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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F10%3A00353458" target="_blank" >RIV/60077344:_____/10:00353458 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GJ - Choroby a škůdci zvířat, veterinární medicina

OECD FORD obor

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Projekt

<a href="/cs/project/LC522" target="_blank" >LC522: ICHTYOPARAZITOLOGIE - centrum základního výzkumu</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Parasitology

ISSN

0031-1820

e-ISSN

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Svazek periodika

137

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

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Kód UT WoS článku

000283794600011

EID výsledku v databázi Scopus

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