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Table of Contents

  1. Introduction: AIACC: Climate Change and Conservation Planning
    1. Chapter1: Evidence for climate change
      1. Chapter 2: Global circulation models
        1. Chapter 4: Biodiversity responses to past changes in climate
          1. Chapter 5: Adaptation of biodiversity to climate change
            1. Chapter 6: Approaches to niche-based modelling
              1. Slide 1: Approaches to niche-based modelling - theory and practice
              2. Slide 2: Lecture Structure
              3. Slide 3: Why model species ranges?
              4. Slide 4: Used in response to
              5. Slide 5: Distribution models have been used to predict
              6. Slide 6: They have also been used to...
              7. Slide 7: Principles: Fundamental niche
              8. Slide 8: Principles: Realised niche
              9. Slide 9: Principles: Range edges
              10. Slide 10: Principles: Response curves
              11. Slide 11: Response curves estimation of different models
              12. Slide 12: Specifics: Niche-based modelling
              13. Slide 13: Niche-based modelling - assumptions
              14. Slide 14: Cautionary note on modelling in general
              15. Slide 15: Specifics: variable selection
              16. Slide 16: Example of how direct/indirect variables may affect a plant species
              17. Slide 17: Variables and their selection
              18. Slide 18: Variables determine specificity of model
              19. Slide 19: Environmental Variables
              20. Slide 20: Derived Variables
              21. Slide 21: Recommendations for variable selection
              22. Slide 22: Species distribution datasets
              23. Slide 23: Species distribution datasets...2
              24. Slide 24: Species distribution datasets...3
              25. Slide 25: How do we choose a model type?
              26. Slide 26: Different types of models
              27. Slide 27: Principles
              28. Slide 28: Various decision trees from the literature
              29. Slide 29: Decision trees from the literature (2)
              30. Slide 30: In conclusion
              31. Slide 31: Model calibration and evaluation
              32. Slide 32: Models and their selection - BioClimatic Envelope
              33. Slide 33: Models and their selection - GAM modeling
              34. Slide 34: Models and their selection - GARP
              35. Slide 35: How good are the predictions?
              36. Slide 36: Kappa statistic
              37. Slide 37: Receiver operating characteristic analysis (ROC)
              38. Slide 38: How good are the predictions?
              39. Slide 39: Test yourself
              40. Slide 40 Links to other chapters
            2. Chapter 7: Ecosystem function modelling
              1. Chapter 8: Climate change implications for conservation planning
                1. Chapter 9: The economic costs of conservation response options for climate change
                  1. Course Resources
                    1. Practical: Conservation for Climate Change
                      1. Tests to Assess your Understanding
                        1. How to run a GAM model in R

                          Slide 27: Principles

                          Duration: 00:01:06

                          Notes:

                          In order to select an appropriate model, we need to take the following into account:

                          What is the question you want to answer? In some cases this will lead directly to a specific model type.

                          The data guides decisions as well:What environmental data do you have access to?

                          What is the resolution and extent of this data?

                          Categorical or continuous data?

                          The scale of the model is important.Thuiller et al showed in 2003 that GAMs are better at performing consistently across scales due to their ability to model complex response curves. However, certain other models might work better at a specific scale.

                          Certain variables are highly relevant at a fine scale but have little overall effect at a larger scale, and vice versa - ie: the guiding factors are scale-depndent.

                          For a good example of an informed model solution, have a look at Gibson et al's paper of 2004.

                          Many papers have been published comparing different models, and a summary of these studies has been prepared by Segurado and Araujo (2005), and Thuiller et al (2003).