Presentation Scandinavian Society of Parasitology. Bergen, Norway.

2. 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 Biology
University of Valencia, Spain

3. 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

4. http://www.codtrace.ie
The European Commission Community Research
Establishing traceability for cod (Gadus
morhua) in European waters: determining
location of spawning and harvest

5. Marine Research Institute – Iceland
(Project coordinator)
University College Dublin
Who Is Behind COTRACE?

6. 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

7. ICELAND
CELTIC
SEA
BALTIC
SEA
NORTH
SEA
IRISH
SEA
BALTIC
SEA
N= 60
NORTH
SEA
N= 28
IRISH
SEA
N=60

8. Cod harboured rich
parasite assemblages
 32 forms: 25 helminths, 6
crustaceans, 1 leech
 About 19,000 parasite individuals
 Only 1 cod parasite free

9. 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

10. Differences could be
visualized with catPCA
Axis 1
210-1-2-3
Axis2
8
6
4
2
0
-2
-4
North Sea
Baltic Sea
Irish Sea

11. Variation was related to
certain species
Axis 1
1.0.50.0-.5-1.0
Axis2
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
LBRANC
CELONG
CCURTUS
CORYNO
SPIROID
ANIS1
PDECIP
HYSTE
CONTRAC
ASIMP
CUCUL
ASCARO
SPLEUR
TRYPANOR
ABOTGADI
STEPHAN
OPECH
HEMIUR
GONOC
DVAR BUCEPH1
EGADI
North Sea
Irish Sea
Baltic Sea
E. gadiE. gadi
D. varicusD. varicus
H. aduncumH. aduncum
Hemiurus sp.Hemiurus sp.
Corynosoma sp.Corynosoma sp.
A. simplexA. simplex

12. LBRANC
CELONG
CCURTUS
CORYNO
SPIROID
ANIS1
PDECIP
HYSTE
CONTRAC
ASIMP
CUCUL
ASCARO
SPLEUR
TRYPANOR
ABOTGADI
STEPHAN
OPECH
HEMIUR
GONOC
DVAR BUCEPH1
EGADI
North Sea
Irish Sea
Baltic Sea
A. simplex
H. aduncum
D. varicus
Contracaecum sp.

13. Sp 1
Sp 4
Sp 2
Sp 3
N
B
I
Hidden LayersInput Layer Output Layer
Neural Networks work
like an artificial Sherlock Holmes

14. 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

15. 04000200Irish
8001910North
10390020Baltic
NorthIrishBalticNorthIrishBaltic
PredictedPredicted
TestTraining
Actual
 Bayesian Neural Network
180012002850North
220011101330Irish
002400121032Baltic
NorthIrishBalticNorthIrishBalticNorthIrishBaltic
PredictedPredictedPredicted
TestValidationTraining
Actual
 Radial Basis Function
97%97%94%
98% 99%
...and these are the
encouraging results

16. 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

17. Thank you