Climate change is increasing the frequency and intensity of summer heatwaves, posing a major threat to temperate forests. European beech (Fagus sylvatica) forests are particularly sensitive to thermal extremes. Given their wide distribution, responses to heatwaves are not uniform, and it remains unclear how sensitivity varies at the continental scale. This study aims to quantify how summer disruptive heatwaves (dHWs) reshape phenological dynamics in European beech-dominated forests (BDFs) over 2003-2023 across four major biogeographical regions: Alpine, Atlantic, Continental and Mediterranean. By combining remotely sensed vegetation time series with machine-learning and anomaly detection approaches, we specifically (i) identified the occurrence of summer dHWs across European BDFs; (ii) quantified the impacts of dHWs both on summer canopy functioning (short-term effect) and on autumn senescence phenology (lagged effect), and (iii) evaluated these phenological responses under different dHW intensity levels. Results revealed that dHWs in BDFs consistently reduced summer canopy greenness and advanced autumn senescence, with distinct regional patterns. The most pronounced dHW impacts were observed in Continental and Atlantic regions. Graded, intensity-dependent responses emerged in Atlantic, Continental and Alpine regions, with the latter showing a comparatively buffered response, likely due to a cooler climate baseline and shorter vegetative phase. By contrast, the Mediterranean region exhibited a threshold-type response, reflecting adaptation mechanisms of southern-edge populations under already limiting summer conditions. Late-summer and early-autumn emerged as the most sensitive time windows for dHW-induced phenological shifts, with temporal patterns varying along the European BDF climatic gradient. Together, these results demonstrate that dHWs drive both immediate and legacy impacts on forest vegetative dynamics, reducing seasonal productivity and advancing the end of the growing season, with intensity effects modulated by regional climate and baseline phenology. Our findings underscore the need for region-specific strategies to safeguard forest ecosystems as disruptive heatwaves continue to intensify.

This paper analyzes the seasonal and annual patterns of precipitation and temperature in the Castelporziano Nature Reserve from 1980. It also considers an index that combines precipitation and evapotranspiration. The results indicate various patterns within this small region, primarily influenced by the distance from the coast. Additionally, there is a clear upward trend in temperature. The joint analysis of precipitation and temperature shows that the most recent years have been characterized as either the hottest and driest or the hottest and most humid. Furthermore, there has been a significant increase in tropical nights and a longer duration of warm spells for maximum temperatures.

Masting, the synchronised interannual variation in seed production, shapes forest regeneration and many ecosystem processes, yet process-based models remain underdeveloped. Here we introduce MASTHING (MASting THeory modellING), an individual-tree model coupling phenology, carbon gain, resource storage, temperature cues, and environmental vetoes on reproduction. We parameterised MASTHING for European beech (Fagus sylvatica L.) using 43 years of data from 100 trees across 11 sites in England to test alternative hypotheses about masting mechanisms. Three formulations were compared: resource budget only (RB), and extensions with additive (RB+WC) and interactive (RB×WC) temperature cue effects. Performance improved with complexity and was highest when cue sensitivity depended on internal resource status. At the site-year scale, explained variance increased from R2 = 0.58 (RB) to R2 = 0.76 (RB×WC), supporting that favourable cues trigger strong reproduction only when sufficient resources have accumulated. The model reproduced mast and failure years, partly captured long-term breakdown in masting intensity under climate warming, and broadly predicted seed production during 2023-2025, although low-seed years were less well predicted. Modelled resource and cue dynamics were sufficient to simulate short-term variation and progressive weakening of reproductive pulses. MASTHING provides a platform for testing masting hypotheses, evaluating climate change impacts, and supporting operational seed forecasting.

Soil respiration (SR) is a major component of carbon fluxes in forest ecosystems, encompassing contributions from both heterotrophic and autotrophic respiration. In this study, we analysed two years of continuous CO₂ flux measurements obtained using an automated chamber monitoring system to investigate the seasonal drivers of SR in an evergreen holm oak forest under a typical Mediterranean climate. Soil water content (SWC) thresholds, marking the transition from moisture- to temperature-limited conditions, were determined using a stepwise regression-based approach. The relative importance of each predictor controlling SR was quantified across the different phases defined by the SWC threshold. Soil moisture exerted a stronger control on SR than soil temperature. Including a SWC threshold to delineate distinct soil moisture phases improved model performance (R2 increased from 0.79 to 0.85).A complementary litter manipulation experiment was conducted to isolate the role of litter surface in modulating soil respiration and to quantify its contribution to soil CO₂ fluxes. Results showed that, under bare-soil condition, the control exerted by soil temperature on SR below the SWC threshold was further weakened.To further investigate the threshold effect on the temperature sensitivity of SR, we calculated Q10 values, which confirmed that under the SWC threshold, the response of SR to temperature is weak. These findings provide important insights for modelling soil respiration in Mediterranean ecosystems exposed to abrupt variations in soil moisture associated with seasonality and climate-change extreme events.

