A persistent challenge for models simulating carbon fluxes, such as gross primary productivity (GPP), is that the inter-annual variability (IAV) is currently not well-represented, often underestimating the peak GPP values, while also struggling with representing the onset and end of vegetation activity. We hypothesize that the difficulty with representing IAV can be attributed to temporally fixed model parameters, and yearly varying parameters can partially alleviate it. We test this hypothesis using two models: a simple light-use efficiency (LUE) model with response functions of solar radiation, air temperature, vapor pressure deficit, cloudiness, and soil water content, and an optimality-based model that includes parameter acclimation and drought stress. These functions have multiple parameters requiring calibration.First, we calibrated all the model parameters per site-year and found that both models can simulate annual GPP better with annually calibrated parameters (median normalized Nash-Sutcliffe efficiency, viz. NNSE: 0.74 for the LUE model) compared to parameter calibration per site (median NNSE: 0.5) or per plant functional types (median NNSE: 0.23). Thereafter, we focused on calibrating parameters of one environmental response function as year-specific (one function at a time), while simultaneously calibrating year-invariant parameters for all other functions. These exercises were conducted for 198 eddy-covariance sites. The ability to represent IAV of GPP in arid sites was substantially improved when hydrological parameters were allowed to vary between years, both for herbaceous and forest ecosystems. However, for tropical, temperate and boreal climates, improvements in IAV emerged from parametric variability controlling the GPP responses to temperature, light or atmospheric dryness. Given the paucity of arid and semi-arid sites in the dataset, allowing year-specific parameters for vapor pressure deficit and atmospheric CO2 effects yielded a median annual NNSE of 0.73 across the whole dataset for the LUE model. These results challenge our perception on temporally static parameterizations, reflecting the need to learn the empirical relationships between observations and temporally-varying parameters, or improve the representation of missing state variables. It further suggests that these may be strongly linked to below-ground plant dynamics, largely unobserved in current Earth observation networks.However, by analyzing mean absolute deviation of parameter values from per site and per site-year model calibrations, we found that temporal variation of parameters was lower than their spatial variation. For example, spatial variability of parameters, such as optimal temperature for photosynthesis, was 82.6% higher than temporal variability. Though we show that the temporal variability of model parameters is important to better capture the IAV of GPP flux, our analyses are currently limited to eddy-covariance sites, and only for the measurement periods at these sites.As a next step, further research is needed to explain or statistically learn the temporal variability of model parameters using environmental variables, which can be used to predict the spatiotemporal variability of model parameters at sites with no observational data or predict the future temporal trend of model parameters. This, in turn, will likely improve the performance of simulated IAV of GPP and, consequently, enhance our ability to represent unknown linkages between IAV and longer time scales.

In recent years, bark beetle outbreaks have become a significant threat to forest ecosystems in Central Europe, a trend exacerbated by climate change, creating favourable environmental conditions for pest proliferation. This has increased the interest in remote-sensing-based detection of bark beetle infestations. The objective of this study is to detect and reconstruct forest disturbances in a mountainous area situated in the Upper Puster Valley (South Tyrol, Italy) and quantify spatio-temporal patterns, with a special focus on bark-beetle-induced damages. The study area was severely impacted by the storm VAIA in autumn 2018 as well as widespread snow breakage in subsequent winters, causing large amounts of residual wood in the forests and providing brood material for bark beetles. Combined with warm and dry summer conditions, populations grew significantly. Using a Sentinel-2 satellite time series spanning from 2018 to 2024, we applied a parametric bi-temporal change detection approach based on linear regression. Validation of infestation data from 2021 to 2023 with a reference dataset yielded detection rates ranging from 52% to 92%. The analysis of elevation influence on damage patterns revealed significant differences between abiotic and biotic disturbances. The interannual clustering of forest disturbances was analysed based on bivariate join count statistics. The results indicate a strong spatial correlation between years for both disturbance types. The highest correlations were found for bark beetle infestations, clustering highly around previous disturbances, underlining the importance of understanding interannual spatial connectivity and implementing timely targeted management strategies to limit the spread of outbreaks.

Abstract Parametric uncertainty can hinder land surface models (LSM) from accurately simulating carbon fluxes, such as gross primary production (GPP). These models generally cannot capture inter–annual variability (IAV) of fluxes well due to missing processes, and temporally varying parameters can partially alleviate this limitation. We evaluated this assumption using two models: a light‐use efficiency (LUE) model with several environmental response functions, and an optimality‐based model that includes parameter acclimation and drought stress. De et al. (2025, https://doi.org/10.1029/2024MS004697) concluded that calibrating all parameters per site–year improves annual performance. As a follow‐up, we now inverted parameters of each environmental response function annually at a time, while simultaneously estimating year‐invariant optima for all other parameters, applying this across 198 eddy‐covariance sites. The IAV of GPP in arid sites was substantially improved when hydrological parameters varied annually, both for herbaceous and forest ecosystems. However, for tropical, temperate and boreal sites, IAV improved from annual variation of parameters controlling the GPP responses to temperature, light or atmospheric dryness. Given the paucity of arid and semi‐arid sites, allowing year‐specific parameters for vapor pressure deficit and atmospheric carbon dioxide effects yielded an overall median annual normalized Nash‐Sutcliffe efficiency of 0.733. Re‐evaluating some experiments from De et al. (2025, https://doi.org/10.1029/2024MS004697), we found that spatial variation of model parameters was higher than temporal variation, even though yearly varying parameters can improve annual performance. Our results challenge the existing perspective on temporally static parameterizations, reflecting the need to statistically learn temporal parameter variation from observations to improve IAV representation in LSMs.

The increased intensity and frequency of heatwaves, coupled with prolonged periods of drought, poses a significant threat to viticulture worldwide. Under these conditions, greater damage may occur when the leaf is also exposed to high radiation intensity. To better understand the impact of drought and high radiation exposure during heatwaves on grapevine physiology, we established a factorial experiment using well-watered or water-deficit Shiraz grapevines with different radiation exposure due to the row orientation. Given the East-West row orientation, the two sides faced directly north or south, receiving different radiation intensities. To monitor the impact of irrigation and radiation exposure on PSII functionality, leaf chlorophyll fluorescence was continuously monitored over a 20-day period on leaves on the north side and on the south side of the canopy of each plant, while leaf gas exchange measurements were performed on adjacent leaves. Water-deficit vines were maintained at a midday stem water potential (SWP) of –1.4 MPa, while well-watered plants had SWP of –0.8 MPa during the experiment. High radiation exposure was the dominant factor impairing PSII performance rather than heat or water stress alone. The north side leaves (N) showed lower maximum efficiency of PSII than the south side leaves (S) on hot days, especially when plants were water-stressed. S leaves had higher photochemical (Y(II)) and lower non-photochemical yields (Y(NPQ)) than N leaves, predominantly at midday. Water stress further decreased Y(II) and increased Y(NPQ) in N leaves, but not in S leaves. Leaf net assimilation, stomatal conductance, and transpiration were higher in N leaves compared to S leaves, notably pronounced in the well-watered plants than in the water-deficit ones. The coupling between stomatal conductance and assimilation showed similar pattern in water-deficit and well-watered vines in N leaves, while in S leaves, water-deficit plants showed lower changes in stomatal conductance compared to well-watered ones for the same increase in assimilation. These findings suggest that despite the positive impact of irrigation to sustain the canopy during heatwaves, additional management strategies (such as shade netting) may be required to reduce radiation exposure and to maintain leaf function during and following heatwaves.

Stomatal pores on plant leaves regulate the gain of carbon through photosynthesis and the loss of water through transpiration. Through their responses to environmental conditions, stomata can constrain plant productivity and transpiration fluxes, exerting a strong control on climate feedbacks over land. Although mechanistic modelling of stomata remains a challenge, semi-empirical and optimisation models have been successfully applied to improve the simulation of land-atmosphere fluxes of water and carbon. Optimisation approaches assume that some aspect of plant function, such as photosynthesis or growth rate, is optimised with respect to an environmental constraint, such as available soil water. Both optimisation models and semi-empirical models predict that stomatal conductance will increase in concert with rising photosynthetic rates as temperatures approach a thermal optimum, beyond which declines in both photosynthesis and stomatal conductance are expected. However, a growing number of experiments have found that while photosynthesis declines beyond its thermal optimum, stomatal conductance often continues to increase at high temperatures. Early modelling work suggests that this phenomenon can be captured and explained by an optimal thermoregulation strategy via increased evaporative cooling at the leaf surface. However, many stomatal conductance models that are embedded within climate and Earth System Models do not correctly account for this feedback and so cannot capture observed decoupling. Here, we demonstrate that if leaf temperature is calculated iteratively outside the optimisation scheme, as is commonly done in Earth System Models, stomatal decoupling will not be captured. However, by calculating leaf temperature in parallel with optimal stomatal conductance, we find that we are able to capture observed decoupling and improve predicted leaf temperature and gas exchange. Correctly implementing the leaf energy balance equation within stomatal optimisation models will be essential for capturing high temperature responses of forests across the globe.

