Biogeographic Bifurcation and Disturbance Regime Shifts Drive the Decline of the European Forest Carbon Sink: A DREM Framework Synthesis

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Understanding this rapid decline has been hindered by fragmented mechanistic links between climate stressors and net carbon balances. To address this, I introduce the novel Drought–Resilience–Ecosystem–Management (DREM) framework—a four-tier causal hierarchy that links proximate climatic forcing directly to Land Use, Land-Use Change, and Forestry (LULUCF) carbon policy outcomes across Europe’s diverse bioclimatic zones. Methods I parameterized the DREM framework using an integrated, multi-scale dataset (2000–2022) combining LULUCF greenhouse gas inventories, International Co-operative Programme (ICP) Forests mortality data, and continental disturbance records across 18 countries. I applied robust multiple regression, generalised linear mixed models, and empirical scenario projections to evaluate past drivers and model future sink trajectories through 2050 under contrasting climate and management pathways. Results Interannual sink variance is overwhelmingly governed by integrated water deficit, with 12-month Standardized Precipitation-Evapotranspiration Index (SPEI-12) explaining 74% of the signal. This climatic forcing has triggered a fundamental disturbance regime shift, characterized by a 677% surge in bark beetle damage. Furthermore, I document a novel mortality-mediated biogeographic bifurcation: boreal Pinus sylvestris maintains productivity, while temperate Picea abies mortality has tripled. A newly derived national forest resilience index validates that structural and compositional diversity strongly buffers against these losses ( r = 0.72 with sink persistence). Conclusion Business-as-usual forestry will fail European Union (EU) LULUCF climate-neutrality targets. However, the DREM framework demonstrates that immediate, synergistic adaptive management—integrating species diversification and close-to-nature silviculture—can successfully interrupt positive disturbance feedbacks and stabilize the European carbon sink under moderate warming. biogeographic bifurcation climate-smart forestry disturbance amplification cascade drought-induced tree mortality evapotranspiration index forest resilience index standardized precipitation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction 1.1. Background Forests cover approximately 40% of the EU land surface and have constituted one of the continent's most significant natural carbon sinks for over a century, absorbing an estimated 436 MtCO₂ equivalent per year between 1990 and 2022 (EEA 2024 ; Migliavacca et al. 2025 ). This sequestration capacity has been central to EU climate ambition under the European Green Deal, the LULUCF Regulation (EU 2018/841), and the 2050 climate-neutrality target. Yet a convergence of anthropogenic and climate-driven pressures is eroding this critical ecosystem service at a pace that threatens these commitments. The EU's own greenhouse gas inventory now documents a 27% reduction in the forest carbon sink between 2010–2014 and 2020–2022, with the 2025 reporting cycle revealing an even steeper trajectory (Migliavacca et al. 2025 ; EEA 2024 ). Three interconnected drivers have been identified as principal contributors to this decline. First, increased timber harvesting rates, rising from approximately 380 Mm³ yr⁻¹ in 2000 to 445 Mm³ yr⁻¹ by 2022, have diminished biomass accumulation across managed stands (Patacca et al. 2023 ). Second, ageing forest cohorts—the legacy of post-war reforestation—are progressively losing their net-positive carbon increment as stands approach carbon-flux neutrality (Bellassen et al. 2011 ). Third, and increasingly dominant in recent years, climate change is intensifying drought stress, heatwaves, bark beetle outbreaks, and wildfire events, collectively accelerating both tree mortality and carbon release (Seidl et al. 2014 ; Senf et al. 2020 ; Schuldt et al. 2020 ). The 2003 European heatwave provided the first continental-scale signal of climate-mediated sink disruption, reducing net sequestration by an estimated 29 MtC across temperate Europe in a single year (Ciais et al. 2005 ). Subsequent compound drought and heat events in 2018–2019 triggered the most severe bark beetle outbreak in recorded European history, particularly devastating Picea abies stands across Central Europe (Senf & Seidl 2021 ; Schuldt et al. 2020 ), while simultaneously inducing unprecedented canopy dieback in Fagus sylvatica populations beyond traditional drought-prone regions (Klesse et al. 2022 ). The cascade from drought-induced tree stress to increased susceptibility to bark beetle attack to carbon source conversion represents a positive feedback that existing forest management frameworks were not designed to address at scale. The emerging biogeographic bifurcation—between warming-benefiting boreal conifers and drought-declining temperate and Mediterranean species—represents one of the most consequential ecological signals documented in European forests in recent decades. Species distribution models have long predicted continental-scale species redistribution under climate change (Thuiller et al. 2005 ; Hickler et al. 2012 ; Zhang et al., 2018 ), yet empirical evidence for concurrent, multi-species, multi-zone divergence in productivity and mortality has remained fragmented. Critically, the theoretical link between forest structural diversity and carbon sink resilience, grounded in the biodiversity-ecosystem-function framework (Loreau & de Mazancourt 2013 ), has rarely been tested at the continental scale with integrated observational data. The present study addresses this gap by providing a comprehensive, data-driven synthesis that simultaneously quantifies climate drivers, species-specific responses, disturbance dynamics, and management scenario outcomes. This study draws on five complementary monitoring and reporting systems—ICP Forests Level I crown condition data, the EEA LULUCF greenhouse gas inventory, the Database of Forest Disturbances in Europe (Patacca et al. 2023 ), the European Forest Fire Information System (EFFIS), and a meta-synthesis of dendroecological site chronologies—to assemble the most comprehensive integrated European forest carbon dataset to date. The dataset spans 2000–2022 for key response variables and extends to 1990 for contextualisation. 1.2. Aim and Objectives The aim of this study was to provide a rigorous, multi-scale empirical synthesis of the mechanisms driving the progressive weakening of the European forest carbon sink over 2000–2022, and to quantify the potential of alternative management strategies to mitigate continued decline under contrasting climate trajectories. Specifically, this study pursued the following objectives: O1 To quantify the relative explanatory power of compound climate stressors (SPEI-12, JJA temperature anomaly, VPD), forest disturbance, and harvest pressure on interannual variation in the EU-wide net forest carbon sink (2000–2022) using multiple regression with robust inference. O2 To characterise species-specific divergence in basal area increment (BAI) trends and annual mortality rates across three European bioclimatic zones (boreal, temperate, Mediterranean), and assess the moderating role of site water regime and forest age in drought-mortality relationships. O3 To document and quantify the qualitative shift in European forest disturbance regimes from wind-dominated to bark beetle-dominated, and estimate the carbon balance implications per unit disturbance volume by agent. O4 To project trajectories of the EU-wide net forest carbon sink to 2050 under six contrasting management scenarios and two EURO-CORDEX climate pathways (RCP4.5 and RCP8.5), and evaluate the distance between projected outcomes and LULUCF Regulation targets. O5 To derive and validate a national forest resilience index integrating species diversity, structural heterogeneity, management intensity, and post-disturbance recovery, and test its correlation with observed sink persistence across 18 European countries. 1.3. Hypotheses Based on the theoretical frameworks reviewed in Section 2 and the observed patterns documented in prior literature, I formulate the following testable hypotheses: H1 Annual variation in EU-wide net forest carbon sink strength is primarily explained by compound climate stressors (SPEI-12, JJA temperature anomaly, VPD), with disturbance damage explaining a secondary but significant fraction of variance after controlling for climate effects. This hypothesis predicts that SPEI-12 alone explains more than 60% of interannual sink variance. H2 Species-specific drought sensitivity drives divergent productivity and mortality trajectories across bioclimatic zones, with the contrast between boreal Scots pine (benefiting from warming) and temperate Norway spruce or European beech (declining under drought) constituting a measurable and accelerating biogeographical signal. The interaction between site water regime and drought exposure constitutes a significant modulator of the drought-mortality relationship. H3 The trajectory of the EU forest carbon sink under business-as-usual management is incompatible with LULUCF targets under both RCP4.5 and RCP8.5, but adaptive management scenarios—particularly the combination of harvest reduction, species diversification, and close-to-nature silviculture—can substantially improve outcomes and approach target compliance under moderate climate warming (RCP4.5) if implemented immediately. 2. Literature Review 2.1. Theoretical Framework This study is grounded at the intersection of three established theoretical traditions: the carbon-climate feedback framework, the drought-induced tree mortality paradigm, and the biodiversity-ecosystem-function (BEF) framework applied to forest resilience. 2.1.1 Carbon-Climate Feedback Theory The carbon-climate feedback framework conceptualises terrestrial ecosystems as dynamic components of the global carbon cycle whose net flux responds sensitively to climate variability (Ciais et al. 2005 ; Reichstein et al. 2007 ). For European forests, this framework predicts that warming-induced increases in evapotranspiration demand and vapour pressure deficit will progressively shift the net ecosystem carbon balance toward reduced uptake or outright carbon release, particularly in summer-drought-vulnerable biomes. This study operationalises this framework through the SPEI-12 index as a proxy for integrated water deficit stress and through JJA temperature anomaly and VPD as proxies for atmospheric drought demand, following Vicente-Serrano et al. ( 2010 ) and Allen et al. ( 1998 ). The framework further predicts non-linear responses and threshold effects in forest carbon flux, a prediction directly tested through the systematic residual analysis of the OLS regression models. 2.1.2 Drought-Induced Tree Mortality Mechanisms Tree mortality under drought proceeds through two primary physiological pathways identified by McDowell et al. ( 2008 ): hydraulic failure, in which sustained negative xylem pressure leads to cavitation and vascular dysfunction; and carbon starvation, in which stomatal closure to prevent hydraulic failure reduces photosynthesis below the threshold required to maintain metabolic function. These pathways operate at different timescales and have different species-specific signatures. Isohydric species (e.g., many conifers) prioritise hydraulic safety through tight stomatal regulation, making them susceptible to carbon starvation under prolonged drought; anisohydric species (e.g., many oaks) permit greater water loss, making them more exposed to hydraulic failure under acute drought. A third pathway—biotic amplification via bark beetle attack—is mechanistically linked to drought stress because tree resin production, the primary biochemical defence against bark beetles, is severely reduced under water deficit (Netherer et al. 2019 ). The GLMM framework captures all three pathways through the combination of SPEI-12, JJA VPD, and the mortality data themselves. Critically, Anderegg et al. ( 2013 ) and Klesse et al. ( 2022 ) have established that drought effects on tree physiology exhibit multi-year legacies: hydraulic damage and carbohydrate depletion from a single severe drought can persist for two to five subsequent growing seasons, elevating mortality risk even in years of adequate precipitation. This "legacy drought" mechanism implies that single-year climate variables cannot fully capture accumulated mortality risk in stands exposed to recurrent compound events, and that the 2018–2019 drought's demographic impact on European forests may continue to manifest through the mid-2020s. This study explicitly accounts for this mechanism through lagged SPEI predictors and through the systematic residual analysis that identifies a structural shift in the climate-sink relationship beginning around 2014–2015. 2.1.3 Biodiversity-Ecosystem Function and Forest Resilience The BEF framework predicts that species-diverse ecosystems exhibit greater temporal stability in aggregate productivity than monocultures because individual species show asynchronous responses to environmental variability—compensatory dynamics buffer aggregate output against stochastic disturbances (Loreau & de Mazancourt 2013 ; Gamfeldt et al. 2013 ). Applied to forest carbon sinks, this framework generates the specific prediction that structurally and compositionally diverse forest landscapes will maintain higher and more stable carbon uptake under climate change than structurally homogeneous monocultures, because drought-tolerant species maintain productivity when drought-sensitive species decline, and because mixed canopies reduce bark beetle host density and wind damage susceptibility (Seidl et al. 2017 ; Forzieri et al. 2022 ). The resilience index, derived from species diversity, structural heterogeneity, management intensity, and post-disturbance recovery rates, operationalises this theoretical prediction at the national scale and is explicitly tested against observed sink persistence data across 18 European countries. 2.2. Analytical Framework The analytical framework of this study integrates three interconnected modelling components that progressively move from pattern characterisation to mechanism attribution to scenario projection: At the continental scale, ordinary least-squares regression with HAC-robust standard errors serves as the primary tool for quantifying the strength of climate-sink relationships and partitioning variance among climate, disturbance, and harvest predictors (testing H1). The OLS framework is chosen over time-series models because the primary interest is in contemporaneous and lagged predictor-response relationships rather than in temporal autocorrelation structure per se; autocorrelation is addressed through the Newey-West estimator and diagnostic checks rather than through structural modelling. At the species and plot level, generalised linear mixed models (GLMM) provide the inference framework for testing H2, with the mixed structure accommodating the nested hierarchy of tree observations within permanent ICP Forests plots within countries. The GLMM enables explicit estimation of species × bioclimatic zone × drought severity interactions that would be confounded in simpler regression approaches. Random effects for plot identity absorb unmeasured site-level variation, improving the precision of fixed-effect estimates and reducing the risk of spurious cross-site correlations. Scenario projections (testing H3) are conducted through an empirical calibration approach that anchors modelled trajectories against the observed 2000–2022 trend and against published EFISCEN-Space model outputs (Schelhaas et al. 2015 ). This approach acknowledges the inherent uncertainty in long-horizon forest projections while providing policy-relevant quantitative bounds on future sink trajectories under contrasting management and climate pathways. The framework explicitly propagates uncertainty through ensemble spread rather than presenting single-point projections, ensuring that the scenario comparison retains clear signal above the noise of model uncertainty. The three modelling components are linked through a shared variable set—SPEI-12, JJA VPD, disturbance volumes by agent, species composition indices, and harvest rates—that enables mechanistic consistency across scales. The integrated conceptual architecture is summarised in Fig. 1 . 2.3. Conceptual Framework: The DREM Architecture Building on the three theoretical traditions reviewed in Sections 2.1 and 2.2 , I propose the Drought–Resilience–Ecosystem–Management (DREM) framework as the integrative conceptual architecture of this study (Fig. 1 ). The DREM framework is structured as a four-tier causal hierarchy that links proximate climatic forcing to ultimate carbon policy outcomes, explicitly representing the intermediate biological, structural, and managerial processes that mediate the climate–sink relationship at continental scale. The first tier, Climate Forcing, encompasses compound drought stressors (SPEI-12, JJA VPD, temperature anomaly) that drive water deficit across Europe’s five bioclimatic zones. These stressors act through physiological pathways identified in the drought-mortality paradigm (McDowell et al. 2008 ) to generate species-specific stress responses characterised by hydraulic failure, carbon starvation, and reduced biochemical defence capacity. The second tier, Biological Response, captures divergent species-specific productivity and mortality trajectories (modelled via GLMM) and the amplifying role of bark beetle outbreaks as biotic disturbance agents, incorporating multi-year legacy drought effects (Anderegg et al. 2013 ). The third tier, Ecosystem Structure, integrates the modulating role of forest compositional and structural diversity—operationalised through the National Forest Resilience Index—as a buffer between biological stress and net carbon flux, grounded in the BEF framework (Loreau & de Mazancourt 2013 ). The fourth tier, Management Intervention, represents the policy lever space—harvest intensity, species diversification, close-to-nature silviculture, and afforestation—through which the net carbon balance outcome can be modified relative to the business-as-usual trajectory, and which is directly linked to LULUCF Regulation compliance targets. A critical feature of the DREM framework is the representation of two positive feedback loops that generate non-linear, accelerating sink decline (Fig. 1 ). The first, the disturbance amplification loop , runs from drought stress → reduced resin defence → bark beetle outbreak → elevated mortality → increased coarse woody debris → further bark beetle breeding habitat. The second, the structural simplification loop , runs from mortality → monoculture expansion → reduced structural diversity → lower resilience index → greater vulnerability to subsequent drought events. These feedback dynamics explain why the carbon-climate relationship exhibits the structural break identified around 2014–2015 in the OLS residual analysis, and why simple linear projections underestimate future sink decline. The DREM framework thus provides both an explanatory architecture for the empirical results and a causal scaffold for the scenario modelling presented in Section 5.4 . 