Introduction
Canonical Correspondence Analysis (CCA) is a powerful multivariate statistical technique used to explore the relationship between species distribution and environmental variables. In this article, we will walk through how to perform a CCA in R using an Excel dataset. We’ll cover how to load the data, run the analysis, and customize the CCA plot to suit your needs. Whether you’re analyzing plant species in an ecosystem or investigating animal distributions, this guide will help you use R to visualize and interpret ecological data efficiently.
Why Use CCA?
CCA is ideal for exploring how species are distributed across different environmental gradients. By using CCA, you can determine which environmental factors most strongly influence species distributions, allowing you to make data-driven decisions for conservation, biodiversity studies, and ecosystem management.
Step 1: Loading and Preparing Data in R
First, you need to install and load the necessary packages in R. We'll use the vegan package for CCA, which is widely used in ecological research.
# Install required packages
install.packages("vegan")
install.packages("readxl")
# Load the packages
library(vegan)
library(readxl)
Next, load your Excel data into R using the readxl package.
# Load the Excel file (adjust the file path to your actual location)
data <- read_excel("species_data.xlsx")
data <- read_excel("env_data.xlsx")
Step 2: Data Preparation for CCA
In your dataset, the columns from Species_1 to Species_10 represent the species data (species abundance), while pH, Temperature, and Dissolved Oxygen, represent the environmental variables.
We'll separate the species data and environmental data.
# Create a data frame for species data
species_data <- data.frame(species_data)
(or)
species_data <- data.frame(
Species_1 = c(5, 6, 4, 0, 7),
Species_2 = c(2, 3, 4, 7, 0),
Species_3 = c(6, 3, 8, 5, 0),
Species_4 = c(8, 1, 3, 5, 7),
Species_5 = c(6, 4, 8, 11, 0),
Species_6 = c(1, 3, 9, 8, 6),
Species_7 = c(6, 4, 12, 8, 9),
Species_8 = c(1, 6, 8, 0, 0),
Species_9 = c(7, 8, 3, 2, 9),
Species_10 = c(7, 1, 0, 3, 8),
)
# Create a data frame for environmental data
env_data <- data.frame(env_data)
(or)
env_data <- data.frame(
pH = c(7.8, 8.2, 6.9, 6.7, 7.3),
Temperature = c(27.5, 32.7, 28.5, 26.2, 29.7),
Dissolved_Oxygen = c(12, 10, 7, 9, 11),
)
Step 3: Running Canonical Correspondence Analysis (CCA)
Now that the data is ready, you can perform the CCA using the cca() function from the vegan package.
# Perform Canonical Correspondence Analysis (CCA)
cca_result <- cca(species_data,env_data)
# View a summary of the CCA model
summary(cca_result)
Step 4: Visualizing the CCA Results
Once the CCA model is created, you can visualize the results using a CCA plot. This plot shows the relationship between species and environmental variables.
# Basic CCA plot
plot(cca_result, scaling = "species")
Once the CCA model is created, you can visualize the results using a CCA plot. This plot shows the relationship between species and environmental variables.
For example, the plot below illustrates how different species relate to environmental factors like pH, temperature, and dissolved oxygen. The blue points represent species, while the red arrows represent environmental variables. The direction and length of the arrows indicate how strongly each environmental factor influences species distributions.
Step 5: Common Customizations for the CCA Plot
Change Point and Text Sizes
You can control the size of the points (species and sites) and the text using the cex argument.
# Customize point and text size
plot(cca_result, scaling = "species", cex = 1.5, cex.lab = 1.2, cex.axis = 1.1)
cex: Changes the overall size of points.
cex.lab: Adjusts the size of the axis labels.
cex.axis: Adjusts the size of axis ticks and labels.
Change Colors
To differentiate between sites and species, you can color them using col or text.col. Here's how to color species and environmental variables differently.
# Customize colors for species and environmental variables
plot(cca_result, scaling = "species", col = "blue", text.col = "darkgreen")
col: Changes the color of points (species and sites).
text.col: Changes the color of text labels for points (species, environmental vectors).
Customize Species and Environmental Arrows
By default, species and environmental variables are plotted together. You can customize the appearance of species and environmental arrows (vectors) separately.
# Customizing arrows for environmental variables
plot(cca_result, scaling = "species")
# Add arrows for environmental variables with specific colors and line widths
arrows(0, 0, cca_result$CCA$biplot[, 1], cca_result$CCA$biplot[, 2], col = "red", length = 0.1, lwd = 2)
length: Adjusts the length of the arrowhead.
lwd: Controls the line width of arrows.
Label Only Species or Sites
To display labels for only species or only sites (samples), you can use the display argument.
# Show only species
plot(cca_result, scaling = "species", display = "species")
# Show only sites
plot(cca_result, scaling = "sites", display = "sites")
Add a Legend
Adding a legend can help distinguish between species and environmental variables or differentiate among groups.
# Basic CCA plot
plot(cca_result, scaling = "species")
# Add legend for species and environmental variables
legend("topright", legend = c("Species", "Environmental"), pch = 1, col = c("blue", "red"))
Use Biplot for Scaling
You can generate a biplot that shows both species and environmental variables in one plot, with species scaled to the site scores.
# Biplot with species and environmental variables
biplot(cca_result, scaling = 2)
Step 6: Interpreting the CCA Plot
In the CCA plot, you will see species represented by blue text and arrows representing environmental gradients (red). The direction and length of the arrows indicate how strongly each environmental variable influences species distributions.
For example, if an arrow for Temperature points in a particular direction, species located near the tip of the arrow are more strongly associated with higher temperatures. The closer a species is to the arrow, the more that environmental factor influences it.
Conclusion
Canonical Correspondence Analysis (CCA) is a powerful tool for ecologists looking to explore the relationship between species and environmental variables. By following the steps in this guide, you can easily perform a CCA in R and customize the resulting plots to make your ecological data more interpretable and visually appealing.
Whether you’re studying plant species in diverse ecosystems or investigating how environmental changes affect animal populations, CCA can provide valuable insights. R’s vegan package, combined with a well-structured dataset, makes it easy to conduct this analysis and generate meaningful visualizations.