Science School on Quantitative Ecology - MB120C28

• Title: Science School on Quantitative Ecology
• Czech title: Letní škola kvantitativní ekologie
• Guaranteed by: Department of Botany (31-120)
• Faculty: Faculty of Science
• Actual: from 2024
• Semester: summer
• E-Credits: 5
• Examination process: summer s.:
• Hours per week, examination: summer s.:0/5, C [DS]
• Capacity: 8
• Min. number of students: 5
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Additional information: https://bit.ly/SSoQE
• Note: enabled for web enrollment
• Guarantor: Mgr. Ondřej Mottl, Ph.D.
• Teacher(s): RNDr. Antonín Macháč, Ph.D.; Mgr. Ondřej Mottl, Ph.D.

Annotation

Join us for an exhilarating five-day international workshop hosted by Charles University and the University of Bayreuth, designed to provide a cutting-edge experience in Quantitative Ecology. This international science school is primarily aimed at master’s students who are eager to explore the complexities of ecological data, with available spots for motivated PhD students. No prior expertise in the field is required—just bring your passion for learning and collaboration!

What You Will Gain:
Advanced Skills: Master the latest statistical modelling, computational techniques, and data analysis methods to tackle complex ecological datasets.
Hands-On Experience: Participate in practical, hands-on projects directly connected to current research. These projects are led by esteemed lecturers from Charles University, the University of Bayreuth, and guest experts from other leading domestic and international institutions. Dive into the most contemporary methods and concepts in Quantitative Ecology.

Networking Opportunities: Engage in evening social activities designed to foster informal networking and cultural exchanges. These gatherings are perfect for enriching your educational experience and forming long-lasting professional and personal relationships.

Dynamic Environment: Our workshop is built on active participation and collaboration, offering a robust platform for scientific exchange. Whether you’re leading a project or engaging in group discussions, you’ll play a vital role in a vibrant learning community.

Beautiful Settings: The workshop alternates between scenic locations in the Czech Republic and Germany. Each site provides a tranquil backdrop conducive to creativity and collaboration, supporting both rigorous academic work and lively social interactions, making it an ideal setting for intensive learning and networking.

Last update: Mottl Ondřej, Mgr., Ph.D. (28.01.2026)

Syllabus

This year, 2026, will be hosted in Germany (the exact location will be specified) and take place between 14.9.2026 and 19.9.2026

Learn more about the course on the project website (bit.ly/SSoQE).

Last update: Mottl Ondřej, Mgr., Ph.D. (28.01.2026)

Learning outcomes

By the end of the week, participants will be able to:

Frame ecological questions and choose methods

• Formulate ecological questions in terms of exploration, inference, and prediction.
  Choose an appropriate analysis strategy for the question, data type, and goal.
  Critically assess model assumptions, uncertainty, and limits of prediction.

Work reproducibly and collaboratively

• Set up and maintain a reproducible project structure (clear folders, readable scripts, documented steps).
• Use dependency-aware workflows where appropriate (e.g., project environments / locked package versions).
• Use Git and GitHub for version control: create repos, commit changes, push/pull, and resolve merge conflicts.
• Collaborate using branches and pull requests; use issues/discussions/task boards to coordinate work.

Apply core quantitative-ecology methods across domains

• Species distribution modelling (SDMs): obtain occurrence + environmental data, perform basic data-quality checks, fit a simple correlative SDM (e.g., GLM), and create spatial predictions (including scenario-based projections).
• Community ecology: compute dissimilarity, run and interpret ordinations (e.g., CA/NMDS), and explore clustering/classification of communities.
• Biodiversity patterns: describe broad-scale richness patterns and compare competing hypotheses using model comparison (e.g., AIC / Akaike weights).
• Hypothesis testing and null models: understand what null models test, how conclusions depend on assumptions, and interpret outputs as evidence about ecological processes.

Handle real-world data complications (bias and sampling)

• Identify common sources of sampling/measurement bias in ecological and paleoecological data.
• Understand standardisation/resampling ideas and their trade-offs.

Connect present-day ecology with (paleo)ecology and evolution

• Fossil pollen workflows: retrieve fossil pollen records from open databases, visualize pollen data (e.g., pollen diagrams), and build a basic age–depth model.
• Phylogenetics & macroecology: account for non-independence among species, fit/compare trait-evolution models (e.g., BM vs OU vs EB), and explore how evolutionary history informs biodiversity patterns.

Understand modern modelling approaches (introductory)

• Explain the basic deep learning workflow (training/validation/evaluation).
• Modify key hyperparameters and interpret changes in performance.
• Reflect on when deep learning is (and is not) appropriate for ecological research.

Last update: Mottl Ondřej, Mgr., Ph.D. (28.01.2026)