BIOMETRY AND STUDY OF ANIMAL POPULATIONS

Knowledge and understanding

•        The main data analysis techniques (statistics, machine learning, artificial intelligence) and their fields of application in biology.

•        How analytical techniques can be used for classification, prediction, and hypothesis testing in biological contexts.

•        A broad range of applications of data analysis techniques to real biological systems from natural populations (morphometric, ecological, genetic, and genomic data).

Applying knowledge and understanding

•        Explore and visualize biometric data using plots (box plots, scatterplots, histograms, etc.).

•        Use the R software for low-difficulty data analysis tasks (using existing packages to perform the analytical techniques learned in the course).

•        Understand how data analysis and machine learning techniques are typically implemented in software with a graphical user interface.

• The course is worth 6 ECTS credits. Teaching activities include frontal lectures (Didattica Erogativa – DE) and computer labs (Didattica Interattiva – DI).

  Frontal lectures are dedicated to covering theoretical concepts with practical examples, and contribute to the acquisition of theoretical and methodological knowledge outlined in the expected learning outcomes.

  Computer labs involve the practical application of data analysis techniques using software (particularly R), fostering students' active engagement and the development of applied skills that form an integral part of the final assessment.

  If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above, in order to ensure consistency with the syllabus.

• Basic knowledge of animal biology.
• Knowledge of the English language (needed for reading scientific articles and educational materials).

Attendance at lectures and labs is fundamental for various reasons, including the fact that the technical skills learned during the course are fundamental for the practical part of the final exam.

Module 1: Biometric Tools

• Introduction to biometry and types of data (morphological, genetic, and ecological) from animal populations.
• Data analysis and biometry: statistics, machine learning, and artificial intelligence.
• Exploration and visualization of biological data through plots (e.g., box plots, scatterplots, histograms).
• Comparing two groups: randomization-based methods and other approaches.
• General linear models and their applications.
• Principal Component Analysis (PCA).
• Classification – Discriminant analysis and classification based on biometric data.
• Quantifying the performance of classification models with cross-validation.

Module 2: Applications in Natural Populations

• Case studies involving the application of techniques covered in the first module.
• Applications using morphometric data.
• Ecological data and associated issues.
• Genetics, genomics, and transcriptomics of animal populations.

There are no “adopted” books in the strict sense, and the main reference material will be provided by the instructor. However, some texts are recommended for consultation and further study.

Books for reference and further study

• Sokal, R.R., Rohlf, F.J. Biometry: The Principles and Practice of Statistics in Biological Research, W.H. Freeman.
• Vu, J., Harrington, D. Introductory Statistics for the Life and Biomedical Sciences. Openintro (available at Openintro).

Assessment will be via a theoretical-practical oral exam. An intermediate written test may be scheduled for students attending lectures regularly; details and timing will be communicated to students during the course.

In the oral exam, the following will be assessed: mastery of theoretical content, ability to apply the analytical techniques learned, clarity of exposition, and mastery of discipline-specific vocabulary.

Learning assessment may also be carried out on-line, should the conditions require it.

To ensure equal opportunities and in compliance with current laws, interested students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per l'integrazione Attiva e Partecipata — Servizi per le Disabilità e/o i DSA) referring teacher within their department (https://www.cinap.unict.it/content/referenti).

1. Describe how principal component analysis (PCA) can be used to explore a biometric dataset.
2. Explain how cross-validation can be used to test the accuracy of machine learning-based predictive models.