Narrow or upright branch angles in shoots and leaves lead to columnar, upright-growing tree architectures, as observed in various tree species such as Lombardy poplar (Populus nigra var. italica). However, the genetic mechanism underlying this unique growth habit in Lombardy poplar has not yet been elucidated. In this study, we identified a nonsense mutation in the PnTAC1-1 gene of Lombardy poplar, an ortholog of the rice TILLER ANGLE CONTROL 1 (TAC1) gene known to regulate branch angles. To confirm the functional role of TAC1 in regulating tree architecture, we generated transgenic hybrid poplar (Populus alba × Populus glandulosa, clone BH) with targeted mutations in TAC1 homologues using CRISPR/Cas9 gene editing. The resulting TAC1-CRISPR hybrid poplars exhibited a stable upright branching phenotype closely resembling that of Lombardy poplar, as confirmed by two consecutive years of living modified organism (LMO) field trials. Anatomical analysis revealed increased cell elongation specifically in the lower petiole region and significantly enhanced gravitropic responses in TAC1-CRISPR hybrid poplars compared to wild-type BH clones. RNA sequencing analysis further demonstrated that TAC1 disruption triggered extensive transcriptomic reprogramming of axillary meristem, notably altering hormonal and photomorphogenic signalling pathways, which redirected auxin accumulation toward the abaxial region and increased gibberellin biosynthesis, ultimately promoting upright growth. This research uncovers the genetic and molecular mechanisms behind columnar growth in poplar and provides a promising approach for engineering tree architecture to enhance planting density, harvest efficiency and woody biomass productivity.

Abstract Background and Aims Chronic kidney disease (CKD) is a progressive condition characterized by the gradual decline of kidney function, often associated with comorbidities such as diabetes, hypertension, and cardiovascular disease. CKD can progress from mild impairment (early stages) to severe kidney damage, ultimately leading to end-stage kidney disease (ESKD), which requires dialysis or transplantation. Early identification of patients at high risk of progression is critical to enable timely interventions and improve clinical outcomes. Current methods of identification include urinary albumin testing which can represent a significant barrier to patient screening and leads to underestimation of risk of CKD progression in the general population. The aim of this study is to develop a non-invasive risk prediction tool without albuminuria testing that identifies CKD patients at high risk of disease progression using routinely available laboratory data and patient characteristics. Implementing such a tool in clinical practice could facilitate earlier interventions, reduce the burden of ESKD, and improve patient quality of life. Method A prediction algorithm was developed using a survival model with a boosted tree architecture to estimate the probability of CKD progression. The model utilizes 11 commonly measured laboratory values and patient characteristics to predict the time to a progression event, defined as either a 40% decline in eGFR, ESRD, or other outcomes. The model was trained and validated using Optum's de-identified Clinformatics® Data Mart Database (CDM), a large real-world dataset. Patients aged 18–95 years with CKD stages 1–4 were included, provided baseline eGFR measurements were available. Patients with prior dialysis dependence or a history of kidney transplant were excluded. After applying these criteria, 534,662 patients were included in the analysis. The dataset was divided into three subsets: 60% for model training, 10% for hyperparameter tuning, and 30% for validation. Right-censoring was applied for events such as study completion, dropout, or kidney transplant failure. Results Diagnoses corresponding to inclusion, progression events, and censoring were identified using ICD-9 and ICD-10 codes. Covariate measurements were aggregated from data available up to 1 month after the date of the first CKD diagnosis. The prediction model achieved a ROC-AUC around 70% for identifying patients at risk of experiencing a progression event within 1 year, 2 years, 3 years, and 4 years, respectively. While precision and sensitivity varied across intervals, the model demonstrated the ability to achieve precision up to 80% and sensitivity up to 60% over the 3-year prediction period. Figure 1 illustrates Kaplan-Meier (KM) curves for patients in the CDM test subset population, stratified by risk score severity. Patients with higher risk scores demonstrated significantly increased risk of early disease progression, as reflected in the KM curves. Conclusion This study demonstrates the feasibility of identifying CKD patients at high risk of progression using data routinely collected in clinical practice without the need for albuminuria testing. The developed model has the potential to support earlier and more targeted clinical interventions, ensuring timely care for patients at greatest risk. By identifying high-risk individuals earlier, this approach could significantly improve patient outcomes by slowing disease progression, enhancing quality of life, and potentially extending life expectancy.

IntroductionUnderstanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon.MethodsSeventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots.ResultsHigher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates.DiscussionOur results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.

