Forest reburns are integral to southern Europe’s disturbance regimes

Forest reburns are integral to southern Europe’s disturbance regimes | 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 Research Article Forest reburns are integral to southern Europe’s disturbance regimes Alba Viana-Soto, Cornelius Senf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Global Ecology and Biogeography → Version 1 posted You are reading this latest preprint version Abstract Fires disturbances are integral to fire-prone landscapes of southern Europe. While evidence of changing fire frequency has been well documented in Europe, the dynamics of forest reburns - defined as previously burned areas that ignite again within intervals shorter than the historical range - remain largely unexplored. Here, we present the first large-scale characterization of reburns in southern Europe, using a novel remote sensing dataset on fire disturbances from 1985 to 2023. We quantified the spatial extent and frequency of reburns, revealing that 30.1% of burned area in southern Europe experienced multiple fire events within the 1985–2023 period (4.24 Mha), with 84.5% of these reburns occurring within a 20-year interval, and thus approaching the lower limit of reproductive maturity for many tree species. Extreme reburns within 10 years were also observed in 22.4%. Reburn hotspots emerged across the Mediterranean, where 19-21.1%yr − 1 of all fires were reburns within 20 years, and in the temperate forests of western Europe, where reburns accounted for 40.8% yr⁻¹. We further show that, although the overall burned area decreased, reburns continued to account for a substantial share of annual burn activity since 2005, with even slight increases in some regions (i.e. Dinaric Mountains and Balkan region). Our results highlight that reburns are integral to southern Europe’s disturbance regimes, and we emphasize the critical role of long time series for understanding forest dynamics. Based on our results, we suggest that reburns may increasingly shape fire regimes in southern Europe under intensifying forest fire activity, which may undermine post-fire recovery and requires special consideration from management. Biogeography Forestry Europe forest fire reburn Earth Observation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Fires play a determinant role in shaping the structure and composition of many ecosystems worldwide (Bowman et al., 2020 ; Pausas & Ribeiro, 2017 ). The complex diversity of fire activity results in geographically distinct fire regimes (Archibald et al., 2013 ), with different gradients of area burned, fire frequency, intensity, size and seasonality (Chuvieco et al., 2008 ; Pausas, 2022 ). Fire frequency is commonly modulated by fuel accumulation, with short-term effects typically reducing available fuel (inhibitory effect, resistance to reburn) (Duane et al., 2019 ), and long-term interactions potentially promoting subsequent fires (e.g. fuel accumulation and continuity, specially where more flammable communities populate after a high-severity fire) (Fernández-Guisuraga & Calvo, 2023 ; Harvey et al., 2016 ; Turner et al., 2019 ). However, climate change and particularly a higher frequency of extreme drought events might overcome those limiting factors (Jones et al., 2022 ; Karavani et al., 2018 ). Fuel accumulation in conjunction with drier and warmer climatic conditions increases susceptibility to repeated fires globally (Stephens et al., 2013 ) and also increases the probability of experiencing multiple fires in rapid succession (McDowell et al., 2020 ). While evidence of changing fire frequency has been documented in several regions worldwide (Cunningham et al., 2024 ), the dynamics of forest reburns, defined as previously burned areas that ignite again in shorter intervals than the historical range (Braziunas et al., 2023 ), remain largely unexplored. In Europe, southern Europe and especially the Mediterranean basin are fire-prone areas, experiencing periodic fire activity according to historical fire disturbance regimes (San-Miguel-Ayanz et al., 2024 ). However, fire regimes have undergone significant shifts driven by climate change, as well as alterations in vegetation structure and productivity linked to land-use changes (e.g. abandonment of rural and agricultural areas; Pausas & Keeley, 2014 ; Stephens et al., 2013 ). These changes contributed to large fire seasons in the 1980s and 1990s (Moreno et al., 2014 ), prompting a strong focus on fire suppression strategies (Moreira et al., 2020 ), which led to a decrease in the burned area reported during early years of the 21st century (San-Miguel-Ayanz et al., 2013 ; Turco et al., 2016 ). Although this strategy may have limited burned area initially, extreme fire seasons, featuring intense and extremely large fires (Grünig et al., 2023 ), have been observed again in recent years, such as 2017 in Portugal (Turco et al., 2019 ), 2021 in Greece (Giannaros et al., 2022 ) and 2022 in Spain and France (Rodrigues et al., 2023 ). Those extreme years highlight how fire activity in Europe may be shifting from being primarily limited by fuel availability to being increasingly climate-driven (Pausas & Fernández-Muñoz, 2012 ; Turco et al., 2019 ). Under more extreme fire weather conditions (Abatzoglou et al., 2019 ), fires are expected to intensify (Duane et al., 2021 ; Jones et al., 2022 ) and projections already indicate an increase in burned area (Dupuy et al., 2020 ; Turco et al., 2018 ), fire frequency (Pimont et al., 2022 ), and fire intensity and size (Grünig et al., 2023 ; Ruffault et al., 2020 ). Future fire frequencies may thus exceed those observed in the past (Galizia et al., 2023 ; McDowell et al., 2020 ), with consequences for reburn dynamics in Europe. With fire frequency expected to increase in Europe, there is likely a higher chance of reburns. Previous studies suggested a change in reburns particularly since the 1970’s in the Iberian Peninsula (Fernandes et al., 2014 ; Pausas & Fernández-Muñoz, 2012 ), with areas that burned in the late 20th century burning again in recent years (Fernández-García et al., 2019 ). Likewise, fire return intervals of less than 25 years were found in several regions of Portugal (Fernandes et al., 2012 ; Oliveira et al., 2012 ) and Greece (Koutsias et al., 2022 ). Those return intervals exceed the natural range of 30–100 years expected for the Mediterranean basin (Keeley & Pausas, 2022 ), and they potentially jeopardize the time needed to reach maturity age in many species, which is the reconstruction of serotinous cones (i.e., 15–20 years in many pine species, Duivenvoorden et al., 2024 ; Eugenio et al., 2006 ). Shorter reburn intervals can pose significant threats to local vegetation communities and soil health (Eugenio & Lloret, 2006 ; Turner et al., 2025 ), leading to more severe damage and prolonged recovery times (García-Llamas et al., 2024 ; Moghli et al., 2022 ; Taboada et al., 2018 ). Reburns thus ultimately endanger forest resilience (Baudena et al., 2020 ; Fernández-García et al., 2019 ). While efforts have been done to characterise fire regimes across southern Europe (Pausas, 2022 ), and to investigate the effects of global warming on changes in fire regimes (Galizia et al., 2023 ; Resco De Dios et al., 2021), our knowledge on forest reburn dynamics is still limited. It remains unknown, for instance, what proportion of fires are reburns, where such reburns occur, and if reburn dynamics are changing across Europe. Long-term satellite archives have changed our understanding of forest disturbances globally (McDowell et al., 2015 ). Remote sensing has a long history in understanding forest fires (Carmona-Moreno et al., 2005 ; Mouillot et al., 2014 ), and there are several global products mapping burned area (Lizundia-Loiola et al., 2020 ; Giglio et al., 2018 ; Andela et al., 2019 ; Artés et al., 2019 ). However, many of those burned area products only provide data since the early 2000s (Chuvieco et al., 2019 ), which limits their capacity for understanding reburns, especially as they miss a relevant period of high fire activity in southern Europe in the 1980s and 1990s (Moreira et al., 2011 ). With a particular focus on Europe, Gincheva et al. ( 2023 ) complied fire data at a 1° grid by aggregating the European Forest Fire Information System (EFFIS) database. Although EFFIS is the official and primary source of fire statistics harmonised for the European Union, data is only distributed at aggregated levels, and official databases thus lack spatially explicit information needed for understanding reburns. Moderate resolution optical satellite data from the Landsat archive, with 30 m spatial resolution and more than four decades of data, have been used in several regions globally to better understand fire dynamics (De Marzo et al., 2021 ; Hislop et al., 2018 ; Oliveira et al., 2012 ). For Europe, the recently developed European Forest Disturbance Atlas (EFDA) (Viana-Soto & Senf, 2025 ) provides annual and spatially explicit data on forest fires since 1985, including multiple fire events. There is thus now opportunity to fully characterize forest reburns across Europe. Here, our aim was to unravel forest reburn dynamics in southern Europe using a novel remote sensing-based forest fire product. We address our aim by three specific objectives. First, to evaluate the detection of reburns from a remote sensing-based product over the last four decades. Second, to quantify forest reburns in terms of spatial and temporal distribution across 59.6 Mha of forests in southern Europe. Third, to analyse how the proportion of reburns has changed since 1985, that is whether reburns are increasing or decreasing across southern Europe. Our study is the first to quantify reburn dynamics consistently across southern Europe, and thus in a global forest fire hotspot. 2. Data and Methods 2.1. Study area This study covers twelve countries in southern Europe and thus in the most active fire region of Europe. The area spans broad climatic gradients and very marked variation in forest types across eight bioregions, from semi-arid woodlands dominated by drought-tolerant sclerophyllous species (e.g., holm oak, cork oak, Aleppo pine) to humid temperate broadleaf and sub-alpine coniferous forests (Fig. 1 ). These bioregions, defined by aggregating the ecoregions of Dinerstein et al. ( 2017 ), reflect the complex interplay of topography, climate, and land-use history that shapes forest composition and fire regimes across the region. Aggregating data by larger spatial units as bioregions facilitates the estimation of key fire parameters and helps disentangle generalized burn and reburn patterns. 2.2. Forest fire dataset We used annual fire information from the European Forest Disturbance Atlas (EFDA) described in detail in (Viana-Soto & Senf, 2025 ). EFDA is Landsat-based disturbance dataset for Europe, containing annual information on forest disturbances from 1985–2023 at a spatial grain of 30 m. Disturbances are detected at the pixel level using year-to-year spectral differences in Landsat imagery. The root cause of disturbance (i.e. fire, wind and bark beetles, harvest) is attributed at the individual patch level using indicators that describe patch size and shape, spectral characteristics before and during disturbance, and its landscape context (see also Seidl & Senf, 2024 ; Senf & Seidl, 2021 ). EFDA’s annual mapping approach captures multiple disturbance events per pixel time series, and thus allows for the analysis of multiple fire events per pixel, in contrast to older datasets (Senf & Seidl, 2021 ). Disturbances are constrained to areas classified as forest land use following FAO’s definition (FAO, 2020 ), including young forests with lower tree cover, reforested areas, and temporarily unstocked forest lands (Viana-Soto & Senf, 2025 ). To evaluate the consistency of EFDA annual burned area estimates, we compared the annual burned area with the one reported by EFFIS for the Mediterranean countries of the European Union (EUMED5), for which annual fire data is available since the 1980s (San-Miguel-Ayanz et al., 2024 ). We further evaluated the spatial accuracy of EFDA’s burned area maps using a validation set of 100 randomly selected samples within burned areas (Figure S1). Each sample was visually interpreted using both Landsat imagery and high-resolution Google Earth images to verify the detection of single and multiple fire events captured by EFDA (see examples in Fig. 2 in the next section). 2.3. Characterising spatiotemporal reburn dynamics We extracted burned area and identified areas burning multiple times by aggregating the annual information on fire disturbances. We first calculated the mean annual burned area fraction (% yr⁻¹) (Bf) on a 20×20 km 2 grid as a baseline of burn activity across bioregions. This metric represents the percentage of forest land area that burned on average since 1985. Then, we identified areas that experienced two or more fire events. Areas burning at shorter intervals relative to the historical frequency are considered reburns (Keeley & Pausas, 2022 ). We here defined reburns as those burning at intervals shorter than 20 years, based on ecological thresholds for forest recovery and maturity risk of the seed bank for many tree species in fire-prone areas (e.g. Aleppo pine, Maritime pine) (Eugenio et al., 2006 ; Santana et al., 2010 ) (Fig. 2 ). To characterize spatiotemporal reburn dynamics, we calculated two key metrics: (1) reburn interval, defined as the time elapsed between fire events at a given location, and (2) reburn fraction (Rf), defined as the percentage of the annual burned area that overlapped with areas burned previously within the preceding 20-years. A pixel burned in year t was considered a reburn if it had also burned at least once between years t –20 and t –1 . We restricted this analysis to the 2005–2023 period to ensure a complete 20-year look-back period for every year, avoiding bias in the earlier years of the time series when burn history prior to 1985 is unavailable. For each 20×20 km 2 grid, we calculated annual reburn fractions and then averaged over the 2005–2023 period to produce a reburn fraction per grid-cell: $$\:Reburn\:fraction\:\left(Rf\right)=\frac{1}{N}{\sum\:}_{t=2005}^{2023}\frac{{R}_{t}}{{B}_{t}}\:\:\:\:,where\:{B}_{t}>0$$ where t ∈ [2005,2023] is each year observation, B t is the total forest area burned in year t and R t is the is the percentage of B t that had also burned at least once in the previous 20 years. A reburn fraction of 50% would indicate that on average half of the burned area since 2005 consists of reburns. Finally, we analysed how annual reburn fractions evolved since 2005, by comparing the annual percentage of reburns versus of new burns. 3. Results 3.1. Evaluating burned area and reburn detection Our analysis resulted in a total burned area of 12.98 Mha for EUMED5 countries, whereas EFFIS reported a total of 15.63 Mha for the same period (1985–2023) (Fig. 3 ). We found a high agreement between both time series, as annual burned area estimates from EFDA and EFFIS correlated closely in time (r = 0.61). Additionally, major fire seasons are captured in our data set (e.g. 1985, 1994 in Spain, 2003–2004 in Portugal, 2007 in Greece, 2017 in Portugal, 2022 in Spain and France). We found a good agreement between the visual interpretation of fire events and EFDA, with 89 out of 100 samples being assigned the same number of fire events (Table 1 ). Disagreements were primarily due to EFDA overestimating the number of fires by detecting one additional fire in visually interpreted single-fire events (5 cases) or in unburned areas (5 cases). The visual interpretation suggests that 45% of all burned areas (45/100) had more than one fire, while EFDA identified 50% as reburns. This indicates a slight overestimation of reburn by EFDA compared to the reference data. Table 1 Confusion matrix showing agreement on number of fire events mapped by EFDA and by visual interpretation. Reference data No fires 1 fire 2 fires 3 fires N Commission errors (%) No fires 0 0 0 0 0 - 1 fire 5 45 0 0 50 10 EFDA 2 fires 0 5 39 0 44 11.4 3 fires 0 0 1 5 6 16.7 N 5 50 40 5 Omission errors (%) 100 8 2.5 0 F1 score - 0.92 0.93 0.91 Overall accuracy = 89% Overall error = 11% 3.2. Overview of burn area fractions Expanding to all southern Europe’s forests, approximately 16.7% burned within the 39 years of observation, burning a total of 14.1 Mha. The mean annual burned area fraction (Bf) revealed widespread fire activity across the Mediterranean Basin, with a mean annual burned area fraction 0.46 ± 2.07% yr⁻¹ over the period 1985–2023 (Fig. 4 ). That is, on average ~ 0.5% of forest area burns per year across across the Mediterranean Basin. The spatial distribution of burned area fractions showed a gradual decrease from west to east (Fig. 4 a), while the time series showed high interannual variability across all bioregions (Fig. 4 b). The highest mean annual burned area fractions were observed in the West Mediterranean (0.65% yr⁻¹) and Central Mediterranean (0.60% yr⁻¹) regions. These regions also featured clusters of high burn fractions, with some grid cells in southern France and Italy exceeding 2% yr⁻¹, and reaching up to 4% yr⁻¹ in central Portugal (Fig. 4 a). Moderate annual burned area fraction were identified for the West European Coastal region (0.47% yr⁻¹), whereas forests in the northwest Iberian Peninsula show higher fractions, commonly reaching 2–3% yr⁻¹ (Fig. 4 a). The Dinaric Mountains and Balkan and the Aegean Sea and East Mediterranean experienced fire activity comparable to western atlantic region (0.45% yr⁻¹ and 0.40% yr⁻¹, respectively). The Alps and Po Basin, as well as the Pannonian and European Interior regions exhibited consistently low fire activity, with mean annual burned fractions below ~ 0.15% yr⁻¹ (Fig. 4 b). 3.3. How common are reburns in southern Europe? The majority of fires mapped in our dataset were single burns (9.86 Mha, 69.9% of total burned area), i.e. pixels where only one fire was detected between 1985 and 2023. Pixels that experienced two or more fires in the same time period accounted for the remaining 30.1% (4.24 Mha), with 84.5% of these reburns occurring within a 20-year interval and thus within our definition of reburns (Fig. 5 ). In general, reburns within the first five years after a fire were rare, but the likelihood increased rapidly after 10 years. The mean temporal distance between initial fire and reburn was 13.3 years, and 68.6% of the reburns occured between 10 and 20 years (Table S1). We found no major differences in reburn intervals across bioregions (see also map in Figure S2 for information on 20 x 20 km 2 grid). However, some regional variations exist: the East Mediterranean region exhibited a wider distribution, with 19.8% of the reburns occuring beyond 20 years, while reburns after 20 years were rare in West European Atlantic and West and Central mediterranean (5–10%, Table S1). See Figure S3 for country-level detail. The reburn fraction (Rf) map, indicating the average proportion of fires that burned two or more times within 20 years, showed distinct regional hotspots of reburning. High reburn fractions concentrated along the West European Atlantic coast (mean 40.8% yr − 1 ) and the West Mediterranean regions (mean 21.1% yr − 1 ), although there is considerable variability within each region (Fig. 6 b). Some areas in the Northwest of the Iberian Peninsula reached a reburn fraction above 70%, and the region thus stands out as a major fire hotspot with both high burned and reburn fractions. Additional regional hotspots were found along the Adriatic Sea and in southern Italy, with average reburn fractions of 19.5% yr⁻¹ in the Central Mediterranean and 19.1% yr⁻¹ in the Dinaric-Balkan region. Lowest reburn fractions were found in the Eastern Mediterranean, with on average only 11% of fires per year being reburns. There was thus also a strong west-east gradient in reburn fractions, with higher reburn fractions in the west than in the east of Southern Europe. 