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Discrimination of fish populations using parasites: Random Forests on a ‘predictable’ host-parasite system

Published online by Cambridge University Press:  06 July 2010

A. PÉREZ-DEL-OLMO ,
F. E. MONTERO ,
M. FERNÁNDEZ ,
J. BARRETT ,
J. A. RAGA  and
A. KOSTADINOVA
A. PÉREZ-DEL-OLMO*
Affiliation:
Department of Applied Zoology/Hydrobiology, University of Duisburg-Essen, Universitätsstrasse 5, D-45141 Essen, Germany
F. E. MONTERO
Affiliation:
Department of Animal Biology, Plant Biology and Ecology, Autonomous University of Barcelona, Campus Universitari, 08193 Bellaterra, Barcelona, Spain
M. FERNÁNDEZ
Affiliation:
Fundación General de la Universitat de València & Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Parc Científic, Universitat de Valencia, PO Box 22 085, 46071 Valencia, Spain
J. BARRETT
Affiliation:
IBERS, University of Aberystwyth, Ceredigion SY23 3DA, UK
J. A. RAGA
Affiliation:
Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Parc Científic, Universitat de València, PO Box 22 085, 46071 Valencia, Spain
A. KOSTADINOVA
Affiliation:
Institute of Parasitology, Biology Centre v.v.i., Academy of Sciences of the Czech Republic, Branišovská 31, 370 05 České Budějovice, Czech Republic Central Laboratory of General Ecology, Bulgarian Academy of Sciences, 2 Gagarin Street, 1113 Sofia, Bulgaria
*
*Corresponding author: University of Duisburg-Essen, Department of Applied Zoology/Hydrobiology, Universitätsstrasse 5, D-45141 Essen, Germany. Tel: +49 2011832250. Fax: +49 2011832179. E-mail: [email protected]
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Summary

We address the effect of spatial scale and temporal variation on model generality when forming predictive models for fish assignment using a new data mining approach, Random Forests (RF), to variable biological markers (parasite community data). Models were implemented for a fish host-parasite system sampled along the Mediterranean and Atlantic coasts of Spain and were validated using independent datasets. We considered 2 basic classification problems in evaluating the importance of variations in parasite infracommunities for assignment of individual fish to their populations of origin: multiclass (2–5 population models, using 2 seasonal replicates from each of the populations) and 2-class task (using 4 seasonal replicates from 1 Atlantic and 1 Mediterranean population each). The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RF provide an efficient means for model cross-validation on the baseline data and this allows sample size limitations in parasite tag studies to be tackled effectively; (iii) the performance of RF is dependent on the complexity and spatial extent/configuration of the problem; and (iv) the development of predictive models is strongly influenced by seasonal change and this stresses the importance of both temporal replication and model validation in parasite tagging studies.

Keywords

predictive modelsRandom Forestsfish population discriminationparasites as tagsBoops boopsMediterraneanNorth-East Atlantic
Type
Research Article
Information
Parasitology , Volume 137 , Issue 12 , October 2010 , pp. 1833 - 1847
Copyright
Copyright © Cambridge University Press 2010

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