DOI: 10.5281/zenodo.15463588
title: Data from: Normalized tree water deficit: An automated dendrometer signal to quantify drought stress in trees

description: This dataset accompanies the manuscript "A normalized dendrometer-based index to detect drought stress across temperate tree species". The study addresses a central challenge in tree ecophysiology: identifying the timing and intensity of drought stress in mature trees using continuous, non-invasive measurements. Trees regularly experience periods of low soil water availability and high atmospheric demand, which can impair physiological processes such as stomatal conductance, leaf turgor, and growth. While automated dendrometers offer high-resolution insights into stem dynamics, a robust framework for interpreting these signals as direct indicators of drought stress has been lacking.

We introduce a novel, biologically grounded index—normalized tree water deficit (TWDnorm)—derived from dendrometer data, in combination with normalized maximum daily shrinkage (MDSnorm). These indices provide a standardized way to detect drought-induced stress responses in trees. To validate the approach, we integrate concurrent measurements of leaf water potential (Ψleaf), stomatal conductance (gₛ), and hydraulic vulnerability across 118 mature trees representing nine widespread European tree species. All data were collected at the Swiss Canopy Crane II research site under diverse environmental conditions.

The dataset includes physiological and stem diameter dynamics from both broadleaved species (Fagus sylvatica L., Acer pseudoplatanus L., Carpinus betulus L., Fraxinus excelsior L., and Quercus petraea/robur L.) and conifers (Abies alba Mill., Picea abies Karst., and Pinus sylvestris L.).

Our findings show that TWDnorm reliably signals the onset of stomatal closure and leaf turgor loss, with species-specific thresholds that can inform drought vulnerability assessments. The combination of TWDnorm and MDSnorm thus represents a novel, automated index for biological drought stress, complementing traditional meteorological and satellite-based drought indicators.
description: Dataset Description

This dataset contains physiological and dendrometric measurements collected as part of the DenMet Project, which investigates tree water status and diurnal stem dynamics. Each row corresponds to a single day of measurement for an individual tree.

Columns:





date: Date of measurement (YYYY-MM-DD).




species_id: Unique species code, composed of the first letter of the genus and species.




tree_id: Unique identifier for each tree.




pdlwp: Pre-dawn leaf water potential (MPa).




mdlwp: Midday leaf water potential (MPa).




gs: Stomatal conductance to water vapour (mol m⁻² s⁻¹), measured using a LI-COR 6800 system.




shrink_amp: Daily stem shrinkage amplitude (μm), derived from dendrometer data using the treenetproc R package.




shrink_dur: Duration of the daily shrinkage phase (minutes).




phase_class: Diel cycle classification; 1 indicates a typical shrinkage-swelling cycle (shrinkage in the afternoon), -1 or NA denote irregular patterns.




mds: Maximum daily shrinkage (μm), filtered for phase_class = 1.




pdtwd: Pre-dawn tree water deficit (μm).




mdtwd: Midday tree water deficit (μm).




mp_mds: Difference between midday and pre-dawn tree water deficit (μm).




mintwd: Daily minimum tree water deficit (μm).




maxtwd: Daily maximum tree water deficit (μm).




mm_mds: Daily range in tree water deficit (μm), i.e., maxtwd - mintwd.



These variables collectively capture daily water potential dynamics, stomatal regulation, and stem diameter fluctuations, enabling detailed analysis of tree water relations under varying environmental conditions.