Revisiting winners and losers in the rewilding of a marginal mountain landscape: two decades of change and the role of fire

Revisiting winners and losers in the rewilding of a marginal mountain landscape: two decades of change and the role of fire | 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 Revisiting winners and losers in the rewilding of a marginal mountain landscape: two decades of change and the role of fire Concepción García-Redondo, Montserrat Díaz-Raviña, Luis Tapia, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7252826/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rewilding is increasingly promoted as a nature-based solution to biodiversity loss and climate change, yet its long-term ecological outcomes remain poorly understood—particularly in fire-prone Mediterranean landscapes. Building on a previous study that analysed post-abandonment dynamics between 2000 and 2010 (Regos et al. 2016 . Reg. Env. Change . (16): 199–211), we revisit the ‘Baixa Limia–Serra do Xurés’ Natural Park (NW Iberia) to assess two decades of land cover change and bird community responses. Using generalised linear mixed models, co-inertia analysis and census plot data from 2000, 2010, and 2020, we demonstrate a strong and consistent covariation between bird assemblages and land-cover transitions, shaped by both natural successional processes and fire disturbance. While the first decade was characterized by forest expansion and increasing bird occurrences—particularly among forest- and shrubland-associated species—the following decade revealed a partial reversal, marked by the re-expansion of early successional habitats such as rocky areas and shrublands, largely driven by increased wildfire activity (> 20,000 ha burned). Species linked to mature forest cover experienced the strongest declines, especially in burnt areas, where co-inertia trajectories diverged sharply from those of unburnt plots. Our findings underscore the dual role of fire as both a threat and a potential management tool in rewilded landscapes. We advocate for a more nuanced vision of rewilding incorporating ‘fire-smart’ strategies that integrate prescribed burning, biodiversity goals, and landscape resilience to address the growing challenges of land abandonment and climate-driven fire regimes in Southern Europe. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rural abandonment in Europe has profoundly transformed traditional landscapes, particularly in marginal and mountainous regions, where agricultural modernization and policy frameworks have concentrated farming on more productive lands, leaving extensive areas unused (Renwick et al. 2013 ). This process has fostered the expansion of semi-natural habitats and the gradual recovery of native vegetation through ecological succession (Navarro and Pereira 2012 ). In this context, rewilding—understood as a passive management approach that promotes the restoration of natural ecological processes following the cessation of traditional land uses—has been proposed as a nature restoration strategy (Navarro and Pereira 2012 ), offering a more cost-effective alternative to conventional, intensively managed conservation models (Helmer et al. 2015 ; Pettorelli et al. 2018 ; Maanen et al. 2021 ). The EU Nature Restoration Law, formally adopted in June 2024, establishes binding targets to restore at least 20% of the EU's land and sea areas by 2030, with the ultimate goal of restoring all degraded ecosystems by 2050. Rewilding, as a nature-based approach, is identified as a key strategy to achieve these ambitious objectives, contributing both to biodiversity recovery and climate change mitigation (European Commission, 2024). The new legal framework enables large-scale ecological restoration across Europe, with rewilding expected to play an increasingly prominent role (Kloibhofer et al. 2025 ). However, rewilding in its current dominant form also presents challenges, including the loss of open habitats and landscape homogenization due to forest expansion following abandonment (Moreira et al. 2011 ), as well as heightened wildfire risk associated with biomass accumulation and increased fuel continuity (Hermoso et al. 2021 ). In the Iberian Peninsula, the progressive abandonment of agricultural lands and pastures has driven the expansion of scrublands and forests, substantially altering landscape structure (Renwick et al. 2013 ; Lasanta et al. 2016 ; Van der Zanden et al. 2017 ). The large-scale reforestation policy during the Franco’s dictatorship has also contributed to creation of monospecific stands in Spain, in contrast to the mixed stands promoted during the previous historical period (Vadell et al. 2016 ). These changes have directly impacted bird biodiversity by favouring forest species while negatively affecting species dependent on open habitats and ecotones (Suarez-Seoane et al. 2002 ; Sirami et al. 2008 ; Gil-Tena et al. 2009 ; Herrando et al. 2014 ; Zakkak et al. 2015 ). This issue is particularly relevant in protected areas, where conservation efforts often focus on species listed under legal protection (e.g. the Annexes of European Directives). In the ‘Baixa Limia–Serra do Xurés’ Natural Park, this process has generated a distinct pattern of “winners and losers” among bird communities: thirteen species associated with shrubland and forest habitats have increased in occurrence between 2000 and 2010, while only four species from ecotonal and open habitats have shown declining trends (see Regos et al., 2016 ). Nevertheless, this study has primarily addressed medium-term changes, overlooking the potential long-term side effects of rewilding, particularly the increasing wildfire hazard related to fuel accumulation (as recently noted by García-Redondo et al. 2024 ). In addition, the potential role of fire in maintaining open-habitat species threatened by long-term rewilding has received limited attention, despite its capacity to counter successional closure and preserve habitat heterogeneity (García-Redondo et al. 2023 ; Navarro-Rosales et al. 2025 ; Puig-Gironès et al. 2025 ). Fire plays a crucial yet often underestimated ecological role. Although traditionally regarded as a destructive agent, recent studies highlight its function as a natural regulator in Mediterranean and Atlantic-Mediterranean ecosystems (Keeley et al. 2012 ; McLauchlan et al. 2020 ). The frequency and severity of wildfires, interacting with vegetation succession, modulate bird community composition: species such as Dartford warbler ( Sylvia undata ) benefit from post-fire mosaics that sustain early successional shrublands (Pons and Bas 2005 ; Pons and Clavero 2010 ; Regos et al. 2015a ), whereas certain forest specialists may decline due to post-fire habitat simplification and structural degradation (Nappi et al. 2010 ; Robinson et al. 2014 ; Stephens et al. 2015 ) In addition to vegetation effects, wildfires can significantly alter soil structure and functioning. Changes in physical and chemical properties influence the ecosystem’s regenerative capacity. High-severity wildfires may cause substantial losses of organic matter and alter soil fertility (Marfella et al. 2024 ; Zhu et al. 2024 ), ultimately affecting plant community composition and, consequently, the bird species that rely on these habitats. For instance, repeated fires can create bare soils (e.g. rocky areas with sparse vegetation), which support rare, specialist species such as the rock thrush ( Monticola saxatilis ), but may also reduce the potential for vegetation recovery and limit the development of more advanced successional stages (Prodon 2022 ). Therefore, the effects of wildfires extend well beyond immediate vegetation dynamics. Recent studies demonstrate that certain post-fire management practices can also influence soil quality and ecological recovery for decades (Neary and Leonard; Jiménez-Morillo et al. 2020 ). In this context, strategies that reduce fuel loads and fire severity can promote structurally and functionally diverse bird habitats (Russell et al. 2009 ; Pons and Clavero 2010 ; Taillie et al. 2018 ; Saab et al. 2022 ). The resulting spatial heterogeneity, often referred to as ‘pyrodiversity’, is essential for sustaining resilient bird communities by accommodating species with differing ecological requirements (Parr and Andersen 2006 ; Taylor et al. 2012 ; Kelly et al. 2016 ; Tingley et al. 2016 ). Recurring low- to moderate-intensity fires may therefore help maintain landscape heterogeneity, slow successional closure, and promote species associated with early-successional stages, supporting recent calls to restore the ecological role of fire as a conservation tool (Nimmo et al. 2013 ; Kelly et al. 2014 ; Durigan and Ratter 2015 ; Pérez et al. 2018 ; Puig-Gironès et al. 2025 ). The ‘Baixa Limia–Serra do Xurés’ Natural Park (PNBL-SX), located in northwestern Spain, offers a paradigmatic example of these interacting dynamics. Following widespread rural abandonment since the mid-20th century, the area has experienced a complex interplay between vegetation succession and recurring fire regimes (García-Redondo et al. 2024 ). Although most fires are anthropogenic, their ecological effects have contributed to the maintenance of open patches within a landscape otherwise trending toward successional closure (García-Redondo et al. 2023 ). The PNBL-SX thus provides a valuable setting to explore how rural abandonment, vegetation succession, and fire regimes can shape a dynamic habitat mosaic to balance both forest and open-habitat bird communities (Regos et al., 2014; Navarro-Rosales et al., 2025 ). Modelling exercises conducted in the broader ‘Gerês-Xurés’ Biosphere Reserve, which includes the PNBL-SX, already suggest that fire may facilitate rewilding by promoting open habitats beneficial to multiple vertebrate species (see Campos et al. 2021 ) −although this has not been yet empirically tested with in situ data. Understanding how land abandonment processes, modulated by fire dynamics, influences biodiversity is therefore critical for designing adaptive conservation strategies that respond to ecosystem feedbacks (Fuhlendorf et al. 2009 ; Campos et al. 2021 ; Navarro-Rosales et al. 2025 ; Plumanns-Pouton et al. 2025 ). Over the past two decades (2000–2020), the PNBL-SX has offered a unique case study to empirically assess: (1) whether land abandonment has produced sustained gains in bird diversity; (2) whether increased wildfire activity has significantly affected shrubland and forest communities; and (3) the extent to which fire may function as a key ecological driver in increasingly wilder landscapes. Building on a previous study that analysed post-abandonment dynamics between 2000 and 2010 (Regos et al. 2016 . Reg. Env. Change . (16): 199–211), we revisit the ‘Baixa Limia–Serra do Xurés’ Natural Park (NW Iberia) to assess the role of fire in shaping bird communities after two decades of passive rewilding. Methods Study area The ‘Baixa Limia–Serra do Xurés’ Natural Park (PNBL-SX), situated in the province of Ourense (north-western Spain), encompasses approximately 29,345 ha across several municipalities (Bande, Calvos de Randín, Entrimo, Lobeira, Lobios, and Muíños; Decreto 64/2009). The park, originally designated in 1993 and expanded in 2009 (Decreto 401/2009), forms part of the Natura 2000 network (SCI ES1130001; SPA ES0000376) and, since 2009, belongs to the transboundary Gerês–Xurés biosphere