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 exercises, 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;

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

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

TEACHING METHODS

The teaching consists of lectures and applied lessons (in the computer room, if possible according to covid restrictions) where students will learn 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

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.

Recomended 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