Ecological Data Analysis

URL study guide

https://studiegids.vu.nl/en/courses/2025-2026/AM_1225

Course Objective

The final attainment levels of this course, include that students:Are acquainted with possible experimental designs for ecological research and can select the most suitable design depending on experimental objectives and hypotheses Are acquainted with possible statistical analyses, understand the theory and the assumptions underlying the various analyses and can test the underlying assumptions Can select the most suitable statistical analysis depending on the design chosen and the statistical assumptions Can interpret the chain of hypotheses, design and analysis to validate hypotheses combining empirical data with statistical models

Course Content

A proper experimental design combined with a suitable statistical analysis is essential to -biological
- science, even though it is considered by many as a necessary evil. In this course, the whole chain of hypothesis and design to analysis and interpretation is covered to allow students to apply a range of statistical techniques independently. The application and implementation of the techniques (using the statistical programming language R) is the basis. Possible experimental designs are discussed in relation to specific biological questions and hypotheses. The application of statistical analysis is treated in relation to these designs. Theory and especially the assumptions underlying the tests are treated to the extent that this information is necessary to apply the tests properly. Both -combinations of
- regression and analysis of variance techniques and multivariate analysis techniques such as unconstrained and constrained ordination are dealt with.

Teaching Methods

As application is central to this course, case studies, assignments and working with real biological data is the core of this course. Starting of with the research question, hypothesis and the lab/field/model situation a proper design and statistical analysis will be discussed. A specific case study is used to illustrate this chain of arguments. Theory, assumptions and tests are all treated in the context of these case studies and are coupled directly to the case study and subsequent assignments. The course is finalised with an assignment involving applying the learned theory and techniques, to a series of case studies. Knowledge of some of the main principles of applied statistics is also tested in a short-answer exam. This set-up translates into 30 contact hours for lectures, 35 contact hours for practicals.

Method of Assessment

Exam (50%)Report on the final case studies (50%)

Literature

There is no required textbook, however students are strongly recommended to have access to one of:Quinn, G.P. and M.J. Keough (2002), Experimental design and data analysis for biologists Cambridge University PressDiscovering Statistics Using R (2012) A. Field, J. Miles & Z. Field SAGE PublicationsThe latter is particularly recommended for students without any previous experience with the R programming language. In addition the following articles and books may be helpful for parts of the course:Dalgaard, P. (2008) Introductory Statistics with RLogan, M. (2010) Biostatistical Design and Analysis Using R: A Practical Guide. Wiley Borcard, D., F. Gillet and P. Legendre (2011) Numerical ecology in R. Springer Bolker, B.M., M.E. Brooks, C.J. Clark, S.W. Geange, J.R. Poulsen, M.H.H. Stevens and J.S. White (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24: 127-135 Gurevitch, J. and L.V. Hedges (1999) Statistical issues in ecological meta-analyses. Ecology 80: 1142-1149This literature is complemented by lecture handouts, explanations of the assignments, answers to the assignments,and additional notes provided on Canvas.

Target Audience

The course is compulsory for MSc Ecology & Evolution students at the VU and for UvA students doing the Ecology and Evolution specialization of the master Biological Science. The course is also open for master students in Biology, Ecology or Earth Sciences and PhD students at the VU and UvA universities with a deficiency in experimental design and statistics.

Additional Information

All contact hours are at VU Amsterdam Lecturers: dr. J.T. Weedon (VU) dr M. Bruijning (UvA)

Entry Requirements

Methodology and statistics 1 and 2 or equivalent statistics courses (min. 12 EC). This implies that we require students to understand the interpretation of P-values, type I and type II errors and statistical hypotheses testing in general. In addition, students are required to have understanding of t-tests (paired and unpaired), linear regression and one-way ANOVAs.

Recommended background knowledge

Students who have doubts about their background knowledge in statistics (see "Entry requirements" above) are strongly encouraged to contact the course coordinator ([email protected]) in August or September before the course to get advice for preparatory readings.