Estimation of herbaceous biomass from species composition and cover

Abstract

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; g·m−2) 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‐median method, 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.

Arctic-alpine vegetation biomass is driven by fine-scale abiotic heterogeneity

“…However, this process is time consuming and destructive (Bobek and Bergström ; Axmanová et al . ; Redjadj et al . ), and in environments with a heterogeneous distribution of biomass, a large number of samples are required for an accurate estimation (Tsutsumi et al .…”

“…; Axmanová et al . ; Redjadj et al . ), especially in remote high‐latitude and high‐altitude regions.…”

Effects of local and landscape factors on exotic vegetation in the riparian zone of a regulated river: Implications for reservoir conservation

“…In our case most species are diffuse and herbaceous, so we used a non-destructive biomass index derived from multiplying coverage by average height of species i (Axmanová et al, 2012). The soil map of the Pengxi River catchment (scale 1:2000, 2013) was used to determine the soil attributes of each sample site.…”

Різноманіття Та Фітоіндикаційні Можливості Рослинного Угруповання

“…Безпосереднє порівняння результатів біоіндикації з вимірюваними властивостями середовища вказує на наявність проблеми адекватності інтерпретації біоіндикації в термінах екологічних градієнтів (Schaffers, Sýkora, 2000;Wamelink et al, 2002Wamelink et al, , 2005. Дослідження вказують, що шкала вмісту азоту чітко віддзеркалює продуктивність екосистем, хоч продуктивність залежить не тільки від азоту, але й від вмісту інших поживних речовин (Hill, Carey, 1997;Schaffers, Sykora, 2000;Wagner et al, 2007;Axmanová et al, 2012). Найменший вміст вологи у ґрунті краще корелює з індикаторною шкалою вологості, ніж середнє значення вмісту вологи в ґрунті, що свідчить про те, що шкала вологості Елленберга найбільш чутлива до стійкості рослин до літньої засухи (Scheffer, Sykora, 2000).…”