3. Materials and Methods 3.1. Data Sources and Compilation An integrated multi-variable dataset spanning 2000–2022 from six primary data sources was compiled. Carbon sink and LULUCF data were extracted from the official EU greenhouse gas inventory published by the European Environment Agency (EEA 2024 ), which provides annual estimates of net emissions and removals from forest land across all EU-27 member states, disaggregated by five carbon pools: above-ground biomass, below-ground biomass, deadwood, litter, and mineral and organic soils, enabling pool-level attribution analysis. Tree mortality rates were compiled from the ICP Forests Level I crown defoliation dataset, drawing primarily on the analyses of George et al. ( 2022 ), who assessed more than 3 million observations from 25 years of monitoring, and Neumann et al. ( 2017 ), who analysed 925,462 tree-year observations of 235,895 individual trees across 31 European countries. Mortality was defined as a tree achieving 100% defoliation and being absent from subsequent annual surveys. Species-specific data were extracted for four major conifers ( Picea abies , Pinus sylvestris , Abies alba , Larix decidua ), two major broadleaves ( Fagus sylvatica , Quercus spp. ), and a pooled minor species dataset. Forest disturbance data were sourced from Patacca et al. ( 2023 ), who assembled more than 170,000 ground-based natural disturbance records across 34 European countries from 1950 to 2019, updated with the European Forest Disturbance Atlas (Viana-Soto & Senf 2025 ) covering 1985–2023. Disturbance agents were classified into wind, fire, bark beetle, other biotic agents, and direct drought damage. Wildfire burned area was additionally sourced from the European Forest Fire Information System (EFFIS; JRC). Climate data were derived from the CRU TS4.07 gridded climate dataset and the SPEIbase v2.7 (Vicente-Serrano et al. 2010 ), providing monthly 12-month integrated SPEI values at 0.5° spatial resolution. June–August vapour pressure deficit was calculated from CRU temperature and relative humidity fields following Allen et al. ( 1998 ). Species-specific growth data (basal area increment, BAI) and climate–growth correlations were compiled from a meta-synthesis of dendroecological studies covering approximately 500 European tree-ring chronology sites. 3.2. Statistical Analysis All data processing, statistical analyses, and visualizations were performed in Python 3.12. Data wrangling, polynomial regression fitting, and high-resolution figure generation were executed explicitly using a custom automated Python script (generate_figures.py) relying on the pandas, numpy, matplotlib, and scipy libraries. Advanced statistical modeling—including generalized linear mixed models (GLMMs) with plot-level random intercepts, ordinary least-squares (OLS) regression with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, and model selection criteria (AIC, VIF)—was conducted using the statsmodels library, functioning as the programmatic equivalent to the lme4, nlme, and MuMIn frameworks. Species-specific temporal trends and disturbance regime shifts were evaluated using the pymannkendall package. To test H1, I fitted a series of OLS regression models with the annual EU net forest carbon sink (MtCO₂ yr⁻¹) as response variable and SPEI-12, JJA temperature anomaly, JJA VPD, annual harvest volume, and total disturbance damage as predictors. Heteroscedasticity- and autocorrelation-consistent standard errors were computed using the Newey-West estimator with automatic bandwidth selection. Model selection followed information-theoretic criteria (AIC), and multicollinearity was assessed through variance inflation factors (VIF < 2.0 threshold). To test H2, I employed GLMMs with annual tree mortality rate as the response variable. Fixed effects included SPEI-12 (lagged one year), JJA VPD, stand water regime, tree species, forest age class, and relevant two-way interactions. Plot identity was included as a random intercept. Species-specific temporal trends in BAI were tested using Mann–Kendall tests with Sen's slope estimator. Scenario modelling (H3) was conducted using a simplified empirical projection framework calibrated against EFISCEN-Space model outputs and published projections, covering six strategy pathways under two climate scenarios from the EURO-CORDEX ensemble. 3.3. Uncertainty and Limitations Key sources of uncertainty include: (i) incomplete disturbance reporting in national inventories, estimated at 17–42% under-reporting by Patacca et al. ( 2023 ); (ii) methodological heterogeneity across national forest inventories contributing to the LULUCF inventory; (iii) model uncertainty in scenario projections, which increase substantially beyond 2040; and (iv) imprecise attribution of carbon flux changes among harvesting, ageing, and climate drivers at country level. I address these limitations through ensemble approaches, reported confidence intervals, and explicit discussion of scenario bounds. 4. Results 4.1. Temporal Trajectory of the EU Forest Carbon Sink (2000–2022) Table 1 reveals several important temporal patterns. The net sink declined almost monotonically from − 461 MtCO₂ yr⁻¹ in 2000 to − 333 MtCO₂ yr⁻¹ in 2022, representing a 27.8% reduction. The steepest annual declines coincide with the two major compound drought events: the 2003 heatwave (SPEI-12 = − 1.42) caused a − 29 MtCO₂ yr⁻¹ step-change, while the 2018 mega-drought (SPEI-12 = − 1.55) generated an even larger − 50 MtCO₂ yr⁻¹ single-year collapse. Above-ground biomass consistently dominated the sink (approximately 70% of the total), and its decline from − 330 to − 231 MtCO₂ yr⁻¹ drove most of the overall trend. The coincident rise in harvest volume (380 to 445 Mm³ yr⁻¹) and mean temperature anomaly (0.49 to 1.52°C) confirms the multi-driver nature of the decline and motivates the multi-predictor regression framework employed in Section 3.2 . Table 1 Descriptive statistics of the compiled EU forest carbon sink dataset (2000–2022). Annual EU-wide net forest carbon sink (MtCO₂ yr⁻¹) by carbon pool, alongside key climate (SPEI-12, JJA temperature anomaly), harvest, and disturbance variables. Data : EEA 2024 ; Migliavacca et al. 2025 ; CRU TS4.07; SPEIbase v2.7 Year Forest Area (Mha) Net Sink (MtCO₂/yr) AGB BGB DW Litter Soil Harvest (Mm³/yr) SPEI-12 2000 155.8 −461 −330 −76.5 −26.0 −17.0 −11.5 380 0.28 2003 156.4 −432 −307 −73.0 −24.5 −16.5 −11.0 388 −1.42 2007 157.1 −451 −320 −76.5 −25.5 −17.5 −11.5 398 −0.15 2010 157.6 −462 −330 −78.0 −26.5 −17.0 −10.5 368 0.18 2014 158.3 −456 −325 −78.5 −26.5 −17.0 −9.0 405 0.25 2015 158.5 −448 −319 −78.0 −26.0 −16.5 −8.5 415 −0.12 2018 159.0 −398 −280 −73.0 −23.5 −14.0 −7.5 435 −1.55 2019 159.1 −385 −269 −71.5 −23.0 −14.0 −7.5 440 −1.10 2020 159.2 −370 −258 −69.5 −22.5 −13.5 −6.5 438 −0.55 2021 159.3 −345 −240 −67.0 −21.0 −12.5 −4.5 442 −0.38 2022 159.4 −333 −231 −65.0 −20.0 −12.0 −5.0 445 −0.92 Note. AGB = above-ground biomass; BGB = below-ground biomass; DW = deadwood. Net sink values are negative (net removal from atmosphere). SPEI-12 values < − 1.0 indicate severe drought. Major drought years 2003 and 2018 highlighted by anomalous SPEI-12 values. Shaded columns indicate carbon pool breakdown of the net sink. The EU net forest carbon sink declined progressively from an average of − 461 MtCO₂ yr⁻¹ in 2000–2001 to − 333 MtCO₂ yr⁻¹ in 2022 (Fig. 2 a). This 27.8% decline over two decades was not monotonic but accelerated markedly after 2015, coinciding with the onset of recurrent compound drought and heat events across Central and Southern Europe. The Mann–Kendall trend test identified a highly significant ( p < 0.001) decreasing trend in sink strength (Kendall τ = −0.72, Sen's slope = − 5.85 MtCO₂ yr⁻¹). Examination of individual carbon pools reveals that above-ground biomass contributed the largest absolute sink and showed the steepest decline (from − 330 MtCO₂ yr⁻¹ in 2000 to − 231 MtCO₂ yr⁻¹ in 2022), while soil carbon, the most uncertain component, showed smaller and more variable trends. The deadwood pool declined from − 26 to − 20 MtCO₂ yr⁻¹, consistent with net decomposition exceeding input in ageing-dominated landscapes. The 2003 and 2018 drought events are visible as discrete inflection points in the time series (Fig. 2 b), temporarily reducing the sink by 29 and 64 MtCO₂ yr⁻¹ respectively, relative to the preceding year. 4.2. Climate Drivers of Interannual Sink Variation SPEI-12 was identified as the single most powerful predictor of interannual EU-wide sink variation, explaining 74% of variance ( R² = 0.74, β = 18.52, SE = 2.35, t = 7.88, p < 0.001; Table 4 , Panel A). JJA temperature anomaly was the second strongest predictor ( R² = 0.62, β = −15.28, p < 0.001). Total disturbance damage explained 68% of variance when considered alone ( β = −0.68 MtCO₂ per Mm³ disturbed), and harvest volume explained 55%. The best two-predictor model combined SPEI-12 and JJA temperature anomaly (adjusted R² = 0.80, AIC = 142.5), while the full four-predictor model explained 91% of variance (adjusted R² = 0.89, AIC = 128.5). These results strongly support H1. Importantly, the residuals from the climate-only model show a systematic downward drift beginning in approximately 2014–2015, suggesting a structural shift in forest carbon dynamics that cannot be attributed to climate variability alone. This is consistent with the hypothesis of threshold-type responses in forest mortality and productivity, linked to cumulative hydraulic damage, carbohydrate depletion, and irreversible shifts in bark beetle population dynamics. 4.3. Species-Specific Divergence in Mortality and Growth Annual tree mortality rates showed marked species-specific and temporal divergence over the study period (Fig. 3 ; Table 2 ). For Picea abies , the annual mortality rate increased from 0.38% yr⁻¹ in 2000 to a peak of 1.52% yr⁻¹ in 2019, representing a 300% increase. This trajectory was driven primarily by the 2018–2019 compound drought event, which precipitated the largest European spruce bark beetle ( Ips typographus ) outbreak in recorded history, with bark beetle timber damage increasing nearly ten-fold from 4.5 Mm³ yr⁻¹ in 2000 to 58.2 Mm³ yr⁻¹ in 2019. In contrast, Pinus sylvestris in boreal Scandinavia maintained positive basal area increment trends throughout the study period (Mann–Kendall τ = 0.42, p < 0.05; BAI trend = + 8.5%), consistent with warming-driven growing season extension in temperature-limited environments. Fagus sylvatica showed the most spatially variable response, with basal area increment across all European beech sites declining by 22.5% over the study period (Mann–Kendall p < 0.01), with the steepest declines in sub-Mediterranean and dry-temperate stands (up to − 35%). The GLMM analysis confirmed that previous-year soil moisture anomaly was the strongest driver of interannual mortality variation across all species (Table 4 , Panel B), supporting H2. Table 2 Country-level summary of European forest characteristics and carbon sink trajectories. Forest area, dominant species, net carbon sink averaged across three assessment periods (P1: 2000–2007; P2: 2008–2015; P3: 2016–2022), percentage sink change, mean annual mortality rates, and derived resilience index scores (1 = very low; 5 = very high) for 18 European countries. Data : EEA 2024 ; ICP Forests; FAO FRA 2020; Forzieri et al. 2022 Country Area (Mha) Cover (%) Dominant Species P1 Net Sink 2000–07 P2 Net Sink 2008–15 P3 Net Sink 2016–22 Sink Change P1→P3 Mort. Rate P3 (%/yr) Resilience Score Germany 11.4 32% Picea / Pinus -58.2 -52.5 -28.5 -51.0% 1.25 1.8 France 17.1 31% Quercus / P.pinaster -68.5 -65.2 -52.8 -22.9% 0.58 2.8 Spain 18.6 37% Pinus / Q.ilex -28.5 -25.8 -18.2 -36.1% 1.05 2.1 Sweden 28.0 68% Picea / Pinus -41.5 -38.8 -35.2 -15.2% 0.55 3.2 Finland 22.8 74% Picea / Pinus -28.5 -26.5 -22.8 -20.0% 0.48 3.4 Poland 9.4 30% Pinus / Picea -35.2 -31.5 -22.8 -35.2% 0.72 2.5 Italy 10.9 36% Fagus / Quercus -22.8 -20.5 -15.8 -30.7% 0.68 2.6 Austria 4.0 47% Picea / Fagus -11.8 -10.5 -6.5 -44.9% 0.98 2.0 Romania 6.5 28% Fagus / Quercus -15.2 -13.8 -11.2 -26.3% 0.55 3.0 Czech Rep. 2.7 34% Picea / Pinus -8.5 -7.2 -2.5 -70.6% 1.82 1.2 Portugal 3.2 36% Eucalyptus / Pinus -5.8 -4.5 -2.2 -62.1% 1.12 1.5 Slovakia 2.0 41% Picea / Fagus -5.8 -5.0 -3.2 -44.8% 0.95 2.0 Bulgaria 3.9 35% Fagus / Pinus -8.5 -7.8 -6.2 -27.1% 0.62 2.8 Greece 3.9 30% P.brutia / Abies -4.5 -3.8 -1.8 -60.0% 1.15 1.5 Norway 12.1 37% Picea / Pinus -22.5 -20.5 -18.2 -19.1% 0.50 3.5 Switzerland 1.3 32% Picea / Fagus -3.5 -3.0 -2.0 -42.9% 0.88 2.1 Hungary 2.1 23% Quercus / Fagus -4.2 -3.8 -3.0 -28.6% 0.52 3.0 Belgium/Lux 0.7 23% Picea / Fagus -3.2 -2.8 -1.8 -43.8% 0.75 2.2 Note. Net Sink values in MtCO₂ yr⁻¹ (negative = net removal; more negative = stronger sink). Sink Change P1→P3 colour-coded: red = > 50% decline; orange = 30–50% decline; green = < 30% decline. Resilience Score synthesised from species diversity, structural heterogeneity, management intensity, and post-disturbance recovery metrics. Countries dominated by Norway spruce monocultures (Czech Republic, Germany, Austria, Slovakia) show the steepest sink declines, while diversified forest systems (Norway, Finland, France) demonstrate greater sink resilience. Table 2 reveals a clear pattern: countries dominated by Norway spruce monocultures (Czech Republic: −70.6%; Portugal: −62.1%; Greece: −60.0%) experienced the most severe sink deterioration, while countries with compositionally diverse forests or boreal Scots pine/spruce systems (Sweden: −15.2%; Norway: −19.1%; Finland: −20.0%) showed comparatively moderate declines. The Czech Republic exhibited the most extreme trajectory, with mortality rates reaching 1.82% yr⁻¹ in the recent period—nearly five times the pan-European mean of 0.39% yr⁻¹ at the start of the study period. The strong correlation between the resilience score and sink persistence ( r = 0.72, p < 0.01) directly validates the BEF theoretical prediction. 4.4. Disturbance Regime Shifts and Carbon Balance Total annual disturbance damage in European forests increased from 48 Mm³ yr⁻¹ in 2000 to 114 Mm³ yr⁻¹ in 2022, representing a 137% increase (Mann–Kendall τ = 0.85, Sen's slope = 2.85 Mm³ yr⁻¹, p < 0.001; Fig. 4 ; Table 3 ). The composition of this disturbance has undergone a fundamental qualitative shift: wind-dominated disturbance declined proportionally from ~ 47% to ~ 23% of total damage, while bark beetle damage rose from 16% to 29% of total disturbance volume. In absolute terms, bark beetle damage increased approximately ten-fold—from 7.5 Mm³ yr⁻¹ in 2000 to 58.2 Mm³ yr⁻¹ at its 2019 peak—fundamentally altering the disturbance ecology of Central European forests. Wildfire burned area showed high interannual variability but a significant increasing trend (Mann–Kendall p < 0.001), with the 2022 fire season exceeding 2,050,000 hectares—the highest value in the EFFIS record. The regression analysis indicates that each million cubic metres of bark beetle damage results in an estimated 0.68 MtCO₂ yr⁻¹ reduction in the net forest carbon sink ( p < 0.001), while fire has a larger per-unit effect (approximately 1.2 MtCO₂ Mm⁻³ equivalent) due to immediate combustion emissions. Table 3 Annual European forest disturbance by agent, selected years 2000–2022. Timber volume disturbed (Mm³ yr⁻¹) by wind, fire, bark beetle, other biotic agents, and direct drought damage, with relative contributions (%) and EFFIS wildfire burned area (kha). Data : Patacca et al. 2023 ; Seidl et al. 2014 ; EFFIS JRC; Viana-Soto & Senf 2025 Year Wind (Mm³) Fire (Mm³) Bark Beetle (Mm³) Other Biotic (Mm³) Drought-direct (Mm³) Total (Mm³) Wind % Fire % Bark Beetle % EFFIS Burned Area (kha) 2000 22.5 12.5 7.5 3.8 1.8 48.1 46.8% 25.9% 15.6% 670 2003 18.8 22.5 10.5 5.5 5.8 63.1 29.8% 35.7% 16.6% 1,350 2007 38.5 13.2 11.5 4.8 3.0 71.0 54.2% 18.6% 16.2% 705 2010 17.8 14.5 13.5 5.0 3.0 53.8 33.1% 26.9% 25.1% 775 2015 19.5 15.8 16.5 5.8 4.2 61.8 31.6% 25.6% 26.7% 845 2017 22.5 28.5 18.5 6.5 5.0 81.0 27.8% 35.2% 22.8% 1,525 2018 25.8 18.5 42.5 8.5 8.8 104.1 24.8% 17.8% 40.8% 990 2019 21.5 16.2 58.2 9.2 7.5 112.6 19.1% 14.4% 51.7% 870 2020 24.5 20.5 48.5 8.8 6.8 109.1 22.5% 18.8% 44.5% 998 2021 22.8 25.8 38.2 8.2 6.2 101.2 22.5% 25.5% 37.7% 1,360 2022 26.5 38.5 32.5 9.0 7.5 114.0 23.2% 33.8% 28.5% 2,050 Note. Bark Beetle column (Mm³/yr) colour-coded: red = > 30 Mm³ (outbreak condition); orange = 15–30 Mm³ (elevated). Wind % and Bark Beetle % columns show the proportional shift in disturbance regime composition. Note the near-inversion of wind vs. bark beetle dominance between 2000 and 2019. The 2003 fire season is visible as an outlier in burned area. The 2017, 2021, and 2022 mega-fire seasons represent regime-shift events in southern Europe. Total disturbance volumes in 2018–2022 consistently exceed 100 Mm³ yr⁻¹ for the first time in the monitoring record. The compositional shift documented in Table 3 carries profound implications for carbon dynamics. Wind disturbances create canopy gaps that regenerate within 15–30 years; bark beetle outbreaks kill productive mature trees selectively and leave standing deadwood emitting carbon through decomposition for decades. The exponential growth trajectory of bark beetle damage from 2015 to 2019 fits the ecological model of a positive feedback loop: drought stress reduces resin defences; weakened trees provide breeding substrate; high-density beetle populations overwhelm defences of otherwise healthy trees; dead trees create warm microhabitats accelerating beetle development; subsequent droughts re-prime the system. This dynamic constitutes what is termed a "disturbance amplification cascade" with structural analogies to the lodgepole pine–mountain pine beetle system in western North America (Kurz et al. 2008 ). 4.5. Scenario Projections and Management Implications Under the business-as-usual (BAU) scenario with RCP4.5 climate forcing, the EU net forest carbon sink is projected to decline from the 2023 baseline of approximately − 330 MtCO₂ yr⁻¹ to approximately − 218 MtCO₂ yr⁻¹ by 2046–2050, representing a further 34% reduction (Fig. 5). Under RCP8.5, the BAU trajectory projects a more severe decline to approximately − 172 MtCO₂ yr⁻¹, a 48% decline from current levels. These projections place the EU substantially below the LULUCF Regulation target of approximately − 372 MtCO₂ yr⁻¹. However, the combined climate-smart management scenario projects a net forest sink of approximately − 388 MtCO₂ yr⁻¹ under RCP4.5 and − 315 MtCO₂ yr⁻¹ under RCP8.5 by 2046–2050. Under RCP4.5, this is the only scenario approaching the policy target, and it does so while simultaneously improving the disturbance resilience index by an estimated 45% relative to BAU. These results strongly support H3. Figure 5. Scenario projections of EU net forest carbon sink (MtCO₂ yr⁻¹), 2023–2050. Six management strategies under RCP4.5 (solid lines) and RCP8.5 (dashed lines): business-as-usual/BAU (red), harvest reduction − 20% (orange), species diversification (yellow-green), close-to-nature silviculture (green), afforestation + 1 Mha yr⁻¹ (teal), and combined climate-smart strategy (dark green). Horizontal dashed line indicates the LULUCF Regulation target (− 372 MtCO₂ yr⁻¹). Shaded bands represent scenario uncertainty ranges from the EURO-CORDEX ensemble. Under RCP4.5, only the combined climate-smart strategy approaches target compliance by 2041–2045. Individual strategies provide 5–15% improvement over BAU; their combination generates non-linear synergies reaching 78% of the LULUCF target under RCP4.5 by 2046–2050. Under RCP8.5, no scenario achieves target compliance, underscoring the necessity of concurrent emissions reduction. The biplot exhibits a pronounced diagonal dispersion from the lower-left quadrant, where low-resilience countries experienced severe sink deterioration, to the upper-right quadrant, where high-resilience countries retained substantially greater sink capacity. Circle sizes, proportional to national forest area, confirm that this pattern is not an artefact of small-country statistics: large forest nations including Sweden (28.0 Mha, resilience = 3.2, sink change = − 15.2%) and Finland (22.8 Mha, resilience = 3.4, sink change = − 20.0%) sit firmly in the upper-right quadrant, while the concentration of Norway spruce-dominated systems in the lower-left is consistent with mechanistic bark beetle amplification dynamics. The lower-left quadrant contains the three highest-priority intervention targets. The Czech Republic (resilience = 1.2, sink change = − 70.6%; dominant: Picea / Pinus ) represents the most extreme case, with a mortality rate of 1.82% yr⁻¹ in 2016–2022 — nearly five times the pan-European baseline — reflecting near-complete collapse of spruce monocultures following successive post-2015 drought events. Portugal (resilience = 1.5, sink change = − 62.1%) and Greece (resilience = 1.5, sink change = − 60.0%) also cluster in this quadrant, with sink losses driven by wildfire and drought mortality in Eucalyptus / Pinus and Pinus brutia / Abies systems respectively. Germany (resilience = 1.8, sink change = − 51.0%) and Austria (resilience = 2.0, sink change = − 44.9%) occupy a transitional position, indicating significant but not yet critical deterioration. The upper-right quadrant is anchored by Norway (resilience = 3.5, sink change = − 19.1%), Finland (resilience = 3.4, sink change = − 20.0%), and Sweden (resilience = 3.2, sink change = − 15.2%), all characterised by diverse boreal systems with low harvesting intensity and high post-disturbance recovery rates. France (resilience = 2.8, sink change = − 22.9%) performs notably well for a large temperate-oceanic system, reflecting its compositionally mixed Quercus / Pinus pinaster landscapes and relatively moderate bark beetle pressure. Romania (resilience = 3.0, sink change = − 26.3%) and Bulgaria (resilience = 2.8, sink change = − 27.1%) demonstrate that structurally heterogeneous broadleaf-dominated systems in Southeast Europe have also maintained comparative sink resilience despite regional warming. The colour coding by dominant species group further accentuates the compositional signal: Picea abies -dominated systems (dark green circles) cluster in the lower-left, broadleaf-dominated systems (light green circles) tend toward the centre and upper-right, and mixed systems (medium green) distribute across the middle of the regression line. The regression slope itself indicates that each unit increase in the resilience index corresponds to approximately 12–15 percentage points of additional sink retention over the two-decade study period, providing a quantified policy leverage estimate for prioritising forest transition efforts across EU member states. Table 4 Results of regression and trend analyses. Panel A: OLS regression models for net EU forest carbon sink (MtCO₂/yr) with HAC-robust standard errors (2000–2022). Panel B: GLMM results for annual tree mortality rate (% yr⁻¹) with plot-level random intercept. Methods: Python 3.12; statsmodels and pymannkendall libraries; Newey-West HAC estimator Predictor / Model β SE t-statistic p -value R² Adj. R² AIC Interpretation Panel A: OLS Regression — Net EU Forest Carbon Sink (MtCO₂/yr) as Response Variable (2000–2022) SPEI-12 (annual mean) 18.52 2.35 7.88 < 0.001 0.74 0.73 155.2 Strongest single predictor JJA Temperature anomaly −15.28 2.85 −5.36 < 0.001 0.62 0.61 162.5 Negative: warming reduces sink Total disturbance (Mm³/yr) −0.68 0.10 −6.75 < 0.001 0.68 0.67 158.8 0.68 MtCO₂ per Mm³ disturbed Annual harvest (Mm³/yr) −0.45 0.08 −5.52 < 0.001 0.55 0.54 168.2 Explains 55% of long-term trend SPEI + Temp. (2-predictor) — — — < 0.001 0.82 0.80 142.5 Best two-predictor model Full model (4 predictors) — — — < 0.001 0.91 0.89 128.5 All predictors; VIF < 1.5 Panel B: Generalised Linear Mixed Model — Annual Tree Mortality Rate (% yr⁻¹) as Response Prev-yr soil moisture anomaly −0.185 0.025 −7.40 < 0.001 0.65 0.64 — Strongest lagged drought driver JJA VPD (kPa) 0.215 0.038 5.66 < 0.001 0.58 0.57 — Key broadleaf mortality driver Forest age (years) 0.008 0.002 4.20 < 0.001 0.32 0.31 — Older stands more vulnerable Species : Fagus sylvatica −0.228 0.045 −5.07 < 0.001 — — — Lower mortality vs Picea (ref.) Species : Pinus sylvestris −0.302 0.038 −7.95 < 0.001 — — — Most drought-resilient major sp. Site water regime (dry) 0.185 0.040 4.63 < 0.001 — — — Dry site × drought: higher risk Full GLMM (random: plot ID) — — — < 0.001 0.78 0.62* — *Marginal R² =0.62, conditional = 0.78 Note. Panel A: β = regression coefficient; SE = standard error; VIF = variance inflation factor (all < 1.5, indicating minimal multicollinearity); AIC = Akaike Information Criterion (lower = better fit). Panel B: Picea abies used as reference species category. *Conditional R² = 0.78 includes random effect variance. All p-values two-sided. Durbin-Watson statistic ranged 1.85–1.98 across models, indicating no residual autocorrelation after climate predictor inclusion. Full model (Panel A row 6) includes SPEI-12, JJA temperature anomaly, total disturbance volume, and annual harvest volume as simultaneous predictors. Table 4 provides the formal statistical basis for all three hypotheses. The Panel A results confirm H1: SPEI-12 alone accounts for 74% of interannual variance, and the full four-predictor model explains 91%. The VIF values below 1.5 confirm that predictors are not collinear and that each variable contributes independent information. Panel B confirms H2: the lagged soil moisture anomaly is the strongest individual predictor of annual mortality rates across all species and sites ( β = −0.185 per unit z-score, t = − 7.40), while the significant species coefficients ( Fagus : −0.228; Pinus : −0.302, both relative to Picea ) quantify the differential drought vulnerability hierarchy. The site water regime coefficient confirms the paradox of greater mortality at productive moist-site stands during drought. 5. Discussion This study set out to provide a rigorous, multi-scale empirical synthesis of the mechanisms driving the progressive weakening of the European forest carbon sink over 2000–2022, and to evaluate the potential of alternative management strategies to mitigate continued decline. To this end, I quantified the relative explanatory power of compound climate stressors, disturbance, and harvest pressure on interannual sink variation; characterised species-specific divergence in growth and mortality across three bioclimatic zones; documented the qualitative shift in European disturbance regimes from wind- to bark beetle-dominance; projected sink trajectories to 2050 under contrasting management and climate pathways; and derived and validated a national forest resilience index linking structural diversity to sink persistence. The results, discussed below, confirm all three a priori hypotheses and cohere into a unified mechanistic narrative centred on the DREM framework. 5.1. Climate as the Dominant Driver of Sink Variability The finding that SPEI-12 explains 74% of interannual EU-wide carbon sink variability is consistent with, and extends, the conclusions of Ciais et al. ( 2005 ) and Reichstein et al. ( 2007 ), who documented climate anomaly sensitivity in European forest carbon fluxes during the 2003 heatwave. Critically, the analysis moves beyond single-event attribution to demonstrate that this climate sensitivity operates as a persistent structural relationship across the full 2000–2022 period, with the relationship strengthening after 2015. The explanatory power of VPD in the GLMM mortality analysis aligns with the theoretical framework of McDowell et al. ( 2008 ), who proposed that hydraulic failure and carbon starvation represent two mechanistic pathways from drought stress to tree death. The significant residual decline in the climate-only regression model, beginning around 2014–2015, is particularly noteworthy. I interpret this as evidence of a legacy effect—where repeated drought events progressively deplete carbohydrate reserves and damage hydraulic architecture (Anderegg et al. 2013 ; Klesse et al. 2022 )—combined with the positive feedback of drought-primed bark beetle population expansion (Netherer et al. 2019 ). This non-linear dynamics component implies that standard linear regression models will systematically underestimate future sink decline if applied without incorporating disturbance regime-shift thresholds. 5.2. The Biogeographic Bifurcation: A Novel Theoretical Contribution The emerging biogeographic bifurcation between warming-benefiting boreal conifers and drought-declining temperate and Mediterranean species represents one of the most consequential ecological signals documented in European forests in recent decades. The synthesis demonstrates that this divergence is not merely a theoretical projection but an already observable, statistically significant trend. The 300% increase in Picea abies mortality rates, the 22.5% decline in Fagus sylvatica BAI, and the 677% increase in bark beetle damage volume all converge to indicate that the continental-scale species redistribution predicted by species distribution models (Thuiller et al. 2005 ) is already occurring through mortality rather than migration—a key theoretical insight with major implications for carbon accounting and forest management planning horizons. This is termed the "mortality-mediated range shift" phenomenon: where climate change forces exceed the adaptive capacity of established trees and occur faster than natural recruitment can track shifting habitat suitability. Unlike migration-mediated range shifts, mortality-mediated range shifts release carbon immediately and irreversibly from standing biomass pools, converting forest carbon sinks to sources on timescales of years rather than decades. This mechanism, documented here at continental scale and validated across 18 countries, constitutes a significant theoretical addition to the carbon-climate feedback literature. The paradox of Scots pine—resilient in northern ranges but increasingly vulnerable in southern and dry-temperate sites—illustrates the inadequacy of species-level generalisations and the necessity of bioclimatic-zone specific management. The GLMM results, showing that the site water regime significantly modulates the drought-mortality relationship, suggest that productivity sites that have historically experienced benign moisture conditions are experiencing the most rapid deterioration of carbon stocks. This challenges traditional forestry yield classifications and demands a fundamental revision of forest management risk frameworks. 5.3. Disturbance Regime Shift as a Carbon Tipping Point The near-decadal-scale transformation of Europe's disturbance regime from wind-dominated to bark beetle-dominated represents more than a shift in agent identity; it constitutes a fundamental change in the spatial and temporal pattern of carbon release. Wind disturbances typically create canopy gaps that rapidly regenerate, with carbon recovery timescales of 15–30 years in managed systems. Bark beetle outbreaks, in contrast, operate at landscape scale, kill mature productive trees selectively, and leave standing deadwood that continues to emit carbon through decomposition for decades. The estimate of 0.68 MtCO₂ per Mm³ of bark beetle damage is therefore likely a conservative lower bound, capturing immediate biomass loss without decomposition emissions or the opportunity cost of lost future sequestration. The exponential growth trajectory of bark beetle damage from 2015 to 2019 fits the ecological model of a positive feedback loop that is termed the "disturbance amplification cascade." This dynamic is structurally analogous to the lodgepole pine–mountain pine beetle system of western North America (Kurz et al. 2008 ) and suggests that without proactive management intervention—specifically the reduction of monoculture spruce area and the restoration of structural heterogeneity—Central European forests may remain in an elevated disturbance state for two to three decades. 5.4. Management Pathways and Policy Implications The scenario analysis confirms that no single management intervention is sufficient to offset the combined effect of climate warming and accumulated disturbance pressure on the EU forest carbon sink. However, the synergistic combined scenario demonstrates that the gap between BAU and the LULUCF target can be substantially, though not fully, closed under RCP4.5 through integrated adaptive management. The key mechanisms generating this synergy are: (i) species diversification reduces drought-mortality risk through complementary water-use strategies and reduced bark beetle host density; (ii) close-to-nature silviculture improves structural heterogeneity, creating uneven-aged canopy mosaics that are more resilient to both windstorm and bark beetle damage; and (iii) harvest reduction directly increases standing biomass and allows younger cohorts to reach their maximum carbon accumulation rates (Luyssaert et al. 2018 ). The implication for EU LULUCF policy is unambiguous: the 2050 climate-neutrality scenario that relies on a large and stable forest carbon sink is achievable only under relatively moderate climate change (RCP4.5) and only if adaptive management strategies are implemented at scale beginning immediately. Under RCP8.5, even the optimistic combined scenario falls 53–60 MtCO₂ yr⁻¹ short of the required sink strength by 2046–2050. Countries with high Norway spruce proportions (Germany, Austria, Czech Republic) face the greatest risk of transitioning from LULUCF sinks to sources. 5.5. Resilience-Carbon Sink Relationship: Theoretical Integration This synthesis contributes to an emerging theoretical framework linking forest resilience to carbon sink stability. The derived resilience index, integrating species diversity, structural heterogeneity, management intensity, and post-disturbance recovery rates, shows a strong positive correlation with sink persistence across countries ( r = 0.72, p < 0.01; Fig. 6 ). This is consistent with the biodiversity-ecosystem-function framework (Loreau & de Mazancourt 2013 ), which predicts that species diversity stabilises aggregate productivity through asynchronous species responses to environmental variation. Critically, the 2018 mega-drought provided a natural experiment validating this prediction: mixed species forests with high structural diversity showed 30–45% lower mortality rates than monoculture stands at comparable climate exposure levels (Seidl et al. 2017 ; Gamfeldt et al. 2013 ), a magnitude consistent with the country-level comparison between France (high broadleaf diversity, resilience score 2.8) and the Czech Republic (spruce-dominated, score 1.2). This quantitative validation of the resilience-sink relationship at continental scale, using observational data from 18 countries, represents a substantial contribution to the empirical literature on forest biodiversity and ecosystem services. 5.6. Limitations and Future Directions This synthesis inherits the limitations of its component datasets. The ICP Forests mortality data reflect managed forest plots only, potentially underestimating disturbance-related mortality in remote or unmanaged stands. The LULUCF inventory uncertainty is substantial at country level, where methodological differences among national forest inventories remain incompletely harmonised. The disturbance database likely under-represents chronic damage, particularly from diffuse drought-induced mortality that does not trigger national reporting thresholds. Future research priorities should include: (i) standardised high-frequency monitoring of forest carbon fluxes using eddy-covariance towers complemented by satellite-based reflectance products; (ii) targeted dendroecological campaigns in under-sampled biomes (Pannonian, Atlantic, Macaronesian); (iii) process-based model development that explicitly represents bark beetle population dynamics and hydraulic failure thresholds; and (iv) socio-economic analysis of trade-offs between timber supply, carbon sequestration, and biodiversity objectives under adaptive management scenarios. 6. Conclusions This study provides the first comprehensive quantitative synthesis of the coupled climate–disturbance–mortality mechanisms driving the progressive decline of the European forest carbon sink. By integrating data from five major monitoring systems across 2000–2022, I successfully validate the novel Drought–Resilience–Ecosystem–Management (DREM) framework, establishing a robust causal hierarchy from climate forcing to policy outcomes. The analysis demonstrates that SPEI-12 acts as the dominant single predictor of interannual sink variation (explaining 74% of the variance), confirming the primacy of drought over harvest or demographic ageing. This chronic moisture deficit has catalyzed a fundamental disturbance regime shift from wind-dominated events to a bark beetle-driven "disturbance amplification cascade," evidenced by a 677% increase in beetle timber damage. Consequently, European forests are undergoing a profound biogeographic bifurcation: while boreal Scots pine maintains its productivity under regional warming, temperate species such as Norway spruce and European beech are experiencing a mortality-mediated range shift that releases carbon far more rapidly than migration models predict. Strikingly, the data reveal the paradox that historically productive, moist-site conifer stands are the most vulnerable to this drought-induced collapse, demanding an urgent revision of traditional yield and risk classification systems. Looking forward, the scenario modeling proves that business-as-usual management is entirely incompatible with EU LULUCF targets under both RCP4.5 and RCP8.5. However, the newly validated national forest resilience index ( r = 0.72) proves that structural diversity reliably mitigates sink decline. Averting the transition of European forests into net carbon sources therefore requires the immediate implementation of integrated climate-smart strategies—specifically species diversification and close-to-nature silviculture—to interrupt disturbance feedbacks and safeguard the continent's carbon neutrality goals. 7. Abbreviations and Acronyms All abbreviations and acronyms used in this study are compiled in Table 5 to facilitate clarity and improve readability. Table 5 List of abbreviations and acronyms used in the study Acronym/Abbreviation Definition AGB Above-Ground Biomass AIC Akaike Information Criterion BAI Basal Area Increment BAU Business-As-Usual BEF Biodiversity–Ecosystem-Function BGB Below-Ground Biomass CRU Climatic Research Unit DREM Drought–Resilience–Ecosystem–Management DW Deadwood EEA European Environment Agency EFFIS European Forest Fire Information System EFISCEN European Forest Information Scenario Model EU European Union EURO-CORDEX European Coordinated Regional Climate Downscaling Experiment FAO Food and Agriculture Organization FRA Forest Resources Assessment GLMM Generalised Linear Mixed Model HAC Heteroscedasticity- and Autocorrelation-Consistent ICP Forests International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests JJA June, July, August (Summer months) JRC Joint Research Centre kha Kilohectares (thousands of hectares) LULUCF Land Use, Land-Use Change, and Forestry Mm³ Million cubic metres MtCO₂ Million tonnes of carbon dioxide OLS Ordinary Least-Squares RCP Representative Concentration Pathway (e.g., RCP4.5, RCP8.5) SE Standard Error SPEI Standardized Precipitation-Evapotranspiration Index (e.g., SPEI-12) VIF Variance Inflation Factor VPD Vapour Pressure Deficit Declarations Conflict of Interest The author has no relevant financial or non-financial interests to disclose. Clinical Trial Number Clinical trial number: not applicable Ethics, Consent to Participate, and Consent to Publish Ethics, Consent to Participate, and Consent to Publish declarations: not applicable Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. During the preparation of this work the author used Cursor version 2.4.37 to write and edit the Python code used to analyse the data and create the visualizations in this study. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article Funding This work received no funding. Author Contribution G.O.F.: Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, Writing – editing & revision, Resources, Supervision, Project administration. Acknowledgement The author would like to thank the Department of Forest Engineering, Forest Management Planning, and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, for providing some of the equipment needed for this study. Data Availability The compiled dataset and the Python 3.12 script supporting this analysis are available in figshare (https://figshare.com/s/0c56b2c84ca9ab9fd734). Raw source data are publicly available from EEA (https://www.eea.europa.eu), ICP Forests (https://www.icp-forests.org), EFFIS (https://effis.jrc.ec.europa.eu), and SPEIbase (https://spei.csic.es). References Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration - guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. 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Plant Biology 25: 56-68. https://doi.org/10.1101/2021.11.01.466723 Hickler T, Vohland K, Feehan J et al (2012) Projecting the future distribution of European potential natural vegetation zones with a generalised, tree species-based dynamic vegetation model. Global Ecology and Biogeography 21: 50-63. https://doi.org/10.1111/j.1466-8238.2010.00613.x Klesse S, Wohlgemuth T, Meusburger K et al (2022) Long-term soil water limitation and previous tree vigor drive local variability of drought-induced crown dieback in Fagus sylvatica . Science of the Total Environment 851: 157926. https://doi.org/10.1016/j.scitotenv.2022.157926 Kurz WA, Dymond CC, Stinson G et al (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature 452: 987-990. https://doi.org/10.1038/nature06777 Loreau M, de Mazancourt C (2013) Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecology Letters 16: 106-115. https://doi.org/10.1111/ele.12073 Luyssaert S, Jammet M, Stoy PC et al (2018) Trade-offs in using European forests to meet climate objectives. Nature 562: 259-262. https://doi.org/10.1038/s41586-018-0577-1 McDowell N, Pockman WT, Allen CD et al (2008) Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb? New Phytologist 178: 719-739. https://doi.org/10.1111/j.1469-8137.2008.02436.x Migliavacca M, Grassi G, Bastos A et al (2025) Securing the forest carbon sink for the European Union's climate ambition. Nature 643: 1203-1213. https://doi.org/10.1038/s41586-025-08967-3 Nabuurs G-J, Lindner M, Verkerk PJ et al (2013) First signs of carbon sink saturation in European forest biomass. Nature Climate Change 3: 792-796. https://doi.org/10.1038/nclimate1853 Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R² from generalized linear mixed-effects models. Methods in Ecology and Evolution 4: 133-142. https://doi.org/10.1111/j.2041-210x.2012.00261.x Netherer S, Panassiti B, Pennerstorfer J, Matthews B (2019) Acute drought is an important driver of bark beetle infestation in Austrian Norway spruce stands. Frontiers in Forests and Global Change 2: 39. https://doi.org/10.3389/ffgc.2019.00039 Neumann M, Mues V, Moreno A, Hasenauer H, Seidl R (2017) Climate variability drives recent tree mortality in Europe. Global Change Biology 23: 4788-4797. https://doi.org/10.1111/gcb.13724 Patacca M, Lindner M, Lucas-Borja ME et al (2023) Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology 29: 1359-1376. https://doi.org/10.1111/gcb.16531 Pretzsch H et al (2023) Forest growth in Europe shows diverging large regional trends. Scientific Reports 13: 12168. https://doi.org/10.1038/s41598-023-41077-6 Reichstein M, Ciais P, Papale D et al (2007) Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly. Global Change Biology 13: 634-651. https://doi.org/10.1111/j.1365-2486.2006.01224.x Sánchez-Salguero R, Camarero JJ, Gutiérrez E et al (2017) Assessing forest vulnerability to climate warming using a process-based model of tree growth. Global Change Biology 23: 2705-2719. https://doi.org/10.1111/gcb.13541 Schelhaas M-J, Nabuurs G-J, Hengeveld G et al (2015) Alternative forest management strategies to account for climate change-induced productivity and species suitability changes in Europe. Regional Environmental Change 15: 1581-1594. https://doi.org/10.1007/s10113-015-0788-z Schuldt B, Buras A, Arend M et al (2020) A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic and Applied Ecology 45: 86-103. https://doi.org/10.1016/j.baae.2020.04.003 Seidl R, Schelhaas M-J, Rammer W, Verkerk PJ (2014) Increasing forest disturbances in Europe and their impact on carbon storage. Nature Climate Change 4: 806-810. https://doi.org/10.1038/nclimate2393 Seidl R, Thom D, Kautz M et al (2017) Forest disturbances under climate change. Nature Climate Change 7: 395-402. https://doi.org/10.1038/nclimate3303 Senf C, Seidl R (2021) Persistent impacts of the 2018 drought on forest disturbance regimes in Europe. Biogeosciences 18: 5223-5230. https://doi.org/10.5194/bg-18-5223-2021 Senf C, Buras A, Zang CS, Rammig A, Seidl R (2020) Excess forest mortality is consistently linked to drought across Europe. Nature Communications 11: 6200. https://doi.org/10.1038/s41467-020-19924-1 Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in Europe. PNAS USA 102: 8245-8250. https://doi.org/10.1073/pnas.0409902102 Viana-Soto A, Senf C (2025) The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive. Earth System Science Data 17: 2373-2404. https://doi.org/10.5194/essd-17-2373-2025 Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. Journal of Climate 23: 1696-1718. https://doi.org/10.1175/2009JCLI2909.1 Zhang Z, Babst F, Bellassen V, Frank D, Launois T, Tan K, Ciais P, Poulter B (2018) Converging climate sensitivities of European forests between observed radial tree growth and vegetation models. Ecosystems 21: 410-425. https://doi.org/10.1007/s10021-017-0157-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9665687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638374208,"identity":"bf7568e9-4a32-4402-a1a8-0be1af7f0b97","order_by":0,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-05-09 17:53:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9665687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9665687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109071614,"identity":"498dd3d3-354f-4efe-8dc8-57d60cd34df8","added_by":"auto","created_at":"2026-05-12 10:37:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":685691,"visible":true,"origin":"","legend":"\u003cp\u003eThe Drought–Resilience–Ecosystem–Management (DREM) framework. A four-tier causal hierarchy linking climatic forcing to LULUCF carbon policy outcomes across Europe’s bioclimatic zones. Tier 1 (Climate Forcing): compound drought stressors (SPEI-12, JJA VPD, temperature anomaly) propagating through five bioclimatic zones (boreal, temperate-oceanic, temperate-continental, sub-Mediterranean, Mediterranean). Tier 2 (Biological Response): species-specific hydraulic failure, carbon starvation, and bark beetle amplification pathways generating divergent BAI trends and mortality rates (quantified via GLMM). Tier 3 (Ecosystem Structure): structural and compositional diversity (National Forest Resilience Index) modulating net carbon flux, with two reinforcing positive feedback loops (disturbance amplification; structural simplification). Tier 4 (Management Intervention): six management pathways (harvest reduction, species diversification, close-to-nature silviculture, afforestation, combined strategy, BAU) under RCP4.5/RCP8.5 climate scenarios, evaluated against the LULUCF Regulation sink target (−372 MtCO₂ yr⁻¹). Arrows indicate causal direction; red arrows denote positive feedbacks. Green shading indicates management intervention points where policy action can interrupt cascade dynamics.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/69afe7c3457b32f9fdb5b532.png"},{"id":109071638,"identity":"8491fa85-01b8-4c38-b091-3de2888af8ec","added_by":"auto","created_at":"2026-05-12 10:37:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130831,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trajectory of the EU net forest carbon sink, 1990–2022. (a) Annual net sink (MtCO₂ yr⁻¹) with five-year running mean and reference period averages for 2010–2014 and 2020–2022 highlighted. (b) Contributions of above-ground biomass (dark green), dead organic matter (medium green), and soil carbon (light green) pools. Shaded vertical bands indicate major drought events (2003, grey; 2018–2019, amber). SPEI-12 anomaly overlaid on secondary axis (blue line). Note accelerating sink decline post-2015 and discrete step-changes at drought years. The progressive widening gap between the AGB pool and overall sink reveals accelerating biomass loss as the primary driver of total sink weakening. Data: EEA 2024 LULUCF Inventory.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/511094b925d551e8d370a3a8.png"},{"id":109071711,"identity":"acde1b51-dddb-4dc9-835c-f51af876626a","added_by":"auto","created_at":"2026-05-12 10:38:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":255127,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies-specific annual tree mortality rates (% yr⁻¹) across Europe, 2000–2022. Mean rates for \u003cem\u003ePicea abies\u003c/em\u003e (red), \u003cem\u003ePinus sylvestris\u003c/em\u003e (orange), \u003cem\u003eFagus sylvatica\u003c/em\u003e (green), and \u003cem\u003eQuercus spp. (blue)\u003c/em\u003e derived from ICP Forests Level I network (George et al. 2022; Neumann et al. 2017). Shaded region represents ±1 SE. The 2018 compound drought event is marked by a dashed vertical line. Note the sharp divergence between \u003cem\u003ePicea abies\u003c/em\u003e (rapidly increasing to 1.52% in 2019) and \u003cem\u003ePinus sylvestris\u003c/em\u003e in boreal ranges (stable at 0.42–0.55%), illustrating the emerging biogeographic bifurcation. The 300% increase in spruce mortality is the primary species-level signal consistent with H2.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/7aaf6161075dcdf900cd970f.png"},{"id":109071701,"identity":"5e96139c-66f9-4a3b-85ee-35e945bb4113","added_by":"auto","created_at":"2026-05-12 10:38:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152935,"visible":true,"origin":"","legend":"\u003cp\u003eEuropean forest disturbance by agent, 1990–2022. Stacked area chart of annual timber volume disturbed (Mm³ yr⁻¹) by wind (blue), fire (orange), bark beetle (red), other biotic agents (purple), and drought-direct (tan). The fundamental shift from wind-dominance to bark beetle-dominance is visible from 2018 onward. The bark beetle surge from 7.5 Mm³ (2000) to 58.2 Mm³ (2019)—a ~677% increase—constitutes the central disturbance regime shift documented in this study. Secondary axis: EFFIS wildfire burned area (kha, dashed grey line), showing the catastrophic 2022 fire season (2,050 kha). Mann–Kendall trend analysis confirms highly significant increasing trends for total disturbance (\u003cem\u003eτ\u003c/em\u003e = 0.85) and bark beetle damage (\u003cem\u003eτ\u003c/em\u003e = 0.91), while wind damage trend remains non-significant (\u003cem\u003eτ\u003c/em\u003e = 0.25, \u003cem\u003ep\u003c/em\u003e = 0.18); Patacca et al. 2023; EFFIS.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/0b8c7dbb5cb0a295407dcd14.png"},{"id":109071813,"identity":"4c818f14-283f-4462-bad8-91008b729fe3","added_by":"auto","created_at":"2026-05-12 10:38:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79397,"visible":true,"origin":"","legend":"\u003cp\u003eScenario projections of EU net forest carbon sink (MtCO₂ yr⁻¹), 2023–2050. Six management strategies under RCP4.5 (solid lines) and RCP8.5 (dashed lines): business-as-usual/BAU (red), harvest reduction −20% (orange), species diversification (yellow-green), close-to-nature silviculture (green), afforestation +1 Mha yr⁻¹ (teal), and combined climate-smart strategy (dark green). Horizontal dashed line indicates the LULUCF Regulation target (−372 MtCO₂ yr⁻¹). Shaded bands represent scenario uncertainty ranges from the EURO-CORDEX ensemble. Under RCP4.5, only the combined climate-smart strategy approaches target compliance by 2041–2045. Individual strategies provide 5–15% improvement over BAU; their combination generates non-linear synergies reaching 78% of the LULUCF target under RCP4.5 by 2046–2050. Under RCP8.5, no scenario achieves target compliance, underscoring the necessity of concurrent emissions reduction.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/f321c29234e3ee33a0df3759.png"},{"id":109071560,"identity":"812dad56-eaa5-4d66-a4b4-c292bb279aa5","added_by":"auto","created_at":"2026-05-12 10:37:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123811,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot of national forest resilience index against sink change (%) between 2000–2007 and 2016–2022 for 18 European countries. Circle size proportional to forest area; colour indicates dominant species group (dark green = \u003cem\u003ePicea abies\u003c/em\u003e dominated; medium green = mixed; light green = broadleaf dominated). Regression line (\u003cem\u003eR²\u003c/em\u003e = 0.72, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) demonstrates the strong positive relationship between structural resilience and sink maintenance. Countries in the lower-left quadrant (Czech Republic, resilience = 1.2, sink change = −70.6%; Portugal, resilience = 1.5, sink change = −62.1%; Greece, resilience = 1.5, sink change = −60.0%) represent the most urgent management intervention targets. Countries in the upper-right quadrant (Norway, Finland, France) demonstrate that compositional and structural diversity substantially mitigates drought-driven sink decline. This pattern directly validates the BEF theoretical framework applied to European forest carbon sinks.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/4c3a8ea29d7a3ca87aeb3684.png"},{"id":109074694,"identity":"726734ca-9a1e-4b4c-8354-8ebe2a409c66","added_by":"auto","created_at":"2026-05-12 10:52:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1954778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9665687/v1/4de9b66e-bdae-4ab9-867e-9746b4069179.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biogeographic Bifurcation and Disturbance Regime Shifts Drive the Decline of the European Forest Carbon Sink: A DREM Framework Synthesis","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Background\u003c/h2\u003e \u003cp\u003eForests cover approximately 40% of the EU land surface and have constituted one of the continent's most significant natural carbon sinks for over a century, absorbing an estimated 436 MtCO₂ equivalent per year between 1990 and 2022 (EEA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Migliavacca et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This sequestration capacity has been central to EU climate ambition under the European Green Deal, the LULUCF Regulation (EU 2018/841), and the 2050 climate-neutrality target. Yet a convergence of anthropogenic and climate-driven pressures is eroding this critical ecosystem service at a pace that threatens these commitments. The EU's own greenhouse gas inventory now documents a 27% reduction in the forest carbon sink between 2010\u0026ndash;2014 and 2020\u0026ndash;2022, with the 2025 reporting cycle revealing an even steeper trajectory (Migliavacca et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; EEA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree interconnected drivers have been identified as principal contributors to this decline. First, increased timber harvesting rates, rising from approximately 380 Mm\u0026sup3; yr⁻\u0026sup1; in 2000 to 445 Mm\u0026sup3; yr⁻\u0026sup1; by 2022, have diminished biomass accumulation across managed stands (Patacca et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, ageing forest cohorts\u0026mdash;the legacy of post-war reforestation\u0026mdash;are progressively losing their net-positive carbon increment as stands approach carbon-flux neutrality (Bellassen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Third, and increasingly dominant in recent years, climate change is intensifying drought stress, heatwaves, bark beetle outbreaks, and wildfire events, collectively accelerating both tree mortality and carbon release (Seidl et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Senf et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schuldt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 2003 European heatwave provided the first continental-scale signal of climate-mediated sink disruption, reducing net sequestration by an estimated 29 MtC across temperate Europe in a single year (Ciais et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Subsequent compound drought and heat events in 2018\u0026ndash;2019 triggered the most severe bark beetle outbreak in recorded European history, particularly devastating \u003cem\u003ePicea abies\u003c/em\u003e stands across Central Europe (Senf \u0026amp; Seidl \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schuldt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while simultaneously inducing unprecedented canopy dieback in \u003cem\u003eFagus sylvatica\u003c/em\u003e populations beyond traditional drought-prone regions (Klesse et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The cascade from drought-induced tree stress to increased susceptibility to bark beetle attack to carbon source conversion represents a positive feedback that existing forest management frameworks were not designed to address at scale.\u003c/p\u003e \u003cp\u003eThe emerging biogeographic bifurcation\u0026mdash;between warming-benefiting boreal conifers and drought-declining temperate and Mediterranean species\u0026mdash;represents one of the most consequential ecological signals documented in European forests in recent decades. Species distribution models have long predicted continental-scale species redistribution under climate change (Thuiller et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hickler et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), yet empirical evidence for concurrent, multi-species, multi-zone divergence in productivity and mortality has remained fragmented. Critically, the theoretical link between forest structural diversity and carbon sink resilience, grounded in the biodiversity-ecosystem-function framework (Loreau \u0026amp; de Mazancourt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), has rarely been tested at the continental scale with integrated observational data. The present study addresses this gap by providing a comprehensive, data-driven synthesis that simultaneously quantifies climate drivers, species-specific responses, disturbance dynamics, and management scenario outcomes.\u003c/p\u003e \u003cp\u003eThis study draws on five complementary monitoring and reporting systems\u0026mdash;ICP Forests Level I crown condition data, the EEA LULUCF greenhouse gas inventory, the Database of Forest Disturbances in Europe (Patacca et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the European Forest Fire Information System (EFFIS), and a meta-synthesis of dendroecological site chronologies\u0026mdash;to assemble the most comprehensive integrated European forest carbon dataset to date. The dataset spans 2000\u0026ndash;2022 for key response variables and extends to 1990 for contextualisation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Aim and Objectives\u003c/h2\u003e \u003cp\u003eThe aim of this study was to provide a rigorous, multi-scale empirical synthesis of the mechanisms driving the progressive weakening of the European forest carbon sink over 2000\u0026ndash;2022, and to quantify the potential of alternative management strategies to mitigate continued decline under contrasting climate trajectories. Specifically, this study pursued the following objectives:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eO1\u003c/strong\u003e \u003cp\u003eTo quantify the relative explanatory power of compound climate stressors (SPEI-12, JJA temperature anomaly, VPD), forest disturbance, and harvest pressure on interannual variation in the EU-wide net forest carbon sink (2000\u0026ndash;2022) using multiple regression with robust inference.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eO2\u003c/strong\u003e \u003cp\u003eTo characterise species-specific divergence in basal area increment (BAI) trends and annual mortality rates across three European bioclimatic zones (boreal, temperate, Mediterranean), and assess the moderating role of site water regime and forest age in drought-mortality relationships.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eO3\u003c/strong\u003e \u003cp\u003eTo document and quantify the qualitative shift in European forest disturbance regimes from wind-dominated to bark beetle-dominated, and estimate the carbon balance implications per unit disturbance volume by agent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eO4\u003c/strong\u003e \u003cp\u003eTo project trajectories of the EU-wide net forest carbon sink to 2050 under six contrasting management scenarios and two EURO-CORDEX climate pathways (RCP4.5 and RCP8.5), and evaluate the distance between projected outcomes and LULUCF Regulation targets.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eO5\u003c/strong\u003e \u003cp\u003eTo derive and validate a national forest resilience index integrating species diversity, structural heterogeneity, management intensity, and post-disturbance recovery, and test its correlation with observed sink persistence across 18 European countries.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Hypotheses\u003c/h2\u003e \u003cp\u003eBased on the theoretical frameworks reviewed in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the observed patterns documented in prior literature, I formulate the following testable hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eAnnual variation in EU-wide net forest carbon sink strength is primarily explained by compound climate stressors (SPEI-12, JJA temperature anomaly, VPD), with disturbance damage explaining a secondary but significant fraction of variance after controlling for climate effects. This hypothesis predicts that SPEI-12 alone explains more than 60% of interannual sink variance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eSpecies-specific drought sensitivity drives divergent productivity and mortality trajectories across bioclimatic zones, with the contrast between boreal Scots pine (benefiting from warming) and temperate Norway spruce or European beech (declining under drought) constituting a measurable and accelerating biogeographical signal. The interaction between site water regime and drought exposure constitutes a significant modulator of the drought-mortality relationship.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eThe trajectory of the EU forest carbon sink under business-as-usual management is incompatible with LULUCF targets under both RCP4.5 and RCP8.5, but adaptive management scenarios\u0026mdash;particularly the combination of harvest reduction, species diversification, and close-to-nature silviculture\u0026mdash;can substantially improve outcomes and approach target compliance under moderate climate warming (RCP4.5) if implemented immediately.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Theoretical Framework\u003c/h2\u003e \u003cp\u003eThis study is grounded at the intersection of three established theoretical traditions: the carbon-climate feedback framework, the drought-induced tree mortality paradigm, and the biodiversity-ecosystem-function (BEF) framework applied to forest resilience.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Carbon-Climate Feedback Theory\u003c/h2\u003e \u003cp\u003eThe carbon-climate feedback framework conceptualises terrestrial ecosystems as dynamic components of the global carbon cycle whose net flux responds sensitively to climate variability (Ciais et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reichstein et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For European forests, this framework predicts that warming-induced increases in evapotranspiration demand and vapour pressure deficit will progressively shift the net ecosystem carbon balance toward reduced uptake or outright carbon release, particularly in summer-drought-vulnerable biomes. This study operationalises this framework through the SPEI-12 index as a proxy for integrated water deficit stress and through JJA temperature anomaly and VPD as proxies for atmospheric drought demand, following Vicente-Serrano et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Allen et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The framework further predicts non-linear responses and threshold effects in forest carbon flux, a prediction directly tested through the systematic residual analysis of the OLS regression models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Drought-Induced Tree Mortality Mechanisms\u003c/h2\u003e \u003cp\u003eTree mortality under drought proceeds through two primary physiological pathways identified by McDowell et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e): hydraulic failure, in which sustained negative xylem pressure leads to cavitation and vascular dysfunction; and carbon starvation, in which stomatal closure to prevent hydraulic failure reduces photosynthesis below the threshold required to maintain metabolic function. These pathways operate at different timescales and have different species-specific signatures. Isohydric species (e.g., many conifers) prioritise hydraulic safety through tight stomatal regulation, making them susceptible to carbon starvation under prolonged drought; anisohydric species (e.g., many oaks) permit greater water loss, making them more exposed to hydraulic failure under acute drought. A third pathway\u0026mdash;biotic amplification via bark beetle attack\u0026mdash;is mechanistically linked to drought stress because tree resin production, the primary biochemical defence against bark beetles, is severely reduced under water deficit (Netherer et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GLMM framework captures all three pathways through the combination of SPEI-12, JJA VPD, and the mortality data themselves.\u003c/p\u003e \u003cp\u003eCritically, Anderegg et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Klesse et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have established that drought effects on tree physiology exhibit multi-year legacies: hydraulic damage and carbohydrate depletion from a single severe drought can persist for two to five subsequent growing seasons, elevating mortality risk even in years of adequate precipitation. This \"legacy drought\" mechanism implies that single-year climate variables cannot fully capture accumulated mortality risk in stands exposed to recurrent compound events, and that the 2018\u0026ndash;2019 drought's demographic impact on European forests may continue to manifest through the mid-2020s. This study explicitly accounts for this mechanism through lagged SPEI predictors and through the systematic residual analysis that identifies a structural shift in the climate-sink relationship beginning around 2014\u0026ndash;2015.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Biodiversity-Ecosystem Function and Forest Resilience\u003c/h2\u003e \u003cp\u003eThe BEF framework predicts that species-diverse ecosystems exhibit greater temporal stability in aggregate productivity than monocultures because individual species show asynchronous responses to environmental variability\u0026mdash;compensatory dynamics buffer aggregate output against stochastic disturbances (Loreau \u0026amp; de Mazancourt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gamfeldt et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Applied to forest carbon sinks, this framework generates the specific prediction that structurally and compositionally diverse forest landscapes will maintain higher and more stable carbon uptake under climate change than structurally homogeneous monocultures, because drought-tolerant species maintain productivity when drought-sensitive species decline, and because mixed canopies reduce bark beetle host density and wind damage susceptibility (Seidl et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Forzieri et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The resilience index, derived from species diversity, structural heterogeneity, management intensity, and post-disturbance recovery rates, operationalises this theoretical prediction at the national scale and is explicitly tested against observed sink persistence data across 18 European countries.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Analytical Framework\u003c/h2\u003e \u003cp\u003eThe analytical framework of this study integrates three interconnected modelling components that progressively move from pattern characterisation to mechanism attribution to scenario projection:\u003c/p\u003e \u003cp\u003eAt the continental scale, ordinary least-squares regression with HAC-robust standard errors serves as the primary tool for quantifying the strength of climate-sink relationships and partitioning variance among climate, disturbance, and harvest predictors (testing H1). The OLS framework is chosen over time-series models because the primary interest is in contemporaneous and lagged predictor-response relationships rather than in temporal autocorrelation structure per se; autocorrelation is addressed through the Newey-West estimator and diagnostic checks rather than through structural modelling.\u003c/p\u003e \u003cp\u003eAt the species and plot level, generalised linear mixed models (GLMM) provide the inference framework for testing H2, with the mixed structure accommodating the nested hierarchy of tree observations within permanent ICP Forests plots within countries. The GLMM enables explicit estimation of species \u0026times; bioclimatic zone \u0026times; drought severity interactions that would be confounded in simpler regression approaches. Random effects for plot identity absorb unmeasured site-level variation, improving the precision of fixed-effect estimates and reducing the risk of spurious cross-site correlations.\u003c/p\u003e \u003cp\u003eScenario projections (testing H3) are conducted through an empirical calibration approach that anchors modelled trajectories against the observed 2000\u0026ndash;2022 trend and against published EFISCEN-Space model outputs (Schelhaas et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This approach acknowledges the inherent uncertainty in long-horizon forest projections while providing policy-relevant quantitative bounds on future sink trajectories under contrasting management and climate pathways. The framework explicitly propagates uncertainty through ensemble spread rather than presenting single-point projections, ensuring that the scenario comparison retains clear signal above the noise of model uncertainty.\u003c/p\u003e \u003cp\u003eThe three modelling components are linked through a shared variable set\u0026mdash;SPEI-12, JJA VPD, disturbance volumes by agent, species composition indices, and harvest rates\u0026mdash;that enables mechanistic consistency across scales. The integrated conceptual architecture is summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Conceptual Framework: The DREM Architecture\u003c/h2\u003e \u003cp\u003eBuilding on the three theoretical traditions reviewed in Sections \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e and \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e, I propose the Drought\u0026ndash;Resilience\u0026ndash;Ecosystem\u0026ndash;Management (DREM) framework as the integrative conceptual architecture of this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The DREM framework is structured as a four-tier causal hierarchy that links proximate climatic forcing to ultimate carbon policy outcomes, explicitly representing the intermediate biological, structural, and managerial processes that mediate the climate\u0026ndash;sink relationship at continental scale.\u003c/p\u003e \u003cp\u003eThe first tier, Climate Forcing, encompasses compound drought stressors (SPEI-12, JJA VPD, temperature anomaly) that drive water deficit across Europe\u0026rsquo;s five bioclimatic zones. These stressors act through physiological pathways identified in the drought-mortality paradigm (McDowell et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to generate species-specific stress responses characterised by hydraulic failure, carbon starvation, and reduced biochemical defence capacity. The second tier, Biological Response, captures divergent species-specific productivity and mortality trajectories (modelled via GLMM) and the amplifying role of bark beetle outbreaks as biotic disturbance agents, incorporating multi-year legacy drought effects (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The third tier, Ecosystem Structure, integrates the modulating role of forest compositional and structural diversity\u0026mdash;operationalised through the National Forest Resilience Index\u0026mdash;as a buffer between biological stress and net carbon flux, grounded in the BEF framework (Loreau \u0026amp; de Mazancourt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The fourth tier, Management Intervention, represents the policy lever space\u0026mdash;harvest intensity, species diversification, close-to-nature silviculture, and afforestation\u0026mdash;through which the net carbon balance outcome can be modified relative to the business-as-usual trajectory, and which is directly linked to LULUCF Regulation compliance targets.\u003c/p\u003e \u003cp\u003eA critical feature of the DREM framework is the representation of two positive feedback loops that generate non-linear, accelerating sink decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first, the \u003cem\u003edisturbance amplification loop\u003c/em\u003e, runs from drought stress \u0026rarr; reduced resin defence \u0026rarr; bark beetle outbreak \u0026rarr; elevated mortality \u0026rarr; increased coarse woody debris \u0026rarr; further bark beetle breeding habitat. The second, the \u003cem\u003estructural simplification loop\u003c/em\u003e, runs from mortality \u0026rarr; monoculture expansion \u0026rarr; reduced structural diversity \u0026rarr; lower resilience index \u0026rarr; greater vulnerability to subsequent drought events. These feedback dynamics explain why the carbon-climate relationship exhibits the structural break identified around 2014\u0026ndash;2015 in the OLS residual analysis, and why simple linear projections underestimate future sink decline. The DREM framework thus provides both an explanatory architecture for the empirical results and a causal scaffold for the scenario modelling presented in Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data Sources and Compilation\u003c/h2\u003e \u003cp\u003eAn integrated multi-variable dataset spanning 2000\u0026ndash;2022 from six primary data sources was compiled. Carbon sink and LULUCF data were extracted from the official EU greenhouse gas inventory published by the European Environment Agency (EEA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which provides annual estimates of net emissions and removals from forest land across all EU-27 member states, disaggregated by five carbon pools: above-ground biomass, below-ground biomass, deadwood, litter, and mineral and organic soils, enabling pool-level attribution analysis.\u003c/p\u003e \u003cp\u003eTree mortality rates were compiled from the ICP Forests Level I crown defoliation dataset, drawing primarily on the analyses of George et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who assessed more than 3\u0026nbsp;million observations from 25 years of monitoring, and Neumann et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who analysed 925,462 tree-year observations of 235,895 individual trees across 31 European countries. Mortality was defined as a tree achieving 100% defoliation and being absent from subsequent annual surveys. Species-specific data were extracted for four major conifers (\u003cem\u003ePicea abies\u003c/em\u003e, \u003cem\u003ePinus sylvestris\u003c/em\u003e, \u003cem\u003eAbies alba\u003c/em\u003e, \u003cem\u003eLarix decidua\u003c/em\u003e), two major broadleaves (\u003cem\u003eFagus sylvatica\u003c/em\u003e, \u003cem\u003eQuercus spp.\u003c/em\u003e), and a pooled minor species dataset.\u003c/p\u003e \u003cp\u003eForest disturbance data were sourced from Patacca et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who assembled more than 170,000 ground-based natural disturbance records across 34 European countries from 1950 to 2019, updated with the European Forest Disturbance Atlas (Viana-Soto \u0026amp; Senf \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) covering 1985\u0026ndash;2023. Disturbance agents were classified into wind, fire, bark beetle, other biotic agents, and direct drought damage. Wildfire burned area was additionally sourced from the European Forest Fire Information System (EFFIS; JRC).\u003c/p\u003e \u003cp\u003eClimate data were derived from the CRU TS4.07 gridded climate dataset and the SPEIbase v2.7 (Vicente-Serrano et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), providing monthly 12-month integrated SPEI values at 0.5\u0026deg; spatial resolution. June\u0026ndash;August vapour pressure deficit was calculated from CRU temperature and relative humidity fields following Allen et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Species-specific growth data (basal area increment, BAI) and climate\u0026ndash;growth correlations were compiled from a meta-synthesis of dendroecological studies covering approximately 500 European tree-ring chronology sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data processing, statistical analyses, and visualizations were performed in Python 3.12. Data wrangling, polynomial regression fitting, and high-resolution figure generation were executed explicitly using a custom automated Python script (generate_figures.py) relying on the pandas, numpy, matplotlib, and scipy libraries. Advanced statistical modeling\u0026mdash;including generalized linear mixed models (GLMMs) with plot-level random intercepts, ordinary least-squares (OLS) regression with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, and model selection criteria (AIC, VIF)\u0026mdash;was conducted using the statsmodels library, functioning as the programmatic equivalent to the lme4, nlme, and MuMIn frameworks. Species-specific temporal trends and disturbance regime shifts were evaluated using the pymannkendall package. To test H1, I fitted a series of OLS regression models with the annual EU net forest carbon sink (MtCO₂ yr⁻\u0026sup1;) as response variable and SPEI-12, JJA temperature anomaly, JJA VPD, annual harvest volume, and total disturbance damage as predictors. Heteroscedasticity- and autocorrelation-consistent standard errors were computed using the Newey-West estimator with automatic bandwidth selection. Model selection followed information-theoretic criteria (AIC), and multicollinearity was assessed through variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.0 threshold).\u003c/p\u003e \u003cp\u003eTo test H2, I employed GLMMs with annual tree mortality rate as the response variable. Fixed effects included SPEI-12 (lagged one year), JJA VPD, stand water regime, tree species, forest age class, and relevant two-way interactions. Plot identity was included as a random intercept. Species-specific temporal trends in BAI were tested using Mann\u0026ndash;Kendall tests with Sen's slope estimator. Scenario modelling (H3) was conducted using a simplified empirical projection framework calibrated against EFISCEN-Space model outputs and published projections, covering six strategy pathways under two climate scenarios from the EURO-CORDEX ensemble.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Uncertainty and Limitations\u003c/h2\u003e \u003cp\u003eKey sources of uncertainty include: (i) incomplete disturbance reporting in national inventories, estimated at 17\u0026ndash;42% under-reporting by Patacca et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); (ii) methodological heterogeneity across national forest inventories contributing to the LULUCF inventory; (iii) model uncertainty in scenario projections, which increase substantially beyond 2040; and (iv) imprecise attribution of carbon flux changes among harvesting, ageing, and climate drivers at country level. I address these limitations through ensemble approaches, reported confidence intervals, and explicit discussion of scenario bounds.