Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty
Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty
复制标题
树木生长的生态预测：树木年轮和森林清查数据的区域融合，以量化驱动因素并描述不确定性
DOI：
10.1111/gcb.16038
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发表时间：
2022-01-13
影响因子：
11.6
通讯作者：
Evans, Margaret E. K.
中科院分区：
文献类型：
--
作者：
Heilman, Kelly A.;Dietze, Michael C.;Evans, Margaret E. K.
对未来气候条件下树木生长进行稳健的生态预测对于预测未来森林碳储量和碳通量至关重要。在这里，我们应用了生态预测的三个要素，这是提高预测技能的关键：数据融合，面对新数据的模型预测，并划分预测的不确定性。具体而言，我们提出了第一次融合的树轮和森林资源清查数据在贝叶斯状态空间模型在多站点，区域尺度，重点是美国黄松。美国西南部的短翅目昆虫。利用这两个数据源的互补性，我们解析了树木生长的生态复杂性到气候，树木大小，林分密度，立地质量，以及它们之间的相互作用的影响，并量化与这些影响相关的不确定性。新的测量树木，森林清查的一个持续的过程中，被用来对抗树木直径的预测与观察，并评估替代树木生长模型。我们预测了树木直径和增量，以响应气候变化预测的集合，并将预测的不确定性分为四个不同的原因：初始条件，参数，气候驱动因素和过程误差。我们发现，秋季和春季最高温度的强烈的负面影响，和水年降水量对树木生长的积极影响。此外，树木对气候压力的脆弱性随着更大的竞争、树木的大小和贫穷的地点而增加。在未来的气候情景下，我们预测增量下降22%-117%，而气候和大小相关趋势的综合影响将导致56%-91%的下降。对预报不确定性的划分表明，直径预报不确定性主要由参数和初始条件的不确定性引起，而增量预报不确定性主要由过程误差和气候驱动因素的不确定性引起。树轮和森林资源清查数据的这种融合为在树、地块和区域尺度上对地上生物量和碳核算进行稳健的生态预测奠定了基础，包括模型技能的迭代改进。
Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree-ring and forest inventory data within a Bayesian state-space model at a multi-site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall-spring maximum temperature, and a positive effect of water-year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%-117%, while the combined effect of climate and size-related trends results in a 56%-91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree-ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill.