欧洲温带落叶林下层温度缓冲的季节性驱动因素,Global Ecology and Biogeography

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欧洲温带落叶林下层温度缓冲的季节性驱动因素
Global Ecology and Biogeography ( IF 6.3 ) Pub Date : 2019-08-22 , DOI: 10.1111/geb.12991
Florian Zellweger _{1,

2} , David Coomes ₁ , Jonathan Lenoir ₃ , Leen Depauw ₄ , Sybryn L Maes ₄ , Monika Wulf ₅ , Keith J Kirby ₆ , Jörg Brunet ₇ , Martin Kopecký _{8,

9} , František Máliš ₁₀ , Wolfgang Schmidt ₁₁ , Steffi Heinrichs ₁₁ , Jan den Ouden ₁₂ , Bogdan Jaroszewicz ₁₃ , Gauthier Buyse ₄ , Fabien Spicher ₃ , Kris Verheyen ₄ , Pieter De Frenne ₄

Affiliation

Forest Ecology and Conservation Group, Department of Plant Sciences University of Cambridge Cambridge UK.
Swiss Federal Institute for Forest, Snow and Landscape Research WSL Birmensdorf Switzerland.
UR "Ecologie et dynamique des systèmes anthropisés" (EDYSAN, UMR 7058 CNRS-UPJV) Université de Picardie Jules Verne Amiens France.
Forest & Nature Lab, Department of Environment Ghent University Melle-Gontrode Belgium.
Leibniz-ZALF e.V. Müncheberg Müncheberg Germany.
Department of Plant Sciences University of Oxford Oxford UK.
Southern Swedish Forest Research Centre Swedish University of Agricultural Sciences Alnarp Sweden.
Institute of Botany Czech Academy of Sciences Průhonice Czech Republic.
Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Czech Republic.
Faculty of Forestry Technical University in Zvolen Zvolen Slovakia.
Department Silviculture and Forest Ecology of the Temperate Zones University of Göttingen Göttingen Germany.
Forest Ecology and Forest Management Group Wageningen University Wageningen The Netherlands.
Białowieża Geobotanical Station Faculty of Biology University of Warsaw Białowieża Poland.

摘要目的森林下层微气候通常可以抵御极热或极冷，这对生活在这些环境中的生物具有重要意义。我们量化了描述冠层结构、冠层组成和地形（即当地因素）以及森林斑块大小和到海岸的距离（即景观因素）的下层小气候预测因子的季节性影响。地点欧洲的温带森林。时间段 2017-2018。主要分类群研究了木本植物。方法我们将来自小气候传感器网络的数据与气象站记录相结合，计算出森林内外测得的温度之间的差异或偏移量。我们使用回归分析来研究当地和景观因素对最低、平均和最高气温季节偏移的影响。结果夏季最高气温室内比室外平均低2.1℃，冬春季最低气温分别高0.4℃和0.9℃。当地的树冠覆盖是夏季最大温度偏移的强大非线性驱动因素，我们发现树种下方的冷却增加，这些树种会产生最深的阴影。最低气温的季节性偏移主要受景观和地形特征的调节，例如到海岸的距离和地形位置。主要结论森林生物经历的极端温度不如目前可用的大气候数据所暗示的那样严重；因此，仅使用大气候数据无法在森林中准确模拟气候-物种关系和物种对人为全球变暖的反应。冠层覆盖和组成的变化将强烈调节森林下层最高温度的变暖，这对于理解森林生物多样性的反应和应对土地利用变化和气候变化的综合威胁具有重要意义。我们的预测模型通常适用于低地温带落叶林，为森林下层提供具有重要生态意义的小气候数据。

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Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe

Abstract Aim Forest understorey microclimates are often buffered against extreme heat or cold, with important implications for the organisms living in these environments. We quantified seasonal effects of understorey microclimate predictors describing canopy structure, canopy composition and topography (i.e., local factors) and the forest patch size and distance to the coast (i.e., landscape factors). Location Temperate forests in Europe. Time period 2017–2018. Major taxa studied Woody plants. Methods We combined data from a microclimate sensor network with weather‐station records to calculate the difference, or offset, between temperatures measured inside and outside forests. We used regression analysis to study the effects of local and landscape factors on the seasonal offset of minimum, mean and maximum temperatures. Results The maximum temperature during the summer was on average cooler by 2.1 °C inside than outside forests, and the minimum temperatures during the winter and spring were 0.4 and 0.9 °C warmer. The local canopy cover was a strong nonlinear driver of the maximum temperature offset during summer, and we found increased cooling beneath tree species that cast the deepest shade. Seasonal offsets of minimum temperature were mainly regulated by landscape and topographic features, such as the distance to the coast and topographic position. Main conclusions Forest organisms experience less severe temperature extremes than suggested by currently available macroclimate data; therefore, climate–species relationships and the responses of species to anthropogenic global warming cannot be modelled accurately in forests using macroclimate data alone. Changes in canopy cover and composition will strongly modulate the warming of maximum temperatures in forest understories, with important implications for understanding the responses of forest biodiversity and functioning to the combined threats of land‐use change and climate change. Our predictive models are generally applicable across lowland temperate deciduous forests, providing ecologically important microclimate data for forest understories.

更新日期：2019-08-22

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