1. Paul Eastwood Centre for Environment, Fisheries and Aquaculture Science (Cefas) Lowestoft, UK INCOFISH WP3 Brazil workshop

2. Species distribution modelling All started in early 1980s with US Fish and Wildlife Service Framework for predicting habitat suitability based on known preferences and tolerances Habitat Suitability Index (HSI) modelling HSI models formulated from word, graphical or mathematical expressions that described the relationship between a species’ life-history stage and its environment

3. HSI modelling Early HSI models were non-spatially structured GIS & digital spatial data were not widely available Models developed primarily for terrestrial species SI 1 SI 2 Geometric mean HSI = (SI 1 + SI 2 ) 0.5

4. HSI & GIS modelling 1.0 0.5 0 2829303132 1.0 0.5 0 7891011 1.0 0.5 0 1020304050 1.0 0.5 0 AB Temperature Substrate type Salinity Depth Modelled fish-habitat relationships (SI’s) Temperature SI map Depth SI map Salinity SI map Substrate SI map    HSI = 1/4 Low suitability High suitability UnsuitableMedium Habitat suitability index map Digital environmental maps recoded with the SI’s

5. Many ways to skin the cat… From Guisan and Thuiller (2005)

6. Why so many methods? Distributional data come in different forms –Relative abundance –Presence-absence –Presence only Try and improve predictions Resolve some of the (false) assumptions made by HSI models, e.g. all habitat variables selected independently And also because we’re scientists and are always looking for better and more efficient solutions

7. Habitat factor Response e.g. catch density Average but non-limiting effect Quantile regression for SDM Limiting effect Common sole in the eastern Channel Non-limitingLimiting

8. Model selection Model construction is not an exact science Environmental factors can be few or many Models fitted using linear and non-linear functions, parametric and non-parametric From Oksanen and Minchin (2002)

9. Modelling procedure Typical procedure for constructing a species distribution model Define input variables species data environmental data Model construction Selection of variables Significance tests Assessment of fit (AIC) Model validation Internal External Final model and distribution map Success Fail ?????

10. Model validation Measures of predictive performance are generally all based on a confusion matrix:

11. Model validation Performance measures based on confusion matrix From Fielding and Bell (1997)

12. Model validation Some measures influenced by species prevalence Not an issue for INCOFISH as only have presence data From Fielding and Bell (1997)

13. Model validation Issues for Aquamaps… Maps generated at global scale using all data Therefore, validation measures would be internal not based on external data Would either have to –accept this –generate bootstrap samples –withhold some data for model testing