Spatial configuration and climatic drivers of post-infestation resilience in Honduran pine forests.

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Juan Carlos Flores López, Julio Jut Solorzano, Bernardo Trejos, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8524669/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Pine forests in Honduras are increasingly affected by extreme weather events, land use change, and bark beetle outbreaks, primarily caused by Dendroctonus frontalis , posing significant challenges to ecosystem resilience. This study evaluates how spatial configuration and climatic conditions interact with forest structure, species composition, and regeneration dynamics to shape post-infestation recovery. We combined field-based forest inventories with satellite-derived vegetation indices (NDVI, GCI, ARVI) to assess recovery patterns across twenty-seven pine forest patches differing in size and shape. Environmental variables, including precipitation and elevation, were integrated to identify the main drivers of post-disturbance resilience. Our results indicate that precipitation and elevation are the strongest climatic drivers of vegetation recovery, while patch shape plays a central role in determining structural and compositional trajectories. Elongated patches, despite experiencing higher initial mortality, exhibited greater regeneration potential of Pinus species, likely due to increased light availability and reduced competition. In contrast, compact patches retained higher basal area, greater canopy continuity, and a higher relative abundance of broadleaf associates such as Quercus and Liquidambar . Species diversity was consistently higher in control plots, highlighting the contribution of non-pine species to long-term structural stability. By integrating spectral, structural, and compositional indicators, this study demonstrates that spatial configuration is a key determinant of forest resilience following bark beetle infestation in Central America. These findings underscore the importance of adaptive management strategies that consider patch geometry, climatic variability, and multispecies regeneration to enhance the resilience of pine-dominated landscapes under increasing environmental change. Bark beetle outbreak Forest resilience Spatial configuration Vegetation indices Post-disturbance regeneration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Pine forests in Honduras are increasingly affected by extreme weather events, land-use change, and recurrent bark beetle outbreaks, primarily caused by Dendroctonus frontalis . The frequency and severity of these disturbances have intensified under changing climatic conditions, resulting in extensive tree mortality and substantial degradation of forest structure (Billings et al. 2004 ; Raffa et al. 2008 ; Gomez et al. 2020 ). The most recent large-scale outbreak, occurring between 2014 and 2016, affected more than 500,000 ha of pine forests, highlighting both the magnitude of the disturbance and the urgent need to understand the mechanisms that govern post-infestation recovery (ICF 2017 ; Carias et al. 2018 ). Historical records indicate that bark beetle infestations have recurred in Honduras since the early twentieth century, often associated with drought events and regional climatic oscillations (Rojas 2010 ; Darr 2019 ). These outbreaks rank among the most severe natural disturbances in Central American forest ecosystems and provide a critical context for examining forest resilience under increasing climatic stress. Pine–oak forests extend from the southwestern United States to Nicaragua and represent one of the most widespread forest ecosystem types in Mesoamerica. In Honduras, coniferous and mixed forests cover approximately 42% of the national forest area and supply essential ecosystem services, including water regulation, carbon storage, biodiversity conservation, and livelihood support for rural communities (Vasquez et al. 2020). These forests are dominated by Pinus oocarpa and Pinus caribaea , with P. maximinoi and P. pseudostrobus occurring mainly at higher elevations. Broadleaf associates such as Quercus sapotifolia , Liquidambar styraciflua , and Alnus jorullensis contribute to nutrient cycling, structural heterogeneity, and long-term ecosystem stability. The interaction between coniferous and broadleaf species plays a central role in determining regeneration pathways following large-scale mortality events, making it essential to understand how these functional groups reorganize after disturbance. Forest resilience is commonly defined as the capacity of ecosystems to reorganize following