Land surface phenology–the seasonal rhythm of leaf emergence and senescence–is shifting in response to climatic changes. These shifts modify how the land and atmosphere exchange energy, water, and carbon, feeding back onto the climate system. This article synthesizes current understanding of phenology-climate interactions and provides a perspective on how to address key uncertainties across biogeophysical and biogeochemical pathways, including changes in surface albedo, turbulent heat fluxes, carbon cycling, and cloud formation. To bridge synthesis and perspective, we complement the review with new fully coupled Earth system model simulations in which satellite-derived leaf area index (LAI) changes serve as an observationally constrained proxy for recent phenological trends, corresponding to approximately 2.1 days per decade earlier spring onset and 1.8 days per decade later autumn senescence. Under these idealized pre-industrial conditions, a 10-day growing-season extension triggers a global surface cooling of -0.10 +/- 0.03 °C, strongest in northern high latitudes. These results identify a potentially important feedback mechanism; as outputs of an idealized single-model setup, however, these simulations only serve as hypothesis-generating tools, not definitive predictions. This first-order quantification motivates a research pathway forward focused on: (1) coordinated integration of multi-stream observations and experimental networks; (2) model-data fusion via causal and hybrid approaches; and (3) a hierarchy of mechanistic models, from single-column frameworks to next-generation Earth system models capable of resolving phenology-climate feedbacks across scales. Emerging priorities include phenological saturation and acclimation, improved representation of autumn phenology and legacy effects, and characterization of non-linear compensation mechanisms. Phenology thus emerges not only as a climate responder but as an active regulator of the Earth system.

Solar-induced chlorophyll fluorescence (SIF) is a plant signal that can currently be retrieved from satellites at regional to global scale. Since SIF originates from the pool of excitation energy absorbed by chlorophyll molecules that also provides the energy for the photosynthetic CO2 assimilation, it has potential for diagnosing vegetation stress, particularly before the stress becomes apparent by optical decreases in greenness. However, the interpretation of satellite-observed SIF (SIFobs) remains challenging because it integrates multiple confounding factors beyond plant physiology, including variations in illumination conditions, canopy structure, and observation geometry. For applications aiming to detect early stress signals, it is essential to disentangle the physiological component, i.e., fluorescence efficiency (ΦF). SIFobs are strongly influenced by illumination conditions which change with actual differences in overpass times that can occur from one day to the next. Also, the canopy structure determines the fraction (fesc) of fluorescence that escapes the canopy to the sensor. Consequently, SIFobs are affected by the spatial heterogeneity of vegetation elements within the satellite footprint. A common practice to account for these effects is to apply corrections after multiple instantaneous SIFobs have been aggregated onto a regular grid of a geographic coordinate system, which may underestimate the uncertainty from spatio-temporal mismatches. We propose that these procedures should be applied prior to spatial gridding to ensure they are done over the correct spatio-temporal supports. We hypothesize that doing so will ensure consistency within the same support of all contributing variables and reduce uncertainties arising from spatial and temporal mismatches.Here we derive ΦF from TROPOMI observations by normalizing SIFobs with radiation and canopy features prior to gridding. We normalize SIFobs by photosynthetically active radiation (PAR) and near-infrared reflectance of vegetation (NIRv), where NIRv serves as a proxy for canopy structure and vegetation greenness status. We explore ΦF using multi-source NIRv and PAR datasets in combination with TROPOMI SIF from three independent retrieval products. PAR is approximated using downward shortwave radiation products with multiple spatio-temporal resolutions (e.g., MSG, ERA5, TROPOMI estimation of radiance). NIRv, derived from other sources (e.g., MODIS, Sentinel-3, and Sentinel-2), is aggregated to the TROPOMI footprint and compared against the native TROPOMI top-of-atmosphere reflectance product. To evaluate the performance of ΦF derived at the individual footprint level, we compare it against flux tower observations from the Austro-SIF dataset. Austro-SIF is a fluorescence-specific dataset that integrates both active and passive measurement approaches from multiple European sites collected over different time periods between 2018 and 2022. It combines meteorological data with photosynthetic measurements of vegetation at both leaf and canopy scales, capturing comprehensive ecosystem responses to environmental variation. Using this dataset, we further assess the cross-scale consistency and uncertainty of ΦF across ecosystems spanning diverse biomes.

In response to a warming and drying climate, viticulture at higher elevations could be a viable adaptation strategy for the wine industry. Nevertheless, our knowledge about the optimal water management in mountain vineyards remains limited. This study evaluated the effect of drought stress on two grapevine cultivars (Sauvignon Blanc and Chardonnay) under irrigated and rainfed conditions. The research took place in the mountainous region of South Tyrol in northern Italy. Soil-water status, several plant-based physiological stress indicators, yield components, berry quality, and wine sensory attributes were monitored across two growing seasons. Drought stress varied in timing and intensity. In 2021, a 10-day drought occurred during berry set, while in 2022, a more severe drought coincided with veraison and was combined with a 9-day heatwave. Reduced soil-water availability caused growth inhibition and impaired photosynthetic activity, particularly in 2022. Conversely, the mild to moderate water deficit during berry set caused a greater reduction in berry size and weight, and consequently in yield, than the more severe water deficit at veraison. As for berry quality traits, drought stress affected berry soluble solids accumulation, but not pH or titratable acidity. Furthermore, sensory analysis revealed distinct differences across vintages and cultivars. Wines from rainfed vines displayed enhanced hedonistic attributes (e.g., complexity, fullness, and length), particularly when the stress occurred early during the season. Generally, despite comparable stress exposure, Sauvignon Blanc vines were more drought-sensitive, showing greater growth inhibition, decreased photosynthetic activity, substantial yield loss, and altered wine sensory profiles. Chardonnay, conversely, seemed more resilient to drought conditions, showing no discernible differences between rainfed and irrigated wines. In conclusion, this study reveals varying levels of drought tolerance between the two cultivars across two seasons. These findings may help develop cultivar-specific irrigation schedules for mountain vineyards, enhancing water conservation, preserving yields, and ultimately achieving the desired wine quality.

Global Navigation Satellite System (GNSS) signal attenuation offers a novel approach to estimate Vegetation Optical Depth (VOD) and thereby monitor vegetation structure and vegetation water status at high temporal resolution. At the FAIR site in Mieming (Austria), a GNSS receiver system has recently been installed, opening new opportunities to explore the applicability and added value of GNSS-based VOD in a well-instrumented forest ecosystem. The exceptional strength of FAIR lies in its dense and diverse sensor infrastructure, including eddy covariance measurements above and below the canopy, dendrometer observations, stem water potential measurements, sapflow systems, cosmic-ray neutron sensing (CRNS), soil water content and soil water potential profiles, detailed observations of precipitation and throughfall, as well as periodic, manual measurements of leaf water content.The co-location of these measurements enables a unique framework to investigate how GNSS-derived VOD relates to plant water status, biomass dynamics, and ecosystem-scale fluxes. Key research questions include the sensitivity of GNSS-VOD to short-term vegetation water dynamics, its coupling with transpiration and carbon exchange at ecosystem levels, and its response to soil moisture variability and atmospheric demand. The FAIR site thus provides an ideal testbed to assess the potential of GNSS-based VOD as an integrative indicator of vegetation–soil–atmosphere interactions and to evaluate its role in multi-sensor ecohydrological monitoring.In addition, we present first GNSS-VOD time series from the newly installed system and present a first draft of a data processing routine, providing a basis for future analyses and for the integration of GNSS-derived VOD into the existing multi-sensor framework at FAIR.

Abstract. As the terrestrial carbon sink remains the most uncertain component of the global CO2 budget, systematic misrepresentation of biospheric CO2 exchange in complex mountainous regions limits the reliability of climate projections. This study employs the Vegetation Photosynthesis and Respiration Model coupled to the Weather Research and Forecasting model (WRF-VPRM) in real-case simulations over the European Alps. It investigates whether Alpine CO2 exchange is appropriately represented when using default or regionally optimized VPRM parameters, quantifies the sensitivity of modelled CO2 exchange to horizontal grid spacing at scales typical for global weather prediction (9 km) and climate models (54 km), and identifies the physical drivers of resolution-induced biases. Simulations with coarser horizontal grid spacing are compared with a regional-scale 1 km reference. Throughout 2012, 12 clear-sky and 12 cloudy/rainy days are simulated using three different VPRM parameter sets: default European (DF), Alpine-optimized (ALPS), and site-specific (SITE). Validation against five Alpine FLUXNET sites indicates that the SITE parameters perform best overall. The ALPS configuration provides a nearly unbiased representation of ecosystem respiration (Reco) but overestimates gross primary production (GPP), whereas the DF configuration strongly underestimates both Reco and GPP. In DF, these biases partially compensate, resulting in comparatively good performance for net ecosystem exchange (NEE) despite physically inconsistent flux components. Systematic biases in CO2 uptake and their magnitude depend on grid spacing and prevailing meteorological conditions. Resolution-induced biases in NEE (relative to 1 km simulations) under clear-sky conditions decrease from several percent (7 % for ALPS, 4 % for DF) at 9 km to near zero at 54 km. For clear sky, coarser resolutions yield higher net CO2 uptake. In contrast, under cloudy and rainy conditions coarse grids have lower simulated uptake than at 1 km, while the biases substantially increase (from order 10 % at 9 km grid spacing to over 40 % at 54 km). If yearly NEE is estimated from 12 days each for clear-sky and cloudy/rainy conditions, differences due to resolution are minimal at 9 km , while differences due to the parameter set (ALPS vs. DF) amount to 15 %. At 54 km grid spacing, resolution effects for both ALPS (17 %) and DF (13 %) exceed parameter effects (8 %). Taken together, the results imply that resolution-induced errors govern annual NEE uncertainty at coarse resolution (O(100 km)), but at finer resolutions (O(10 km)) the relative impact of parameter optimization dominates. Analytical estimates based on temperature derivatives indicate that 35–42 % of the differences in GPP and 71–85 % in Reco differences between resolutions can be attributed directly to temperature. Additionally, a linear perturbation analysis confirms the key role of temperature in unresolved topography, while it clarifies that radiation accounts for most of the remaining GPP variance and that e.g. water stress and vegetation types from satellite data add smaller but systematic biases.

Climate change is shifting drought frequency and severity in alpine regions, affecting plant physiological processes, including the production and emission of biogenic volatile organic compounds (BVOCs), that influence atmospheric chemistry and the radiative properties of the atmosphere.While constitutive BVOC emissions are well-characterized for some plant species, drought-induced changes in monoterpene and sesquiterpene emissions remain poorly constrained, limiting predictions for future climate scenarios. We quantified the gas exchange of two conifer species, Pinus sylvestris and Juniperus communis, in a multi-week plant cuvette experiment with four drought intensity treatments. Continuous gas exchange measurements resolved temporal dynamics of CO₂ assimilation, transpiration, and BVOC emissions. Under severe drought, P. sylvestris maintained a positive carbon balance (21.0 g C m⁻² leaf area), while J. communis experienced net carbon loss (-0.48 g C m⁻² leaf area), reflecting contrasting carbon-uptake versus water-use strategies under drought stress. Total monoterpene emissions were largely drought-insensitive, although our data revealed a compound-specific regulation in P. sylvestris. Sesquiterpene emissions were strongly induced in both species, but diverged during the course of the experiment: sustained elevation in J. communis versus a bell-shaped response in P. sylvestris. Methyl salicylate responses were opposite: stress-induced and unimodal in P. sylvestris, but declining with drought severity in J. communis.These findings demonstrate that BVOC drought responses are determined by species-specific carbon balance and physiological thresholds rather than phylogeny alone, providing important insights for models of BVOC emission dynamics under climate change.

Plant litter decomposition governs how much carbon soils store and emit, yet the microbial traits that shape ecosystem-scale decay remain unresolved. Metagenomes can quantify genes encoding plant cell-wall-degrading enzymes, but it is unclear whether ecosystem differences in decay reflect distinct enzymatic repertoires, and whether these data improve prediction beyond climate and soil properties. We paired standardized green and rooibos tea-bag decomposition assays across 3–24 months with 295 soil metagenomes from 264 global sites. Using 196 European plots for primary inference, we built a stage-resolved catalogue of 17.6 million carbohydrate-active enzyme (CAZyme) genes. Forest microbiomes decomposed tea faster than grasslands, but this was not explained by greater CAZyme family richness. Instead, ecosystems differed in CAZyme abundance, subfamily and protein-sequence variation, and allocation across biochemical stages of plant cell-wall decay, with evidence of ecosystem-specific selection. CAZyme profiles added explanatory power for 24-month mass loss and improved within-ecosystem prediction but generalized poorly across ecosystems and continents. By showing that ecosystem differences in decomposition arise from the stage-specific distribution of shared enzymatic functions rather than their presence alone, this work shifts microbial trait inference beyond gene inventories and provides a mechanistic genomic framework for carbon-cycle modelling within defined environmental limits.

Carbonyl sulphide (COS) is an atmospheric trace gas that has been suggested as a proxy to estimate carbon uptake by plants. To this end, the concept of leaf relative uptake (LRU), the ratio of deposition velocities of COS and CO2, has been introduced to obtain plant CO2 uptake fluxes from COS flux measurements. In our study we use a coupled soil-canopy-atmospheric mixed layer model to simulate CO2 and COS uptake by vegetation explicitly, and derive LRU. In this modelling framework, the exchange of COS is coupled to the exchange of H2O and CO2 via stomatal conductance. The latter is calculated using an assimilation-stomatal conductance (A-g(s)) photosynthesis model, accounting for separate exchange at sunlit and shaded leaves. Despite limited complexity, our coupled model includes most key processes involved in daytime land atmosphere exchange. The model is embedded in an inverse modelling framework, allowing for a structured model parameter estimation. We performed a parameter optimisation for a boreal forest in Finland (Hyytiala), using observation data from July 2015. We took a holistic approach and aimed to obtain model parameters consistent with a large set of observations, including COS and CO2 molar fractions (measured in and above the canopy) and fluxes. By optimising parameters, we obtained a good fit to many observation types simultaneously. Analysing the corresponding modelled LRU, we found strong within-canopy variations at the leaf scale, with highest LRU values for shaded leaves near the bottom of the canopy. These variations can be explained to a large extent by differences in photosynthetically active radiation (PAR), vapour pressure and lea ftemperature. Based on these findings, we propose a new parameterisation of canopy-scale LRU based on absorbed PAR and vapour-pressure deficit of sunlit leaves near the canopy top. We performed several additional optimisations, without re-optimising leaf exchange parameters: two for the same location, but for the months August and September, and two for a needleleaf forest in Austria (Mieming). We obtained a generally good fit with observations in all of these optimisations, suggesting transferability of model parameters to different months and locations. When testing the LRU parameterisation using Hyytialamodel data from August and September (data not used for deriving the parameterisation), the results of the physical model were well-approximated, although observations suggest somewhat lower LRU values for a large part of the day. For Mieming, the parameterisation also provided a satisfactory fit to the physical model. For both locations we found that the LRU of sunlit leaves near the top of the canopy provides a good approximation of the canopy-scale LRU. Our results provide insight in the behaviour of LRU in the canopy, and the new parameterisation, based on both absorbed PAR and vapour pressure deficit, can contribute to improving COS-based ecosystem plant carbon uptake estimates in needleleaf ecosystems, but further validation is needed.

Assessing ecosystem functioning is crucial for managing and conserving ecosystems and their services. Numerous ways to evaluate ecosystem functioning have been developed, using species traits, such as Plant Functional Types (PFTs), flux measurements with the Eddy Covariance (EC) technique, and remote sensing techniques. We propose that the spatial heterogeneity in ecosystem functioning at a regional scale can be assessed and monitored using satellite-derived Ecosystem Functional Types (EFTs): groups of ecosystems or patches of the land surface that share similar dynamics of matter and energy exchanges. We hypothesize that, as observed for PFTs, different EFTs should have distinct patterns and magnitudes of Net Ecosystem Exchange (NEE) of carbon dioxide measured using the EC technique. We derived EFTs from 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) and compared them with NEE measurements (derived from in situ field observations using the EC technique) across 50 European sites. Our results show that distinct EFTs classes display significantly different dynamics and magnitudes of NEE and that EFTs perform marginally better than PFTs in explaining NEE regional patterns. Land-cover maps based on PFTs are difficult to update on an annual basis and are not sensitive to changes in ecosystem performance (e.g., droughts or pests) that do involve short-term changes in PFT composition. In contrast, satellite-derived EFTs are sensitive to short-term changes in ecosystem performance. Satellite-derived EFTs are an ecosystem functional classification built from satellite observations that allow the identification of homogeneous land patches based on ecosystem functions, e.g., ecosystem net productivity measured on the ground as NEE. Satellite-derived EFTs can be recalculated annually, providing a straightforward way to assess and monitor interannual changes in ecosystem functioning and functional diversity.

