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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F11%3A00054653%21RIV12-MSM-14310___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
Aim: Phytosociological databases often contain unbalanced samples of real vegetation, which should be carefully resampled before any analyses. We propose a new resampling method based on species composition, called heterogeneity-constrained random (HCR) resampling. Method: Many subsets of the source vegetation database are selected randomly. These subsets are sorted by decreasing mean dissimilarity between pairs of the vegetation plots, and then sorted again by increasing variance of these dissimilarities. Ranks from both sortings are summed for each subset, and the subset with the lowest summed rank is considered as the most representative. Results: Both stratified and HCR resampling yielded selection patterns more similar to the reference than resampling without these tools. Outcomes from the resampling that combined these two methods were the most similar to the reference. The efficiency of the HCR resampling method varied with different levels of aggregation in the database. Aim: Phytosociological databases often contain unbalanced samples of real vegetation, which should be carefully resampled before any analyses. We propose a new resampling method based on species composition, called heterogeneity-constrained random (HCR) resampling. Method: Many subsets of the source vegetation database are selected randomly. These subsets are sorted by decreasing mean dissimilarity between pairs of the vegetation plots, and then sorted again by increasing variance of these dissimilarities. Ranks from both sortings are summed for each subset, and the subset with the lowest summed rank is considered as the most representative. Results: Both stratified and HCR resampling yielded selection patterns more similar to the reference than resampling without these tools. Outcomes from the resampling that combined these two methods were the most similar to the reference. The efficiency of the HCR resampling method varied with different levels of aggregation in the database.
dcterms:title
Heterogeneity-constrained random resampling of phytosociological databases Heterogeneity-constrained random resampling of phytosociological databases
skos:prefLabel
Heterogeneity-constrained random resampling of phytosociological databases Heterogeneity-constrained random resampling of phytosociological databases
skos:notation
RIV/00216224:14310/11:00054653!RIV12-MSM-14310___
n6:predkladatel
n10:orjk%3A14310
n3:aktivita
n13:Z
n3:aktivity
Z(MSM0021622416)
n3:cisloPeriodika
1
n3:dodaniDat
n14:2012
n3:domaciTvurceVysledku
n5:2938219 n5:3447456
n3:druhVysledku
n7:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
201952
n3:idVysledku
RIV/00216224:14310/11:00054653
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Data representativeness; Point pattern; Releve; Ripley's K function; Sample plot; Selection; Stratification; Vegetation survey
n3:klicoveSlovo
n8:Point%20pattern n8:Selection n8:Releve n8:Sample%20plot n8:Vegetation%20survey n8:Stratification n8:Data%20representativeness n8:Ripley%27s%20K%20function
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[4D05974DEE64]
n3:nazevZdroje
Journal of Vegetation Science
n3:obor
n19:EF
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n14:2011
n3:svazekPeriodika
22
n3:tvurceVysledku
Lengyel, Attila Tichý, Lubomír Chytrý, Milan
n3:wos
000286146800016
n3:zamer
n4:MSM0021622416
s:issn
1100-9233
s:numberOfPages
9
n17:doi
10.1111/j.1654-1103.2010.01225.x
n18:organizacniJednotka
14310