Essential Ocean Variables (EOV) plot for reporting

Source: R/plot_timeseries.R

Create a three-panel figure showing Essential Ocean Variables over time, including the raw time series, anomalies, and climatology. This format is designed for scientific reporting and State of Environment assessments.

pr_plot_EOVs(
  df,
  EOV = "Biomass_mgm3",
  trans = "identity",
  col = "blue",
  labels = TRUE
)

Arguments

df

A dataframe from pr_get_EOVs() containing Essential Ocean Variable data

EOV

The Essential Ocean Variable parameter to plot (must match a parameter in df)

trans

Transformation for the y-axis scale:

  • "identity" - No transformation (default)

  • "log10" - Log base 10 transformation

  • "sqrt" - Square root transformation

col

Colour for the time series line and points (e.g., "blue", "darkred", "#FF5733")

labels

Logical. Should x-axis labels be shown? Set to FALSE when combining multiple plots vertically to save space.

Value

A patchwork object containing three ggplot2 panels stacked vertically

Details

Essential Ocean Variables (EOVs) are key measurements identified by the Global Ocean Observing System (GOOS) as critical for understanding ocean health and change. For plankton, the two primary EOVs are:

  • Biomass - Total plankton biomass (proxy for ecosystem productivity)

  • Diversity - Species richness and diversity (proxy for ecosystem health)

This function creates a three-panel figure:

Panel 1: Time Series

Shows the raw data over time with a smoothed trend line (LOESS). Useful for identifying long-term trends and interannual variability.

Panel 2: Anomalies

Shows deviations from the long-term mean, highlighting periods of unusually high (positive anomalies) or low (negative anomalies) values. Anomalies are calculated by subtracting the overall mean from each observation.

Panel 3: Climatology

Shows the monthly climatology (mean ± standard error), revealing the typical seasonal cycle. Useful for understanding natural seasonal variability.

The function automatically handles NRS station data and CPR bioregion data, detecting which type is present in the input dataframe.

See also

pr_get_EOVs() for preparing the input data, pr_remove_outliers() for outlier removal before plotting, pr_get_coeffs() for extracting trend coefficients

Examples

# Plot zooplankton biomass EOV for Port Hacking
df <- pr_get_EOVs("NRS") %>%
  dplyr::filter(StationCode == "PHB") %>%
  pr_remove_outliers(2)
pr_plot_EOVs(df, EOV = "Biomass_mgm3", trans = "identity", col = "blue")


# Stack multiple EOVs by removing x-axis labels on upper panels
library(patchwork)
p1 <- pr_plot_EOVs(df, EOV = "Biomass_mgm3", col = "blue", labels = FALSE)
p2 <- pr_plot_EOVs(df, EOV = "ShannonCopepodDiversity", col = "darkgreen")
p1 / p2


# Plot phytoplankton diversity for CPR South-east bioregion
df_cpr <- pr_get_EOVs("CPR") %>%
  dplyr::filter(BioRegion == "South-east") %>%
  pr_remove_outliers(2)
pr_plot_EOVs(df_cpr, EOV = "ShannonPhytoDiversity", 
             trans = "identity", col = "darkgreen")