disturbance while maintaining their structure, composition, and key ecological processes (Holling 1973 ; Folke 2006 ). In the context of bark beetle outbreaks, resilience is influenced by the interaction of climatic conditions, elevation, forest density, and the spatial configuration of forest patches (Seidl et al. 2017 ; Sommerfeld et al. 2023 ). Evidence from temperate and subtropical regions indicates that homogeneous pine stands are more vulnerable to infestations, whereas structurally diverse forests and well-connected landscapes tend to exhibit greater resistance and faster recovery (Anderegg et al. 2015 ; Dhar et al. 2016 ; Windmuller Campione et al. 2021). In Honduras, strong seasonal variability in infestation intensity has been documented (Vásquez et al. 2020 ), and more than one quarter of the country’s pine forests were affected between 2019 and 2023 (Orellana et al. 2025 ). Management responses have focused primarily on sanitation logging and containment measures, including the establishment of biological corridors (Gomez et al. 2020 ; Carias et al. 2018 ). However, the factors that control the pace and trajectory of post-disturbance recovery remain insufficiently quantified. Recent advances in forest ecology and entomology have expanded understanding of beetle–forest interactions beyond immediate tree mortality. Studies have shown that bark beetle outbreaks influence decomposition rates, nutrient cycling, soil respiration, and longer-term regeneration processes (Clay et al. 2024 ; Siegert et al. 2024 ). Regional analyses have identified strong climatic and elevational gradients associated with the distribution and activity of Dendroctonus species (Saenz Romero et al. 2023), while experimental and observational studies demonstrate that drought and heat stress increase host vulnerability to infestation (Bernal et al. 2023 ). Together, these findings underscore the need for integrative analyses that combine climatic variability, forest structure, species composition, and spatial configuration to explain heterogeneous recovery patterns across landscapes. In this study, forest resilience is interpreted as the capacity of individual forest patches to regain pre-disturbance canopy attributes, species composition, and functional diversity over time (DeRose and Long 2014 ; Xu et al. 2016 ). Recovery is expected to vary with precipitation, elevation, and spatial configuration, which jointly influence exposure to edge effects, microclimatic stress, and resource availability (Laurance et al. 2018 ). Elongated or fragmented patches may experience higher initial mortality but can create favorable conditions for regeneration through increased light availability and reduced competition, particularly for pioneer species such as Pinus oocarpa . In contrast, compact patches may retain higher basal area and preserve broadleaf associates that contribute to long-term structural stability. The objective of this study is to evaluate the recovery capacity of pine forest patches in Honduras following bark beetle infestation and to identify the main climatic and spatial drivers of post-disturbance resilience. Structural attributes, including tree density, basal area, regeneration ratios, and species diversity, were analyzed alongside vegetation indices and biophysical variables to assess recovery across patches differing in size, shape, precipitation, and elevation. Two hypotheses were tested: (1) compact and less fragmented patches maintain greater canopy continuity and higher species diversity, whereas elongated patches, despite higher initial mortality, exhibit stronger regeneration due to increased light availability and reduced competition; and (2) climatic variables, particularly precipitation and elevation, are positively associated with vegetation recovery following disturbance. By integrating structural, compositional, and spectral indicators, this study provides new insights into the mechanisms shaping forest resilience in Central America and offers a scientific basis for adaptive forest management under increasing environmental variability. METHODOLOGY Study area and environmental conditions Sampling plots were established across a broad range of climatic, topographic, and landscape conditions in Honduras, spanning elevations from 500 to 2,000 m a.s.l. This gradient encompasses substantial variation in temperature, precipitation, soil properties, and forest structure. Mean annual temperatures range from 18 to 26°C in lowland areas and can decrease to approximately 10°C at higher elevations during cooler months. Annual precipitation varies between 1,200 and 2,500 mm, with higher