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Effects of interannual weather on understory plant traits in old-growth beech forests
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Abstract
We aimed to estimate the effects of annual mean temperature and precipitation on understory plant traits and trait diversity in old-growth beech forests. We combined sub-canopy and open-field measurements from local weather stations with data on aboveground and belowground traits.
Relationships and potential confounding effects within the system were analysed using a causal model. The resulting statistical model estimated the response of traits to interannual weather fluctuations within a site, exploiting the longitudinal data from a 12-year monitoring program.
Overall, temperature effects were uncertain, while precipitation effects were more precisely estimated but small. Aboveground traits and trait diversity showed a positive response to temperature, while belowground traits a negative response to precipitation, highlighting the importance of considering distinct functional compartments and different sets of traits when assessing plant responses to environmental variability.
More than half of the variation in trait diversity occurred among neighbouring sub-plots, rather than among sites and years. This fine-scale heterogeneity suggests that micro-environmental factors and biotic interactions might have a larger effect on the coexistence of different functional strategies than broader-scale variation, environmental or otherwise.
We conclude that interannual weather variability, especially in precipitation amount, has limited effects on the functional properties of understory plant communities in undisturbed beech forests. However, this apparent functional stability may be threatened by more frequent climatic extremes or altered seasonal weather patterns.
DOI
https://doi.org/10.32942/X2569N
Subjects
Ecology and Evolutionary Biology, Forest Biology, Forest Sciences, Life Sciences, Plant Sciences
Keywords
Temperate forest, Beech forest, Weather, Sub-canopy, Temperature, Precipitation, Plant traits, Functional diversity, Causal inference, Bayesian models
Dates
Published: 2026-11-13 11:47
Last Updated: 2026-11-13 11:47
License
CC BY Attribution 4.0 International
Additional Metadata
Data and Code Availability Statement:
Data and code supporting the results in the paper are available at https://doi.org/10.5281/zenodo.24167466 and https://doi.org/10.5281/zenodo.24167182, respectively.
Language:
English
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