "cm"))) +
ggplot2::scale_x_date(date_breaks = "2 year", date_labels = "%Y", expand = expansion(mult = c(0,0))) +
ggplot2::geom_line(data = coral_oni, size = 1,
aes(x = date, y = simpson_index, color = location)) +
ggplot2::scale_color_viridis_d() +
ggplot2::guides(color = guide_legend(order = 1)) +
ggplot2::labs(fill = "Oceanic Ni\u00f1o Index", color = "Sites",
x = "Date", y = "Simpsons Diversity Index") +
ggplot2::theme_minimal()
Simpson’s Diversity Index calculated by site from 14 taxa of corals plotted from 1994 to 2021. The Oceanic Niño Index monthly values are plotted as a colorbar to highlight duration and intensity of sea surface temperatures in the Niño region 3.4.
Data Preservation
Our group used a Knowledge Network for Biocomplexity (KNB) project repository to preserve our data due to its reputation, reliability, and commitment to the future access regardless of transitions with in the organization. Data will be preserved and relevant until the Simpson’s Diversity Index methodology has been superseded by more significant research.
Data Sources:
James P Gilmour, Kylie L Cook, and Nicole M Ryan. 2021. Scott Reef
and Rowley Shoals Coral Bleaching Data. Environmental Data
Initiative.
LTER
Data Access
- Download Method: metajam
data_downloade API
- Spatial Coverage: Scott Reef
and Rowley Reef Ecosystems in - Northwestern Australia
-
Temporal Coverage: 1994 to 2020
-
Parameters of Interest for Analysis: Species
Population
NOAA/ National Weather Service National Centers for Environmental
Prediction Climate Prediction Center Oceanic Niño Index (ONI).
NOAA
ENSO Data
- Download Method: read.table from
base R
- Spatial Coverage: 5N to 5S, 170W to
120W
- Temporal Coverage: 1950 to 2021
-
Parameters of Interest for Analysis: SST
Anomaly