Alarming decline in the carbon sink of European forests driven by disturbances

Alarming decline in the carbon sink of European forests driven by disturbances | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Alarming decline in the carbon sink of European forests driven by disturbances Francois Ritter, Philippe Ciais, Cornelius Senf, Maurizio Santoro, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3671432/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Forests are carbon sinks essential for climate change mitigation. However, increased harvests and natural disturbances across Europe have recently challenged this role. To project the future carbon sink capacity of Europe's forests, we integrated country reports from the United Nations Framework Convention on Climate Change (UNFCCC) with remote sensing maps of disturbances and above-ground biomass. Our model simulates biomass dynamics at 18 km resolution from 2010 to 2030, predicting a 44% decrease in the EU-27 forest carbon sink, driven by disturbances outpacing biomass recovery. Consequently, the 2030 forest carbon sink will fall 29% short of EU-27 targets. We demonstrate that the three billion trees initiative is insufficient for climate change mitigation and needs to be combined with a 26% reduction in forest harvests from 2025 to 2030 to meet these targets. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Climate sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text [1] European forests have gradually recovered from major timber exploitation during and following the two World Wars (1) . Today, they cover 33% of the continent and hold 12.1 PgC of above-ground biomass carbon (AGC, Fig. 1A), based on a recent dataset established from National Forest Inventories (NFIs) at a sub-national scale for 2020 (2) . Forests constitute the main carbon sink of the European Union (EU-27), which has implemented a revised regulation aiming to achieve an annual carbon sink of 310 MtCO 2 eq in the land use sector by 2030 (3) . In this study, we define the forest carbon sink as the sum of five components: the net carbon stock change in AGC, below-ground biomass, soils (organic and mineral), deadwood and litter, and harvested wood products. [2] Despite their importance for carbon sequestration, Europe’s forests are facing increasing pressure (Fig. 1B) from timber harvest (4,5) , as well as from natural disturbances such as wildfires, storms, bark beetle outbreaks (6) , and drought and heatwave events (7) . Timber harvest is the most significant disturbance in Europe (Fig. 1C), accounting for 83-86% of all the forest area losses from 2001 to 2019, followed by storms (6-7%), fires (3-5%) and bark-beetles (less than 3%) based on the data from two independent studies (6,8) . The increasing rate of forest disturbances was already predicted in the 1990s (9) and has been confirmed by recent in-situ (6) and satellite (10) observations. Over the past three decades, the mortality of forest trees has almost doubled in Europe (11) , raising concerns about the future resilience of forests to disturbances (12) and their capacity to maintain their role as major carbon sinks (13-15) . Annual summaries of country reports under the UNFCCC indicate that the carbon sink of 69% of European forests has declined from 2010 to 2021, despite the forest area of Europe slightly increasing by 1.6% (Fig. 1D). [3] NFIs routinely monitor forest wood stocks through regular measurements of numerous field plots with statistical sampling schemes specific to each country (16) . However, inventories typically have a revisit cycle of five years, which complicates the tracking of changes in forest growth or stocks, and individual plot observations are not easily accessible to the scientific community due to economic interests and legislative issues (e.g., 47% of forests are privately owned (4) ). Spaceborne remote sensing offers an attractive data source for obtaining spatially explicit estimates of forest carbon stocks. We used two state-of-the-art annual AGC maps: one from CCI-ESA v5 (17) (100 m resolution, 2015 to 2021) and another from PlanetScope imagery v0.1 (18) (30 m resolution aggregated from 3 m nanosatellite images, available for 2019). The two map products are independent, allowing for the assessment of uncertainties, and have been bias-corrected to align with the forest cover and AGC levels reported by NFIs at a sub-national scale (2) . [4] To assess current and predict future AGC changes, we leveraged a recent European disturbance map based on 30 m resolution Landsat data from 1986 to 2020 (10) . This map allowed us to estimate trends in AGC loss due to disturbances by spatially and temporally aggregating forest cover loss data from 30 m to 18 km. Then, we estimated AGC gains from forest regrowth following disturbances (19) using a space-for-time methodology to derive spatially explicit local recovery curves across all Europe from AGC maps (Fig. 2A). These curves have been validated with an independent in-situ dataset on forest age and AGC (20) (Fig. 2B). Our approach extends the method originally developed for tropical forests (21) by considering local recovery curves (18 km grid) instead of continental-average curves. Finally, according to UNFCCC reports, the carbon sink of forests across all five European biogeographical regions (miniature in Fig. 1A) is primarily driven by the net carbon stock change in AGC rather than in soils, deadwood, and harvested wood products (supplementary Fig. S4c). We therefore estimated each non-AGC forest sink component based on annual AGC changes using linear relationships derived from UNFCCC data for each biogeographical region. Remote-sensing and ground-based data have been integrated into our study to reconcile differences that have sparked debate these last years (see Matters Arising in Nature (22) ). [5] The resulting data-driven carbon model (DDCM, Fig. 3D) simulates annual AGC stocks and each forest sink component from 2010 to 2030 on an 18×18 km² grid, based on the local imbalance between AGC loss due to disturbances and subsequent AGC recovery. To project the future forest carbon sink, we conservatively assumed that future disturbances would follow the same local trends as in the past 35 years while future AGC recovery curves would remain unchanged. Our projections for carbon sink trajectories are spatially explicit and can be aggregated at the national level for each EU-27 country. This allows for comparison with the 2030 carbon sink target for the forest sector, which contributes to the broader land-use sector mitigation goal set by the European Commission (Fig. 4 and supplementary Fig. S7). By partitioning harvests and natural disturbances based on their constant ratio at 18 km (supplementary Fig. S13 and S14) and adjusting the harvest trends in the DDCM, we infer the reduction in