Modeling persistence of coarse woody debris residuals in boreal forests as an ecological property. Issue 11 (11th November 2021) - Menichetti, Lorenzo; Mäkinen, Harri; Stendahl, Johan; Ågren, Göran I.; Hyvönen, Riitta
Abstract: There is a trade‐off between leaving coarse woody debris (CWD) in the stand, providing desirable ecosystem services, and harvesting it. To consider this trade‐off, forest management needs to model describing the decomposition of CWD. When a trunk is lying on the ground, it can be attacked by microorganisms faster than when it is still standing. Current decomposition models fail to account for these local differences in processes, which may give rise to errors in the estimation of stand C balance. We extended the Q decomposition model to represent the influences of tree species and the local position of the wood. We utilized data from two studies on long‐term deadwood decomposition in forests. We first calibrated the model on the whole dataset, and then divided the data into different CWD decomposition classes, and then allowed some of the parameters to vary between different CWD decomposition classes. The calibrations were performed within a Bayesian framework, which allowed for a statistically sound comparison of the calibration results. The difference between the remaining C mass predicted by the two versions of the model, one considering one single calibration for all decomposition classes and one specific to decomposition classes, depended on the CWD class but was in general substantial. Some classes, when modeled with a specific parameterization, resulted in C stocks after 50 yr 1–5 times less than that predicted by the single parameterization model. Logs decayed faster than snags, and birch wood much faster than pine and spruce wood, with little difference between the two conifers. Russian spruce wood decomposed somewhat faster than Finnish spruce wood. Incorporating our calibration, describing specifically the processes driving the wood decay locally, into a C balance model of forests may change model estimates substantially.