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

Cite this dataset

Zellweger, Florian et al. (2019). Data from: Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe [Dataset]. Dryad.


Aim: Forest understory microclimates are often buffered against extreme heat or cold, with important implications for the organisms living in these environments. We quantified seasonal effects of understory microclimate predictors describing canopy structure, canopy composition and topography (i.e. local factors), as well as forest patch size and distance to 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: Maximum temperature during summer was on average cooler by 2.1 °C and minimum temperature during winter and spring were 0.4 °C and 0.9 °C warmer inside than outside forests. The local canopy cover was a strong non-linear 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 coast and topographic position. Main conclusions: Forest organisms experience less severe temperature extremes than suggested by currently available macroclimate data, so climate-species relationships and species’ responses to anthropogenic global warming cannot be modelled accurately in forests using macroclimate data alone. Changes in canopy cover and composition will strongly modulate warming of maximum temperatures in forest understories, with important implications for understanding 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.

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