Megafire attributes and pre-fire structural legacies shape short-term avian responses in an Atlantic-Mediterranean ecotone

Megafire attributes and pre-fire structural legacies shape short-term avian responses in an Atlantic-Mediterranean ecotone | 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 Megafire attributes and pre-fire structural legacies shape short-term avian responses in an Atlantic-Mediterranean ecotone Fernando García-Fernández, Adrián Regos, Miguel Cánibe, María Vidal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8423628/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Fire regimes are rapidly shifting due to climate change and increasing vegetation flammability, with these dynamics often intensified in areas undergoing widespread rural abandonment, a trend particularly evident in mountainous landscapes of sub-Mediterranean Europe. We assessed avian community responses, including post-fire beta diversity, during the first breeding season following a megafire—the largest recorded in the region—within a depopulated mountain landscape in northwestern Iberia, located in a poorly studied transitional biogeographic zone. We employed a stratified sampling design across major habitat types to survey bird communities and quantify fire attributes and vegetation structure, combining field-based measurements with satellite-derived spectral indices. Results We recorded 2,928 individuals representing 56 bird species, classified into multiple functional guilds. Fire severity was the main negative driver of community structure and composition, significantly impacting most functional groups. In contrast, spatial heterogeneity in fire severity fostered a broader range of ecological niches, enhancing the coexistence of diverse guilds and buffering the immediate effects of high fire severity. Postfire vegetation structure was a key determinant of community reassembly: snag-rich stands, unburned forest patches, and early post-fire open habitats facilitated both avian persistence and recolonization. These components also provided critical resources for highly specialized guilds, including cavity-nesting and open-habitat species. Bird community composition differed significantly but weakly between burned and unburned areas, and fire heterogeneity had a strong positive effect on post-fire beta diversity only when interacting with post-fire habitat structure. Conclusions Our findings demonstrate that fire attributes alone cannot account for short-term avian responses; rather, their interaction with pre-fire structural legacies is critical to understanding community reassembly. The conservation of snag-rich stands, early-successional open habitats, and unburned forest refugia—alongside the maintenance of fine-scale heterogeneity—should be prioritized to support post-fire bird community recovery in abandoned sub-Mediterranean mountain landscapes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. BACKGROUND Fire is a global ecosystem process (Keeley et al. 2011a ) that plays a fundamental role in influencing biome distribution and ecosystem functioning, while also being strongly correlated with vegetation types across many landscapes (Bond et al. 2005 ; Keeley et al. 2011b ). It is also recognised as an evolutionary driving force that exerts selective pressure on the life-history traits of many species, thereby promoting functional differentiation and a wide range of ecological strategies (Blondel et al. 2010 ; Rundel et al. 2018 ; He et al. 2019 ). In Mediterranean regions, summer drought and winter growing conditions lead to a high fire risk, making their landscapes some of the most fire-prone in the world (Keeley et al. 2011b ). Despite covering only about 2.4% of global land surface (Dinerstein et al. 2017 ), these regions contribute disproportionately to global biodiversity and exhibit high levels of species endemism (Rundel and Cowling 2024 ). Yet these fire-prone regions are now facing rapid and unprecedented shifts in fire regimes, driven by warming fire-weather conditions and increased fuel flammability, posing significant challenges for the long-term persistence of their unique biodiversity (Kelly et al. 2020 ; Hantson et al. 2024 ). Beyond climatic factors, widespread rural abandonment has emerged as a major socioecological driver of these changes, especially in Mediterranean mountain landscapes of southern Europe (Moreira et al. 2020 ; García-Redondo et al. 2023 ). The cessation of traditional land-use practices has led to increased fuel loads and continuity, reduced landscape heterogeneity, and diminished the capacity of ecosystems to buffer fire spread. These changes, in turn, are heightening the likelihood of large and extreme wildfire events (Tedim et al. 2020 ; Regos et al. 2023 ; Sil et al. 2024 ). Novel fire regimes that emerge from these complex interactions between climatic and anthropogenic factors, are expected to have profound effects on Mediterranean bird communities (De Cáceres et al. 2013 ; Kelly et al. 2020 ). Drastic changes in vegetation structure and resource availability following fire events can alter species distributions and local abundances, ultimately modifying the composition and functional structure of avian communities (Smucker et al. 2005 ; Fontaine and Kennedy 2012 ; Arrogante-Funes et al. 2024 ). Understanding how bird communities respond to these novel fire regimes remains a complex challenge (Puig-Gironès et al. 2025 ), as avian responses are strongly mediated by specific fire attributes and post-fire habitat dynamics (Pons 2007 ). Key drivers of variation include the time elapsed since the fire (Chalmandrier et al. 2013 ; Hutto et al. 2015 ; Rainsford et al. 2023 ), the spatial configuration and diversity of pre- and post-fire vegetation conditions (Zozaya et al. 2011 ; Brotons et al. 2018 ; Stillman et al. 2019 ), and the severity gradient across the burned landscape (Puig-Gironès et al. 2022 ; García-Redondo et al. 2023 ). Notably, the relative importance of these factors can differ markedly across ecosystems and biogeographic contexts, underscoring the need for context-specific assessments of avian community responses to fire (Hutto et al. 2015 ; Rainsford et al. 2023 ). An increasingly recognised yet sometimes overlooked attribute of fire is its capacity to generate a fine-scale mosaic of habitats at varying successional stages, potentially facilitating the coexistence of species with different ecological requirements adapted to distinct phases of post-fire recovery (Burrows et al. 2021 ; Steel et al. 2024 ). The pyrodiversity hypothesis posits that heterogeneity in fire frequency, spatial extent, type, and severity can enhance biodiversity. However, empirical support for this hypothesis remains context-dependent and difficult to assess without considering local variations (Kelly et al. 2017a ; Jones and Tingley 2022 ). While spatial variability in fire severity is expected to promote habitat differentiation, creating a range of resources and ecological opportunities after the fire (Steel et al. 2024 ), empirical studies explicitly linking this spatial dimension of pyrodiversity to avian beta diversity are still scarce (Tingley et al. 2016 ; Jones and Tingley 2022 ). This is particularly relevant given that the spatial heterogeneity of fire severity may foster turnover in species composition and support functionally distinct assemblages, with divergence from unburned communities potentially increasing over time (Tingley et al. 2016 ; Burkle et al. 2022 ). Furthermore, the extent to which this relationship is mediated by post-fire habitat structure—shaped jointly by disturbance heterogeneity and pre-fire legacies—remains largely unexplored. Although fire attributes largely influence post-fire vegetation development, growing evidence indicates that the resulting structure is not a linear function of fire severity or time since fire, but rather on pre-fire structural and historical legacies (Johnstone et al. 2016 ; Bowd et al. 2021 ). Because post-fire animal succession closely tracks vegetation regeneration, integrating post-fire habitat structure alongside fire attributes may provide a more reliable basis for understanding wildlife responses to fire, particularly in heterogeneous landscapes where post-fire regeneration can be highly variable (Swan et al. 2015 ). Given the shifting nature of post-fire environments, short-term assessments are critical to avoid misleading conclusions about the temporal dynamics of avian responses to fire disturbance (Smucker et al. 2005 ). Although recolonisation of recently burned areas is largely attributed to adjacent populations (Zozaya et al. 2012 ; Pérez-Granados et al. 2018 ), there is growing evidence of post-fire survival, demonstrating that birds never completely abandon the affected area. Instead, they continue to use the burned area immediately after the fire and even during the harsh conditions of the first post-fire winter (Pons 2002 ; Prodon 2021 ; Puig-Gironés et al. 2023 ). The first breeding season following fire represents a critical ecological window, offering a detailed picture of how bird communities reorganise and begin to recover. Grouping species into functional guilds provides a powerful framework for interpreting these early-stage dynamics, as it links community structure with resource availability and life-history traits, and can provide causal insight into the interaction between species and fire (Gosper et al. 2019 ; Scott and Korb 2024 ). Understanding the variability in avian responses to changing fire regimes is essential for developing adaptive management strategies in fire-prone landscapes (Novoa et al. 2021 ; PuigGironès et al. 2025). Yet, although several studies have examined these responses across Mediterranean landscapes, research remains scarce in transitional zones between the Mediterranean and Eurosiberian biogeographic regions (López and Guitián 1988 ; Moreira et al. 2001 ; García-Redondo et al. 2023 , 2025 ), where, to our knowledge, no study has evaluated the direct impacts on bird communities as early as one year after fire. These transitional areas, characterized by overlapping biotic components and distinct climate-vegetation dynamics, may elicit community responses that differ from those documented in core Mediterranean regions. Here, in a sub-Mediterranean mountainous landscape of northwestern Spain undergoing widespread rural abandonment, we examine how key fire-related attributes and post-fire vegetation structure influence short-term avian responses at both community and functional levels, and we assess how fire shapes beta diversity within the burned area and between burned and unburned zones. Our study was conducted one year after a megafire—the largest documented in the area at the time—offering a valuable opportunity to assess avian resilience under conditions of severe ecological disturbance. By focusing on a biogeographic transition zone between Mediterranean and Eurosiberian regions, we address an underexplored ecological context and provide new insights into post-fire bird–habitat associations, contributing knowledge relevant to conservation and management in fire-prone landscapes. 2. METHODS 2.1. STUDY AREA The study was conducted in “Serra do Courel”, a rural mountainous area located in the province of Lugo, Galicia (NW Spain), covering approximately 30,000 ha (Fig. 1 ). This region exhibits significant altitudinal variation, ranging from 400 to 1,650 m a.s.l., and is characterized by steep slopes and rugged terrain (Guitián and Villar 2014 ). Biogeographically, the mountain range represents a convergence zone where Eurosiberian elements coexist with distinctly Mediterranean influences (Rodríguez and Ramil 2008 ; Amigo and Rodríguez-Guitián 2025 ). Combined with its diverse lithology and elevation gradients, this results in high taxonomic diversity (Guitián 1985 ), contributing to its designation as a Special Area of Conservation (SAC) within the Natura 2000 Network (Nat-2000 Site Code ES1120001). According to the Köppen Climate Classification, the study area has a temperate climate with dry and mild summers (AEMET 2011 ). Over the past decade, the lowest recorded monthly temperature occurred in January, with an average minimum of 2°C, while the highest was in August, with an average maximum of 27.6°C. The mean annual precipitation was 1,640 mm, though during July, the driest summer month, average rainfall was less than 20 mm (Meteogalicia 2025 ). The predominant vegetation units, ranked by area coverage, include heathlands dominated by Cytisus spp., Erica spp., and Genista spp., followed by natural forests primarily composed of Pyrenean oak ( Quercus pyrenaica Willdenow) and holm oak ( Quercus rotundifolia Lamarck). Additionally, chestnut woodlands ( Castanea sativa Miller) and pine plantations, predominantly Scots pines ( Pinus sylvestris Linnaeus), are also important (González-Varo et al. 2008 ; Losada et al. 2023 ). Alongside its ecological richness, Serra do Courel has undergone significant land use changes over the past several decades. Since the mid-20th century, the region has experienced widespread land abandonment, leading to vegetation encroachment and forest expansion (Guitián and Villar 2014 ). This process has been driven largely by rural exodus, profoundly transforming traditional land use practices. Since 1970 alone, O Courel has lost nearly 75% of its population (IGE 2025a ), along with approximately 85% of its livestock (IGE 2025b ) and over 60% of its farms (Munilla et al. 2008 ). This region is classified within the Rare-Intense-Large (RIL) pyrome, characterized by high intensity fires, long fire return intervals and short fire seasons (Archibald et al. 2013 ). In the summer of 2022—the warmest year on record in Spain (García 2023)—a severe dry thunderstorm on 14 July ignited a wildfire that burned continuously until 28 July, affecting 12,768 ha (García 2023). At the time, this was the largest wildfire recorded in Galicia since official record keeping began (Xunta de Galicia 2024 ). The combination of extreme fire weather conditions, high pre-fire fuel load and the region's steep terrain resulted in predominantly high fire severity within the burned area. However, spatial heterogeneity created a mosaic of varying severity levels, including patches of lower fire severity and unburned islands (Figs. 1 and 2 ). 2.2. BIRD DATA Bird communities were surveyed using point counts (PoCs) during the 2023 breeding season, one year after the wildfire, from early May to mid-July. To compare bird assemblages between burned and unburned areas, two sampling zones were established: (i) the burned area and (ii) a peripheral unburned control zone (Fig. 1 ). A stratified sampling design was employed based on the dominant vegetation types in the study area: heathlands, natural forests, chestnut woodlands, and pine plantations. The placement of sampling plots was guided by the latest edition of the Spanish Forest Map (MITECO 2011 ), while also considering the rugged topography and access constraints of the region. A total of 215 PoCs were conducted, with 107 in the burned area and 108 in the unburned control zone (Fig. 1 ). The final sampling effort averaged 26.9 ± 0.81 PoCs per vegetation type (range: 24–30 PoCs) (see Fig. 1 , Table S1 ). To prevent double-counting bias, a minimum distance of 200 m was maintained between PoCs, with each placed at least 30 m from vegetation type edges to minimize edge effects (Calviño-Cancela 2013 ; García-Fernández et al. 2025 ). Each PoC lasted 10 min, preceded by a 1-min settling period. A single observer (FGF) recorded all birds heard or seen within two distance bands (0–30 m and 31–100 m) (Gibbons and Gregory 2006 ; Gregory et al. 2007 ). Surveys were conducted within 4 h after sunrise, under favourable weather conditions (calm or low wind, no fog, and no precipitation). To ensure temporal consistency, each vegetation type was surveyed on the same day in both sampling zones (Greenberg et al. 2019 ; García-Fernández et al. 2025 ). Birds in flight without landing, as well as raptors, owls, waterfowl, and aerial foragers (Hirundinidae and Apodidae), were excluded from the counts, as the method was not suited for these groups (Bibby et al. 1992 ; Sergio 2018 ). To characterise functional structure within bird communities, we grouped the relative abundance of all recorded species into five ecologically meaningful trait categories: dietary guild, foraging stratum, habitat breadth, habitat selection, and cavity-nesting behaviour. Dietary guilds included: (1) insectivores, (2) granivores, (3) frugivores, and (4) omnivores. Foraging stratum was categorised as (1) canopy, (2) understorey or shrub layer, and (3) ground. Habitat breadth was determined by the number of distinct habitat types used by each species, following a classification into: (1) specialists (1–2 habitats), (2) intermediates (3–4), and (3) generalists (≥ 5). Habitat selection was defined according to primary habitat use, grouping species into: (1) forest specialists, (2) facultative forest users, (3) shrubland species, and (4) open-habitat species. Cavity-nesting species were further classified as (1) primary excavators (species that excavate their own cavities) and (2) non-excavators (species that use existing cavities). Functional assignments were based on multiple sources: dietary and foraging traits were obtained primarily from Mikusiński et al. ( 2018 ) and the EltonTraits database (Wilman et al. 2014 ); cavity-nesting behaviour followed classifications from Wesołowski and Martin ( 2018 ) and Mikusiński et al. ( 2018 ); and habitat preferences were derived from Guitián et al. ( 2004 ). Habitat breadth was quantified following the framework proposed by Ausprey et al. ( 2022 ), using species-level habitat use data extracted from the Spanish Breeding Bird Atlas (Molina et al. 2022 ). A full list of species along with their assigned functional categories is provided in Table S2, and further methodological details are available in Text S1. 