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dc.contributor.author Perez-Del-Olmo, A.
dc.contributor.author Montero, F. E.
dc.contributor.author Fernandez, M.
dc.contributor.author Barrett, J.
dc.contributor.author Raga, J. A.
dc.contributor.author Kostadinova, A.
dc.date.accessioned 2011-06-01T12:50:16Z
dc.date.available 2011-06-01T12:50:16Z
dc.date.issued 2010-10-01
dc.identifier.citation Perez-Del-Olmo , A , Montero , F E , Fernandez , M , Barrett , J , Raga , J A & Kostadinova , A 2010 , ' Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system ' Parasitology , vol 137 , no. 12 , pp. 1833-1847 . , 10.1017/S0031182010000739 en
dc.identifier.issn 0031-1820
dc.identifier.other PURE: 163023
dc.identifier.other dspace: 2160/6874
dc.identifier.uri http://hdl.handle.net/2160/6874
dc.description Perez-Del-Olmo, A., Montero, F.E., Fernandez, M., Barrett, J., Raga, J.A., Kostadinova, A. (2010). Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system.   Parasitology, 137, (12), 1833-1847. IMPF: 02.52 en
dc.description.abstract 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. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Parasitology en
dc.subject predictive models en
dc.subject random forests en
dc.subject fish population discrimination en
dc.subject parasites as tags en
dc.subject boops boops en
dc.subject mediterranean en
dc.subject north-east atlantic en
dc.title Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1017/S0031182010000739
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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