LIVE ONLINE COURSE – Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) using R (SDMR06)
22 September 2025 - 26 September 2025
£480.00
Event Date
Monday, September 22nd, 2025
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
About This Course
By the end of the course, participants should be able to:
- Calculate ecological niche models and specie distribution models
- Understand their results, as well as to choose and apply the correct.
- How to choose the best methodology depending on the aim of their type of study and data.
Intended Audiences
- Academics and post-graduate students working on projects related to spatial data.
- Students and researchers working on biogeography, spatial ecology, or related disciplines with experience in ecological niche models.
Venue
Course Details
Availability – 30 Places
Duration – 5 days, 7 hours a day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Assumed quantitative knowledge
Assumed computer background
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Monday 22nd
Elementary concepts on Ecological Niche Modelling
Module 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory, types of ecological niches, types of ENM, diagram BAM, ENMs as approximations to species’ niches.
Module 2: Problems and limitations on ENM. Assumptions and uncertainties, equilibrium concept, niche conservatism, autocorrelation and intensity, sample size, correlation of environmental variables, size and form of study area, thresholds, model validation, model projections.
Module 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis, Biomod, Domain, Habitat, Distance of Mahalanobis, ENFA, GARP, Maxent, Logistic regression, Generalised Linear Models, Generalised Additive Models, Generalised Boosted Regression Models, Random Forest, Support Vector Machines, Artificial Neural Network.
Module 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step.
Module 5: Applications of ENM. Ecological niche identification, Identification of contact zones, Integration with genetical data, Species expansions, Species invasions, Dispersion hypotheses, Species conservation status, Prediction of future conservation problems, Projection to future and past climate change scenarios, Modelling past species, Modelling species richness, Road-kills, Diseases, Windmills, Location of protected areas.
Tuesday 23rd
Prepare environmental variables and run ecological niche models with dismo package.
Module 6: Preparing variables. Choosing environmental data sources, Downloading variables, Clipping variables, Aggregating variables, Checking pixel size, Checking raster limits, Checking NoData, Correlating variables.
Module 7: Dismo practice. How to run an ENM using the R package dismo.
Wednesday 24th
Run ecological niche models with Biomod2 package and Maxent.
Module 8: Biomod2 practice. How to run an ENM using the R package Biomod2.
Module 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software.
Thursday 25th
Compare ecological niche models with ecospat.
Module 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat.
Module 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM.
Friday 26th
Run ecological niche models with your own data.
Module 12: Final practical. In this practical, the students will run ENM with their own data or with a new dataset, applying all the methods showed during the previous days.
Dr. Neftali Sillero
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Teaches
- Ecological Niche Modelling Using R (ENMR)
- Advanced Ecological Niche Modelling Using R (ANMR)
- GIS And Remote Sensing Analyses With R (GARM)
Teaches
- Ecological Niche Modelling Using R (ENMR)
- Advanced Ecological Niche Modelling Using R (ANMR)
- GIS And Remote Sensing Analyses With R (GARM)