dates

13-17 July 2026

To foster international participation, this course will be held online

Landscape genomics is a research field that studies how genetic diversity is distributed across space and how environmental features can modify this structure through local adaptation. In this workshop, students will learn the basics of this approach and train using state of the art methods. The course will provide an overview of the types of dataset that can be used for a landscape genomics analysis. Firstly, students will learn how to obtain environmental data from publicly available databases, how to process it with Geographic Information Systems (GIS) and how to use the latter to produce indicators able to describe the characteristics of the landscape. Next, the course will discuss the different approaches to obtain genetic data and subsequently show how to study genetic variation and population structure across space in the R environment. Students will be given an overview of the different statistical approaches to study local adaptation, and will be trained in using the following methods: Sambada, latent factor mixed models (LFMM), and redundancy analysis (RDA). The course will also cover the critical task of the interpretation and validation of the results. Finally, the workshop will consider the crucial aspects and good habits to account for when planning a landscape genomics experiment (e.g. sampling design). 

This workshop is aimed at all biologists, ecologists, geneticists, veterinarians that want to implement the landscape genomics approach in their own studies of evolutionary biology and conservation. Even though the course is not intended for a specialized audience, basic knowledge in evolutionary biology and population genetics would help. Students will learn how to use GIS, but basic computer skills are desirable (e.g. in the R environment). A basic understanding of statistics is also necessary.

The course is organized in ten learning sessions. During the first two sessions, the course will provide a contextualization of the research field. Then, students will be guided through a landscape genomics experiment with sessions that couple brief theoretical introductions with practical work.

Monday –  2-6 PM Berlin time

 -          Overview of the course program

 -          Introduction on Landscape Genomics

 -          The environmental data

-          The genetic data

       -          GIS, coordinate systems, modelling modes

Tuesday – 2-6 PM Berlin time

-          Environmental characterization of the samples

-          Point versus surface

-          Analysis of environmental diversity of samples

-          Vegetation indices and land surface temperature based on remote sensing data and Google Earth Engine

Wednesday –2-6 PM Berlin time

 -          Planning a Landscape Genomics Experiment

-          Sampling Design

-          Genetic ata filtering

-          Spatial genetic variation

-          Population structure

Thursday –2-6 PM Berlin time

-          Combining environmental and genetic data with samβada

-          Correction for multiple comparisons (Bonferroni, FDR)

-           Identification of markers under selection, sorting of association models obtained

-          Calculation of spatial autocorrelation and their cartography

Friday –2-6 PM Berlin time

-          Thematic mapping

-          Take home messages

-         Q&A

COst overview

"Our instructors were knowledgeable about the content but were happy to help each student. I appreciated how patient they were with everyone and making sure everyone understood the concepts. "

"Very informative course"

1-  GIS in R - ONLINE, 02-06 March

2 - Population Genomics - ONLINE, 16-20 March

3- Dealing with messy data in R - ONLINE, 8-10 April

4 - Conservation Genomics - ONLINE, 7-10 April

5 - Interactive Maps with R - ONLINE, 13-14 May

6 - Introduction to GWAS - ONLINE, 18-22 May

7 - Species distribution and ecological niche modelling in R - ONLINE, 14-18 September