Environmental Niche Factor Analysis (ENFA)

Environmental Niche Factor Analysis (ENFA) is a powerful statistical method used in ecology and conservation biology to model species distributions using presence-only data. Unlike traditional methods that require both presence and absence data, ENFA leverages environmental variables to characterize the ecological niche of a species and assess habitat suitability across a landscape.

ENFA is particularly valuable in conservation contexts where absence data is unreliable or unavailable—such as for rare, elusive, or poorly surveyed species. It helps researchers and conservationists:

• Identify critical habitats for endangered species.
• Assess the impact of environmental change on species distributions.
• Support reserve design and spatial planning by highlighting areas of high suitability.
• Compare niche characteristics across species or populations.

By calculating the marginality (how different the species' niche is from the average environment) and specialization (how restricted the species is to specific conditions), ENFA provides a nuanced view of species–environment relationships. The resulting suitability maps can guide conservation priorities and inform ecological research.

How ENFA Works

ENFA operates by comparing the environmental conditions at species presence locations to the overall environmental conditions in the study area. It calculates:

• Marginality: The difference between the mean environmental conditions of the species and the global mean.
• Specialization: The ratio of variance in the global environment to the variance in the species environment.
• Mahalanobis distance: A multivariate measure of distance from each location to the species niche centroid.

In this implementation, suitability is derived using the inverse of the chi-square distribution applied to the Mahalanobis distance matrix. This transformation allows for a probabilistic interpretation of habitat suitability across the landscape.

Workflow Summary

1. Load Species Spatial Data

• Presence records: Loaded from CSV files.
• Species range: Loaded from IUCN shapefiles.
• Study area: Defined by buffering the species range and selecting intersecting countries.

2. Load Environmental Data

• Uses WorldClim bioclimatic variables at 2.5 arc-minute resolution.
• Cropped to the study area.

3. Clean Records

• Filters presence points to those within the IUCN range.

4. Prepare Data for ENFA

• Extracts environmental values for presence and background locations.
• Constructs a presence/background index.

5. Run ENFA

• Calculates niche centroid, marginality, specialization, and suitability.
• Outputs raster layers for visualization.

6. Visualize Results

• Plots:
  • Mahalanobis distance to niche centroid
  • Marginality
  • Specialization
  • Suitability

Main Outputs

• ENFA raster layers:
  • Mahalanobis_dist
  • Marginality
  • Specificity
  • Suitability
• Niche centroid coordinates
• Marginality loadings

Dependencies

• sf, terra, geodata, viridis, dplyr, ggplot2
• Custom functions: ENFA_function, plot_enfa

Load the functions from the repository using the R console

```{r}

0. Load/install the needed packages

list.of.packages<-c("httr","tidyverse")

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] if(length(new.packages)) install.packages(new.packages)

lapply(list.of.packages,require,character.only=TRUE) # conflicts_prefer(dplyr::filter) rm(list.of.packages,new.packages)

1. Connect to the ENFA GitHub repository

github <- "https://api.github.com/repos/BioDivHealth/ENFA/git/trees/main?recursive=1" ENFARepo <- GET(github) # Extract the repo information ENFARepo

2. Get the route to the functions

filepath <- data.frame(unlist(lapply(content(ENFARepo)$tree, function(x) x$path))) colnames(filepath) = c('Path') head(file_path)

Extract routes

filepath <- filepath %>% separate(Path,c('folder','filename'),'/') %>% filter(folder == 'Functions') %>% filter(str_detect(filename,'.R'))

3. Configure the routes, download, and export scripts

raw_route <- "https://raw.githubusercontent.com/BioDivHealth/ENFA/refs/heads/main" #This is the raw route to the gitHub repository MyRoute <- paste(getwd(),"ENFA",sep="/") ; dir.create(MyRoute)

for(i in 1:nrow(filepath)){ writelines(content(GET(paste(rawroute,filepath$folder[i],filepath$filename[i],sep="/"))), paste(MyRoute,filepath$filename[i],sep="/")) }

4. Load the functions

functions <- MyRoute %>% list.files(recursive = FALSE,pattern = ".R$",full.names = TRUE) lapply(functions,function(x) source(x)) ```

Downloading the full repository

1. Clone the repository.
2. Place species records and range shapefiles in ./Data/Sp_info/Records and ./Data/Sp_info/Ranges.
3. Run the R script or R Markdown file.
4. Outputs will be saved in ./Data/Env_vars and visualized in the notebook.

Author

Gonzalo Albaladejo Robles
Bioinformatician, Wellcome Henry Dale Fellow