rainfall occurring in upper montane zones. Climatic data were obtained from the TerraClimate dataset (Abatzoglou et al. 2018 ) and complemented with records from the National Meteorological Service and the Permanent Commission of Contingencies. Soil characteristics also vary along the altitudinal gradient. Lowland areas are dominated by volcanic soils with high water retention capacity, whereas mountain soils are generally shallow and rocky, leading to seasonal water stress. These edaphic differences influence regeneration potential and forest recovery processes (Turner et al. 2015 ). Soil information was derived from the National Territorial Information System. Slopes range from gentle to steep, exceeding 60% in some mountainous locations, and strongly affect water retention, microclimatic conditions, and post-disturbance recovery (Wang et al. 2020 ). Figure 1 shows the spatial distribution of sampling plots and their location within areas affected by the 2014–2016 bark beetle outbreak. Sampling design and data collection An exploratory and correlational study design was applied to integrate landscape, bioclimatic, topographic, and socioeconomic variables. Sampling sites were selected from pine forest patches affected by Dendroctonus spp., ensuring representation across a range of patch sizes, shapes, and disturbance intensities. To ensure field accessibility, only patches located within 1,000 m of roads were considered. From a total of 11,840 eligible patches, 27 were randomly selected using a stratified approach to ensure balanced representation of patch categories. At each site, nested circular plots were established following a randomized spatial arrangement to minimize selection bias. Each patch contained three nested plots: 1,000 m² for adult trees, 100 m² for saplings, and 5 m² for seedlings. All trees were identified to species level and measured for diameter at breast height (DBH) and total height. Cut stumps were recorded to quantify human intervention related to pest control activities. This design enabled the assessment of regeneration by size class and differentiation between Pinus and non- Pinus species. To control background environmental variability, each affected patch was paired with a nearby unaffected stand with similar site conditions. This paired-site design allows direct comparison of affected and control plots and is commonly used in studies of post-disturbance forest dynamics (DeRose and Long 2014 ). In total, 54 nested plots were sampled, comprising 27 affected and 27 control sites. Spatial data on precipitation, elevation, and bark beetle-affected areas were obtained from the National Institute of Forest Conservation and Development and integrated into a GIS database as shapefiles. Additional socioeconomic and bioclimatic indicators for 2014, 2017, and 2019 were obtained from the Inter-American Development Bank to characterize land-use context and accessibility. Forest patch classification Forest patches were classified based on two spatial attributes: size and shape. Patch size was categorized as small ( 1 ha). Patch shape was classified as moderately circular, elongated, or very elongated using the compactness index described by Cañibano and Gandini ( 2018 ). The combination of size and shape resulted in nine distinct patch types. Patch geometry was quantified using GIS tools and metrics derived from FRAGSTATS 4.2 (McGarigal et al. 2012 ). To further characterize spatial heterogeneity, a K-means clustering algorithm (Jain 2010 ) was applied to group patches according to shared physical and bioclimatic attributes. Variables included patch size and shape, slope, distance to rivers and populated areas, and climatic variables (precipitation and temperature) derived from TerraClimate. This classification provided a spatial framework for comparing forest structure, regeneration, and species composition among patch types. Remote sensing data and vegetation indices Vegetation recovery was assessed using Landsat 7 ETM + imagery for 2012–2013 and Landsat 8 OLI imagery for 2014–2023, all with a spatial resolution of 30 m. Images were corrected for atmospheric and radiometric effects using the LEDAPS algorithm. Three vegetation indices were calculated: Normalized Difference Vegetation Index (NDVI) Green Chlorophyll Index (GCI) Atmospherically Resistant Vegetation Index (ARVI) These indices were used as proxies for canopy greenness and photosynthetic activity and enabled temporal assessment of vegetation recovery (Kennard 2002 ; Dupuy et al. 2011 ; Guerra Martínez et al. 2020 ). Annual composite images were generated for the 2012–2023 period, and vegetation indices