harvesting necessary to meet the 2030 target (while accounting for the observed increase in natural disturbances). The DDCM calibration involved only two parameters to ensure that simulated changes in AGC match UNFCCC reports across the recent historical period (2010-2021, Fig. 3E). The first parameter is the annual percentage of AGC loss from disturbances at 18 km. The second is the maximum AGC achievable by mature forests at 18 km (AGC pot ). This parameterization implicitly accounts for losses from low-severity disturbances (e.g., selective logging, thinning), common across Europe but often undetected by Landsat (23) . It also ensures we do not underestimate AGC pot due to the scarcity of old-growth reference forest data in Europe (only 2.2% of forests remain untouched by humans (4) ). Forest recovery after disturbance [6] Forest recovery after disturbance shows significant variations across different biogeographical regions of Europe (19) (Fig. 2A). In the Boreal region, forests typically need 118 [93,163] years on average to regain 90% of their maximum reachable AGC (AGC pot ) after a stand-replacing disturbance event. The confidence intervals in brackets show the range obtained from model parameterization conducted on different AGC maps at different scales. Forests in the Atlantic also take about a century to recover (101 [64,181] years), while recovery in the Alpine and Continental regions is twice as slow (239 [157,312] years). Their recovery is slower because it is defined here as a percentage of AGC pot , which is much higher in the Alpine and Continental regions (184 [162,223] MgC/haF, with haF standing for hectares of forest) compared to the Boreal and Atlantic regions (104 [85,147] MgC/haF). However, the Mediterranean region has the longest recovery time (more than 300 years) with the lowest potential AGC (72 [59,92] MgC/haF) due to being water-limited. The accuracy of local recovery curves is confirmed by the small mean anomalies between satellite-derived and field-observed AGC (Fig. 2B) when the sample size is large enough for a given age. However, the precision of these curves is limited (large variability seen in the anomalies) due to the influence of varying factors (e.g., climate, soil, forest management) as well as the inherent uncertainties in in-situ measurements (supplementary Fig. S10). Carbon storage potential in a world without disturbance [7] On a global scale, studies have suggested that forests could significantly increase their biomass if allowed to regrow partially or fully (24-28) . We estimate that Europe could potentially increase its AGC stock by 15.8 [11.9,19.0] PgC, corresponding to a ~130% increase in AGC carbon stocks relative to the period 2017-2020 (for the same forest area). This result is consistent with the conclusions of a recent study based on old-growth forests in Europe (29) . Being far from their saturation point, these forests reflect a broader shift in forest dynamics (5,30) characterized by a rapid increase in forest turnover rates. Mature forests steadily decline due to increased tree mortality, leading to significant ecological consequences (31) . Repeated disturbances affecting forests are not accounted for in these estimations, therefore the disturbance trends are implemented in the DDCM to provide more realistic projections of future AGC stocks (Fig. 3D). Inconsistencies in biomass change detection using remote sensing [9] While the CCI-ESA biomass maps correctly capture the spatial variability of AGC among young, mature, and old forests (Fig. 2B), calculating AGC changes as the year-on-year difference between two consecutive AGC maps is unsuitable, as it produces AGC changes that are inconsistent with UNFCCC reports across the recent historical period (Fig. 3E, blue triangles versus black dots). For instance, this ‘brute force’ approach produces a net loss of AGC in the Continental region from 2017 to 2021 (except in 2020), contradicting the AGC accumulation reported by the UNFCCC for that period. In contrast, the AGC change simulations from the DDCM closely match UNFCCC data across all biogeographical regions (Fig. 3E). The NFI data used in the UNFCCC reports provide robust national-scale estimates of biomass carbon stock changes over time with a ~30% uncertainty (35-38) . However, they cannot provide insights into fine-scale spatial patterns; a gap effectively addressed by the DDCM that is spatially explicit (see next sections). Future carbon sink of Europe’s forests [10] According to the DDCM, the net carbon sink of EU-27 forests is projected to decrease from 496 [459,521] MtCO 2 eq/year in 2010 to 279 [269,294] MtCO 2 eq/year by 2030 (Fig. 4A). This projection assumes that forest recovery curves remain unchanged in the near future (Fig. 2A) and that forest management and natural disturbances will continue linearly until 2030, following past trends observed in the mean percentage of AGC loss across 18 km grids (Fig. 3D). The largest decrease in the forest carbon sink is expected in the Boreal region, with a decline of 62%, while the Mediterranean region is predicted to maintain a stable sink. The forest carbon sink consists of five components, each with different significance and behavior (Fig. 4A). The net carbon stock change in living above- and below-ground biomass (ΔAGC + ΔBGC) will see a large decrease of 63% from 2010 to 2030 in the EU-27, which is the primary driver of the overall decline. However, this decline will be partially offset by increases in the net carbon stock changes of harvested wood products (39) (ΔHWP, +96%) and deadwood and litter resulting from recent disturbances (ΔDWL, +71%). The net carbon stock change in soils is expected to decrease by 17%, consistent with our current understanding of the impact of harvests on soil carbon dynamics (40) . While ΔHWP and ΔDWL only accounted for ~11% of the forest carbon sink in 2010, they are projected to contribute ~35% by 2030, acting as a temporary buffer against the declining carbon sink of living biomass and soils. [11] The resolution of the DDCM allows for the detailed computation of spatial variations in the AGC sink (Fig. 4B) in addition to the overall forest carbon budget (Fig. 4C) from 2010 to 2030. Regions such as Southern Germany, Northern Belarus, Northern Sweden, Southern Romania, Central Spain, the Pyrenees, and the Dinaric mountains are expected to continue to accumulate large proportions of AGC from 2010 to 2030 (Fig. 4B). These gains are attributed to either stationary or decreasing trends in disturbance levels (partly due to recovery from old disturbances like in Southern Germany), unlike other parts of the continent (Fig. 1B), where 12% of forests are projected to experience a net AGC loss (agreement between CCI-ESA and PlanetScope). Particularly, forests in Portugal, Slovenia, Southern France, Austria, Czechia, Estonia, Latvia, Northern