A novel method to predict dark diversity using unconstrained ordination analysis

dc.contributor.author	Majekova, Maria
dc.date.accessioned	2020-03-09T15:28:58Z
dc.date.available	2020-03-09T15:28:58Z
dc.date.issued	2019
dc.identifier.issn	1654-1103
dc.identifier.uri	http://hdl.handle.net/10900/98818
dc.language.iso	en	en
dc.publisher	Wiley	de_DE
dc.relation.uri	http://dx.doi.org/10.1111/jvs.12757
dc.subject.ddc	570	de_DE
dc.subject.ddc	610	de_DE
dc.subject.ddc	630	de_DE
dc.title	A novel method to predict dark diversity using unconstrained ordination analysis	de_DE
dc.type	Article	de_DE
utue.quellen.id	20190926111821_00768
utue.publikation.seiten	610-619	de_DE
utue.personen.roh	Brown, Joel J.
utue.personen.roh	Mennicken, Sophie
utue.personen.roh	Massante, Jhonny C.
utue.personen.roh	Dijoux, Samuel
utue.personen.roh	Telea, Alexandra
utue.personen.roh	Benedek, Ana M.
utue.personen.roh	Gotzenberger, Lars
utue.personen.roh	Majekova, Maria
utue.personen.roh	Leps, Jan
utue.personen.roh	Smilauer, Petr
utue.personen.roh	Hrcek, Jan
utue.personen.roh	de Bello, Francesco
dcterms.isPartOf.ZSTitelID	Journal of Vegetation Science	de_DE
dcterms.isPartOf.ZS-Issue	4	de_DE
dcterms.isPartOf.ZS-Volume	30	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät