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• The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day–night conditions on tree growth remained uncertain. • Here we present the first comprehensive field study of hourly-resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 yr. • We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species-specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. • We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.

• A valid representation of intra-annual wood formation processes in global vegetation models is vital for assessing climate change impacts on the forest carbon stock. Yet, wood formation is generally modelled with photosynthesis, despite mounting evidence that cambial activity is rather directly constrained by limiting environmental factors. • Here, we apply a state-of-the-art turgor-driven growth model to simulate 4 yr of hourly stem radial increment from Picea abies (L.) Karst. and Larix decidua Mill. growing along an elevational gradient. For the first time, wood formation observations were used to validate weekly to annual stem radial increment simulations, while environmental measurements were used to assess the climatic constraints on turgor-driven growth. • Model simulations matched the observed timing and dynamics of wood formation. Using the detailed model outputs, we identified a strict environmental regulation on stem growth (air temperature > 2°C and soil water potential > −0.6 MPa). Warmer and drier summers reduced the growth rate as a result of turgor limitation despite warmer temperatures being favourable for cambial activity. • These findings suggest that turgor is a central driver of the forest carbon sink and should be considered in next-generation vegetation models, particularly in the context of global warming and increasing frequency of droughts.

Wood formation consumes around 15% of the anthropogenic CO₂ emissions per year and plays a critical role in long-term sequestration of carbon on Earth. However, the exogenous factors driving wood formation onset and the underlying cellular mechanisms are still poorly understood and quantified, and this hampers an effective assessment of terrestrial forest productivity and carbon budget under global warming. Here, we used an extensive collection of unique datasets of weekly xylem tissue formation (wood formation) from 21 coniferous species across the Northern Hemisphere (latitudes 23 to 67°N) to present a quantitative demonstration that the onset of wood formation in Northern Hemisphere conifers is primarily driven by photoperiod and mean annual temperature (MAT), and only secondarily by spring forcing, winter chilling, and moisture availability. Photoperiod interacts with MAT and plays the dominant role in regulating the onset of secondary meristem growth, contrary to its as-yet-unquantified role in affecting the springtime phenology of primary meristems. The unique relationships between exogenous factors and wood formation could help to predict how forest ecosystems respond and adapt to climate warming and could provide a better understanding of the feedback occurring between vegetation and climate that is mediated by phenology. Our study quantifies the role of major environmental drivers for incorporation into state-of-the-art Earth system models (ESMs), thereby providing an improved assessment of long-term and high-resolution observations of biogeochemical cycles across terrestrial biomes.

Trees play a key role in the global hydrological cycle and measurements performed with the thermal dissipation method (TDM) have been crucial in providing whole-tree water-use estimates. Yet, different data processing to calculate whole-tree water use encapsulates uncertainties that have not been systematically assessed. We quantified uncertainties in conifer sap flux density (F d) and stand water use caused by commonly applied methods for deriving zero-flow conditions, dampening and sensor calibration. Their contribution has been assessed using a stem segment calibration experiment and 4 yr of TDM measurements in Picea abies and Larix decidua growing in contrasting environments. Uncertainties were then projected on TDM data from different conifers across the northern hemisphere. Commonly applied methods mostly underestimated absolute F d. Lacking a site- and species-specific calibrations reduced our stand water-use measurements by 37% and induced uncertainty in northern hemisphere F d. Additionally, although the interdaily variability was maintained, disregarding dampening and/or applying zero-flow conditions that ignored night-time water use reduced the correlation between environment and F d. The presented ensemble of calibration curves and proposed dampening correction, together with the systematic quantification of data-processing uncertainties, provide crucial steps in improving whole-tree water-use estimates across spatial and temporal scales.

Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.

Trees are integral to ecosystems and hold considerable economic importance. Their exceptional longevity and modular structure also make them valuable models for studying the long-term accumulation of somatic mutations and epimutations in plants. Empirical evidence indicates that the annual rate of these stochastic events correlates negatively with generation time, suggesting that species with long lifespans have evolved mechanisms to mitigate the build-up of deleterious somatic variants. It has been hypothesized that this reduction is achieved by slowing growth and minimizing the number of cell divisions per unit time, thereby reducing errors associated with DNA replication. However, a direct test of this “mitotic-rate hypothesis” remains technically challenging. Here we take advantage of a 150 year-old experiment in European beech to show that a thinning-induced growth acceleration increases the annual rate of somatic epimutations in main stems and lateral branches of trees. We demonstrate that this effect is accompanied by a proportional increase in the rate of cell divisions per unit time. These findings support the notion that life-history constraints on growth rates in trees are not merely a trade-off between resource allocation and structural stability but also a strategy to preserve genetic and epigenetic fidelity over extended lifespans. Trees live centuries by slowing growth to protect their DNA. This study shows that faster growth increases the epimutation rate in European beech trees, supporting the idea that slow growth helps maintain genome stability.

The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder ( Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech ( Fagus sylvatica L.); and a ring-porous species, sessile oak ( Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the “learning process” defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms’ performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.