Ukraine, Northern Germany, the Eastern Alps, and parts of Scandinavia are projected to face reductions in AGC (Fig. 4B). However, the impacts of these losses will be partially offset by increases in the net carbon stock changes in HWP and DWL (Fig. 4A), which will help compensate for the AGC loss in about half of these areas. Overall, our estimates indicate that at least 6% of European forests will become net carbon sources between 2010 and 2030 (Fig. 4C, agreement between CCI-ESA and PlanetScope). Challenges and mitigation strategies [12] A common assumption is that sustainable harvests carry a low carbon cost according to the rationale that (i) HWPs serve as a reliable long-term carbon sequestration asset, and (ii) young stands replacing mature forests are compensating for the carbon deficit because they are more productive (41). Based on these premises, the EU-27 has endorsed wood use for bioenergy, which now represents 59% of renewable energy (42) . Regarding point (i), 69% of global HWPs have very short lifespans, significantly reducing their contribution to the forest carbon sink (41) . For instance, estimates from the French NFIs show that 68% of HWPs are used as bioenergy (43) . Concerning point (ii), the DDCM demonstrates that the recovery of young and productive stands is insufficient to offset carbon losses from current natural and anthropogenic disturbances (Fig. 4A). These projections also address point (i) as they implicitly incorporate carbon transfers across different pools (especially HWPs and DWLs). The EU-27 plan could be made more effective by extending HWP lifespans (for example, using wood for construction material) and reducing wood use for bioenergy (by promoting other renewable energies) to lower harvest rates, especially as natural disturbances continue to increase dramatically (Fig. 3B). The impact of reducing harvest rates on the forest carbon sink is estimated below. [13] We estimate that the forest carbon sink of the EU-27 will be 29% lower than the 2030 sink target (forest state of 2016-2018), resulting in a carbon deficit of -113 MtCO 2 eq/year upon the target sink in the forest sector. Despite the EU-27's plan to plant 3 billion trees by 2030, this initiative will only contribute an additional 15 MtCO 2 eq/year to the carbon sink (44) , which is insufficient to close the gap. According to DDCM simulations, a 26 [20,31] % decrease in harvest from 2025 to 2030, in addition to the 3 billion new trees, would be sufficient for the EU-27 to reach the target (Fig. 4A). This estimate is a first-order assessment and should be refined in future studies by examining other forest management options than simply reducing harvest (45,46) . Forest biomass expansion could, for instance, be promoted by regenerating forests with thinning, changing rotations, considering biodiversity restoration versus monocultures, or choosing non-intervention versus salvage logging after a disturbance (34) . All these solutions need to be explored, as a continent-wide reduction in harvests will increase carbon market leakage, with harvests increasing outside Europe to meet European demand. Three recent studies predict that the forest sink values in 2030 will fall below the EU-27 target, based on different modeling approaches: large-scale simulations from a forestry carbon model (CBM) with business-as-usual forest management assumptions (46) , multiple statistical extrapolations of current trends (39) , and land-climate models under different Representative Concentration Pathway scenarios (47) . Our data-driven model assumes that disturbances will evolve in the next six years as they did in the past, forecasting a less optimistic carbon sink for living biomass (ΔAGC and ΔBGC) compared to CBM predictions: 152 MtCO 2 eq/year (DDCM) versus 240 MtCO 2 eq/year (CBM) by 2030 for the EU-27. However, it is important to note that the CBM forestry model did not capture the recent declines in forest carbon sinks as reported in the latest UNFCCC data from 2023 (46) . [14] Several limitations are acknowledged in our study. First, shifts in disturbance trends or changes in the growth rates of recovering forests (19) , whether due to natural or anthropogenic reasons, will impact the simulated trajectories. This is why we chose not to extend forecasts beyond 2030. Secondly, land-use changes such as deforestation and reforestation are not factored in, with the reasonable assumption that forest cover remains largely constant (only +1.6% increase from 2000 to 2021, Fig. 1D). Thirdly, we assumed that forests would continue to recover as they have in the past, regardless of disturbance frequency and severity (or increasing droughts). Notably, our model does not capture non-linear processes such as cascading effects in disturbance interactions (e.g., bark beetle outbreaks after a heatwave). This overlooks the potential for ecological tipping points (12) , beyond which the resilience of ecosystems is altered. Finally, the type and severity of disturbance (fires, storms, harvests) or the disturbance patch size, along with variations in forest structure (e.g., old versus young, coniferous versus deciduous, plantations versus natural forests), might change the way forests recover (48) . We plan to address these factors and their potential legacy effects in future studies. Conclusions [15] We present evidence that Europe’s forests are increasingly at risk of losing their role as carbon sinks, primarily due to a dramatic increase in natural disturbances alongside a moderate increase in harvests. Although these forests have the potential to double their AGC stocks within the same forest area, disturbances are currently outpacing AGC recovery in 12% of European forests. Alarmingly, half of these endangered forests are projected to become net carbon sources by 2030. The carbon sequestration capacity of the remaining forests is progressively deteriorating, a trend exacerbated by business-as-usual forest management practices. Our projections for the near future are less optimistic than the EU-27’s target, which will significantly impact European climate change mitigation plans that rely on increasing forest area. Over the past two decades, forest expansion has been limited. By November 2024, only 22 million trees had been planted in Europe (49) , falling short of the ambitious pledge to plant 3 billion trees by 2030. Even if this pledge were met, we estimate that a 26% decrease in forest harvest from 2025 to 2030 would be necessary for the EU-27 to reach their target. Historically, forest management in Europe has accumulated a substantial carbon debt (50) , further exacerbated by recent natural disturbances. To mitigate the decline in the European carbon sink, a major shift in forest management practices is essential, focusing on increasing resilience and better adapting to natural disturbances. Declarations ACKNOWLEDGEMENTS The views expressed are purely those of the writers and may not in any circumstances be regarded as stating an official position of the European Commission. Funding: This work benefited from: the French state aid managed by the ANR under the "Investissements d'avenir" programme with the reference ANR-16-CONV-0003; European Space Agency (ESA) Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/4000123662); RECCAP2 project 1190 (ESA ESRIN/4000123002/18/I-NB); DFF Sapere Aude (9064-00049B); MSCA “PARDI” (101109551). Author contributions: F.R. and P.C. conceived the idea and designed the methodology. 