Threshold Indicator Taxa Analysis (TITAN) 

Pre Requirements:- You will need two separate CSVs as follows: 

# Install and load the TITAN2 package for Threshold Indicator Taxa Analysis

install.packages("TITAN2")

library(TITAN2)

# Load species and environmental data from CSV files

# species_data: matrix of species (columns) by sites (rows), values = abundance or presence/absence

# env_data: data frame with environmental gradient(s), here assumed to be tree or bare cover

species_data <- read.csv("C:/Users/Rounak Choudhary/Desktop/titanmetrix.csv", row.names = 1)

env_data <- read.csv("C:/Users/Rounak Choudhary/Desktop/titanenv.csv", row.names = 1)

# Extract the environmental gradient (e.g., % of bare cover)

tree_cover <- env_data$BareP

# Count total number of sampling sites

n_sites <- nrow(species_data)

# Count how many sites each species appears in (non-zero entries)

species_occurrence <- colSums(species_data > 0)

# Filter: Keep only species found in at least 3 sites and not in all sites

# This avoids overfitting and ensures variability

filtered_species <- species_data[, species_occurrence >= 3 & species_occurrence < n_sites]

# Print how many species were retained after filtering

cat("Species retained:", ncol(filtered_species), "\n")

# Run TITAN analysis using the filtered species and environmental gradient

# minSplt = 5: Minimum number of sites needed in each group during splitting

# boot = 500: Number of bootstrap replicates for estimating uncertainty

# imax = 100: Max number of iterations for internal procedures (optional)

# pur.cut and rel.cut = 0.95: High stringency for considering a taxon a reliable indicator

titan_result <- titan(

  env = tree_cover,

  txa = filtered_species,

  minSplt = 5,

  boot = 500,

  imax = 100,

  pur.cut = 0.95,

  rel.cut = 0.95

)

# Print a summary of significant taxa ordered by their indicator strength

summary(titan_result, sort = TRUE)

# Access the core table with maximum z-scores and thresholds for each taxon

titan_result$sppmax

# Save this result table to a CSV file for external review

write.csv(titan_result$sppmax, "C:/Users/Rounak Choudhary/Desktop/bare_cover_thresholds.csv")

# Convert sppmax result to a data frame for further analysis

summary_df <- as.data.frame(titan_result$sppmax)

# Check column names to understand what's available (e.g., filter, maxgrp, z-scores, purity)

colnames(summary_df)

# Filter to keep only species:

# - with filter > 0 (meaning they passed the purity/reliability criteria)

# - that are negatively responding to the gradient (maxgrp == 2)

good_species <- summary_df[summary_df$filter > 0 & summary_df$maxgrp == 2, ]

# Print the species that are good negative indicators (decline with more bare cover)

print(good_species)