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doi: 10.1111/nrm.12173
handle: 2454/28508
AbstractScarce and uncertain data on woody debris decomposition rates are available for calibrating forest ecosystem models, owing to the difficulty of their empirical estimations. Using field data from three experimental sites which are part of the North American Long‐Term Soil Productivity (LTSP) Study in south‐eastern British Columbia (Canada), we developed probability distributions of standard wood stake mass loss ofPopulus tremuloidesandPinus contorta. Using a Monte Carlo approach, 50 synthetic decomposition rate values per debris type were used to calibrate the ecosystem‐level forest model FORECAST. Significant effects of uncertainty of pine stake mass loss rates on estimated tree growth were found, especially in moderately managed forests, as estimations of available nitrogen were affected. Consequently, our work has shown that projections of tree growth under management conditions depend on accurate estimations of woody debris decomposition rates, and special effort should be done in create reliable databases of decomposition rates for their use in tree growth and yield modelling.Recommendations for Resource ManagersMaintaining woody debris on site, particularly large roots, should be favored. Significant influences of wood decomposition rates on tree growth were found, especially in moderately managed forests, because belowground woody debris became an important reservoir of nutrients needed to maintain tree growth rates. Forest floor and stump removal are therefore discouraged.When using ecological models for estimating tree growth, uncertainty associated with calibrating woody debris decomposition processes should be taken into consideration if moderate management is planned.Special efforts should be made to gather site‐ and species‐specific woody debris decomposition rates, particularly for medium and coarse roots (diameter above 2.5 cm). Creating a database of standardized branch and root decomposition rates would greatly reduce the uncertainty of model estimations of tree growth.
Ecosystem-level model, Forest management, Forest ecosystem, Uncertainty analysis, FORECAST model, Sensitivity analysis
Ecosystem-level model, Forest management, Forest ecosystem, Uncertainty analysis, FORECAST model, Sensitivity analysis
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