Analysis of environmental data and modelling

1. It is intended that students acquire the skills to integrate tools and strategies from the areas of ecological and environmental modeling, in the resolution of environmental problems and ecological issues.

2. Basic knowledge in numerical ecology shall be consolidated, including the description of environmental parameters at the soil, atmosphere, and hydrosphere, and of ecological parameters regarding species distribution, the structure and composition of biological communities, patterns of richness and specific diversity, and their interdependencies.

3. Practical skills will be developed in the use of tools for modelling environmental parameters and ecological information, using freeware applications and real ecological situations.

4. Finally, it is intended to develop skills and aptitude for teamwork in an interdisciplinary way, integrating students with different basic backgrounds in joint exercises.

I. Tools for describing environmental parameters.

II. Tools for describing biological and ecological parameters.

III. Development of Numerical Ecology in historical perspective.

IV. Analysis of species distribution patterns and their relationship with abiotic, biotic, and anthropic parameters.

V. Tools for describing ecological communities and specific richness patterns.

VI. Similarity indices, distances, and biological diversity indices.

VII. Ordering of species, communities, and environmental factors.

VIII. Models applied to environmental and ecological parameters without spatialization.

IX. Models applied to environmental and ecological parameters with spatialization.

X. Processes for training, validation, selection, and projection of ecological and environmental models.

XI. Some cutting-edge methods in numerical ecology: machine learning, Bayesian models.

XII. Modeling methods available in free-to-use applications.

Classes will be taught in a theoretical-practical format, with a brief introduction to the topic by the professor, peer-to-peer discussion of case studies and scientific articles, clarification of the topics covered, and development of skills related to the description and analysis of different types of environmental and ecological parameters. Sessions will always include the use of computer applications, especially open-source ones, including R and QGIS, to allow contact with real data, also preferably open-source (e.g., land use, climate). A flipped classroom strategy will be followed, in which students prepare for the in-person component by reading articles, watching videos, and using tutorial files during the asynchronous period. Discussion forums will be used to encourage this preparation.

Borcard D, Gillet F, P Legendre (2011). Numerical Ecology with R. Springer, New York, 306 pp.

Danilson, Romeiras Maria M, Silva Luís. Implications of climate change on the distribution and conservation of Cabo Verde endemic trees.

Global Ecology and Conservation 34 (2022): e02025.

Dutra Silva L, Elias RB, Silva L (2021). Modelling invasive alien plant distribution: A literature review of concepts and bibliometric analysis.

Environmental Modelling and Software, 145: 105203.

Humphries G, Magness DR, Huettmann Falk (Eds.) (2018) Machine learning for ecology and sustainable natural resource management.

Springer International Publishing, 441 pp.

Pavão D, Elias R, Silva L (2019) Comparison of discrete and continuum community models: Insights from numerical ecology and Bayesian methods applied to Azorean plant communities. Ecological Modelling, 402: 93-106.

Scutari M. Denis J-B (2015) Bayesian networks with examples in R. CRC Press, Taylor &amp: Francis Group, Boca Raton, 221 pp.