CUrriculum

Monday – Lectures from 15:00 to 18:00 + 4 hours practicals

 

- [PRE-COURSE] Self-guided introduction / refresher on base R language

 

- Underlying theory in SDM and ENM: geographic distributions and ecological niches

 

- Differences between SDM and ENM

 

- Types of niches and types of models

 

- Data for building SDM and ENM: species occurrence records and predictor variables

 

- Identifying and addressing data quality issues

 

- Practical: data gathering, cleaning and formatting

 

 

 

Tuesday – Lectures from 15:00 to 18:00 + 4 hours practicals

 

- Presence-only, presence-background, and presence-absence models

 

- Defining the modelling region: resolution and extent

 

- Methods and R packages for SDM and ENM: single-model vs. multi-model approaches

 

- Practical: model building

 

 

 

Wednesday – Lectures from 15:00 to 18:00 + 4 hours practicals

 

- Different facets of model accuracy: discrimination vs. calibration

 

- Metrics and packages for model performance analysis

 

- Model cross-validation: random, spatial and environmental blocks

 

- Practical: model evaluation and cross-validation

 

 

 

Thursday – Lectures from 15:00 to 18:00 + 4 hours practicals

 

- Overview of applications of SDM/ENM

 

- Strengths, limitations and caveats of SDM/ENM

 

- Model projection and validation in space and time

 

- Assessing extrapolation beyond the model domain

 

- Practical: model projection and extrapolation analysis

 

 

 

Friday – Lectures from 15:00 to 18:00

 

- Students’ presentations

- Final discussion and outlook