3.4. Trends in reburns in Southern Europe Percentages of new burns and reburns varied annually and per bioregion from 2005 to 2023 (Fig. 7 ), highlighting distinct regional trajectories in repeated burning across southern Europe. A significant decreasing trend in reburn fraction was found in the West European Atlantic Coast (slope: − 1.12 percentage points per year, p = 0.0017). Smaller, non-significant declines were observed in the Balearic Sea & West Mediterranean (–0.18%/yr, p = 0.22) and the Aegean Sea & East Mediterranean (–0.29%/yr, p = 0.073). A positive but non-significant trend emerged in the Adriatic Sea & Central Mediterranean (+ 0.23%/yr, p = 0.51), whereas the Dinaric Mountains and Balkan region showed a statistically significant increase in reburn share (+ 0.91%/yr, p = 0.034). See Figure S4 for country-level detail. Overall, there is thus no clear pattern of increasing reburn fractions across Europe. 4. Discussion Here, we provide a quantitative and spatially explicit characterization of reburn dynamics in southern Europe, unravelling the frequency, abundance and extent of reburns across bioregions. The absence of long-term, spatially detailed fire disturbance data has previously hindered assessments of reburns at a sub-continental scale. We fill this gap by leveraging a newly developed remote sensing-based dataset that captures multiple fire events, allowing us to analyse the spatial and temporal variability of reburns over the past four decades. We estimate that almost a third (30.1%) of the area burned in southern Europe between 1985 and 2023 was affected by repeated fires. While slightly higher than the 20% reported by Ermitão et al. ( 2024 ) for a shorter period (2001–2022), differences in spatial coverage and temporal framework limit direct comparison. We found that short-term reburns (i.e. repeated fires with return intervals < 20 years) to be common among repeated fires, and even more extreme reburns (< 15 years) are common in southern Europe. Reburns are uncommon to happen within the first five years, likely reflecting insufficient time for fuel accumulation. This might be true especially for Mediterranean dry environments, where post-fire regrowth can be constrained by water-limited conditions (Blanco-Rodríguez et al., 2023 ). The dominance of the 10–20-year reburn interval in southern Europe likely results from a convergence of ecological, climatic and contextual conditions (Ganteaume et al., 2013 ; Peris-Llopis et al., 2024 ; Resco De Dios et al., 2022). In many Mediterranean landscapes, forests may require more than a decade to build up the continuous and flammable fuel loads needed for fire recurrence (Pausas & Paula, 2012 ). This pattern is further shaped by the legacy of large fire footprints from the 1980s and 1990s (Moreira et al., 2011 ; San-Miguel-Ayanz et al., 2013 ), which created a substantial pool of previously burned areas that reburned after 2000s (Fernández-García et al., 2019 ; Pausas & Fernández-Muñoz, 2012 ). While overall differences in reburn intervals across bioregions were small, subtle variations were observed. For instance, slightly longer intervals in the Aegean may indicate slower vegetation recovery due to limited productivity (Koutsias et al., 2022 ; Roder et al., 2008 ). Conversely, short fire return intervals in the west of the Iberian Peninsula might be explained by large-scale afforestation throughout the 20th century (Fernandes et al., 2014 ; Pausas & Fernández-Muñoz, 2012 ), creating high fuel continuity that increases the likelihood of reburns (Duane et al., 2019 ). Local studies support this pattern, with reburn intervals of five years before 2000s reported in landscapes of northern Portugal (Fernandes et al., 2015 ), Corsica (Mouillot et al., 2003 ) and eastern Spain, where approximately 12% of the total area that burned during 23 years (1975–1998) experienced at least two fires (Díaz-Delgado et al., 2004 ). Although similar reburn intervals appear in the Dinaric and Balkan regions, the lack of detailed fire history data limits interpretation, and the underlying drivers remain unclear. This highlights the need for further investigation in these regions. In summary, we conclude that short-term reburns are common across southern Europe, with the majority of reburns happening within 10 and 20 years. Our results showed a very strong west-east gradient in reburn fractions. The largest hotspot of reburns was in the Western basin, with up to 40.8% of all fires being short-term reburns on average (i.e. within 20 years), while for all other regions, average reburn fractions ranged between 11% and 21%. Differences in land use, fire suppression strategies, and post-fire management likely play a fundamental role in shaping the observed patterns (Moreira et al., 2020 ; Pausas, 2022 ). The high prevalence of reburns in the Western basin may be explained by a combination of frequent fire activity, high ignition pressure, and fast fuel accumulation (Peris-Llopis et al., 2024 ), particularly in densely vegetated areas where fire weather, forest structure, and land management contribute to frequent fire recurrence (e.g. productive forest plantations in humid areas in Portugal, Davim et al., 2023 ). Beyond the legacy of large areas burned in the late 20th century, strict fire suppression policies may have unintentionally amplified reburn potential by limiting low-intensity fires that would otherwise naturally regulate fuel loads (Moghli et al., 2022 ). Over time, fuel accumulation of fire-prone species and greater fuel continuity create conditions that favour large-scale wildfires (Duane et al., 2021 ; Piñol et al., 2005 ), which will ultimately increase the likelihood of reburns. Our results also highlight that regions outside the classical fire-prone regions of Europe (i.e. Spain, Portugal; Archibald et al., 2013 ; Pausas, 2022 ) are likewise affected by reburns. Although the largest hotspot of reburns remains the Western Mediterranean basin, parts of the Central Mediterranean and the Dinaric Mountains and Balkans also showed high reburn fractions. The Dinaric mountains and Balkan regions are often overlooked in terms of European fire dynamics and generally considered as fire rare, likely due to lack of long-term data (Resco De Dios et al., 2022). However, our findings suggest that this region plays a larger role for Europe’s fire regimes than previously reported. Despite the observed decline in annual burned area across southern Europe (Turco et al., 2016 ), reburn proportions have remained stable (e.g. West and Central Mediterranean) or even increased in some regions such as the Dinaric mountains and Balkans. We note, though, that the actual time frame for characterizing reburns using our long-term dataset was limited to 2005–2023. We did not observe a sharp increase in reburn proportions that would suggest fuel limitations are no longer a factor, yet the continued occurrence of reburns indicates that more areas may become susceptible to reburning soon. Thus, reburn dynamics are not confined to regions with historically frequent fire activity, but might also emerge in areas where recent fire activity has increased, post-fire conditions allow for rapid fuel regeneration, or even where fuel moisture used to be a limiting factor (Agne et al., 2023 ; Fernández-Guisuraga & Calvo, 2023 ; Resco De Dios et al., 2021). The only significant decrease in reburn percentages was found in the Western Mediterranean basin related to the largest fire seasons of the last century that now fall outside the 20-year reburn interval. However, recent major fire seasons in 2017 and 2022 (Rodrigues et al., 2023 ; Turco et al., 2019 ) may have set a new stage for reburns in the coming decades. While we present the first continental-scale analysis of reburn dynamics, some caveats need to be considered when interpreting our results. First, four decades of data may be a relatively short time to capture clear long-term trends in reburning, but it has been adequate to reveal reburn patterns in recent years, significantly extending insights beyond previous research. It is important to note that although our dataset spans nearly four decades, we can only reliably assess reburns for the last 19 years, because a historic baseline is needed to compare reburn fractions over time. Without applying a temporal threshold, reburn fractions will inherently increase over time simply as a function of expanding burn records (Buma et al., 2020 ). Further, identifying reburns requires knowledge of earlier fires, making a sufficiently long look-back period essential. Since MODIS data is only available since 2001, a complete 20-year reburn interval can only be assessed for fires from 2021 onward, limiting long-term analysis. Second, remote sensing products are always prone to errors in mapping burned area (Franquesa et al., 2022 ). Comparison between our annual burned area estimates from EFDA maps and EFFIS showed a good agreement, supporting the suitability of our dataset for analysing long-term and spatial patterns of reburn dynamics. That said, our estimates tend to be slightly lower than official statistics, partly due to constraints in our forest land use definition (Viana-Soto & Senf, 2025 ). For instance, some patches in open landscapes characterised by evergreen shrubs (maquis, garrigue) intermixed with small trees (oak, Aleppo pine) may not always be fully included in EFDA. We also observed small mismatches between EFFIS and EFDA time series related to fires occurring in late summer or early autumn, which are recorded in the following year due to constraints in disturbance mapping during the summer season (e.g. fires in 1985 captured in 1986 or large fires in 2017 partially recorded in 2018, Fig. 3 ). From a spatial perspective, we also found good agreement when comparing our results to visual interpretation, effectively capturing multiple fire events, with differences primarily arising in single-fire events and unburned areas. Despite the uncertainties discussed above, our estimates provide a reliable representation of reburn dynamics for southern Europe. 