Author: Martin R. Smith
Date: 2026-02-03

# Holdridge plots

#### Martin R. Smith

#### 2026-02-03

`HoldridgePlot()` creates a blank triangular plot, as proposed by Holdridge (1947, 1967), onto which potential evapotranspiration (PET) ratio and annual precipitation data can be plotted (using the `AddToHoldridge()` family of functions) in order to interpret climatic life zones.

`HoldridgePoints()`, `HoldridgeText()` and related functions allow data points to be added to an existing plot; `AddToHoldridge()` allows plotting using any of the standard plotting functions.

`HoldridgeBelts()` and `HoldridgeHexagons()` plot interpretative lines and hexagons allowing plotted data to be linked to interpreted climate settings.

Please cite Tsakalos et al. (2023) when using this function.

    # Install the Ternary package, if it's not already installed
    if (!requireNamespace("Ternary", quietly = TRUE)) {
      install.packages("Ternary")
    }

    # Load the Ternary package
    library("Ternary")

    # Load some example data from the Ternary package
    data(holdridge, holdridgeLifeZonesUp, package = "Ternary")

    # Suppress plot margins
    par(mar = c(0, 0, 0, 0))

    # Create blank Holdridge plot
    HoldridgePlot(hex.labels = holdridgeLifeZonesUp)
    HoldridgeBelts()

    # Plot data, shaded by latitude
    HoldridgePoints(holdridge$PET, holdridge$Precipitation,
                    col = hcl.colors(91)[abs(holdridge$Latitude) + 1],
                    lwd = 2)

    # Add legend to interpret shading
    PlotTools::SpectrumLegend(
      "topright", bty = "n", # No box
      horiz = TRUE, # Horizontal
      x.intersp = -0.5, # Squeeze in X direction
      legend = paste0(seq(0, 90, 15), "°"),
      palette = hcl.colors(91),
      title = "Latitude"
    )

# Your own data

The next step is to try plotting your own data. There are many online tutorials for loading data in a variety of formats into R. Two of the most common cases are:

- Reading data from an Excel file. Here you can use:

    # On first use only, install the 'readxl' package
    install.packages("readxl")

    # Load data into an object called `myData`
    myData <- readxl::read_excel("path_to/your_excel_file.xlsx")

    # View the loaded data
    View(myData)

    # View manual page for `read_excel` function, with details for advanced use
    ?read_excel

- Reading data from a delimited file, such as a `.csv`:

    # Read data into an object called `myData`
    myData <- read.csv("path_to/your_data_file.csv")

    # View the loaded data
    View(myData)

    # For more flexibility - for instance for tab-separated data - see:
    ?read.table

    # Read data from a tab-separated file with a header row
    myData <- read.table("path_to/your_data_file.txt", sep = "\t", header = TRUE)

    View(myData)

Once your data are within R, you need to select the values for the potential evapotranspiration ratio (pet) and annual precipitation (in mm).

If your PET data are in the third row of `myData`, you can call:

    myPet <- myData[3, ] # Access row number 3.  Note the position of the comma.

If your precipitation data is in a column with the heading “Prec”, you can call:

    myPrec <- myData[, "Prec"] # Access column with name "Prec".
                               # Columns go after the comma.

This will allow you to plot your data using:

    HoldridgePlot()
    HoldridgePoints(pet = myPet, prec = myPrec)

# Customization

Details of how to customize Holdridge plots are given on the relevant manual pages.

See:

- `HoldridgePlot()`

- `AddToHoldridge()`

# References

Holdridge (1947), “Determination of world plant formations from simple climatic data”, Science 105:367–368.

Holdridge (1967), Life zone ecology. Tropical Science Center, San José.

Tsakalos, Smith, Luebert & Mucina (2023). “climenv: Download, extract and visualise climatic and elevation data.”, Journal of Vegetation Science 6:e13215.