ABSTRACT Stand biomass in boreal forests has gained recognition for its crucial role in carbon cycling. While stand biomass is influenced by multiple factors, including stand age, tree species, and site fertility, stand age is often considered the most important factor. Most research has focused on conifer stands, with limited attention to foliage biomass and belowground biomass in peatland sites dominated by deciduous tree species. This study examined foliage and coarse root biomass allocation in three downy birch (Betula pubescens Ehrh.) stands on drained peatland sites in Central Finland. The stands represented three developmental classes: young (10 years), middle-aged (50 years), and mature (80 years). Results showed that foliage biomass ranged from 0.7 t ha−¹ to 6.6 t ha−¹¹, peaking in the middle-aged stand compared to the young and mature stands. Additionally, coarse root biomass in the 0–50 cm peat layer totaled 0.45 t ha−¹, 2.50 t ha−¹, and 1.10 t ha−¹ for the young, middle-aged, and mature stands, respectively, with 77% occurring in the top 20 cm layer. These findings enhance our understanding of biomass allocation patterns, particularly foliage biomass, over time and their impact on litter inputs into the soil.

Comprehensive evaluation of hybrid progeny of Catalpa tree species and selection of superior hybrid combinations

Aspen-associated soil microbiomes reveal different strategies for nitrogen acquisition across ecosystems in Mexico and Canada.

Species distribution patterns at range edges can reveal effects of landscape and climate changes which can drive population expansions or contractions. Southern flying squirrel (Glaucomys volans Linnaeus, 1758) populations are common in eastern United States and southeastern Canada, and extend westwardly into the Great Plains. Space-use patterns within their range interior are well documented, though it is unclear if patterns are similar along western range edges. We tracked 10 southern flying squirrels in southeastern Kansas, USA to estimate home-range sizes and patterns in nest-site and tree selection. Forest structure and composition at western range edges differ compared to forest habitat at range interiors, and we predicted differences in home-range sizes and nest-site and tree selection patterns in Kansas compared to previous research. Mean home-range size was 1.55 ha (95% minimum convex polygon) and 5.44 ha (95% kernel density), similar to some populations in the interior of their range. Greater tree genera richness and average tree diameter (DBH) increased probability of nest-site use. Non-hard mast species and greater tree DBH increased nest-tree selection probabilities. Maintaining forest diversity while allowing all tree species to reach maturity could increase available nesting habitat for southern flying squirrels on their western range periphery.

Abstract Key Message Stands stocked with European beech ( Fagus sylvatica L.), sessile oak ( Quercus petraea (Matt.) Liebl.), and Scots pine ( Pinus sylvestris L.) show distinct deep seepage patterns. An increasing importance of extreme summer precipitation contributing to deep seepage in the northeastern German lowlands was detected. Extreme summer precipitation events contributed 71% (pine), 22% (young oak), and 15% (beech) of the annual deep seepage. Adapted forest management may promote deep seepage caused by extreme summer precipitation and by precipitation during the winter half-year. Context To date, deep seepage and groundwater recharge in temperate lowland forests occured mainly during the winter half year, the only period in which precipitation exceeds potential evapotranspiration. The increasing occurrence of extreme summer precipitation events, however, has the potential to promote deep seepage during summer. Aims This study aims to quantify the deep seepage feed by extreme summer precipitation events, utilising three large-scale lysimeters below canopies of beech ( Fagus sylvatica L.), young oak ( Quercus petraea (Matt.) Liebl.), and pine ( Pinus sylvestris L.), respectively. Methods Using a seepage hydrograph separation method, we were able to identify two major types of deep seepage: slow deep seepage due to winter precipitation and rapid deep seepage due to extreme summer precipitation events. Results Our measurements attributed substantial portions of deep seepage to extreme summer precipitation events, with distinct differences among lysimeters related to tree species and stand structure. The highest ratio of deep seepage by extreme summer precipitation to annual deep seepage occurred below pine, whereas the highest quantities of deep seepage by extreme summer precipitation were found under young oak. Conclusion Rapid deep seepage due to an increase in extreme summer precipitation events could be the most important mechanism for recharging near-surface groundwater aquifers under pine forests in the northeastern Germany lowlands. Deep seepage may be influenced by the choice of tree species and stand structure.

Agarwood (Aquilaria malaccensis Lamk) is a tree species with high economic value due to its resin, widely used in the perfume, pharmaceutical, and religious industries. However, overexploitation and habitat degradation have led to a significant decline in its natural population. This study was conducted using a controlled experiment to evaluate the effects of various planting media on the growth of A. malaccensis seedlings. The experiment employed a Completely Randomized Design (CRD) with seven media combinations and three replications, using 84 seedlings in total. The results showed that the combination of topsoil, peat, and river sand yielded the highest survival rate (100%) and significantly better growth performance compared to other treatments, with average plant height of 28.7 cm, stem diameter of 0.29 cm, and 27.4 leaves. This medium supports optimal growth by combining organic nutrients (peat), structural stability (topsoil), and effective drainage (river sand). Other viable alternatives include peat + river sand or topsoil + peat, depending on local availability. The findings offer a practical, evidence-based model for sustainable agarwood cultivation and a replicable approach to ex-situ conservation. Keywords: Agarwood (Aquilaria malaccensis Lamk), ethnobotanical studies, planting media, conservation.

Abstract Global diversification of the structure and composition of artificial forests and partial or complete conversion to natural forests is an important task for improving long-term biodiversity and counteracting climate change. Larix kaempferi is a tree species used widely in forests throughout northeast Asia that plays an important role in converting artificial forests to mixed forests. However, the phylogenetic diversity (PD) of the species remains unclear. We investigated L. kaempferi forests formed in Gayasan National Park, South Korea, categorized the community types, and quantified species composition, PD, and phylogenetic community structure depending on the vegetation type. Furthermore, we explored the factors regulating biodiversity in L. kaempferi forests to provide insights for promoting forests with high structural diversity. We observed unique vegetation characteristics and community formation mechanisms depending on the local environment, with vegetation types located in valleys and at the bottom of slopes having the highest PD. We revealed how the structural properties and local conditions of forests affect phylogenetic community structure for each vegetation type, leading to competitive interactions and competitive exclusion. For all vegetation types, PD showed a gradually increasing trend with older stand age, but piecewise structural equation modeling analysis showed that topographic environmental factors are the main factors regulating PD. Our findings highlight the need to introduce customized management approaches suited to the characteristics of each community rather than using the same method for all communities. This approach is crucial because species composition, ecological properties, rate of succession, and surrounding environmental conditions differ between vegetation types. In addition, by presenting management strategies to improve biodiversity depending on vegetation type, we expanded existing knowledge on the conversion of artificial forests to mixed forests. Our study provides important insights into establishing strategies for managing artificial coniferous forests and the mechanisms of community formation with changes in species composition after forest creation.

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m3 (20.4%), 20.778 m3 (7.8%), 32.776 m3 (37.3%), and 4.825 m3 (26.3%), respectively. The model is executed within Maple, a symbolic algebra system.

Abstract Background Practitioners rely on sample-based estimates of street tree population characteristics when complete inventories are not feasible. Selecting a sample size is a primary consideration when implementing a sample-based inventory, as it involves a tradeoff between costs and data quality. Methods We used street tree inventory data from 16 municipalities in Indiana, USA, to assess how data quality improves with increasing sample size. Specifically, we conducted 1,000 random draws of street segments at increasing sample depths to observe how estimates improved for the number of total trees citywide, species richness, species diversity, and vulnerability to an invasive pest. Results Compared to previous research, our results indicate that a larger percent of sampled street segments is needed to achieve relative standard error values below the heuristic target of 10%. We also calculated reliability thresholds that showed the percent of street segments that would need to be inventoried to achieve estimates within a given margin of the true citywide value in 95% of random draws. Again, relatively large random samples were needed to reliably achieve accurate estimates of street tree characteristics, especially in smaller municipalities. Conclusions This study provides information that practitioners can consider when planning street tree sampling given the community’s size, capacity to inventory trees, and level of data quality needed for planning and management activities. In general, we suggest that municipalities may need to acquire larger samples than previously thought to achieve accurate estimates of citywide street tree characteristics, and smaller municipalities should conduct complete inventories when possible.

