Time series plot of functional group composition

Source: R/plot_timeseries.R

Create stacked area or bar plots showing the relative or absolute abundance of different functional groups over time. This visualisation is particularly useful for examining changes in community composition.

pr_plot_tsfg(df, Scale = "Actual", Trend = "Raw")

Arguments

df

A dataframe from pr_get_FuncGroups() containing functional group data

Scale

Scaling of the y-axis:

  • "Actual" - Plot actual abundance values (stacked)

  • "Proportion" - Plot as proportions summing to 1 (or 100%)

Trend

The temporal scale for plotting:

  • "Raw" - Plot all data points over time (default)

  • "Month" - Monthly climatology averaged across years

  • "Year" - Annual means for each year

Value

A ggplot2 object showing functional group composition over time

Details

This function creates stacked area plots (for Raw trends) or stacked bar plots (for Month/Year trends) showing how functional group composition changes over time.

Functional Groups Plotted

Phytoplankton (5 groups):

  • Centric diatoms (radially symmetrical, bloom-forming)

  • Pennate diatoms (bilaterally symmetrical)

  • Dinoflagellates (flagellated protists)

  • Cyanobacteria (photosynthetic bacteria)

  • Other (remaining groups)

Zooplankton (7 groups):

  • Copepods (dominant marine zooplankton)

  • Appendicularians (larvaceans, gelatinous filter feeders)

  • Molluscs (pteropods - sea butterflies and angels)

  • Cladocerans (water fleas, e.g., Penilia, Evadne)

  • Chaetognaths (arrow worms, predatory)

  • Thaliaceans (salps, doliolids, pyrosomes)

  • Other (remaining groups)

Interpretation

Actual Scale: Shows true abundance patterns. Useful for seeing:

  • Total community biomass/abundance changes

  • Bloom events

  • Which groups dominate numerically

Proportion Scale: Shows relative composition. Useful for seeing:

  • Community shifts (e.g., diatoms to dinoflagellates)

  • Seasonal succession patterns

  • Long-term regime shifts

  • Changes that might be masked by overall abundance changes

Colours are assigned consistently across plots for each functional group.

See also

pr_get_FuncGroups() for preparing the input data, pr_plot_PieFG() for pie chart visualisation of functional groups

Examples

# Plot actual abundances over time
df <- pr_get_FuncGroups("NRS", "Phytoplankton") %>%
  dplyr::filter(StationCode %in% c('MAI', 'PHB'))
pr_plot_tsfg(df, Scale = "Actual", Trend = "Raw")
#> Warning: Removed 5 rows containing non-finite outside the scale range (`stat_align()`).


# Plot as proportions to see community shifts
pr_plot_tsfg(df, Scale = "Proportion", Trend = "Raw")
#> Warning: Removed 5 rows containing non-finite outside the scale range (`stat_align()`).


# Monthly climatology showing seasonal patterns
pr_plot_tsfg(df, Scale = "Proportion", Trend = "Month")


# Zooplankton functional groups
df_zoo <- pr_get_FuncGroups("CPR", "Zooplankton", near_dist_km = 250) %>%
  dplyr::filter(BioRegion == "South-east")
pr_plot_tsfg(df_zoo, Scale = "Actual", Trend = "Raw")
#> Warning: Removed 77 rows containing non-finite outside the scale range (`stat_align()`).