5. Conclusion Our findings establish a robust baseline for understanding the role of reburns in forest dynamics of southern Europe. From our analysis we conclude that reburns, i.e. fires occurring a maximum 20 years after another fire, are integral to Europe’s fire regimes. We also highlight the critical value of long-term time series for studying reburn patterns, as our analysis would have been impossible with existing shorter burned area products. As environmental conditions continue to change, examining the interplay of multiple fires will be essential for predicting future fire regimes and their socio-ecological consequences (Duane et al., 2019 ; García-Llamas et al., 2024 ). Given the projected increase in fire size and frequency (Grünig et al., 2023 ; Pimont et al., 2022 ), understanding fire patterns and trends is increasingly urgent due to their potential to drive shifts in ecosystem structure and carbon dynamics (Pellegrini et al., 2018 ). Climate change is expected to strengthen the fire-aridity relationship (Grünig et al., 2023 ), causing more frequent burning in historically fire-limited regions before vegetation can adapt. More frequent reburning may potentially compromise forest resilience (Stevens-Rumann & Morgan, 2016 ; Turner et al., 2019 ) by preventing ecosystems to fully recover or lead to shift in ecosystems composition (Baudena et al., 2020 ; Viana-Soto et al., 2022 ). The consequences of this recurrence cycle are far-reaching also in the impact to ecosystem services (e.g. soil degradation, biodiversity loss, air quality, water availability) (Lecina-Diaz et al., 2024 ). Shortened fire intervals can significantly reduce ecosystem services and diminish ecosystem multifunctionality. In this context, the spatially explicit data on forest reburns provided here are a valuable source for guiding restoration efforts and supporting management in fire-prone landscapes. Declarations Conflict of interest The authors declare no competing interests. Acknowledgements We acknowledge funding from ForestPaths project (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation, ID No 101056755) funded from the European Union's Horizon Europe Research and Innovation, from the AI4Forest project funded by the Federal Ministry of Education and Research, Germany (BMBF; Grant number: 01IS23025C) and from ESA Project CLIMATE SPACE RECCAP2 (ESA contract 4000144908/24/I-LR). Data Availability Statement The data used in this study from the European Forest Disturbance Atlas is freely available at https://zenodo.org/records/13333034 . Further information on the code and processing workflows will be made publicly available upon publication. References Abatzoglou, J. T., Williams, A. P., & Barbero, R. (2019). Global Emergence of Anthropogenic Climate Change in Fire Weather Indices. Geophysical Research Letters , 46 (1), 326-336. https://doi.org/10.1029/2018GL080959 Agne, M. C., Fontaine, J. B., Enright, N. J., Bisbing, S. 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The European Forest Disturbance Atlas: A forest disturbance monitoring system using the Landsat archive. Earth System Science Data , 17 (6), 2373-2404. https://doi.org/10.5194/essd-17-2373-2025 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Global Ecology and Biogeography → 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. 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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-6937064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478201583,"identity":"145440bb-7c10-445e-96c4-69e24d2e715d","order_by":0,"name":"Alba Viana-Soto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACdh4QKQHny4FJHnxamFG0JDAY8xCpBQYSGBJ7CGnhb+Y9+IGxzULenP0A84ePP2zS90tkJzC8qcCtReIwX7IEY5uE4c6eBDbJGQlpuT0SuRsY55zBY81hHgMJhjMSjBsO5H9j5kk4DNbCzNuGW4f8YR7jH0At9hvOP2D+DNSSzgPW8g+3FoPDPGYSDBUSiRtuJDBIA7UkQLQ04NZieJgvzSKhQiJ5w40HQL+kpRn2nHm74eCcY7i1yB3vPXzjg0Gd7YbzCcwfPtjYyLO352588KYGj/dBIAFd4AABDaNgFIyCUTAKCAAAFWNLiJLdxTEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5595-2170","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Alba","middleName":"","lastName":"Viana-Soto","suffix":""},{"id":478207482,"identity":"cfbb5ffb-55ad-4675-84b7-d71443013181","order_by":1,"name":"Cornelius Senf","email":"","orcid":"https://orcid.org/0000-0002-2389-2158","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Cornelius","middleName":"","lastName":"Senf","suffix":""}],"badges":[],"createdAt":"2025-06-20 08:45:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6937064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6937064/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1111/geb.70198","type":"published","date":"2026-01-30T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85836303,"identity":"d3bfa44c-cb69-445d-8a5f-faee68f5a318","added_by":"auto","created_at":"2025-07-02 08:23:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237608,"visible":true,"origin":"","legend":"\u003cp\u003eBioregions of southern Europe and overlapping countries affected by forest reburns.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/f508015f3fa0098a82611dcd.png"},{"id":85836305,"identity":"344b0346-c96e-4d74-9bd8-c2a49ff9aca2","added_by":"auto","created_at":"2025-07-02 08:23:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1025853,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of reburns at different return intervals. Background images are false colour Landsat composites (USGS) and ©Google Earth imagery (left).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/d36108c1e5e4ec140b7a3be7.png"},{"id":85836306,"identity":"caac555e-f352-4997-b1dd-b9a7655993fb","added_by":"auto","created_at":"2025-07-02 08:23:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210930,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual burned area for EUMED5 countries (Portugal, Spain, France, Italy and Greece) estimated by EFFIS versus EFDA.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/6d5d98015061991c4cea830e.png"},{"id":85836309,"identity":"70c341a0-fa15-4f5c-a272-1b3723885bdf","added_by":"auto","created_at":"2025-07-02 08:23:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":738837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eBurned area fraction (Bf) per 20x20 km\u003csup\u003e2 \u003c/sup\u003egrid. Values indicate the percentage of forest land that burned on average over 1985-2023; b) Mean annual burned area fraction per bioregion.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/b353cd5c4b41ed6cd2395ee8.png"},{"id":85836311,"identity":"fae58b17-5020-44a1-b31c-e182dfe27b57","added_by":"auto","created_at":"2025-07-02 08:23:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":372465,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of reburns per reburn interval and bioregion (map indicates the bioregions affected by reburns). Black dot curve shows the overall distribution.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/4457e8ecf6277116402f0e7e.png"},{"id":85837719,"identity":"612ce3e5-cd49-484f-84aa-4092010d8d74","added_by":"auto","created_at":"2025-07-02 08:31:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":586346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Reburn fraction per 20-km grid cell over 2005-2023. Reburn fraction (Rf) represents the percentage of burned pixels that reburn on average over 2005-2023; \u003cstrong\u003eb)\u003c/strong\u003e Boxplots showing the distribution of reburn fractions across bioregions.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/7234d8c541d3216cb019257d.png"},{"id":85836320,"identity":"900d62cd-8420-44ca-b258-01a05b552d9b","added_by":"auto","created_at":"2025-07-02 08:23:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Annual burned area per bioregion attributed to reburns and new burns and \u003cstrong\u003eb)\u003c/strong\u003e percentage of annual burned area attributed to reburns and new burns. Dot black line indicates the linear trend line.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/e1d1b83c26c20cdc664d2096.png"},{"id":101799644,"identity":"bd994f1c-3de8-4371-96fe-d507dd085f9f","added_by":"auto","created_at":"2026-02-03 17:45:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3991463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/f65fe2d6-8121-45c7-b58f-77602cf8e157.pdf"},{"id":85837718,"identity":"49736bb9-21cf-477c-ba4c-66d4d45ff698","added_by":"auto","created_at":"2025-07-02 08:31:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4047529,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6937064/v1/60ce754358220d342d11d321.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForest reburns are integral to southern Europe’s disturbance regimes\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFires play a determinant role in shaping the structure and composition of many ecosystems worldwide (Bowman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pausas \u0026amp; Ribeiro, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The complex diversity of fire activity results in geographically distinct fire regimes (Archibald et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with different gradients of area burned, fire frequency, intensity, size and seasonality (Chuvieco et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pausas, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fire frequency is commonly modulated by fuel accumulation, with short-term effects typically reducing available fuel (inhibitory effect, resistance to reburn) (Duane et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and long-term interactions potentially promoting subsequent fires (e.g. fuel accumulation and continuity, specially where more flammable communities populate after a high-severity fire) (Fern\u0026aacute;ndez-Guisuraga \u0026amp; Calvo, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Harvey et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Turner et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, climate change and particularly a higher frequency of extreme drought events might overcome those limiting factors (Jones et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karavani et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fuel accumulation in conjunction with drier and warmer climatic conditions increases susceptibility to repeated fires globally (Stephens et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and also increases the probability of experiencing multiple fires in rapid succession (McDowell et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While evidence of changing fire frequency has been documented in several regions worldwide (Cunningham et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the dynamics of forest reburns, defined as previously burned areas that ignite again in shorter intervals than the historical range (Braziunas et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), remain largely unexplored.