diff --git a/workflows/phenotype_pca_and_hc/results/PCoA_contributions.csv b/workflows/phenotype_pca_and_hc/results/PCoA_contributions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2ef880ab46dbde9ca79098c014540c6ffb9ce7c7
--- /dev/null
+++ b/workflows/phenotype_pca_and_hc/results/PCoA_contributions.csv
@@ -0,0 +1,4154 @@
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diff --git a/workflows/phenotype_pca_and_hc/results/R_screeplot_transparent_pcoa.png b/workflows/phenotype_pca_and_hc/results/R_screeplot_transparent_pcoa.png
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diff --git a/workflows/phenotype_pca_and_hc/scripts/pheno_and_climate_pcoa.R b/workflows/phenotype_pca_and_hc/scripts/pheno_and_climate_pcoa.R
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--- /dev/null
+++ b/workflows/phenotype_pca_and_hc/scripts/pheno_and_climate_pcoa.R
@@ -0,0 +1,118 @@
+# Title: PCoA Analysis of Teosinte Phenotype and Environmental Data
+#
+# Description:
+# This script performs a Principal Coordinates Analysis (PCoA) on teosinte phenotype
+# and environmental data. The analysis aims to investigate the relationships
+# among individuals based on phenotypic traits and their environmental context.
+#
+# Key Steps:
+# 1. Data Import: Reads phenotype and environmental data from specified sources.
+# 2. Data Preprocessing: Cleans, scales, and formats the data for analysis.
+# 3. Distance Calculation: Computes a dissimilarity matrix based on selected metrics.
+# 4. PCoA Execution: Applies PCoA to the distance matrix and extracts coordinates.
+# 5. Visualization: Generates plots, including:
+#    - A scree plot of eigenvalues to visualize variance explained by axes.
+#    - PCoA scatter plots for PC1 vs. PC2 and PC1 vs. PC3.
+# 6. Output: Saves plots in PNG, PDF, and SVG formats for reporting.
+#
+# Author: Joseph Atemia
+
+library("vegan")
+library("factoextra")
+library("RColorBrewer")
+
+setwd("workflows/phenotype_pca_and_hc/scripts")
+
+# Load the data
+phen.data.raw <- read.csv("../data/phenotype_and_env_data.csv")
+phen.data.raw.full.labs <- read.csv("../data/phenotype_and_env_data_vars_labled.csv")
+
+# Update classification labels
+phen.data.raw$classification <- gsub("Zea mays", "Z. m. ssp.", phen.data.raw$classification)
+phen.data.raw$classification <- gsub("\\bZea\\b", "Z.", phen.data.raw$classification)
+
+# Select relevant columns for the analysis
+phen.data <- phen.data.raw.full.labs[, c(2:256)]
+rownames(phen.data) <- phen.data.raw$POBL
+
+# Na's
+any(is.na(phen.data)) # Currently outputs FALSE
+
+# Preprocess the data (center and scale)
+phen.data.scaled <- scale(phen.data)
+
+# Compute the distance matrix using Euclidean distance
+phen.dist <- vegdist(phen.data.scaled, method = "euclidean")
+
+#' Mahalanobis Distance for Multivariate data where variables are
+#' correlated (eg. environmental variables like tem in our case).
+#' It Accounts for correlations between variables.
+#' Scales distances based on the variance-covariance structure of the data.
+# Remove near-zero variance variables
+# library(caret)
+# phen.data <- phen.data[, -nearZeroVar(phen.data)]
+# nzv <- nearZeroVar(phen.data, freqCut = 99/1, uniqueCut = 5)  # Less aggressive thresholds
+# phen.data <- phen.data[, -nzv]
+# phen.dist <- mahalanobis(as.matrix(phen.data), colMeans(phen.data), cov(phen.data))
+
+# Perform PCoA
+phen.pcoa <- cmdscale(phen.dist, eig = TRUE, k = 2) # k = 2 for 2D coordinates
+
+# Scree plot for eigenvalues
+eigenvalues <- phen.pcoa$eig
+
+png("../results/R_screeplot_transparent_pcoa.png", width = 6, height = 6, units = "in", res = 300, bg = "transparent")
+
+barplot(eigenvalues[1:10] / sum(eigenvalues),
+  main = "Screeplot - Eigenvalues",
+  col = heat.colors(10),
+  xlab = "PCoA Axes",
+  ylab = "Proportion of Variance Explained",
+  names.arg = NULL # Temporarily remove default labels
+)
+# Add custom labels to the x-axis
+axis(1, at = scree_plot, labels = paste0(1:10), tick = TRUE)
+
+dev.off()
+
+# Contribution of variables to PCoA axes
+phen.pcoa_coords <- as.data.frame(phen.pcoa$points)
+colnames(phen.pcoa_coords) <- c("Axis1", "Axis2")
+
+# Save contributions
+write.csv(phen.pcoa_coords, "../results/PCoA_contributions.csv", row.names = TRUE)
+
+# Individuals plot
+groups <- as.factor(phen.data.raw$classification)
+cols <- c("#E515A1", "#2E7312", "#8FBC8F", "#79DDFC", "#E11D20", "#FCAB2A", "#5EFF27", "#FDBDE7", "#4286FC", "#686868", "#A800E2")
+
+# Calculate the proportion of variance explained by each axis
+variance_explained <- 100 * eigenvalues / sum(eigenvalues)
+x_label <- paste0("PC.1 (", round(variance_explained[1], 2), "%)")
+y_label <- paste0("PC.2 (", round(variance_explained[2], 2), "%)")
+
+png("../results/pcoa_plot.png", width = 17, height = 11, units = "in", res = 320)
+
+plot(phen.pcoa_coords$Axis1, phen.pcoa_coords$Axis2,
+  col = cols[groups],
+  pch = 19,
+  xlab = x_label,
+  ylab = y_label,
+  main = "PCoA"
+)
+legend("topright", legend = levels(groups), col = cols, pch = 19, title = "Teosinte Taxa")
+
+dev.off()
+
+pdf("../results/pcoa_plot.pdf", width = 17, height = 11)
+
+plot(phen.pcoa_coords$Axis1, phen.pcoa_coords$Axis2,
+  col = cols[groups],
+  pch = 19,
+  xlab = x_label,
+  ylab = y_label,
+  main = "PCoA"
+)
+legend("topright", legend = levels(groups), col = cols, pch = 19, title = "Teosinte Taxa")
+
+dev.off()