DOI: 10.5281/zenodo.18362577
title: Dataset for: Coupled decline in growth and fine-branching signals the onset of drought-induced European beech mortality

description: Code and data for “Coupled decline in fine-branching and growth signals the onset of drought-induced European beech mortality"

This archive contains all data and code required to reproduce the analyses and figures presented in the manuscript.

All scripts are fully commented and designed to be run in a defined order (see below).

TLS data, as well as growth and vitality data are zipped files that need to be extracted prior to usage.

Metadata

Explaining the rows and columns from the vitality_survey_intense.csv and growth_data_intense.csv datasets (see also below).

Main scripts (R)





growth.RPreparing growth data for subsequent analyses (growth curve estimation, drought-induced growth reductions).




tree_qsm.RCorrections of QSM models, visualisation and derivation of tree metrics.




statistical_analyses.RBayesian statistical analyses and modelling, including predictions and visualisation of results.



Recommended workflow (script execution order)

To reproduce the full workflow, run scripts in the following order:





growth.R




tree_qsm.R




statistical_analyses.R



The output generated by earlier scripts is used as input for later scripts.

Folder structure



.


├── README.md


├── metadata.csv 

├── growth.R


├── tree_qsm.R


├── statistical_analyses.R


├── tls_data/


│   ├── 2214/


│   │   ├── 1_pointclouds/


│   │   ├── 2_treegraph_qsm/


│   │   └── 3_rtwig_adjusted_qsm/


│   ├── 2215/


│   │   ├── 1_pointclouds/


│   │   ├── 2_treegraph_qsm/


│   │   └── 3_rtwig_adjusted_qsm/


│   ├── 2218/


│   │   ├── 1_pointclouds/


│   │   ├── 2_treegraph_qsm/


│   │   └── 3_rtwig_adjusted_qsm/


│   ├── 2219/


│   │   ├── 1_pointclouds/


│   │   ├── 2_treegraph_qsm/


│   │   └── 3_rtwig_adjusted_qsm/


│   ├── 2220/


│   │   ├── 1_pointclouds/


│   │   ├── 2_treegraph_qsm/


│   │   └── 3_rtwig_adjusted_qsm/

│   ├── 2221/

│   │ ├── 1_pointclouds/

│   │ ├── 2_treegraph_qsm/

│   │ └── 3_rtwig_adjusted_qsm/

 └── growth_and_vitality_data/

 ├── growth_data_intense.csv

 └── vitality_survey_intense.csv



Data description

1) Terrestrial laser scanning (TLS) data (tls_data/)

Scanning instrument: We used a RIEGL VZ-400i with a wavelength of 1550 nm, 0.35 mrad beam divergence, and pulse repetition rate of up to 1200kHz.

The folder tls_data/ contains all terrestrial laser scanning (TLS) data used for deriving quantitative structure models (QSMs) and fine-branching metrics. Data are grouped by study site:





2214, 2215, 2218, 2219, 2220, 2221



Each site folder contains three subfolders:

1_pointclouds/

Raw TLS point cloud data used to generate QSMs.Point clouds are voxelized to 0.015 m.

2_treegraph_qsm/

QSMs derived using the Treegraph library:





Treegraph library v1.8 (Python v3.7)




Source code for Treegraph: https://github.com/wanxinyang/treegraph


Resulting QSMs from Treegraph include four files by default. (1) The .centre.ply files include the skeleton nodes of the reconstructed tree. (2) The .txt files provide the pointcloud data in text format. (3) The .mesh.ply files include fitted cylinders from the QSM models. (4) The .json files include all relevant information and are used for the tree_qsm.R script. More information can be found at Yang et al. 2024 (https://doi.org/10.1002/rse2.399).


These QSMs represent the initial QSM reconstruction prior to additional correction/adjustment steps.

3_rtwig_adjusted_qsm/

This folder contains the adjusted/corrected QSM outputs produced by running tree_qsm.R.The script reads the Treegraph QSMs from 2_treegraph_qsm/, applies corrections using the rTwig library (https://cran.r-project.org/web/packages/rTwig/index.html), and derives tree-level metrics used in the manuscript.

2) Growth and vitality data (growth_and_vitality_data/)

growth_data_intense.csv

Retrospective growth dataset used for growth analyses and drought-related growth reduction estimates.The growth data originate from Schmied et al. (2023): https://doi.org/10.1016/j.foreco.2023.121305

Columns:





site_idUnique site identifier (2214, 2215, 2218, 2219, 2220, 2221)




siteSite name (e.g. Oberschwarzach, …)




yearGrowth year




tree_idUnique tree identifier (includes the site_id, e.g. 221401, 221402, …)




rwRing width (mm)




dbh_springDiameter at breast height (1.3 m) measured before the vegetation period of the respective year (cm)



vitality_survey_intense.csv

Vitality assessment dataset used in the manuscript (tree-level).

Columns:





site_idUnique site identifier (2214, 2215, 2218, 2219, 2220, 2221)




tree_idUnique tree identifier (includes the site_id, e.g. 221401, 221402, …)




dbhDiameter at breast height (1.3 m) measured at the end of 2022 (cm)




crown_volumeCrown volume (m³) derived from crown hulls fitted to point clouds(see Bohnhorst et al. 2025: https://doi.org/10.1016/j.foreco.2025.123274)




vitality_classBinary vitality classification (0/1) from Schmied et al. (2023).





0 = modest defoliation category




1 = pronounced defoliation category






defoliation_01Estimated defoliation level in % for year 2022




defoliation_02Estimated defoliation level in % for year 2023




defoliation_03Estimated defoliation level in % for year 2024




defoliation_04Estimated defoliation level in % for year 2025



Script details

growth.R

Topic: Preparing growth data for subsequent analyses

This script:





estimates the potential population-level growth curve




quantifies drought-induced growth reductions




creates Figure S3 (Supplementary Material)



tree_qsm.R

Topic: Corrections of QSM models, visualisation and derivation of tree metrics

This script:





reads QSM models created with the Treegraph library




adjusts/corrects QSM models using the rTwig library




derives tree metrics from the QSM models




calculates relative twig length




generates visualisations for publication (Figures 2, 3, S2, S5)



statistical_analyses.R

Topic: Statistical analyses

This script:





develops the nonlinear model used for statistical analyses




derives mortality and damage probabilities and generates predictions




visualises model results (Figures 4, 5, S4, S6)




checks model quality and performance




uses data generated from growth.R and tree_qsm.R



Software requirements

Analyses were performed in R.The Bayesian modelling requires a functional Stan installation.

Citation and reuse

If you use this code or data, please cite:





this Zenodo archive




the associated manuscript



Contact

For questions, issues, or reproduction problems, please contact Gerhard Schmied (gerhard.schmied@tum.de).