Is There a Theoretical Limit to Soil Carbon Storage in Old-Growth Forests? A Model Analysis with Contrasting Approaches
2009
Reichstein , Markus (Max Planck Institute for Biogeochemistry, Jena(Allemagne). Biogeochemical Model-Data Integration Group) | Agren , Göran I. (Max Planck Institute for Biogeochemistry, Jena(Allemagne). Biogeochemical Model-Data Integration Group) | Fontaine , Sébastien (INRA , Clermont-Ferrand (France). UR 0874 Unité de recherche sur l'Ecosystème Prairial ) | Christian Wirth | Gerd Gleixner | Martin Heimann
The question of long-term carbon uptake by old-growth forests has lead to extensive debate between modelling and experimental communities in the past. Based on so-called “carbon pool models”, where the soil carbon is assumed to be distributed among different pools, and to decay according to a first-order kinetic with pool-specific turnover constants, large factions of the modelling community have put forward a strong case that there cannot be long-term uptake of carbon by ecosystems because there is a limit defined by the steady state where total input equals total efflux of carbon. However, this theoretical deduction from first-order kinetic pool models seems to contradict a number of observations where long-term carbon uptake has been perceived. In this chapter, however, we will show that the modelling view stated here is completely contingent on the first-order reaction kinetics paradigm, and that there exist both old and recent alternative model formulations predicting that, under certain conditions, soil carbon pools never reach a steady state. Hence, from a modelling point of view, there is no justification for excluding the possibility of long-term old-growth forest carbon uptake. Since several plausible model formulations currently exist, in particular new models that include the role of soil microbial limitations, we need initiatives and experimental designs that can distinguish between, and potentially exclude, the respective decomposition modelling paradigms. From the perspective of scientific theory, this example reminds us that models should never be confounded with the truth and that they must be critically examined and tested again and again. Otherwise models can turn into fairy tales.
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