Climate change is significantly reshaping forest ecosystems, with vegetation phenology providing a sensitive indicator of these transformations. Understanding the spatial variability of forest phenological patterns and their environmental drivers is critical for anticipating ecosystem responses. Using a 21-year (2003–2023) MODIS Normalized Difference Vegetation Index (NDVI) time series, this study aims to identify groups of European beech (Fagus sylvatica) forests with similar seasonal timing (i.e., pheno-clusters) across Europe and quantify the role of climatic and geographical variables in their discrimination. Four distinct pheno-clusters were identified, capturing a gradient in seasonal amplitude and profile shape, from sharply peaked trajectories in colder, high-elevation environments to flatter seasonal profiles in warmer lowlands. Temperature emerged as the primary driver of cluster differentiation, with elevation acting as a key geographical constraint, while precipitation played a secondary role. Despite clear macroclimatic patterns, substantial local-scale variability highlighted the marked ecological plasticity of European beech forests. The strong association between pheno-clusters and European biogeographical regions further reflects region-specific climatic constraints on phenological dynamics. Multivariate statistical analyses confirmed the robustness of the classification and the significance of the underlying environmental gradients. Overall, these results suggest that phenology-based clusters may represent functional phenotypes associated with distinct environmental niches, providing a useful framework for linking spatially distant forests with shared seasonal phenology and for monitoring forest ecosystem responses to ongoing climate change.

Intra-stand variability in autumn leaf senescence is increasingly recognized as ecologically relevant, yet its structural and management-related drivers remain poorly understood. Here, we investigated whether crown architectural traits shape within-stand phenological dynamics in European beech (Fagus sylvatica L.) under contrasting management regimes. We combined multitemporal UAV-derived NDVI time series with LiDAR-based structural data collected during the autumn senescence seasons of 2024 and 2025 at two managed beech stands in the Alpe di Catenaia (Central Italy): one under thinned treatment (THT) and one under a seed-tree system (STS). Clustering of NDVI temporal profiles consistently identified two phenological groups per site and year, differing in senescence timing and rate, differently associated to architectural traits. Results revealed that crown area, crown volume, and crown insertion height were the structural traits most strongly associated with senescence timing in STS stand across both years. In contrast, THT stand showed an unstable and year-dependent structural configuration, indicating that thinning weakens the linkage between crown architecture and autumn phenology and increases sensitivity to inter-annual climate variability. Vertical crown asymmetry consistently explained residual within-cluster variability in STS, suggesting a hierarchically structured architectural control on canopy decline. These results demonstrate that seed-tree management preserves a robust, architecturally driven senescence diversity buffered against climatic fluctuations, while thinning promotes stand-level homogenization of senescence responses and greater climate contingency. Our findings highlight how silvicultural practices mediate the balance between structural and climatic controls on autumn phenology, with implications for forest resilience under increasing climate variability.

Accurate estimates of aboveground biomass (AGB) are essential for forest policies to reduce carbon emissions. Unmanned aerial laser scanning (UAV-LS) offers unprecedented millimetric detail but is underutilized in monitoring broadleaf Mediterranean forests compared to coniferous ones. This study aims to design and evaluate a procedure for AGB estimates based on the predictive power of crown features. In the first step, we manually created Quantitative Structure Models (QSMs) for 320 trees using data from UAV laser scanning (UAV-LS), airborne laser scanning (ALS), and co-registered terrestrial laser scanning (TLS). This provided the most accurate non-destructive estimate of aboveground biomass (AGB) in the absence of destructive measurements. For each reference tree we also measured crown projection and crown volume to build two separated models relating AGB to such crown features. In the second phase, we evaluated the potential of UAV-LS for quantifying AGB in a pure European beech (Fagus sylvatica) forest and compared it with traditional ALS estimates, using fully automatic procedures. The two obtained tree-level AGB models were then tested using three datasets derived from 35 sampling plots over the same study area: (a) 1130 trees manually segmented (phase-2 reference); (b) trees automatically extracted from ALS data; and (c) trees automatically extracted from UAV-LS data. Results demonstrate that detailed UAV-LS data improve model sensitivity compared to ALS data (RMSE = 45.6 Mg ha−1, RMSE% = 13.4%, R2 = 0.65, for the best ALS model; RMSE = 44.0 Mg ha−1, RMSE% = 12.9%, R2 = 0.67, for the best UAV-LS model), allowing for the detection of AGB differences even in quite homogenous forest structures. Overall, this study demonstrates the combined use of both laser scanner data can foster non-destructive and more precise AGB estimation than the use of only one, in forested areas across hectare scales (1 to 100 ha).

Climate change has a major impact on the current environment, with vegetation phenology being the earliest indicator of these effects. Long-term phenological observations, such as those provided by satellite remote sensing, are fundamental for understanding spatio-temporal forest dynamics. Normalized Difference Vegetation Index (NDVI) data represent a well-known proxy for monitoring forest productivity and detecting seasonal variations. The objectives of this work are to identify phenological clusters of beech forests, and to quantify the role of geographic and physiographic variables in the phenological timing of each cluster. The research focuses also on examining the influence of environmental variables on the mechanisms of phenological response to climate change. To this end, we used the EU-Forest dataset to derive the beech forest location across Europe. Then, for each location, NDVI data were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua sensors, from 2003 to 2023, with spatial resolution of 250 m and temporal frequency of 8 days. To identify groups of different forest types with similar seasonal timing (i.e., pheno-clusters), we carried out K-means Cluster Analysis on the NDVI temporal profiles. Finally, we characterized each pheno-cluster based on latitude, elevation, temperature, and precipitation, to identify gradients and discriminant environmental conditions. Results showed that the obtained pheno-clusters follow a clear elevation gradient, with a high variability at local scale even within the same macroclimatic conditions. This study indicates that characterizing vegetation phenology can provide valuable information about how forests ecosystems respond to both environmental conditions and climate change.

Vegetation phenology is closely linked to the functioning of multiple aspects of forest ecosystems and is regulated by a complex interaction between climatic and environmental factors. In particular, the end of the growing season has proven to be very sensitive to extreme weather events, leading to alterations in the regular physiological behaviour of forests. Autumn phenology represents a little-explored season due to the highly variable response of forests to environmental factors. This work aims to investigate late-season dynamics by comparing ground-based and satellite observations in European beech forests. The objectives of this research are: (i) quantify the temporal discrepancy between phenology obtained from ground-based observations (PEP725 stations) and satellite-derived data (MODIS EVI time series); (ii) assess the influence of the main biophysical factors, i.e. latitude, elevation, total annual precipitation and mean annual temperature, on the mismatch. The results identified key end-of-season metrics, distinguishing different stages during the season that were affected differently by biophysical factors, such as temperature and precipitations. This study highlights the complexity of late-season phenology, emphasizing the crucial role of remotely sensed phenometric analysis compared to ground-based observations, revealing a fundamental contribution to understanding of late-season phenology in the context of climate change.

Continuous monitoring of forest canopy structure and phenology is pivotal for the assessment of ecosystem responses to environmental variability and changes. The present study evaluated the use of repeat digital trail cameras as a low-cost, flexible, and accessible in situ monitoring solution for quantifying daily canopy attributes, including effective leaf area index (Le) and canopy cover. A trial camera monitoring network (CrowNet) was established encompassing 20 forest stands in Italy, under different management and environmental conditions, resulting in over 44,000 daily images collected over three years. We demonstrated that taking the mean daily canopy attribute allowed to obtain smooth time series from trail cameras, from which phenological transition dates can be inferred. Daily canopy attributes were validated against manual digital cover photography measurement. To further explore the applicability of this monitoring solution, we performed a comparison between daily Le time series derived from a subset of trail cameras located in beech forests and data collected by multitemporal UAV LiDAR. Results demonstrated the close agreement between the two methods across the entire phenological period (start and end of season). We also illustrated use of continuous trail camera estimates to calibrate a vegetation index (NDVI) to infer leaf area and canopy cover from optical multi-temporal UAV data. We further investigated use of trail camera to detect species-specific differences in tree phenology from time series acquired in a mixed oak-hornbeam forest. We found different canopy structure and phenological transition dates in three broadleaved species (oak, ash, hornbeam), supporting the effectiveness of trail cameras for species-oriented phenology monitoring. We conclude that trail cameras provide a reliable solution for daily canopy monitoring, offering a significant cost-effective and flexible alternative to traditional field methods and providing potential to calibrate, validate or integrate remotely-sensed information. However, camera failures during adverse weather, and the need for more efficient image data quality checking procedures, still represent open challenges. Future improvements, such as weatherproof housing and automated pre-processing screening procedures, are therefore recommended for making trail camera fully operational in ground canopy and phenology monitoring.

