Temperature and Precipitation Diversely Control Seasonal and Annual Dynamics of Litterfall in a Temperate Mixed Mature Forest, Revealed by Long‐Term Data Analysis
Temperature and Precipitation Diversely Control Seasonal and Annual Dynamics of Litterfall in a Temperate Mixed Mature Forest, Revealed by Long‐Term Data Analysis
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DOI：
10.1029/2020jg006204
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发表时间：
2021-06
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通讯作者：
C. Wang;X. Zheng;A. Z. Wang;G. Dai;B. Zhu;Y. M. Zhao;S. J. Dong;W. Zu;W. Wang;Y. G. Zheng;J. G. Li;M.‐H. Li
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作者：
C. Wang;X. Zheng;A. Z. Wang;G. Dai;B. Zhu;Y. M. Zhao;S. J. Dong;W. Zu;W. Wang;Y. G. Zheng;J. G. Li;M.‐H. Li
凋落物是森林生态系统中森林整体功能的一个良好指标。在全球范围内，森林凋落物已被广泛调查，然而，缺乏长期的数据分析，以显示与环境因素有关的各种凋落物成分在月度和年度尺度上。在这里，收集了中国东北长白山红松混交成熟林30年来每月（5月至10月）和每年（1981-2018年）的落叶量，包括树叶、树枝、树皮、生殖部分和杂项部分。基于这些长期凋落物数据，我们分析了不同凋落物组分的季节和年度变化以及内部/外部驱动因素。我们观察到，树叶和总凋落物都表现出强烈的，相似的季节性模式，最高水平在9月至10月之间，从1981年到2018年，年凋落物量呈“S形”增长模式。其他凋落物组分在30年中表现出明显的月度和年度波动。平均月蒸散量和温度（最低和最高）是最好的预测月凋落物。相比之下，最能预测年落叶量的模型包括年平均降水量以及5月和10月的月平均降水量和温度。我们的研究，使用一个独特的数据集，详细的长期凋落物动态，有潜在的重大意义，提高我们的气候因素的作用，控制森林凋落物量和季节性在温带混合成熟森林的理解。这一认识对于气候变化下温带森林土壤固碳和养分循环的模拟和估算具有重要意义。
Litterfall is a good indicator of overall forest functions in forest ecosystems. Globally, forest litterfall has been extensively investigated, however, there is a lack of long‐term data analysis to show the various litterfall components in relation to environmental factors on the monthly and yearly scales. Here, monthly (May–October) and annual (1981–2018) litterfall including leaves, twigs, bark, reproductive, and miscellaneous fractions were collected in a mixed mature Pinus koraiensis forest on Changbai Mountain in Northeast, China, across 30 years. Based on these long‐term litterfall data, we analyzed the seasonal and annual variations in different litterfall fractions and the internal/external drivers. We observed that both the leaf and total litterfall exhibited a strong, similar seasonal pattern, with the highest levels between September and October, and the annual litterfall had an “S‐shaped” increasing pattern from 1981 to 2018. The other litterfall fractions showed distinct monthly and yearly fluctuations across the 30 years. Mean monthly evapotranspiration and temperature (minimum and maximum) were the best predictors for monthly litterfall. By contrast, the models that best predicted the annual litterfall production included mean annual precipitation and mean monthly precipitation and temperature in May and October. Our study, using a unique dataset of detailed long‐term litterfall dynamics, has potentially major significance for enhancing our understanding of the role of climatic factors controlling forest litterfall amount and seasonality in temperate mixed mature forests. This insight is of paramount importance for modeling and estimating soil carbon sequestration and nutrient cycling of temperate forests under climate change.