course_structure.md

Course structure:

3 day course by Nick Golding and Gerry Ryan

Day 1:

AM:

• Welcome / icebreaker (ROpenSci activity - stand on a line thing)
• Introductory concepts and discussion. Slides and whiteboard.

PM:

• Simulate data
  • Abundance and relative abundance
  • bias
  • PA data from planned surveys (random, biased, abundance-biased)
  • understand how to simulate presence/absence from abundance
  • understand how to calculate probability of presence from average abundance
    • simulate_prob_presence.R
  • PO data from presence and bias process
  • prepare_raster_data.R - run through but encourage students to download files from figshare as travel and bioclim files are large.
  • simulate_data.R - students run through alongside instructors

Day 2:

AM:

• Modelling
  • Logistic regression on random PA data
  • Logistic regression on presence-only with random background
  • models.R - students run through alongside instructors
• Theory: link functions. Whiteboard and code.
  • link_demo.R

PM:

• Modelling
  • Maxent presence only with random background points
  • Maxent presence only with random bg and bias layer offset
  • Students can run models.R alongside instructors
  • Students explore other PA data or other covariates if happy
• Theory: target-group background and bias cancellation

Day 3:

AM:

• Modelling
  • Fithian PA-PO-bg model
  • models.R!
  • continue explore alternatives from existing model set
• Theory: Fithian model
• Discussion: other topics in SDMs

PM:

• Modelling
  • own data and models
  • continue explore alternatives from existing model set
• Discussion
  • Papers
  • Own models and data