Show simple item record Perez-Del-Olmo, A. Montero, F. E. Fernandez, M. Barrett, J. Raga, J. A. Kostadinova, A. 2011-06-01T12:50:16Z 2011-06-01T12:50:16Z 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 . DOI: 10.1017/S0031182010000739 en
dc.identifier.issn 0031-1820
dc.identifier.other PURE: 163023
dc.identifier.other PURE UUID: 86bd9af9-8b03-46ab-b391-78b228d130f4
dc.identifier.other dspace: 2160/6874
dc.identifier.other DSpace_20121128.csv: row: 4149
dc.identifier.other IBERS: 0000018965
dc.identifier.other Ibers_20121112_1204.csv: row: 968
dc.identifier.other Scopus: 78650513320
dc.identifier.other PubMed: 20602856
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.rights 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 /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en

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