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Carbon benefits from Forest Transitions promoting biomass expansions and thickening. Glob. Change Biol. 26 , 5365-5370 (2020). https://doi.org/10.1111/gcb.15292 Korosuo, A., Pilli, R., Abad Viñas, R. et al. The role of forests in the EU climate policy: are we on the right track?. Carbon Balance Manage. 18 , 15 (2023). https://doi.org/10.1186/s13021-023-00234-0 Pilli, R., Alkama, R., Cescatti, A., Kurz, W. A. and Grassi, G. The European forest carbon budget under future climate conditions and current management practices. Biogeosci. 19 , 3263-3284(2022). https://doi.org/10.5194/bg-19-3263-2022 Babst, F. et al. Twentieth century redistribution in climatic drivers of global tree growth. Sci. Adv. 5 , eaat4313(2019). https://doi.org/10.1126/sciadv.aat4313 Forest Information System in Europe (9 November, 2024). https://forest.eea.europa.eu/3-billion-trees/introduction Naudts, K. et al. Forest management: Europe’s forest management did not mitigate climate warming. Science 351 , 597–600(2016). https://doi.org/10.1126/science.aad7270 Additional Declarations There is NO Competing Interest. Supplementary Files RITTERsupplementaryDDCMv1.1.docx Supplementary Material 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-3671432","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443731795,"identity":"12cfa4b2-df49-4a8b-98fd-1c6d1168eecd","order_by":0,"name":"Francois 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09:15:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3671432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3671432/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80731285,"identity":"2c04aee0-cf6a-4686-b5f2-19885b9c1d40","added_by":"auto","created_at":"2025-04-16 12:38:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":481840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest state in Europe during the historical period. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eAbove-ground biomass for the year 2019, averaged from two products: CCI-ESA v5 (solid lines in the histograms \u003cem\u003e(17)\u003c/em\u003e) and PlanetScope v0.1 (dashed lines \u003cem\u003e(18)\u003c/em\u003e), both bias-corrected with NFI data at a sub-national scale. Histograms show the distributions within five biogeographical regions indicated in the miniature. (\u003cstrong\u003eB\u003c/strong\u003e) Trends in forest cover loss (Landsat \u003cem\u003e(10)\u003c/em\u003e). Scatterplots display the annual percentage of forest cover loss due to natural and anthropogenic disturbances. (\u003cstrong\u003eC\u003c/strong\u003e) Disturbance partitioning assessed from ground-based \u003cem\u003e(6)\u003c/em\u003e and Landsat-based \u003cem\u003e(8)\u003c/em\u003e data. Some disturbance agents are merged due to Landsat’s limited sensitivity. (\u003cstrong\u003eD\u003c/strong\u003e) Country reports to UNFCCC of forest area (FA) and forest sink (FS) from 2010 to 2021. Four groups are separated based on the national forest sink trends: small decrease (in orange), large decrease (in red), increase (in green, observed in Hungary and Liechtenstein only) and no trend (in gray). The unit haF stands for hectares of forests.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/c1b7aee44dd5212aac18b251.png"},{"id":80730529,"identity":"3c650ad4-7a7c-4e6f-8e30-f47a259b4c5e","added_by":"auto","created_at":"2025-04-16 12:30:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest recovery after disturbance.\u003c/strong\u003e (A) AGC recovery curves at 18 km, with the median AGC values post-disturbance shown across the 30-year Landsat range. The potential AGC (AGC\u003csub\u003epot\u003c/sub\u003e) is shown at 250 years for visualization purposes (AGC approaches AGC\u003csub\u003epot\u003c/sub\u003e asymptotically, without ever fully reaching it), and the black vertical bar represents the time required for forests to recover 90% of their AGC\u003csub\u003epot\u003c/sub\u003e. Whiskers represent the 95% range of values across all 18 km pixels. (B) Validation with age and AGC of trees from 383 sites in Europe. Thick curves represent the moving mean of anomalies. (C) Locations of in-situ sites and biogeographical regions.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/aa466e90b213e1d31f526220.png"},{"id":80731287,"identity":"b58a6d78-8e2d-4f6e-9ecc-1c91e9b0e327","added_by":"auto","created_at":"2025-04-16 12:38:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":215359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in forest growth and disturbances, DDCM procedure, and remote sensing inconsistencies in biomass change\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Comparison of mean AGC (CCI-ESA) of forests from 5 to 30 years old (Landsat range) between the periods 2015-2017 and 2019-2021 across Europe. (\u003cstrong\u003eB\u003c/strong\u003e) The volume of wood loss in blue\u0026nbsp;\u003cem\u003e(6)\u003c/em\u003e and the percentage of forest cover loss in red\u0026nbsp;\u003cem\u003e(8)\u003c/em\u003e due to natural disturbances in the EU-27 from 2010 to 2019. (\u003cstrong\u003eC\u003c/strong\u003e) Roundwood removals in blue\u0026nbsp;\u003cem\u003e(32)\u003c/em\u003e and the percentage of forest cover loss in red\u0026nbsp;\u003cem\u003e(8)\u0026nbsp;\u003c/em\u003edue to harvest or salvage logging in the EU-27 from 2010 to 2020.\u0026nbsp;(\u003cstrong\u003eD\u003c/strong\u003e) AGC simulations from the data-driven carbon model (DDCM) for an 18 km pixel, with the mean AGC loss percentage scaled from forest cover loss. (\u003cstrong\u003eE\u003c/strong\u003e) Annual AGC changes from CCI-ESA maps, UNFCCC reports, and DDCM simulations from 2010 to 2021.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/62de5fd27fe96dc6b6551fed.png"},{"id":80731877,"identity":"8effcdd0-80ec-4b78-ae88-5ad2d6e88927","added_by":"auto","created_at":"2025-04-16 12:46:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1036463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial and temporal changes in the carbon sink of European forests. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Forest sink for five biogeographical regions and the EU-27 under two management scenarios: business as usual and a 26% harvest reduction with 3 billion trees planted by 2030 (their total forest sink is in pink). The EU-27 forest sink target is estimated at 392 MtCO\u003csub\u003e2\u003c/sub\u003eeq (average for 2016-2018). The black-shaded area shows variability from PlanetScope or CCI-ESA maps with country- or region-specific parameterization. (\u003cstrong\u003eB\u003c/strong\u003e) Simulated difference in AGC between 2030 and 2010. (\u003cstrong\u003eC\u003c/strong\u003e) Simulated difference in total forest carbon stocks (AGC, BGC, HWP, soils, deadwood, and litter) between 2030 and 2010 (values are divided by two to match the legend). Inconsistencies between CCI-ESA and PlanetScope are shown in gray (when one predicts a carbon source and the other a carbon sink).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/da7152f257314bdd64d7d723.jpeg"},{"id":96708101,"identity":"6793743a-b5e5-4882-8f60-a518482d8e8f","added_by":"auto","created_at":"2025-11-25 09:56:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2812474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/63b62686-c76c-4082-bf1b-6f552ef6daf7.pdf"},{"id":80730549,"identity":"5a7cedb0-8228-4966-b7de-d13619864124","added_by":"auto","created_at":"2025-04-16 12:30:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7955810,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"RITTERsupplementaryDDCMv1.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3671432/v1/25e588b17b2656b3fb828f28.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Alarming decline in the carbon sink of European forests driven by disturbances","fulltext":[{"header":"Full Text","content":"\u003cp\u003e[1]\u0026nbsp;European forests have gradually recovered from major timber exploitation during and following the two World Wars\u0026nbsp;\u003cem\u003e(1)\u003c/em\u003e. Today, they cover 33% of the continent and hold 12.1 PgC of above-ground biomass carbon (AGC, Fig. 1A), based on a recent dataset established from National Forest Inventories (NFIs) at a sub-national scale for 2020\u0026nbsp;\u003cem\u003e(2)\u003c/em\u003e. Forests constitute the main carbon sink of the European Union (EU-27), which has implemented a revised regulation aiming to achieve an annual carbon sink of 310 MtCO\u003csub\u003e2\u003c/sub\u003eeq in the land use sector by 2030\u0026nbsp;\u003cem\u003e(3)\u003c/em\u003e. In this study, we define the forest carbon sink as the sum of five components: the net carbon stock change in AGC, below-ground biomass, soils (organic and mineral), deadwood and litter, and harvested wood products.\u003c/p\u003e\n\u003cp\u003e[2] Despite their importance for carbon sequestration, Europe\u0026rsquo;s forests are facing increasing pressure (Fig. 1B) from timber harvest \u003cem\u003e(4,5)\u003c/em\u003e, as well as from natural disturbances such as wildfires, storms, bark beetle outbreaks \u003cem\u003e(6)\u003c/em\u003e, and drought and heatwave events \u003cem\u003e(7)\u003c/em\u003e. Timber harvest is the most significant disturbance in Europe (Fig. 1C), accounting for 83-86% of all the forest area losses from 2001 to 2019, followed by storms (6-7%), fires (3-5%) and bark-beetles (less than 3%) based on the data from two independent studies \u003cem\u003e(6,8)\u003c/em\u003e. The increasing rate of forest disturbances was already predicted in the 1990s \u003cem\u003e(9)\u003c/em\u003e and has been confirmed by recent in-situ \u003cem\u003e(6)\u003c/em\u003e and satellite \u003cem\u003e(10)\u003c/em\u003e observations. Over the past three decades, the mortality of forest trees has almost doubled in Europe \u003cem\u003e(11)\u003c/em\u003e, raising concerns about the future resilience of forests to disturbances \u003cem\u003e(12)\u003c/em\u003e and their capacity to maintain their role as major carbon sinks \u003cem\u003e(13-15)\u003c/em\u003e. Annual summaries of country reports under the UNFCCC indicate that the carbon sink of 69% of European forests has declined from 2010 to 2021, despite the forest area of Europe slightly increasing by 1.6% (Fig. 1D).\u003c/p\u003e\n\u003cp\u003e[3]\u0026nbsp;NFIs routinely monitor forest wood stocks through regular measurements of numerous field plots with statistical sampling schemes specific to each country\u0026nbsp;\u003cem\u003e(16)\u003c/em\u003e. However, inventories typically have a revisit cycle of five years, which complicates the tracking of changes in forest growth or stocks, and individual plot observations are not easily accessible to the scientific community due to economic interests and legislative issues (e.g., 47% of forests are privately owned\u0026nbsp;\u003cem\u003e(4)\u003c/em\u003e). Spaceborne remote sensing offers an attractive data source for obtaining spatially explicit estimates of forest carbon stocks. We used two state-of-the-art annual AGC maps: one from CCI-ESA v5\u0026nbsp;\u003cem\u003e(17)\u003c/em\u003e (100 m resolution, 2015 to 2021) and another from PlanetScope imagery v0.1\u0026nbsp;\u003cem\u003e(18)\u003c/em\u003e (30 m resolution aggregated from 3 m nanosatellite images, available for 2019). The two map products are independent, allowing for the assessment of uncertainties, and have been bias-corrected to align with the forest cover and AGC levels reported by NFIs at a sub-national scale\u0026nbsp;\u003cem\u003e(2)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e[4]\u0026nbsp;To assess current and predict future AGC changes, we leveraged a recent European disturbance map based on 30 m resolution Landsat data from 1986 to 2020\u0026nbsp;\u003cem\u003e(10)\u003c/em\u003e. This map allowed us to estimate trends in AGC loss due to disturbances by spatially and temporally aggregating forest cover loss data from 30 m to 18 km. Then, we estimated AGC gains from forest regrowth following disturbances\u0026nbsp;\u003cem\u003e(19)\u003c/em\u003e using a space-for-time methodology to derive spatially explicit local recovery curves across all Europe from AGC maps (Fig. 2A). These curves have been validated with an independent in-situ dataset on forest age and AGC\u0026nbsp;\u003cem\u003e(20)\u003c/em\u003e (Fig. 2B). Our approach extends the method originally developed for tropical forests\u0026nbsp;\u003cem\u003e(21)\u003c/em\u003e by considering local recovery curves (18 km grid) instead of continental-average curves. Finally, according to UNFCCC reports, the carbon sink of forests across all five European biogeographical regions (miniature in Fig. 1A) is primarily driven by the net carbon stock change in AGC rather than in soils, deadwood, and harvested wood products (supplementary Fig. S4c). We therefore estimated each non-AGC forest sink component based on annual AGC changes using linear relationships derived from UNFCCC data for each biogeographical region. Remote-sensing and ground-based data have