Sun-induced chlorophyll fluorescence (SIF) is used to infer canopy photosynthesis, yet its ability to isolate physiological from canopy structural signals remains uncertain. We aimed at testing whether SIF can disentangle the onset and progression of physiological drought stress amidst concurrent canopy biochemical/physical changes, and to evaluate links between leaf- and canopy-scale fluorescence. To this end, a mesocosm experiment manipulating water availability (control vs. drought) in two herbaceous canopies with contrasting leaf angle distributions (planophile vs. erectophile) was conducted. We monitored environmental conditions, chlorophyll fluorescence at leaf- and canopy-scale using an active and passive approach, canopy biochemical/physical properties, and used the SCOPE model to upscale from leaf- to canopy-scale and analyse drivers. Drought progressively reduced soil water content, depressed stomatal conductance, and upregulated non-photochemical quenching, yielding lower fluorescence yields at leaf and canopy scales. TOC SIF showed a significant response to drought; however, neither TOC SIF nor down-scaled SIF detected stress earlier than NIRv. Modelling and observations indicated strong non-physiological influences on TOC SIF, especially as drought altered biochemical/physical canopy attributes. Leaf-scale TOC fluorescence yields were higher and poorly correlated with canopy-scale yields, whereas SCOPE-based upscaling improved agreement. We conclude that SIF robustly captures drought onset, but offers limited early-detection advantage over greenness indices when concurrent canopy structural/biochemical changes are substantial.

This study reports a comparative investigation of two alpine research sites situated in the Aosta Valley (Italian Alps), representing distinct neighbouring ecosystems: a high-altitude grassland and a mature larch forest. Eddy covariance flux measurements have been operational since 2008 at the grassland site (2168 m a.s.l.) and since 2012 at the larch forest site (2100 m a.s.l.). Each station is fully instrumented for flux and meteorological observations using identical instrumentation. The straight-line distance between the two sites is approximately 2.7 km and they experience comparable climatic conditions, thereby enabling direct inter-site comparisons.The primary aim of this study is to quantify and interpret differences in the carbon dioxide exchange between these ecosystems, with particular attention to the peculiarities of the years showing extreme meteorological conditions.The two sites represent contrasting stages along a land‑use transition gradient, where the abandoned grasslands — no longer subject to livestock grazing since 2008, when the area was fenced and permanently excluded from grazing — exhibit a progressive encroachment by woody species, ultimately evolving into mature larch stands. This is a widely documented process in the Alpine region: the abandonment of traditional grazing practices and the subsequent natural recolonization of former grasslands by forest species.To complement this analysis, preliminary results from a third eddy covariance station, installed in 2024 within a transitional ecotone characterized by scattered small larch saplings and shrub species, will also be presented.Overall, this study demonstrates how multi-year eddy covariance measurements can reveal differences in ecosystem functioning under the same climatic conditions but across distinct vegetation types and successional stages, offering new insights into carbon flux dynamics along alpine land-use gradients.

Growing evidence has shown that, apart from local environmental factors, changes in landscape-level factors by accelerated land-use change can also shape soil pathogenic fungal diversity. However, the global representativeness of such patterns remains unclear. Here, we assess how pathogenic fungal diversity in 511 soil samples worldwide responds to landscape factors, including landscape complexity index based on eight landscape metrics and quantity of different land cover types across six spatial scales (i.e., surrounding landscape, 250 m to 10,000 m radii from the sampling coordinate). We find that while soil variables explain over half of the variance, pathogenic fungal alpha diversity increases with landscape complexity and crop cover proportion, but decreases with grass and tree cover proportion, together explaining 23.4% of the total variance. Landscape factors have weaker impacts on beta diversity, explaining 13.0% of the variance. Across spatial scales, grassland ecosystems exhibit increasingly stronger responses to landscape variables compared to forest ecosystems. Landscape factors have a higher relative contribution to root-associated fungi than leaf/fruit/seed-associated fungi. Our results emphasize the importance of local factors and the complementary role of landscape patterns in shaping global soil pathogenic fungal distributions, highlighting scale-dependent effects across ecosystems and fungal functional groups.

Gross primary productivity (GPP) drives the land carbon sink, but its response to climate change and extreme weather events like drought remains uncertain. However, GPP cannot be measured directly but must be inferred through proxies, which introduces uncertainties that limit predictions. One promising approach is to measure carbonyl sulfide (COS) fluxes, supported by a thorough understanding of the relative uptake ratio between COS and CO2, the leaf relative uptake (LRU). We derived plant-scale COS and CO2 fluxes and calculated the LRU of Pinus sylvestris (pine) and Juniperus communis (juniper), under controlled drought conditions. The LRU remained constant (median daytime value of 1.47) in pine across the whole drought gradient due to opposing physiological processes: adjustment of conductances to COS and changes in the ratio of intercellular-to-ambient CO2 concentration. In juniper, the LRU also had a constant value (daytime median of 1.41) for soil water content (SWC) above 17 % and increased with decreasing SWC below this threshold, driven by a decline in the stomatal to internal conductance to COS. Under drought stress, both COS and CO2 uptake declined more in pine than in juniper. This study highlights LRU variability among species and water availability levels, providing insights into the underlying ecophysiological processes.

