Ecological Data Analysis

 
Course code:
AM_1225
Period:
Period 1
Credits:
6.0
Language of tuition:
English
Faculty:
Faculty of Science
Coordinator:
dr. J.T. Weedon
Examinator:
dr. J.T. Weedon
Lecturers:
dr. J.T. Weedon
Teaching method(s):
Computer lab, Lecture, Practical, Study Group, Seminar
Level:
500

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 test 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.

Form of tuition

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 extensive case study, to
which the theory is applied. 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, 15 contact hours for
practicals and 20 contact hours for feedback on the daily assignments .

Type of assessment

Exam (30%)
Report on the final case study (70%)

Course reading

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 Press

Discovering Statistics Using R (2012) A. Field, J. Miles & Z. Field SAGE
Publications

The 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 R

Logan, 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: 1142-1149

This literature is complimented by lecture handouts, explanations of the
assignments, answers to the assignments,and additional notes provided on
Canvas.

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 on t-tests (paired and unpaired), linear regression
and one-way ANOVAs.

Target audience

The course is compulsory for MSc Ecology 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.

Remarks

The course is organized by the Department of Ecolgical Science at the VU
and the Institute for Biodiversity and Ecosystem Dynamics of the UvA.
All contact hours are at VU University.

Lecturers:
dr. J.T. Weedon,
dr. J. Duivenvoorden,
dr. M. Egas

© Copyright VU University Amsterdam
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