Quantitative methods in marine science

Semester 1

Semester 2

Semester 3

Semester 4



Ghent University, Université de Bretagne Occidentale, University of Algarve, University of Oviedo


Numerical tools help to ask scientific questions more efficiently and extract appropriate answers. This course will introduce students to many basic techniques in data analysis and numerical modelling, to help them summarise a problem in mathematical terms, plan experiments or field sampling campaigns, and gather insights from the data collected.
Students will learn how to identify sources of variation in biological data and decide on sampling/experimental units and replicates. Major inferential statistical and data exploration techniques will be taught. Numerical models will be introduced as a way to simplify and formalise a system. A programming language (R) will be used to apply all those techniques.


The class will consist of theoretical parts and applications to actual data sets. The themes tackled are presented below. While the core of the programme will be the same in all universities, some classes are optional (in brackets: [] ) and the specific time spent on each part will vary between universities.

Maths and programming basics
notion of variable and of assignation; data types; data import; data manipulation, repetition of operations.
numerical integration of differential equations; matrix computation
data representation (plotting)

Experimental/sampling design
best practices in experiment and sampling design for optimal statistical power

Linear model
revision of simple linear regression, revision of ANOVA (as a particular case of linear model)
multiple regression and multi-factor ANOVA; model selection
introduction to generalised linear model: logistic regression, Poisson regression
[introduction to mixed effects models]

Non parametric tests
notion of rank, basic non-parametric version of inferential tests (Wilcoxon-Mann-Whitney, Kruskall-Wallis)
[notion of bootstrap and bootstrap tests]

Introduction to multivariate data analysis
Principal Component Analysis
[Correspondence Analysis or Multidimensional Scaling]

Numerical modelling
0D dynamical box and flux models (Fasham-like NPZD model)
Population dynamics models (Leslie-like matrix models)


Bachelor in sciences. Basic knowledge in sampling and experimental design (notion of replicate), descriptive statistics (distributions, statistical moments), and basic statistical inference (comparison of means, correlation, one-way ANOVA, simple linear regression).


The general aim of the course is to make students “operational” for data acquisition and analysis in science: they will know how to plan an experiment or sampling campaign for maximum inferential power, read and manipulate the dataset collected, perform appropriate exploratory analyses and implement statistical or numerical models to test hypotheses, interpret and plot the results.


Students will learn how to translate a marine sciences question or hypothesis in mathematical terms and how to select the factors that are more relevant to answer it. They should realise that this formalisation should precede and information data acquisition rather than be considered after the fact.

Key skills acquired

Students will learn:
- how to use computer code to read and manipulate data, to implement statistical tests or dynamical models
- how to efficiently plan an experiment or field sampling campaign
- how to choose an appropriate data analysis technique
- how to interpret the output of basic inferential statistics
- how to represent data and model output graphically


UPMC: Biostatistique (Scherrer), Numerical Ecology (Legendre & Legendre),
Uniovi: Sampling, 3rd Ed (S.K. Thompson),
Ugent: Experimental design and analysis for Biologists (Quinn & Keough (2002))


UALG: 3 h final exam, open notes with broad interpretation questions
UPMC: 3h written exam, no documents, exercises and interpretation questions
UGent: 3h written exam + oral feedback
Uniovi: Assignment describing a complete sampling protocol/experimental design on a realistic scenario

Involved teachers

Margarida Castro mcastro@ualg.pt UAlg Marleen De Troch marleen.detroch@ugent.be UGent Nele De Meester nele.demeester@ugent.be UGent Ann Vanreusel ann.vanreusel@ugent.be UGent Julio Arrontes arrontes@uniovi.es UniOvi Jean-Olivier Irisson irisson@obs-vlfr.fr UPMC Jean-Marc Guarini jean-marc.guarini@upmc.fr UPMC Lars Stemmann stemmann@obs-vlfr.fr UPMC Éric Thiebaut eric.thiebaut@sb-roscoff.fr UPMC Stéphane Gasparini gasparini@obs-vlfr.fr UPMC

Contact hours






University lectures (h) practicals (h) seminars (h) computer class (h) field work (h) other (h) UGent 24 … .. 32 … ... UAlg … … … 60 ... … UPMC 24 … … 36 … … UniOvi 24 … … 24 … …