reserve together with the Peneda-Gerês National Park in Portugal (Xunta de Galicia, 2024). The area lies within the Eurosiberian–Mediterranean transition zone, characterized by rugged topography (323–1,529 m a.s.l., ~ 13% average slope) and a temperate oceanic climate with sub-Mediterranean influences (Csb, Köppen), receiving 1,200–1,600 mm of annual precipitation and average annual temperatures of 8–12°C. Land cover is dominated by rocky areas with sparse vegetation (hereafter ‘rocky areas’) and closed shrublands (~ 69%), primarily heathlands and gorse, while deciduous forests (mainly Quercus robur and Q. pyrenaica ) occupy ~ 21%, and agricultural land represents less than 5% (Regos et al. 2015b ). Long-term rural abandonment since the mid-20th century has favoured spontaneous vegetation succession, shrub encroachment, and biomass accumulation, contributing to a landscape increasingly vulnerable to recurrent wildfires, the vast majority of which are human induced (~ 87% arson origin)(García-Redondo et al. 2024 ). Between 2001 and 2010, a total of 9,995.03 ha burned, whereas during the following decade (2011–2020), 19,584.83 ha were affected by fire—a near two-fold increase compared to the previous period. Moreover, the number of large fires (defined as those exceeding 500 ha) rose from 2 events in 2001–2010 to 6 events in 2011–2020. For comparison, during 1990–1999, 17,796 ha burned across 4 large fire events, illustrating that while total burned area fluctuates, recent years show a marked increase in both burned extent and fire severity (see Fig. 1 ). The interplay between rural abandonment, wildfire regime, and ecological succession has shaped a mosaic-like landscape, where open patches, shrublands, and forest formations at different successional stages coexist, making this natural park an ideal setting for studying the ecological effects of rewilding in marginal mountain landscapes. Bird data Bird data for the period 2000–2010 were obtained from the same standardized point-count surveys used by Regos et al. ( 2016 ), which analysed the effects of rural abandonment and vegetation succession on bird assemblages in the ‘Baixa Limia–Serra do Xurés’ Natural Park. These surveys were conducted during the breeding season (May–June), using 5-min unlimited-distance point counts (following Bibby et al. 1992 ) at 209 locations stratified across the park’s major land-cover types (Fig. 1 ). In 2020, surveys were repeated following the same protocol to ensure temporal comparability. All point-count stations were separated by at least 250 m to avoid double counting (Gregory et al., 2004 ). The original sampling design was part of a broader ecological monitoring initiative assessing the impacts of land-use change and passive rewilding in marginal mountain landscapes of northwestern Iberia. Point-count locations were selected to ensure spatial representativeness across the park’s heterogeneous habitat mosaic, including shrublands, woodlands, rocky outcrops, and ecotones. The point-count method was chosen for its effectiveness in detecting small- and medium-sized passerines by both sound and sight in structurally complex Mediterranean habitats, consistent with standard protocols (Bibby et al. 1992 ; Regos et al. 2016 ). To minimize detection bias, all surveys were conducted during optimal weather conditions (i.e., no strong wind or rainfall) within the first four hours after sunrise, coinciding with peak avian vocal activity. Only occurrence data (presence/absence) were used to reduce potential interannual variability and observer bias in abundance estimation. Land cover and fire history To assess vegetation dynamics and fire disturbance over time, we used land cover maps and wildfire perimeter data for the ‘Baixa Limia–Serra do Xurés’ Natural Park. Land cover maps were obtained from García-Redondo et al. ( 2024 ) and derived from Landsat imagery (TM, ETM+, and OLI/TIRS) for the years 2000, 2010, and 2020. These maps classify the landscape into six dominant land cover types: croplands/grasslands, deciduous forest, evergreen forest, shrubland, rocky areas, and water bodies. Classification was performed using a supervised ensemble approach that combined multiple machine learning algorithms (Random Forest, Support Vector Machines, Neural Networks, and AdaBoost), with ensemble outputs integrated via majority voting and validated through confusion matrices. Land cover transitions were quantified using cross-tabulation matrices calculated between consecutive time steps (2000–2010 and 2010–2020), as well as across the entire study period (2000–2020). Analyses were conducted at two spatial scales: (1) across the full extent of the Natural Park, and (2) within a 100-m buffer around each bird census plot to capture local habitat dynamics potentially influencing bird assemblages. To evaluate the effect of wildfires on bird communities, we used official spatial fire data provided by the Department of Rural Environment of the Xunta de Galicia. This dataset includes georeferenced fire perimeters, ignition dates, and surface areas affected by each event. Fire perimeters were overlaid with the bird sampling locations to identify census plots impacted by wildfires. Each plot was then classified as either burnt or unburnt based on its spatial intersection with fire perimeters during the 2010–2020 period (n = 87). The year 2020 was excluded from this classification, as wildfires occurred after the bird surveys had been completed (October), ensuring temporal consistency between ecological and fire data. Statistical Analyses To assess temporal changes in the presence–absence patterns of breeding bird species in the ‘Baixa Limia–Serra do Xurés’ Natural Park, we applied generalized linear mixed models (GLMMs) with a binomial error distribution and a logit link function, as described in Regos et al. ( 2016 ). The models included‘year’ as a fixed effect and ‘point count’ as a random effect to account for repeated measures at the same sampling locations. Statistical significance was set at p-value < 0.05. All analyses were conducted using the ‘lme4’ package in R (Bolker et al. 2009 ; Bates et al. 2014 ). To explore the joint structure between bird communities and environmental conditions, we conducted a co-inertia analysis (CoIA) using the ‘ade4’ package in R, as described in Regos et al. ( 2016 ). This multivariate technique allows the simultaneous ordination of two datasets measured on the same sampling units—in this case, bird species abundance and a set of environmental predictors including land cover types (LCT) and fire-related variables. Bird community data were compiled for the years 2000, 2010, and 2020, and included only species consistently recorded across all time periods (n = 32). Environmental data included proportional land cover composition (extracted from classified raster maps at 100 m buffers around sampling plots) and, for 2020, a stack of three fire-related variables derived from raster layers. The CoIA was performed in two stages: 1) Full temporal trajectories (2000–2020) We matched bird community data with land cover composition for each year and computed two separate PCA ordinations: one for the species matrix (dudiY) and another for the environmental matrix (dudiX). These were linked using the coinertia() function. The resulting co-inertia axes represent shared structure between species composition and land cover variation over time. Site scores (coi$ls and coi$lY) were summarized by dominant LCT (from 2000) and year to visualize temporal trajectories of both environmental conditions and bird communities. 2) Fire influence on bird–environment association (2020) To assess the influence of recent fire history, we performed a second CoIA restricted to bird community data from 2020, matched with 2010 land cover composition and mean fire variable values extracted around each plot. This allowed us to examine how past land cover and recent fire activity jointly structure current bird community composition. The biplot of this analysis displays associations between bird species and both land cover types and fire descriptors. In all cases, significance of the co-structure was assessed using a Monte Carlo permutation test (randtest.coinertia). Additionally, we visualized temporal and group-specific trajectories (by characterising each census plot by the dominant LC class) using mean site scores in the co-inertia space, highlighting directional changes across time periods (2000–2010 vs. 2010–2020) and between burnt and unburnt areas. Results Changes at the landscape level The land cover change analysis revealed an overall increase in forested areas—particularly deciduous woodlands—and a notable decline in both open and closed shrublands between 2000 and 2020. Reductions were also observed, though to a lesser extent, in grasslands and croplands. However, a closer look at decadal trends highlights contrasting dynamics. Between 2000 and 2010, open shrublands—represented by rocky areas with sparse vegetation—experienced a sharp decline of approximately 50,000 ha (Fig. 2 ). In contrast, this land cover class expanded by around 30,000 ha between 2010 and 2020, while grasslands, croplands, and closed shrublands showed continued declines. Transition matrices clearly illustrated dominant land cover shifts (Fig. 2 ). From 2000 to 2010, the main transitions were from rocky areas with sparse vegetation to shrublands, and from shrublands to both deciduous forests—consistent with natural successional processes—and evergreen forests, likely due to plantation expansion. In contrast, the 2010–2020 period showed a reversal of these patterns, with major transitions from shrublands back to ‘rocky areas’ and from evergreen forests to shrublands. Nonetheless, deciduous forest continued to expand slightly, primarily at the expense of shrublands. In terms of bird species, we found that 10 species significantly increased their occurrence across the natural park over the past 20 years, while only 4 species showed a contraction in their distribution. The majority of species did not exhibit significant changes (Fig. 3 ). However, when analysed at the decadal scale, contrasting patterns emerged: (1) between 2000 and 2010, 14 species increased in occurrence while only 4 declined; (2) between 2010 and 2020, only 6 species showed an increase, whereas 10 experienced a decline (see complete list of species associated with each trend in Table 1 ). Table 1 Number of occurrences (i.e. presences in census plots) for each species and year. P-values were derived from Generalized Linear Mixed Models (GLMM). Statistical significance is considered at p-value < 0.05, and significant values are marked with an asterisk (*). Species acronim Scientific name 2000 2010 2020 p-value (2000–2010) p-value (2010–2020) p-value (2000–2020) Cpal Columba palumbus 8 20 15 0.02* 0.13 0.27 Stur Streptopelia turtur 8 17 8 0.01* 0.00* 0.25 Ccan Cuculus canorus 69 48 18 0.01* 0.00* 0.00* Pvir Picus sharpei 15 10 11 0.30 0.90 0.23 Larb Lullula arborea 18 14 32 0.45 0.02* 0.11 Aarv Alauda arvensis 43 33 47 0.02* 0.02* 0.99 Atri Anthus trivialis 10 4 6 0.00* 0.00* 0.00* Ttro Troglodytes troglodytes 92 144 137 0.00* 0.00* 0.00* Pmod Prunella modularis 46 46 91 1.00 0.00* 0.00* Erub Erithacus rubecula 25 50 76 0.00* 0.07 0.00* Stor Saxicola rubicola 34 46 63 0.10 0.28 0.01* Tmer Turdus merula 37 107 98 0.00* 0.01* 0.00* Sund Curruca undata 33 78 91 0.00* 0.92 