Leaf area index (LAI) is an important biophysical parameter describing vegetation. LAI is typically retrieved from optical remote sensing by empirical models relating LAI to vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). As the relationship between LAI and NDVI is non-linear and crop type dependant, several specific empirical equations relating LAI to NDVI have been developed using field data. This study presented LAIr, an R package to derive LAI from NDVI data from the most comprehensive library of conversion equations. In the package, the range of functions differs on environmental factors, sensors, and vegetation types, allowing flexibility in choosing appropriate options based on specific application, scale of investigation and data availability. We illustrated the use of the package with a case study to compare a generic LAI product with specific NDVI-based LAI estimations. By leveraging empirical knowledge, LAIr enables accurate and context-specific estimation of LAI. The deployment of an open-source R package serves as a valuable tool for aiding researchers in selecting the most appropriate equations for conducting NDVI-to-LAI conversion.

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Multifunctional forest management should provide the opportunity to create, conserve, modify or eliminate forest roads. Within protected areas, it is difficult to make a single assessment of the degree of accessibility to different forest areas, having to mediate among productive forestry, protection needs and other benefits deriving from forest stands. A GIS-based methodology, with the support of a Forest Information System (FIS) and available Forest Plans for the study area, were applied to create an accessibility map (based on the forest roads network) for the Abruzzo, Lazio, and Molise National Park (PNALM). Results were related to several FIS metadata, highlighting that accessibility in the study area was sufficient, but not optimal, in the productive management units, being rather poor in those where soil protection and biodiversity conservation are the main functions (only 38.8% of them were accessible). Forest roads density (28.5 m ha(-1)) was not homogeneously distributed within the study area and the ratio between forest road length (199.4 km) and planned forest surface (13,355.3 ha) is only 14.9 m ha(-1). In contrast to what is commonly found in forest accessibility works, the innovative element of this study was the involvement of PNALM's technical office in evaluating the results and exploring the opportunity to adopt a different policy for forest roads network management.

The distribution of species is primarily driven by the availability of trophic resources. In a given forest type, insects trophically related to the dominant tree are expected to be evenly distributed due to the abundance of their foodplant. However, their distribution is also influenced by complex relationships with abiotic and biotic parameters such as available space, predatory pressure, and morphometric traits. In this study, we investigated how the three-dimensional structure of space below the canopy may affect the composition of nocturnal lepidoptera communities. To synthesise the complexity of the dispersal behaviour of these insects, we evaluated easily measurable traits such as wingspan and the presence of tympanic organs, both connected to their mobility and thus potentially influenced by the structure of the available flight space. The study was conducted in the Sila National Park (Italy), where 12 sampling sites were selected in pine forests and an additional 12 in beech forests. Forest spatial structure was investigated using a portable terrestrial laser scanner. Moths were sampled monthly using light traps from May to October in both 2019 and 2020. Among measured forest traits, we observed that the space above three meters from the ground is the only factor influencing community composition. Larger species with tympanic organs prefer environments with less space below tree canopies. Our findings could be the starting point for future studies that investigate a potential defence strategy of moths against bats, as tympanate and larger species not only actively avoid chiropter predation but could also choose denser forests because of a lower bat activity. Moths' distribution and community composition thus appear to be significantly shaped by the spatial structure of forests.

Managing forests to sustain their diversity and functioning is a major challenge in a changing world. Despite the key role of understory vegetation in driving forest biodiversity, regeneration and functioning, few studies address the functional dimensions of understory vegetation response to silvicultural management. We assessed the influence of the silvicultural regimes on the functional diversity and redundancy of European forest understory. We gathered vascular plant abundance data from more than 2000 plots in European forests, each associated with one out of the five most widespread silvicultural regimes. We used generalized linear mixed models to assess the effect of different silvicultural regimes on understory functional diversity (Rao's quadratic entropy) and functional redundancy, while accounting for climate and soil conditions, and explored the reciprocal relationship between three diversity components (functional diversity, redundancy and dominance) across silvicultural regimes through a ternary diversity diagram. Intensive silvicultural regimes are associated with a decrease in functional diversity and an increase in functional redundancy, compared with unmanaged conditions. This means that although intensive management may buffer communities' functions against species or functional losses, it also limits the range of understory response to environmental changes. Policy implications. Different silvicultural regimes influence different facets of understory functional features. While unmanaged forests can be used as a reference to design silvicultural practices in compliance with biodiversity conservation targets, different silvicultural options should be balanced at landscape scale to sustain the multiple forest functions that human societies are increasingly demanding. Different silvicultural regimes influence different facets of understory functional features. While unmanaged forests can be used as a reference to design silvicultural practices in compliance with biodiversity conservation targets, different silvicultural options should be balanced at landscape scale to sustain the multiple forest functions that human societies are increasingly demanding.image

The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5–10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM1/3) and the corresponding standard deviation (SD1/3), while (ii) for the lower understory layer (2–5 m above ground), the most related metric is HM1/3 alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data.