DOI: 10.5061/dryad.1c59zw495
title: Data from: Convergence and variation in tree growth trends at the aggregate level

description: Individual trees in natural forests often exhibit complex, inconsistent,
 and variable growth trajectories influenced by genetics, climate change,
 and uneven stand structure. These growth divergences pose a challenge in
 predicting the overall growth trend of trees at the aggregate level. Here,
 we propose a radius-driven metabolic growth model (IGMR) to explain the
 radial growth of trees. The IGMR suggests that the best radial growth
 trajectory (BGT) at the aggregate level varies within a predictable range
 and can be derived from the maximum radius and total growth time of an
 individual tree. Analyses based on a global database confirmed the
 applicability of the IGMR and found that the average radial growth trend
 closely follows half of the BGT, with the strength of this association
 potentially related to functional trait trade-offs. Further analyses show
 that climate change and uneven stand structure may cause the overall
 growth trajectory to undergo more drifts (changes in growth rate only)
 than adaptations (changes in maximum size). Synthesis: Our results reveal
 not only a convergent growth trajectory in tree size (or radius) at the
 aggregate level, but also suggest that climate regulates the tree
 growth–climate relationship by influencing the height (i.e., maximum
 radial growth rate) of this unimodal trajectory, whereas the
 length (i.e., with maximum tree radius) of the trajectory shows greater
 dependence on species. These findings further imply that climate change is
 more likely to affect the forest’s maximum carbon sequestration capacity
 through shifts in community composition, rather than through direct
 changes in individual tree growth rates.
description: # Data from: Convergence and variation in tree growth trends at the
 aggregate level ## 1. Overview This dataset was compiled to investigate
 large-scale patterns and drivers of tree radial growth across multiple
 regions. It integrates tree-ring measurement data from the NOAA
 International Tree-Ring Data Bank (ITRDB) and processed climate variables
 derived from WorldClim v2.1. All data were curated, filtered, harmonized,
 and aggregated to meet the analytical objectives of the study, resulting
 in a final dataset containing more than 130,000 individual records. Note:
 The WorldClim data included here are processed derivatives (summaries and
 extracted site-level values), not the original raster files. These derived
 variables are consistent with Dryad’s CC0 license requirements, as they
 represent newly generated, value-added data. ## **2. Data Availability**
 \- Data and code underlying Figure 1 are not included, as they overlap
 with an ongoing unpublished study. Relevant files will be made available
 in a future update. \- data2cfa_new2.csv → used to generate Figure 2. \-
 Datasets used for Figure 3: \- f.csv → Figure 3A \- f2.csv → Figures 3B
 and 3C \- f4.csv → Figure 3D ## **3. Data Sources** 1\. Tree radial growth
 data: NOAA International Tree-Ring Data Bank
 [https://www1.ncdc.noaa.gov/pub/data/paleo/treering/measurements/](https://www1.ncdc.noaa.gov/pub/data/paleo/treering/measurements/) 2\. Climate data: Derived from WorldClim v2.1 [https://www.worldclim.org/data/worldclim21.html](https://www.worldclim.org/data/worldclim21.html) Only derived site-level variables (mean values and summary statistics) are included, not original raster products. ## **4. File Descriptions** 1\. **data2cfa_new2.csv** Contains site-level or population-level metrics used for analyses and for producing Figure 2. 2\. **f.csv, f2.csv, f4.csv** Contain subsets of data used for generating Figure 3 (panels A–D). Each file includes tree growth, climate, and metadata variables. 