Abstract Tree cultivation provides food, raw materials, carbon sequestration, and many other ecosystem services. Developing innovative approaches for tree analysis to help optimize their management is crucial. Cherry trees provide numerous health and economic benefits, with Michigan home to 75% of the cherry trees grown in the United States. In this study, we investigated the coarse root architecture of tart cherry trees using non‐invasive imaging techniques to reconstruct their spatial distribution and extent. Roots from matured orchards in Michigan were imaged using ground‐penetrating radar (GPR) with an 800 MHz antenna. The processed radiograms were analyzed using MALA Vision software, through which a three‐dimensional cube was generated. Depth slices extracted from this cube were subsequently analyzed using convolutional neural networks—a novel approach employed to identify and extract root patterns from the imaging data. A nondestructive, controlled root experiment was conducted to validate and assess the detection capabilities of the GPR frequency employed. The findings from this experiment informed the image interpretation process used to reconstruct root geometry. Results indicated that the GPR could detect and reconstruct coarse roots with diameters as small as 4.3 cm. To establish an allometric relationship between root systems and canopy size, an unmanned aerial vehicle was utilized to estimate tree canopy dimensions. Comparative analysis revealed that the lateral extent of coarse roots was approximately 1.2 times larger than the canopy area. Finally, a separate experiment involving root proxies was developed to create a predictive model for root biomass, achieving an accuracy of 95%.

ABSTRACTGray jujube (Ziziphus jujuba Mill) is an important economic fruit crop in Xinjiang, China, whose fruit quality is regulated by complex interactions among tree architecture, physiological functions, and environmental factors. Based on 2 years of field experiments, we developed an interpretable artificial neural network model integrating 13 structural and physiological indicators to predict four quality parameters: vitamin C (VC), soluble sugar, titratable acid, and sugar‐acid ratio. The model architecture was optimized through Bayesian optimization, resulting in a 13–4–1/13–5–1 network structure with high prediction accuracy (R2 = 0.89–0.98). Biological interpretation of the connection weights revealed that the elongation of bearing shoots (1.2–3.1 cm/month) and SPAD values (33–41.5) were key drivers of VC accumulation, reflecting their roles in photosynthate transport and light‐harvesting efficiency. Canopy structural characteristics, particularly leaf inclination angles of 26°–34° combined with a direct beam transmittance of 0.32–0.43, were found to synergistically enhance sugar accumulation by optimizing light distribution while maintaining sufficient gas exchange. Furthermore, net photosynthetic rates exceeding 12 μmol·m−2·s−1 significantly reduced organic acid content, indicating a shift in carbon partitioning toward sugar synthesis. These findings demonstrate that the model successfully bridges computational analysis with biological processes, providing both a predictive tool and mechanistic insights for gray jujube quality management. The integration of architectural, physiological, and environmental parameters in this framework offers a comprehensive approach for precision cultivation of this important crop.

ABSTRACT This study evaluates the potential of low-altitude airborne laser scanning (ALS) and terrestrial laser scanning (TLS) for characterizing structural complexity in Southern Finland. Unlike species diversity, structural complexity reflects realized niche occupancy by describing how vegetation utilizes light, water, and space, providing key insights into ecosystem functioning. We analyzed 99 circular sample plots (r = 20 m) scanned with helicopter-borne ALS at 80 m altitude and TLS data from nine scan locations per plot. Structural complexity metrics were derived at both grid level (variability in canopy height models and voxel occupancy) and object level (variability in individual tree attributes). High-density ALS effectively captured vertical and horizontal complexity through object-level analysis, showing close agreement with TLS. However, differences in measurement geometry affected volumetric complexity, with ALS and TLS characterizing tree architecture and vegetation occupancy differently. Object-level approaches captured a broader range of horizontal and vertical complexity, while grid-level approaches better captured volumetric variability, facilitating the identification of forest stand properties and biodiversity hotspots. The strongest agreement between ALS and TLS occurred for variation in tree height (R² = 0.66, Spearman = 0.80), while lowest agreement was found for fractal dimensions of tree architecture (R² = 0.04, Spearman = 0.25).