0.00* Scom Curruca communis 16 27 23 0.04* 0.26 0.31 Satr Sylvia atricapilla 34 82 49 0.00* 0.00* 0.26 Pbon Phylloscopus bonelli 3 11 19 0.01* 0.13 0.00* Pibe Pylloscopus ibericus 23 38 28 0.02* 0.02* 0.85 Rign Regulus ignicapilla 26 35 17 0.05 0.00* 0.01* Pcri Lophophanes cristatus 14 20 11 0.25 0.03* 0.26 Pate Periparus ater 34 35 48 0.88 0.40 0.32 Pcae Cyanistes caeruleus 3 10 9 0.02* 0.31 0.11 Pmaj Parus major 7 18 23 0.02* 0.00* 0.01* Cbra Certhia brachydactyla 8 11 11 0.30 0.38 0.86 Oori Oriolus oriolus 15 14 8 0.76 0.01* 0.00* Lcol Lanius collurio 9 3 6 0.02* 0.31 0.12 Ggla Garrulus glandarius 14 7 16 0.12 0.12 0.96 Ccor Corvus corone 5 27 16 0.00* 0.02* 0.04* Fcoe Fringilla coelebs 37 51 81 0.05* 0.02* 0.00* Sser Serinus serinus 15 24 27 0.11 0.82 0.15 Cchl Chloris chloris 8 9 10 0.74 0.62 0.87 Lcan Linnaria cannabina 24 36 38 0.07 0.68 0.15 Ecia Emberiza cia 44 43 37 0.89 0.12 0.09 Changes at the census-plot level The co-inertia analysis revealed a consistent and statistically significant structure of covariation between bird assemblages and land-cover composition across the three sampling years (2000, 2010, and 2020). This suggests that directional shifts in bird community composition were closely associated with temporal changes in land-cover types. The strength of this relationship was supported by a Monte Carlo permutation test, which yielded a significant RV coefficient (p < 0.01), confirming a non-random, shared structure between the two datasets. Most bird species that showed increasing occurrences in the census plots over the last decade were associated with open habitats and fire-related variables, as indicated by the green dots in the co-inertia biplot (Fig. 4 ). In contrast, species linked to forested habitats—particularly evergreen forests—were among the most negatively affected by land-cover changes between 2010 and 2020. This trend contrasts with the previous decade (2000–2010), during which many forest-associated species experienced increases in occurrence (see circular symbols in the co-inertia biplot in Fig. 4 , and Table 1 ). When separating plots based on fire history, the split co-inertia trajectories revealed contrasting dynamics between burnt and unburnt sites. Burnt sites—those affected by fire at least once over the last 10 years—exhibited sharp directional shifts in both land cover and bird community composition, often diverging from the general trajectory observed in unburnt areas (Fig. 4 ). These findings suggest that fire disturbance has played a central role in reshaping both vegetation and avifaunal structure, reinforcing its importance as a driver of ecological change in the study area. In unburnt plots, the dominant patterns were shaped by natural successional processes, with transitions from rocky areas to shrubland and from shrubland to forest cover (see ‘Unburnt sites – Land cover’ in Fig. 4 ). In contrast, burnt sites exhibited markedly different trajectories. For example, rocky areas initially shifted toward the shrubland region in co-inertia space during 2000–2010 but returned to the rocky area space by 2020, suggesting a reversal driven by fire disturbance. A similar pattern was observed for shrubland, which shifted back toward rocky areas in the last decade. Evergreen forests showed a pronounced directional shift toward the rocky area space, indicating structural degradation or conversion following fire events (Fig. 4 ). Bird communities also exhibited distinct trajectories in burnt versus unburnt plots. In unburnt sites, communities progressively shifted from associations with rocky areas to shrubland, and slightly from shrubland to forest, reflecting vegetation succession. In burnt sites, however, bird communities tended to return toward the co-inertia space associated with rocky areas, reversing the successional trend. Forest species—particularly those most strongly linked to evergreen forests—shifted markedly toward communities typical of open habitats, underscoring the disruptive impact of fire on bird assemblage structure (see ‘Burnt vs. Unburnt Sites – Bird Communities’ co-inertia plot in Fig. 4 ). Discussion Our findings highlight the importance of assessing the impacts of rewilding over both medium- and long-term (interdecadal) timeframes. While the overall trend across the last 20 years suggests a generally positive effect on most bird species—particularly those associated with forest habitats—interdecadal assessments revealed contrasting patterns. During the 2000–2010 period, we observed an overall increase in forest cover, accompanied by distributional expansion in many target species linked to both forest and shrubland habitats (Figs. 3 and 4 , and Table 1 ; Regos et al. 2016 ). These patterns likely reflect post-fire vegetation recovery following major wildfire events that affected approximately 17,800 ha (Figs. 2 and 4 ). Our analysis of land-cover trajectories over the past two decades reveals a complex and temporally heterogeneous pattern of ecological change that strongly influenced bird community composition. While the general trend pointed toward an expansion of forested habitats—particularly deciduous woodlands—this was not uniform across time (Figs. 2 and 4 ). Between 2000 and 2010, the landscape underwent progressive natural succession, with widespread transitions from rocky areas with sparse vegetation to closed shrublands and from closed shrublands to forest cover. This shift was accompanied by a marked increase in the occurrence of bird species associated with both shrubland and forest habitats, as reflected in the positive trends observed for 14 species during this decade. However, the 2010–2020 period showed a partial reversal of these trends, with a significant recovery of open rocky areas and declines in shrubland, cropland, and grassland cover (Figs. 2 and 4 ). These land-cover shifts coincided with reduced gains—or even contractions—in bird species distribution, particularly among forest-associated taxa, suggesting a breakdown of successional recovery patterns in the most recent decade (Fig. 4 ). The observed reversal in land-cover trends and bird community trajectories during the last decade aligns with the increasing frequency and extent of large wildfires in the region (Fig. 1 and García-Redondo et al. 2024 ). Our split co-inertia analysis clearly differentiates the ecological pathways of burnt versus unburnt plots (see Fig. 4 ), highlighting the central role of fire in reshaping both vegetation structure and avifaunal assemblages. In unburnt areas, bird and vegetation communities followed expected successional trajectories, moving from open habitats to shrublands and forests. In contrast, burnt sites displayed directional shifts back toward co-inertia spaces associated with rocky and open habitats, indicating a disruption of successional processes. This was particularly evident for evergreen forests, which showed strong degradation signals, and for forest-specialist bird species, which shifted toward communities typical of open landscapes. These patterns reinforce the idea that, while land abandonment may foster biodiversity recovery under stable conditions, the increasing intensity and frequency of fire events can override successional gains, underscoring the need for ‘fire-smart’ strategies in Mediterranean landscapes (Pais et al. 2020 ; Campos et al. 2022 ; Cánibe et al. 2022 ; Regos et al. 2023 ). However, after a decade of vegetation encroachment, the frequency of fire seasons with wildfires larger than 500 ha markedly increased—from only two years between 1983 and 2010 to six in the following ten years alone (see García-Redondo et al. 2024 ). Between 2010 and 2020, nearly 20,000 ha burned—more than double the area affected during the previous decade. This surge in severe fire activity likely contributed to the negative trends observed in many bird species during this period (Figs. 3 and Table 1 ), particularly those associated with evergreen forests. A previous study conducted in the natural park had already documented an increase in large fires (> 500 ha) during the 2000–2010 period and their strong impacts on soil and vegetation dynamics (García-Redondo et al. 2024 ). Our findings confirm the negative ecological consequences of large wildfires in the ‘Baixa Limia–Serra do Xurés’ Natural Park. At the same time, our results showed that post-fire recovery rates during the 1990s and 2000s supported a broad-scale recovery in the distribution of most studied bird species (see Regos et al. 2016 ; see period ‘2000–2010’ in Fig. 3 and Table 1 ). In this context, if fire impacts are not too severe, bird communities dominated by open-habitat and early successional species can recover relatively quickly following fire, in line with post-fire recovery trajectories reported for other Mediterranean ecosystems worldwide (Pons and Bas 2005 ; Brotons et al. 2005 ; Albanesi et al. 2012 ; Watson et al. 2012 ; Chalmandrier et al. 2013 ; Lindenmayer et al. 2014 ). These findings are consistent with those from a previous study in the same region, where species distribution models (fitted using generalized linear models with Poisson error distributions) revealed that up to 71% of our studied bird species exhibited a significant linear relationship with at least one attribute of the fire regime (García-Redondo et al. 2023 ). Importantly, spatial and temporal variability in burnt area and fire severity emerged as key predictors of bird abundance for 39% of species, and 60% showed a significant quadratic response to at least one fire regime variable. Moreover, the legacy of past land use—including vegetation structure a decade after disturbance—was critical for understanding the long-term influence of fire on avian communities (García-Redondo et al. 2023 ). These findings, together with our co-inertia results, reinforce the need for spatially explicit, fire-informed approaches to biodiversity conservation in fire-prone Mediterranean landscapes. Recent research in the ‘Gerês-Xurés’ Biosphere Reserve—where the natural park is located—modelled habitat availability for over 100 vertebrate species, including the bird species considered in our study, under contrasting land-use scenarios (Pais et al. 2020 ). That work found that policies promoting the recovery of extensive management approaches based on High Nature Value (HNV) farmlands offered the most beneficial outcomes for both biodiversity conservation and fire regime regulation. However, traditional rewilding scenarios—representing the continuation of current trends in agropastoral abandonment—also had positive effects for some species (Pais et al. 2020 ; Campos et al. 2021 ). In this sense, integrating more explicitly disturbances like fire into rewilding strategies, both in the form of unplanned wildfires and planned prescribed burns, has been proposed as a viable management approach (Campos et al. 2021 ; Pais et al. under review ). Prescribed burning in agroforestry mosaics was identified as particularly effective for simultaneously reducing wildfire hazard and preserving habitat for biodiversity (Pais et al. 2023 ;Pais et al. under review ). Despite these findings, the widespread recovery of traditional agropastoral practices that shaped these landscapes in the second half of the 20th century now appears unlikely (Renwick et al. 2013 ; Pe’er et al. 2014 ; Estoque et al. 2019 ). In this context, rewilding emerges as both a nature restoration strategy