School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Institute of Silviculture, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.
Nat Commun. 2023 Mar 18;14(1):1515. doi: 10.1038/s41467-023-37166-9.
Litter decomposition / accumulation are rate limiting steps in soil formation, carbon sequestration, nutrient cycling and fire risk in temperate forests, highlighting the importance of robust predictive models at all geographic scales. Using a data set for the Australian continent, we show that among a range of models, >60% of the variance in litter mass over a 40-year time span can be accounted for by a parsimonious model with elapsed time, and indices of aridity and litter quality, as independent drivers. Aridity is an important driver of variation across large geographic and climatic ranges while litter quality shows emergent properties of climate-dependence. Up to 90% of variance in litter mass for individual forest types can be explained using models of identical structure. Results provide guidance for future decomposition studies. Algorithms reported here can significantly improve accuracy and reliability of predictions of carbon and nutrient dynamics and fire risk.
凋落物分解/积累是温带森林土壤形成、碳固存、养分循环和火灾风险的限速步骤,这凸显了在所有地理尺度上建立稳健预测模型的重要性。利用澳大利亚大陆的数据集,我们表明,在所研究的一系列模型中,超过 60%的凋落物质量在 40 年时间跨度内的变化可以用一个简洁的模型来解释,该模型的自变量为时间流逝、干旱指数和凋落物质量指数。干旱是跨越大地理和气候范围的变化的重要驱动因素,而凋落物质量则表现出对气候依赖性的新兴特性。使用相同结构的模型可以解释高达 90%的个别森林类型的凋落物质量的变化。研究结果为未来的分解研究提供了指导。这里报告的算法可以显著提高对碳和养分动态以及火灾风险预测的准确性和可靠性。