DOI: 10.5061/dryad.z8w9ghxrq
title: Decreasing stem growth in common European tree species despite earlier growth onset

description: Recent findings suggest that global warming is altering the timing of
 trees’ phenological activities including earlier emergence from winter
 dormancy. While early-season warming can boost carbon uptake, tree growth
 does not seem to benefit. The underlying mechanisms and the altered intra-
 and inter-annual growth dynamics, as well as their interaction with
 environmental factors, remain poorly understood. We analysed
 daily-resolved dendrometer data from 228 trees across 48 Swiss forest
 sites over 2012–2022 to examine stem radial growth timing, intra- and
 inter-annual dynamics, and environmental controls for five tree species.
 We examined how weekly tree growth is related to envrionmental variables
 including day length, temperature, preciipitation, vapor pressure deficit,
 soil water potential and their interactions. We found a significant
 negative growth trend for Picea abies, Abies alba, and
 Fagus sylvatica across a wide climatic gradient. The
 reduction in growth was associated with the decrease in number of days
 with growth. The positive effect of higher temperatures in spring was
 canceled out by a negative effect towards the end of the growth period.
 Overall, such negative effect of increased temperature at annual scale was
 strongest in Pinus sylvestris and persisted over 2012-2022. To provide a
 comprehensive understanding of the data analysis process, we have uploaded
 an R script that details all analyses and datasets used. The script
 includes thorough documentation of the analyses corresponding to each
 figure, table, and supplementary material. The dataset contains
 information on tree growth, site characteristics, and the environmental
 conditions in which the trees are growing. It is available for reuse in
 other scientific studies, with no legal or ethical restrictions.
description: The data is a part of an extensive tree growth and drought
 monitoring network (www.treenet.info) in
 Switzerland. In this network, radial stem growth is derived from
 measurements of stem radius changes of trees with high-precision point
 dendrometers on mainly mature dominant trees. Tree radial growth was
 measured at about 1.3 m above the ground. The data was recorded and
 transmitted with Decentlab data transfer nodes (Decentlab GmbH,
 Duebendorf, Switzerland) with a logging resolution &lt;1 µm at every
 5-10 mins. The measurements were processed to a 10-min time aligned data
 set using the R-package ‘treenetproc’, including outlier removal, jump
 correction and linear interpolation of short gaps. Radial growth data,
 extracted with the zero-growth assumption were aggregated on a daily,
 weekly, monthly, and yearly sum basis. As growth strongly varies within
 individual trees, across years, species, sites and climatic regions,
 annual growth was standardized by converting it to relative annual growth.
 This was done by calculating the difference between each tree’s annual
 growth to its tree-specific long-term average, calculated over the
 available data from 2012 to 2022. Air temperature (°C)
 data was obtained from weather stations www.meteoswiss.admin.ch, mean distance: 8 km, max: 15 km) or were measured at the site and aggregated to daily values. Air temperature and relative humidity (%) at the sites were measured at 2 m height within the forest stands. Precipitation was extracted from MeteoSwiss model generated CombiPrecip combining data from weather stations and precipitation radar. The vapour pressure deficit (VPD, in kPa) was calculated using temperature and relative humidity. Soil water potential (SWP) was measured with dielectric MPS-2/MPS-6 sensors (Decagon Devices, Pullman, US) at 10-20 cm soil depth at each site and the values were corrected for soil temperature fluctuations. All analyses, including statistical model runs and figures, were produced using the R statistical software. 
description: # Decreasing stem growth in common European tree species despite earlier
 growth onset #### GENERAL INFORMATION Daily-resolved dendrometer-based
 tree radial growth, characteristics of site and climate where trees are
 growing for seven tree species in Switzerland from Bose et al (2025).
 Decreasing stem growth in common European tree species despite earlier
 growth onset. Global Change Biology DOI:10.1111/gcb.70318 Author
 Information A. Principal Investigator Name: Arun K. Bose Contact
 Information: Swiss Federal Institute for Forest, Snow and Landscape
 Research, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland. Email:
 [arun.bose@wsl.ch](mailto:arun.bose@wsl.ch) B. Co-investigator
 information: Names: Sophia Etzold, Katrin Meusburger, Arthur Gessler,
 Andri Baltensweiler, Sabine Braun, Nina Buchmann, J. Julio Camarero,
 Matthias Haeni, Ansgar Kahmen, Richard L. Peters, Frank J. Sterck, Simon
 Tresch, Lorenz Walthert, and Roman Zweifel Date of data collection: From
 2012 to 2022 #### DATA & FILE OVERVIEW File List: 1. growth_data.rds
 2. temperature_data.rds 3. climate_doy_5.csv 4. climate_doy_25.csv 5.
 climate_doy_50.csv 6. climate_doy_75.csv 7. metadata.csv 8. data_cwb.rds
 9. trend_precip_annual.rds 10. trend_temp_annual.rds 11. Trees.DBH.csv 12.
 Daylength.csv 13. broadleave_proportion_250_lv95.tif 14.
 mogli_hill_1km_lv95_smooth.tif 15. See_500.shp 16. ch_lv95.shp 17.