\u003c/p\u003e \u003cp\u003eIn Europe, southern Europe and especially the Mediterranean basin are fire-prone areas, experiencing periodic fire activity according to historical fire disturbance regimes (San-Miguel-Ayanz et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, fire regimes have undergone significant shifts driven by climate change, as well as alterations in vegetation structure and productivity linked to land-use changes (e.g. abandonment of rural and agricultural areas; Pausas \u0026amp; Keeley, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stephens et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These changes contributed to large fire seasons in the 1980s and 1990s (Moreno et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), prompting a strong focus on fire suppression strategies (Moreira et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which led to a decrease in the burned area reported during early years of the 21st century (San-Miguel-Ayanz et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Turco et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although this strategy may have limited burned area initially, extreme fire seasons, featuring intense and extremely large fires (Gr\u0026uuml;nig et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), have been observed again in recent years, such as 2017 in Portugal (Turco et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 2021 in Greece (Giannaros et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e in Spain and France (Rodrigues et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Those extreme years highlight how fire activity in Europe may be shifting from being primarily limited by fuel availability to being increasingly climate-driven (Pausas \u0026amp; Fern\u0026aacute;ndez-Mu\u0026ntilde;oz, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Turco et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Under more extreme fire weather conditions (Abatzoglou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), fires are expected to intensify (Duane et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and projections already indicate an increase in burned area (Dupuy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Turco et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), fire frequency (Pimont et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and fire intensity and size (Gr\u0026uuml;nig et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ruffault et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future fire frequencies may thus exceed those observed in the past (Galizia et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McDowell et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with consequences for reburn dynamics in Europe.\u003c/p\u003e \u003cp\u003eWith fire frequency expected to increase in Europe, there is likely a higher chance of reburns. Previous studies suggested a change in reburns particularly since the 1970\u0026rsquo;s in the Iberian Peninsula (Fernandes et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pausas \u0026amp; Fern\u0026aacute;ndez-Mu\u0026ntilde;oz, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with areas that burned in the late 20th century burning again in recent years (Fern\u0026aacute;ndez-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Likewise, fire return intervals of less than 25 years were found in several regions of Portugal (Fernandes et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Greece (Koutsias et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Those return intervals exceed the natural range of 30\u0026ndash;100 years expected for the Mediterranean basin (Keeley \u0026amp; Pausas, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and they potentially jeopardize the time needed to reach maturity age in many species, which is the reconstruction of serotinous cones (i.e., 15\u0026ndash;20 years in many pine species, Duivenvoorden et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Eugenio et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Shorter reburn intervals can pose significant threats to local vegetation communities and soil health (Eugenio \u0026amp; Lloret, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Turner et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), leading to more severe damage and prolonged recovery times (Garc\u0026iacute;a-Llamas et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Moghli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Taboada et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Reburns thus ultimately endanger forest resilience (Baudena et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fern\u0026aacute;ndez-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While efforts have been done to characterise fire regimes across southern Europe (Pausas, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and to investigate the effects of global warming on changes in fire regimes (Galizia et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Resco De Dios et al., 2021), our knowledge on forest reburn dynamics is still limited. It remains unknown, for instance, what proportion of fires are reburns, where such reburns occur, and if reburn dynamics are changing across Europe.\u003c/p\u003e \u003cp\u003eLong-term satellite archives have changed our understanding of forest disturbances globally (McDowell et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Remote sensing has a long history in understanding forest fires (Carmona-Moreno et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mouillot et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and there are several global products mapping burned area (Lizundia-Loiola et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giglio et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Andela et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Art\u0026eacute;s et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, many of those burned area products only provide data since the early 2000s (Chuvieco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which limits their capacity for understanding reburns, especially as they miss a relevant period of high fire activity in southern Europe in the 1980s and 1990s (Moreira et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). With a particular focus on Europe, Gincheva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) complied fire data at a 1\u0026deg; grid by aggregating the European Forest Fire Information System (EFFIS) database. Although EFFIS is the official and primary source of fire statistics harmonised for the European Union, data is only distributed at aggregated levels, and official databases thus lack spatially explicit information needed for understanding reburns. Moderate resolution optical satellite data from the Landsat archive, with 30 m spatial resolution and more than four decades of data, have been used in several regions globally to better understand fire dynamics (De Marzo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hislop et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For Europe, the recently developed European Forest Disturbance Atlas (EFDA) (Viana-Soto \u0026amp; Senf, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provides annual and spatially explicit data on forest fires since 1985, including multiple fire events. There is thus now opportunity to fully characterize forest reburns across Europe.\u003c/p\u003e \u003cp\u003eHere, our aim was to unravel forest reburn dynamics in southern Europe using a novel remote sensing-based forest fire product. We address our aim by three specific objectives. First, to evaluate the detection of reburns from a remote sensing-based product over the last four decades. Second, to quantify forest reburns in terms of spatial and temporal distribution across 59.6 Mha of forests in southern Europe. Third, to analyse how the proportion of reburns has changed since 1985, that is whether reburns are increasing or decreasing across southern Europe. Our study is the first to quantify reburn dynamics consistently across southern Europe, and thus in a global forest fire hotspot.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThis study covers twelve countries in southern Europe and thus in the most active fire region of Europe. The area spans broad climatic gradients and very marked variation in forest types across eight bioregions, from semi-arid woodlands dominated by drought-tolerant sclerophyllous species (e.g., holm oak, cork oak, Aleppo pine) to humid temperate broadleaf and sub-alpine coniferous forests (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These bioregions, defined by aggregating the ecoregions of Dinerstein et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reflect the complex interplay of topography, climate, and land-use history that shapes forest composition and fire regimes across the region. Aggregating data by larger spatial units as bioregions facilitates the estimation of key fire parameters and helps disentangle generalized burn and reburn patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Forest fire dataset\u003c/h2\u003e \u003cp\u003eWe used annual fire information from the European Forest Disturbance Atlas (EFDA) described in detail in (Viana-Soto \u0026amp; Senf, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). EFDA is Landsat-based disturbance dataset for Europe, containing annual information on forest disturbances from 1985\u0026ndash;2023 at a spatial grain of 30 m. Disturbances are detected at the pixel level using year-to-year spectral differences in Landsat imagery. The root cause of disturbance (i.e. fire, wind and bark beetles, harvest) is attributed at the individual patch level using indicators that describe patch size and shape, spectral characteristics before and during disturbance, and its landscape context (see also Seidl \u0026amp; Senf, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Senf \u0026amp; Seidl, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). EFDA\u0026rsquo;s annual mapping approach captures multiple disturbance events per pixel time series, and thus allows for the analysis of multiple fire events per pixel, in contrast to older datasets (Senf \u0026amp; Seidl, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Disturbances are constrained to areas classified as forest land use following FAO\u0026rsquo;s definition (FAO, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), including young forests with lower tree cover, reforested areas, and temporarily unstocked forest lands (Viana-Soto \u0026amp; Senf, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo evaluate the consistency of EFDA annual burned area estimates, we compared the annual burned area with the one reported by EFFIS for the Mediterranean countries of the European Union (EUMED5), for which annual fire data is available since the 1980s (San-Miguel-Ayanz et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We further evaluated the spatial accuracy of EFDA\u0026rsquo;s burned area maps using a validation set of 100 randomly selected samples within burned areas (Figure S1). Each sample was visually interpreted using both Landsat imagery and high-resolution Google Earth images to verify the detection of single and multiple fire events captured by EFDA (see examples in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e in the next section).