Life History Theory deals with species-specific adaptive schemes of the distribution of the reproductive effort over the life of an organism. 

Programming can serve multiple purposes. Purposes like developing applications and working with data are also very useful for research. For dealing with these issues, Python offers many libraries. Getting the skills of working with some of these libraries will enable future learning. This can be for more advanced programming applications, but also for self-learning to apply different libraries.

Researchers trying to summarize the constantly growing body of published research are increasingly using meta-analysis. The focus of this 2-day course will be on concepts of linear models and mixed linear models in meta-analysis. The statistical software R will be used.

This course will empower attendees to maximize and defend their scientific interpretations based on their expert knowledge as informed by quantitative results. A new procedure, “causal knowledge analysis” will be introduced as a way to document expert knowledge and set the stage for SEM analyses. Once that is accomplished, structural equation modeling techniques will be presented as a methodology for representing and evaluating hypotheses involving networks of relationships.

Starting a PhD is an exciting step but it can also feel a bit overwhelming at times. Our First year Retreat is designed to help you get a better sense of what lies ahead and how to make the most of your PhD experience.

This retreat invites you to hit pause, look back, and explore how things are going  and how things can be improved!

Join us for an inspiring two-day retreat designed for PhD candidates in their final year!

The focus of this edition of the FSRLA course is on farm household systems in multifunctional landscapes, as affected by poverty, pressures of climate change, population growth, land-use change, and changes in markets. 

In this post graduate course, we will explore photosynthesis from the chloroplast to the whole-plant canopy level. 

The course Multivariate Analysis offers a thorough introduction to multivariate statistical methods, tailored for researchers working with complex datasets where multiple variables are measured simultaneously.

The aim of this course is to provide an introduction to R and R Studio. It introduces the participants to R language syntax, to enable them to write their own R code. They will also learn about R data-types and data-structures, and they will be taught how to explore the data and produce plots. The course will be a combination of lectures and practicals.

This course will make you familiar with a modern toolbox of analysis techniques at the interface of statistics and machine learning. You will develop the skills to build and evaluate modeling strategies for complex (big, high-dimensional, hierarchically-structured) data as occurring in the areas relevant to WIAS and PE&RC. 

This course focuses on concepts such as spatial self-organization, scale-free movement, and biophysical interactions at multiple scales. Furthermore, modern techniques to quantify plant and animal movement, analyse animal movement strategies, and model the implications of spatial self-organization in an ecosystem using a relatively simple modelling approach will be introduced.

In this workshop, participants will learn the principle of tidy data, how to transform and combine datasets using the tools from the tidyverse and how to generate advanced visualization with the ggplot2 package.

This is a refresher course aimed at PhD candidates. The level is that of a second course in Statistics. 

European forests have a central role to play in biodiversity conservation, as is recognized by recent publications and policy documents.

In this module we discuss how to analyse data for which the assumption of independence is violated. In this course, you will learn all about it!

In this module we study how to analyse data that are not normally distributed. We look at fractions (logistic regression), counts (Poisson regression, log-linear models), ordinal data (threshold models), and overdispersion. We discuss (quasi-) maximum likelihood estimation and the deviance.

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. 

Extend participants' basic knowledge of R by teaching them more advanced programming concepts and the use of R for more complex problem solving, going beyond just statistics.