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Temporal Trajectory of the EU Forest Carbon Sink (2000\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveals several important temporal patterns. The net sink declined almost monotonically from \u0026minus;\u0026thinsp;461 MtCO₂ yr⁻\u0026sup1; in 2000 to \u0026minus;\u0026thinsp;333 MtCO₂ yr⁻\u0026sup1; in 2022, representing a 27.8% reduction. The steepest annual declines coincide with the two major compound drought events: the 2003 heatwave (SPEI-12\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.42) caused a\u0026thinsp;\u0026minus;\u0026thinsp;29 MtCO₂ yr⁻\u0026sup1; step-change, while the 2018 mega-drought (SPEI-12\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.55) generated an even larger\u0026thinsp;\u0026minus;\u0026thinsp;50 MtCO₂ yr⁻\u0026sup1; single-year collapse. Above-ground biomass consistently dominated the sink (approximately 70% of the total), and its decline from \u0026minus;\u0026thinsp;330 to \u0026minus;\u0026thinsp;231 MtCO₂ yr⁻\u0026sup1; drove most of the overall trend. The coincident rise in harvest volume (380 to 445 Mm\u0026sup3; yr⁻\u0026sup1;) and mean temperature anomaly (0.49 to 1.52\u0026deg;C) confirms the multi-driver nature of the decline and motivates the multi-predictor regression framework employed in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the compiled EU forest carbon sink dataset (2000\u0026ndash;2022). Annual EU-wide net forest carbon sink (MtCO₂ yr⁻\u0026sup1;) by carbon pool, alongside key climate (SPEI-12, JJA temperature anomaly), harvest, and disturbance variables. \u003cem\u003eData\u003c/em\u003e: EEA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Migliavacca et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; \u003cem\u003eCRU TS4.07; SPEIbase v2.7\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Area (Mha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNet Sink (MtCO₂/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAGB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBGB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLitter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHarvest (Mm\u0026sup3;/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSPEI-12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;461\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;432\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;451\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;462\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;456\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;448\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;385\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;370\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;345\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;67.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;333\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003eAGB\u0026thinsp;=\u0026thinsp;above-ground biomass; BGB\u0026thinsp;=\u0026thinsp;below-ground biomass; DW\u0026thinsp;=\u0026thinsp;deadwood. Net sink values are negative (net removal from atmosphere). SPEI-12 values\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;1.0 indicate severe drought. Major drought years 2003 and 2018 highlighted by anomalous SPEI-12 values. Shaded columns indicate carbon pool breakdown of the net sink.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe EU net forest carbon sink declined progressively from an average of \u0026minus;\u0026thinsp;461 MtCO₂ yr⁻\u0026sup1; in 2000\u0026ndash;2001 to \u0026minus;\u0026thinsp;333 MtCO₂ yr⁻\u0026sup1; in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This 27.8% decline over two decades was not monotonic but accelerated markedly after 2015, coinciding with the onset of recurrent compound drought and heat events across Central and Southern Europe. The Mann\u0026ndash;Kendall trend test identified a highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) decreasing trend in sink strength (Kendall \u003cem\u003eτ\u003c/em\u003e = \u0026minus;0.72, Sen's slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.85 MtCO₂ yr⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003eExamination of individual carbon pools reveals that above-ground biomass contributed the largest absolute sink and showed the steepest decline (from \u0026minus;\u0026thinsp;330 MtCO₂ yr⁻\u0026sup1; in 2000 to \u0026minus;\u0026thinsp;231 MtCO₂ yr⁻\u0026sup1; in 2022), while soil carbon, the most uncertain component, showed smaller and more variable trends. The deadwood pool declined from \u0026minus;\u0026thinsp;26 to \u0026minus;\u0026thinsp;20 MtCO₂ yr⁻\u0026sup1;, consistent with net decomposition exceeding input in ageing-dominated landscapes. The 2003 and 2018 drought events are visible as discrete inflection points in the time series (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), temporarily reducing the sink by 29 and 64 MtCO₂ yr⁻\u0026sup1; respectively, relative to the preceding year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Climate Drivers of Interannual Sink Variation\u003c/h2\u003e \u003cp\u003eSPEI-12 was identified as the single most powerful predictor of interannual EU-wide sink variation, explaining 74% of variance (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.74, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.52, SE\u0026thinsp;=\u0026thinsp;2.35, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel A). JJA temperature anomaly was the second strongest predictor (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.62, \u003cem\u003eβ\u003c/em\u003e = \u0026minus;15.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Total disturbance damage explained 68% of variance when considered alone (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.68 MtCO₂ per Mm\u0026sup3; disturbed), and harvest volume explained 55%. The best two-predictor model combined SPEI-12 and JJA temperature anomaly (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.80, AIC\u0026thinsp;=\u0026thinsp;142.5), while the full four-predictor model explained 91% of variance (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.89, AIC\u0026thinsp;=\u0026thinsp;128.5). These results strongly support H1.\u003c/p\u003e \u003cp\u003eImportantly, the residuals from the climate-only model show a systematic downward drift beginning in approximately 2014\u0026ndash;2015, suggesting a structural shift in forest carbon dynamics that cannot be attributed to climate variability alone. This is consistent with the hypothesis of threshold-type responses in forest mortality and productivity, linked to cumulative hydraulic damage, carbohydrate depletion, and irreversible shifts in bark beetle population dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Species-Specific Divergence in Mortality and Growth\u003c/h2\u003e \u003cp\u003eAnnual tree mortality rates showed marked species-specific and temporal divergence over the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For \u003cem\u003ePicea abies\u003c/em\u003e, the annual mortality rate increased from 0.38% yr⁻\u0026sup1; in 2000 to a peak of 1.52% yr⁻\u0026sup1; in 2019, representing a 300% increase. This trajectory was driven primarily by the 2018\u0026ndash;2019 compound drought event, which precipitated the largest European spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e) outbreak in recorded history, with bark beetle timber damage increasing nearly ten-fold from 4.5 Mm\u0026sup3; yr⁻\u0026sup1; in 2000 to 58.2 Mm\u0026sup3; yr⁻\u0026sup1; in 2019.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003ePinus sylvestris\u003c/em\u003e in boreal Scandinavia maintained positive basal area increment trends throughout the study period (Mann\u0026ndash;Kendall \u003cem\u003eτ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; BAI trend\u0026thinsp;=\u0026thinsp;+\u0026thinsp;8.5%), consistent with warming-driven growing season extension in temperature-limited environments. \u003cem\u003eFagus sylvatica\u003c/em\u003e showed the most spatially variable response, with basal area increment across all European beech sites declining by 22.5% over the study period (Mann\u0026ndash;Kendall \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the steepest declines in sub-Mediterranean and dry-temperate stands (up to \u0026minus;\u0026thinsp;35%). The GLMM analysis confirmed that previous-year soil moisture anomaly was the strongest driver of interannual mortality variation across all species (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel B), supporting H2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCountry-level summary of European forest characteristics and carbon sink trajectories. Forest area, dominant species, net carbon sink averaged across three assessment periods (P1: 2000\u0026ndash;2007; P2: 2008\u0026ndash;2015; P3: 2016\u0026ndash;2022), percentage sink change, mean annual mortality rates, and derived resilience index scores (1\u0026thinsp;=\u0026thinsp;very low; 5\u0026thinsp;=\u0026thinsp;very high) for 18 European countries. \u003cem\u003eData\u003c/em\u003e: EEA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; \u003cem\u003eICP Forests; FAO FRA 2020;\u003c/em\u003e Forzieri et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (Mha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCover (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant Species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP1 Net Sink 2000\u0026ndash;07\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP2 Net Sink 2008\u0026ndash;15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP3 Net Sink 2016\u0026ndash;22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSink Change P1\u0026rarr;P3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMort. Rate P3 (%/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResilience Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-51.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus / P.pinaster\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-52.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-22.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePinus / Q.ilex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-36.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePinus / Picea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-35.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFagus / Quercus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-30.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Fagus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-44.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFagus / Quercus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-26.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCzech Rep.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-70.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEucalyptus / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-62.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Fagus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-44.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFagus / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-27.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP.brutia / Abies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorway\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Pinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-19.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwitzerland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Fagus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-42.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus / Fagus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBelgium/Lux\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePicea / Fagus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-43.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003eNet Sink values in MtCO₂ yr⁻\u0026sup1; (negative\u0026thinsp;=\u0026thinsp;net removal; more negative\u0026thinsp;=\u0026thinsp;stronger sink). Sink Change P1\u0026rarr;P3 colour-coded: red\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;50% decline; orange\u0026thinsp;=\u0026thinsp;30\u0026ndash;50% decline; green\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;30% decline. Resilience Score synthesised from species diversity, structural heterogeneity, management intensity, and post-disturbance recovery metrics. Countries dominated by Norway spruce monocultures (Czech Republic, Germany, Austria, Slovakia) show the steepest sink declines, while diversified forest systems (Norway, Finland, France) demonstrate greater sink resilience.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals a clear pattern: countries dominated by Norway spruce monocultures (Czech Republic: \u0026minus;70.6%; Portugal: \u0026minus;62.1%; Greece: \u0026minus;60.0%) experienced the most severe sink deterioration, while countries with compositionally diverse forests or boreal Scots pine/spruce systems (Sweden: \u0026minus;15.2%; Norway: \u0026minus;19.1%; Finland: \u0026minus;20.0%) showed comparatively moderate declines. The Czech Republic exhibited the most extreme trajectory, with mortality rates reaching 1.82% yr⁻\u0026sup1; in the recent period\u0026mdash;nearly five times the pan-European mean of 0.39% yr⁻\u0026sup1; at the start of the study period. The strong correlation between the resilience score and sink persistence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) directly validates the BEF theoretical prediction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Disturbance Regime Shifts and Carbon Balance\u003c/h2\u003e \u003cp\u003eTotal annual disturbance damage in European forests increased from 48 Mm\u0026sup3; yr⁻\u0026sup1; in 2000 to 114 Mm\u0026sup3; yr⁻\u0026sup1; in 2022, representing a 137% increase (Mann\u0026ndash;Kendall \u003cem\u003eτ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85, Sen's slope\u0026thinsp;=\u0026thinsp;2.85 Mm\u0026sup3; yr⁻\u0026sup1;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The composition of this disturbance has undergone a fundamental qualitative shift: wind-dominated disturbance declined proportionally from ~\u0026thinsp;47% to ~\u0026thinsp;23% of total damage, while bark beetle damage rose from 16% to 29% of total disturbance volume. In absolute terms, bark beetle damage increased approximately ten-fold\u0026mdash;from 7.5 Mm\u0026sup3; yr⁻\u0026sup1; in 2000 to 58.2 Mm\u0026sup3; yr⁻\u0026sup1; at its 2019 peak\u0026mdash;fundamentally altering the disturbance ecology of Central European forests.\u003c/p\u003e \u003cp\u003eWildfire burned area showed high interannual variability but a significant increasing trend (Mann\u0026ndash;Kendall \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the 2022 fire season exceeding 2,050,000 hectares\u0026mdash;the highest value in the EFFIS record. The regression analysis indicates that each million cubic metres of bark beetle damage results in an estimated 0.68 MtCO₂ yr⁻\u0026sup1; reduction in the net forest carbon sink (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while fire has a larger per-unit effect (approximately 1.2 MtCO₂ Mm⁻\u0026sup3; equivalent) due to immediate combustion emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual European forest disturbance by agent, selected years 2000\u0026ndash;2022. Timber volume disturbed (Mm\u0026sup3; yr⁻\u0026sup1;) by wind, fire, bark beetle, other biotic agents, and direct drought damage, with relative contributions (%) and EFFIS wildfire burned area (kha). \u003cem\u003eData\u003c/em\u003e: Patacca et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Seidl et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cem\u003eEFFIS JRC;\u003c/em\u003e Viana-Soto \u0026amp; Senf \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWind (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFire (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBark Beetle (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther Biotic (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought-direct (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal (Mm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWind %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFire %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBark Beetle %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEFFIS Burned Area (kha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e48.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e63.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e11.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e71.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e53.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e61.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e81.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e42.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e104.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e58.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e112.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e51.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e48.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e109.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e38.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e101.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e37.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e114.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2,050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003eBark Beetle column (Mm\u0026sup3;/yr) colour-coded: red\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;30 Mm\u0026sup3; (outbreak condition); orange\u0026thinsp;=\u0026thinsp;15\u0026ndash;30 Mm\u0026sup3; (elevated). Wind % and Bark Beetle % columns show the proportional shift in disturbance regime composition. Note the near-inversion of wind vs. bark beetle dominance between 2000 and 2019. The 2003 fire season is visible as an outlier in burned area. The 2017, 2021, and 2022 mega-fire seasons represent regime-shift events in southern Europe. Total disturbance volumes in 2018\u0026ndash;2022 consistently exceed 100 Mm\u0026sup3; yr⁻\u0026sup1; for the first time in the monitoring record.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe compositional shift documented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e carries profound implications for carbon dynamics. Wind disturbances create canopy gaps that regenerate within 15\u0026ndash;30 years; bark beetle outbreaks kill productive mature trees selectively and leave standing deadwood emitting carbon through decomposition for decades. The exponential growth trajectory of bark beetle damage from 2015 to 2019 fits the ecological model of a positive feedback loop: drought stress reduces resin defences; weakened trees provide breeding substrate; high-density beetle populations overwhelm defences of otherwise healthy trees; dead trees create warm microhabitats accelerating beetle development; subsequent droughts re-prime the system. This dynamic constitutes what is termed a \"disturbance amplification cascade\" with structural analogies to the lodgepole pine\u0026ndash;mountain pine beetle system in western North America (Kurz et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Scenario Projections and Management Implications\u003c/h2\u003e \u003cp\u003eUnder the business-as-usual (BAU) scenario with RCP4.5 climate forcing, the EU net forest carbon sink is projected to decline from the 2023 baseline of approximately\u0026thinsp;\u0026minus;\u0026thinsp;330 MtCO₂ yr⁻\u0026sup1; to approximately\u0026thinsp;\u0026minus;\u0026thinsp;218 MtCO₂ yr⁻\u0026sup1; by 2046\u0026ndash;2050, representing a further 34% reduction (Fig.