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Characterising spatiotemporal reburn dynamics\u003c/h2\u003e \u003cp\u003eWe extracted burned area and identified areas burning multiple times by aggregating the annual information on fire disturbances. We first calculated the mean annual burned area fraction (% yr⁻\u0026sup1;) (Bf) on a 20\u0026times;20 km\u003csup\u003e2\u003c/sup\u003e grid as a baseline of burn activity across bioregions. This metric represents the percentage of forest land area that burned on average since 1985. Then, we identified areas that experienced two or more fire events. Areas burning at shorter intervals relative to the historical frequency are considered reburns (Keeley \u0026amp; Pausas, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We here defined reburns as those burning at intervals shorter than 20 years, based on ecological thresholds for forest recovery and maturity risk of the seed bank for many tree species in fire-prone areas (e.g. Aleppo pine, Maritime pine) (Eugenio et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Santana et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo characterize spatiotemporal reburn dynamics, we calculated two key metrics: (1) reburn interval, defined as the time elapsed between fire events at a given location, and (2) reburn fraction (Rf), defined as the percentage of the annual burned area that overlapped with areas burned previously within the preceding 20-years. A pixel burned in year \u003cem\u003et\u003c/em\u003e was considered a reburn if it had also burned at least once between years \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026ndash;20\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026ndash;1\u003c/em\u003e\u003c/sub\u003e. We restricted this analysis to the 2005\u0026ndash;2023 period to ensure a complete 20-year look-back period for every year, avoiding bias in the earlier years of the time series when burn history prior to 1985 is unavailable. For each 20\u0026times;20 km\u003csup\u003e2\u003c/sup\u003e grid, we calculated annual reburn fractions and then averaged over the 2005\u0026ndash;2023 period to produce a reburn fraction per grid-cell:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Reburn\\:fraction\\:\\left(Rf\\right)=\\frac{1}{N}{\\sum\\:}_{t=2005}^{2023}\\frac{{R}_{t}}{{B}_{t}}\\:\\:\\:\\:,where\\:{B}_{t}\u0026gt;0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e \u0026isin; [2005,2023] is each year observation, B\u003csub\u003et\u003c/sub\u003e is the total forest area burned in year \u003cem\u003et\u003c/em\u003e and R\u003csub\u003et\u003c/sub\u003e is the is the percentage of B\u003csub\u003et\u003c/sub\u003e that had also burned at least once in the previous 20 years. A reburn fraction of 50% would indicate that on average half of the burned area since 2005 consists of reburns. Finally, we analysed how annual reburn fractions evolved since 2005, by comparing the annual percentage of reburns versus of new burns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1. Evaluating burned area and reburn detection\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eOur analysis resulted in a total burned area of 12.98 Mha for EUMED5 countries, whereas EFFIS reported a total of 15.63 Mha for the same period (1985\u0026ndash;2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We found a high agreement between both time series, as annual burned area estimates from EFDA and EFFIS correlated closely in time (r\u0026thinsp;=\u0026thinsp;0.61). Additionally, major fire seasons are captured in our data set (e.g. 1985, 1994 in Spain, 2003\u0026ndash;2004 in Portugal, 2007 in Greece, 2017 in Portugal, 2022 in Spain and France).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe found a good agreement between the visual interpretation of fire events and EFDA, with 89 out of 100 samples being assigned the same number of fire events (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Disagreements were primarily due to EFDA overestimating the number of fires by detecting one additional fire in visually interpreted single-fire events (5 cases) or in unburned areas (5 cases). The visual interpretation suggests that 45% of all burned areas (45/100) had more than one fire, while EFDA identified 50% as reburns. This indicates a slight overestimation of reburn by EFDA compared to the reference data.\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\u003eConfusion matrix showing agreement on number of fire events mapped by EFDA and by visual interpretation.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eReference data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 fire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCommission errors (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 fire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 fires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmission errors (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eOverall accuracy\u0026thinsp;=\u0026thinsp;89% Overall error\u0026thinsp;=\u0026thinsp;11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Overview of burn area fractions\u003c/h2\u003e \u003cp\u003eExpanding to all southern Europe\u0026rsquo;s forests, approximately 16.7% burned within the 39 years of observation, burning a total of 14.1 Mha. The mean annual burned area fraction (Bf) revealed widespread fire activity across the Mediterranean Basin, with a mean annual burned area fraction 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07% yr⁻\u0026sup1; over the period 1985\u0026ndash;2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). That is, on average\u0026thinsp;~\u0026thinsp;0.5% of forest area burns per year across across the Mediterranean Basin. The spatial distribution of burned area fractions showed a gradual decrease from west to east (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), while the time series showed high interannual variability across all bioregions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The highest mean annual burned area fractions were observed in the West Mediterranean (0.65% yr⁻\u0026sup1;) and Central Mediterranean (0.60% yr⁻\u0026sup1;) regions. These regions also featured clusters of high burn fractions, with some grid cells in southern France and Italy exceeding 2% yr⁻\u0026sup1;, and reaching up to 4% yr⁻\u0026sup1; in central Portugal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Moderate annual burned area fraction were identified for the West European Coastal region (0.47% yr⁻\u0026sup1;), whereas forests in the northwest Iberian Peninsula show higher fractions, commonly reaching 2\u0026ndash;3% yr⁻\u0026sup1; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The Dinaric Mountains and Balkan and the Aegean Sea and East Mediterranean experienced fire activity comparable to western atlantic region (0.45% yr⁻\u0026sup1; and 0.40% yr⁻\u0026sup1;, respectively). The Alps and Po Basin, as well as the Pannonian and European Interior regions exhibited consistently low fire activity, with mean annual burned fractions below ~\u0026thinsp;0.15% yr⁻\u0026sup1; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. How common are reburns in southern Europe?\u003c/h2\u003e \u003cp\u003eThe majority of fires mapped in our dataset were single burns (9.86 Mha, 69.9% of total burned area), i.e. pixels where only one fire was detected between 1985 and 2023. Pixels that experienced two or more fires in the same time period accounted for the remaining 30.1% (4.24 Mha), with 84.5% of these reburns occurring within a 20-year interval and thus within our definition of reburns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In general, reburns within the first five years after a fire were rare, but the likelihood increased rapidly after 10 years. The mean temporal distance between initial fire and reburn was 13.3 years, and 68.6% of the reburns occured between 10 and 20 years (Table S1). We found no major differences in reburn intervals across bioregions (see also map in Figure S2 for information on 20 x 20 km\u003csup\u003e2\u003c/sup\u003e grid). However, some regional variations exist: the East Mediterranean region exhibited a wider distribution, with 19.8% of the reburns occuring beyond 20 years, while reburns after 20 years were rare in West European Atlantic and West and Central mediterranean (5\u0026ndash;10%, Table S1). See Figure S3 for country-level detail.