and a climate-smart solution—particularly in areas hampered by longer socioeconomically viable (Navarro and Pereira 2012 ; Helmer et al. 2015 ; Pettorelli et al. 2018 ; Svenning 2020 ; Plumanns-Pouton et al. 2025 ). Our results confirm that land abandonment can have positive effects on biodiversity in the ‘Baixa Limia–Serra do Xurés’ Natural Park (Regos et al. 2016 ), but they also highlight a key trade-off: increased wildfire hazard resulting from fuel accumulation and vegetation encroachment (Figs. 3 and 4 , and Table 1 ). In this regard, the use of fire emerges as key process to incorporate more explicitly in the rewilding framework. There is a growing recognition of the need to rewild fire regimes, using fire as both an ecological process and an ancient tool for landscape management in fire-prone ecosystems (Plumanns-Pouton et al 2025 ). Future research should explore the potential of prescribed burning or allowing natural wildfires to burn under controlled fire-weather conditions to help restore fire regimes that support biodiversity while mitigating wildfire risk—especially under projected climate warming scenarios. Taken together, our findings highlight the urgent need to integrate biodiversity conservation with adaptive fire and land-use management in rewilding contexts. As rewilding continues to shape the future of abandoned rural landscapes, fire regimes must be seen not only as disturbance agents but also as potential tools for ecological restoration (Campos et al. 2021 ; Navarro-Rosales et al. 2025 ; Plumanns-Pouton et al. 2025 ). A ‘fire-smart’ rewilding approach—grounded in ecological thresholds, post-fire recovery trajectories, and socioecological viability—will be critical to maintaining ecosystem functionality and mitigating future wildfire risk, particularly in Mediterranean mountain areas experiencing climate-driven intensification of fire events. Aligning such strategies with broader EU biodiversity and climate objectives will be key to building resilient socioecological systems across fire-prone landscapes. Conclusion This study demonstrates that rewilding processes in the ‘Baixa Limia–Serra do Xurés’ Natural Park have led to heterogeneous ecological outcomes over time, shaped by the interplay between vegetation succession, fire disturbance, and species-specific responses. While initial phases of rewilding supported forest expansion and bird community recovery, the subsequent increase in large wildfires in recent years has reversed many of these gains—particularly for forest-specialist species. By combining co-inertia analysis, satellite-based land cover transitions, and decadal-scale bird monitoring, we provide compelling evidence that fire is both a driver of ecological disruption and a potential ally in managing post-abandonment landscapes. Moving forward, ‘fire-smart’ rewilding strategies that incorporate fire as a key ecological process, historical land use legacies, and biodiversity-based planning will be essential to reconciling nature restoration with wildfire risk mitigation in Mediterranean mountains under climate change. Declarations Acknowledgements This work was developed under the research project RESFIRE (PID2023-152690OA-C22, C21), funded by the Spanish Ministry of Science, Innovation and Universities. It was also supported by wildE Horizon Europe (GAP-101081251) project. AR was funded by the ‘Ramón y Cajal’ fellowship program of the Spanish Ministry of Science and Innovation (RYC2022-036822-I). References Albanesi S, Dardanelli S, Bellis LM (2012) Effects of fire disturbance on birds of mountain Serrano Forest of Central Argentina. Journal of Forest Research 19:105–114 Bates D, Maechler M, Bolker B, Walker S (2014) lme4: Linear Mixed-effects Models Using Eigen and S4. R Package Version 1.1-23. 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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-7252826","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498134290,"identity":"989a22db-906b-4b3b-bdcb-c8a9140d516e","order_by":0,"name":"Concepción García-Redondo","email":"","orcid":"","institution":"Universidade de Vigo","correspondingAuthor":false,"prefix":"","firstName":"Concepción","middleName":"","lastName":"García-Redondo","suffix":""},{"id":498134291,"identity":"aace60b2-8913-4434-8d9f-21327d8501ec","order_by":1,"name":"Montserrat Díaz-Raviña","email":"","orcid":"","institution":"Misión Biológica de Galicia del Consejo Superior de Investigaciones Científicas (MBG-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Montserrat","middleName":"","lastName":"Díaz-Raviña","suffix":""},{"id":498134296,"identity":"3419acf6-7326-424b-84e2-56187b59bdfb","order_by":2,"name":"Luis Tapia","email":"","orcid":"","institution":"Dirección Xeral de Patrimonio Natural. 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The background raster represents the number of years since the last fire recorded between 2010 and 2020, with lighter tones indicating older fire events or long-unburned areas. The park boundary is outlined in black. The inset barplot (top right) summarizes total burned area per decade for 2000–2010 and 2011–2020, with a dashed red line indicating the reference level from 1990–1999 (17,796 ha). The figure illustrates the spatial heterogeneity in fire history and highlights the recent increase in both burned area and fire activity within the study landscape.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7252826/v1/3002497ae8fce1b85a3ea5c1.png"},{"id":88913113,"identity":"59cabaf1-7ced-4c5b-a235-b7e13641a92c","added_by":"auto","created_at":"2025-08-12 15:48:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":732583,"visible":true,"origin":"","legend":"\u003cp\u003eNet land-cover changes and transitions by decanal periods estimated from the ensembled land cover maps developed for each year.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7252826/v1/b5ba428ecb5a6d8e27349698.png"},{"id":88913115,"identity":"f29441cf-016b-41fb-beef-ebce22f9198d","added_by":"auto","created_at":"2025-08-12 15:48:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253949,"visible":true,"origin":"","legend":"\u003cp\u003eBarplot showing changes in species occurrences across decadal intervals. Species with significant changes (marked with an asterisk in Table 1) are highlighted in green (increased occurrence) and red decreased occurrence). Significance was determined based on GLMM-derived p-values (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7252826/v1/fd67f525070ca639f5b0bbd7.png"},{"id":88914555,"identity":"8c8fb11b-0757-47d7-8db7-7cb30fe004dd","added_by":"auto","created_at":"2025-08-12 16:04:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":879236,"visible":true,"origin":"","legend":"\u003cp\u003eCo-inertia analysis of bird assemblages and environmental variables across time and fire regimes. Co-inertia biplot for bird communities in 2020 versus land cover composition in 2020 and fire-related variables, showing the joint structure of species and environmental variables. Trajectories of land cover types in the co-inertia ordination space across the three sampling years (2000, 2010, and 2020), showing directional changes in environmental composition. Corresponding trajectories of bird communities by dominant land cover type over the same period, based on species composition scores. Separate trajectories for burnt and unburnt sites, highlighting differences in land cover and bird community responses to fire disturbance. Arrows represent temporal transitions (solid for 2000–2010, dashed for 2010–2020).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7252826/v1/4874982e4b220a2e3bfde4d6.png"},{"id":95654755,"identity":"a07630e6-00c2-405a-a51a-2b00bb3a5038","added_by":"auto","created_at":"2025-11-11 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3987340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7252826/v1/b04a2730-ac28-486b-aafb-35beff0697e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revisiting winners and losers in the rewilding of a marginal mountain landscape: two decades of change and the role of fire","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRural abandonment in Europe has profoundly transformed traditional landscapes, particularly in marginal and mountainous regions, where agricultural modernization and policy frameworks have concentrated farming on more productive lands, leaving extensive areas unused (Renwick et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This process has fostered the expansion of semi-natural habitats and the gradual recovery of native vegetation through ecological succession (Navarro and Pereira \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this context, rewilding—understood as a passive management approach that promotes the restoration of natural ecological processes following the cessation of traditional land uses—has been proposed as a nature restoration strategy (Navarro and Pereira \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), offering a more cost-effective alternative to conventional, intensively managed conservation models (Helmer et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pettorelli et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maanen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe EU Nature Restoration Law, formally adopted in June 2024, establishes binding targets to restore at least 20% of the EU's land and sea areas by 2030, with the ultimate goal of restoring all degraded ecosystems by 2050. Rewilding, as a nature-based approach, is identified as a key strategy to achieve these ambitious objectives, contributing both to biodiversity recovery and climate change mitigation (European Commission, 2024). The new legal framework enables large-scale ecological restoration across Europe, with rewilding expected to play an increasingly prominent role (Kloibhofer et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, rewilding in its current dominant form also presents challenges, including the loss of open habitats and landscape homogenization due to forest expansion following abandonment (Moreira et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), as well as heightened wildfire risk associated with biomass accumulation and increased fuel continuity (Hermoso et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Iberian Peninsula, the progressive abandonment of agricultural lands and pastures has driven the expansion of scrublands and forests, substantially altering landscape structure (Renwick et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lasanta et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Van der Zanden et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The large-scale reforestation policy during the Franco’s dictatorship has also contributed to creation of monospecific stands in Spain, in contrast to the mixed stands promoted during the previous historical period (Vadell et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These changes have directly impacted bird biodiversity by favouring forest species while negatively affecting species dependent on open habitats and ecotones (Suarez-Seoane et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sirami et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gil-Tena et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Herrando et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zakkak