Abstract Key message Although global changes are expected to intensify the impact of wind as a hazard, recent studies have emphasized the critical role wind plays in tree growth and development. Wind-induced swaying generates strains that the tree perceives, triggering a process known as thigmomorphogenesis. This process alters the tree’s growth patterns and wood properties to enhance its mechanical stability. Thus, wind functions not only as a hazard but also as a growth factor, enabling the tree to acclimate to wind loads and reduce its mechanical risk. Despite the significant role of thigmomorphogenesis in tree growth and carbon allocation, this process remains largely overlooked in forest ecology and management models. We strongly advocate for the integration of wind-induced strain sensing, the primary driver of thigmomorphogenesis, alongside established environmental factors in tree and forest growth models, as well as in instrumented forest stands aimed at studying the effects of environmental factors on tree growth. This crucial step is essential for a comprehensive understanding of forest growth dynamics and for informed decision-making in forest management.

In this study, we analysed how the tree growth in stem and roots reacts to thinning, focusing on the consequences for mechanical stability of the root-soil plate quantified by field mechanical bending tests. In order to disentangle the role of the biomechanical control of growth (thigmomorphogenesis) from other factors, half of the studied trees were guyed to remove mechanical stimulation due to the wind of living cells. Surprisingly, our results show a decrease in the root-soil plate mechanical performances for a given stem biomass after thinning. This decrease was however explained by boosted biomass allocation to the stem at the expense of the root system. Further, relationship between the initial stiffness and the strength (overturning moment) of the root-soil plate was modified by thinning. It is suggested that at this development stage (poles), as stem break is the weakest point of tree resistance to wind loads, the biomechanical control of growth strengthens preferentially the stem and not the anchorage. Further developments should study the diversity of behaviours between development stages and between species for a unified theory on the role of the thigmomorphogenetic syndrome in tree resistance to wind risk, with synergies and trade-offs with other processes and functions.

Flexure wood formation is not systematically observed as a part of thigmomorphogenetic syndrome induced by wind. Its formation depends likely on the dose of mechanical signal perceived and the tree size-dependent strategy to resist wind loads. Formation of a specific wood tissue called flexure wood often accompanies the thigmomorphogenetic syndrome in mechanically stimulated trees. Flexure wood exhibits high microfibril angle allowing for increase in the stem flexibility and higher resistance to post-elastic damage during repeated loadings. In this study, we examine the microstructure and the properties of wood produced by beech poles submitted to increased mechanical stimulus. Contrary to previous results obtained on poplar saplings, aside a little decrease in density no changes in the microstructure or the post-elastic properties of wood were observed in beech poles. While in saplings wood properties explained 25

Key message The TreeTrace_spruce database contains images and measurements of 100 Norway spruce ( Picea abies (L.) H.Karst.) logs from Northeastern France, each about 4.5 m long. The image database includes RGB images of large and small ends of the logs and hyperspectral and computed tomography (CT) images of wood discs sampled at both log ends. The 100 logs were also fully X-ray scanned with a CT device for roundwoods and their top surface was scanned with a terrestrial LiDAR device. The measurements performed on discs include wood local density, growth ring widths and pith location. This database is complementary to another one (TreeTrace_Douglas) resulting from the same ANR project TreeTrace, but if the objectives are similar, the protocols and conditions of acquisition are not the same for these two databases. TreeTrace_spruce dataset is available at https://doi.org/10.57745/WKLTJI and associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/cffee2f1-18e1-4b53-9f5b-6cc4c66f1cb8 .

Tree risk assessment requires mechanical response studies, but simplification of the shape, material, or boundary conditions is necessary when dealing with such complex structures. To observe overall tree response, sub-structuring to several levels of detail can be used, enabled by recent developments in numerical methods and three-dimensional laser scanning (3D scan). This study aimed to determine an appropriate level of geometry and loading simplification allowed for high-order branches at the crown border, which is useful for the mechanical analysis of structured tree models. Four higher-order branches were pruned and experimentally tested by single-point loading. Beam and solid finite-element models (FEMs) were created based on measured geometric parameters and detailed 3D scans, respectively. The FEMs were used to analyze seven loading scenarios with force applied at (a) the center of gravity, (b) the top of side branches, (c) key discrete points, and (d) uniformly to the whole volume (to each finite element). Force was distributed by ratios weighted according to the mass, area, and diameter of side branches; or according to the mass of each finite element. The results showed no significant difference between the beam model and 3D scan-based model. The scenarios with finite elements' mass-based force distribution deviated significantly from those of the other scenarios. The most simplified single-point loading caused a deviation in the deflection curve. The deviation of single-point loading in the case of the bending moment was related to force distribution ratios given by the branches architecture. Therefore, such loading simplification is not considered always appropriate. Consistency between the bending moment and branch deflection provided a representative mechanical response, recommended for further modeling of trees by sub-structuring.