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reburn fraction (Rf) map, indicating the average proportion of fires that burned two or more times within 20 years, showed distinct regional hotspots of reburning. High reburn fractions concentrated along the West European Atlantic coast (mean 40.8% yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the West Mediterranean regions (mean 21.1% yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), although there is considerable variability within each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Some areas in the Northwest of the Iberian Peninsula reached a reburn fraction above 70%, and the region thus stands out as a major fire hotspot with both high burned and reburn fractions. Additional regional hotspots were found along the Adriatic Sea and in southern Italy, with average reburn fractions of 19.5% yr⁻\u0026sup1; in the Central Mediterranean and 19.1% yr⁻\u0026sup1; in the Dinaric-Balkan region. Lowest reburn fractions were found in the Eastern Mediterranean, with on average only 11% of fires per year being reburns. There was thus also a strong west-east gradient in reburn fractions, with higher reburn fractions in the west than in the east of Southern Europe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Trends in reburns in Southern Europe\u003c/h2\u003e \u003cp\u003ePercentages of new burns and reburns varied annually and per bioregion from 2005 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e), highlighting distinct regional trajectories in repeated burning across southern Europe. A significant decreasing trend in reburn fraction was found in the West European Atlantic Coast (slope: \u0026minus;\u0026thinsp;1.12 percentage points per year, p\u0026thinsp;=\u0026thinsp;0.0017). Smaller, non-significant declines were observed in the Balearic Sea \u0026amp; West Mediterranean (\u0026ndash;0.18%/yr, p\u0026thinsp;=\u0026thinsp;0.22) and the Aegean Sea \u0026amp; East Mediterranean (\u0026ndash;0.29%/yr, p\u0026thinsp;=\u0026thinsp;0.073). A positive but non-significant trend emerged in the Adriatic Sea \u0026amp; Central Mediterranean (+\u0026thinsp;0.23%/yr, p\u0026thinsp;=\u0026thinsp;0.51), whereas the Dinaric Mountains and Balkan region showed a statistically significant increase in reburn share (+\u0026thinsp;0.91%/yr, p\u0026thinsp;=\u0026thinsp;0.034). See Figure S4 for country-level detail. Overall, there is thus no clear pattern of increasing reburn fractions across Europe.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHere, we provide a quantitative and spatially explicit characterization of reburn dynamics in southern Europe, unravelling the frequency, abundance and extent of reburns across bioregions. The absence of long-term, spatially detailed fire disturbance data has previously hindered assessments of reburns at a sub-continental scale. We fill this gap by leveraging a newly developed remote sensing-based dataset that captures multiple fire events, allowing us to analyse the spatial and temporal variability of reburns over the past four decades. We estimate that almost a third (30.1%) of the area burned in southern Europe between 1985 and 2023 was affected by repeated fires. While slightly higher than the 20% reported by Ermit\u0026atilde;o et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for a shorter period (2001\u0026ndash;2022), differences in spatial coverage and temporal framework limit direct comparison.\u003c/p\u003e \u003cp\u003eWe found that short-term reburns (i.e. repeated fires with return intervals\u0026thinsp;\u0026lt;\u0026thinsp;20 years) to be common among repeated fires, and even more extreme reburns (\u0026lt;\u0026thinsp;15 years) are common in southern Europe. Reburns are uncommon to happen within the first five years, likely reflecting insufficient time for fuel accumulation. This might be true especially for Mediterranean dry environments, where post-fire regrowth can be constrained by water-limited conditions (Blanco-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The dominance of the 10\u0026ndash;20-year reburn interval in southern Europe likely results from a convergence of ecological, climatic and contextual conditions (Ganteaume et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Peris-Llopis et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Resco De Dios et al., 2022). In many Mediterranean landscapes, forests may require more than a decade to build up the continuous and flammable fuel loads needed for fire recurrence (Pausas \u0026amp; Paula, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This pattern is further shaped by the legacy of large fire footprints from the 1980s and 1990s (Moreira et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; San-Miguel-Ayanz et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which created a substantial pool of previously burned areas that reburned after 2000s (Fern\u0026aacute;ndez-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pausas \u0026amp; Fern\u0026aacute;ndez-Mu\u0026ntilde;oz, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While overall differences in reburn intervals across bioregions were small, subtle variations were observed. For instance, slightly longer intervals in the Aegean may indicate slower vegetation recovery due to limited productivity (Koutsias et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Roder et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Conversely, short fire return intervals in the west of the Iberian Peninsula might be explained by large-scale afforestation throughout the 20th century (Fernandes et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pausas \u0026amp; Fern\u0026aacute;ndez-Mu\u0026ntilde;oz, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), creating high fuel continuity that increases the likelihood of reburns (Duane et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Local studies support this pattern, with reburn intervals of five years before 2000s reported in landscapes of northern Portugal (Fernandes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Corsica (Mouillot et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and eastern Spain, where approximately 12% of the total area that burned during 23 years (1975\u0026ndash;1998) experienced at least two fires (D\u0026iacute;az-Delgado et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Although similar reburn intervals appear in the Dinaric and Balkan regions, the lack of detailed fire history data limits interpretation, and the underlying drivers remain unclear. This highlights the need for further investigation in these regions. In summary, we conclude that short-term reburns are common across southern Europe, with the majority of reburns happening within 10 and 20 years.\u003c/p\u003e \u003cp\u003eOur results showed a very strong west-east gradient in reburn fractions. The largest hotspot of reburns was in the Western basin, with up to 40.8% of all fires being short-term reburns on average (i.e. within 20 years), while for all other regions, average reburn fractions ranged between 11% and 21%. Differences in land use, fire suppression strategies, and post-fire management likely play a fundamental role in shaping the observed patterns (Moreira et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pausas, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The high prevalence of reburns in the Western basin may be explained by a combination of frequent fire activity, high ignition pressure, and fast fuel accumulation (Peris-Llopis et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly in densely vegetated areas where fire weather, forest structure, and land management contribute to frequent fire recurrence (e.g. productive forest plantations in humid areas in Portugal, Davim et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond the legacy of large areas burned in the late 20th century, strict fire suppression policies may have unintentionally amplified reburn potential by limiting low-intensity fires that would otherwise naturally regulate fuel loads (Moghli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Over time, fuel accumulation of fire-prone species and greater fuel continuity create conditions that favour large-scale wildfires (Duane et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pi\u0026ntilde;ol et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which will ultimately increase the likelihood of reburns. Our results also highlight that regions outside the classical fire-prone regions of Europe (i.e. Spain, Portugal; Archibald et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pausas, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) are likewise affected by reburns. Although the largest hotspot of reburns remains the Western Mediterranean basin, parts of the Central Mediterranean and the Dinaric Mountains and Balkans also showed high reburn fractions. The Dinaric mountains and Balkan regions are often overlooked in terms of European fire dynamics and generally considered as fire rare, likely due to lack of long-term data (Resco De Dios et al., 2022). However, our findings suggest that this region plays a larger role for Europe\u0026rsquo;s fire regimes than previously reported.\u003c/p\u003e \u003cp\u003eDespite the observed decline in annual burned area across southern Europe (Turco et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reburn proportions have remained stable (e.g. West and Central Mediterranean) or even increased in some regions such as the Dinaric mountains and Balkans. We note, though, that the actual time frame for characterizing reburns using our long-term dataset was limited to 2005\u0026ndash;2023. We did not observe a sharp increase in reburn proportions that would suggest fuel limitations are no longer a factor, yet the continued occurrence of reburns indicates that more areas may become susceptible to reburning soon. Thus, reburn dynamics are not confined to regions with historically frequent fire activity, but might also emerge in areas where recent fire activity has increased, post-fire conditions allow for rapid fuel regeneration, or even where fuel moisture used to be a limiting factor (Agne et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fern\u0026aacute;ndez-Guisuraga \u0026amp; Calvo, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Resco De Dios et al., 2021). The only significant decrease in reburn percentages was found in the Western Mediterranean basin related to the largest fire seasons of the last century that now fall outside the 20-year reburn interval. However, recent major fire seasons in 2017 and 2022 (Rodrigues et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Turco et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) may have set a new stage for reburns in the coming decades.