et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This issue is particularly relevant in protected areas, where conservation efforts often focus on species listed under legal protection (e.g. the Annexes of European Directives). In the ‘Baixa Limia–Serra do Xurés’ Natural Park, this process has generated a distinct pattern of “winners and losers” among bird communities: thirteen species associated with shrubland and forest habitats have increased in occurrence between 2000 and 2010, while only four species from ecotonal and open habitats have shown declining trends (see Regos et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nevertheless, this study has primarily addressed medium-term changes, overlooking the potential long-term side effects of rewilding, particularly the increasing wildfire hazard related to fuel accumulation (as recently noted by García-Redondo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, the potential role of fire in maintaining open-habitat species threatened by long-term rewilding has received limited attention, despite its capacity to counter successional closure and preserve habitat heterogeneity (García-Redondo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Navarro-Rosales et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Puig-Gironès et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFire plays a crucial yet often underestimated ecological role. Although traditionally regarded as a destructive agent, recent studies highlight its function as a natural regulator in Mediterranean and Atlantic-Mediterranean ecosystems (Keeley et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McLauchlan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The frequency and severity of wildfires, interacting with vegetation succession, modulate bird community composition: species such as Dartford warbler (\u003cem\u003eSylvia undata\u003c/em\u003e) benefit from post-fire mosaics that sustain early successional shrublands (Pons and Bas \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pons and Clavero \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Regos et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), whereas certain forest specialists may decline due to post-fire habitat simplification and structural degradation (Nappi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Robinson et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stephens et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn addition to vegetation effects, wildfires can significantly alter soil structure and functioning. Changes in physical and chemical properties influence the ecosystem’s regenerative capacity. High-severity wildfires may cause substantial losses of organic matter and alter soil fertility (Marfella et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), ultimately affecting plant community composition and, consequently, the bird species that rely on these habitats. For instance, repeated fires can create bare soils (e.g. rocky areas with sparse vegetation), which support rare, specialist species such as the rock thrush (\u003cem\u003eMonticola saxatilis\u003c/em\u003e), but may also reduce the potential for vegetation recovery and limit the development of more advanced successional stages (Prodon \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the effects of wildfires extend well beyond immediate vegetation dynamics. Recent studies demonstrate that certain post-fire management practices can also influence soil quality and ecological recovery for decades (Neary and Leonard; Jiménez-Morillo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, strategies that reduce fuel loads and fire severity can promote structurally and functionally diverse bird habitats (Russell et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pons and Clavero \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taillie et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saab et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The resulting spatial heterogeneity, often referred to as ‘pyrodiversity’, is essential for sustaining resilient bird communities by accommodating species with differing ecological requirements (Parr and Andersen \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Taylor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tingley et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recurring low- to moderate-intensity fires may therefore help maintain landscape heterogeneity, slow successional closure, and promote species associated with early-successional stages, supporting recent calls to restore the ecological role of fire as a conservation tool (Nimmo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Durigan and Ratter \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pérez et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Puig-Gironès et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe ‘Baixa Limia–Serra do Xurés’ Natural Park (PNBL-SX), located in northwestern Spain, offers a paradigmatic example of these interacting dynamics. Following widespread rural abandonment since the mid-20th century, the area has experienced a complex interplay between vegetation succession and recurring fire regimes (García-Redondo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although most fires are anthropogenic, their ecological effects have contributed to the maintenance of open patches within a landscape otherwise trending toward successional closure (García-Redondo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The PNBL-SX thus provides a valuable setting to explore how rural abandonment, vegetation succession, and fire regimes can shape a dynamic habitat mosaic to balance both forest and open-habitat bird communities (Regos et al., 2014; Navarro-Rosales et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Modelling exercises conducted in the broader ‘Gerês-Xurés’ Biosphere Reserve, which includes the PNBL-SX, already suggest that fire may facilitate rewilding by promoting open habitats beneficial to multiple vertebrate species (see Campos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) −although this has not been yet empirically tested with \u003cem\u003ein situ\u003c/em\u003e data.\u003c/p\u003e\u003cp\u003eUnderstanding how land abandonment processes, modulated by fire dynamics, influences biodiversity is therefore critical for designing adaptive conservation strategies that respond to ecosystem feedbacks (Fuhlendorf et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Campos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Navarro-Rosales et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Plumanns-Pouton et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Over the past two decades (2000–2020), the PNBL-SX has offered a unique case study to empirically assess: (1) whether land abandonment has produced sustained gains in bird diversity; (2) whether increased wildfire activity has significantly affected shrubland and forest communities; and (3) the extent to which fire may function as a key ecological driver in increasingly wilder landscapes. Building on a previous study that analysed post-abandonment dynamics between 2000 and 2010 (Regos et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e. \u003cem\u003eReg. Env. Change\u003c/em\u003e. (16): 199–211), we revisit the ‘Baixa Limia–Serra do Xurés’ Natural Park (NW Iberia) to assess the role of fire in shaping bird communities after two decades of passive rewilding.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy area\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park (PNBL-SX), situated in the province of Ourense (north-western Spain), encompasses approximately 29,345 ha across several municipalities (Bande, Calvos de Rand\u0026iacute;n, Entrimo, Lobeira, Lobios, and Mu\u0026iacute;\u0026ntilde;os; Decreto 64/2009). The park, originally designated in 1993 and expanded in 2009 (Decreto 401/2009), forms part of the Natura 2000 network (SCI ES1130001; SPA ES0000376) and, since 2009, belongs to the transboundary Ger\u0026ecirc;s\u0026ndash;Xur\u0026eacute;s biosphere reserve together with the Peneda-Ger\u0026ecirc;s National Park in Portugal (Xunta de Galicia, 2024).\u003c/p\u003e\n\u003cp\u003eThe area lies within the Eurosiberian\u0026ndash;Mediterranean transition zone, characterized by rugged topography (323\u0026ndash;1,529 m a.s.l., ~\u0026thinsp;13% average slope) and a temperate oceanic climate with sub-Mediterranean influences (Csb, K\u0026ouml;ppen), receiving 1,200\u0026ndash;1,600 mm of annual precipitation and average annual temperatures of 8\u0026ndash;12\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eLand cover is dominated by rocky areas with sparse vegetation (hereafter \u0026lsquo;rocky areas\u0026rsquo;) and closed shrublands (~\u0026thinsp;69%), primarily heathlands and gorse, while deciduous forests (mainly \u003cem\u003eQuercus robur\u003c/em\u003e and \u003cem\u003eQ. pyrenaica\u003c/em\u003e) occupy\u0026thinsp;~\u0026thinsp;21%, and agricultural land represents less than 5% (Regos et al. \u003cspan class=\"CitationRef\"\u003e2015b\u003c/span\u003e). Long-term rural abandonment since the mid-20th century has favoured spontaneous vegetation succession, shrub encroachment, and biomass accumulation, contributing to a landscape increasingly vulnerable to recurrent wildfires, the vast majority of which are human induced (~\u0026thinsp;87% arson origin)(Garc\u0026iacute;a-Redondo et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Between 2001 and 2010, a total of 9,995.03 ha burned, whereas during the following decade (2011\u0026ndash;2020), 19,584.83 ha were affected by fire\u0026mdash;a near two-fold increase compared to the previous period. Moreover, the number of large fires (defined as those exceeding 500 ha) rose from 2 events in 2001\u0026ndash;2010 to 6 events in 2011\u0026ndash;2020. For comparison, during 1990\u0026ndash;1999, 17,796 ha burned across 4 large fire events, illustrating that while total burned area fluctuates, recent years show a marked increase in both burned extent and fire severity (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe interplay between rural abandonment, wildfire regime, and ecological succession has shaped a mosaic-like landscape, where open patches, shrublands, and forest formations at different successional stages coexist, making this natural park an ideal setting for studying the ecological effects of rewilding in marginal mountain landscapes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBird data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBird data for the period 2000\u0026ndash;2010 were obtained from the same standardized point-count surveys used by Regos et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), which analysed the effects of rural abandonment and vegetation succession on bird assemblages in the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park. These surveys were conducted during the breeding season (May\u0026ndash;June), using 5-min unlimited-distance point counts (following Bibby et al. \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e) at 209 locations stratified across the park\u0026rsquo;s major land-cover types (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2020, surveys were repeated following the same protocol to ensure temporal comparability. All point-count stations were separated