Defoliator insects are a major disturbance agent in many forests worldwide. During outbreaks, they can strongly reduce photosynthetic carbon uptake and impact tree growth. In the Alps, larch budmoth ( Zeiraphera diniana ) outbreaks affect European larch ( Larix decidua ) radial growth over several years. However, immediate and legacy effects on xylem formation, structure, and functionality are still largely unknown. In this study, we aimed at assessing the impact of budmoth defoliations on larch xylem anatomical features and tree-ring structure. Analyses were performed in the Lötschental (Swiss Alps) within (1,900 m a.s.l.) and above (2,200 m a.s.l.) the optimum elevational range of larch budmoth. We investigated variability of xylem anatomical traits along century-long tree-ring series of larch (host) and Norway spruce (non-host) trees. We identified eight outbreaks affecting larch xylem anatomy during the 20th century, particularly at 1,900 m a.s.l. Tracheid number always showed a higher percent reduction than properties of individual cells. Cell lumen size was slightly reduced in the first 2–3 years of outbreaks, especially in the early part of the ring. The more carbon-demanding cell wall was thinned along the entire ring, but more evidently in the last part. Theoretical tree-ring hydraulic conductivity was reduced for several years (up to 6), mostly due to cell number decrease. Reduced cell wall area and cell number resulted in a strong reduction of the tree-ring biomass, especially in the first year of outbreak. Our study shows that, under carbon source limitations caused by natural defoliation, cell division is more impacted than wall thickening and cell enlargement (the least affected process). Consequences on both xylem hydraulic properties and tree-ring biomass should be considered when assessing long-term defoliator effects on xylem functioning, forest dynamics, and terrestrial carbon cycle.

Quantitative wood anatomy is critical for establishing climate reconstruction proxies, understanding tree hydraulics, and quantifying carbon allocation. Its accuracy depends upon the image acquisition methods, which allows for the identification of the number and dimensions of vessels, fibres, and tracheids within a tree ring. Angiosperm wood is analysed with a variety of different image acquisition methods, including surface pictures, wood anatomical micro-sections, or X-ray computed micro-tomography. Despite known advantages and disadvantages, the quantitative impact of method selection on wood anatomical parameters is not well understood. In this study, we present a systematic uncertainty analysis of the impact of the image acquisition method on commonly used anatomical parameters. We analysed four wood samples, representing a range of wood porosity, using surface pictures, micro-CT scans, and wood anatomical micro-sections. Inter-annual patterns were analysed and compared between methods from the five most frequently used parameters, namely mean lumen area ( ), vessel density ( ), number of vessels ( ), mean hydraulic diameter ( ), and relative conductive area ( ). A novel sectorial approach was applied on the wood samples to obtain intra-annual profiles of the lumen area ( ), specific theoretical hydraulic conductivity ( ), and wood density ( ). Our quantitative vessel mapping revealed that values obtained for hydraulic wood anatomical parameters are comparable across different methods, supporting the use of easily applicable surface picture methods for ring-porous and specific diffuse-porous tree species. While intra-annual variability is well captured by the different methods across species, wood density ( ) is overestimated due to the lack of fibre lumen area detection. Our study highlights the potential and limitations of different image acquisition methods for extracting wood anatomical parameters. Moreover, we present a standardized workflow for assessing radial tree ring profiles. These findings encourage the compilation of all studies using wood anatomical parameters and further research to refine these methods, ultimately enhancing the accuracy, replication, and spatial representation of wood anatomical studies.

Human-driven peatland drainage has occurred in Europe for centuries, causing habitat degradation and leading to the emission of greenhouse gases. As such, in the last decades, there has been an increase in policies aiming at restoring these habitats through rewetting. Alder ( Alnus glutinosa L.) is a widespread species in temperate forest peatlands with a seemingly high waterlogging tolerance. Yet, little is known about its specific response in growth and wood traits relevant for tree functioning when dealing with changing water table levels. In this study, we investigated the effects of rewetting and extreme flooding on alder growth and wood traits in a peatland forest in northern Germany. We took increment cores from several trees at a drained and a rewetted stand and analyzed changes in ring width, wood density, and xylem anatomical traits related to the hydraulic functioning, growth, and mechanical support for the period 1994–2018. This period included both the rewetting action and an extreme flooding event. We additionally used climate-growth and climate-density correlations to identify the stand-specific responses to climatic conditions. Our results showed that alder growth declined after an extreme flooding in the rewetted stand, whereas the opposite occurred in the drained stand. These changes were accompanied by changes in wood traits related to growth (i.e., number of vessels), but not in wood density and hydraulic-related traits. We found poor climate-growth and climate-density correlations, indicating that water table fluctuations have a stronger effect than climate on alder growth. Our results show detrimental effects on the growth of sudden water table changes leading to permanent waterlogging, but little implications for its wood density and hydraulic architecture. Rewetting actions should thus account for the loss of carbon allocation into wood and ensure suitable conditions for alder growth in temperate peatland forests.