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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.

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one publication added to basket [206699]

Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling Merckx, B.; Steyaert, M.; Vanreusel, A.; Vincx, M.; Vanaverbeke, J. (2011). Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling, in: Merckx, B. Habitat suitability and community modelling of marine benthos = Modeleren van habitatgeschiktheid en gemeenschapsstructuren van marien benthos. pp. 83-102 In: Merckx, B. (2011). Habitat suitability and community modelling of marine benthos = Modeleren van habitatgeschiktheid en gemeenschapsstructuren van marien benthos. PhD Thesis. Ghent University: Gent. ISBN 978-90-77713-87-7. 309 pp., meer

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Beschikbaar in	Auteurs
VLIZ: Open access [224652] [ download pdf ]

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Trefwoorden

Analysis > Mathematical analysis > Statistical analysis > Correlation analysis > Autocorrelation Modelling Sampling Nematoda [WoRMS] ANE, Noordzee, Zuidelijke Bocht [Marine Regions] Marien/Kust

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Auteurs		Top
Merckx, B., meer Steyaert, M., meer Vanreusel, A., meer	Vincx, M., meer Vanaverbeke, J., meer

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Abstract

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area.

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