2.3. VEGETATION DATA Vegetation structure and composition were assessed using 30 m radius plots centred on each of the 215 PoCs locations (García-Fernández et al. 2025 ). Data collection was tailored to the dominant vegetation type at each plot, distinguishing between forested habitats (natural forests, chestnut woodlands, pine plantations) and open heathlands. In forested plots, two 10 m transects were laid out in opposite directions (north and south) from the plot centre. Along each transect, all living trees and standing dead trees (snags) within a 1 m wide band on either side were recorded, and each living tree was identified to species level. Within the entire 30 m radius plot, additional variables were measured, including the average height and girth at breast height (GBH) of both live trees and snags, the number of mature trees (GBH > 100 cm), and the volume of lying deadwood. Tree canopy cover was assessed using a GRS vertical densitometer at six equidistant points (1, 5, and 10 m along each transect), following Huynh ( 2005 ). To characterise the understory, two 25 m² subplots were established 10 m from the plot centre along the transects. Within each subplot, we estimated vegetation cover for the herb and shrub layers, as well as litter ground cover (Ascoli et al. 2013 ). Additionally, we recorded the average height of the shrub layer and the number of shrub species exceeding 10% cover (Ambarli and Bilgin 2014 ). In heathland plots, only the two 25 m² subplots were used. We visually estimated shrub, herbaceous, tree, and bare ground cover, and recorded average shrub height and the number of shrub species with > 10% cover. Topographic variables recorded for all plots included elevation (using a handheld GPS) and slope (calculated in QGIS v3.34 from a 10 m DEM provided by IGN). Following Skowno and Bond ( 2003 ), woody plants taller than 3 m were classified as trees, and those under 3 m as shrubs. A summary of all vegetation predictors is provided in the Supplementary Table S3. Based on ecological relevance to post-fire habitat structure and suitability for multivariate approaches, we focused on eight continuous vegetation variables, excluding those with excessive zeros or strong collinearity. These variables included canopy cover, herbaceous cover, the number and mean height of both living trees and snags, the number of shrub species exceeding 10% cover, and a shrub coverage index calculated as the product of mean shrub height and mean shrub cover divided by 100 (García-Fernández et al. 2025 ). 2.4. FIRE ATTRIBUTES To characterise fire attributes relevant to post-fire bird responses, we considered two key variables: fire severity and its spatial heterogeneity. Fire severity was evaluated using two complementary approaches that targeted the immediate biophysical effects of fire, rather than post-fire ecosystem responses (Keeley 2009 ). First, a field-based visual index adapted from Keeley ( 2009 ) was applied during PoCs surveys to capture structural ecosystem changes while avoiding bias from post-fire recovery dynamics. Within a 50 m radius of each sampling plot, the area was divided into four quadrants. In each quadrant, the dominant severity class—based on the loss or decomposition of organic matter above and below ground—was visually identified and assigned a numerical score ranging from 0 (no apparent effects) to 3 (high severity) (Table 1 ; Fig. 2 ). The scores from the four quadrants were then summed to produce a fire severity index for each plot, ranging from 0 to 12. This method allowed direct assessment of fire-induced ecological impacts, such as canopy mortality of non-resprouting trees and loss of soil organic matter, while minimizing the influence of post-fire vegetation regrowth. Second, satellite-derived estimates of fire severity were obtained using the delta Normalized Burn Ratio (dNBR), calculated from Sentinel-2 imagery by comparing pre-fire (14 July 2022) and post-fire (1 August 2022) scenes. Mean dNBR values were extracted within a 50 m buffer around each plot for comparison with the field-based index. Although both metrics were strongly correlated (Spearman ρ = 0.93, p < 0.001), only the field-based visual severity index was retained for subsequent ecological analyses (see Section 2.5.1 . for justification). Table 1 Field-based classification of fire severity within sampling plots. Adapted from Keeley ( 2009 ). Fire severity Description No apparent effects Unburned plants with no visible direct effects of fire. Soil organic layer intact. Light Some canopy stems scorched; surface litter consumed; soil organic layer largely intact. Moderate Partial canopy mortality; all understory plants charred or consumed; fine dead twigs on the soil surface consumed and logs charred; soil organic layer largely consumed. High Complete canopy mortality; surface litter and soil organic layer largely consumed; charred organic matter extending several centimetres in depth. To quantify spatial heterogeneity in fire severity, we calculated the standard deviation (SD) of dNBR values within a 120 m buffer around each sampling plot, using the same pre- and post-fire Sentinel-2 imagery as for the fire severity metric. This buffer radius was selected to capture local-scale variability in fire effects while minimizing spatial autocorrelation among nearby plots. The SD of dNBR values provides a quantitative measure of variation in fire severity across neighbouring pixels, with higher values indicating a more heterogeneous burn mosaic in the surrounding landscape (Zlonis et al. 2019 ). Fire severity and heterogeneity values for each sampling plot are provided in Table S4. 2.5. DATA ANALYSIS 2.5.1. Predictor variables To characterise vegetation structure, we conducted a Principal Component Analysis (PCA) based on the subset of ecologically relevant vegetation variables described in Section 2.3. The first three principal components (PCs), each with eigenvalues > 1, jointly explained 69.3% of the total variance (Table S5). PC1 represented severely burned forest stands, characterised by high snag abundance, low shrub regeneration, and sparse live tree cover. PC2 reflected unburned forest structure, with high values associated with the dominance of living trees. PC3 captured early post-fire recovery in open shrublands, with high herbaceous cover and minimal woody vegetation. To compare fire severity metrics, we performed a Spearman correlation between field-based visual severity and satellite-derived dNBR values across all sampling plots. The two metrics were strongly correlated (ρ = 0.93, p < 0.001; Fig. S1 ), indicating a shared spatial pattern of severity. For subsequent analyses, we retained the visual index as the primary fire severity metric due to its finer spatial resolution, its direct assessment of structural fire effects, and its independence from pre-fire vegetation biomass (See Text S2 for further details). Finally, the following predictor variables were selected to assess the effects of fire and vegetation characteristics on bird community structure and functional guild composition: (1) visual fire severity, (2) fire heterogeneity (SD of dNBR within a 120 m buffer), (3) vegetation structure (summarised by the first three PCs from the PCA), (4) burn status (burned vs. unburned plots), and (5) elevation. Elevation was retained as a covariate due to significant differences observed between burned and unburned areas. 2.5.2. Modelling bird community and functional responses To assess avian responses to post-fire vegetation structure and fire attributes, we modelled a suite of response variables encompassing both community-level and functional group metrics. These included total bird abundance, species richness, and aggregated abundances across previously defined functional groups (see Section 2.2): dietary guilds, foraging stratum, habitat breadth classes, habitat preference, and cavity-nesting guilds. For each response variable, we fitted a Generalized Linear Model (GLM) using either a Poisson or negative binomial error distribution, depending on the degree of overdispersion. All predictor variables were standardised prior to analysis. Although burn status showed moderate collinearity with fire severity and fire heterogeneity (assessed via variance inflation factors), it was retained in all models due to its consistent statistical significance and improved model performance (lower AIC), indicating it provided complementary explanatory power (See Text S3 for further details). Model assumptions and fit were evaluated using the easystats package in R (Lüdecke et al. 2022 ), including diagnostics for overdispersion, zero inflation, residual patterns, influential outliers, variance homogeneity, and multicollinearity. Model explanatory power was quantified using Nagelkerke’s pseudo-R². We applied an information-theoretic approach with the MuMIn package (Bartoń 2023 ), generating a full set of candidate models via the dredge function. Models with ΔAICc 0.5 considered influential for interpretation (Burnham and Anderson 2002 ). 2.5.3. Beta diversity and community composition analysis To assess differences in bird community structure between burned and unburned areas, we conducted a multistep analysis focusing on beta diversity, compositional dissimilarity, and indicator species. A site-by-species abundance matrix was constructed from the 215 PoCs. For dissimilarity measures, we used both Bray–Curtis on raw abundances and Euclidean distances on Hellinger-transformed data (Legendre and Gallagher, 2001 ; Magurran and McGill, 2011). Results from the two approaches were broadly consistent; however, Bray–Curtis was retained as the primary metric in subsequent analyses because it provided better model performance and more closely reflected the ecological signal of post-fire bird responses. Beta diversity, measured as multivariate dispersion within groups (Legendre and De Cáceres 2013 ), was assessed with PERMDISP (Anderson 2006 ) using 999 permutations. Patterns of dispersion and centroid separation were visualized with a Principal Coordinates Analysis (PCoA). Differences in species composition between burned and unburned plots were then tested with a permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis dissimilarities. To identify species most strongly associated with each zone, we used the Indicator Value index (IndVal; Cáceres and Legendre, 2009 ), which combines measures of fidelity (frequency within a group) and specificity (exclusivity to a group). Statistical significance was assessed through permutation tests, and species with p-values < 0.05 were considered significant indicators. Finally, we quantified gamma diversity in each zone by comparing the total number of species and the number of unique species —defined as those detected exclusively in either burned or unburned plots—in order to evaluate whether burned areas contributed to regional diversity by incorporating new species. 2.5.4. Heterogeneity in fire severity and beta diversity To evaluate whether spatial heterogeneity in fire severity influenced bird community composition within the burned area, we applied distance-based redundancy analysis (dbRDA; Jiang et al., 2024 ; Legendre and Anderson, 1999 ; Rosas-Espinoza et al., 2024; Shi et al., 2021). This constrained ordination method relates community dissimilarities to environmental predictors while providing estimates of explained variance and partitioning among predictors. Analyses were restricted to burned plots, using Bray–Curtis dissimilarities of species abundances as the response matrix (consistent, though slightly weaker results were obtained with Hellinger + Euclidean distances). We first fitted a simple dbRDA with fire heterogeneity as the sole predictor to test its independent contribution to beta diversity. We then built a full model including additional predictors (fire severity, elevation, and vegetation structure PCs) to assess the robustness of the fire heterogeneity effect and the relative contribution of other variables. To further explore context-dependent effects, we also tested interaction terms between fire heterogeneity and vegetation structure PCs. All predictors were standardized prior to analysis, and model significance was evaluated using 4,999 permutations with the function capscale () in the vegan R package (Oksanen et al. 2023 ). 3. RESULTS 3.1. Bird census A total of 2928 individual birds representing 56 species were recorded across the study area. In the control (unburned) zone, 51 species and 1766 individuals were observed, while 47 species and 1162 individuals were recorded in the burned zone. Mean species richness and bird abundance per sampling point were 8.67 ± 0.20 species and 13.61 ± 0.41 individuals, respectively, across all surveyed locations. These values were higher in the control zone, with 9.74 ± 0.26 species and 16.35 ± 0.58 individuals per PoC, compared to 7.59 ± 0.27 species and 10.86 ± 0.45 individuals per PoC in the burned zone (Table S6, Fig. S2). 3.2. Community level and functional bird responses The fitted GLMs, which included both fire attributes and vegetation structure (PCs), showed generally high explanatory power across community and functional group models (Table S7), supporting the importance of integrating local habitat context in post-fire ecological assessments. Elevation showed weak but occasionally significant effects across models (Fig. S3). When comparing sampling plots located inside versus outside the burned area no community-level metric or functional bird group was significantly favoured by fire. Habitat specialists and granivores exhibited the strongest negative responses (Fig. 3 A). This pattern is consistent with short-term reductions in habitat suitability following high-severity fire, potentially due to post-fire resource limitation. Fire severity emerged as the strongest negative driver of bird community structure and composition one year after the fire (Fig. 3 A). Most functional guilds were significantly affected: all dietary groups except omnivores, all habitat breadth classes, and all foraging strata—although the response of canopy foragers overlapped zero. Negative responses were also evident among forest facultative and, most notably, shrubland species. Increasing fire severity was associated with a marked reduction in both species richness and overall bird abundance. No community-level metric or functional guild showed a positive response to higher fire severity, and only three of the 47 recorded species were significantly associated with more severely burned plots (Text S4). These patterns underscore the short-term disruptive and impoverishing effects of high-severity fire on post-fire bird assemblages. However, spatial heterogeneity in fire severity appeared to promote a diversity of ecological niche opportunities from the very first post-fire breeding season (Fig. 3 A). All functional guilds that were negatively affected by high fire severity tended to show positive associations with greater spatial variability in severity. Notably, no functional guild or diversity metric exhibited negative responses to fire heterogeneity. Post-fire vegetation structure significantly influenced all functional guilds and community metrics, emphasising the importance of considering it alongside the attributes of fire (Fig. 3 B). PC1 - characterised by severely burned stands with abundant snags and almost no understory - showed positive associations with habitat specialists (HB1), forest-dependent species, and cavity nesters, particularly non-excavators. PC2 – representing unburned forest structure, including remnant unburned forest patches within the fire perimeter - showed expected positive associations with forest-related guilds, such as cavity nesters (both excavators and non-excavators), canopy foragers, and both forest specialists and facultative forest users, while being negatively associated with shrubland and open-habitat species. PC3 – associated with post-fire open habitats characterised by herbaceous recovery and minimal woody vegetation - showed a significant positive association only with open-habitat species, while displaying negative relationships with forest-associated guilds (forest specialists, facultative forest users, canopy foragers, and cavity nesters) and with generalist species (HB3). 3.3. Beta diversity and community turnover between zones Beta diversity, assessed with PERMDISP, was slightly higher in burned plots, although differences between burned and unburned areas were not statistically significant (Bray–Curtis: p = 0.09; Hellinger + Euclidean: p = 0.14; Fig. S4). Both dissimilarity approaches yielded consistent patterns, showing greater multivariate dispersion in burned plots. This may reflect a weak tendency toward higher compositional heterogeneity in fire-affected areas, although this pattern should be interpreted with caution. PERMANOVA based on Bray–Curtis dissimilarity revealed significant differences in bird community composition between burned and unburned plots (F = 6.97, R² = 0.032, p = 0.001), indicating that post-fire conditions significantly influenced species composition across sites (Fig. 4 ). Although the proportion of explained variance was modest, the results point to subtle yet detectable compositional shifts in burned areas one year after the disturbance. Indicator species analysis (IndVal) identified five species significantly associated with unburned plots and two with burned plots (Table 2 ). Of the two species linked to burned areas, the black redstart ( Phoenicurus ochruros Gmelin) also showed a positive association with high fire severity (Text S4), whereas the tawny pipit ( Anthus campestris Linnaeus) occurred exclusively in open habitats generated by fire, following the structural collapse of burned shrublands. Table 2 Results of the Indicator Species Analysis (IndVal) showing species significantly associated with burned and unburned plots (p < 0.05). The IndVal statistic (“stat”) quantifies the strength of association between each species and a given zone, combining specificity and fidelity. Common name Scientific name Authority Zone Stat p.value Eurasian Nuthatch Sitta europaea Linnaeus (1758) Unburned 0.47168679 0.001 Dartford Warbler Curruca undata Boddaert (1783) Unburned 0.49650655 0.001 Song thrush Turdus philomelos Brehm (1831) Unburned 0.41343247 0.002 Tawny pipit Anthus campestris Linnaeus (1758) Burned 0.25697808 0.007 Common Cuckoo Cuculus canorus Linnaeus (1758) Unburned 0.27216553 0.007 Black redstart Phoenicurus ochruros Gmelin (1774) Burned 0.25697808 0.008 Golden oriole Oriolus oriolus Linnaeus (1758) Unburned 0.23570226 0.034 A total of 56 species were recorded across both zones, with 47 species in burned plots and 51 in unburned ones. The burned area contained five exclusive species, representing 8.9% of total gamma diversity and indicating a positive contribution to regional diversity. In contrast, the unburned area harboured nine exclusive species (16.1% of gamma diversity), thus contributing more markedly to overall regional diversity (Table 3 ). Table 3 List of bird species exclusively recorded in burned and unburned zones one year after the fire. BURNED ZONE UNBURNED ZONE Common name Scientific name Authority Common name Scientific name Authority Tawny pipit Anthus campestris Linnaeus (1758) Common cuckoo Cuculus canorus Linnaeus (1758) Crested lark Galerida cristata Linnaeus (1758) Cirl bunting Emberiza cirlus Linnaeus (1766) Black redstart Phoenicurus ochruros Gmelin (1774) Yellowhammer Emberiza citrinella Linnaeus (1758) Marsh tit Poecile palustris Linnaeus (1758) Melodious warbler Hippolais polyglotta Vieillot (1817) Sardinian warbler Sylvia melanocephala Gmelin (1789) Red crossbill Loxia curvirostra Linnaeus (1758) Golden oriole Oriolus oriolus Linnaeus (1758) Bonelli’s warbler Phylloscopus bonelli Vieillot (1819) Turtle dove Streptopelia turtur Linnaeus (1758) Spotless starling Sturnus unicolor Temminck (1820) 3.4. Influence of fire heterogeneity on beta diversity dbRDA revealed that spatial heterogeneity in fire severity had a statistically significant but weak effect on community composition within the burned area (F = 2.01, p = 0.012), explaining 1.9% of the total variance (Fig. S5). This suggests that fine-scale fire severity mosaics were associated with modest increases in compositional dissimilarity among plots. When additional predictors were included, the model explained 20% of the variance in community composition (p < 0.001). Among predictors, elevation had the strongest influence (p < 0.001), followed by post-fire vegetation structure (PC2, PC3, and PC1; all p < 0.05), while fire heterogeneity showed a near-significant trend (p = 0.066). These results indicate that post-fire habitat structure and topography exert stronger influences on community turnover than fire heterogeneity per se. However, incorporating interaction terms between fire heterogeneity and post-fire vegetation structure improved model performance, particularly the interaction with PC1 (p = 0.006), which increased the explained variance to 21.7% (Fig. 5 ). This finding supports the idea that the ecological effects of fire heterogeneity on bird community composition are mediated by post-fire habitat structure, which is in turn a product of both fire attributes and pre-fire ecosystem legacies. 4. DISCUSSION This study provides one of the first empirical assessments of short-term wildfire effects on bird communities in sub-Mediterranean mountain ecosystems. By characterizing avian responses through functional guilds and biodiversity structure metrics, we offer a robust framework for understanding the mechanisms driving community reassembly during the early stages of postfire succession, while also facilitating broader inference beyond species-level patterns. Our results show that local-scale fire attributes (severity and heterogeneity), landscape-level burn status, and fine-scale post-fire vegetation structure jointly shaped avian community structure, significantly influencing all functional groups and biodiversity metrics considered. Additionally, beta diversity within burned area was better explained by interactions between fire heterogeneity and post-fire vegetation structure, underscoring the importance of accounting for post-fire habitat conditions when assessing community composition in the aftermath of megafires. Contrasting roles of key fire attributes To disentangle the effects of different fire attributes, we first explore burn status as a coarse indicator of post-fire landscape configuration, capturing broad-scale patterns of habitat availability and continuity. However, additional landscape metrics—such as distance to unburned patches or individual patch size—may further refine this perspective (Steel et al. 2022; Ray et al. 2025). Within the burned area, species with specialized diets (insectivores and granivores), habitat specialists, and overall bird abundance exhibited the most pronounced negative responses, while no functional group was significantly favoured (Fig. 3A). These patterns are consistent with short-term post-fire responses of bird communities reported in other Mediterranean mountain ecosystems (e.g., Pons & Clavero, 2010; Pons & Prodon, 1996) and in the limited research from alpine systems of non-Mediterranean Europe (Rey et al. 2019). They align with the habitat fragmentation hypothesis (Fahrig 2003), whereby fire reduces habitat amount via the loss or downsizing of habitat patches, decreases habitat quality through structural simplification at patch edges, and increases spatial isolation among unburned remnants (Brotons 2007). In addition, the landscape complementation hypothesis (Dunning et al. 1992) may help explain contrasts between burned and unburned areas, as the availability and spatial proximity of complementary resources across habitat patches and the surrounding matrix is likely greater in the unburned zone, particularly following large wildfires that tend to homogenize post-fire landscapes (Herrando 2001; Brotons 2007; Brotons et al. 2018). At finer spatial scales, fire severity and its spatial heterogeneity exerted contrasting effects on the bird community during the first breeding season after fire. Fire severity emerged as the primary negative driver of community structure and functional composition, acting as a major disruptive force with an overall impoverishing effect on bird assemblages. In our study, increasing severity notably altered the structure of shrub and understorey layers, particularly affecting habitat conditions for shrubland species and understorey foragers (Fig. 3A). As fire severity increases, resource availability and habitat complexity tend to decline, with larger high-severity patches generally considered detrimental to biodiversity (Steel et al. 2022; Gibson et al. 2025). This structural simplification may reduce site fidelity and promote dispersal, especially in strata where vertical vegetation structure had nearly collapsed (Puig-Gironés et al. 2023; Scott and Korb 2024). At the same time, high-severity patches can reset successional dynamics and provide suitable conditions for early-seral species, offering notable ecological value in abandoned montane landscapes where open habitats are increasingly rare (DellaSala and Hanson 2015; Hutto et al. 2015; Tingley et al. 2016). Yet, this pattern was not detected in our study, likely due to the predominance (76%) of sampling plots in forested environments, which may have obscured colonization signals in open areas created by high-severity fire in shrublands. Moreover, in the short term, colonization may have been constrained by the scarcity of nearby source populations across the dense, unmanaged landscape that characterize the study area (Pons and Clavero 2010; Puig-Gironès et al. 2022). While early-successional colonizers typically peak within 2–8 years post-fire in Mediterranean systems (Pons et al. 2012; Prodon 2021; García-Redondo et al. 2023), these dynamics remain largely undocumented in our Atlantic– Mediterranean transition zone, where longer-term monitoring may be required to capture such responses. By contrast, spatial heterogeneity in fire severity acted as a positive driver of bird functional diversity from the very first post-fire breeding season. Unlike the uniform negative effects of high fire severity, this heterogeneity fosters a broader range of ecological niches, likely contributing to buffering the immediate impacts of fire and enhancing short-term ecological resilience. The fine-scale mosaic of fire severities promotes post-fire community recovery by facilitating dispersal and recolonization across a gradient of habitat conditions (Rey et al. 2019; Rainsford et al. 2023). Extremes within this gradient must play distinct ecological roles: unburned refugia support the persistence of fire-sensitive guilds—such as shrubland species and habitat specialists—while severely burned patches create favourable conditions for opportunistic or disturbance-tolerant ones, including open-habitat and ground-foraging birds (Watson et al., 2012; Zozaya et al., 2011; Fig. 3A). Increased edge habitats associated with spatial heterogeneity further enhance resource diversity (Adorno et al. 2025), benefiting functional groups such as insectivores and granivores (Fig. 3A) through improved availability and detectability of seeds and invertebrates during early post-fire successional stages (López and Guitián 1988; Edenius 2011; Banza et al. 2021). This structural complexity is also likely to allow many birds to exploit complementary resources distributed across patches in different stages of post-fire recovery, as some species benefit from the variety of resources that this mosaic provides (Stephens et al. 2015; Tingley et al. 2016; Brotons et al. 2018). Moreover, patchily burned areas may be colonised more rapidly, as they concentrate a greater diversity of resources than uniformly burned sites, thereby facilitating faster avifaunal recovery (Watson et al. 2012). These positive responses to fine-scale spatial heterogeneity in fire severity suggest that even small-scale variations in the burned landscape may help retain species that might otherwise be lost (Pons and Prodon 1996; Brotons 2007; Pons 2007). This underscores how the habitat complementation hypothesis (Dunning et al. 1992) may be scale-dependent (Herrando et al. 2002): at the landscape level, burned areas appear more homogeneous than unburned controls, potentially limiting access to a diverse array of resources. In contrast, at finer spatial scales, fire heterogeneity enhances both structural and functional habitat diversity, supporting richer and functionally more diverse bird communities than would be expected from the additive effects of individual severity patches (Brotons et al. 2018). Post-fire vegetation structure as a major driver of avian community reassembly Fire initiates dynamic ecological processes that shape vegetation structure and composition across multiple spatial and temporal scales. These changes can profoundly influence bird community composition for decades or longer (Bitani et al. 2023; Ray et al. 2025). Our results indicate that incorporating in situ post-fire vegetation variables provides high explanatory power of models describing bird community responses (Kelly et al., 2017; Morin et al., 2021; Table S7). In our study, three main components (PCs) emerged as key predictors of avian community patterns. Among these, post-fire snags (PC1) may confer important structural and functional value during the first breeding season in severely burned forest stands. They contribute notably to habitat provisioning for cavity nesters and forest specialists (Fig. 3B) by providing new nesting cavities, roosting sites, perches, and enhanced foraging opportunities (Zozaya et al. 2011; Brown et al. 2015; White et al. 2016; Scott and Korb 2024). Snags may also provide new trophic resources, such as xylophagous and saproxylic insects or pinecone-delivered seeds (Moreira et al. 2003; Hutto et al. 2015; Scott and Korb 2024), although we did not detect significant associations with any dietary guild (Fig. 3B). While previous studies suggest that primary excavators respond rapidly to fire due to increased snag availability, non-excavators often show delayed increases in abundance, relying on cavities generated over time by primary excavation and wood decay (Saab et al. 2004; Lowe et al. 2011; Steel et al. 2022). However, our results indicate that just one year after fire, non-excavators already exhibit stronger positive associations with snag-rich habitats than primary excavators (Fig. 3B). This suggests that the early availability of suitable cavities—likely pre-existing or rapidly formed through fire damage and subsequent structural degradation—may provide high-quality nesting substrates for secondary cavity users without requiring prior creation by excavators. Long-term monitoring of forest specialists and cavity nesters will be essential to assess the future ecological value of these post-fire forest stands. In the short term, site fidelity may promote persistence despite drastic habitat alteration, creating a temporal lag between fire disturbance and observable population shifts (Moreira et al. 2003; Zozaya et al. 2011; Prodon 2021). Beyond this initial phase, two contrasting scenarios may emerge. In many systems, early post-fire peaks in bird abundance are followed by gradual declines as snags decay and collapse (e.g., Lindenmayer et al., 2021; Ray et al., 2025; Steel et al., 2021). Alternatively, snags may persist for decades as structural legacies within these post-fire forests (DellaSala and Hanson 2015; Stephens et al. 2015). When combined with the epicormic resprouting of fire-resilient tree species - such as cork oak ( Quercus suber Linnaeus), Pyrenean oak, or holm oak– and the recovery of understorey cover, these elements may enhance habitat quality over time by increasing structural complexity and resource availability for these two functional groups. Post-fire unburned forest stands (PC2) acted as refugia for fire-sensitive species and likely served as early sources for recolonization of adjacent burned areas. By contributing to spatial heterogeneity within the burned matrix, these patches may also facilitate movement across fragmented habitats by providing stepping stones for dispersal (Rainsford et al. 2023; Gibson et al. 2025; Adorno et al. 2025). Even small unburned remnants may play a key role in post-fire recovery, harbouring higher bird abundances and serving as important sources of individuals for both fauna and flora (Watson et al. 2012; Garnett et al. 2023). PC3, associated with early post-fire open habitats, supported low overall functional diversity but provided critical habitat for open-habitat specialists (Fig. 3B), currently among the guilds of highest conservation concern in Europe (Clavero et al. 2011; Puig-Gironès et al. 2022). These findings underscore the value of using a comprehensive functional guild classification, which enables the detection of subtle responses often overlooked in species-level analyses or general diversity metrics due to the rarity of these taxa (Smith and Lim 2025). In this case, a guild-based approach revealed the window of opportunity created by fire through the emergence of new, temporary open habitats. Despite their structural simplicity, these areas hold disproportionate biological value, particularly in mountain landscapes dominated by rural abandonment and fire suppression, where open-habitat specialists face the strongest declines (Brotons et al. 2018; García-Redondo et al. 2023; Pais et al. 2025). Beta diversity across landscape and within the burned area In landscapes dominated by rural abandonment, fire is often expected to increase community differentiation (beta diversity), thereby