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利用长期森林清查数据和模拟水力损伤预测美国西部森林死亡率。

Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage.

机构信息

School of Biological Sciences, University of Utah, Salt Lake City, UT, 84112, USA.

Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.

出版信息

New Phytol. 2021 Jun;230(5):1896-1910. doi: 10.1111/nph.17043. Epub 2020 Nov 23.

DOI:10.1111/nph.17043
PMID:33112415
Abstract

Global warming is expected to exacerbate the duration and intensity of droughts in the western United States, which may lead to increased tree mortality. A prevailing proximal mechanism of drought-induced tree mortality is hydraulic damage, but predicting tree mortality from hydraulic theory and climate data still remains a major scientific challenge. We used forest inventory data and a plant hydraulic model (HM) to address three questions: can we capture regional patterns of drought-induced tree mortality with HM-predicted damage thresholds; do HM metrics improve predictions of mortality across broad spatial areas; and what are the dominant controls of forest mortality when considering stand characteristics, climate metrics, and simulated hydraulic stress? We found that the amount of variance explained by models predicting mortality was limited (R median = 0.10, R range: 0.00-0.52). HM outputs, including hydraulic damage and carbon assimilation diagnostics, moderately improve mortality prediction across the western US compared with models using stand and climate predictors alone. Among factors considered, metrics of stand density and tree size tended to be some of the most critical factors explaining mortality, probably highlighting the important roles of structural overshoot, stand development, and biotic agent host selection and outbreaks in mortality patterns.

摘要

全球变暖预计将加剧美国西部干旱的持续时间和强度,这可能导致树木死亡率增加。干旱导致树木死亡的一个主要的近端机制是水力损伤,但从水力理论和气候数据预测树木死亡率仍然是一个主要的科学挑战。我们使用森林清查数据和一个植物水力模型(HM)来解决三个问题:我们能否用 HM 预测的损伤阈值来捕捉因干旱导致的树木死亡的区域模式;HM 指标是否能提高在广泛的空间区域内对死亡率的预测;在考虑林分特征、气候指标和模拟水力胁迫时,森林死亡率的主要控制因素是什么?我们发现,预测死亡率的模型解释的方差量有限(R 中位数= 0.10,R 范围:0.00-0.52)。与单独使用林分和气候预测因子的模型相比,HM 输出,包括水力损伤和碳同化诊断,适度提高了整个美国西部的死亡率预测。在所考虑的因素中,林分密度和树木大小的指标往往是解释死亡率的最关键因素之一,这可能突出了结构过冲、林分发育以及生物因子宿主选择和爆发在死亡率模式中的重要作用。

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