6-8 October 2026  

This course will introduce the basics of Capture-Mark-Recapture (CMR) analyses, main concepts and commonly used models and their assumptions. We will specifically focus on Cormack-Jolly-Seber model (for aparent survival estimation), Jolly-Seber model and POPAN models (for abundance estimation), while giving a rather brief overview of other model types. 
Practical exercises will mainly focus on using R package RMark to fit CMR models. We will walk through a complete workflow for analysing the field data: from data formatting to obtain encounter history to fitting CMR models with different structures, to interpreting and visualising the results. 

The course is aimed at researchers who would like to learn how to analyse capture-mark-recapture data in R. Researchers who have experience in running CMR analyses in MARK but would like to learn how to do it in R, are welcome to join. However, previous experience with MARK is not required to attend the course. Participants are expected to have basic experience with R, meaning they should be able to import, manipulate, and visualize data. Familiarity with RStudio is also required. 

‒    Have a basic understanding of CMR analyses and knowledge of the main model types and their assumptions
‒    Being able to specify and fit Cormack-Jolly-Seber and POPAN models in R
‒    Know how to perform goodness-of-fit test and how to interpret it
‒    Being able to interpret the fitted CMR models and choose the one that fits the data best (if there is such a single model)
‒    Being able to interpret the parameter estimates of the fitted CMR model and visualize the results

Sessions from 12:00 to 15:30 (Berlin time, Tuesday-Thursday) will consist of lectures and practical exercises (walk-throughs, excercises and Q&A sessions)

Tuesday – Classes from 12:00-15:30 Berlin time
Lectures: 
-    Introduction to Capture-Mark-Recapture analyses
-    Assumptions of Cormack-Jolly-Seber model
-    Goodness-of-fit test, overdispersion
-    Recap on GLM
-    Behind the scenes: encounter history, Parameter Index Matrices (PIMs), design data

Practical exercises:
-    Fitting a simple CMR model with Rmark
-    Running goodness-of-fit test and the fitted model and interpreting the output

Wednesday – Classes from 12:00-15:30 Berlin time
Lectures: 
-    Encounter histories 
-    Contrasting models with different structures with the data

Practical exercises:
-    From raw data to encounter history
-    Full workflow to fitting a CMR data, interpeting and visualising the results

Thursday – Classes from 12:00-15:30 Berlin time
Lectures: 
-    Multistate capture-mark-recapture models 
-    Overview of the models used to estimate abundance from CMR data: Jolly-Seber model, POPAN, Link-Barker, Pradel-λ

Practical exercises:
-    Fitting and interpreting multistate CMR
-    Estimating abundance 

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