by at least 250 m to avoid double counting (Gregory et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe original sampling design was part of a broader ecological monitoring initiative assessing the impacts of land-use change and passive rewilding in marginal mountain landscapes of northwestern Iberia. Point-count locations were selected to ensure spatial representativeness across the park\u0026rsquo;s heterogeneous habitat mosaic, including shrublands, woodlands, rocky outcrops, and ecotones. The point-count method was chosen for its effectiveness in detecting small- and medium-sized passerines by both sound and sight in structurally complex Mediterranean habitats, consistent with standard protocols (Bibby et al. \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e; Regos et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). To minimize detection bias, all surveys were conducted during optimal weather conditions (i.e., no strong wind or rainfall) within the first four hours after sunrise, coinciding with peak avian vocal activity. Only occurrence data (presence/absence) were used to reduce potential interannual variability and observer bias in abundance estimation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLand cover and fire history\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess vegetation dynamics and fire disturbance over time, we used land cover maps and wildfire perimeter data for the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park. Land cover maps were obtained from Garc\u0026iacute;a-Redondo et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and derived from Landsat imagery (TM, ETM+, and OLI/TIRS) for the years 2000, 2010, and 2020. These maps classify the landscape into six dominant land cover types: croplands/grasslands, deciduous forest, evergreen forest, shrubland, rocky areas, and water bodies. Classification was performed using a supervised ensemble approach that combined multiple machine learning algorithms (Random Forest, Support Vector Machines, Neural Networks, and AdaBoost), with ensemble outputs integrated via majority voting and validated through confusion matrices.\u003c/p\u003e\n\u003cp\u003eLand cover transitions were quantified using cross-tabulation matrices calculated between consecutive time steps (2000\u0026ndash;2010 and 2010\u0026ndash;2020), as well as across the entire study period (2000\u0026ndash;2020). Analyses were conducted at two spatial scales: (1) across the full extent of the Natural Park, and (2) within a 100-m buffer around each bird census plot to capture local habitat dynamics potentially influencing bird assemblages.\u003c/p\u003e\n\u003cp\u003eTo evaluate the effect of wildfires on bird communities, we used official spatial fire data provided by the Department of Rural Environment of the Xunta de Galicia. This dataset includes georeferenced fire perimeters, ignition dates, and surface areas affected by each event. Fire perimeters were overlaid with the bird sampling locations to identify census plots impacted by wildfires. Each plot was then classified as either \u003cem\u003eburnt\u003c/em\u003e or \u003cem\u003eunburnt\u003c/em\u003e based on its spatial intersection with fire perimeters during the 2010\u0026ndash;2020 period (n\u0026thinsp;=\u0026thinsp;87). The year 2020 was excluded from this classification, as wildfires occurred after the bird surveys had been completed (October), ensuring temporal consistency between ecological and fire data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess temporal changes in the presence\u0026ndash;absence patterns of breeding bird species in the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park, we applied generalized linear mixed models (GLMMs) with a binomial error distribution and a logit link function, as described in Regos et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The models included\u0026lsquo;year\u0026rsquo; as a fixed effect and \u0026lsquo;point count\u0026rsquo; as a random effect to account for repeated measures at the same sampling locations. Statistical significance was set at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using the \u0026lsquo;lme4\u0026rsquo; package in R (Bolker et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bates et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo explore the joint structure between bird communities and environmental conditions, we conducted a co-inertia analysis (CoIA) using the \u0026lsquo;ade4\u0026rsquo; package in R, as described in Regos et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). This multivariate technique allows the simultaneous ordination of two datasets measured on the same sampling units\u0026mdash;in this case, bird species abundance and a set of environmental predictors including land cover types (LCT) and fire-related variables.\u003c/p\u003e\n\u003cp\u003eBird community data were compiled for the years 2000, 2010, and 2020, and included only species consistently recorded across all time periods (n\u0026thinsp;=\u0026thinsp;32). Environmental data included proportional land cover composition (extracted from classified raster maps at 100 m buffers around sampling plots) and, for 2020, a stack of three fire-related variables derived from raster layers.\u003c/p\u003e\n\u003cp\u003eThe CoIA was performed in two stages:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1) \u0026nbsp; Full temporal trajectories (2000\u0026ndash;2020)\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe matched bird community data with land cover composition for each year and computed two separate PCA ordinations: one for the species matrix (dudiY) and another for the environmental matrix (dudiX). These were linked using the coinertia() function. The resulting co-inertia axes represent shared structure between species composition and land cover variation over time. Site scores (coi$ls and coi$lY) were summarized by dominant LCT (from 2000) and year to visualize temporal trajectories of both environmental conditions and bird communities. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2) \u0026nbsp; Fire influence on bird\u0026ndash;environment association (2020)\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the influence of recent fire history, we performed a second CoIA restricted to bird community data from 2020, matched with 2010 land cover composition and mean fire variable values extracted around each plot. This allowed us to examine how past land cover and recent fire activity jointly structure current bird community composition. The biplot of this analysis displays associations between bird species and both land cover types and fire descriptors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn all cases, significance of the co-structure was assessed using a Monte Carlo permutation test (randtest.coinertia). Additionally, we visualized temporal and group-specific trajectories (by characterising each census plot by the dominant LC class) using mean site scores in the co-inertia space, highlighting directional changes across time periods (2000\u0026ndash;2010 vs. 2010\u0026ndash;2020) and between burnt and unburnt areas.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eChanges at the landscape level\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe land cover change analysis revealed an overall increase in forested areas\u0026mdash;particularly deciduous woodlands\u0026mdash;and a notable decline in both open and closed shrublands between 2000 and 2020. Reductions were also observed, though to a lesser extent, in grasslands and croplands. However, a closer look at decadal trends highlights contrasting dynamics. Between 2000 and 2010, open shrublands\u0026mdash;represented by rocky areas with sparse vegetation\u0026mdash;experienced a sharp decline of approximately 50,000 ha (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, this land cover class expanded by around 30,000 ha between 2010 and 2020, while grasslands, croplands, and closed shrublands showed continued declines.\u003c/p\u003e\u003cp\u003eTransition matrices clearly illustrated dominant land cover shifts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From 2000 to 2010, the main transitions were from rocky areas with sparse vegetation to shrublands, and from shrublands to both deciduous forests\u0026mdash;consistent with natural successional processes\u0026mdash;and evergreen forests, likely due to plantation expansion. In contrast, the 2010\u0026ndash;2020 period showed a reversal of these patterns, with major transitions from shrublands back to \u0026lsquo;rocky areas\u0026rsquo; and from evergreen forests to shrublands. Nonetheless, deciduous forest continued to expand slightly, primarily at the expense of shrublands.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn terms of bird species, we found that 10 species significantly increased their occurrence across the natural park over the past 20 years, while only 4 species showed a contraction in their distribution. The majority of species did not exhibit significant changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, when analysed at the decadal scale, contrasting patterns emerged: (1) between 2000 and 2010, 14 species increased in occurrence while only 4 declined; (2) between 2010 and 2020, only 6 species showed an increase, whereas 10 experienced a decline (see complete list of species associated with each trend in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of occurrences (i.e. presences in census plots) for each species and year. P-values were derived from Generalized Linear Mixed Models (GLMM). Statistical significance is considered at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and significant values are marked with an asterisk (*).