Imputation of environmental variables for vegetation plots
based on compositional similarity

J 2010

Imputation of environmental variables for vegetation plots based on compositional similarity

TICHÝ, Lubomír, Michal HÁJEK and David ZELENÝ

Basic information

Original name

Imputation of environmental variables for vegetation plots based on compositional similarity

Name in Czech

Dopočet proměnných prostředí pro fytocenologické snímky založený na podobnosti druhového složení

Authors

TICHÝ, Lubomír (203 Czech Republic, guarantor, belonging to the institution), Michal HÁJEK (203 Czech Republic, belonging to the institution) and David ZELENÝ (203 Czech Republic, belonging to the institution)

Edition

Journal of Vegetation Science, Oxford, Wiley-Blackwell, 2010, 1100-9233

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10600 1.6 Biological sciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 2.457

RIV identification code

RIV/00216224:14310/10:00051275

Organization unit

Faculty of Science

UT WoS

000273668300009

Keywords (in Czech)

Konduktivita, Ellenbergovy indikační hodnoty, mokřady, fytocenologie, pH

Keywords in English

Conductivity; Ellenberg indicator values; Fens; Phytosociology; Water pH

Abstract

ORIG CZ

V originále

Question: Vegetation plot data combined with measured environmental variables such as soil pH or conductivity are often used for gradient analyses, species response modelling, quantifying recent vegetation change and prediction of plant species composition. Large vegetation databases contain high numbers of plots, but only small subsets with measured environmental data. To obtain broader datasets, researchers often use expert-based plant indicator values as surrogates of measured factors. Alternatively, missing environmental factors for vegetation plots may be estimated by imputation. In this study we tested whether imputation provides more exact approximations than do indicator values. Location: Fens in the West Carpathians (Slovakia, Poland, Czech Republic) and Bulgaria. Methods: We developed a simple imputation method based on vegetation plot similarity that estimates the missing environmental variables for vegetation plots, and named it the MOSS (mean of similar samples) method. The method was tested for water pH and conductivity, the most important environmental factors influencing vegetation composition and structure within wetlands, on two datasets of 485 (West Carpathians) and 118 (Bulgaria) vegetation plots for which directly measured values were available. The West Carpathian dataset was used as a source of calibration. Imputation was based on calculating the mean of the measured factor from a group of the most similar vegetation plots. According to pre-defined similarity criteria we selected subsets of both datasets for which we compared estimated and measured values. Using the root mean squared error of prediction we compared the predictive power of the method with the widely used averages of Ellenberg indicator values as well as with other recently published methods. Results: Within one study region, the MOSS method predicted the sample pH and conductivity more precisely than Ellenberg indicator values and similar calibration methods. The predictive power slightly decreased when the method was transferred to a distant region. Conclusions: Imputation using the MOSS method appears to be the best way to predict pH or conductivity values from existing composition data within a single geographical region and thus increase the number of replicates. The method does not require expert-based indicator values, which may contain considerable imprecision. We provide examples of situations in which our method can be utilised without the risk of circular reasoning or introducing pseudo-replications.

In Czech

Fytocenologická data kombinovaná s měřenými faktory prostředí jakými jsou např. konduktivita a půdní pH jsou často používány v gradientových analýzách, při simulaci odpovědních křivek druhů, kvantifikaci vegetačních změn atd. Vegetační databáze však obsahují velké množství zápisů, u nichž parametry prostředí nebyly přímo měřeny. Navržená metoda proto řeší možnost dopočtu takovýchto hodnot na základě celkové druhové podobnosti snímků se zápisy, u nichž byly proměnné prostředí přímo měřeny. Metoda nazvaná akronymem MOSS byla testována na datových souborech obsahujících snímky rašelinišť a mokřadů Západních Karpat a Bulharska. Dopočet hodnot metodou MOSS vykázal nejpřesnější odhady pH a konduktivity v porovnání s jinými metodami publikovanými v literatuře. Metoda nevyžaduje žádné expertní indikační hodnoty pro druhy, a je tedy aplikovatelná i v územích s dosud málo prozkoumanou vegetací. V práci jsou uvedeny také příklady možné budoucí aplikace metody bez rizika kruhového důkazu nebo zavedení pseudoreplikací do datového souboru.

Links

MSM0021622416, plan (intention)

Name: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase

Investor: Ministry of Education, Youth and Sports of the CR, Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time

Detailed Information on Publication Record