The study was intended to investigate the effects of parkland Mangifera indica and Ziziphus spina-christi trees species on the selected physicochemical properties of soil within and outside the canopy of the tree and at varying soil depths in Sofi district, Harari Region, Ethiopia. Accordingly, twelve trees isolated and nearly similar M. indica and Z. spina-christi trees in crop field growing on similar site conditions, management practices, canopy coverage and height were selected. Soil samples were collected from under the canopy of trees, edge of canopy, outside of the canopy and three soil depth; 0-20 cm, 20-40 cm and 40-60 cm for analysis of selected soil physicochemical properties. The result of the study revealed that lower soil bulk density was observed under trees canopy and surface soil of both tree species than open field and subsurface soil (p<0.01), While soil water content at FC, PWP and AWHC were significantly (p<0.01) higher on subsurface than surface soil and under canopy of trees than open fields. However, no significant differences were observed in the texture and pH of soil (p>0.05). Soil EC, SOC, SCS, TN, AP and CEC were significantly (p<0.01) higher under the trees canopy than open field and on the surface than subsurface soil for both trees species in sorghum field. Thus, the finding suggests that retaining these important trees species with proper management on farmland improve and maintain soil fertility and reduce chemical fertilizer amendments.

ABSTRACT Vegetation traits are essential indicators of plant health and ecosystem functioning, providing critical information for biodiversity assessment, forest management, and ecological modelling. This study investigates the variability of vegetation traits across spatial and seasonal scales in the floodplain forest of Lanžhot, Czech Republic, using in-situ leaf trait measurements, airborne CASI hyperspectral imagery, and Sentinel-2 satellite data. Our objectives were to: (i) compare the performance of various trait retrieval methods, including radiative transfer modelling (RTM), partial least squares regression (PLSR), and neural networks (NN); (ii) assess the temporal variability of functional traits from leaf- and canopy-level spectral retrievals; and (iii) evaluate the spatial variability of key vegetation traits derived from high-resolution CASI hyperspectral and Sentinel-2 data. In addition, we examined differences in trait dynamics between managed and unmanaged regions of floodplain forests. Our findings reveal substantial seasonal fluctuations in chlorophyll content (Cab), equivalent water thickness (Cw), dry matter content (Cm), and leaf area index (LAI), with distinct patterns across tree species. The results demonstrate the effectiveness of hyperspectral remote sensing in capturing forest trait dynamics and emphasize the relevance of both environmental drivers and management regimes in ecosystem monitoring. PLSR achieved the highest accuracy in retrieving leaf functional traits from leaf-level spectra, with R2 values of 0.84, 0.86, and 0.68 and NRMSE values of 0.20, 0.14, and 0.18 for Cab, Cw, and Cm, respectively. For canopy-level spectra, the NN model yielded the highest accuracy in Cab and LAI retrievals, with R2 > 0.5 across seasons for both CASI and Sentinel-2 data. By clearly showing how different methods influence trait estimates and by identifying key factors driving their spatial and temporal variability, this study provides valuable contributions to forest management strategies and ecological modelling, particularly in endangered floodplain forest ecosystems.

Understanding the nature of trees should provide an important step for improving the productivity of the components and designing agroforestry systems. The study was aimed to evaluate the impacts of parkland Christ's thorn and Mango trees species on the yield of sorghum within and outside the canopy of the tree in Harari Region Ethiopia. Accordingly, twelve trees (six for each) isolated and nearly similar Mango and Christ's thorn trees growing on more or less similar site conditions, management practices, canopy coverage and height were selected. Sorghum variety (bullo) which is dominantly grown around the study area was sown during rainy season along with existing both tree species in crop field. Three quadrates 1 m*1 m were laid out under, edge and outside the canopy of the trees in the sorghum farmland to assess the sorghum grain and biomass yield in parkland. The finding of the study also showed that the grain and biomass yield of sorghum were significantly (P<0.05) increased under canopy of Christ's thorn trees. Mango -sorghum interaction showed highly significant (p<0.01) reduction of sorghum biomass and grain yield under the tree canopy compared with open area. It can be concluded that the integration of trees particularly Mango with sorghum in parkland agroforestry should be promoted with effective tree crown management to reduce crop shading, particularly near the tree where the shading effect is high.

