Courses open for registration

Ecological modelling, based on field data, has become an indispensable tool in ecological research. This course presents a conceptual framework for ecological modelling: covering elementary growth models and probability distributions needed to mathematically model processes.

This course will provide an overview of concepts and tools that can contribute to the design of multifunctional landscapes that are better suited to capitalize on ecosystem services in conjunction with other land-use functions. The focus of the course will be on consumer-resource interactions underlying pollination and biocontrol services, the spatial ecology of pollinators and natural enemies, the impact of pest management practices on arthropod-mediated ecosystem services, and the design of multifunctional landscapes that support arthropod-mediated ecosystem services as well as other land-use functions.

Survival analysis, or time-to-event analysis, was originally developed in medical statistics to analyse the effects of covariates on survival times of patients (hence its name). Techniques from survival analysis can be applied in a variety of biological contexts, whenever data consist of time until occurrence of a certain event. Examples are many types of behavioural data, times until recapture, and latency data.

The course is mainly based on the book "Multivariate Analysis of Ecological Data Using CANOCO 5" by Petr Smilauer and Jan Leps (2014). Practical exercises, the use of Canoco for Windows (4.5) and GenStat for Windows and interpretation of the output are important elements of the course.

The global food system is under stress. Crop yields are expected to decline due to increases in the frequencies of heat waves and prolonged periods of droughts. This course will explore the effects of elevated CO2, temperature and drought on crop physiology. Crop traits that can mitigate or even enhance yield under the stress of a changing world will be explored through a toolbox of options, modelling being a central one. We will be integrating the different physiological processes in relation to change using a systems approach, rather than studying them separately. Focus will be on selecting or breeding plant cultivars that are adapted to these stresses, drought in particular.

Modelling is a crucial part of today's science. Among other things, models are used for assessing sensitivity of systems to disturbances or changes in external factors, and for predictions of future system states. This course provides an introduction to modelling with a focus on systems analysis using dynamic simulation models.

This is a refresher course. The level is that of a second course in Statistics. We will refresh basic knowledge of Probability, Statistical Inference (Estimation and Testing), t-tests, simple cases of Regression and ANOVA, Experimental Design, Nonparametric Tests, and Chi-square Tests. Some time is reserved to discuss statistical problems of the participants.

The aim of this course is to provide an introduction to R, a language and environment for statistical computing and graphics. Focus of the course will be on getting familiar with the R environment, to use R for manipulation and exploration of data, and to perform simple statistical analyses. Hands-on exercises will form a large part of the workshop.

From 20 - 24 May 2019, ISRIC - World Soil Information will organise a Spring School on digital soil mapping, classification and assessment for soil and environmental scientists, students, soil experts and professionals in natural resources management. The spring school will take place at the Wageningen Campus in the Netherlands and will consist of two five-day courses that are run in parallel.

This course introduces methods and software for management, analysis and mapping of soil type and soil properties within the R environment for statistical computing. After this course participants will be able to apply the methods learnt to their own datasets.

The design and analysis of experiments, using plants, animals, or humans, are an important part of the scientific process. Proper design of an experiment, apart from its proper analysis and interpretation, is important to convince a researcher that your results are valid and that your conclusions are meaningful.

In this course participants will learn how to model patterns and structures contained in data. The course will be focused on statistical and machine learning approaches, where the relationships between the observed data and the phenomenon under study are learned directly from observations. Through a series of lectures and practical exercises (in Matlab), the participants will learn about different strategies and their pertinence for specific problems in environmental sciences. Most applications considered in the course will be remote sensing-based, but the course will remain general for a broader audience.

In this module we continue with Regression, ANOVA, and ANCOVA, set in the general framework of Linear Models. We look at topics like parameter estimation and interpretation, checking model assumptions, regression diagnostics, analysis of unbalanced designs and multiple comparisons.

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.

In this module we discuss how to analyse data for which the assumption of independence is violated. So: Do you have a nested experimental set-up? Or repeated measurements? Or weight of the same animal over time? Or pseudo-replication? Then, you are likely to need Mixed Models. In this course, you will learn all about it!

The aim of this course is to provide participants with skills to assist them in working with animal movement data including data management and organization, working with large tracking datasets, data exploration, visualization and analysis of movement data. The course combines several guest lectures from international experts in the field of animal movement research.

This postgraduate course Geocomputation using free and open source software is an immersive 5-day experience opening new horizons in the use of the outstanding power of Linux and the command line approach for processing geospatial data. Jumpstart with R, Grass, Python, Gdal/Ogr library and linux operating system. We will guide newbies who have never used a command line terminal to a stage where they will be able to understand and use very advanced open source data processing routines. Our focus is to give you the tools and competencies to continue developing your skills independently. This self-learning approach allows participants to continue progressing and improving in an ever-evolving technology environment.