区分成熟和未成熟树木可从林分结构中估算森林碳吸收量

Samuel M. Fischer, Xugao Wang, Andreas Huth
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引用次数: 0

摘要

摘要将森林生产力与森林结构的局部变化联系起来是一项长期挑战。以往的研究通常侧重于森林结构与林分水平光合作用(总初级生产力 - GPP)之间的联系。然而,生物量生产(净初级生产量--NPP)和净生态系统交换(NEE)还受制于呼吸作用和其他碳损失,而这些又会随着当地条件和生活史特征的变化而变化。在此,我们采用模拟方法来研究这些损失如何影响森林生产力并在森林结构中显现出来。我们将基于过程的森林模型 FORMIND 拟合到中国温带原始森林的 25 公顷清单中,并根据树木的内在碳利用效率将其分为 "成熟"(完全生长)和 "不成熟 "两类。我们的研究结果表明,林分碳利用效率与成熟树木的普遍性之间存在紧密的负相关:总初级生产力(GPP)随总基部面积的增加而增加,而净初级生产力(NPP)和净碳排放量(NEE)则受未成熟树木基部面积的影响。因此,基部面积熵作为未成熟树木普遍程度的结构代表,与净初级生产力和净初级能源效率有很好的相关性,并且比其他结构特征(如香农多样性和高度标准偏差)具有更高的预测能力。我们的研究结果在不同的空间尺度(0.04-1 公顷)上都是稳健的,为实地研究和新的理论工作提供了有前景的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distinguishing mature and immature trees allows estimating forest carbon uptake from stand structure
Abstract. Relating forest productivity to local variations in forest structure has been a long-standing challenge. Previous studies often focused on the connection between forest structure and stand-level photosynthesis (gross primary production – GPP). However, biomass production (net primary production – NPP) and net ecosystem exchange (NEE) are also subject to respiration and other carbon losses, which vary with local conditions and life history traits. Here, we use a simulation approach to study how these losses impact forest productivity and reveal themselves in forest structure. We fit the process-based forest model FORMIND to a 25 ha inventory of an old-growth temperate forest in China and classify trees as “mature” (fully grown) or “immature” based on their intrinsic carbon use efficiency. Our results reveal a strong negative connection between the stand-level carbon use efficiency and the prevalence of mature trees: gross primary production (GPP) increases with the total basal area, whereas net primary production (NPP) and NEE are driven by the basal area of immature trees. Accordingly, the basal area entropy, a structural proxy for the prevalence of immature trees, correlated well with NPP and NEE and had a higher predictive power than other structural characteristics, such as the Shannon diversity and height standard deviation. Our results were robust across spatial scales (0.04–1 ha) and yield promising hypotheses for field studies and new theoretical work.
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