Key message This study presents the first attempt to quantify how the thigmomorphogenetic syndrome is involved in Fagus sylvatica L. tree growth responses to thinning. An experimental design preventing mechanosensing in half of the trees demonstrated that radial growth distribution in roots and along the tree stem is under strong biomechanical control. Context Studies on the mechanosensitive control of growth under real forest conditions are rare and those existing to date all deal with conifer species. In the current context of global changes, it is important to disentangle how different biotic and abiotic factors affect tree growth. Aims Whereas growth changes after thinning are usually interpreted as responses to decreased competition for resources, this study investigates the importance of how mechanosensing controls growth distribution inside the tree. Methods In an even-aged beech stand, 40 pole-sized trees (size class at first thinning) were selected, half of the plot was thinned and, within each sub-plot (thinned and unthinned), half of the tree were guy-wired in order to remove mechanical stimulations to the lower part of the stem. Four years later, all trees were felled and volume increment, ring width distribution along the tree height, and the largest ring width of the structural roots were measured. The effect of mechanical stimulation in the two treatments (thinned and unthinned) was assessed. Results Removal of mechanical stimulation decreased the volume increment in the lower part of the stem as well as radial root growth but did not affect axial growth. When mechanical strain was removed, the ring width distribution along the stem height changed drastically to an ice-cream cone-like distribution, indicating a strong mechanosensitive control of tree shape. Conclusion In a forest stand, the growth allocation inside the tree is under strong mechanical control. Mechanical stimulations explain more than 50% of the increment stimulated by thinning, whatever the growth indicator. A further challenge is to better understand how cambial cells perceive strains during growth in order to integrate mechanosensing into process-based tree-growth modeling.

The analysis of trunk shape and, in particular, the geometric structures on the bark surface are of main interest for different applications linked to the wood industry or biological studies. Bark singularities are often external records of the history of the development of internal elements. The actors of the forest sector grade the trees by considering these singularities through standards. In this paper, we propose a method using terrestrial LiDAR data to automatically segment singularities on tree surfaces. It is based on the construction of a relief map combined with a convolutional neural network. The algorithms and the source code are available with an online demonstration allowing to test the defect detection without any software installation.

We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark. Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality. Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface. First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator. The defects were detected and classified with a high accuracy with an average $${F}_{1}$$ score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects. The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods.

Estimating the quality of standing trees or roundwood after felling is a crucial step in forest production trading. The on-going revolution in the forest sector resulting from the use of 3D sensors can also contribute to this step. Among them the terrestrial lidar scanning is a reference descriptive method offering the possibility to segment defects. In this paper, we propose a new reproducible method allowing to automatically segment the defects. It is based on the construction of a relief map inspired from a previous strategy and combining with a convolutional neural network to improve the resulting segmentation quality. The proposed method outperforms the previous results and the source code is publicly available with an online demonstration allowing to test the defect detection without any software installation.

Three-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These singularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epicormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F-1 score of 0.86.

PREMISE OF THE STUDY:Thinning is a frequent disturbance in managed forests, especially to increase radial growth. Due to buckling and bending risk associated with height and mass growth, tree verticality is strongly constrained in slender trees growing in dense forests and poor light conditions. Tree verticality is controlled by uprighting movements implemented from local curvatures induced by wood maturation stresses and/or eccentric radial growth. This study presents the first attempt to compare the real uprighting movements in mature trees using a theoretical model of posture control.METHODS:Stem lean and curvature were measured by Terrestrial LiDAR Scanner (TLS) technology before and 6 years after thinning and compared to unthinned control poles. Measures for several tree and wood traits were pooled together to implement a widely used biomechanical model of tree posture control. Changes in observed stem lean were then compared with the model predictions, and discrepancies were reviewed.KEY RESULTS:Even under a highly constrained environment, most control poles were able to counterbalance gravitational curvature and avoid sagging. Thinning stimulated uprighting movements. The theoretical uprighting curvature rate increased just after thinning, then slowed after 2 years, likely due to the stem diameter increase. The biomechanical model overestimated the magnitude of uprighting.CONCLUSIONS:Most suppressed beech poles maintain a constant lean angle, and uprighting movements occur after thinning, indicating that stem lean is plastic in response to light conditions. Acclimation of posture control to other changes in growth condition should be investigated, and lean angles should be measured in forest inventories as an indicator of future wood quality.