enhancing landscape-scale richness (gamma diversity) (Farnsworth et al. 2014). However, although gamma diversity increased when combining burned and unburned areas (Table 3), our results indicate that beta diversity was similar and that community composition differed only modestly between zones one year after the fire (Fig. 4). These findings are consistent with earlier results on the effects of burn status, reinforcing the idea that the Courel megafire did not lead to a substantial increase in landscape-scale heterogeneity within the burned area, possibly due to the predominance of high fire severities (Steel et al. 2022). Such conditions align with the concept of habitat fragmentation per se (Fahrig 2003), in which habitat is subdivided into smaller and more isolated patches without generating a mosaic of distinct ecological conditions. By contrast, the unburned area likely retained substantial spatial heterogeneity due to its rugged topography and the biological legacy of pre-abandonment land use, which may facilitate the coexistence of species with diverse habitat requirements. In this context, only the burned zone hosted indicator species associated with open habitats (Table 2), highlighting the ecological contribution of these scarce habitat types created by fire. The reduced representation of open-habitat species in community composition may reflect short-term constraints imposed by the surrounding landscape context, which may have limited species turnover between zones (Puig-Gironès et al. 2022). Therefore, the modest differences in species composition and beta diversity between zones—despite higher functional diversity in the unburned area—may reflect that both local abandonment (limited by site fidelity) and recolonization (constrained by landscape dispersal barriers) processes are still in early stages, and that habitat fragmentation may currently outweigh habitat heterogeneity in the burned zone. While spatial heterogeneity in fire severity showed positive effects on bird functional diversity (Fig. 3A), its influence on species composition was more limited (Fig. 5). Our analysis revealed that the interaction between fire heterogeneity and post-fire habitat structure explained the largest share of variation in bird community composition within the burned area. This suggests that the main effects of fire heterogeneity on beta diversity are mediated by the structural outcomes of fire, which depend not only on fire characteristics but also on pre-fire vegetation conditions and their capacity to respond to disturbance. In our study, fire heterogeneity alone barely increased species turnover; instead, its influence became evident when it interacted with snag-rich post-fire stands. These findings support the notion that post-fire beta diversity results from the combined effects of fire attributes and the structural legacies that remain in the landscape after disturbance. As key components of a system’s ecological memory, post-fire habitat structures act both as sources of biotic colonization and as structural features that influence post-fire regeneration (Johnstone et al. 2016). In our analyses, post-fire vegetation structure was only partially explained by fire attributes (Text S5), suggesting a degree of short-term ecological resilience, whereby post-fire reorganization is driven not only by the disturbance itself but also by the persistence and expression of pre-fire ecological legacies. This is consistent with the view that fire interacts with pre-existing legacies, which in turn influence both fire severity and its spatial configuration, as well as the potential recovery trajectories of the ecosystem. However, the extent to which these legacies promote resilience depends on their own nature and their alignment with the prevailing disturbance regime (Johnstone et al. 2016; Foster et al. 2017). 5. CONCLUSIONS Mechanistic pathways linking burn status, fire severity, fire heterogeneity, and post-fire habitat structure explained the variation observed across all functional groups and biodiversity metrics considered. This integrative framework provides a valuable basis for predicting short-term avian responses to wildfire and highlights that post-fire biodiversity dynamics cannot be understood solely through fire attributes. Ignoring detailed information on post-fire vegetation structure may lead to incomplete interpretations of how fire shapes ecosystems in fire-prone landscapes (Gibson et al. 2025 ). The ability to link a comprehensive classification of avian functional groups with fine-scale habitat variables and fire attributes at the plot level is critical for understanding avian responses to fire and informing future management actions. Our results revealed that mixed-severity fires promote functional diversity, whereas extensive high-severity fires have the opposite effect. Fire heterogeneity and post-fire habitat structure jointly shaped community composition within burned areas, underscoring that fire interacts with structurally and functionally complex ecosystems whose resilience and pre-existing legacies ultimately condition post-fire avian reorganization. Three structural components of post-fire habitat emerged as pivotal for community reassembly: (i) open patches that favour declining early-seral colonizers and may be critical for their persistence in abandoned landscapes, (ii) unburned stands and residual refugia that enable recolonization by fire-sensitive guilds, and (iii) snag-rich forest stands that sustain structural legacies and forest specialist resilience. Preserving the structural and functional integrity of these post-fire elements, together with the fine-scale heterogeneity of the post-fire mosaic, should be prioritized during the early stages of recovery in sub-Mediterranean mountain landscapes affected by land abandonment. Declarations FUNDING DECLARATION This research was supported by the Regional Government of Galicia through the CMA-2022-0129 project and by the RESFIRE project (PID2023-152690OA-C22, C21), funded by the Spanish Ministry of Science, Innovation and Universities. AR was supported by the “Ramon y Cajal” fellowship program of the Spanish Ministry of Science and Innovation (RYC2022- 036822-I). Author Contribution Conceptualization, F.G., A.R., J.D.; methodology, F.G., A.R., M.V., J.D.; formal analysis, F.G., M.C.; investigation, F.G.; writing – original draft preparation, F.G.; writing – review and editing, F.G., A.R., J.D.; supervision, A.R., J.D. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Adorno B, Ribeiro MC, Hasui E, et al (2025) Fire size and vegetation productivity shape bird diversity across burned landscapes in the Atlantic Forest. 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13:31:09","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":246361,"visible":true,"origin":"","legend":"","description":"","filename":"6d19c93582d344059700967a9018e2661structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/178c2d8d600f31abf03a519d.xml"},{"id":99572388,"identity":"ba12ae92-3d30-4067-aef8-3a1376d61c77","added_by":"auto","created_at":"2026-01-06 02:54:50","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267497,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/ed7016117503ce4fe01a66c4.html"},{"id":99572356,"identity":"f1121916-22ef-4850-8190-cb4909850c23","added_by":"auto","created_at":"2026-01-06 02:54:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":599016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy area and distribution of sampling plots within and outside the burned perimeter. A total of 107 plots were established inside the burned perimeter (29 in chestnut woodlands, 27 in natural forests, 25 in pine plantations, and 26 in heathlands), and 108 plots in the unburned control area (29 in chestnut woodlands, 30 in natural forests, 24 in pine plantations, and 25 in heathlands). The red colour scale represents fire severity levels derived from the delta Normalized Burn Ratio (dNBR), with darker tones indicating higher severity. Insets show examples of post-fire conditions in different habitat types: (a) burned heathlands, (b) burned pine plantations, (c) burned chestnut woodlands, and (d) burned natural forests.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/67f596cbadf77797c08bf1ac.jpeg"},{"id":99791716,"identity":"18159fd7-3564-439a-b42e-4c0b2bcbe0e3","added_by":"auto","created_at":"2026-01-08 13:09:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":721394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDifferent degrees of fire severity recorded in the field one year after the 2022 wildfire. (a) High severity in heathland: dominance of bare ground. (b) Moderate severity in pine plantation: partial canopy scorch (c) Low severity in natural forest: scorching limited to lower tree trunks. (d) High severity in pine plantation: widespread canopy mortality.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/cbbeea691ba3e2d27f2976d1.png"},{"id":99572392,"identity":"9f30108f-8138-41b1-8a4d-43ae06b13b08","added_by":"auto","created_at":"2026-01-06 02:54:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":427730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModel-averaged regression coefficients (±95% CI) showing the effects of fire and vegetation predictors with high relative importance (ΣWi \u0026gt; 0.5) across retained models (ΔAICc ≤ 2) on bird community and functional guild metrics. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Effects of burn status (burned vs. unburned plots), fire heterogeneity and fire severity. For burn status, positive coefficients indicate higher values in unburned plots. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEffects of post-fire vegetation structure represented by the first three principal components (PC1 = severely burned stands with high snag density, PC2 = unburned forest dominated by live trees, PC3 = early post-fire recovery in open shrubland). Functional guilds include dietary groups (insectivores, granivores, frugivores, omnivores), foraging strata (canopy, understorey, ground), habitat breadth classes (HB1 = specialists, HB2 = intermediates, HB3 = generalists), habitat selection groups (F_specialist = forest specialists, F_facultative = facultative forest users, Shrubland, Open = open-habitat species), and cavity nesters (Cavnest) further divided into primary excavators (Cavnest_Exc) and non-excavators (Cavnest_NExc).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/637114213fbcb1ba6c5f4d8c.jpeg"},{"id":99572363,"identity":"72a83e4c-741d-40da-9998-38657841b541","added_by":"auto","created_at":"2026-01-06 02:54:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrincipal Coordinates Analysis (PCoA) based on Bray–Curtis dissimilarity of bird communities in burned (red) and unburned (green) plots. Each circle represents a sampling point, and dashed ellipses depict the dispersion of each group in multivariate space (encompassing 95% of the variation under a t-distribution model). PERMANOVA revealed significant differences in species composition between zones (p = 0.001), whereas PERMDISP showed no significant differences in within-group dispersion (p = 0.09), indicating that both zones exhibited almost comparable beta diversity but differed in their average community composition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/99613d985b3614ecf96244e8.jpeg"},{"id":99793275,"identity":"038e768b-eb99-4b64-9f10-f09d64d9b822","added_by":"auto","created_at":"2026-01-08 13:31:18","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistance-based redundancy analysis (dbRDA) ordination plot showing the effects of environmental predictors on bird community composition (Bray–Curtis dissimilarity) within the burned area. Arrows indicate the direction and strength of correlations between each predictor and the canonical axes (CAP1 and CAP2). Severity: fire severity estimated visually in situ within a 50 m radius; Elevation: altitude of each bird point count; Fire_Hetero: spatial heterogeneity of fire severity within a 120 m radius; PC1: intensely burned forest stands, with abundant snags and minimal shrub or live tree cover; PC2: unburned forest stands; PC3: open shrublands in early stages of post-fire recovery. Fire_Hetero:PC1: interaction between fire heterogeneity and PC1. CAP1 and CAP2 together explain 74.5% of the variance explained by the model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/7dd94764ad97ab4020d8719c.jpeg"},{"id":100356364,"identity":"c94a5e46-4355-4d13-a3c9-9b005aaff2bf","added_by":"auto","created_at":"2026-01-16 07:05:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3107966,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/4ee96e6a-ab7e-4169-a435-7db85342d44c.pdf"},{"id":99572358,"identity":"b130837a-8b29-4179-8f28-10ac11b5bc2c","added_by":"auto","created_at":"2026-01-06 02:54:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":753710,"visible":true,"origin":"","legend":"","description":"","filename":"2.MaterialSupl.docx","url":"https://assets-eu.researchsquare.com/files/rs-8423628/v1/dd621eebb28ba89f93726977.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Megafire attributes and pre-fire structural legacies shape short-term avian responses in an Atlantic-Mediterranean ecotone","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eFire is a global ecosystem process (Keeley et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e) that plays a fundamental role in influencing biome distribution and ecosystem functioning, while also being strongly correlated with vegetation types across many landscapes (Bond et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Keeley et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). It is also recognised as an evolutionary driving force that exerts selective pressure on the life-history traits of many species, thereby promoting functional differentiation and a wide range of ecological strategies (Blondel et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rundel et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Mediterranean regions, summer drought and winter growing conditions lead to a high fire risk, making their landscapes some of the most fire-prone in the world (Keeley et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). Despite covering only about 2.4% of global land surface (Dinerstein et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), these regions contribute disproportionately to global biodiversity and exhibit high levels of species endemism (Rundel and Cowling \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yet these fire-prone regions are now facing rapid and unprecedented shifts in fire regimes, driven by warming fire-weather conditions and increased fuel flammability, posing significant challenges for the long-term persistence of their unique biodiversity (Kelly et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hantson et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond climatic factors, widespread rural abandonment has emerged as a major socioecological driver of these changes, especially in Mediterranean mountain landscapes of southern Europe (Moreira et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The cessation of traditional land-use practices has led to increased fuel loads and continuity, reduced landscape heterogeneity, and diminished the capacity of ecosystems to buffer fire spread. These changes, in turn, are heightening the likelihood of large and extreme wildfire events (Tedim et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Regos et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sil et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNovel fire regimes that emerge from these complex interactions between climatic and anthropogenic factors, are expected to have profound effects on Mediterranean bird communities (De C\u0026aacute;ceres et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Drastic changes in vegetation structure and resource availability following fire events can alter species distributions and local abundances, ultimately modifying the composition and functional structure of avian communities (Smucker et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fontaine and Kennedy \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arrogante-Funes et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding how bird communities respond to these novel fire regimes remains a complex challenge (Puig-Giron\u0026egrave;s et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as avian responses are strongly mediated by specific fire attributes and post-fire habitat dynamics (Pons \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Key drivers of variation include the time elapsed since the fire (Chalmandrier et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hutto et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rainsford et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the spatial configuration and diversity of pre- and post-fire vegetation conditions (Zozaya et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Brotons et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stillman et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the severity gradient across the burned landscape (Puig-Giron\u0026egrave;s et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the relative importance of these factors can differ markedly across ecosystems and biogeographic contexts, underscoring the need for context-specific assessments of avian community responses to fire (Hutto et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rainsford et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn increasingly recognised yet sometimes overlooked attribute of fire is its capacity to generate a fine-scale mosaic of habitats at varying successional stages, potentially facilitating the coexistence of species with different ecological requirements adapted to distinct phases of post-fire recovery (Burrows et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Steel et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pyrodiversity hypothesis posits that heterogeneity in fire frequency, spatial extent, type, and severity can enhance biodiversity. However, empirical support for this hypothesis remains context-dependent and difficult to assess without considering local variations (Kelly et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Jones and Tingley \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile spatial variability in fire severity is expected to promote habitat differentiation, creating a range of resources and ecological opportunities after the fire (Steel et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), empirical studies explicitly linking this spatial dimension of pyrodiversity to avian beta diversity are still scarce (Tingley et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jones and Tingley \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is particularly relevant given that the spatial heterogeneity of fire severity may foster turnover in species composition and support functionally distinct assemblages, with divergence from unburned communities potentially increasing over time (Tingley et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Burkle et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the extent to which this relationship is mediated by post-fire habitat structure\u0026mdash;shaped jointly by disturbance heterogeneity and pre-fire legacies\u0026mdash;remains largely unexplored.