B2111 - SPATIAL ECOLOGY IN R

Conoscenze e abilità da conseguire

This course mainly focuses on the application of free and open source algorithms - which ensure high reproducibility and robustness of ecological analysis - to study ecological change in space and time, due to both human impact and global change. Particular emphasis will be given to: 1) population ecology: how organisms spread in space and how to study it by point pattern analysis, 2) how community are structured and how to study such structure by multivariate analysis; 3) monitoring species distributions and their change in space and time by species distribution modelling; 4) monitoring ecosystem change in space and time by remote sensing data. The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. Students will finally be able to create their own project on monitoring of spatial and temporal changes of species and ecosystems at different spatial scales.

Contenuti

Lab plan:

1. R (intro)

[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!]

2. Spatial R

[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!]

3. Population Ecology | Point Patterns Analysis

[Spatial statistics: deriving continuous maps from in-situ data, principles of autocorrelation and spatial interpolation]

4. Multivariate analysis in R

[Population and community monitoring]

5. Remote sensing in R

[Remote sensing: performing amazing spatio-temporal monitoring with satellite data at global scales]

[Geographical data and reference systems: how to deal with the spatial reference of geographical data]

6. Multitemporal analysis of ecosystem functions

[Multitemporal analysis: monitoring change in time]
 
7. Species Distribution Modelling

[Species distribution modelling: modelling the main drivers shaping species distributions]

Testi/Bibliografia

Rocchini, D. (2026). R Coding for Ecology. Springer.

https://doi.org/10.1007/978-3-031-99665-8

Metodi didattici

Lectures and practical exercises.

Modalità di verifica e valutazione dell'apprendimento

- Markdown project by the students based on R coding

- Questions on the code

Strumenti a supporto della didattica

Free and Open Source Software. Free geographic and remote sensing data.

Orario di ricevimento

Consulta il sito web di Duccio Rocchini