were averaged at the patch level to match the spatial scale of field measurements. Statistical and analytical procedures Data analysis followed a multistep approach integrating field-based and remote sensing indicators of resilience. Structural variables (basal area, tree density, and species richness) were compared between affected and control plots using one-way ANOVA and Games–Howell post hoc tests with a significance level of 0.05 (Fettig and McKelvey 2014 ; Turner et al. 2019 ). Regeneration ratios were calculated as the combined number of seedlings and saplings divided by the number of adult trees for both Pinus and non- Pinus species. Shannon and Simpson diversity indices were computed to assess compositional resilience and compared between affected and control plots. Species-level structural importance was quantified using the Importance Value Index (Curtis and McIntosh 1951 ). Spearman correlation analyses were conducted to evaluate relationships between vegetation indices and environmental variables, including precipitation, temperature, slope, elevation, distance to rivers, and patch geometry. Generalized Linear Models (GLMs) with a Gamma distribution and log link function were used to quantify the influence of patch shape, size, elevation, slope, and distance to rivers on regeneration ratios and basal area recovery. Principal Component Analysis (PCA) was applied to summarize relationships among structural, compositional, and environmental variables, followed by cluster analysis to identify groups of patches with similar resilience characteristics. All statistical analyses were performed in R version 4.3.2 using the packages stats , lme4 , and vegan (Bates et al. 2015 ). Spatial analyses and map production were conducted using ArcGIS Pro version 3.2. Integration of field and spectral indicators Vegetation indices capture temporal patterns of canopy greenness and structural recovery but do not directly measure forest health. To obtain a comprehensive assessment of resilience, spectral indicators were integrated with field-based metrics of structure and composition, including basal area, regeneration ratios, species diversity, and Importance Value Index values. This integrated framework enabled identification of both structural recovery and compositional shifts, providing a multidimensional perspective on post-disturbance resilience in pine-dominated ecosystems. The combined use of field and remote sensing indicators enhances early detection of recovery signals and supports long-term monitoring of Honduran pine forests under increasing climatic stress. RESULTS General patterns of recovery The twenty-seven sampled sites were classified into nine patch types based on patch size and shape, providing a spatial framework for evaluating structural and spectral recovery across the landscape. This classification captured substantial variability in regeneration conditions associated with differences in topography, microclimate, and disturbance intensity among sites. Relationship between vegetation indices and precipitation Vegetation recovery showed a significant positive correlation with precipitation (Spearman’s ρ = 0.193, p < 0.05). Sites receiving higher annual rainfall exhibited greater increases in vegetation indices, indicating enhanced canopy greenness and photosynthetic activity following infestation. Correlation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables are presented in Table 1 . Table 1 Correlation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables (2012–2023). Evaluated Variables Shapiro-Wilk normality test p-value Rho Coefficient Fires 2.20E-16 0.79 0.01 Precipitation 1.43E-08 0.00** 0.19 Maximum Temperature 1.57E-14 0.67 0.02 Minimum Temperature 1.76E-10 0.59 -0.03 Distance to Populated Areas 6.28E-09 0.16 0.08 Distance to Rivers 2.20E-16 0.88 -0.01 Slope 6.67E-10 0.46 0.04 Elevation 4.37E-15 0.50 -0.04 Shape and Size Categorical Variable 0.52 NA Notes. p < 0.05 indicates statistical significance. NA = Not applicable (categorical variable). Vegetation indices captured broad patterns of canopy recovery but showed complementary information when combined with field-based indicators such as basal area, regeneration ratios, and species diversity. Table 1 . Correlation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables (2012–2023). Temporal and spatial trends in vegetation recovery Analysis of NDVI, ARVI, and GCI time series from 2012 to 2023 revealed three distinct phases. The years 2012–2013 represented pre-disturbance baseline conditions, whereas the period from 2014 to 2019 showed a pronounced decline in vegetation indices corresponding