 R_script_for_data_analyses.r #### METHODOLOGICAL INFORMATION For a
 complete description of the methodologies used for data collection, see
 Bose et al. (2025), Global Change Biology DATA FILE-SPECIFIC INFORMATION
 1.growth_data.rds Variable description * series_name: ID of the
 dendrometer * Site_id: ID of the site * Year: Calendar year * doy: Day of
 the year * temp: Temperature * swp: Soil water potential * precip:
 Precipitation * vpd: Vapor pressure deficit * species: Tree species name *
 ts: Time series * growth: Daily growth * growth_cum: Cumulative growth *
 growth_percent: Percentage of annual growth achieved at that given doy *
 cum_percent: Percentage of annual growth achieved until that doy
 2.temperature_data.rds Variable description * series_name: ID of the
 dendrometer * species: Tree species name * Site_id: ID of the site * Year:
 Calendar year * Month: Months of the year * mat: Daily average monthly
 temperature (°C) 3.climate_doy_5.csv Variable description * series_name:
 ID of the dendrometer * species: Tree species name * Site_id: ID of the
 site * Year: Calendar year * cum_percent: Percentage of annual growth
 achieved * min_doy_5: Day of year (DOY) when the tree exceeded 5% of its
 annual growth * spring_temp: Average daily temperature of the spring
 (March to May) * past_year_winter_temp: Average daily temperature of the
 previous winter (December to February) * summer_temp: Average daily
 temperature of the summer (June to August) 4.climate_doy_25.csv Variable
 description * series_name: ID of the dendrometer * species: Tree species
 name * Site_id: ID of the site * Year: Calendar year * cum_percent:
 Percentage of annual growth achieved * min_doy_25: Day of year (DOY) when
 the tree exceeded 25% of its annual growth * spring_temp: Average daily
 temperature of the spring (March to May) * past_year_winter_temp: Average
 daily temperature of the previous winter (December to February) *
 summer_temp: Average daily temperature of the summer (June to August)
 5.climate_doy_50.csv Variable description * series_name: ID of the
 dendrometer * species: Tree species name * Site_id: ID of the site * Year:
 Calendar year * cum_percent: Percentage of annual growth achieved *
 min_doy_50: Day of year (DOY) when the tree exceeded 50% of its annual
 growth * spring_temp: Average daily temperature of the spring (March to
 May) * past_year_winter_temp: Average daily temperature of the previous
 winter (December to February) * summer_temp: Average daily temperature of
 the summer (June to August) 6.climate_doy_75.csv Variable description *
 series_name: ID of the dendrometer * species: Tree species name * Site_id:
 ID of the site * Year: Calendar year * cum_percent: Percentage of annual
 growth achieved * min_doy_75: Day of year (DOY) when the tree exceeded 75%
 of its annual growth * spring_temp: Average daily temperature of the
 spring (March to May) * past_year_winter_temp: Average daily temperature
 of the previous winter (December to February) * summer_temp: Average daily
 temperature of the summer (June to August) 7.metadata.csv Variable
 description * Site_id: ID of the site * species: Tree species name *
 sensor_name: ID of the dendrometer (same as series_name in other data
 files) * site_xcor: Longitudinal coordinates * site_ycor: Latitudinal
 coordinates * tree_dbh: Tree diameter at breast height (cm) * tree_height:
 Tree height (m) * site_altitude: Elevation of the site (m) 8.data.cwb.rds
 Variable description * grid_id: ID of the grid at 250 m resolution * yr:
 Calendar year * x: Longitude (Swiss national coordinates; Coordinate
 system: CH1903/LV03,EPSG:21781) * y: Latitude (Swiss national coordinates;
 Coordinate system: CH1903/LV03,EPSG:21781) * cwb: Climatic water balance
 (precipitation - potential evapotranspiration) 9.trend_precip_annual.rds
 Variable description * Type: Data type: Annual scale * grid_id: ID of the
 grid at 250 m resolution * Slope: Coefficient of the model where the
 annual precipitation sum is modeled as a function of year * SE: Standard
 error of the model * SIG: Whether the coefficient was statistically
 significant or not * p_value: p-value of the model
 10.trend_temp_annual.rds Variable description * Type: Data type: Annual
 scale * grid_id: ID of the grid at 250 m resolution * Slope: Coefficient
 of the model where the mean annual temperature is modeled as a function of
 year * SE: Standard error of the model * SIG: Whether the coefficient was
 statistically significant or not * p_value: p-value of the model
 11.Trees.DBH.csv Variable description * series_name: ID of the dendrometer
 * Site_id: ID of the site * genus: Genus of the tree species * species:
 Tree species name * tree_dbh_test: Diameter at breast height of the tree
 (cm) * type: Tree size: Small or Large, based on the site-specific
 characterization 12.Daylength.csv Variable description * Month: Month of
 the year * Day: Monthly days * DOY: Day of the year * Daylength: Length of
 the day in hours, minutes, and seconds * DAY_DECIMAL: Length of the day in
 decimal * WEEK: Weeks of the year 13.broadleave_proportion_250_lv95 Raster
 layer for forest vegetation areas of Switzerland
 14.mogli_hill_1km_lv95_smooth Shape file for the hills of Switzerland
 15.See_500.shp Shape file for water bodies in Switzerland 16.ch_lv95.shp
 Shape file for the country's boundary of Switzerland
 17.R_script_for_data_analyses.r The script outlines all analyses conducted
 for Bose et al. (2025), Global Change Biology. Analyses are organized in
 the order of appearance, beginning with the tables and figures in the main
 manuscript, followed by those presented in the supplementary materials.
 All analyses, including statistical model runs and figures, were produced
 using the R statistical software.