Discrimination of fish populations using parasites: Random Forests on a predictable? host-parasite system
Result description
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. The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RFprovide 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
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Discrimination of fish populations using parasites: Random Forests on a predictable? host-parasite system
Original language description
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. The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RFprovide 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.
Czech name
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Czech description
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Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
GJ - Diseases and animal vermin, veterinary medicine
OECD FORD branch
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Result continuities
Project
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Parasitology
ISSN
0031-1820
e-ISSN
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Volume of the periodical
137
Issue of the periodical within the volume
12
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
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UT code for WoS article
000283794600011
EID of the result in the Scopus database
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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
GJ - Diseases and animal vermin, veterinary medicine
Year of implementation
2010