been integrated into our study to reconcile differences that have sparked debate these last years (see \u003cem\u003eMatters Arising\u003c/em\u003e in \u003cem\u003eNature\u003c/em\u003e \u003cem\u003e(22)\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e[5]\u0026nbsp;The resulting data-driven carbon model (DDCM, Fig. 3D) simulates annual AGC stocks and each forest sink component from 2010 to 2030 on an 18\u0026times;18 km\u0026sup2; grid, based on the local imbalance between AGC loss due to disturbances and subsequent AGC recovery. To project the future forest carbon sink, we conservatively assumed that future disturbances would follow the same local trends as in the past 35 years while future AGC recovery curves would remain unchanged. Our projections for carbon sink trajectories are spatially explicit and can be aggregated at the national level for each EU-27 country. This allows for comparison with the 2030 carbon sink target for the forest sector, which contributes to the broader land-use sector mitigation goal set by the European Commission (Fig. 4 and supplementary Fig. S7). By partitioning harvests and natural disturbances based on their constant ratio at 18 km (supplementary Fig. S13 and S14) and adjusting the harvest trends in the DDCM, we infer the reduction in harvesting necessary to meet the 2030 target (while accounting for the observed increase in natural disturbances). The DDCM calibration involved only two parameters to ensure that simulated changes in AGC match UNFCCC reports across the recent historical period (2010-2021, Fig. 3E). The first parameter is the annual percentage of AGC loss from disturbances at 18 km. The second is the maximum AGC achievable by mature forests at 18 km (AGC\u003csub\u003epot\u003c/sub\u003e). This parameterization implicitly accounts for losses from low-severity disturbances (e.g., selective logging, thinning), common across Europe but often undetected by Landsat \u003cem\u003e(23)\u003c/em\u003e. It also ensures we do not underestimate AGC\u003csub\u003epot\u003c/sub\u003e due to the scarcity of old-growth reference forest data in Europe (only 2.2% of forests remain untouched by humans \u003cem\u003e(4)\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eForest recovery after disturbance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[6] Forest recovery after disturbance shows significant variations across different biogeographical regions of Europe \u003cem\u003e(19)\u003c/em\u003e (Fig. 2A). In the Boreal region, forests typically need 118 [93,163] years on average to regain 90% of their maximum reachable AGC (AGC\u003csub\u003epot\u003c/sub\u003e) after a stand-replacing disturbance event. The confidence intervals in brackets show the range obtained from model parameterization conducted on different AGC maps at different scales. Forests in the Atlantic also take about a century to recover (101 [64,181] years), while recovery in the Alpine and Continental regions is twice as slow (239 [157,312] years). Their recovery is slower because it is defined here as a percentage of AGC\u003csub\u003epot\u003c/sub\u003e, which is much higher in the Alpine and Continental regions (184 [162,223] MgC/haF, with haF standing for hectares of forest) compared to the Boreal and Atlantic regions (104 [85,147] MgC/haF). However, the Mediterranean region has the longest recovery time (more than 300 years) with the lowest potential AGC (72 [59,92] MgC/haF) due to being water-limited. The accuracy of local recovery curves is confirmed by the small mean anomalies between satellite-derived and field-observed AGC (Fig. 2B) when the sample size is large enough for a given age. However, the precision of these curves is limited (large variability seen in the anomalies) due to the influence of varying factors (e.g., climate, soil, forest management) as well as the inherent uncertainties in in-situ measurements (supplementary Fig. S10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon storage potential in a world without disturbance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[7] On a global scale, studies have suggested that forests could significantly increase their biomass if allowed to regrow partially or fully \u003cem\u003e(24-28)\u003c/em\u003e. We estimate that Europe could potentially increase its AGC stock by 15.8 [11.9,19.0] PgC, corresponding to a ~130% increase in AGC carbon stocks relative to the period 2017-2020 (for the same forest area). This result is consistent with the conclusions of a recent study based on old-growth forests in Europe \u003cem\u003e(29)\u003c/em\u003e. Being far from their saturation point, these forests reflect a broader shift in forest dynamics \u003cem\u003e(5,30)\u003c/em\u003e characterized by a rapid increase in forest turnover rates. Mature forests steadily decline due to increased tree mortality, leading to significant ecological consequences \u003cem\u003e(31)\u003c/em\u003e. Repeated disturbances affecting forests are not accounted for in these estimations, therefore the disturbance trends are implemented in the DDCM to provide more realistic projections of future AGC stocks (Fig. 3D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eInconsistencies in biomass change detection using remote sensing\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[9] While the CCI-ESA biomass maps correctly capture the spatial variability of AGC among young, mature, and old forests (Fig. 2B), calculating AGC changes as the year-on-year difference between two consecutive AGC maps is unsuitable, as it produces AGC changes that are inconsistent with UNFCCC reports across the recent historical period (Fig. 3E, blue triangles versus black dots). For instance, this \u0026lsquo;brute force\u0026rsquo; approach produces a net loss of AGC in the Continental region from 2017 to 2021 (except in 2020), contradicting the AGC accumulation reported by the UNFCCC for that period. In contrast, the AGC change simulations from the DDCM closely match UNFCCC data across all biogeographical regions (Fig. 3E). The NFI data used in the UNFCCC reports provide robust national-scale estimates of biomass carbon stock changes over time with a ~30% uncertainty \u003cem\u003e(35-38)\u003c/em\u003e. However, they cannot provide insights into fine-scale spatial patterns; a gap effectively addressed by the DDCM that is spatially explicit (see next sections).