Learning objectives
On the completion of this course, the students will acquire skills for the understanding and presentation of ecological data, useful for professional activities, for dissertation projects and for further studies.
In particular, the students will understand the rationale behind the scientific method, will develop the ability to critically read the scientific literature and will be able to plan an experiment, both in the phase of sampling design and in the subsequent steps of data analysis.
Prerequisites
None.
Course unit content
This course introduces the basic concepts for the collection and analysis of biological and environmental data, and provides the opportunity to experience methods and instruments useful for the understanding of ecological systems.
Full programme
- During the first class, information about learning outcomes, course programme, teaching methods and assessment will be provided.
- The scientific method and the role of statistical analysis: the logic of the scientific approach to the study of nature, what is statistics and why is it needed. DD: 1. Knowledge and understanding, 4. Communication
- The approaches to the investigation of ecological problems: mensurative and manipulative studies, lab and field experiments, environmental gradients and space-for-time substitution. DD: 1. Knowledge and understanding
- The logic of hypothesis testing and the development of a research: the role of inferential statistics, variability and uncertainty metrics, null hypothesis, probability and significance. DD: 1. Knowledge and understanding
- The different types of variables and the choice of statistical models: numerical and categorical data, relationships between data type and statistical model. DD: 1. Knowledge and understanding, 4. Communication
- Sampling design and samples selection: statistical population and sample, inferential and sampling units, independence, replication and random sampling. DD: 1. Knowledge and understanding, 2. Applying knowledge and understanding
- Methods and tools for data collection and processing: disruptive and non-disruptive vegetation samplings, approaches to primary production and decomposition estimation. DD: 1. Knowledge and understanding
- Linear models in R: correlation and regression, analysis of variance and covariance, introduction to the R statistical suite. DD: 1. Knowledge and understanding, 2. Applying knowledge and understanding, 3. Making judgements, 5. Lifelong learning skills
- A protocol for explorative analysis of data: descriptive statistics, the recognition of outliers, data distribution. 1. Knowledge and understanding, 2. Applying knowledge and understanding, 3. Making judgements
- The procedures for applying statistical models to data: model selection and evaluation of assumptions, complex and parsimonious models. DD: 1. Knowledge and understanding, 2. Applying knowledge and understanding, 3. Making judgements
- The interpretation and presentation of results: the structure of a scientific work, graphical display of data. DD: 1. Knowledge and understanding, 3. Making judgements
- Some case studies and common problems: critical evaluation of experiments, how to avoid common statistical errors. DD: 1. Knowledge and understanding, 3. Making judgements, 5. Lifelong learning skills
Bibliography
During the course students will be provided with lecture notes and scientific papers.
Text books:
Crawley MJ (2013) The R Book. Wiley.
Quinn GP, Keough MJ (2002) Experimental Design and Data Analysis for Biologist. Cambridge University Press.
Teaching methods
The course includes lectures, with multimedia devices, and practical activities both in the field, such as sampling design and data collection, and in the classroom, such as data processing and analysis with appropriate software.
Theoretical topics will be coupled with illustrative study cases, experiment simulations and critical class discussions.
Assessment methods and criteria
Knowledge will be assessed by means of a practical test (three problems, each with three essay questions) and a following oral examination.
Other information
For some activity is essential to have a laptop; during the course open source programs for data processing will be installed.
“Dublin Descriptors” (DD) included in the course are shown for each point of the programme in the respective section.
Dublin Descriptors:
1. Knowledge and understanding;
2. Applying knowledge and understanding;
3. Making judgments;
4. Communication;
5. Lifelong learning skills.