Mango (Mangifera indica L.), a highly valued tropical fruit, faces challenges in productivity due to the use of non-descriptive rootstocks and large tree architecture. To address this, a field experiment was conducted at ICAR-IARI, New Delhi (2021-2024), using Olour as rootstock and scion, with three grafting combinations: without interstock, with Amrapali interstock, and with Mallika interstock. The study aimed to evaluate their effects on morpho-physio-chemical traits, leaf and soil nutrient content, and anatomical parameters. The results revealed significant differences in plant performance based on the treatment combinations. The Olour/Mallika/Olour combination showed the highest leaf width (3.71 cm),intercellular CO2 concentration (356.20μmole m-2 s-1), net photosynthetic rate (8.51 μmole m-2 s-1), leaf total soluble protein (4.34 mg/g FW), leaf total sugars (119.05 mg/g FW), total chlorophyll (4.04 mg/g FW), total carotenoid (0.22 mg/g FW) and stomatal density (746.00 mm-2) and lowest apical bud phenols (1014.31 mg/100 g) and leaf proline content (0.36 µg g-1 FW). Conversely, the Olour/Amrapali/Olour combination exhibited lowest rootstock girth(7.11 mm), scion girth (4.32 mm), leaf fresh weight (1.26 g), leaf dry weight (0.40 g), leaf net photosynthesis (3.50 μmole m-2 s-1), leaf total soluble protein (1.25 mg/g FW), total chlorophyll (1.65 mg/g FW), total carotenoid (0.13 mg/g FW) and stomatal density (380.75 mm-2) and demonstrated higher proline (1.06 µg g-1 FW) and apical bud phenols (3067.53 mg/100 g)indicating dwarfing potential. Among the single-graft combinations, Amrapali/Olour exhibited moderate vigour and nutrient content, while the Mallika/Olour combination maintained high stomatal conductance and favourable growth traits. These findings confirm that both interstock and direct scion-rootstock combinations significantly influence plant physiology and nutrient dynamics. Anatomically, stomatal density and the complexity of the area were also significantly affected by the choice of interstock. Overall, these findings highlight the important role of interstocks in modifying plant vigour, physiology, and nutrient acquisition. Future studies are needed to assess the long-term field performance of these combinations under various agro-climatic conditions.

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding.

Abstract Key message We introduce an approach to studying partial crown dieback that accounts for height profiles of bark thickness and stem vulnerability to fire, resulting in improved modeling of biomass loss. Abstract Fire mediates tree cover in savannas by causing topkill, typically represented as a binary process in which the whole stem either survives or dies, overlooking losses of foliage and branches from partial canopy dieback. To overcome this limitation, we introduce an approach that focuses on conditional probabilities of dieback of stems and branches, which we demonstrate with a Brazilian savanna tree. We quantified the probability of branch death as a function of bark thickness and height above ground, to parameterize a model of tree architecture for simulating aerial biomass losses under scenarios of differing fire intensity, maximum tree height, and investment in bark. The study population experienced a 43% loss of stem biomass when exposed to a prescribed fire, but the traditional all-or-nothing approach that ignores partial dieback accounts for only half of this loss. Simulations show that, in absolute terms, the traditional approach more substantially underestimates carbon losses in severe fires, but in relative terms, the underestimation is greater in mild fires. A benefit–cost analysis revealed that the observed investment in bark more closely matches the predicted optimal investment when we account for partial dieback. In scenarios of low fire intensity or taller tree stature, the model predicts lower investment in bark, compared to the default scenario. We introduce the concept of bark safety margin, which quantifies the relative protection afforded by bark in the main stem and branches. This study thus demonstrates the importance of considering partial stem dieback, in addition to offering a new approach for quantifying this dieback.

ABSTRACTAfrican elephants (Loxodonta africana), in conjunction with the community of browser species, exert substantial top‐down control over the woody vegetation in savannas by utilizing large amounts of plant biomass, as well as through non‐consumptive effects. However, how much browsers affect the pattern of proportional growth between different tree components remains understudied. Using vegetation data collected in 2000–2001 and 2019 for more than 3500 trees inside and outside Madikwe Game Reserve, South Africa, we determined the long‐term effects of an increasing elephant population, in conjunction with the community of meso‐browsers, on structural relationships in 13 tree species. The number of trees utilized by elephants increased between 2000 and 2019, but individual trees were not more intensively utilized. After almost two decades of use by elephants, we observed a reduction in the logged initial growth rate of the structural relationship between tree height and stem diameter, without modification of the asymptotic change in growth rate. Despite species‐specific variability, tree height was overall reduced for a given stem diameter. Canopy area, as well as its structural relationship with stem diameter, remained mostly stable. We suggest that elephants are responsible for hedging by reducing tree height. Together with impala (Aepyceros melampus), the dominant species in this meso‐browser community, they could stimulate regrowth by browsing the canopy of the vegetation maintained in the browsing trap. Our study emphasizes the necessity of long‐term, species‐specific studies to improve our understanding of how the browser community, and elephants in particular, affect structural relationships in trees.

