Intensive forest harvesting and pools of base cations in forest ecosystems: A modeling study using the Heureka decision support system

摘要

Intensive forestry practices, defined as the additional removal of tree tops, branches, and sometimes stumps, have helped nations meet renewable energy goals, but can also deplete long-term base cation (i.e. Ca, Mg, and K) pools in forest mineral soils. Decreasing pools of base cations (BC) are of great concern in forest areas that became acidified during the 1970s and 1980s and have recently started to recover because many soils do not contain large reserves of available BCs and are therefore particularly sensitive to further BC loss and the threat of re-acidification. This project combined field data on tree growth and a database of BC concentrations in tree tissue component types (i.e. stem wood, bark, foliage, branches, stumps, and roots) compiled from the literature to simulate tree growth over 150 years and investigate the sustainability of BC removals from forests resulting from different harvest strategies and under different management scenarios. Our simulation results indicated that BC removals were greatest under the maximum tree growth management scenario, being roughly 50-100% greater than the business as usual, nature conservation, or reindeer husbandry scenarios. Additionally, removal of tree tops, branches, and stumps consistently resulted in removal rates of Ca, Mg, and K 3-4 times greater than conventional stem-only harvests. Intensified forestry practices may result in large decreases in available BC pools, however it is difficult to assess the potential sustainability of estimated BC removal rates because uncertainty surrounding current weathering estimates to compensate for BC removals is large and does not provide sufficient precision for robust comparisons of forestry practices. In order to help decision makers set effective policy, ensuring the long-term health and production of forest soils, we need improved accuracy of weathering estimates and a better understanding at the spatial and temporal scales relevant to forest management.