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture carbon sink of Europe\u0026rsquo;s forests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[10]\u0026nbsp;According to the DDCM, the net carbon sink of EU-27 forests is projected to decrease from 496 [459,521] MtCO\u003csub\u003e2\u003c/sub\u003eeq/year in 2010 to 279 [269,294] MtCO\u003csub\u003e2\u003c/sub\u003eeq/year by 2030 (Fig. 4A). This projection assumes that forest recovery curves remain unchanged in the near future (Fig. 2A) and that forest management and natural disturbances will continue linearly until 2030, following past trends observed in the mean percentage of AGC loss across 18 km grids (Fig. 3D). The largest decrease in the forest carbon sink is expected in the Boreal region, with a decline of 62%, while the Mediterranean region is predicted to maintain a stable sink. The forest carbon sink consists of five components, each with different significance and behavior (Fig. 4A). The net carbon stock change in living above- and below-ground biomass (\u0026Delta;AGC + \u0026Delta;BGC) will see a large decrease of 63% from 2010 to 2030 in the EU-27, which is the primary driver of the overall decline. However, this decline will be partially offset by increases in the net carbon stock changes of harvested wood products\u0026nbsp;\u003cem\u003e(39)\u003c/em\u003e (\u0026Delta;HWP, +96%) and deadwood and litter resulting from recent disturbances (\u0026Delta;DWL, +71%). The net carbon stock change in soils is expected to decrease by 17%, consistent with our current understanding of the impact of harvests on soil carbon dynamics\u0026nbsp;\u003cem\u003e(40)\u003c/em\u003e. While \u0026Delta;HWP and \u0026Delta;DWL only accounted for ~11% of the forest carbon sink in 2010, they are projected to contribute ~35% by 2030, acting as a temporary buffer against the declining carbon sink of living biomass and soils.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[11]\u0026nbsp;The resolution of the DDCM allows for the detailed computation of spatial variations in the AGC sink (Fig. 4B) in addition to the overall forest carbon budget (Fig. 4C) from 2010 to 2030. Regions such as Southern Germany, Northern Belarus, Northern Sweden, Southern Romania, Central Spain, the Pyrenees, and the Dinaric mountains are expected to continue to accumulate large proportions of AGC from 2010 to 2030 (Fig. 4B). These gains are attributed to either stationary or decreasing trends in disturbance levels (partly due to recovery from old disturbances like in Southern Germany), unlike other parts of the continent (Fig. 1B), where 12% of forests are projected to experience a net AGC loss (agreement between CCI-ESA and PlanetScope). Particularly, forests in Portugal, Slovenia, Southern France, Austria, Czechia, Estonia, Latvia, Northern Ukraine, Northern Germany, the Eastern Alps, and parts of Scandinavia are projected to face reductions in AGC (Fig. 4B). However, the impacts of these losses will be partially offset by increases in the net carbon stock changes in HWP and DWL (Fig. 4A), which will help compensate for the AGC loss in about half of these areas. Overall, our estimates indicate that at least 6% of European forests will become net carbon sources between 2010 and 2030 (Fig. 4C, agreement between CCI-ESA and PlanetScope).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChallenges and mitigation strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[12] A common assumption is that sustainable harvests carry a low carbon cost according to the rationale that (i) HWPs serve as a reliable long-term carbon sequestration asset, and (ii) young stands replacing mature forests are compensating for the carbon deficit because they are more productive \u003cem\u003e(41).\u003c/em\u003e Based on these premises, the EU-27 has endorsed wood use for bioenergy, which now represents 59% of renewable energy \u003cem\u003e(42)\u003c/em\u003e. Regarding point (i), 69% of global HWPs have very short lifespans, significantly reducing their contribution to the forest carbon sink \u003cem\u003e(41)\u003c/em\u003e. For instance, estimates from the French NFIs show that 68% of HWPs are used as bioenergy \u003cem\u003e(43)\u003c/em\u003e.\u0026nbsp;Concerning point (ii), the DDCM demonstrates that the recovery of young and productive stands is insufficient to offset carbon losses from current natural and anthropogenic disturbances (Fig. 4A). These projections also address point (i) as they implicitly incorporate carbon transfers across different pools (especially HWPs and DWLs). The EU-27 plan could be made more effective by extending HWP lifespans (for example, using wood for construction material) and reducing wood use for bioenergy (by promoting other renewable energies) to lower harvest rates, especially as natural disturbances continue to increase dramatically (Fig. 3B). The impact of reducing harvest rates on the forest carbon sink is estimated below.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e[13]\u0026nbsp;We estimate that the forest carbon sink of the EU-27 will be 29% lower than the 2030 sink target (forest state of 2016-2018), resulting in a carbon deficit of -113 MtCO\u003csub\u003e2\u003c/sub\u003eeq/year upon the target sink in the forest sector. Despite the EU-27\u0026apos;s plan to plant 3 billion trees by 2030, this initiative will only contribute an additional 15 MtCO\u003csub\u003e2\u003c/sub\u003eeq/year to the carbon sink \u003cem\u003e(44)\u003c/em\u003e, which is insufficient to close the gap. According to DDCM simulations, a 26 [20,31] % decrease in harvest from 2025 to 2030, in addition to the 3 billion new trees, would be sufficient for the EU-27 to reach the target (Fig. 4A). This estimate is a first-order assessment and should be refined in future studies by examining other forest management options than simply reducing harvest \u003cem\u003e(45,46)\u003c/em\u003e. Forest biomass expansion could, for instance, be promoted by regenerating forests with thinning, changing rotations, considering biodiversity restoration versus monocultures, or choosing non-intervention versus salvage logging after a disturbance \u003cem\u003e(34)\u003c/em\u003e. All these solutions need to be explored, as a continent-wide reduction in harvests will increase carbon market leakage, with harvests increasing outside Europe to meet European demand. Three recent studies predict that the forest sink values in 2030 will fall below the EU-27 target, based on different modeling approaches: large-scale simulations from a forestry carbon model (CBM) with business-as-usual forest management assumptions \u003cem\u003e(46)\u003c/em\u003e, multiple statistical extrapolations of current trends \u003cem\u003e(39)\u003c/em\u003e, and land-climate models under different Representative Concentration Pathway scenarios \u003cem\u003e(47)\u003c/em\u003e. Our data-driven model assumes that disturbances will evolve in the next six years as they did in the past, forecasting a less optimistic carbon sink for living biomass (\u0026Delta;AGC and \u0026Delta;BGC) compared to CBM predictions: 152 MtCO\u003csub\u003e2\u003c/sub\u003eeq/year (DDCM) versus 240 MtCO\u003csub\u003e2\u003c/sub\u003eeq/year (CBM) by 2030 for the EU-27. However, it is important to note that the CBM forestry model did not