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Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons62608

Wutzler,  T.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62606

Wirth,  C.
Research Group Organismic Biogeochemistry, Dr. C. Wirth, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Wutzler, T., Wirth, C., & Schumacher, J. (2008). Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty. Canadian Journal of Forest Research, 38(6), 1661-1675. doi:10.1139/X07-194.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-D7A3-5
Zusammenfassung
This study provides a comprehensive set of functions for predicting biomass for Common beech (Fagus sylvatica L.) in Central Europe for all major tree compartments. The equations are based on data of stem, branch, timber, brushwood (wood with diameter below 5 or 7 cm), foliage, root, and total aboveground biomass of 443 trees from 13 studies. We used nonlinear mixed-effects models to assess the contribution of fixed effects (tree dimensions, site descriptors), random effects (grouping according to studies), and residual variance to the total variance and to obtain realistic estimates of uncertainity of biomass on an aggregated level. Candidate models differed in their basic form, the description of the variance, and inclusion of various combinations of additional fixed and random effects and were compared using the Akaike information criterion. Model performance increased most when accounting for between-study differences in the variability of biomass predictions. Further performance increased with the inclusion of the age, site index, and altitude as predictor variables. We show that neglecting variance partitioning and the fact that prediction errors of trees are not independent with respect to their predictor variables will lead to a significant underestimation of prediction variance. [References: 67]