Establishing Harvest Location for Atlantic Cod: Playing Sherlock

Holmes with Parasites

Francisco Montero, Eugenia Ferrer, Diana Perdiguero, Toni Raga

& Juan Antonio Balbuena Cavanilles Institute of Biodiversity and Evolutionary

BiologyUniversity of Valencia, Spain

Establishing harvest location is relevant to fisheries and

consumer policies IUU Fishing (up to 30% of total catches)

Legal disputes between countries

Geographical characterization of seafood

Ensuring safety and enhancing confidence of consumers

http://www.codtrace.ie

The European Commission Community Research

Establishing traceability for cod (Gadus morhua) in European waters: determining

location of spawning and harvest

Marine Research Institute – Iceland

(Project coordinator)University College Dublin

Who Is Behind COTRACE?

Faunistic characterization

Differences between localities (exploratory)

Predictive tool assigning individual cod to its harvest location

Conclusions (preliminary)

This presentation concerns preliminary analyses of 148 cod

from 3 Localities

ICELAND

CELTIC SEA

BALTIC SEA

NORTH SEA

IRISH SEA

BALTIC SEA

N= 60

NORTH SEA

N= 28

IRISH SEA

N=60

Cod harboured rich parasite assemblages

32 forms: 25 helminths, 6 crustaceans, 1 leech

About 19,000 parasite individuals

Only 1 cod parasite free

Diversity of parasite assemblages differred

between localities

Species richness Diversity* Mean SD Range Mean SD

Baltic Sea 1.6 0.8 0-4 0.22 0.29

Irish Sea 9.3 2.2 4-15 1.43 0.35

North Sea 8.0 2.3 4-13 1.35 0.34*Shannon’s

Differences could be visualized with catPCA

Axis 1

210-1-2-3

Axis

28

6

4

2

0

-2

-4

North Sea

Baltic Sea

Irish Sea

Variation was related to certain species

Axis 1

1.0.50.0-.5-1.0

Axis 2

0.9

0.7

0.5

0.3

0.1

-0.1

-0.3

-0.5

LBRANC

CELONG

CCURTUS

CORYNO

SPIROID

ANIS1

PDECIPHYSTE

CONTRAC

ASIMP

CUCUL

ASCARO

SPLEUR

TRYPANORABOTGADI

STEPHAN

OPECH

HEMIUR

GONOC

DVAR BUCEPH1

EGADI

North Sea

Irish Sea

Baltic Sea

E. gadi

D. varicus

H. aduncum

Hemiurus sp.

Corynosoma sp.A. simplex

LBRANC

CELONG

CCURTUS

CORYNO

SPIROID

ANIS1

PDECIPHYSTE

CONTRAC

ASIMP

CUCUL

ASCARO

SPLEUR

TRYPANORABOTGADI

STEPHAN

OPECH

HEMIUR

GONOC

DVAR BUCEPH1

EGADI

North Sea

Irish Sea

Baltic Sea

A. simplex

H. aduncumD. varicus

Contracaecum sp.

Sp 1

Sp 4

Sp 2

Sp 3N

B

I

Hidden LayersInput Layer Output Layer

Neural Networks worklike an artificial Sherlock Holmes

We applied 2 different Neural Networks

Radial Basis Function: equalize training set

Bayesian NN: no validation set

33 Baltic33 Baltic

34 Irish34 Irish

33 North33 North

12 Baltic12 Baltic

12 Irish12 Irish

12 North12 North

24 Baltic24 Baltic

22 Irish22 Irish

18 North18 North

Training Validation Test

20 Baltic20 Baltic

20 Irish20 Irish

20 North20 North

40 Baltic40 Baltic

40 Irish40 Irish

8 North8 North

Training Test

04000200Irish

8001910North

10390020Baltic

NorthIrishBalticNorthIrishBalticPredictedPredictedTestTraining

Act

ual

Bayesian Neural Network

180012002850North

220011101330Irish002400121032Baltic

NorthIrishBalticNorthIrishBalticNorthIrishBalticPredictedPredictedPredictedTestValidationTraining

Act

ual

Radial Basis Function

97%97%94%

98% 99%

...and these are the encouraging results

Conclusions

1. Geographical differences in parasite fauna

2. Parasites show promise for cod traceability

3. A few parasite species may do the job (cost efficiency)

4. Artificial Neural Networks seem appropriate statistical tools for this type of problem

Thank you