Short heat treatments are commonly used to estimate the leaf-level thermal tolerance of tree species. The effect of varying treatment duration has recently been challenged to have significant effects on the estimates of thermal tolerance thresholds and how it reflects the development of leaf necrosis. Here, we aim to elucidate the effect of treatment duration on the thermal tolerance estimates of three Mediterranean oak species. We applied heat treatments with varying treatment durations (15, 30, 60, 120 and 240 min) to excised leaves. The temperature response of the leaf maximum quantum yield of photosystem II (Fv/Fm) was measured immediately, 24 h, and 2 weeks after the treatment. Necrotic leaf damage was visually assessed 2 weeks after the treatment. Both the intensity of decline in Fv/Fm and the development of necrotic tissue due to longer heat treatments were described by a logistic function. The longer the heat treatment lasted, the lower was the required temperature to induce irreversible damage to the photosystems and to reach a certain degree of visible damage to the leaf tissue. Fv/Fm measurements carried out 24 h after the treatment predicted well the development of necrotic tissue. Leaf-level thermal tolerance of Mediterranean oak species depends largely on the exposure duration to heat. The heat duration response follows a predictable logarithmic relationship, which allows for modelling the potential heat damage during heatwaves and forecasting climate change responses of Mediterranean forests.

Ecological restoration in protected areas plays a key role in halting biodiversity loss, particularly within the context of global and national biodiversity targets. This study evaluates the success of a restoration project implemented in the buffer zone of the Special Nature Reserve “Ludaš Lake” and Nature Park “Palić”, encompassing wetland and Pannonian forest-steppe habitats, potentially contributing to the resilience of the landscape in the face of ongoing climate change. The goal was to restore degraded habitats using functionally diverse native plant species to enhance ecological stability. This study evaluated the effectiveness of the initial restoration phase, focusing on a 3-year assessment of the survival and establishment of 6,310 planted native tree and shrub seedlings. The monitoring of the initial restoration phase evaluated the performance of 29 planted native species. It also aimed to identify challenges in planning and implementation and to determine which species show the best adaptation to local conditions. Climate projections, a dynamic soil–vegetation model, and the Habitat Suitability Index were used to evaluate species’ future persistence. Results from the initial monitoring phase (3-year period), indicate that Acer campestre, Acer tataricum, Ulmus minor, Prunus spinosa, Prunus tenella, Viburnum lantana , and Ligustrum vulgare exhibited the highest survival rates. According to projections, shrub communities will dominate, with a limited persistence of tree species. The restoration approach presented in this study supports climate-adapted species selection and provides practical guidance for planning restoration in ecologically sensitive and climatically vulnerable areas.

Gene trees play an important role in various areas of phylogenomics. However, their reconstruction often relies on limited-length sequences and may not account for complex evolutionary events, such as gene duplications, losses, or incomplete lineage sorting (ILS), which are not modeled by standard phylogenetic methods. To address these challenges, it is common to first infer gene trees using fast algorithms for conventional models, then refine them through species tree-aware correction methods. Recently, it has been argued that such corrections can lead to overfitting and force gene trees to resemble the species tree, thereby obscuring genuine gene-level variation caused by ILS. In this paper, we challenge and refute this hypothesis, and we demonstrate that, when applied carefully, correction methods can offer significant benefits, even in the presence of ILS.

