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