J 2012

Estimation of herbaceous biomass from species composition and cover

AXMANOVÁ, Irena; Lubomír TICHÝ; Zuzana FAJMONOVÁ; Petra HÁJKOVÁ; Eva HETTENBERGEROVÁ et al.

Základní údaje

Originální název

Estimation of herbaceous biomass from species composition and cover

Autoři

AXMANOVÁ, Irena; Lubomír TICHÝ; Zuzana FAJMONOVÁ; Petra HÁJKOVÁ; Eva HETTENBERGEROVÁ; Ching-Feng LI; Kristina MERUNKOVÁ; Martina NEJEZCHLEBOVÁ; Zdenka PREISLEROVÁ; Marie VYMAZALOVÁ a David ZELENÝ

Vydání

Applied Vegetation Science, Wiley-Blackwell, 2012, 1402-2001

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10600 1.6 Biological sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.263

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/12:00058023

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Ellenberg indicator values; plant cover; plant height; productivity; species richness– productivity relationship

Štítky

Příznaky

Recenzováno
Změněno: 10. 4. 2013 12:59, Ing. Andrea Mikešková

Anotace

V originále

Questions: Biomass is an important ecological property, but its measurement is destructive and time-consuming and therefore generally missing for historical vegetation plots. Here we propose and test indirect estimation of herbaceous biomass using models based on easily obtainable variables, namely plant height and cover. We compare these models with Ellenberg indicator values for nutrients(EIVs Nutrients), which are sometimes used as an alternative measure of productivity. Location: Czech Republic,western Slovakia. Methods: Above-ground biomass (dry weight; gm2) was regressed against the following explanatory variables: (1) Cover E1, total percentage cover of the herb layer visually estimated in the field; (2) Biomass estimate-raw, -adjusted and -median, calculated from plant covers and heights (according to a local flora); and(3) mean EIVs Nutrients calculated per plot. For the analyses, we used four data sets containing a total of 469 plots from different vegetation types: ‘Wet meadows’, ‘Dry grasslands’, ‘Fen–dry grassland transects’ and ‘Forest herb layer’. To test the applicability of different biomass estimates we chose an example of a species richness–productivity relationship in the ‘Wet meadows’ data set and describe differences in resulting patterns. Results: Both cover of herb layer and calculated ‘biomass volumes’ were more accurate in predicting biomass dry weight than EIVs Nutrients. The best results were obtained from the Biomass estimate-median model that combines median stand height and total cover of the herb layer. Cover E1 showed relatively tight correlations with biomass, particularly in sparse vegetation, but was a rather poor predictor when cover values were high. This was especially noticeable in application of the Cover E1 model in analysis of the species richness–productivity relationship.Conclusions: In contrast to biomass, cover of the herb layer has a fixed upper limit (100%), which may lead to misinterpretations in dense, structurally diverse vegetation. Most promising is the Biomass estimate-medianmethod, which can be applied both to already sampled plots by calculating median height from average species heights according to local floras and to newly sampled plots using the median of plant heights measured in the field. Therefore, we propose it as a rapid, non-destructive alternative to biomass harvest.

Návaznosti

GAP505/11/0732, projekt VaV
Název: Zobecněná řízená klasifikace v ekologii společenstev
Investor: Grantová agentura ČR, Generalized supervised classification in community ecology
GD526/09/H025, projekt VaV
Název: Evolučně-ekologická analýza společenstev a populací
Investor: Grantová agentura ČR, Evolučně-ekologická analýza společenstev a populací
MSM0021622416, záměr
Název: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase
Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Diverzita biotických společenstev: kauzální analýza variability v prostoru a čase