Abstract Deep learning methods such as Mask R‑CNN enable the precise delineation of single-tree crowns from remote sensing data. However, their segmentation performance still depends on local stand conditions. Using UAV multispectral imagery and lidar canopy height models (CHM), we assessed the influence of tree species composition, stand density, and foliage condition on the robustness of deep-learning-based single-tree segmentation. High-resolution laser data and multispectral data were collected over several hectares of forest area (Bavarian Forest National Park; DBU Natural Heritage, Schönau Foundation; Black Forest National Park, Kinzigtal) using a DJI 600 Pro drone. The Fraunhofer Lightweight Airborne Profiler collected a multispectral point cloud using a 905-nm laser and two integrated RGB cameras with 4112 × 3008 pixels. Another multispectral camera captured RGB imagery with 4112 × 3008 pixels and two monochrome bands (725 nm RE, 850 nm NIR; 2164 × 2056 pixels each). Flights were conducted at 80 m altitude with ≥ 50% lateral overlap, resulting in an average point density of 150 points/m 2 . Different models were trained and validated using multispectral images (RGB, CIR), images derived from the CHM, and images fused from the CHM and two near-infrared channels (RE, NIR). Highly accurate tree positions and manually processed tree segments were available for accuracy analyses. When the best-performing CHM channel combination was used, the average F1 scores across the three study areas were 70% (range: 36–100%). In the Bavarian Forest, the highest F1 score was 82%, surpassing that obtained with baseline methods by up to 39%. In the Black Forest, the highest F1 score was 85%, but it was > 50% lower in complex deciduous plots. The gains in the DBU Schönau Foundation area were smaller, with F1 scores up to 90%, about 20% above baseline. Multispectral channel combinations, such as CIR or CHM with two IR bands (725 nm, 850 nm), contributed only marginally to tree segmentation. The accuracy in coniferous areas reached 81%, which was about 20% higher than in deciduous stands, although this was influenced by the high stem densities (1000 stems per hectare). In a single reference plot, the results improved substantially under leaf-off conditions. Polygons from delineated tree crowns were of significantly better quality than those from baseline methods. Overall, this study demonstrated the superiority of deep-learning-based tree segmentation in complex, dense forest structures.

Coast redwood (Sequoia sempervirens) is a uniquely-tall and long-lived tree species that occupies a narrow fog-belt along the Pacific coast of North America. Despite its ecological and conservation significance, existing maps of redwood distribution remain limited in spatial resolution, accuracy, and timeliness. In this study, we present an updatable and field-validated distribution map of S. sempervirens across its entire native range—from southwestern Oregon to central California—developed using freely available Sentinel-2 multispectral and Sentinel-1 SAR data. We compiled a georeferenced canopy classification dataset of 960 points, combining field surveys conducted in October 2024 with field-based ground truth points collected in 2017 from a prior mapping study, and externally sourced field-based redwood presence records. This dataset was used to train machine learning models (Random Forest and Gradient Boosted Trees) within a cloud computing framework to classify redwood presence and absence at 10 m spatial resolution. Binary classification models achieved high predictive performance, with the best model yielding over 88% overall accuracy and an AUC of 0.92 on a 30% hold-out validation set. Ten-fold cross-validation further confirmed model consistency (TPR = 0.89; FPR = 0.17). To assess geographic transferability, spatial block cross-validation yielded a mean overall accuracy of 0.78, demonstrating robust performance across the species' range despite reduced accuracy in under-sampled southern regions. A secondary multi-class model differentiated between redwood-dominated and mixed-conifer forest types, achieving an overall accuracy of 73.82%. Comparison with previous redwood distribution datasets revealed substantial agreement but also significant discrepancies, with the new model suggesting redwood presences in previously unmapped redwood fragments and absences in locations previously mapped as redwood. Validation against field data suggested higher accuracy in the new map. The resulting range-wide redwood map offers a current, field-validated, and updateable platform for conservation planning, habitat monitoring, and ecological research. It also establishes a high-confidence baseline for tracking redwood distribution dynamics under ongoing climate and land-use change.