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies acronim\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScientific name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value \u003csub\u003e(2000\u0026ndash;2010)\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value \u003csub\u003e(2010\u0026ndash;2020)\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value \u003csub\u003e(2000\u0026ndash;2020)\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCpal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eColumba palumbus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStreptopelia turtur\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCcan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCuculus canorus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvir\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePicus sharpei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLullula arborea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAarv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAlauda arvensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnthus trivialis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTtro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTroglodytes troglodytes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePmod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePrunella modularis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErub\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eErithacus rubecula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\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\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSaxicola rubicola\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTurdus merula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSund\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCurruca undata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCurruca communis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSatr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSylvia atricapilla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePbon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePhylloscopus bonelli\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePibe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePylloscopus ibericus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRegulus ignicapilla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePcri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLophophanes cristatus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeriparus ater\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePcae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCyanistes caeruleus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePmaj\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eParus major\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCbra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCerthia brachydactyla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOori\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eOriolus oriolus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLcol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLanius collurio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGgla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGarrulus glandarius\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCcor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCorvus corone\u003c/em\u003e\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\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFcoe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFringilla coelebs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSser\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSerinus serinus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCchl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eChloris chloris\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLcan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLinnaria cannabina\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEmberiza cia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eChanges at the census-plot level\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe co-inertia analysis revealed a consistent and statistically significant structure of covariation between bird assemblages and land-cover composition across the three sampling years (2000, 2010, and 2020). This suggests that directional shifts in bird community composition were closely associated with temporal changes in land-cover types. The strength of this relationship was supported by a Monte Carlo permutation test, which yielded a significant RV coefficient (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming a non-random, shared structure between the two datasets. Most bird species that showed increasing occurrences in the census plots over the last decade were associated with open habitats and fire-related variables, as indicated by the green dots in the co-inertia biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, species linked to forested habitats\u0026mdash;particularly evergreen forests\u0026mdash;were among the most negatively affected by land-cover changes between 2010 and 2020. This trend contrasts with the previous decade (2000\u0026ndash;2010), during which many forest-associated species experienced increases in occurrence (see circular symbols in the co-inertia biplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen separating plots based on fire history, the split co-inertia trajectories revealed contrasting dynamics between burnt and unburnt sites. Burnt sites\u0026mdash;those affected by fire at least once over the last 10 years\u0026mdash;exhibited sharp directional shifts in both land cover and bird community composition, often diverging from the general trajectory observed in unburnt areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings suggest that fire disturbance has played a central role in reshaping both vegetation and avifaunal structure, reinforcing its importance as a driver of ecological change in the study area.\u003c/p\u003e\u003cp\u003eIn unburnt plots, the dominant patterns were shaped by natural successional processes, with transitions from rocky areas to shrubland and from shrubland to forest cover (see \u0026lsquo;Unburnt sites \u0026ndash; Land cover\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, burnt sites exhibited markedly different trajectories. For example, rocky areas initially shifted toward the shrubland region in co-inertia space during 2000\u0026ndash;2010 but returned to the rocky area space by 2020, suggesting a reversal driven by fire disturbance. A similar pattern was observed for shrubland, which shifted back toward rocky areas in the last decade. Evergreen forests showed a pronounced directional shift toward the rocky area space, indicating structural degradation or conversion following fire events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Bird communities also exhibited distinct trajectories in burnt versus unburnt plots. In unburnt sites, communities progressively shifted from associations with rocky areas to shrubland, and slightly from shrubland to forest, reflecting vegetation succession. In burnt sites, however, bird communities tended to return toward the co-inertia space associated with rocky areas, reversing the successional trend. Forest species\u0026mdash;particularly those most strongly linked to evergreen forests\u0026mdash;shifted markedly toward communities typical of open habitats, underscoring the disruptive impact of fire on bird assemblage structure (see \u0026lsquo;Burnt vs. Unburnt Sites \u0026ndash; Bird Communities\u0026rsquo; co-inertia plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings highlight the importance of assessing the impacts of rewilding over both medium- and long-term (interdecadal) timeframes. While the overall trend across the last 20 years suggests a generally positive effect on most bird species\u0026mdash;particularly those associated with forest habitats\u0026mdash;interdecadal assessments revealed contrasting patterns. During the 2000\u0026ndash;2010 period, we observed an overall increase in forest cover, accompanied by distributional expansion in many target species linked to both forest and shrubland habitats (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Regos et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These patterns likely reflect post-fire vegetation recovery following major wildfire events that affected approximately 17,800 ha (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur analysis of land-cover trajectories over the past two decades reveals a complex and temporally heterogeneous pattern of ecological change that strongly influenced bird community composition. While the general trend pointed toward an expansion of forested habitats\u0026mdash;particularly deciduous woodlands\u0026mdash;this was not uniform across time (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Between 2000 and 2010, the landscape underwent progressive natural succession, with widespread transitions from rocky areas with sparse vegetation to closed shrublands and from closed shrublands to forest cover. This shift was accompanied by a marked increase in the occurrence of bird species associated with both shrubland and forest habitats, as reflected in the positive trends observed for 14 species during this decade. However, the 2010\u0026ndash;2020 period showed a partial reversal of these trends, with a significant recovery of open rocky areas and declines in shrubland, cropland, and grassland cover (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These land-cover shifts coincided with reduced gains\u0026mdash;or even contractions\u0026mdash;in bird species distribution, particularly among forest-associated taxa, suggesting a breakdown of successional recovery patterns in the most recent decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe observed reversal in land-cover trends and bird community trajectories during the last decade aligns with the increasing frequency and extent of large wildfires in the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our split co-inertia analysis clearly differentiates the ecological pathways of burnt versus unburnt plots (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), highlighting the central role of fire in reshaping both vegetation structure and avifaunal assemblages. In unburnt areas, bird and vegetation communities followed expected successional trajectories, moving from open habitats to shrublands and forests. In contrast, burnt sites displayed directional shifts back toward co-inertia spaces associated with rocky and open habitats, indicating a disruption of successional processes. This was particularly evident for evergreen forests, which showed strong degradation signals, and for forest-specialist bird species, which shifted toward communities typical of open landscapes. These patterns reinforce the idea that, while land abandonment may foster biodiversity recovery under stable conditions, the increasing intensity and frequency of fire events can override successional gains, underscoring the need for \u0026lsquo;fire-smart\u0026rsquo; strategies in Mediterranean landscapes (Pais et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Campos et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; C\u0026aacute;nibe et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Regos et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHowever, after a decade of vegetation encroachment, the frequency of fire seasons with wildfires larger than 500 ha markedly increased\u0026mdash;from only two years between 1983 and 2010 to six in the following ten years alone (see Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Between 2010 and 2020, nearly 20,000 ha burned\u0026mdash;more than double the area affected during the previous decade. This surge in severe fire activity likely contributed to the negative trends observed in many bird species during this period (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), particularly those associated with evergreen forests. A previous study conducted in the natural park had already documented an increase in large fires (\u0026gt;\u0026thinsp;500 ha) during the 2000\u0026ndash;2010 period and their strong impacts on soil and vegetation dynamics (Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our findings confirm the negative ecological consequences of large wildfires in the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park.