Paul Eastwood

Centre for Environment, Fisheries and Aquaculture Science (Cefas)Lowestoft, UK

INCOFISH WP3 Brazil workshop

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

HSI modelling

• Early HSI models were non-spatially structured

• GIS & digital spatial data were not widely available

• Models developed primarily for terrestrial species

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HSI & GIS modelling

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Temperature

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Salinity

Depth

Modelled fish-habitat relationships (SI’s)

Temperature SI map

Depth SI map

Salinity SI map

Substrate SI map

HSI =

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Low suitability

High suitability

Unsuitable Medium

Habitat suitability index map

Digital environmental maps recoded with the SI’s

Many ways to skin the cat…

From Guisan and Thuiller (2005)

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

Habitat factor

Response e.g. catch

density Average but non-limiting effect

Quantile regression for SDM

Limiting effect

Common sole in the eastern Channel Absent High

Catch density

Spawning Spawning

Nursery Nursery

Adult summerfeeding

Adult summerfeeding

Models based on upper RQs Models based on central RQsNon-limitingLimiting

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)

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 anddistribution map

Success Fail?????

Model validation

• Measures of predictive performance are generally all based on a confusion matrix:

Model validation

• Performance measures based on confusion matrix

From Fielding and Bell (1997)

Model validation

• Some measures influenced by species prevalence

• Not an issue for INCOFISH as only have presence data

From Fielding and Bell (1997)

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