Decreasing stem growth in common European tree species despite earlier growth onset
Data files
Jul 08, 2025 version files 98.05 MB
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R_script_for_data_analyses.r
299.75 KB
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broadleave_proportion_250_lv95.tif
866.22 KB
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ch_lv95.shp
188.68 KB
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climate_doy_25.csv
214.38 KB
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climate_doy_5.csv
217.18 KB
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climate_doy_50.csv
215.20 KB
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climate_doy_75.csv
219.22 KB
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data_cwb.rds
75.27 MB
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Daylength.csv
10.69 KB
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growth_data.rds
10.95 MB
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metadata.csv
55.48 KB
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mogli_hill_1km_lv95_smooth.tif
182.67 KB
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README.md
7.16 KB
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See_500.shp
86.68 KB
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temperature_data.rds
61.79 KB
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Trees.DBH.csv
15.75 KB
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trend_precip_annual.rds
4.62 MB
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trend_temp_annual.rds
4.57 MB
Abstract
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.
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
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:
- growth_data.rds
- temperature_data.rds
- climate_doy_5.csv
- climate_doy_25.csv
- climate_doy_50.csv
- climate_doy_75.csv
- metadata.csv
- data_cwb.rds
- trend_precip_annual.rds
- trend_temp_annual.rds
- Trees.DBH.csv
- Daylength.csv
- broadleave_proportion_250_lv95.tif
- mogli_hill_1km_lv95_smooth.tif
- See_500.shp
- ch_lv95.shp
- 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.
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 <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.