\u003c/p\u003e\u003cp\u003eAt the same time, our results showed that post-fire recovery rates during the 1990s and 2000s supported a broad-scale recovery in the distribution of most studied bird species (see Regos et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; see period \u0026lsquo;2000\u0026ndash;2010\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this context, if fire impacts are not too severe, bird communities dominated by open-habitat and early successional species can recover relatively quickly following fire, in line with post-fire recovery trajectories reported for other Mediterranean ecosystems worldwide (Pons and Bas \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Brotons et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Albanesi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Watson et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chalmandrier et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lindenmayer et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings are consistent with those from a previous study in the same region, where species distribution models (fitted using generalized linear models with Poisson error distributions) revealed that up to 71% of our studied bird species exhibited a significant linear relationship with at least one attribute of the fire regime (Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, spatial and temporal variability in burnt area and fire severity emerged as key predictors of bird abundance for 39% of species, and 60% showed a significant quadratic response to at least one fire regime variable. Moreover, the legacy of past land use\u0026mdash;including vegetation structure a decade after disturbance\u0026mdash;was critical for understanding the long-term influence of fire on avian communities (Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings, together with our co-inertia results, reinforce the need for spatially explicit, fire-informed approaches to biodiversity conservation in fire-prone Mediterranean landscapes.\u003c/p\u003e\u003cp\u003eRecent research in the \u0026lsquo;Ger\u0026ecirc;s-Xur\u0026eacute;s\u0026rsquo; Biosphere Reserve\u0026mdash;where the natural park is located\u0026mdash;modelled habitat availability for over 100 vertebrate species, including the bird species considered in our study, under contrasting land-use scenarios (Pais et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). That work found that policies promoting the recovery of extensive management approaches based on High Nature Value (HNV) farmlands offered the most beneficial outcomes for both biodiversity conservation and fire regime regulation. However, traditional rewilding scenarios\u0026mdash;representing the continuation of current trends in agropastoral abandonment\u0026mdash;also had positive effects for some species (Pais et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Campos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this sense, integrating more explicitly disturbances like fire into rewilding strategies, both in the form of unplanned wildfires and planned prescribed burns, has been proposed as a viable management approach (Campos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pais et al. \u003cem\u003eunder review\u003c/em\u003e). Prescribed burning in agroforestry mosaics was identified as particularly effective for simultaneously reducing wildfire hazard and preserving habitat for biodiversity (Pais et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;Pais et al. \u003cem\u003eunder review\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eDespite these findings, the widespread recovery of traditional agropastoral practices that shaped these landscapes in the second half of the 20th century now appears unlikely (Renwick et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pe\u0026rsquo;er et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Estoque et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this context, rewilding emerges as both a nature restoration strategy and a climate-smart solution\u0026mdash;particularly in areas hampered by longer socioeconomically viable (Navarro and Pereira \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Helmer et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pettorelli et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Svenning \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Plumanns-Pouton et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our results confirm that land abandonment can have positive effects on biodiversity in the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park (Regos et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but they also highlight a key trade-off: increased wildfire hazard resulting from fuel accumulation and vegetation encroachment (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this regard, the use of fire emerges as key process to incorporate more explicitly in the rewilding framework. There is a growing recognition of the need to rewild fire regimes, using fire as both an ecological process and an ancient tool for landscape management in fire-prone ecosystems (Plumanns-Pouton et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Future research should explore the potential of prescribed burning or allowing natural wildfires to burn under controlled fire-weather conditions to help restore fire regimes that support biodiversity while mitigating wildfire risk\u0026mdash;especially under projected climate warming scenarios.\u003c/p\u003e\u003cp\u003eTaken together, our findings highlight the urgent need to integrate biodiversity conservation with adaptive fire and land-use management in rewilding contexts. As rewilding continues to shape the future of abandoned rural landscapes, fire regimes must be seen not only as disturbance agents but also as potential tools for ecological restoration (Campos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Navarro-Rosales et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Plumanns-Pouton et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A \u0026lsquo;fire-smart\u0026rsquo; rewilding approach\u0026mdash;grounded in ecological thresholds, post-fire recovery trajectories, and socioecological viability\u0026mdash;will be critical to maintaining ecosystem functionality and mitigating future wildfire risk, particularly in Mediterranean mountain areas experiencing climate-driven intensification of fire events. Aligning such strategies with broader EU biodiversity and climate objectives will be key to building resilient socioecological systems across fire-prone landscapes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that rewilding processes in the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park have led to heterogeneous ecological outcomes over time, shaped by the interplay between vegetation succession, fire disturbance, and species-specific responses. While initial phases of rewilding supported forest expansion and bird community recovery, the subsequent increase in large wildfires in recent years has reversed many of these gains\u0026mdash;particularly for forest-specialist species. By combining co-inertia analysis, satellite-based land cover transitions, and decadal-scale bird monitoring, we provide compelling evidence that fire is both a driver of ecological disruption and a potential ally in managing post-abandonment landscapes. Moving forward, \u0026lsquo;fire-smart\u0026rsquo; rewilding strategies that incorporate fire as a key ecological process, historical land use legacies, and biodiversity-based planning will be essential to reconciling nature restoration with wildfire risk mitigation in Mediterranean mountains under climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was developed under the research project RESFIRE (PID2023-152690OA-C22, C21), funded by the Spanish Ministry of Science, Innovation and Universities. It was also supported by wildE Horizon Europe (GAP-101081251) project. AR was funded by the \u0026lsquo;Ram\u0026oacute;n y Cajal\u0026rsquo; fellowship program of the Spanish Ministry of Science and Innovation (RYC2022-036822-I). \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbanesi S, Dardanelli S, Bellis LM (2012) Effects of fire disturbance on birds of mountain Serrano Forest of Central Argentina. Journal of Forest Research 19:105\u0026ndash;114\u003c/li\u003e\n\u003cli\u003eBates D, Maechler M, Bolker B, Walker S (2014) lme4: Linear Mixed-effects Models Using Eigen and S4. R Package Version 1.1-23. Available from: https://cran.r-project.org/web/pac\u003c/li\u003e\n\u003cli\u003eBibby CJ, Burgess ND, Hill DA (1992) Bird census techniques. Cambridge University Press, Cambridge. Cambridge University Press, Cambridge, UK\u003c/li\u003e\n\u003cli\u003eBolker BM, Brooks ME, Clark CJ, et al (2009) Generalized linear mixed models: a practical guide for ecology and evolution. 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Water Res 266:. https://doi.org/10.1016/j.watres.2024.122382\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7252826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7252826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRewilding is increasingly promoted as a nature-based solution to biodiversity loss and climate change, yet its long-term ecological outcomes remain poorly understood\u0026mdash;particularly in fire-prone Mediterranean landscapes. Building on a previous study that analysed post-abandonment dynamics between 2000 and 2010 (Regos et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e. \u003cem\u003eReg. Env. Change\u003c/em\u003e. (16): 199\u0026ndash;211), we revisit the \u0026lsquo;Baixa Limia\u0026ndash;Serra do Xur\u0026eacute;s\u0026rsquo; Natural Park (NW Iberia) to assess two decades of land cover change and bird community responses. Using generalised linear mixed models, co-inertia analysis and census plot data from 2000, 2010, and 2020, we demonstrate a strong and consistent covariation between bird assemblages and land-cover transitions, shaped by both natural successional processes and fire disturbance. While the first decade was characterized by forest expansion and increasing bird occurrences\u0026mdash;particularly among forest- and shrubland-associated species\u0026mdash;the following decade revealed a partial reversal, marked by the re-expansion of early successional habitats such as rocky areas and shrublands, largely driven by increased wildfire activity (\u0026gt;\u0026thinsp;20,000 ha burned). Species linked to mature forest cover experienced the strongest declines, especially in burnt areas, where co-inertia trajectories diverged sharply from those of unburnt plots. Our findings underscore the dual role of fire as both a threat and a potential management tool in rewilded landscapes. We advocate for a more nuanced vision of rewilding incorporating \u0026lsquo;fire-smart\u0026rsquo; strategies that integrate prescribed burning, biodiversity goals, and landscape resilience to address the growing challenges of land abandonment and climate-driven fire regimes in Southern Europe.\u003c/p\u003e","manuscriptTitle":"Revisiting winners and losers in the rewilding of a marginal mountain landscape: two decades of change and the role of fire","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 15:48:16","doi":"10.21203/rs.3.rs-7252826/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":"4b29660b-bf2e-4365-9cde-c9786f657323","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T12:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 15:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7252826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7252826","identity":"rs-7252826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}