Introduction to ENM and MaxEnt
Introduction to Ecological Niche Modeling (ENM) and MaxEnt, ecological niche concepts and Species Distribution Models (SDMs).
Certificate Course
5-Day Hybrid Certificate Course on Ecological Niche Modelling using MaxEnt
Build a foundation in ecological niche modelling and learn how species occurrence data and environmental variables can be used to understand and predict species distributions.
Date8–13 December 2026
VenueOnline + KAHM Unity Women's College, Manjeri
Number of SeatsTo be announced
Fee Details₹3,000
About the event
About the Course
Ecological Niche Modelling (ENM) is an important approach in ecology and biodiversity research. It uses species occurrence records and environmental variables to identify areas with suitable environmental conditions for a species. These models support the study of species distributions, habitat suitability, potential range shifts and ecological responses to environmental change.
This five-day hybrid certificate course introduces participants to the concepts, datasets and analytical workflow involved in ecological niche modelling using MaxEnt. The programme is designed to connect ecological understanding with geospatial data processing and model interpretation.
Learning approach: A combination of online learning and in-person practical training. Add the final delivery format and session split here once confirmed.
Ecological niche modelling for biodiversity research and conservation planning
Course overview
Course Overview
Software Installation
Installation and use of QGIS, R, RStudio, Java and MaxEnt.
Data Acquisition
Species occurrence and environmental data acquisition from relevant databases.
Spatial Data Processing
Spatial data processing and GIS analysis using QGIS and R.
MaxEnt Model Development
MaxEnt model development, validation and habitat suitability mapping.
Future Projections and Learning Resources
Future climate projections, case studies, lecture notes and tutorial videos.
Who Can Participate?
What Participants Will Learn
Understand the fundamental concepts of ecological niche modelling.
Prepare and organise species occurrence data.
Work with environmental and bioclimatic predictor layers.
Understand the workflow of MaxEnt modelling.
Interpret habitat suitability maps and model outputs.
Recognise applications of ENM in ecology and conservation.
Detailed schedule
Detailed Schedule
DAY
01
01
Foundations of Ecological Niche Modelling
Introduction to ecological niches, species distribution patterns, ENM concepts, applications and the MaxEnt approach.
DAY
02
02
Species Occurrence Data and Environmental Variables
Occurrence data sources, data cleaning, coordinate preparation, environmental variables and predictor selection.
DAY
03
03
GIS and Spatial Data Preparation
Introduction to spatial datasets, raster preparation, projections, clipping, resolution and preparation of modelling inputs.
DAY
04
04
MaxEnt Model Development
MaxEnt interface and workflow, model configuration, running models and generating habitat suitability outputs.
DAY
05
05
Model Evaluation, Interpretation and Applications
Model evaluation and interpretation, understanding output maps, ecological applications and discussion of participant learning.
Course syllabus
Detailed Course Syllabus
A five-day learning programme combining theoretical sessions and practical training in ecological niche modelling.
Course Objective
Provide participants with theoretical knowledge and hands-on training in Ecological Niche Modeling (ENM) using MaxEnt, QGIS and R, enabling them to independently develop, evaluate and interpret species distribution models for conservation, ecology and climate change applications.
01
Day 1 · 2 hours
Fundamentals of Ecological Niche Modeling
Topics Covered
- Introduction to Ecological Niche Modeling (ENM)
- Ecological niche concepts and Species Distribution Models (SDMs)
- Overview of modelling approaches (presence-only, presence–absence, machine learning)
- Introduction to MaxEnt: principles, assumptions, advantages and disadvantages
- Applications of ENM in biodiversity conservation, invasive species, climate change and restoration
- Limitations of ENM and situations where ENM should not be applied
- Discussion of published case studies and complete ENM workflow
Learning Outcomes
- Understand the concepts and workflow of ENM.
- Differentiate modelling approaches and identify appropriate applications.
- Recognize strengths and limitations of MaxEnt.
02
Day 2 · 2 hours
Software Installation and Data Acquisition
Topics Covered
- Introduction to spatial data: raster and vector
- Installation and configuration of QGIS, R, RStudio, Java and MaxEnt
- Downloading occurrence data (GBIF, iNaturalist, India Biodiversity Portal)
- Downloading environmental variables (WorldClim, CHELSA and related databases)
- Basic processing and management of spatial datasets
Learning Outcomes
- Install and configure all required software.
- Acquire and organize occurrence and environmental datasets.
- Understand raster and vector data structures.
03
Day 3 · 2 hours
Spatial Data Processing Using QGIS and R
Topics Covered
- Working with raster and vector data in QGIS
- Projection, clipping, buffering and extraction tools
- Introduction to R and RStudio
- Importing, cleaning and visualizing spatial datasets in R
- Preparation of study area and modelling inputs
Learning Outcomes
- Perform essential GIS operations.
- Use basic R commands for ENM workflows.
- Prepare datasets for modelling.
04
Day 4 · 7 hours
Building MaxEnt Models
Topics Covered
- Occurrence data collection from field surveys and online repositories
- Data cleaning and spatial thinning
- Environmental variable selection and multicollinearity assessment
- Feature Classes and Regularization Multiplier selection
- Running MaxEnt models
- Interpreting response curves, jackknife analysis and habitat suitability maps
- Theory and practical on model validation (AUC, ROC, Partial ROC, TSS, Cohen's Kappa, Sensitivity and Specificity)
- Threshold selection and uncertainty
Learning Outcomes
- Develop robust MaxEnt models.
- Optimize model parameters.
- Interpret model outputs correctly.
- Evaluate model performance using standard metrics.
05
Day 5 · Duration to be confirmed
Future Projection, Mapping, Visualization and Case Study
Topics Covered
- Future climate projections
- Habitat suitability map generation
- Map layout preparation in QGIS
- Visualization and interpretation of outputs
- Individual case study
- Participant presentation
Learning Outcomes
- Generate future projections.
- Produce publication-quality maps and visualizations.
- Complete and present an ENM case study.
Prerequisites
Basic knowledge of ecology, biodiversity or related life sciences. No prior experience in GIS, R or MaxEnt is required.
Organisers & collaborators
About the organising institutions
The institutions supporting this learning programme.
Tropical Biodiversity Action and Research Centre
Organiser · TBARC IndiaTropical Biodiversity Action and Research Centre (TBARC) is an organisation working to promote biodiversity research, conservation awareness and scientific engagement. Through training programmes, workshops, research initiatives and outreach activities, TBARC aims to connect students, researchers and communities with emerging themes and methods in biodiversity science.
Visit TBARC website ↗KAHM Unity Women's College
Academic partner · Manjeri, KeralaKAHM Unity Women's College, Manjeri, is the academic institution associated with this programme. Add the approved institutional profile here, including its academic departments, research activities and facilities relevant to the course.
College website link to be added ↗Ready to explore ecological niche modelling?
Join the five-day hybrid certificate course and develop your understanding of MaxEnt and its applications in ecological research.
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