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<article language="en">
	<journal>
		<journal_title>Hydrology and Earth System Sciences</journal_title>
		<journal_url>www.hydrol-earth-syst-sci.net</journal_url>
		<issn>1027-5606</issn>
		<eissn>1607-7938</eissn>
		<volume_number>12</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2008</publication_year>
	</journal>
	<doi>10.5194/hess-12-603-2008</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/12/603/2008/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/12/603/2008/hess-12-603-2008.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/12/603/2008/hess-12-603-2008.pdf</fulltext_pdf>
	<start_page>603</start_page>
	<end_page>613</end_page>
	<publication_date>2008-03-19</publication_date>
	<article_title content_type="html">Modelling groundwater-dependent vegetation patterns using ensemble learning</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>J. Peters</name>
			<email>jan.peters@ugent.be</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>B. De Baets</name>
		</author>
		<author numeration="3" affiliations="3">
			<name>R. Samson</name>
		</author>
		<author numeration="4" affiliations="1">
			<name>N. E. C. Verhoest</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Department of Forest and Water Management, Ghent University, Coupure links 653, 9000 Gent, Belgium</affiliation>
		<affiliation numeration="2" content_type="html">Department of Applied Mathematics, Biometrics and Process Control, Coupure links 653, 9000 Gent, Belgium</affiliation>
		<affiliation numeration="3" content_type="html">Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgium</affiliation>
	</affiliations>
	<abstract content_type="html">Vegetation patterns arise from the interplay between intraspecific and interspecific
biotic interactions and from different abiotic constraints and interacting driving
forces and distributions. In this study, we constructed an ensemble learning model that,
based on spatially distributed environmental variables, could model vegetation
patterns at the local scale. The study site was an alluvial floodplain with marked
hydrologic gradients on which different vegetation types developed. The model
was evaluated on accuracy, and could be concluded to perform well. However, model
accuracy was remarkably lower for boundary areas between two distinct vegetation
types. Subsequent application of the model on a spatially independent data set
showed a poor performance that could be linked with the niche concept to conclude
that an empirical distribution model, which has been constructed on local observations,
is incapable to be applied beyond these boundaries.</abstract>
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</article>