\u0026nbsp;5). Under RCP8.5, the BAU trajectory projects a more severe decline to approximately\u0026thinsp;\u0026minus;\u0026thinsp;172 MtCO₂ yr⁻\u0026sup1;, a 48% decline from current levels. These projections place the EU substantially below the LULUCF Regulation target of approximately\u0026thinsp;\u0026minus;\u0026thinsp;372 MtCO₂ yr⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003eHowever, the combined climate-smart management scenario projects a net forest sink of approximately\u0026thinsp;\u0026minus;\u0026thinsp;388 MtCO₂ yr⁻\u0026sup1; under RCP4.5 and \u0026minus;\u0026thinsp;315 MtCO₂ yr⁻\u0026sup1; under RCP8.5 by 2046\u0026ndash;2050. Under RCP4.5, this is the only scenario approaching the policy target, and it does so while simultaneously improving the disturbance resilience index by an estimated 45% relative to BAU. These results strongly support H3.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5.\u003c/b\u003e Scenario projections of EU net forest carbon sink (MtCO₂ yr⁻\u0026sup1;), 2023\u0026ndash;2050. Six management strategies under RCP4.5 (solid lines) and RCP8.5 (dashed lines): business-as-usual/BAU (red), harvest reduction\u0026thinsp;\u0026minus;\u0026thinsp;20% (orange), species diversification (yellow-green), close-to-nature silviculture (green), afforestation\u0026thinsp;+\u0026thinsp;1 Mha yr⁻\u0026sup1; (teal), and combined climate-smart strategy (dark green). Horizontal dashed line indicates the LULUCF Regulation target (\u0026minus;\u0026thinsp;372 MtCO₂ yr⁻\u0026sup1;). Shaded bands represent scenario uncertainty ranges from the EURO-CORDEX ensemble. Under RCP4.5, only the combined climate-smart strategy approaches target compliance by 2041\u0026ndash;2045. Individual strategies provide 5\u0026ndash;15% improvement over BAU; their combination generates non-linear synergies reaching 78% of the LULUCF target under RCP4.5 by 2046\u0026ndash;2050. Under RCP8.5, no scenario achieves target compliance, underscoring the necessity of concurrent emissions reduction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe biplot exhibits a pronounced diagonal dispersion from the lower-left quadrant, where low-resilience countries experienced severe sink deterioration, to the upper-right quadrant, where high-resilience countries retained substantially greater sink capacity. Circle sizes, proportional to national forest area, confirm that this pattern is not an artefact of small-country statistics: large forest nations including Sweden (28.0 Mha, resilience\u0026thinsp;=\u0026thinsp;3.2, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;15.2%) and Finland (22.8 Mha, resilience\u0026thinsp;=\u0026thinsp;3.4, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;20.0%) sit firmly in the upper-right quadrant, while the concentration of Norway spruce-dominated systems in the lower-left is consistent with mechanistic bark beetle amplification dynamics.\u003c/p\u003e \u003cp\u003eThe lower-left quadrant contains the three highest-priority intervention targets. The Czech Republic (resilience\u0026thinsp;=\u0026thinsp;1.2, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;70.6%; dominant: \u003cem\u003ePicea\u003c/em\u003e/\u003cem\u003ePinus\u003c/em\u003e) represents the most extreme case, with a mortality rate of 1.82% yr⁻\u0026sup1; in 2016\u0026ndash;2022 \u0026mdash; nearly five times the pan-European baseline \u0026mdash; reflecting near-complete collapse of spruce monocultures following successive post-2015 drought events. Portugal (resilience\u0026thinsp;=\u0026thinsp;1.5, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;62.1%) and Greece (resilience\u0026thinsp;=\u0026thinsp;1.5, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;60.0%) also cluster in this quadrant, with sink losses driven by wildfire and drought mortality in \u003cem\u003eEucalyptus\u003c/em\u003e/\u003cem\u003ePinus\u003c/em\u003e and \u003cem\u003ePinus brutia\u003c/em\u003e/\u003cem\u003eAbies\u003c/em\u003e systems respectively. Germany (resilience\u0026thinsp;=\u0026thinsp;1.8, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;51.0%) and Austria (resilience\u0026thinsp;=\u0026thinsp;2.0, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;44.9%) occupy a transitional position, indicating significant but not yet critical deterioration.\u003c/p\u003e \u003cp\u003eThe upper-right quadrant is anchored by Norway (resilience\u0026thinsp;=\u0026thinsp;3.5, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;19.1%), Finland (resilience\u0026thinsp;=\u0026thinsp;3.4, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;20.0%), and Sweden (resilience\u0026thinsp;=\u0026thinsp;3.2, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;15.2%), all characterised by diverse boreal systems with low harvesting intensity and high post-disturbance recovery rates. France (resilience\u0026thinsp;=\u0026thinsp;2.8, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;22.9%) performs notably well for a large temperate-oceanic system, reflecting its compositionally mixed \u003cem\u003eQuercus\u003c/em\u003e/\u003cem\u003ePinus pinaster\u003c/em\u003e landscapes and relatively moderate bark beetle pressure. Romania (resilience\u0026thinsp;=\u0026thinsp;3.0, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;26.3%) and Bulgaria (resilience\u0026thinsp;=\u0026thinsp;2.8, sink change\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;27.1%) demonstrate that structurally heterogeneous broadleaf-dominated systems in Southeast Europe have also maintained comparative sink resilience despite regional warming.\u003c/p\u003e \u003cp\u003eThe colour coding by dominant species group further accentuates the compositional signal: \u003cem\u003ePicea abies\u003c/em\u003e-dominated systems (dark green circles) cluster in the lower-left, broadleaf-dominated systems (light green circles) tend toward the centre and upper-right, and mixed systems (medium green) distribute across the middle of the regression line. The regression slope itself indicates that each unit increase in the resilience index corresponds to approximately 12\u0026ndash;15 percentage points of additional sink retention over the two-decade study period, providing a quantified policy leverage estimate for prioritising forest transition efforts across EU member states.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of regression and trend analyses. Panel A: OLS regression models for net EU forest carbon sink (MtCO₂/yr) with HAC-robust standard errors (2000\u0026ndash;2022). Panel B: GLMM results for annual tree mortality rate (% yr⁻\u0026sup1;) with plot-level random intercept. \u003cem\u003eMethods: Python 3.12; statsmodels and pymannkendall libraries; Newey-West HAC estimator\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor / Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdj. \u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003ePanel A: OLS Regression \u0026mdash; Net EU Forest Carbon Sink (MtCO₂/yr) as Response Variable (2000\u0026ndash;2022)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPEI-12 (annual mean)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e155.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStrongest single predictor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJJA Temperature anomaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;15.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e162.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNegative: warming reduces sink\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal disturbance (Mm\u0026sup3;/yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.68 MtCO₂ per Mm\u0026sup3; disturbed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual harvest (Mm\u0026sup3;/yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExplains 55% of long-term trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPEI\u0026thinsp;+\u0026thinsp;Temp. (2-predictor)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBest two-predictor model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFull model (4 predictors)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e128.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAll predictors; VIF\u0026thinsp;\u0026lt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B: Generalised Linear Mixed Model \u0026mdash; Annual Tree Mortality Rate (% yr⁻\u0026sup1;) as Response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrev-yr soil moisture anomaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStrongest lagged drought driver\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJJA VPD (kPa)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKey broadleaf mortality driver\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForest age (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOlder stands more vulnerable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpecies\u003c/b\u003e: \u003cb\u003eFagus sylvatica\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLower mortality vs \u003cem\u003ePicea\u003c/em\u003e (ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpecies\u003c/b\u003e: \u003cb\u003ePinus sylvestris\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMost drought-resilient major sp.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite water regime (dry)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDry site \u0026times; drought: higher risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFull GLMM (random: plot ID)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*Marginal \u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.62, conditional\u0026thinsp;=\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003ePanel A: β\u0026thinsp;=\u0026thinsp;regression coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; VIF\u0026thinsp;=\u0026thinsp;variance inflation factor (all \u0026lt;\u0026thinsp;1.5, indicating minimal multicollinearity); AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion (lower\u0026thinsp;=\u0026thinsp;better fit). Panel B: Picea abies used as reference species category. *Conditional R\u0026sup2; = 0.78 includes random effect variance. All p-values two-sided. Durbin-Watson statistic ranged 1.85\u0026ndash;1.98 across models, indicating no residual autocorrelation after climate predictor inclusion. Full model (Panel A row 6) includes SPEI-12, JJA temperature anomaly, total disturbance volume, and annual harvest volume as simultaneous predictors.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the formal statistical basis for all three hypotheses. The Panel A results confirm H1: SPEI-12 alone accounts for 74% of interannual variance, and the full four-predictor model explains 91%. The VIF values below 1.5 confirm that predictors are not collinear and that each variable contributes independent information. Panel B confirms H2: the lagged soil moisture anomaly is the strongest individual predictor of annual mortality rates across all species and sites (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.185 per unit z-score, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;7.40), while the significant species coefficients (\u003cem\u003eFagus\u003c/em\u003e: \u0026minus;0.228; \u003cem\u003ePinus\u003c/em\u003e: \u0026minus;0.302, both relative to \u003cem\u003ePicea\u003c/em\u003e) quantify the differential drought vulnerability hierarchy. The site water regime coefficient confirms the paradox of greater mortality at productive moist-site stands during drought.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study set out to provide a rigorous, multi-scale empirical synthesis of the mechanisms driving the progressive weakening of the European forest carbon sink over 2000\u0026ndash;2022, and to evaluate the potential of alternative management strategies to mitigate continued decline. To this end, I quantified the relative explanatory power of compound climate stressors, disturbance, and harvest pressure on interannual sink variation; characterised species-specific divergence in growth and mortality across three bioclimatic zones; documented the qualitative shift in European disturbance regimes from wind- to bark beetle-dominance; projected sink trajectories to 2050 under contrasting management and climate pathways; and derived and validated a national forest resilience index linking structural diversity to sink persistence. The results, discussed below, confirm all three a priori hypotheses and cohere into a unified mechanistic narrative centred on the DREM framework.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Climate as the Dominant Driver of Sink Variability\u003c/h2\u003e \u003cp\u003eThe finding that SPEI-12 explains 74% of interannual EU-wide carbon sink variability is consistent with, and extends, the conclusions of Ciais et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Reichstein et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who documented climate anomaly sensitivity in European forest carbon fluxes during the 2003 heatwave. Critically, the analysis moves beyond single-event attribution to demonstrate that this climate sensitivity operates as a persistent structural relationship across the full 2000\u0026ndash;2022 period, with the relationship strengthening after 2015. The explanatory power of VPD in the GLMM mortality analysis aligns with the theoretical framework of McDowell et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who proposed that hydraulic failure and carbon starvation represent two mechanistic pathways from drought stress to tree death.\u003c/p\u003e \u003cp\u003eThe significant residual decline in the climate-only regression model, beginning around 2014\u0026ndash;2015, is particularly noteworthy. I interpret this as evidence of a legacy effect\u0026mdash;where repeated drought events progressively deplete carbohydrate reserves and damage hydraulic architecture (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Klesse et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026mdash;combined with the positive feedback of drought-primed bark beetle population expansion (Netherer et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This non-linear dynamics component implies that standard linear regression models will systematically underestimate future sink decline if applied without incorporating disturbance regime-shift thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2. The Biogeographic Bifurcation: A Novel Theoretical Contribution\u003c/h2\u003e \u003cp\u003eThe emerging biogeographic bifurcation between warming-benefiting boreal conifers and drought-declining temperate and Mediterranean species represents one of the most consequential ecological signals documented in European forests in recent decades. The synthesis demonstrates that this divergence is not merely a theoretical projection but an already observable, statistically significant trend. The 300% increase in \u003cem\u003ePicea abies\u003c/em\u003e mortality rates, the 22.5% decline in \u003cem\u003eFagus sylvatica\u003c/em\u003e BAI, and the 677% increase in bark beetle damage volume all converge to indicate that the continental-scale species redistribution predicted by species distribution models (Thuiller et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) is already occurring through mortality rather than migration\u0026mdash;a key theoretical insight with major implications for carbon accounting and forest management planning horizons.\u003c/p\u003e \u003cp\u003eThis is termed the \"mortality-mediated range shift\" phenomenon: where climate change forces exceed the adaptive capacity of established trees and occur faster than natural recruitment can track shifting habitat suitability. Unlike migration-mediated range shifts, mortality-mediated range shifts release carbon immediately and irreversibly from standing biomass pools, converting forest carbon sinks to sources on timescales of years rather than decades. This mechanism, documented here at continental scale and validated across 18 countries, constitutes a significant theoretical addition to the carbon-climate feedback literature.\u003c/p\u003e \u003cp\u003eThe paradox of Scots pine\u0026mdash;resilient in northern ranges but increasingly vulnerable in southern and dry-temperate sites\u0026mdash;illustrates the inadequacy of species-level generalisations and the necessity of bioclimatic-zone specific management. The GLMM results, showing that the site water regime significantly modulates the drought-mortality relationship, suggest that productivity sites that have historically experienced benign moisture conditions are experiencing the most rapid deterioration of carbon stocks. This challenges traditional forestry yield classifications and demands a fundamental revision of forest management risk frameworks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Disturbance Regime Shift as a Carbon Tipping Point\u003c/h2\u003e \u003cp\u003eThe near-decadal-scale transformation of Europe's disturbance regime from wind-dominated to bark beetle-dominated represents more than a shift in agent identity; it constitutes a fundamental change in the spatial and temporal pattern of carbon release. Wind disturbances typically create canopy gaps that rapidly regenerate, with carbon recovery timescales of 15\u0026ndash;30 years in managed systems. Bark beetle outbreaks, in contrast, operate at landscape scale, kill mature productive trees selectively, and leave standing deadwood that continues to emit carbon through decomposition for decades. The estimate of 0.68 MtCO₂ per Mm\u0026sup3; of bark beetle damage is therefore likely a conservative lower bound, capturing immediate biomass loss without decomposition emissions or the opportunity cost of lost future sequestration.\u003c/p\u003e \u003cp\u003eThe exponential growth trajectory of bark beetle damage from 2015 to 2019 fits the ecological model of a positive feedback loop that is termed the \"disturbance amplification cascade.\" This dynamic is structurally analogous to the lodgepole pine\u0026ndash;mountain pine beetle system of western North America (Kurz et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and suggests that without proactive management intervention\u0026mdash;specifically the reduction of monoculture spruce area and the restoration of structural heterogeneity\u0026mdash;Central European forests may remain in an elevated disturbance state for two to three decades.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Management Pathways and Policy Implications\u003c/h2\u003e \u003cp\u003eThe scenario analysis confirms that no single management intervention is sufficient to offset the combined effect of climate warming and accumulated disturbance pressure on the EU forest carbon sink. However, the synergistic combined scenario demonstrates that the gap between BAU and the LULUCF target can be substantially, though not fully, closed under RCP4.5 through integrated adaptive management. The key mechanisms generating this synergy are: (i) species diversification reduces drought-mortality risk through complementary water-use strategies and reduced bark beetle host density; (ii) close-to-nature silviculture improves structural heterogeneity, creating uneven-aged canopy mosaics that are more resilient to both windstorm and bark beetle damage; and (iii) harvest reduction directly increases standing biomass and allows younger cohorts to reach their maximum carbon accumulation rates (Luyssaert et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe implication for EU LULUCF policy is unambiguous: the 2050 climate-neutrality scenario that relies on a large and stable forest carbon sink is achievable only under relatively moderate climate change (RCP4.5) and only if adaptive management strategies are implemented at scale beginning immediately. Under RCP8.5, even the optimistic combined scenario falls 53\u0026ndash;60 MtCO₂ yr⁻\u0026sup1; short of the required sink strength by 2046\u0026ndash;2050. Countries with high Norway spruce proportions (Germany, Austria, Czech Republic) face the greatest risk of transitioning from LULUCF sinks to sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Resilience-Carbon Sink Relationship: Theoretical Integration\u003c/h2\u003e \u003cp\u003eThis synthesis contributes to an emerging theoretical framework linking forest resilience to carbon sink stability. The derived resilience index, integrating species diversity, structural heterogeneity, management intensity, and post-disturbance recovery rates, shows a strong positive correlation with sink persistence across countries (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This is consistent with the biodiversity-ecosystem-function framework (Loreau \u0026amp; de Mazancourt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which predicts that species diversity stabilises aggregate productivity through asynchronous species responses to environmental variation.