Generalized biomass and leaf area allometric equations for European tree species incorporating stand structure, tree age and climate

Abstract

Biomass and leaf area equations are often required to assess or model forest productivity, carbon stocks and other ecosystem services. These factors are influenced by climate, age and stand structural attributes including stand density and tree species diversity or species composition. However, such covariates are rarely included in biomass and leaf area equations. We reviewed the literature and built a database of biomass and leaf area equations for 24 European tree species and 3 introduced species. The final dataset contained 973 equations. Most of the equations were site-specific and therefore restricted to the edaphic, climatic and stand structural conditions of the given site. To overcome this limitation, the database was used to develop regional species-specific equations that can be used in a wide range of stands and to quantify the effects of climate, age and stand structure on biomass or leaf area. The analysis showed considerable inter-and intra-specific variability in biomass relationships. The intra-specific variability was related to climate, age or stand characteristics, while the inter-specific variability was correlated with traits such as wood density, specific leaf area and shade tolerance. The analysis also showed that foliage mass is more variable than stem or total aboveground biomass, both within and between species, and these biomass components have contrasting responses to age and changes in stand structure. Despite the large number of published equations, many species are still not well represented. Therefore, generic equations were developed that include species-specific wood density instead of species identity. Further improvements may be possible if future studies quantify the stand structure of individual tree neighbourhoods instead of using the stand means for all trees sampled with the given stand.

Tree biomass allometry during the early growth of Norway spruce (Picea abies) varies between pure stands and mixtures with European beech (Fagus sylvatica)

“…However, when height alone was used, the AB biomass prediction bias between pure and mixed stands (at approximately 19%, Table 3) was greater than using DBH alone (approximately 15%, Table 3). Biomass predictions that include height and wood density as covariates with DBH can be effective in offsetting site and species effects (Wirth et al 2004, Chave et al 2014, Forrester et al 2017b). This is related to H-DBH relationship being one of the main influences of biomass-DBH site variability and to species variations in wood density.…”

Local and General Above-Ground Biomass Functions for Pinus palustris Trees

“…For branch and foliage, the negative coefficient of BA implies that for a given DBH and A, trees growing in stands with higher stocking will have lower crown biomass than trees growing in less stocked stands. (12) The only difference between this system (Equation (12)) and the system IV (Equation (10)) is associated with the branch biomass equation. In addition to DBH and HT, the branch biomass equation included BA in system VI, instead of including A in the system IV.…”

“…These models are widely used but limited to certain stand characteristics and geographical areas, particularly those from which the data originated. However, inclusion of additional stand variables in the models such as stand age, density and/or productivity may result in general models that provide more accurate predictions [10][11][12]. Moreover, Gonzalez-Benecke et al [10] concluded that stand characteristics used as covariates in general biomass functions resulted in significant improvements in model fitting and prediction ability for all tree components, especially when age and HT were unknown.…”

Allometric Models to Predict Aboveground Woody Biomass of Black Locust (Robinia pseudoacacia L.) in Short Rotation Coppice in Previous Mining and Agricultural Areas in Germany

“…Modelling by employing mixed models, as used by Forrester et al [74], Grote et al [75], and Pretzsch et al [76] has the advantage of being applicable at different geographical and differently structured sites. In this study, RCD had the strongest correlation with BMD (M 01 ).…”