Course of the Department of Physical Geography at Marburg University

Species distribution modelling (SDM) is a key competence for ecogeographical research and applied nature conservation. It allows researchers to estimate current distributions of species and to also predict their future distributions under climate change scenarios. However, SDM is a dynamic field characterized by rapid advancements and many uncertainties.

This course will go beyond the basic SDM knowledge and will delve into all the small and very specific challenges it presents. Participants will gain ability in generating virtual species, critically evaluating the selection of environmental predictors, identifying knowledge gaps in the field, and independently constructing and fine-tune SDMs. The main focus of the course will be on generating an experimental research setup in which we will tackle some of the uncertainties.

Intended learning outcomes

At the end of this course you should

be familiar with virtual species and gained the ability to generate them.
have explored the impact of selecting environmental predictors on SDM.
be able to identified gaps in current knowledge within a specific field of research.
be able to independently constructed your own SDMs.
engage with various modeling strategies including tuning, variable selection, training, testing, and validation techniques.
have designed, executed, and analyzed your own scientific experiments.
have developed awareness regarding the capabilities and limitations of SDMs.

Course features

This course is intended as a blended learning module in our study program although the provided introductions, explanations and examples might be useful for self-study, too.

This course will take place in the classroom (F 14 | 00A19). The first session will take place on Thursday 24.04.2025 at 09:00 am. Course material will be provided in the Ilias course environment (only accessible for members of the course who are logged-in into Ilias).

Course times

Thursday 09:00 - 11:45.

Syllabus

Session	Date	Topic	Content
		SDM Basics
01	24.04.2025	SDM basics	Course introduction, expectations, organisational matters, R, R Studio, R Markdown
	01.05.2025	Public holiday	No session today
02	08.05.2025	SDM methods	What is SDM, how does it works and why do we need it?
		SDM workflow
03	15.05.2025	Prepare some data for SDM	Overview, conceptualisation, data processing for SDM
04	22.05.2025	Train your first model	Model fitting, assessment, and predictions
	29.05.2025	Public holiday	No session today
		Artificial landscapes
05	05.06.2025	Artificial landscapes	Create neutral landscape models
		Virtual species
06	12.06.2025	Virtual species	Generate virtual species with different ecological niches
	19.06.2025	Public holiday	No session today
07	26.06.2025	Sample presence-absence points	Create realistic presence-absence data for your virtual species and create training and testing datasets.
		From idea to insight: Your personal research project
08	03.07.2025	Your research project	Select your research question and start to work on your project outline.
09	10.07.2025	Free working session	No input in this session. Its just you and your project. Work on it and use the time to ask questions.
10	17.07.2025	Present your project outline	Present project outlines to peers for constructive feedback.
		Synthesis
11	24.07.2025	Wrap up	Get and give feedback from your peers and instructors, tell us how you self-assess your skills, and happy holidays

Deliverables

The course grading will be based on on your final SDM project.

Preparation and prerequisites

Knowledge of R and of handling spatial data is beneficial. Initial experience with species distribution modeling is helpful, for example from our basic species distribution modeling course. All software needed for this course is free and open source.

If you have no experience with R we highly recommend the base R course, which can be found here.