3\. **main_analysis.R** R script for loading data, cleaning, calculating summary metrics, and generating Figures 2–3. Software requirements: R ≥ 4.3; packages: dplyr, tidyr, ggplot2, lme4, nlme, ggpubr, sf, terra. ### **Variable Definitions** ##### Tree Growth Variables | Variable | Description | Unit / Notes | | --------------- | ------------------------------------------------------------------------ | ------------ | | mean\_mean.f | Population mean growth rate over the entire history | mm yr⁻¹ | | age\_mean.f | Population mean age | years | | mean\_max.f | Maximum population growth rate over the entire history (99th percentile) | mm yr⁻¹ | | rc\_max.f | Maximum tree radius in the population | mm | | age\_max.f | Maximum age in the population | years | | No.file | Number of cores (tree-ring samples) for each tree | — | | mean\_c\_mean.f | Population mean growth rate over the last 20 years | mm yr⁻¹ | | max\_mean.f | Mean of individual maximum growth rates in the population | mm yr⁻¹ | | rc\_mean.f | Population mean radius | mm | | mean\_c\_max.f | Maximum population growth rate over the last 20 years | mm yr⁻¹ | | treeid | Individual tree ID | — | | rc | Individual current radius | mm | | age | Individual current age | years | | mean\_c | Individual mean growth rate over the last 20 years | mm yr⁻¹ | | mean\_csd | Standard deviation of individual growth rate | mm yr⁻¹ | ##### Metadata and Site Variables | Variable | Description | Unit / Notes | | --------- | ------------------------------------------ | ------------------------------------------------------------- | | species | Tree species code (abbreviated Latin name) | For specific details, please refer to the Species Description | | latitude | Latitude of sampling site | decimal degrees | | longitude | Longitude of sampling site | decimal degrees | | elevation | Elevation of sampling site | meters | | region | Broad geographic region | e.g., continent, country, or study region | | type | Data type / category | e.g., tree-ring width | ##### Climate Variables | Variable | Description | Unit | | ------------- | ---------------------------------------------------- | ------------- | | AMT (bio1) | Annual Mean Temperature | °C | | MDR (bio2) | Mean Diurnal Range (monthly max – min temperature) | °C | | ISO (bio3) | Isothermality (bio2/bio7 × 100) | % | | TSE (bio4) | Temperature Seasonality (SD × 100) | °C × 100 | | MTWAM (bio5) | Max Temperature of Warmest Month | °C | | MTCM (bio6) | Min Temperature of Coldest Month | °C | | TAR (bio7) | Temperature Annual Range (bio5 – bio6) | °C | | MTWEQ (bio8) | Mean Temperature of Wettest Quarter | °C | | METDQ (bio9) | Mean Temperature of Driest Quarter | °C | | MTWAQ (bio10) | Mean Temperature of Warmest Quarter | °C | | METCQ (bio11) | Mean Temperature of Coldest Quarter | °C | | AP (bio12) | Annual Precipitation | mm | | PWEM (bio13) | Precipitation of Wettest Month | mm | | PDM (bio14) | Precipitation of Driest Month | mm | | PS (bio15) | Precipitation Seasonality (Coefficient of Variation) | % | | PWEQ (bio16) | Precipitation of Wettest Quarter | mm | | PDQ (bio17) | Precipitation of Driest Quarter | mm | | PWAQ (bio18) | Precipitation of Warmest Quarter | mm | | PCQ (bio19) | Precipitation of Coldest Quarter | mm | | SH (rcv.cv.f) | Coefficient of Variation of Tree Radial Growth | dimensionless | 
 
 **Species Description** | Species | Full name | | :------ |
 :-------------------------- | | PCGL | Picea glehnii | | PCSI | Picea
 sitchensis | | TSME | Tsuga mertensiana | | PCMA | Picea mariana | | CHNO
 | Chamaecyparis nootkatensis | | PIPA | Pinus palustris | | PIHE | Pinus
 heldreichii | | PIHA | Pinus halepensis | | CDAT | Cedrus atlantica | |
 PINI | Pinus nigra | | PINE | Pinus nigra ssp. nigra | | PIEC | Pinus
 echinata | | TADI | Taxodium distichum | | JUVI | Juniperus virginiana | |
 QUST | Quercus stellata | | QUAL | Quercus alba | | NOPB | Nothofagus
 pumilio | | NOPD | Nothofagus dombeyi | | NOPU | Nothofagus pumilio | |
 FICU | Fitzroya cupressoides | | CEAN | Cedrus deodara | | JUAU | Juglans
 australis | | NOBE | Nothofagus betuloides | | CESP | Abies spectabilis |
 | ARAR | Araucaria araucana | | PIUV | Pilgerodendron uviferum | | AUCH |
 Austrocedrus chilensis | | ATSE | Athrotaxis cupressoides | | PHAS |
 Phyllocladus aspleniifolius | | LGFR | Lagarostrobus franklinii | | CACO |
 Callitris columellaris | | ATCU | Athrotaxis cupressoides | | QUPE |
 Quercus petraea | | QUSP | Quercus sp. | | PIPO | Pinus ponderosa | | PIED