\u003c/p\u003e \u003cp\u003eAlthough fire attributes largely influence post-fire vegetation development, growing evidence indicates that the resulting structure is not a linear function of fire severity or time since fire, but rather on pre-fire structural and historical legacies (Johnstone et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bowd et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Because post-fire animal succession closely tracks vegetation regeneration, integrating post-fire habitat structure alongside fire attributes may provide a more reliable basis for understanding wildlife responses to fire, particularly in heterogeneous landscapes where post-fire regeneration can be highly variable (Swan et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the shifting nature of post-fire environments, short-term assessments are critical to avoid misleading conclusions about the temporal dynamics of avian responses to fire disturbance (Smucker et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Although recolonisation of recently burned areas is largely attributed to adjacent populations (Zozaya et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; P\u0026eacute;rez-Granados et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), there is growing evidence of post-fire survival, demonstrating that birds never completely abandon the affected area. Instead, they continue to use the burned area immediately after the fire and even during the harsh conditions of the first post-fire winter (Pons \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Prodon \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Puig-Giron\u0026eacute;s et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first breeding season following fire represents a critical ecological window, offering a detailed picture of how bird communities reorganise and begin to recover. Grouping species into functional guilds provides a powerful framework for interpreting these early-stage dynamics, as it links community structure with resource availability and life-history traits, and can provide causal insight into the interaction between species and fire (Gosper et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Scott and Korb \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the variability in avian responses to changing fire regimes is essential for developing adaptive management strategies in fire-prone landscapes (Novoa et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; PuigGiron\u0026egrave;s et al. 2025). Yet, although several studies have examined these responses across Mediterranean landscapes, research remains scarce in transitional zones between the Mediterranean and Eurosiberian biogeographic regions (L\u0026oacute;pez and Guiti\u0026aacute;n \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Moreira et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Garc\u0026iacute;a-Redondo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), where, to our knowledge, no study has evaluated the direct impacts on bird communities as early as one year after fire. These transitional areas, characterized by overlapping biotic components and distinct climate-vegetation dynamics, may elicit community responses that differ from those documented in core Mediterranean regions.\u003c/p\u003e \u003cp\u003eHere, in a sub-Mediterranean mountainous landscape of northwestern Spain undergoing widespread rural abandonment, we examine how key fire-related attributes and post-fire vegetation structure influence short-term avian responses at both community and functional levels, and we assess how fire shapes beta diversity within the burned area and between burned and unburned zones. Our study was conducted one year after a megafire\u0026mdash;the largest documented in the area at the time\u0026mdash;offering a valuable opportunity to assess avian resilience under conditions of severe ecological disturbance. By focusing on a biogeographic transition zone between Mediterranean and Eurosiberian regions, we address an underexplored ecological context and provide new insights into post-fire bird\u0026ndash;habitat associations, contributing knowledge relevant to conservation and management in fire-prone landscapes.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. STUDY AREA\u003c/h2\u003e \u003cp\u003eThe study was conducted in \u0026ldquo;Serra do Courel\u0026rdquo;, a rural mountainous area located in the province of Lugo, Galicia (NW Spain), covering approximately 30,000 ha (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region exhibits significant altitudinal variation, ranging from 400 to 1,650 m a.s.l., and is characterized by steep slopes and rugged terrain (Guiti\u0026aacute;n and Villar \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Biogeographically, the mountain range represents a convergence zone where Eurosiberian elements coexist with distinctly Mediterranean influences (Rodr\u0026iacute;guez and Ramil \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Amigo and Rodr\u0026iacute;guez-Guiti\u0026aacute;n \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Combined with its diverse lithology and elevation gradients, this results in high taxonomic diversity (Guiti\u0026aacute;n \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), contributing to its designation as a Special Area of Conservation (SAC) within the Natura 2000 Network (Nat-2000 Site Code ES1120001). According to the K\u0026ouml;ppen Climate Classification, the study area has a temperate climate with dry and mild summers (AEMET \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Over the past decade, the lowest recorded monthly temperature occurred in January, with an average minimum of 2\u0026deg;C, while the highest was in August, with an average maximum of 27.6\u0026deg;C. The mean annual precipitation was 1,640 mm, though during July, the driest summer month, average rainfall was less than 20 mm (Meteogalicia \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe predominant vegetation units, ranked by area coverage, include heathlands dominated by \u003cem\u003eCytisus\u003c/em\u003e spp., \u003cem\u003eErica\u003c/em\u003e spp., and \u003cem\u003eGenista\u003c/em\u003e spp., followed by natural forests primarily composed of Pyrenean oak (\u003cem\u003eQuercus pyrenaica\u003c/em\u003e Willdenow) and holm oak (\u003cem\u003eQuercus rotundifolia\u003c/em\u003e Lamarck). Additionally, chestnut woodlands (\u003cem\u003eCastanea sativa\u003c/em\u003e Miller) and pine plantations, predominantly Scots pines (\u003cem\u003ePinus sylvestris\u003c/em\u003e Linnaeus), are also important (Gonz\u0026aacute;lez-Varo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Losada et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Alongside its ecological richness, Serra do Courel has undergone significant land use changes over the past several decades. Since the mid-20th century, the region has experienced widespread land abandonment, leading to vegetation encroachment and forest expansion (Guiti\u0026aacute;n and Villar \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This process has been driven largely by rural exodus, profoundly transforming traditional land use practices. Since 1970 alone, O Courel has lost nearly 75% of its population (IGE \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), along with approximately 85% of its livestock (IGE \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e) and over 60% of its farms (Munilla et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis region is classified within the Rare-Intense-Large (RIL) pyrome, characterized by high intensity fires, long fire return intervals and short fire seasons (Archibald et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the summer of 2022\u0026mdash;the warmest year on record in Spain (Garc\u0026iacute;a 2023)\u0026mdash;a severe dry thunderstorm on 14 July ignited a wildfire that burned continuously until 28 July, affecting 12,768 ha (Garc\u0026iacute;a 2023). At the time, this was the largest wildfire recorded in Galicia since official record keeping began (Xunta de Galicia \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The combination of extreme fire weather conditions, high pre-fire fuel load and the region's steep terrain resulted in predominantly high fire severity within the burned area. However, spatial heterogeneity created a mosaic of varying severity levels, including patches of lower fire severity and unburned islands (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. BIRD DATA\u003c/h2\u003e \u003cp\u003eBird communities were surveyed using point counts (PoCs) during the 2023 breeding season, one year after the wildfire, from early May to mid-July. To compare bird assemblages between burned and unburned areas, two sampling zones were established: (i) the burned area and (ii) a peripheral unburned control zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A stratified sampling design was employed based on the dominant vegetation types in the study area: heathlands, natural forests, chestnut woodlands, and pine plantations. The placement of sampling plots was guided by the latest edition of the Spanish Forest Map (MITECO \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while also considering the rugged topography and access constraints of the region. A total of 215 PoCs were conducted, with 107 in the burned area and 108 in the unburned control zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The final sampling effort averaged 26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 PoCs per vegetation type (range: 24\u0026ndash;30 PoCs) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo prevent double-counting bias, a minimum distance of 200 m was maintained between PoCs, with each placed at least 30 m from vegetation type edges to minimize edge effects (Calvi\u0026ntilde;o-Cancela \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Garc\u0026iacute;a-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Each PoC lasted 10 min, preceded by a 1-min settling period. A single observer (FGF) recorded all birds heard or seen within two distance bands (0\u0026ndash;30 m and 31\u0026ndash;100 m) (Gibbons and Gregory \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gregory et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Surveys were conducted within 4 h after sunrise, under favourable weather conditions (calm or low wind, no fog, and no precipitation). To ensure temporal consistency, each vegetation type was surveyed on the same day in both sampling zones (Greenberg et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Garc\u0026iacute;a-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBirds in flight without landing, as well as raptors, owls, waterfowl, and aerial foragers (Hirundinidae and Apodidae), were excluded from the counts, as the method was not suited for these groups (Bibby et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sergio \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo characterise functional structure within bird communities, we grouped the relative abundance of all recorded species into five ecologically meaningful trait categories: dietary guild, foraging stratum, habitat breadth, habitat selection, and cavity-nesting behaviour. Dietary guilds included: (1) insectivores, (2) granivores, (3) frugivores, and (4) omnivores. Foraging stratum was categorised as (1) canopy, (2) understorey or shrub layer, and (3) ground. Habitat breadth was determined by the number of distinct habitat types used by each species, following a classification into: (1) specialists (1\u0026ndash;2 habitats), (2) intermediates (3\u0026ndash;4), and (3) generalists (\u0026ge;\u0026thinsp;5). Habitat selection was defined according to primary habitat use, grouping species into: (1) forest specialists, (2) facultative forest users, (3) shrubland species, and (4) open-habitat species. Cavity-nesting species were further classified as (1) primary excavators (species that excavate their own cavities) and (2) non-excavators (species that use existing cavities).\u003c/p\u003e \u003cp\u003eFunctional assignments were based on multiple sources: dietary and foraging traits were obtained primarily from Mikusiński et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the EltonTraits database (Wilman et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); cavity-nesting behaviour followed classifications from Wesołowski and Martin (\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Mikusiński et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and habitat preferences were derived from Guiti\u0026aacute;n et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Habitat breadth was quantified following the framework proposed by Ausprey et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), using species-level habitat use data extracted from the Spanish Breeding Bird Atlas (Molina et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A full list of species along with their assigned functional categories is provided in Table S2, and further methodological details are available in Text S1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. VEGETATION DATA\u003c/h2\u003e \u003cp\u003eVegetation structure and composition were assessed using 30 m radius plots centred on each of the 215 PoCs locations (Garc\u0026iacute;a-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Data collection was tailored to the dominant vegetation type at each plot, distinguishing between forested habitats (natural forests, chestnut woodlands, pine plantations) and open heathlands.\u003c/p\u003e \u003cp\u003eIn forested plots, two 10 m transects were laid out in opposite directions (north and south) from the plot centre. Along each transect, all living trees and standing dead trees (snags) within a 1 m wide band on either side were recorded, and each living tree was identified to species level.\u003c/p\u003e \u003cp\u003eWithin the entire 30 m radius plot, additional variables were measured, including the average height and girth at breast height (GBH) of both live trees and snags, the number of mature trees (GBH\u0026thinsp;\u0026gt;\u0026thinsp;100 cm), and the volume of lying deadwood. Tree canopy cover was assessed using a GRS vertical densitometer at six equidistant points (1, 5, and 10 m along each transect), following Huynh (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo characterise the understory, two 25 m\u0026sup2; subplots were established 10 m from the plot centre along the transects. Within each subplot, we estimated vegetation cover for the herb and shrub layers, as well as litter ground cover (Ascoli et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, we recorded the average height of the shrub layer and the number of shrub species exceeding 10% cover (Ambarli and Bilgin \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn heathland plots, only the two 25 m\u0026sup2; subplots were used. We visually estimated shrub, herbaceous, tree, and bare ground cover, and recorded average shrub height and the number of shrub species with \u0026gt;\u0026thinsp;10% cover.