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INCOFISH WP3 Brazil workshop

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

  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. SI1 SI2 Geometric mean HSI = (SI1 + SI2)0.5 HSI modelling • Early HSI models were non-spatially structured • GIS & digital spatial data were not widely available • Models developed primarily for terrestrial species

  4. Modelled fish-habitat relationships (SI’s) Digital environmental maps recoded with the SI’s 1.0 Temperature Habitat suitability index map 0.5 Depth Unsuitable Medium 0 Low suitability High suitability 7 8 9 10 11 Salinity Substrate type 1.0 0.5 0 10 20 30 40 50 1.0 0.5 0 28 29 30 31 32 1.0 0.5 0 A B 1/4 Temperature SI map ´ Depth SI map HSI = ´ Salinity SI map ´ Substrate SI map HSI & GIS modelling

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

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

  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

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