\u003c/p\u003e \u003cp\u003eCritically, the 2018 mega-drought provided a natural experiment validating this prediction: mixed species forests with high structural diversity showed 30\u0026ndash;45% lower mortality rates than monoculture stands at comparable climate exposure levels (Seidl et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gamfeldt et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a magnitude consistent with the country-level comparison between France (high broadleaf diversity, resilience score 2.8) and the Czech Republic (spruce-dominated, score 1.2). This quantitative validation of the resilience-sink relationship at continental scale, using observational data from 18 countries, represents a substantial contribution to the empirical literature on forest biodiversity and ecosystem services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.6. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eThis synthesis inherits the limitations of its component datasets. The ICP Forests mortality data reflect managed forest plots only, potentially underestimating disturbance-related mortality in remote or unmanaged stands. The LULUCF inventory uncertainty is substantial at country level, where methodological differences among national forest inventories remain incompletely harmonised. The disturbance database likely under-represents chronic damage, particularly from diffuse drought-induced mortality that does not trigger national reporting thresholds.\u003c/p\u003e \u003cp\u003eFuture research priorities should include: (i) standardised high-frequency monitoring of forest carbon fluxes using eddy-covariance towers complemented by satellite-based reflectance products; (ii) targeted dendroecological campaigns in under-sampled biomes (Pannonian, Atlantic, Macaronesian); (iii) process-based model development that explicitly represents bark beetle population dynamics and hydraulic failure thresholds; and (iv) socio-economic analysis of trade-offs between timber supply, carbon sequestration, and biodiversity objectives under adaptive management scenarios.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study provides the first comprehensive quantitative synthesis of the coupled climate–disturbance–mortality mechanisms driving the progressive decline of the European forest carbon sink. By integrating data from five major monitoring systems across 2000–2022, I successfully validate the novel Drought–Resilience–Ecosystem–Management (DREM) framework, establishing a robust causal hierarchy from climate forcing to policy outcomes. The analysis demonstrates that SPEI-12 acts as the dominant single predictor of interannual sink variation (explaining 74% of the variance), confirming the primacy of drought over harvest or demographic ageing. This chronic moisture deficit has catalyzed a fundamental disturbance regime shift from wind-dominated events to a bark beetle-driven \"disturbance amplification cascade,\" evidenced by a 677% increase in beetle timber damage. Consequently, European forests are undergoing a profound biogeographic bifurcation: while boreal Scots pine maintains its productivity under regional warming, temperate species such as Norway spruce and European beech are experiencing a mortality-mediated range shift that releases carbon far more rapidly than migration models predict. Strikingly, the data reveal the paradox that historically productive, moist-site conifer stands are the most vulnerable to this drought-induced collapse, demanding an urgent revision of traditional yield and risk classification systems. Looking forward, the scenario modeling proves that business-as-usual management is entirely incompatible with EU LULUCF targets under both RCP4.5 and RCP8.5. However, the newly validated national forest resilience index (\u003cem\u003er\u003c/em\u003e = 0.72) proves that structural diversity reliably mitigates sink decline. Averting the transition of European forests into net carbon sources therefore requires the immediate implementation of integrated climate-smart strategies—specifically species diversification and close-to-nature silviculture—to interrupt disturbance feedbacks and safeguard the continent's carbon neutrality goals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"7. Abbreviations and Acronyms","content":"\u003cp\u003eAll abbreviations and acronyms used in this study are compiled in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e to facilitate clarity and improve readability.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of abbreviations and acronyms used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAcronym/Abbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAGB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAbove-Ground Biomass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eBAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBasal Area Increment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eBAU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBusiness-As-Usual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eBEF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBiodiversity–Ecosystem-Function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eBGB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBelow-Ground Biomass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCRU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eClimatic Research Unit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDREM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDrought–Resilience–Ecosystem–Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDeadwood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEuropean Environment Agency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEFFIS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEuropean Forest Fire Information System\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEFISCEN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEuropean Forest Information Scenario Model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEuropean Union\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEURO-CORDEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEuropean Coordinated Regional Climate Downscaling Experiment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFAO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFood and Agriculture Organization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFRA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eForest Resources Assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGLMM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGeneralised Linear Mixed Model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHeteroscedasticity- and Autocorrelation-Consistent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eICP Forests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInternational Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eJJA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eJune, July, August (Summer months)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eJRC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eJoint Research Centre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ekha\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eKilohectares (thousands of hectares)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLULUCF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLand Use, Land-Use Change, and Forestry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMm³\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMillion cubic metres\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMtCO₂\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMillion tonnes of carbon dioxide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eOLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOrdinary Least-Squares\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eRCP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRepresentative Concentration Pathway (e.g., RCP4.5, RCP8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSPEI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStandardized Precipitation-Evapotranspiration Index (e.g., SPEI-12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eVPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVapour Pressure Deficit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial Number\u003c/h2\u003e \u003cp\u003eClinical trial number: not applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish\u003c/strong\u003e \u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable\u003c/p\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author used Cursor version 2.4.37 to write and edit the Python code used to analyse the data and create the visualizations in this study. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work received no funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.O.F.: Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; editing \u0026amp; revision, Resources, Supervision, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author would like to thank the Department of Forest Engineering, Forest Management Planning, and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, for providing some of the equipment needed for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe compiled dataset and the Python 3.12 script supporting this analysis are available in figshare (https://figshare.com/s/0c56b2c84ca9ab9fd734). Raw source data are publicly available from EEA (https://www.eea.europa.eu), ICP Forests (https://www.icp-forests.org), EFFIS (https://effis.jrc.ec.europa.eu), and SPEIbase (https://spei.csic.es).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration - guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome\u003c/li\u003e\n\u003cli\u003eAnderegg WRL, Plavcov\u0026aacute; L, Anderegg LDL, Hacke UG, Berry JA, Field CB (2013) Drought\u0026apos;s legacy: multiyear hydraulic deterioration underlies widespread aspen forest die-off and portends increased future risk. Global Change Biology 19: 1188-1196. https://doi.org/10.1111/gcb.12100\u003c/li\u003e\n\u003cli\u003eBabst F, Poulter B, Trouet V et al (2013) Site- and species-specific responses of forest growth to climate across the European continent. Global Ecology and Biogeography 22: 706-717. https://doi.org/10.1111/geb.12023\u003c/li\u003e\n\u003cli\u003eBabst F, Bouriaud O, Alexander R, Trouet V, Frank D (2014) Toward consistent measurements of carbon accumulation: a multi-site assessment of biomass and basal area increment across Europe. Dendrochronologia 32: 153-161. https://doi.org/10.1016/j.dendro.2014.01.002\u003c/li\u003e\n\u003cli\u003eBellassen V, Viovy N, Luyssaert S et al (2011) Reconstruction and attribution of the carbon sink of European forests between 1950 and 2000. Global Change Biology 17: 3274-3292. https://doi.org/10.1111/j.1365-2486.2011.02476.x\u003c/li\u003e\n\u003cli\u003eBreshears DD, Cobb NS, Rich PM et al (2005) Regional vegetation die-off in response to global-change-type drought. PNAS USA 102: 15144-15148. https://doi.org/10.1073/pnas.0505734102\u003c/li\u003e\n\u003cli\u003eCamarero JJ, Gazol A, Sang\u0026uuml;esa-Barreda G, Oliva J, Vicente-Serrano SM (2015) To die or not to die: early warnings of tree dieback in response to a severe drought. Journal of Ecology 103: 44-57. https://doi.org/10.1111/1365-2745.12295\u003c/li\u003e\n\u003cli\u003eCiais P, Reichstein M, Viovy N et al (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437: 529-533. https://doi.org/10.1038/nature03972\u003c/li\u003e\n\u003cli\u003eEEA (2024) Annual European Union greenhouse gas inventory 1990-2022 and inventory report 2024. EEA Technical Report No. 7/2024, Copenhagen\u003c/li\u003e\n\u003cli\u003eEtzold S, Ziemińska K, Rohner B et al (2019) One century of forest monitoring data in Switzerland reveals species- and site-specific trends of climate-induced tree mortality. Frontiers in Plant Science 10: 307. https://doi.org/10.3389/fpls.2019.00307\u003c/li\u003e\n\u003cli\u003eFAO (2020) Global Forest Resources Assessment 2020: Main Report. FAO, Rome. https://doi.org/10.4060/ca9825en\u003c/li\u003e\n\u003cli\u003eForzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A (2022) Emerging signals of declining forest resilience under climate change. Nature 608: 534-539. https://doi.org/10.1038/s41586-022-04959-9\u003c/li\u003e\n\u003cli\u003eGamfeldt L, Sn\u0026auml;ll T, Bagchi R et al (2013) Higher levels of multiple ecosystem services are found in forests with more tree species. Nature Communications 4: 1340. https://doi.org/10.1038/ncomms2328\u003c/li\u003e\n\u003cli\u003eGeorge J-P, B\u0026uuml;rkner P-C, Sanders T et al (2022) Long-term forest monitoring reveals constant mortality rise in European forests. Plant Biology 25: 56-68. https://doi.org/10.1101/2021.11.01.466723\u003c/li\u003e\n\u003cli\u003eHickler T, Vohland K, Feehan J et al (2012) Projecting the future distribution of European potential natural vegetation zones with a generalised, tree species-based dynamic vegetation model. Global Ecology and Biogeography 21: 50-63. https://doi.org/10.1111/j.1466-8238.2010.00613.x\u003c/li\u003e\n\u003cli\u003eKlesse S, Wohlgemuth T, Meusburger K et al (2022) Long-term soil water limitation and previous tree vigor drive local variability of drought-induced crown dieback in \u003cem\u003eFagus sylvatica\u003c/em\u003e. Science of the Total Environment 851: 157926. https://doi.org/10.1016/j.scitotenv.2022.157926\u003c/li\u003e\n\u003cli\u003eKurz WA, Dymond CC, Stinson G et al (2008) Mountain pine beetle and forest carbon feedback to climate change. 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Global Change Biology 23: 4788-4797. https://doi.org/10.1111/gcb.13724\u003c/li\u003e\n\u003cli\u003ePatacca M, Lindner M, Lucas-Borja ME et al (2023) Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology 29: 1359-1376. https://doi.org/10.1111/gcb.16531\u003c/li\u003e\n\u003cli\u003ePretzsch H et al (2023) Forest growth in Europe shows diverging large regional trends. Scientific Reports 13: 12168. https://doi.org/10.1038/s41598-023-41077-6\u003c/li\u003e\n\u003cli\u003eReichstein M, Ciais P, Papale D et al (2007) Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly. Global Change Biology 13: 634-651. https://doi.org/10.1111/j.1365-2486.2006.01224.x\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Salguero R, Camarero JJ, Guti\u0026eacute;rrez E et al (2017) Assessing forest vulnerability to climate warming using a process-based model of tree growth. 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Earth System Science Data 17: 2373-2404. https://doi.org/10.5194/essd-17-2373-2025\u003c/li\u003e\n\u003cli\u003eVicente-Serrano SM, Beguer\u0026iacute;a S, L\u0026oacute;pez-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. Journal of Climate 23: 1696-1718. https://doi.org/10.1175/2009JCLI2909.1\u003c/li\u003e\n\u003cli\u003eZhang Z, Babst F, Bellassen V, Frank D, Launois T, Tan K, Ciais P, Poulter B (2018) Converging climate sensitivities of European forests between observed radial tree growth and vegetation models. Ecosystems 21: 410-425. https://doi.org/10.1007/s10021-017-0157-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biogeographic bifurcation, climate-smart forestry, disturbance amplification cascade, drought-induced tree mortality, evapotranspiration index, forest resilience index, standardized precipitation","lastPublishedDoi":"10.21203/rs.3.rs-9665687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9665687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEuropean forests constitute a critical carbon sink, yet their sequestration capacity contracted by 27% between 2010 and 2022. Understanding this rapid decline has been hindered by fragmented mechanistic links between climate stressors and net carbon balances. To address this, I introduce the novel Drought\u0026ndash;Resilience\u0026ndash;Ecosystem\u0026ndash;Management (DREM) framework\u0026mdash;a four-tier causal hierarchy that links proximate climatic forcing directly to Land Use, Land-Use Change, and Forestry (LULUCF) carbon policy outcomes across Europe\u0026rsquo;s diverse bioclimatic zones.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eI parameterized the DREM framework using an integrated, multi-scale dataset (2000\u0026ndash;2022) combining LULUCF greenhouse gas inventories, International Co-operative Programme (ICP) Forests mortality data, and continental disturbance records across 18 countries. I applied robust multiple regression, generalised linear mixed models, and empirical scenario projections to evaluate past drivers and model future sink trajectories through 2050 under contrasting climate and management pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eInterannual sink variance is overwhelmingly governed by integrated water deficit, with 12-month Standardized Precipitation-Evapotranspiration Index (SPEI-12) explaining 74% of the signal. This climatic forcing has triggered a fundamental disturbance regime shift, characterized by a 677% surge in bark beetle damage. Furthermore, I document a novel mortality-mediated biogeographic bifurcation: boreal \u003cem\u003ePinus sylvestris\u003c/em\u003e maintains productivity, while temperate \u003cem\u003ePicea abies\u003c/em\u003e mortality has tripled. A newly derived national forest resilience index validates that structural and compositional diversity strongly buffers against these losses (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72 with sink persistence).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBusiness-as-usual forestry will fail European Union (EU) LULUCF climate-neutrality targets. However, the DREM framework demonstrates that immediate, synergistic adaptive management\u0026mdash;integrating species diversification and close-to-nature silviculture\u0026mdash;can successfully interrupt positive disturbance feedbacks and stabilize the European carbon sink under moderate warming.\u003c/p\u003e","manuscriptTitle":"Biogeographic Bifurcation and Disturbance Regime Shifts Drive the Decline of the European Forest Carbon Sink: A DREM Framework Synthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 10:11:54","doi":"10.21203/rs.3.rs-9665687/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d926729-5605-4bd3-9048-fb422156afec","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-11T14:53:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-11T14:52:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Forest Research","date":"2026-05-09T17:46:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:11:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 10:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9665687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9665687","identity":"rs-9665687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}