Data and code from: Partitioning tree growth into light interception and use efficiencies clarifies the role of light competition in secondary forest succession
Data files
May 28, 2026 version files 2.38 MB
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data_and_code_Dryad.zip
2.38 MB
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README.md
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Abstract
Light competition is a fundamental force driving tree height growth and secondary succession. However, the costs and benefits of light interception and growth at the individual tree level remain poorly understood, largely due to the technical challenges of measuring light interception in natural forests. By adopting in situ measurement of light interception with a novel analytical framework, we decomposed the individual relative growth rate (RGR) into two components: light interception efficiency (LIE), defined as light intercepted per unit of aboveground biomass, and light use efficiency (LUE), defined as biomass gain per unit of intercepted light (that is, RGR = LIE × LUE). Using this analytical framework, we analysed individual tree growth rates in relation to light interception and use to examine how light competition drives size variations and its consequences for forest development during secondary succession. This study was carried out in 12 forest stands of varying ages in Hokkaido, Japan. For all co-occurring trees with stem diameters > 1 cm, we quantified growth and light interception using 3D crown geometries and forest light profiles. In young stands, RGR was positively correlated with tree height, driving rapid vertical growth and stratification. In older stands, this relationship weakened or reversed, contributing to size-structure stabilization and the coexistence of different-size species. Across all stages, taller trees had higher LIE, indicating size-asymmetric competition. Conversely, taller trees had consistently lower LUE than shorter trees, likely due to ecophysiological constraints. In young stands, the advantage of higher LIE outweighed the lower LUE of taller trees, resulting in their higher RGR. In older stands, the relative benefit of higher LIE diminished while the LUE of smaller trees increased; this reduced the growth advantage of canopy individuals and promoted the regeneration of the understory. This mechanistic framework clarifies how light competition drives forest dynamics. The transition from rapid height stratification to structural stabilization and species coexistence is governed by the shifting balance between size-dependent light interception and use efficiencies.
Dataset DOI: 10.5061/dryad.r2280gbth
Description of the data and file structure
Data Overview
This dataset comprises three files collected from 12 natural forest plots of varying stand ages (ranging from 16 to > 100 years old as of 2019) within the Tomakomai Experimental Forest.
File Descriptions
1. Tomakomai_all_LIE_LUE.xlsx (Main Dataset)
This file contains primary structural measurements and derived functional parameters for individual trees within the plots.
- Field Measurements:
- Tree species identification
- Spatial coordinates (x, y)
- Diameter at breast height (DBH)
- Canopy heights (both top and bottom bounds)
- Crown radius measured in four cardinal directions
- Derived Parameters:
- Above-ground biomass
- Tree growth rates
- Relative growth rates
- Total light interception
- Light Use Efficiency (LUE)
- Light Interception Efficiency (LIE)
- File contains two sheets: "Sheet 1" contains the data. "meta_data" contains a description of all column variables in "Sheet 1"
- Note: blank cells are used to represent missing data
2. litter_summary.csv
This file contains monthly litterfall data collected from August through November for the 12 study plots..
3. Plot_ageclass.xlsx
This file contains stand age classifications and metadata for the 12 forest plots.
Files and variables
File: data_and_code_Dryad.zip
This compressed archive contains five R scripts used to replicate and generate all figures presented in the main text and online supplementary materials, with the exception of the conceptual diagram. Before executing the scripts, the working directory path must be updated within the code to match your local file structure.
Code/software
Statistical Software
All data analyses were performed using R (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria).
Code File Descriptions
Figure2.RGenerates Figure 2 in the main text.Figure3.RGenerates Figure 3 in the main text.Figure4&5.RGenerates Figures 4 and 5 in the main text.Figure6_SI1.RGenerates Figure 6 in the main text, as well as Supplementary Figures S3, S4, S6, S7, and S8.FigureSI2.RGenerates Supplementary Figures S2 and S5.
