AIMS AND CONTENT

LEARNING OUTCOMES

The teaching aims to provide students the fundamental knowledge for the organization and study of ecological data, the structuring of an experimental design in ecology and the choice and use of the main univariate and multivariate analysis techniques. Students will also be introduced to the use of the open source software R for the creation of graphs, data exploration and statistical analysis of univariate and multivariate data.

AIMS AND LEARNING OUTCOMES

Attendance and active participation in the proposed training activities, which include lectures and practical sessions (in the field and with the software R), will allow the student to:

- learn how to structure an experimental design in the ecological field;

- organize and explore the data collected through an experiment;

- choose and use the correct uni or multivariate analysis techniques to test hypotheses;

- use the open-source software R for the creation of graphs, data exploration and statistical analysis on univariate and multivariate data;

- interpret and comment on the results of a statistical test.

TEACHING METHODS

The teaching consists of lectures and applied lessons (in the field and in the computer room or with personal computers) where students will learn how to design an experiment and to apply data analysis techniques using the R software.

Lectures are delivered through multimedia presentations, video, quiz and individual or team practice exercises, which can be performed both in the classroom or on-line.

Seminars will complete the teaching with focus on specific topics.

Please refer to AulaWeb application specific for the teaching for potential updates due to changes in the health and epidemiological situation.

SYLLABUS/CONTENT

INTRODUCTION TO QUANTITATIVE ECOLOGY:

1) The experimental design in ecology, the hypothetical deductive method

2) Parameters of a population, statistical inference

UNIVARIATE ANALYSIS

1) Frequency distributions, asymmetry and kurtosis

2) The Analysis of Variance: algebraic distribution of variability, the linear model, parametric and non parametric tests

3) Multi-factorial, hierarchical and orthogonal designs

4) Multi-factorial designs for impact assessment: BACI and beyond BACI 

5) Correlation and linear regression

6) Multiple regressions and Regression Trees

MULTIVARIATE ANALYSIS

1) Similarity coefficients and cluster analysis

2) PCA and MDS orders

3) Multivariate tests (ANOSIM, PERMANOVA)

SOFTWARE R

1) Introduction to the use of R software

2) Frequency distributions

3) Data exploration

4) Univariate analyses

5) Multivariate analyses

RECOMMENDED READING/BIBLIOGRAPHY

Available (downloadable from the AulaWeb or Teams) presentations, videos and all material of the lessons. The provided material is sufficient for the exam preparation.

Suggested reading:

Fowler, Cohen. Statistics for Ornithologists and Naturalists. Natural Sciences Texts, Franco Muzzio Editore, 2010.

Dytham, Calvin. Choosing and using statistics: a biologist's guide. John Wiley & Sons, 2011.

D. Borcard et al., Numerical Ecology with R, Use R, 1 DOI 10.1007/978-1-4419-7976-6_1, © Springer Science+Business Media, LLC 2011

Underwood A.J., 1997. Experiments in ecology. Cambridge University Press

Gambi M.C., Dappiano M., 2003. Handbook of sampling methodology and study of Mediterranean marine benthos. Marine Mediterranean Biology, vol 10 (Suppl.).

Camussi A., Möller F., Ottaviano E., Sari Gorla M., 1995. Statistical methods for biological experimentation. Zanichelli.

Zar J.H., 1999. Biostatistical Analysis. Fourth Editino. Prentice Hall, Upper Saddle River, New Jersey 07458.

Legendre, Pierre & Louis Legendre. 1998. Numerical ecology. 2nd English edition. Elsevier Science BV, Amsterdam. xv + 853 pages.

DC Schneider Quantitative Ecology, 2nd edn, 2009. London: Academic Press. 432 pp.

A.F. Zuur, E.N. Ieno, G. M. Smith. Analysing Ecological Data. Statistics for Biology and Health. Springer, 2007

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/corsi/11770/studenti-orario

Class schedule

QUANTITATIVE ECOLOGY

EXAMS

EXAM DESCRIPTION

The exam consists of an oral test concerning the topics covered in the course. The exam will start with the exposition of a scientific article or case study provided by the lecturer or of student's choice (preferably exposed with a short digital presentation, e.g. in power point or similar), and will continue with other 2/3 questions asked by the lecturer on topics covered during classes.The exam is passed if the student has obtained a grade greater than or equal to 18/30.

Four appeals will be available in the winter session (January-February) and 2 calls in the summer session  (June-July) .

Please refer to AulaWeb application specific for the teaching for potential updates due to changes in the health and epidemiological situation.

ASSESSMENT METHODS

Details on how to prepare for the exam and the degree of detail required for each topic will be provided during the lessons. The oral exam will mainly focus on the topics covered during the lectures and will aim to evaluate the achievement of the adequate level of knowledge. The ability to present the topics clearly and with correct terminology will also be assessed. The oral exam starts from the critical report of a scientific article or case study (among those provided by the teacher and available on Aulaweb or of choice of the student, after discussion with the teacher) presented in the form of a oral presentatio, e.g. Power Point presentation. The ability to understand a scientific text, identify the hypothesis at the basis of the experiment, depict the experimental design, understand and discuss the reported results, will be evaluated. Questions about the main topics covered during the teaching will follow.

FURTHER INFORMATION

The topics covered, the numerous examples discussed in class and high number of practical activities encourage assiduous attendance. 

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sara Ferrando (sara.ferrando@unige.it), the Department’s disability liaison."