\u003c/p\u003e \u003cp\u003eTopographic variables recorded for all plots included elevation (using a handheld GPS) and slope (calculated in QGIS v3.34 from a 10 m DEM provided by IGN). Following Skowno and Bond (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), woody plants taller than 3 m were classified as trees, and those under 3 m as shrubs. A summary of all vegetation predictors is provided in the Supplementary Table S3.\u003c/p\u003e \u003cp\u003eBased on ecological relevance to post-fire habitat structure and suitability for multivariate approaches, we focused on eight continuous vegetation variables, excluding those with excessive zeros or strong collinearity. These variables included canopy cover, herbaceous cover, the number and mean height of both living trees and snags, the number of shrub species exceeding 10% cover, and a shrub coverage index calculated as the product of mean shrub height and mean shrub cover divided by 100 (Garc\u0026iacute;a-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. FIRE ATTRIBUTES\u003c/h2\u003e \u003cp\u003eTo characterise fire attributes relevant to post-fire bird responses, we considered two key variables: fire severity and its spatial heterogeneity.\u003c/p\u003e \u003cp\u003eFire severity was evaluated using two complementary approaches that targeted the immediate biophysical effects of fire, rather than post-fire ecosystem responses (Keeley \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). First, a field-based visual index adapted from Keeley (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) was applied during PoCs surveys to capture structural ecosystem changes while avoiding bias from post-fire recovery dynamics. Within a 50 m radius of each sampling plot, the area was divided into four quadrants. In each quadrant, the dominant severity class\u0026mdash;based on the loss or decomposition of organic matter above and below ground\u0026mdash;was visually identified and assigned a numerical score ranging from 0 (no apparent effects) to 3 (high severity) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The scores from the four quadrants were then summed to produce a fire severity index for each plot, ranging from 0 to 12. This method allowed direct assessment of fire-induced ecological impacts, such as canopy mortality of non-resprouting trees and loss of soil organic matter, while minimizing the influence of post-fire vegetation regrowth.\u003c/p\u003e \u003cp\u003eSecond, satellite-derived estimates of fire severity were obtained using the delta Normalized\u003c/p\u003e \u003cp\u003eBurn Ratio (dNBR), calculated from Sentinel-2 imagery by comparing pre-fire (14 July 2022) and post-fire (1 August 2022) scenes. Mean dNBR values were extracted within a 50 m buffer around each plot for comparison with the field-based index.\u003c/p\u003e \u003cp\u003eAlthough both metrics were strongly correlated (Spearman ρ\u0026thinsp;=\u0026thinsp;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), only the field-based visual severity index was retained for subsequent ecological analyses (see Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.5.1\u003c/span\u003e. for justification).\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\u003eField-based classification of fire severity within sampling plots.\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003eAdapted from Keeley (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire severity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo apparent effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnburned plants with no visible direct effects of fire. Soil organic layer intact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome canopy stems scorched; surface litter consumed; soil organic layer largely intact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial canopy mortality; all understory plants charred or consumed; fine dead twigs on the soil surface consumed and logs charred; soil organic layer largely consumed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplete canopy mortality; surface litter and soil organic layer largely consumed; charred organic matter extending several centimetres in depth.\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\u003eTo quantify spatial heterogeneity in fire severity, we calculated the standard deviation (SD) of dNBR values within a 120 m buffer around each sampling plot, using the same pre- and post-fire Sentinel-2 imagery as for the fire severity metric. This buffer radius was selected to capture local-scale variability in fire effects while minimizing spatial autocorrelation among nearby plots. The SD of dNBR values provides a quantitative measure of variation in fire severity across neighbouring pixels, with higher values indicating a more heterogeneous burn mosaic in the surrounding landscape (Zlonis et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFire severity and heterogeneity values for each sampling plot are provided in Table S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. DATA ANALYSIS\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Predictor variables\u003c/h2\u003e \u003cp\u003eTo characterise vegetation structure, we conducted a Principal Component Analysis (PCA) based on the subset of ecologically relevant vegetation variables described in Section 2.3. The first three principal components (PCs), each with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, jointly explained 69.3% of the total variance (Table S5). PC1 represented severely burned forest stands, characterised by high snag abundance, low shrub regeneration, and sparse live tree cover. PC2 reflected unburned forest structure, with high values associated with the dominance of living trees. PC3 captured early post-fire recovery in open shrublands, with high herbaceous cover and minimal woody vegetation.\u003c/p\u003e \u003cp\u003eTo compare fire severity metrics, we performed a Spearman correlation between field-based visual severity and satellite-derived dNBR values across all sampling plots. The two metrics were strongly correlated (ρ\u0026thinsp;=\u0026thinsp;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicating a shared spatial pattern of severity. For subsequent analyses, we retained the visual index as the primary fire severity metric due to its finer spatial resolution, its direct assessment of structural fire effects, and its independence from pre-fire vegetation biomass (See Text S2 for further details).\u003c/p\u003e \u003cp\u003eFinally, the following predictor variables were selected to assess the effects of fire and vegetation characteristics on bird community structure and functional guild composition: (1) visual fire severity, (2) fire heterogeneity (SD of dNBR within a 120 m buffer), (3) vegetation structure (summarised by the first three PCs from the PCA), (4) burn status (burned vs. unburned plots), and (5) elevation. Elevation was retained as a covariate due to significant differences observed between burned and unburned areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Modelling bird community and functional responses\u003c/h2\u003e \u003cp\u003eTo assess avian responses to post-fire vegetation structure and fire attributes, we modelled a suite of response variables encompassing both community-level and functional group metrics. These included total bird abundance, species richness, and aggregated abundances across previously defined functional groups (see Section 2.2): dietary guilds, foraging stratum, habitat breadth classes, habitat preference, and cavity-nesting guilds.\u003c/p\u003e \u003cp\u003eFor each response variable, we fitted a Generalized Linear Model (GLM) using either a Poisson or negative binomial error distribution, depending on the degree of overdispersion. All predictor variables were standardised prior to analysis. Although burn status showed moderate collinearity with fire severity and fire heterogeneity (assessed via variance inflation factors), it was retained in all models due to its consistent statistical significance and improved model performance (lower AIC), indicating it provided complementary explanatory power (See Text S3 for further details).\u003c/p\u003e \u003cp\u003eModel assumptions and fit were evaluated using the \u003cem\u003eeasystats\u003c/em\u003e package in R (L\u0026uuml;decke et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including diagnostics for overdispersion, zero inflation, residual patterns, influential outliers, variance homogeneity, and multicollinearity. Model explanatory power was quantified using Nagelkerke\u0026rsquo;s pseudo-R\u0026sup2;. We applied an information-theoretic approach with the \u003cem\u003eMuMIn\u003c/em\u003e package (Bartoń \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), generating a full set of candidate models via the \u003cem\u003edredge\u003c/em\u003e function. Models with ΔAICc\u0026thinsp;\u0026lt;\u0026thinsp;2 were retained, and model averaging was conducted to estimate effect sizes. Predictor importance was evaluated using the sum of Akaike weights (\u0026sum;wi), with variables exceeding \u0026sum;wi\u0026thinsp;\u0026gt;\u0026thinsp;0.5 considered influential for interpretation (Burnham and Anderson \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3. Beta diversity and community composition analysis\u003c/h2\u003e \u003cp\u003eTo assess differences in bird community structure between burned and unburned areas, we conducted a multistep analysis focusing on beta diversity, compositional dissimilarity, and indicator species.\u003c/p\u003e \u003cp\u003eA site-by-species abundance matrix was constructed from the 215 PoCs. For dissimilarity measures, we used both Bray\u0026ndash;Curtis on raw abundances and Euclidean distances on Hellinger-transformed data (Legendre and Gallagher, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Magurran and McGill, 2011). Results from the two approaches were broadly consistent; however, Bray\u0026ndash;Curtis was retained as the primary metric in subsequent analyses because it provided better model performance and more closely reflected the ecological signal of post-fire bird responses.\u003c/p\u003e \u003cp\u003eBeta diversity, measured as multivariate dispersion within groups (Legendre and De C\u0026aacute;ceres \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), was assessed with PERMDISP (Anderson \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) using 999 permutations. Patterns of dispersion and centroid separation were visualized with a Principal Coordinates Analysis (PCoA). Differences in species composition between burned and unburned plots were then tested with a permutational multivariate analysis of variance (PERMANOVA) based on Bray\u0026ndash;Curtis dissimilarities.\u003c/p\u003e \u003cp\u003eTo identify species most strongly associated with each zone, we used the Indicator Value index (IndVal; C\u0026aacute;ceres and Legendre, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which combines measures of fidelity (frequency within a group) and specificity (exclusivity to a group). Statistical significance was assessed through permutation tests, and species with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant indicators.\u003c/p\u003e \u003cp\u003eFinally, we quantified gamma diversity in each zone by comparing the total number of species and the number of unique species \u0026mdash;defined as those detected exclusively in either burned or unburned plots\u0026mdash;in order to evaluate whether burned areas contributed to regional diversity by incorporating new species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4. Heterogeneity in fire severity and beta diversity\u003c/h2\u003e \u003cp\u003eTo evaluate whether spatial heterogeneity in fire severity influenced bird community composition within the burned area, we applied distance-based redundancy analysis (dbRDA; Jiang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Legendre and Anderson, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rosas-Espinoza et al., 2024; Shi et al., 2021). This constrained ordination method relates community dissimilarities to environmental predictors while providing estimates of explained variance and partitioning among predictors.\u003c/p\u003e \u003cp\u003eAnalyses were restricted to burned plots, using Bray\u0026ndash;Curtis dissimilarities of species abundances as the response matrix (consistent, though slightly weaker results were obtained with Hellinger\u0026thinsp;+\u0026thinsp;Euclidean distances). We first fitted a simple dbRDA with fire heterogeneity as the sole predictor to test its independent contribution to beta diversity. We then built a full model including additional predictors (fire severity, elevation, and vegetation structure PCs) to assess the robustness of the fire heterogeneity effect and the relative contribution of other variables. To further explore context-dependent effects, we also tested interaction terms between fire heterogeneity and vegetation structure PCs. All predictors were standardized prior to analysis, and model significance was evaluated using 4,999 permutations with the function \u003cem\u003ecapscale\u003c/em\u003e() in the \u003cem\u003evegan\u003c/em\u003e R package (Oksanen et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Bird census\u003c/h2\u003e \u003cp\u003eA total of 2928 individual birds representing 56 species were recorded across the study area. In the control (unburned) zone, 51 species and 1766 individuals were observed, while 47 species and 1162 individuals were recorded in the burned zone. Mean species richness and bird abundance per sampling point were 8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 species and 13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 individuals, respectively, across all surveyed locations. These values were higher in the control zone, with 9.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 species and 16.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 individuals per PoC, compared to 7.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 species and 10.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 individuals per PoC in the burned zone (Table S6, Fig. S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Community level and functional bird responses\u003c/h2\u003e \u003cp\u003e The fitted GLMs, which included both fire attributes and vegetation structure (PCs), showed generally high explanatory power across community and functional group models (Table S7), supporting the importance of integrating local habitat context in post-fire ecological assessments. Elevation showed weak but occasionally significant effects across models (Fig. S3).\u003c/p\u003e \u003cp\u003eWhen comparing sampling plots located inside versus outside the burned area no community-level metric or functional bird group was significantly favoured by fire. Habitat specialists and granivores exhibited the strongest negative responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This pattern is consistent with short-term reductions in habitat suitability following high-severity fire, potentially due to post-fire resource limitation.\u003c/p\u003e \u003cp\u003eFire severity emerged as the strongest negative driver of bird community structure and composition one year after the fire (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Most functional guilds were significantly affected: all dietary groups except omnivores, all habitat breadth classes, and all foraging strata\u0026mdash;although the response of canopy foragers overlapped zero. Negative responses were also evident among forest facultative and, most notably, shrubland species. Increasing fire severity was associated with a marked reduction in both species richness and overall bird abundance. No community-level metric or functional guild showed a positive response to higher fire severity, and only three of the 47 recorded species were significantly associated with more severely burned plots (Text S4). These patterns underscore the short-term disruptive and impoverishing effects of high-severity fire on post-fire bird assemblages.\u003c/p\u003e \u003cp\u003eHowever, spatial heterogeneity in fire severity appeared to promote a diversity of ecological niche opportunities from the very first post-fire breeding season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). All functional guilds that were negatively affected by high fire severity tended to show positive associations with greater spatial variability in severity. Notably, no functional guild or diversity metric exhibited negative responses to fire heterogeneity.\u003c/p\u003e \u003cp\u003ePost-fire vegetation structure significantly influenced all functional guilds and community metrics, emphasising the importance of considering it alongside the attributes of fire (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). PC1 - characterised by severely burned stands with abundant snags and almost no understory - showed positive associations with habitat specialists (HB1), forest-dependent species, and cavity nesters, particularly non-excavators. PC2 \u0026ndash; representing unburned forest structure, including remnant unburned forest patches within the fire perimeter - showed expected positive associations with forest-related guilds, such as cavity nesters (both excavators and non-excavators), canopy foragers, and both forest specialists and facultative forest users, while being negatively associated with shrubland and open-habitat species. PC3 \u0026ndash; associated with post-fire open habitats characterised by herbaceous recovery and minimal woody vegetation - showed a significant positive association only with open-habitat species, while displaying negative relationships with forest-associated guilds (forest specialists, facultative forest users, canopy foragers, and cavity nesters) and with generalist species (HB3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Beta diversity and community turnover between zones\u003c/h2\u003e \u003cp\u003eBeta diversity, assessed with PERMDISP, was slightly higher in burned plots, although differences between burned and unburned areas were not statistically significant (Bray\u0026ndash;Curtis: p\u0026thinsp;=\u0026thinsp;0.09; Hellinger\u0026thinsp;+\u0026thinsp;Euclidean: p\u0026thinsp;=\u0026thinsp;0.14; Fig. S4). Both dissimilarity approaches yielded consistent patterns, showing greater multivariate dispersion in burned plots. This may reflect a weak tendency toward higher compositional heterogeneity in fire-affected areas, although this pattern should be interpreted with caution.\u003c/p\u003e \u003cp\u003ePERMANOVA based on Bray\u0026ndash;Curtis dissimilarity revealed significant differences in bird community composition between burned and unburned plots (F\u0026thinsp;=\u0026thinsp;6.97, R\u0026sup2; = 0.032, p\u0026thinsp;=\u0026thinsp;0.001), indicating that post-fire conditions significantly influenced species composition across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although the proportion of explained variance was modest, the results point to subtle yet detectable compositional shifts in burned areas one year after the disturbance.\u003c/p\u003e \u003cp\u003eIndicator species analysis (IndVal) identified five species significantly associated with unburned plots and two with burned plots (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the two species linked to burned areas, the black redstart (\u003cem\u003ePhoenicurus ochruros\u003c/em\u003e Gmelin) also showed a positive association with high fire severity (Text S4), whereas the tawny pipit (\u003cem\u003eAnthus campestris\u003c/em\u003e Linnaeus) occurred exclusively in open habitats generated by fire, following the structural collapse of burned shrublands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Indicator Species Analysis (IndVal) showing species significantly associated with burned and unburned plots (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The IndVal statistic (\u0026ldquo;stat\u0026rdquo;) quantifies the strength of association between each species and a given zone, combining specificity and fidelity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon name\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\u003eAuthority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Nuthatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSitta europaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47168679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDartford Warbler\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\u003eBoddaert (1783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49650655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong thrush\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTurdus philomelos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrehm (1831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41343247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTawny pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus campestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBurned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25697808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Cuckoo\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\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27216553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack redstart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhoenicurus ochruros\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGmelin (1774)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBurned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25697808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGolden oriole\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\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23570226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\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\u003eA total of 56 species were recorded across both zones, with 47 species in burned plots and 51 in unburned ones. The burned area contained five exclusive species, representing 8.9% of total gamma diversity and indicating a positive contribution to regional diversity. In contrast, the unburned area harboured nine exclusive species (16.1% of gamma diversity), thus contributing more markedly to overall regional diversity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of bird species exclusively recorded in burned and unburned zones one year after the fire.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBURNED ZONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eUNBURNED ZONE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuthority\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTawny pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus campestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommon cuckoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCuculus canorus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrested lark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGalerida cristata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCirl bunting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEmberiza cirlus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1766)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack redstart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhoenicurus ochruros\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGmelin (1774)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellowhammer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEmberiza citrinella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarsh tit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoecile palustris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelodious warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHippolais polyglotta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVieillot (1817)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSardinian warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSylvia melanocephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGmelin (1789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRed crossbill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLoxia curvirostra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGolden oriole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOriolus oriolus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBonelli\u0026rsquo;s warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus bonelli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVieillot (1819)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTurtle dove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptopelia turtur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinnaeus (1758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpotless starling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSturnus unicolor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemminck (1820)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Influence of fire heterogeneity on beta diversity\u003c/h2\u003e \u003cp\u003edbRDA revealed that spatial heterogeneity in fire severity had a statistically significant but weak effect on community composition within the burned area (F\u0026thinsp;=\u0026thinsp;2.01, p\u0026thinsp;=\u0026thinsp;0.012), explaining 1.9% of the total variance (Fig. S5). This suggests that fine-scale fire severity mosaics were associated with modest increases in compositional dissimilarity among plots.\u003c/p\u003e \u003cp\u003eWhen additional predictors were included, the model explained 20% of the variance in community composition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among predictors, elevation had the strongest influence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by post-fire vegetation structure (PC2, PC3, and PC1; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while fire heterogeneity showed a near-significant trend (p\u0026thinsp;=\u0026thinsp;0.066). These results indicate that post-fire habitat structure and topography exert stronger influences on community turnover than fire heterogeneity per se.\u003c/p\u003e \u003cp\u003eHowever, incorporating interaction terms between fire heterogeneity and post-fire vegetation structure improved model performance, particularly the interaction with PC1 (p\u0026thinsp;=\u0026thinsp;0.006), which increased the explained variance to 21.7% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding supports the idea that the ecological effects of fire heterogeneity on bird community composition are mediated by post-fire habitat structure, which is in turn a product of both fire attributes and pre-fire ecosystem legacies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study provides one of the first empirical assessments of short-term wildfire effects on bird communities in sub-Mediterranean mountain ecosystems. By characterizing avian responses through functional guilds and biodiversity structure metrics, we offer a robust framework for understanding the mechanisms driving community reassembly during the early stages of postfire succession, while also facilitating broader inference beyond species-level patterns. Our results show that local-scale fire attributes (severity and heterogeneity), landscape-level burn status, and fine-scale post-fire vegetation structure jointly shaped avian community structure, significantly influencing all functional groups and biodiversity metrics considered. Additionally, beta diversity within burned area was better explained by interactions between fire heterogeneity and post-fire vegetation structure, underscoring the importance of accounting for post-fire habitat conditions when assessing community composition in the aftermath of megafires.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eContrasting roles of key fire attributes \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo disentangle the effects of different fire attributes, we first explore burn status as a coarse indicator of post-fire landscape configuration, capturing broad-scale patterns of habitat availability and continuity. However, additional landscape metrics\u0026mdash;such as distance to unburned patches or individual patch size\u0026mdash;may further refine this perspective (Steel et al. 2022; Ray et al. 2025). Within the burned area, species with specialized diets (insectivores and granivores), habitat specialists, and overall bird abundance exhibited the most pronounced negative responses, while no functional group was significantly favoured (Fig. 3A). These patterns are consistent with short-term post-fire responses of bird communities reported in other Mediterranean mountain ecosystems (e.g., Pons \u0026amp; Clavero, 2010; Pons \u0026amp; Prodon, 1996) and in the limited research from alpine systems of non-Mediterranean Europe (Rey et al. 2019). They align with the habitat fragmentation hypothesis (Fahrig 2003), whereby fire reduces habitat amount via the loss or downsizing of habitat patches, decreases habitat quality through structural simplification at patch edges, and increases spatial isolation among unburned remnants (Brotons 2007). In addition, the landscape complementation hypothesis (Dunning et al. 1992) may help explain contrasts between burned and unburned areas, as the availability and spatial proximity of complementary resources across habitat patches and the surrounding matrix is likely greater in the unburned zone, particularly following large wildfires that tend to homogenize post-fire landscapes (Herrando 2001; Brotons 2007; Brotons et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt finer spatial scales, fire severity and its spatial heterogeneity exerted contrasting effects on the bird community during the first breeding season after fire. Fire severity emerged as the primary negative driver of community structure and functional composition, acting as a major disruptive force with an overall impoverishing effect on bird assemblages. In our study, increasing severity notably altered the structure of shrub and understorey layers, particularly affecting habitat conditions for shrubland species and understorey foragers (Fig. 3A). As fire severity increases, resource availability and habitat complexity tend to decline, with larger high-severity patches generally considered detrimental to biodiversity (Steel et al. 2022; Gibson et al. 2025). This structural simplification may reduce site fidelity and promote dispersal, especially in strata where vertical vegetation structure had nearly collapsed (Puig-Giron\u0026eacute;s et al. 2023; Scott and Korb 2024). At the same time, high-severity patches can reset successional dynamics and provide suitable conditions for early-seral species, offering notable ecological value in abandoned montane landscapes where open habitats are increasingly rare (DellaSala and Hanson 2015; Hutto et al. 2015; Tingley et al. 2016). Yet, this pattern was not detected in our study, likely due to the predominance (76%) of sampling plots in forested environments, which may have obscured colonization signals in open areas created by high-severity fire in shrublands. Moreover, in the short term, colonization may have been constrained by the scarcity of nearby source populations across the dense, unmanaged landscape that characterize the study area (Pons and Clavero 2010; Puig-Giron\u0026egrave;s et al. 2022). While early-successional colonizers typically peak within 2\u0026ndash;8 years post-fire in Mediterranean systems (Pons et al. 2012; Prodon 2021; Garc\u0026iacute;a-Redondo et al. 2023), these dynamics remain largely undocumented in our Atlantic\u0026ndash; Mediterranean transition zone, where longer-term monitoring may be required to capture such responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy contrast, spatial heterogeneity in fire severity acted as a positive driver of bird functional diversity from the very first post-fire breeding season. Unlike the uniform negative effects of high fire severity, this heterogeneity fosters a broader range of ecological niches, likely contributing to buffering the immediate impacts of fire and enhancing short-term ecological resilience. The fine-scale mosaic of fire severities promotes post-fire community recovery by facilitating dispersal and recolonization across a gradient of habitat conditions (Rey et al. 2019; Rainsford et al. 2023). Extremes within this gradient must play distinct ecological roles: unburned refugia support the persistence of fire-sensitive guilds\u0026mdash;such as shrubland species and habitat specialists\u0026mdash;while severely burned patches create favourable conditions for opportunistic or disturbance-tolerant ones, including open-habitat and ground-foraging birds (Watson et al., 2012; Zozaya et al., 2011;\u0026nbsp;Fig. 3A). Increased edge habitats associated with spatial heterogeneity further enhance resource diversity (Adorno et al. 2025), benefiting functional groups such as insectivores and granivores (Fig. 3A) through improved availability and detectability of seeds and invertebrates during early post-fire successional stages (L\u0026oacute;pez and Guiti\u0026aacute;n 1988; Edenius 2011; Banza et al. 2021). This structural complexity is also likely to allow many birds to exploit complementary resources distributed across patches in different stages of post-fire recovery, as some species benefit from the variety of resources that this mosaic provides (Stephens et al. 2015; Tingley et al. 2016; Brotons et al. 2018). Moreover, patchily burned areas may be colonised more rapidly, as they concentrate a greater diversity of resources than uniformly burned sites, thereby facilitating faster avifaunal recovery (Watson et al. 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese positive responses to fine-scale spatial heterogeneity in fire severity suggest that even small-scale variations in the burned landscape may help retain species that might otherwise be lost (Pons and Prodon 1996; Brotons 2007; Pons 2007). This underscores how the habitat complementation hypothesis (Dunning et al. 1992) may be scale-dependent (Herrando et al. 2002): at the landscape level, burned areas appear more homogeneous than unburned controls, potentially limiting access to a diverse array of resources. In contrast, at finer spatial scales, fire heterogeneity enhances both structural and functional habitat diversity, supporting richer and functionally more diverse bird communities than would be expected from the additive effects of individual severity patches (Brotons et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePost-fire vegetation structure as a major driver of avian community reassembly\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFire initiates dynamic ecological processes that shape vegetation structure and composition across multiple spatial and temporal scales. These changes can profoundly influence bird community composition for decades or longer (Bitani et al. 2023; Ray et al. 2025). Our results indicate that incorporating in situ post-fire vegetation variables provides high explanatory power of models describing bird community responses (Kelly et al., 2017; Morin et al., 2021;\u0026nbsp;Table S7). In our study, three main components (PCs) emerged as key predictors of avian community patterns.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong these, post-fire snags (PC1) may confer important structural and functional value during the first breeding season in severely burned forest stands. They contribute notably to habitat provisioning for cavity nesters and forest specialists (Fig. 3B) by providing new nesting cavities, roosting sites, perches, and enhanced foraging opportunities (Zozaya et al. 2011; Brown et al. 2015; White et al. 2016; Scott and Korb 2024). Snags may also provide new trophic resources, such as xylophagous and saproxylic insects or pinecone-delivered seeds (Moreira et al. 2003; Hutto et al. 2015; Scott and Korb 2024), although we did not detect significant associations with any dietary guild (Fig. 3B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile previous studies suggest that primary excavators respond rapidly to fire due to increased snag availability, non-excavators often show delayed increases in abundance, relying on cavities generated over time by primary excavation and wood decay (Saab et al. 2004; Lowe et al. 2011; Steel et al. 2022). However, our results indicate that just one year after fire, non-excavators already exhibit stronger positive associations with snag-rich habitats than primary excavators (Fig. 3B). This suggests that the early availability of suitable cavities\u0026mdash;likely pre-existing or rapidly formed through fire damage and subsequent structural degradation\u0026mdash;may provide high-quality nesting substrates for secondary cavity users without requiring prior creation by excavators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLong-term monitoring of forest specialists and cavity nesters will be essential to assess the future ecological value of these post-fire forest stands. In the short term, site fidelity may promote persistence despite drastic habitat alteration, creating a temporal lag between fire disturbance and observable population shifts (Moreira et al. 2003; Zozaya et al. 2011; Prodon 2021). Beyond this initial phase, two contrasting scenarios may emerge. In many systems, early post-fire peaks in bird abundance are followed by gradual declines as snags decay and collapse (e.g., Lindenmayer et al., 2021; Ray et al., 2025; Steel et al., 2021). Alternatively, snags may persist for decades as structural legacies within these post-fire forests (DellaSala and Hanson 2015; Stephens et al. 2015). When combined with the epicormic resprouting of fire-resilient tree species - such as cork oak (\u003cem\u003eQuercus suber\u0026nbsp;\u003c/em\u003eLinnaeus), Pyrenean oak, or holm oak\u0026ndash; and the recovery of understorey cover, these elements may enhance habitat quality over time by increasing structural complexity and resource availability for these two functional groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePost-fire unburned forest stands (PC2) acted as refugia for fire-sensitive species and likely served as early sources for recolonization of adjacent burned areas. By contributing to spatial heterogeneity within the burned matrix, these patches may also facilitate movement across fragmented habitats by providing stepping stones for dispersal (Rainsford et al. 2023; Gibson et al. 2025; Adorno et al. 2025). Even small unburned remnants may play a key role in post-fire recovery, harbouring higher bird abundances and serving as important sources of individuals for both fauna and flora (Watson et al. 2012; Garnett et al. 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePC3, associated with early post-fire open habitats, supported low overall functional diversity but provided critical habitat for open-habitat specialists (Fig. 3B), currently among the guilds of highest conservation concern in Europe (Clavero et al. 2011; Puig-Giron\u0026egrave;s et al. 2022). These findings underscore the value of using a comprehensive functional guild classification, which enables the detection of subtle responses often overlooked in species-level analyses or general diversity metrics due to the rarity of these taxa (Smith and Lim 2025). In this case, a guild-based approach revealed the window of opportunity created by fire through the emergence of new, temporary open habitats. Despite their structural simplicity, these areas hold disproportionate biological value, particularly in mountain landscapes dominated by rural abandonment and fire suppression, where open-habitat specialists face the strongest declines (Brotons et al. 2018; Garc\u0026iacute;a-Redondo et al. 2023; Pais et al. 2025).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eBeta diversity across landscape and within the burned area\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn landscapes dominated by rural abandonment, fire is often expected to increase community differentiation (beta diversity), thereby enhancing landscape-scale richness (gamma diversity) (Farnsworth et al. 2014). However, although gamma diversity increased when combining burned and unburned areas (Table 3), our results indicate that beta diversity was similar and that community composition differed only modestly between zones one year after the fire (Fig. 4). These findings are consistent with earlier results on the effects of burn status, reinforcing the idea that the Courel megafire did not lead to a substantial increase in landscape-scale heterogeneity within the burned area, possibly due to the predominance of high fire severities (Steel et al. 2022). Such conditions align with the concept of habitat fragmentation \u003cem\u003eper se\u003c/em\u003e (Fahrig 2003), in which habitat is subdivided into smaller and more isolated patches without generating a mosaic of distinct ecological conditions. By contrast, the unburned area likely retained substantial spatial heterogeneity due to its rugged topography and the biological legacy of pre-abandonment land use, which may facilitate the coexistence of species with diverse habitat requirements. In this context, only the burned zone hosted indicator species associated with open habitats (Table 2), highlighting the ecological contribution of these scarce habitat types created by fire. The reduced representation of open-habitat species in community composition may reflect short-term constraints imposed by the surrounding landscape context, which may have limited species turnover between zones (Puig-Giron\u0026egrave;s et al. 2022). Therefore, the modest differences in species composition and beta diversity between zones\u0026mdash;despite higher functional diversity in the unburned area\u0026mdash;may reflect that both local abandonment (limited by site fidelity) and recolonization (constrained by landscape dispersal barriers) processes are still in early stages, and that habitat fragmentation may currently outweigh habitat heterogeneity in the burned zone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile spatial heterogeneity in fire severity showed positive effects on bird functional diversity (Fig. 3A), its influence on species composition was more limited (Fig. 5). Our analysis revealed that the interaction between fire heterogeneity and post-fire habitat structure explained the largest share of variation in bird community composition within the burned area. This suggests that the main effects of fire heterogeneity on beta diversity are mediated by the structural outcomes of fire, which depend not only on fire characteristics but also on pre-fire vegetation conditions and their capacity to respond to disturbance. In our study, fire heterogeneity alone barely increased species turnover; instead, its influence became evident when it interacted with snag-rich post-fire stands. These findings support the notion that post-fire beta diversity results from the combined effects of fire attributes and the structural legacies that remain in the landscape after disturbance. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs key components of a system\u0026rsquo;s ecological memory, post-fire habitat structures act both as sources of biotic colonization and as structural features that influence post-fire regeneration (Johnstone et al. 2016). In our analyses, post-fire vegetation structure was only partially explained by fire attributes (Text S5), suggesting a degree of short-term ecological resilience, whereby post-fire reorganization is driven not only by the disturbance itself but also by the persistence and expression of pre-fire ecological legacies. This is consistent with the view that fire interacts with pre-existing legacies, which in turn influence both fire severity and its spatial configuration, as well as the potential recovery trajectories of the ecosystem. However, the extent to which these legacies promote resilience depends on their own nature and their alignment with the prevailing disturbance regime (Johnstone et al. 2016; Foster et al. 2017).\u0026nbsp;\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eMechanistic pathways linking burn status, fire severity, fire heterogeneity, and post-fire habitat structure explained the variation observed across all functional groups and biodiversity metrics considered. This integrative framework provides a valuable basis for predicting short-term avian responses to wildfire and highlights that post-fire biodiversity dynamics cannot be understood solely through fire attributes. Ignoring detailed information on post-fire vegetation structure may lead to incomplete interpretations of how fire shapes ecosystems in fire-prone landscapes (Gibson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The ability to link a comprehensive classification of avian functional groups with fine-scale habitat variables and fire attributes at the plot level is critical for understanding avian responses to fire and informing future management actions.\u003c/p\u003e \u003cp\u003eOur results revealed that mixed-severity fires promote functional diversity, whereas extensive high-severity fires have the opposite effect. Fire heterogeneity and post-fire habitat structure jointly shaped community composition within burned areas, underscoring that fire interacts with structurally and functionally complex ecosystems whose resilience and pre-existing legacies ultimately condition post-fire avian reorganization. Three structural components of post-fire habitat emerged as pivotal for community reassembly: (i) open patches that favour declining early-seral colonizers and may be critical for their persistence in abandoned landscapes, (ii) unburned stands and residual refugia that enable recolonization by fire-sensitive guilds, and (iii) snag-rich forest stands that sustain structural legacies and forest specialist resilience. Preserving the structural and functional integrity of these post-fire elements, together with the fine-scale heterogeneity of the post-fire mosaic, should be prioritized during the early stages of recovery in sub-Mediterranean mountain landscapes affected by land abandonment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFUNDING DECLARATION\u003c/h2\u003e\u003cp\u003e This research was supported by the Regional Government of Galicia through the CMA-2022-0129 project and by the RESFIRE project (PID2023-152690OA-C22, C21), funded by the Spanish Ministry of Science, Innovation and Universities. AR was supported by the \u0026ldquo;Ramon y Cajal\u0026rdquo; fellowship program of the Spanish Ministry of Science and Innovation (RYC2022- 036822-I).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, F.G., A.R., J.D.; methodology, F.G., A.R., M.V., J.D.; formal analysis, F.G., M.C.; investigation, F.G.; writing \u0026ndash; original draft preparation, F.G.; writing \u0026ndash; review and editing, F.G., A.R., J.D.; supervision, A.R., J.D.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdorno B, Ribeiro MC, Hasui E, et al (2025) Fire size and vegetation productivity shape bird diversity across burned landscapes in the Atlantic Forest. 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Ardea 99:73\u0026ndash;84. https://doi.org/10.5253/078.099.0109\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8423628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8423628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFire regimes are rapidly shifting due to climate change and increasing vegetation flammability, with these dynamics often intensified in areas undergoing widespread rural abandonment, a trend particularly evident in mountainous landscapes of sub-Mediterranean Europe. We assessed avian community responses, including post-fire beta diversity, during the first breeding season following a megafire\u0026mdash;the largest recorded in the region\u0026mdash;within a depopulated mountain landscape in northwestern Iberia, located in a poorly studied transitional biogeographic zone. We employed a stratified sampling design across major habitat types to survey bird communities and quantify fire attributes and vegetation structure, combining field-based measurements with satellite-derived spectral indices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe recorded 2,928 individuals representing 56 bird species, classified into multiple functional guilds. Fire severity was the main negative driver of community structure and composition, significantly impacting most functional groups. In contrast, spatial heterogeneity in fire severity fostered a broader range of ecological niches, enhancing the coexistence of diverse guilds and buffering the immediate effects of high fire severity. Postfire vegetation structure was a key determinant of community reassembly: snag-rich stands, unburned forest patches, and early post-fire open habitats facilitated both avian persistence and recolonization. These components also provided critical resources for highly specialized guilds, including cavity-nesting and open-habitat species. Bird community composition differed significantly but weakly between burned and unburned areas, and fire heterogeneity had a strong positive effect on post-fire beta diversity only when interacting with post-fire habitat structure.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings demonstrate that fire attributes alone cannot account for short-term avian responses; rather, their interaction with pre-fire structural legacies is critical to understanding community reassembly. The conservation of snag-rich stands, early-successional open habitats, and unburned forest refugia\u0026mdash;alongside the maintenance of fine-scale heterogeneity\u0026mdash;should be prioritized to support post-fire bird community recovery in abandoned sub-Mediterranean mountain landscapes.\u003c/p\u003e","manuscriptTitle":"Megafire attributes and pre-fire structural legacies shape short-term avian responses in an Atlantic-Mediterranean ecotone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 02:54:42","doi":"10.21203/rs.3.rs-8423628/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T18:56:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T12:31:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T07:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106841569863215431033055059326124010480","date":"2026-03-09T07:55:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161234649182215555012005512335199237967","date":"2026-03-04T16:51:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T09:28:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309095254626670784778367080128871749875","date":"2026-01-21T17:39:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250497674009704051363535802813231970024","date":"2026-01-21T07:10:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T23:01:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T03:03:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T03:00:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2025-12-22T09:35:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7870d89b-4413-40b4-8935-33cf58d9b54b","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T00:55:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 02:54:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8423628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8423628","identity":"rs-8423628","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}