Species Distribution Models with GIS & Machine Learning in R

Name: Species Distribution Models with GIS & Machine Learning in R
Rating: 4.1 (706 reviews)

Mapping Habitat Suitability for Conservation Using Machine Learning and GIS in R

Created byMinerva Singh

Last updated 10/2025

English

What you'll learn

You will have a greater clarity of basic spatial data concepts and data types
Carry out practical spatial data analysis tasks in freely available software in R
Analyze spatial data using R

Course content

6 sections • 44 lectures • 3h 57m total length

INTRODUCTION TO THE COURSE: Instructor & Course Details1:20
Explore species distribution models with GIS and machine learning in R, including pre-processing raster and spatial data, classical models, and habitat suitability using real-life Peninsular Malaysia data.
What is Species Distribution Modelling?5:53
Explore species distribution modeling with GIS-based habitat suitability and ecological niche concepts. Map habitat suitability from presence data and environmental parameters, using methods such as random forest for conservation outcomes.
Data used in the course0:10
Introduction to R for habitat suitability modelling5:16
Install R and RStudio, set up packages for raster and geo-referenced data, and build habitat suitability models using machine learning in R with Garett and related tools.
Conclusion to Section 11:09

Where to Obtain Raster Data for Building SDMs4:30
Accessing and Cleaning GBIF Data8:25
Accessing GBIF Data via R10:16
Other Sources of Species Geo-location Data3:31
Explore alternative sources of species geo-location data beyond GPA and GBR, including bird-related platforms like eBird and iNaturalist, to access observations, checklists, and hotspots for field study.
Extract Species Geo-location Data from Other Sources in R5:32
Access Climate & Other Data via R4:13
Access climate data and country outlines in R for species distribution modeling, pulling bio climatic variables (bio1–bio19) at 2.5-minute resolution and clipping to country or state boundaries.
Working With Elevation Data in R6:32
Read in elevation data from RTM 59 and SRT 60 rosters, merge adjacent rosters into a single mosaic, and use the mean for overlapping areas for species distribution models.
Deriving Topographic Products from Elevation Data5:41
Conclusions to Section 21:38
Conclude section 2 by outlining data sources for species distribution modeling, including occurrence data from the G-B website and GBI API, plus climate, topography, and optional land cover data.

Some Prerequisites3:01
Ensure raster predictors share the same extent, resolution, and coordinate reference system for reliable species distribution models in R, using a Peninsular Malaysia case study.
CRS of the Data3:52
Clip Raster Data to a Given Extent4:07
Clip rasters to a defined extent and crop all rasters to Peninsular Malaysia using the crop function from the raster package and a Malaysia shape file.
Resize the Raster Data3:27
Basic Data Visualization3:40
Conclusions to Section 31:39

Underlying Rationale9:32
Explore classical species distribution modeling techniques using presence data to map habitat suitability and ecological niches, including climate-based methods, the domain model, and maximum entropy (MaxEnt) approaches.
Bioclim7:30
Model Evaluation4:02
Maxent Interface in R2:49
Maxent SDM in R7:09
Learn to use Maxent in R for presence-only species distribution modeling with raster predictors such as altitude and temperature. Evaluate performance (AUC 0.84) and predict habitat suitability using background points.
Maxent Analysis with the red package4:09
Use the red package to perform maxent analyses for species distribution modeling with multiple species, building predictor stacks from presence data. Evaluate with 20 percent test data and auc metrics.
Domain SDM in R8:18
Conclusion to Section 42:09
Conclude section 4 by reiterating bioclim and maxent, showing how to build models with our data, and preview domain-based modeling with Gayford data splitting and area under the curve evaluation.

Machine Learning Modelling10:45
Apply machine learning to build species distribution and habitat suitability maps, using logistic regression, k-nearest neighbors, support vector machines, random forests, and gradient boosting, with cross-validation and AUC evaluation.
Pre-processing Steps Prior to Modelling With Presence & Absence Data7:44
Learn to prepare presence and absence data for machine learning based species distribution models by compiling raster predictors, extracting values, and generating pseudo absences with ecologically informed backgrounds.
Prior to Implementing Machine Learning5:32
Prepare presence/absence data with land cover, climate, and topography predictors; read and inspect it, then create a reproducible 75/25 train/test split in R using Garrett before building habitat suitability models.
GLMs for Habitat Suitability13:18
Support Vector Machines7:24
kNN4:35
Random Forest (RF)11:34
Gradient Boosting Machine (GBM)8:28
Further Model Evaluation6:53
Conclusions to Section 52:17

Obtain Elevation Data rom Within R6:18
demonstrate obtaining digital elevation models within R using the elevator package, by defining an extent and zoom level to produce a merged elevation raster at 250 m or 2.5 km.
Evaluate Point Density5:21
Identify the spatial distribution and concentration of geo locational points by applying 2D density mapping in R, visualizing forest fire locations in Australia with ggplot and world map overlays.
Introduction to Leaflet7:59
Github5:16
Brazil Time Lapse0:39
Assign Legends in QGIS3:48

Requirements

Ability to Install Packages in R and RStudio
Interest in learning the implementation of GIS techniques in R
Interest in applying machine learning to spatial data

Description

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R?

Are you an ecologist/conservationist looking to carry out habitat suitability mapping?
Are you an ecologist/conservationist looking to get started with R for accessing ecological data and GIS analysis?
Do you want to implement practical machine learning models in R?

Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R.

My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

In this course, actual spatial data from Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests. The underlying motivation for the course is to ensure you can put spatial data and machine learning analysis into practice today. Start ecological data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your GIS and Machine Learning skills in R.

So Many R based Machine Learning and GIS Courses Out There, Why This One?

This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real ecological data in R. Plus, you will gain exposure to working your way through a common ecological modelling technique- species distribution modelling (SDM) using real life data. Students will also gain exposure to implementing some of the most common Geographic Information Systems (GIS) and spatial data analysis techniques in R. Additionally, students will learn how to access ecological data via R.

You will learn to harness the power of both GIS and Machine Learning in R for ecological modelling.

I have designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Yes, even non-ecologists can get started with practical machine learning techniques in R while working their way through real data.

What you will Learn in this Course

This is how the course is structured:

Introduction – Introduction to SDMs and mapping habitat suitability
The Basics of GIS for Species Distribution Models (SDMs) – You will learn some of the most common GIS and data analysis tasks related to SDMs including accessing species presence data via R
Pre-Processing Raster and Spatial Data for SDMs - Your R based GIS training and will continue and you will earn to perform some of the most common GIS techniques on raster and other spatial data
Classical SDM Techniques - Introduction to the classical models and their implementation in R (MaxENT and Bioclim)
Machine Learning Models for Habitat Suitability - Implement and interpret common ML techniques to build habitat suitability maps for the birds of Peninsular Malaysia.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts . However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.

Who this course is for:

Academics
Researchers
Conservation managers
Anybody who works/will work with spatial data

Species Distribution Models with GIS & Machine Learning in R

What you'll learn

Explore related topics

Course content

Introduction to the Species Distribution Modelling Course5 lectures • 14min

The Basics of GIS for Species Distribution Models (SDMs)-Part 19 lectures • 50min

Pre-Processing Raster and Spatial Data for SDMs6 lectures • 20min

Classical SDM Techniques8 lectures • 46min

Machine Learning Models for Habitat Suitability10 lectures • 1hr 19min

Extra Lectures6 lectures • 29min

Requirements

Description

Who this course is for: