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INCOFISH WP3 Brazil workshop

INCOFISH WP3 Brazil workshop. Paul Eastwood Centre for Environment, Fisheries and Aquaculture Science (Cefas) Lowestoft, UK. Species distribution modelling. All started in early 1980s with US Fish and Wildlife Service

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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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