The security of public key cryptosystems has become a major concern due to recent developments in the field of quantum computing. Despite efforts to enhance defenses against quantum attacks, current methods are impractical due to safety and efficacy concerns. A recent study explores hash-based digital signature methods and evaluates their effectiveness using Merkle trees. Furthermore, novel approaches based on Verkle trees and vector commitments have been studied to reduce quantum threats. First, we introduce a post-quantum digital signature system that combines vector commitments based on lattices with Verkle trees. This architecture optimizes traditional Merkle tree architecture by preserving resistance to quantum attacks while improving cryptographic proofs. Second, in order to ensure secure initial seed generation without sacrificing operational viability, we create a hybrid random number generation framework that combines quantum random number generation (QRNG) with pseudorandom approaches. We provide a detailed analysis of generating random numbers in our article, which makes it easier to build a post quantum cryptosystem that uses our generator to provide initial random values. Our system is notable for its robust security against quantum threats, speed, and efficiency.

Saturating allometric relationships reveal how wood density shapes global tree architecture

Labor shortages threaten global apple production, thereby encouraging new strategies to improve orchard management. The growth of columnar apples, controlled by the MdDOX-Co gene, enables vertical growth with minimal lateral branching, allowing for high-density planting and easier harvesting. MdDOX-Co encodes 2-oxoglutarate-dependent dioxygenase (2ODD, DOX). This study aimed to identify selective chemical inhibitors of MdDOX-Co. We synthesized the parental C6-based analogs featuring a heterocyclic 1,3,4-oxathiazol-2-one ring and evaluated their inhibitory activity. Compounds retaining the 1,3,4-oxathiazol-2-one core exhibited strong in vitro inhibition and promoted seedling elongation in MdDOX-Co overexpressing Arabidopsis. Structure-activity analysis confirmed that the 1,3,4-oxathiazol-2-one ring was essential, with tolerance for side-chain variations, including bulky groups. Selectivity assays indicated minimal off-target effects on the related 2ODD enzymes. Molecular modeling suggested the compatibility of the lead compounds with the MdDOX-Co active site. These findings encourage us to develop MdDOX-Co-targeted agrochemicals to chemically regulate tree architecture and enhance productivity during apple cultivation.

The survey aimed to examine the factors influencing local tourists’ Willingness-To-Pay (WTP) for the ecopark, focusing on tree crown architecture and the georeferencing of indigenous trees. The study was conducted at the Ecotrail and Reservation Site of Indigenous Species (ECOTRIS) at Ifugao State University. The results revealed that ECOTRIS entails a significant consumption cost, as 90% of the tourists surveyed expressed a willingness to pay for its management. Among these, 72.2% were willing to pay ₱10, 18.5% ₱15, and 9.3% ₱20.The majority of respondents (69%) were aged 15–25 years, while 21% were 26–39 years old. The WTP results showed that ecological enhancements and approval of existing facilities were statistically significant factors. Regarding trail preferences, concreted trails were favored over unconcreted ones, with land use, situational features, and natural obstacles also influencing tourists’ willingness to pay and satisfaction with the facilities. The study found that ECOTRIS is predominantly visited by younger male tourists and university students, rather than older individuals, women, or high school students, with significant statistical differences observed among these groups. Additionally, the tree crown architecture of native trees was perceived to have higher aesthetic value compared to non-native or exotic species. Non-native species were noted to be potentially invasive, often outcompeting native trees for sunlight in forest stands.

Hybrid Transactional/Analytical Processing (HTAP) systems, which enable real-time analytics on live transactional data, can benefit significantly from blockchain technology. Incorporating features like immutability, provenance tracking, and tamper-proof storage becomes crucial as data volumes grow and systems become more decentralized. However, challenges such as high latency, limited support for range and authenticated queries, and the lack of adaptive optimization strategies persist in blockchain-based HTAP systems. Recent advancements focus on Merkle tree architectures, optimization algorithms, and hybrid indexing strategies to address these issues. Notable developments include query authentication methods, Verkle Trees, and Multi-State Merkle Patricia Tries. Additionally, bio-inspired metaheuristics, such as the Giant Armadillo Optimization (GAO), are gaining traction in overcoming performance bottlenecks. This review highlights the evolving landscape of query processing techniques and outlines future directions to enhance scalability, flexibility, and multi-query handling in blockchain-integrated HTAP platforms.