Tree canopies in urban areas have many important ecosystem functions, and councils have targets to increase urban tree canopy area, which has proved challenging. Urban centres have large areas of impervious surfaces, and there is a perception that impervious surfaces are harmful for urban tree growth as water cannot penetrate the soil in which trees are growing. We investigated tree crown growth of young trees of eight common urban tree species in a suburb of Melbourne, Australia, that either grew in streets that were impacted by impervious surfaces or in parks. Four tree species had shown sensitivity to rainfall by lower crown growth in a low-rainfall suburb before, while the other four species had similar crown growth in high- and low-rainfall suburbs. We identified 40 trees per species and location (street vs. park) that were planted between 2009 and 2011 and measured their tree crown area in 2014 and again in 2018 using remote-sensed images. Trees that grew in streets that were impacted by impervious surfaces had larger crowns in 2014 than trees in parks, but both showed similar crown growth rates of 2.3 m2 yr−1 in the four-year period. Only one species (Eucalyptus sideroxylon A.Cunn ex Woolls) had a statistically significant greater relative crown growth rate in parks compared to streets. There was no statistically significant difference in the relative or absolute crown growth rates in tree species that have shown a previous sensitivity to rainfall compared to those that were insensitive to rainfall. Our data indicate that impervious surfaces had no detrimental impact on tree crown growth. It is possible that trees grown in streets have sufficient water resources and may benefit from the lack of competition for water, whereas trees in parks must compete for water resources with other plants.

Global climate change is a major challenge for forestry because it fundamentally alters the growth conditions of tree species. Sycamore maple ( Acer pseudoplatanus L.), often overlooked in forest management, is increasingly recognized as a valuable species for sustainable forestry in Central Europe. This tree species offers a unique combination of high economic and ecological value, characterized by rapid juvenile growth, abundant natural regeneration, and adaptability to diverse environmental conditions, including mountainous regions. Its contribution to biodiversity, nutrient cycling and soil stabilization underlines its ecological importance. Despite its many advantages, sycamore maple faces significant threats from global climate change, such as prolonged drought and increased susceptibility to pathogens. Nevertheless, its genetic diversity and phenotypic plasticity allow it to thrive in diverse habitats, including areas affected by human activities. This review synthesizes current knowledge of the species distribution, site requirements, silviculture, and associated threats and provides an economic evaluation of sycamore maple wood assortments. Its wood is highly valued for its exceptional versatility and quality, underlining its economic attractiveness. To maximize its potential, effective forest management practices are essential. These include strategies such as establishing mixed stands and implementing careful regeneration techniques to ensure the species’ resilience in the face of climate change. By incorporating ecological, economic, and climate-resilience perspectives, this review demonstrates the vital role of sycamore maple in sustainable forestry and biodiversity conservation in Central Europe. The results highlight its ecological and adaptive capacity and economic viability as a resource for future forest ecosystems.

Abstract Scots pine ( Pinus sylvestris L.) is widely distributed, phenotypically plastic forest tree species with modest ecological demands, therefore it is a very suitable, drought tolerant species for afforestation at present. This is especially important given Europe’s changing climate, with rising extremes and unpredictable rainfall challenging forest regeneration. Drought resistance of seedlings is essential for their survival during current reforestation efforts, however, its relation to ecotypic variation is yet not well understood. The objective of this study was to investigate the response of seedlings from two Czech Scots pine ecotypes (upland and lowland), exposed to water deficit at the beginning of the vegetative season – a critical period for successful afforestation from the perspective of precipitation availability. During a greenhouse experiment with nursery pre-grown seedlings, terminal shoot length and selected leaf functional traits (leaf mass per area; water and pigment contents; needle anatomy), chlorophyll fluorescence kinetics and seedling reflectance were monitored during ten-week irrigation reduction and after rewatering. The photochemical reflectance index (PRI) and the red edge position (REP) were calculated from spectral reflectance to distinguish differently treated seedlings. The lowland ecotype grew faster under control but suffered stronger growth reduction and higher mortality under drought. In contrast, across all recorded responses, the upland ecotype responds more consistently to changes in water availability, does not reduce terminal growth, accumulates less biomass and exhibits lower mortality. In general, for terminal growth, there was a significant effect of treatment and also an interaction of treatment and ecotype during the recovery period, unlike the drought period. REP was responsive in recovery period for upland ecotype while PRI showed no consistent drought-related pattern. Our results, in agreement with the fluorescence-based indicators, suggest that current-year needles are more suitable for drought stress detection using spectral indices. The upland ecotype showed several functional traits corresponding to better resilience to drought stress compared to the lowland ecotype. Understanding drought stress and recovery responses via effective leaf functional traits will help forest management to select suitable ecotypes for reforestation, ensuring a higher survival under changing climatic conditions.