Volatile organic compound (VOC) emissions from forests are typically attributed to tree canopies, while the forest floor remains comparatively understudied despite its complex mixture of litter, soil microbes, and understory vegetation. Here we report first results from a pilot study, in which we measured forest-floor VOC fluxes in a mountain pine–juniper (Pinus sylvestris, Juniperus communis) stand, at the Forest Atmosphere Interaction Research (FAIR) site of the University of Innsbruck in Mieming, Austria. The site is characterized by a relatively open tree canopy dominated by pines, and a forest floor that is almost completely covered by vegetation, including dominant juniper individuals. We compared mean soil emissions with ecosystem-scale VOC fluxes obtained at the same site during a period shortly after the soil measurements, to assess the relative contribution of the forest floor to whole-ecosystem VOC exchange.Using a dynamic chamber approach coupled to online VOC detection using Proton Transfer Reaction Mass Spectrometry (PTR-MS), we quantified forest floor emissions at two locations: (i) a site dominated by pine needle litter and mosses, and (ii) a site where moss and litter co-occurred with heather (Erica herbacea), some Polygala chamaebuxus and various grasses (Sesleria ssp., Carex ssp.) understory.Both sites emitted substantial amounts of terpenoid compounds, such as monoterpenes and sesquiterpenes, but also isoprene, as well as oxygenated compounds such as methanol, acetaldehyde and acetone. The emissions were temporally variable and differed between the two micro-sites, consistent with differences in biological composition, substrate and meteorological conditions. While the exact sources cannot be resolved from these measurements alone, plausible contributors include microbial activity within soil and the litter–moss layer, as well as root and shoot emissions from understory shrubs.A comparison of the forest floor VOC fluxes with the total ecosystem exchange revealed that the mean soil fluxes of many VOCs were on the order of a few to about 40 % of the respective ecosystem fluxes. Strikingly, for sesquiterpenes the soil emissions at both microsites exceeded ecosystem-scale fluxes by about a factor three and seven, respectively. This discrepancy suggests substantial within-canopy loss processes (e.g., rapid oxidation or deposition) and/or differences in temporal representativeness between the datasets.These findings expand the known role of the forest floor as an active VOC source and suggest that bottom-up budgets that focus solely on canopy emissions may underestimate ecosystem-scale fluxes, especially under conditions favorable to microbial or understory vegetation activity.

Grasslands are worldwide spread ecosystems involved in the provision of multiple functional services, including biomass production and carbon storage. However, the increasingly adverse climate and non-optimised farm management are threatening these ecosystems. In this study, the original semi-mechanistic remotely senseddriven VISTOCK model, which simulates grass growth as limited by thermal and water stress, was modified and integrated with the RothC model to simulate the ecosystem fluxes. The new model (GRASSVISTOCK) showed satisfactory performance in simulating above-ground biomass (AGB) in dry matter (d.m.) and fractional transpirable soil water (FTSW) along Alps (AGB, RMSE = 85.39 g d.m. m- 2; FTSW, RMSE = 0.21) and Mediterranean (AGB, RMSE = 136.84 g d.m. m- 2; FTSW, RMSE = 0.13) grasslands. Also, GRASSVISTOCK was able to simulate the net ecosystem exchange (NEE - RMSE = 0.03 Mg C ha- 1), the gross primary production (RMSE = 0.04 Mg C ha- 1), the ecosystem respiration (RMSE = 0.04 Mg C ha- 1) and the evapotranspiration (RMSE = 1.44 mm), where these observations were available (Alps). The model was applied under present and two climate datasets characterised by temperature increase and precipitation decrease (+2 degrees C temperature, -10 % precipitation) and reference or enriched CO2 concentration (394 vs. 540.5 ppm) scenarios. The results showed that, while changes in temperature and precipitation alone had a negative impact by increasing NEE (+0.69 Mg C ha- 1) and decreasing total biomass (-0.20 Mg d.m. ha- 1) in the reference CO2 scenario, the enriched atmospheric CO2 concentration partially smoothed the NEE trend (+0.27 Mg C ha- 1) and increased total biomass (+0.60 Mg d.m. ha- 1) compared to the present period. It is concluded that the GRASSVISTOCK model represents a first step towards an integrated tool for estimating the performance of the agro-pastoral systems in terms of biomass production, water and carbon fluxes, in the face of ongoing climate change.