capture the recent declines in forest carbon sinks as reported in the latest UNFCCC data from 2023 \u003cem\u003e(46)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e[14] Several limitations are acknowledged in our study. First, shifts in disturbance trends or changes in the growth rates of recovering forests \u003cem\u003e(19)\u003c/em\u003e, whether due to natural or anthropogenic reasons, will impact the simulated trajectories. This is why we chose not to extend forecasts beyond 2030. Secondly, land-use changes such as deforestation and reforestation are not factored in, with the reasonable assumption that forest cover remains largely constant (only +1.6% increase from 2000 to 2021, Fig. 1D). Thirdly, we assumed that forests would continue to recover as they have in the past, regardless of disturbance frequency and severity (or increasing droughts). Notably, our model does not capture non-linear processes such as cascading effects in disturbance interactions (e.g., bark beetle outbreaks after a heatwave). This overlooks the potential for ecological tipping points \u003cem\u003e(12)\u003c/em\u003e, beyond which the resilience of ecosystems is altered. Finally, the type and severity of disturbance (fires, storms, harvests) or the disturbance patch size, along with variations in forest structure (e.g., old versus young, coniferous versus deciduous, plantations versus natural forests), might change the way forests recover \u003cem\u003e(48)\u003c/em\u003e. We plan to address these factors and their potential legacy effects in future studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e[15] We present evidence that Europe\u0026rsquo;s forests are increasingly at risk of losing their role as carbon sinks, primarily due to a dramatic increase in natural disturbances alongside a moderate increase in harvests. Although these forests have the potential to double their AGC stocks within the same forest area, disturbances are currently outpacing AGC recovery in 12% of European forests. Alarmingly, half of these endangered forests are projected to become net carbon sources by 2030. The carbon sequestration capacity of the remaining forests is progressively deteriorating, a trend exacerbated by business-as-usual forest management practices. Our projections for the near future are less optimistic than the EU-27\u0026rsquo;s target, which will significantly impact European climate change mitigation plans that rely on increasing forest area. Over the past two decades, forest expansion has been limited. By November 2024, only 22\u0026nbsp;million trees had been planted in Europe \u003cem\u003e(49)\u003c/em\u003e, falling short of the ambitious pledge to plant 3\u0026nbsp;billion trees by 2030. Even if this pledge were met, we estimate that a 26% decrease in forest harvest from 2025 to 2030 would be necessary for the EU-27 to reach their target. Historically, forest management in Europe has accumulated a substantial carbon debt \u003cem\u003e(50)\u003c/em\u003e, further exacerbated by recent natural disturbances. To mitigate the decline in the European carbon sink, a major shift in forest management practices is essential, focusing on increasing resilience and better adapting to natural disturbances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe views expressed are purely those of the writers and may not in any circumstances be regarded as stating an official position of the European Commission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work benefited from: the French state aid managed by the ANR under the \u0026quot;Investissements d\u0026apos;avenir\u0026quot; programme with the reference ANR-16-CONV-0003; European Space Agency (ESA) Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/4000123662); RECCAP2 project 1190 (ESA ESRIN/4000123002/18/I-NB); DFF Sapere Aude (9064-00049B); MSCA \u0026ldquo;PARDI\u0026rdquo; (101109551).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e F.R. and P.C. conceived the idea and designed the methodology. F.R. wrote the R scripts, designed the figures and wrote the manuscript with contributions from P.C., C.S., Y.X., S.B., M.B., R.F., V.A., M.Sa., A.P-T., N.C., M.Sc. and I.F.. All authors contributed critically to interpreting the results and gave final approval for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and material availability:\u003c/strong\u003e The data used in this study are permanently and publicly available on a Zenodo repository (https://doi.org/10.5281/zenodo.14060104). The raw in-situ data are available upon request ([email protected]). The code in R and Python used in this study is permanently and publicly available on a Zenodo repository (https://doi.org/10.5281/zenodo.14060085).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFuchs, R., Herold, M., Verburg, P. H. and Clevers, J. G. A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe. \u003cem\u003eBiogeosciences \u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 1543\u0026ndash;1559(2013). https://doi.org/10.5194/bg-10-1543-2013\u003c/li\u003e\n\u003cli\u003eAvitabile, V., Pilli, R., Migliavacca, M. et al. Harmonised statistics and maps of forest biomass and increment in Europe. \u003cem\u003eSci. 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However, increased harvests and natural disturbances across Europe have recently challenged this role. To project the future carbon sink capacity of Europe's forests, we integrated country reports from the United Nations Framework Convention on Climate Change (UNFCCC) with remote sensing maps of disturbances and above-ground biomass. Our model simulates biomass dynamics at 18 km resolution from 2010 to 2030, predicting a 44% decrease in the EU-27 forest carbon sink, driven by disturbances outpacing biomass recovery. Consequently, the 2030 forest carbon sink will fall 29% short of EU-27 targets. We demonstrate that the three billion trees initiative is insufficient for climate change mitigation and needs to be combined with a 26% reduction in forest harvests from 2025 to 2030 to meet these targets.\u003c/p\u003e","manuscriptTitle":"Alarming decline in the carbon sink of European forests driven by disturbances","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 12:30:15","doi":"10.21203/rs.3.rs-3671432/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":"2efddeb3-6c95-4cbd-b2d8-50a9fdb08c48","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47242714,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47242715,"name":"Earth and environmental sciences/Climate sciences"}],"tags":[],"updatedAt":"2025-11-21T14:26:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 12:30:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3671432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3671432","identity":"rs-3671432","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}