 | Pinus edulis | | PSME | Pseudotsuga menziesii | | PIST | Pinus strobus |
 | PISF | Pinus sylvestris | | PCEN | Picea engelmannii | | QURO | Quercus
 robur | | CEMC | Centrolobium microchaete | | PISY | Pinus sylvestris | |
 PCAB | Picea abies | | TSDU | Tsuga dumosa | | PCSP | Picea smithiana | |
 PIWA | Pinus wallichiana | | PIRO | Pinus roxburghii | | LAGR | Larix
 gmelinii | | JURE | Juniperus turkestanica | | PILE | Pinus heldreichii |
 | PIFL | Pinus flexilis | | PIJE | Pinus jeffreyi | | PIBA | Pinus
 balfouriana | | PICO | Pinus contorta | | PILA | Pinus lambertiana | |
 QUCF | Quercus coccinea | | LIDE | Larix decidua | | ABCO | Abies concolor
 | | ABMA | Abies magnifica | | PIAL | Pinus albicaulis | | PSMA |
 Pseudotsuga macrocarpa | | QUDG | Quercus douglassii | | QULO | Quercus
 lobata | | JUOC | Juniperus occidentalis | | PLRA | Platanus racemosa | |
 PILO | Pinus longaeva | | PIMR | Pinus merkusii | | CADE | Cedrus deodara
 | | ABAM | Abies amabilis | | PIBN | Pinus banksiana | | ABBA | Abies
 balsamea | | ABLA | Abies lasiocarpa | | QUMA | Quercus macrocarpa | |
 PIRE | Pinus resinosa | | THOC | Thuja occidentalis | | LALY | Larix
 lyallii | | PIAM | Pinus armandii | | JUPR | Juniperus przewalskii | |
 PCSH | Picea schrenkiana | | PCLI | Picea likiangensis | | LASI | Larix
 sibirica | | PCTI | Picea smithiana | | PITB | Pinus tabuliformis | | PCPU
 | Picea pungens | | PIBR | Pinus brutia | | CEBR | Cedrela odorata | |
 ABSP | Abies spectabilis | | FASY | Fagus sylvatica | | QULY | Quercus
 lyrata | | PICE | Picea engelmannii | | LITU | Liriodendron tulipifera | |
 TSCA | Tsuga canadensis | | QUPR | Quercus prinus | | QUVE | Quercus
 velutina | | TEGR | Tectona grandis | | PISP | Pinus sp. | | PIPN | Pinus
 pinea | | PIPI | Pinus pinaster | | LADE | Larix decidua | | HEHE | Hevea
 brasiliensis | | QUCE | Quercus cerris | | PCGN | Picea glauca | | CHOB |
 Chamaecyparis obtusa | | CMJA | Cunninghamia lanceolata | | PIKO | Pinus
 koraiensis | | JUSP | Juniperus scopulorum | | JUTU | Juniperus
 turkestanica | | PIRI | Pinus rigida | | TAMU | Taxodium mucronatum | |
 ABPI | Abies pindrow | | QURU | Quercus rubra | 
 
 ## **5. R Code Description** All analyses were conducted in R (≥4.3). The
 scripts generate Figures 2–3 based on IGMR predictions and
 climate-structure relationships. 1\. figure2ABCD.R \- Generates Figure 2
 panels A–D: ``` \- 2A–B: Predicted recent mean (A) and maximum (B) radial
 growth rates of populations vs IGMR predictions. \- 2C: Relationship
 between population mean growth rates and their standard deviation. \- 2D:
 Relationship between tree size heterogeneity and deviation of recent
 growth from theoretical predictions. ``` \- Includes model fitting,
 residual checks, and plotting. 2\. figure3A.R, figure3B.R, figure3C.R,
 figure3D.R \- Generate Figure 3 panels A–D: ``` \- 3A: Effects of climate
 and size heterogeneity on maximum radius (rc_max.f) \- 3B: Effects on
 maximum growth time (age_max.f) \- 3C: Effects on mean radial growth rate
 (mean_mean.f) \- 3D: Effects on maximum radial growth rate (mean_max.f)
 ``` \- Fit linear mixed-effects models or nonlinear quantile regressions
 with climate variables and structural heterogeneity as predictors. \-
 Extract model coefficients, significance, and generate summary plots.
 Workflow Summary: 1\. Load and clean the dataset 2\. Standardize growth
 and climate variables (z-scores) 3\. Calculate population-level summary
 metrics 4\. Fit IGMR predictions (Figure 2) 5\. Fit climate-structure
 models (Figure 3) 6\. Export figures using ggplot2 and cowplot ## **6.
 License and Data Use** All data are released under **CC0 1.0 Public Domain
 Dedication**, permitting unrestricted reuse, redistribution, and
 modification. Derived climate variables were computed from publicly
 available WorldClim v2.1 datasets (CC BY 4.0), only aggregated, derived
 outputs are included, not the original raster files. ## **7. Citation**
 Data and code supporting the results of this study are available in the
 Dryad Digital Repository at
 [https://datadryad.org/dataset/doi:10.5061/dryad.1c59zw495](https://datadryad.org/dataset/doi:10.5061/dryad.1c59zw495) (Shu & Wang, 2025).