Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system.
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.
Pérez-Del-Olmo
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A
Department of Applied Zoology/Hydrobiology, University of Duisburg-Essen, Universitätsstrasse 5, D-45141 Essen, Germany. ana.perez-delolmo@uni-due.de
Montero
F E
FE
Fernández
M
M
Barrett
J
J
Raga
J A
JA
Kostadinova
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A
D000465:Algorithms; D000818:Animals; D001267:Atlantic Ocean; D017753:Ecosystem; D005393:Fish Diseases / Q000469:parasitology*; D006790:Host-Parasite Interactions; D008522:Mediterranean Sea; D010271:Parasites / Q000502:physiology*; D010473:Perciformes / Q000469:parasitology*; D011157:Population Dynamics; D013030:Spain