HIGH-SPEED MULTIPLIER DESIGN BASED ON AN OPTIMIZED PARALLEL PREFIX TREE ARCHITECTURE

Blockchain technology relies heavily on cryptographic hashing for ensuring data integrity, transaction security, and tamper resistance. However, with increasing data volume and demand for real-time processing, traditional hashing mechanisms often struggle to balance speed and security. This paper proposes and evaluates a novel Hybrid Hash Tree architecture that integrates multiple hash algorithms to optimize both performance and cryptographic strength. By combining the efficiency of lightweight hash functions with the robustness of secure hashing algorithms, the hybrid model aims to accelerate block validation while maintaining high levels of security. Through experimental analysis and performance benchmarking, the study compares various algorithmic combinations to identify optimal configurations suitable for cloud-integrated blockchain environments. The results demonstrate that the proposed hybrid approach significantly enhances transaction speed without compromising security, offering a scalable solution for next-generation blockchain systems.

Enhancing energy efficiency and operational reliability is crucial in power plant management, particularly for high-energy-consuming machines such as boiler feed water pumps (BFPs). These pumps play a vital role in the continuous generation of steam and electricity and must operate 24/7 to maintain power production stability. This study proposes the development of predictive models based on machine learning and deep learning techniques to accurately predict energy consumption and applies best models to detect anomalous behaviors in BFPs, enabling timely and preventive interventions. A dataset comprising 43,082 hourly records over five years, with 18 critical operational features, was analyzed using preprocessing and feature engineering techniques. Various predictive models were trained and evaluated, including Multiple Linear Regression, Regularized Regressions (Ridge, Lasso, ElasticNet), Support Vector Regression (SVR), Decision Tree, Ensemble Methods (Random Forest, XGBoost, CatBoost, LightGBM), and Deep Learning Architectures (DNN, RNN, GRU, LSTM). Among these models, SVR demonstrated the highest accuracy (MSE: 13.5573, R²: 0.9838), followed closely by LightGBM. Feature importance analysis revealed that boiler feed pump discharge pressure and bearing housing vibration levels were the most influential variables in energy consumption prediction. Anomaly detection using the Interquartile Range (IQR) method classified deviations into two warning levels, enabling proactive maintenance strategies. Additionally, a Graphical User Interface (GUI) web application was developed for real-time monitoring, integrating predictive models, anomaly detection, and an automated email alert system to assist operators in responding to abnormal energy consumption events promptly. These results highlight the potential of predictive analytics and real-time monitoring in optimizing power plant operations, providing a foundation for extending predictive capabilities to other critical energy-intensive systems.

Tree architecture, defined as the arrangement in space of the elements above the ground, is closely related to the biological and physiological processes of the tree. In particular, the quantitative study of the branch consists of classifying branches into orders, estimating lengths, insertion angles, diameters and volumes. In the case of the olive tree (Olea europaea L.), the knowledge of its architecture is important to determine which varieties are suitable for high-density planting systems and to guide canopy pruning, which allow simplification of field management and reduction of costs. Up to date, measurements are mainly done manually, using measuring tape and caliper, with high time expense and operator-related uncertainty. In this study, the analysis is extended to the three-dimensional case using photogrammetry. Next, the point cloud is processed using a modified version of the open-source code TreeQSM (version 2.4.1). Moreover, a new methodology, based on photogrammetry and branch segmentation using TreeQSM, is proposed to measure not only average branch diameter, but also node diameter and internodal distance along the principal axis of the twig point cloud. The main characteristics of the principal axis of the twig are obtained to prove the validity of the proposed method. The node average diameter is 2.94 mm with a standard deviation of 1.20 mm while the average internode is 14.38 mm with a standard deviation of 7.78 mm.

Tree architecture is an important component of forest community dynamics - taller trees with larger crowns often outcompete their neighbors, but they are generally at higher risk of wind-induced damage. Yet, we know little about wind impacts on tree architecture in natural forest settings, especially in complex tropical forests. Here, we use airborne light detection and ranging (LiDAR) and 30 yr of forest inventory data in Puerto Rico to ask whether and how chronic winds alter tree architecture. We randomly sampled 124 canopy individuals of four dominant tree species (n = 22-39). For each individual, we measured slenderness (height/stem diameter) and crown area (m2) and evaluated whether exposure to chronic winds impacted architecture after accounting for topography (curvature, elevation, slope, and soil wetness) and neighborhood variables (crowding and previous hurricane damage). We then estimated the mechanical wind vulnerability of trees. Three of four species grew significantly shorter (2-4 m) and had smaller crown areas in sites exposed to chronic winds. A short-lived pioneer species, by contrast, showed no evidence of wind-induced changes. We found that three species' architectural acclimation to chronic winds resulted in reduced vulnerability. Our findings demonstrate that exposure to chronic, nonstorm winds can lead to architectural changes in tropical trees, reducing height and crown areas.