\u003c/p\u003e \u003cp\u003eWhile we present the first continental-scale analysis of reburn dynamics, some caveats need to be considered when interpreting our results. First, four decades of data may be a relatively short time to capture clear long-term trends in reburning, but it has been adequate to reveal reburn patterns in recent years, significantly extending insights beyond previous research. It is important to note that although our dataset spans nearly four decades, we can only reliably assess reburns for the last 19 years, because a historic baseline is needed to compare reburn fractions over time. Without applying a temporal threshold, reburn fractions will inherently increase over time simply as a function of expanding burn records (Buma et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further, identifying reburns requires knowledge of earlier fires, making a sufficiently long look-back period essential. Since MODIS data is only available since 2001, a complete 20-year reburn interval can only be assessed for fires from 2021 onward, limiting long-term analysis. Second, remote sensing products are always prone to errors in mapping burned area (Franquesa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Comparison between our annual burned area estimates from EFDA maps and EFFIS showed a good agreement, supporting the suitability of our dataset for analysing long-term and spatial patterns of reburn dynamics. That said, our estimates tend to be slightly lower than official statistics, partly due to constraints in our forest land use definition (Viana-Soto \u0026amp; Senf, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, some patches in open landscapes characterised by evergreen shrubs (maquis, garrigue) intermixed with small trees (oak, Aleppo pine) may not always be fully included in EFDA. We also observed small mismatches between EFFIS and EFDA time series related to fires occurring in late summer or early autumn, which are recorded in the following year due to constraints in disturbance mapping during the summer season (e.g. fires in 1985 captured in 1986 or large fires in 2017 partially recorded in 2018, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). From a spatial perspective, we also found good agreement when comparing our results to visual interpretation, effectively capturing multiple fire events, with differences primarily arising in single-fire events and unburned areas. Despite the uncertainties discussed above, our estimates provide a reliable representation of reburn dynamics for southern Europe.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur findings establish a robust baseline for understanding the role of reburns in forest dynamics of southern Europe. From our analysis we conclude that reburns, i.e. fires occurring a maximum 20 years after another fire, are integral to Europe\u0026rsquo;s fire regimes. We also highlight the critical value of long-term time series for studying reburn patterns, as our analysis would have been impossible with existing shorter burned area products. As environmental conditions continue to change, examining the interplay of multiple fires will be essential for predicting future fire regimes and their socio-ecological consequences (Duane et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Garc\u0026iacute;a-Llamas et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the projected increase in fire size and frequency (Gr\u0026uuml;nig et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pimont et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), understanding fire patterns and trends is increasingly urgent due to their potential to drive shifts in ecosystem structure and carbon dynamics (Pellegrini et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Climate change is expected to strengthen the fire-aridity relationship (Gr\u0026uuml;nig et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), causing more frequent burning in historically fire-limited regions before vegetation can adapt. More frequent reburning may potentially compromise forest resilience (Stevens-Rumann \u0026amp; Morgan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Turner et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) by preventing ecosystems to fully recover or lead to shift in ecosystems composition (Baudena et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Viana-Soto et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The consequences of this recurrence cycle are far-reaching also in the impact to ecosystem services (e.g. soil degradation, biodiversity loss, air quality, water availability) (Lecina-Diaz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Shortened fire intervals can significantly reduce ecosystem services and diminish ecosystem multifunctionality. In this context, the spatially explicit data on forest reburns provided here are a valuable source for guiding restoration efforts and supporting management in fire-prone landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledge funding from ForestPaths project (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation, ID No 101056755) funded from the European Union's Horizon Europe Research and Innovation, from the AI4Forest project funded by the Federal Ministry of Education and Research, Germany (BMBF; Grant number: 01IS23025C) and from ESA Project CLIMATE SPACE RECCAP2 (ESA contract 4000144908/24/I-LR).\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data used in this study from the European Forest Disturbance Atlas is freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/13333034\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/13333034\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Further information on the code and processing workflows will be made publicly available upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbatzoglou, J. T., Williams, A. P., \u0026amp; Barbero, R. (2019). Global Emergence of Anthropogenic Climate Change in Fire Weather Indices. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(1), 326-336. https://doi.org/10.1029/2018GL080959\u003c/li\u003e\n\u003cli\u003eAgne, M. C., Fontaine, J. B., Enright, N. J., Bisbing, S. M., \u0026amp; Harvey, B. J. (2023). Rapid fuel recovery after stand-replacing fire in closed-cone pine forests and implications for short-interval severe reburns. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e545\u003c/em\u003e, 121263. https://doi.org/10.1016/j.foreco.2023.121263\u003c/li\u003e\n\u003cli\u003eAndela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., \u0026amp; Hantson, S. 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The European Forest Disturbance Atlas: A forest disturbance monitoring system using the Landsat archive. \u003cem\u003eEarth System Science Data\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(6), 2373-2404. https://doi.org/10.5194/essd-17-2373-2025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Technical University of Munich","isAcceptedByJournal":true,"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":"Europe, forest fire, reburn, Earth Observation","lastPublishedDoi":"10.21203/rs.3.rs-6937064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6937064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFires disturbances are integral to fire-prone landscapes of southern Europe. While evidence of changing fire frequency has been well documented in Europe, the dynamics of forest reburns - defined as previously burned areas that ignite again within intervals shorter than the historical range - remain largely unexplored. Here, we present the first large-scale characterization of reburns in southern Europe, using a novel remote sensing dataset on fire disturbances from 1985 to 2023. We quantified the spatial extent and frequency of reburns, revealing that 30.1% of burned area in southern Europe experienced multiple fire events within the 1985\u0026ndash;2023 period (4.24 Mha), with 84.5% of these reburns occurring within a 20-year interval, and thus approaching the lower limit of reproductive maturity for many tree species. Extreme reburns within 10 years were also observed in 22.4%. Reburn hotspots emerged across the Mediterranean, where 19-21.1%yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of all fires were reburns within 20 years, and in the temperate forests of western Europe, where reburns accounted for 40.8% yr⁻\u0026sup1;. We further show that, although the overall burned area decreased, reburns continued to account for a substantial share of annual burn activity since 2005, with even slight increases in some regions (i.e. Dinaric Mountains and Balkan region). Our results highlight that reburns are integral to southern Europe\u0026rsquo;s disturbance regimes, and we emphasize the critical role of long time series for understanding forest dynamics. Based on our results, we suggest that reburns may increasingly shape fire regimes in southern Europe under intensifying forest fire activity, which may undermine post-fire recovery and requires special consideration from management.\u003c/p\u003e","manuscriptTitle":"Forest reburns are integral to southern Europe’s disturbance regimes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 08:22:58","doi":"10.21203/rs.3.rs-6937064/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":"e122db95-8014-4af0-a6d7-3df01e69b00d","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50767494,"name":"Biogeography"},{"id":50767495,"name":"Forestry"}],"tags":[],"updatedAt":"2026-02-03T17:45:34+00:00","versionOfRecord":{"articleIdentity":"rs-6937064","link":"https://doi.org/10.1111/geb.70198","journal":{"identity":"global-ecology-and-biogeography","isVorOnly":true,"title":"Global Ecology and Biogeography"},"publishedOn":"2026-01-30 00:00:00","publishedOnDateReadable":"January 30th, 2026"},"versionCreatedAt":"2025-07-02 08:22:58","video":"","vorDoi":"10.1111/geb.70198","vorDoiUrl":"https://doi.org/10.1111/geb.70198","workflowStages":[]},"version":"v1","identity":"rs-6937064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6937064","identity":"rs-6937064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}