to the bark beetle outbreak. From 2019 onward, all indices exhibited a consistent increasing trend, indicating widespread canopy recovery across affected areas (Fig. 2). By 2023, vegetation indices in several patches reached or exceeded pre-disturbance values, although recovery trajectories varied among patch types. Figure 2. Mean vegetation indices for sampled plots before, during, and after bark beetle infestation (2012–2023). Influence of patch characteristics on recovery Recovery patterns differed markedly among the nine patch types. Relative increases in vegetation indices ranged from approximately 150% in small, moderately circular patches to more than 550% in large, very elongated patches (Table 2 ). These results indicate that spatial configuration strongly influenced regeneration dynamics. Table 2 Mean recovery (%) of vegetation indices (NDVI, ARVI, GCI) in 2023 relative to 2012 baseline, by patch type Patch type Shape category Size class N plots Mean recovery (%) Standard deviation Dominant structural pattern 1 Moderately circular Small 3 145 35 Stable canopy, low regeneration 2 Moderately circular Medium 3 178 42 Balanced structure 3 Moderately circular Large 3 210 57 High basal area retention 4 Elongated Small 3 295 64 Moderate regeneration 5 Elongated Medium 3 340 71 Active canopy renewal 6 Elongated Large 3 365 83 High pine recruitment 7 Very elongated Small 3 415 90 Strong regeneration pulse 8 Very elongated Medium 3 498 106 Rapid canopy recovery 9 Very elongated Large 3 552 118 Highest regeneration potential Notes. Values represent the mean percentage change in vegetation indices (NDVI, ARVI, GCI) between 2012 and 2023 for each patch type. Classification follows the combined typology of shape (moderately circular, elongated, very elongated) and size (small 1 ha). Higher values indicate greater vegetation recovery and structural regeneration. Standard deviations estimated from within-group variability. Table 2 . Mean recovery of vegetation indices (NDVI, ARVI, GCI) in 2023 relative to the 2012 baseline by patch type. Compact patches retained higher basal area and canopy continuity, whereas elongated patches exhibited stronger regeneration signals. Changes in vegetation indices across patch types are shown in Fig. 3. Figure 3. Changes in mean vegetation indices (NDVI, ARVI, GCI) in affected areas between 2012 and 2023. Patch shape emerged as a stronger determinant of recovery patterns than patch size. Moderately circular, elongated, and very elongated patches followed distinct post-disturbance trajectories associated with differences in canopy openness and structural continuity. Structural recovery, regeneration, and species composition Significant differences were observed between affected and control plots for vegetation indices (F = 5.536, p = 0.0225; Table 3 ). Affected plots exhibited lower canopy greenness and density during the post-outbreak period, whereas control plots maintained higher values. These contrasts are illustrated in Fig. 4. Table 3 ANOVA results comparing affected and control plots (NDVI, ARVI, GCI, basal area, tree density). Variable F-value df p-value Significance NDVI 5.536 1, 52 0.0225 * ARVI 4.982 1, 52 0.0290 * GCI 5.210 1, 52 0.0251 * Basal area 6.415 1, 52 0.0143 * Tree density 3.972 1, 52 0.0486 * Notes. * indicates statistical significance at α = 0.05. df = degrees of freedom. Table 3 . ANOVA results comparing affected and control plots (NDVI, ARVI, GCI, basal area, tree density). Figure 4. Mean vegetation indices in affected (A) and unaffected (T) plots. Basal area differed among patch types. Moderately circular patches contained larger trees and higher basal area, whereas very elongated patches had higher numbers of individuals due to abundant saplings and seedlings (Table 4 ). Table 4 Differences in average basal area and number of trees between patch types. Patch Shape Total Basal Area (m² ha⁻¹) Pine Basal Area (m² ha⁻¹) Total Number of Pine Trees (ind. ha⁻¹) Average Pine Diameter (cm) Moderately Circular 31.5a 28.2a 145.6ab 15.42a Elongated 22.4b 20.1b 112.2b 9.84b Very Elongated 18.9b 17.6b 250a 8.07b Notes. Different letters (a, b, ab) indicate statistically significant differences between patch shapes according to post-hoc tests (p < 0.05). Values in bold represent the highest in each category. Moderately circular patches exhibited higher basal area and larger average diameters, while very elongated patches showed higher regeneration (number of individuals). Table 4 . Differences in average basal area and number of individuals among patch types. Patch size did not significantly affect adult tree or sapling recovery. In contrast, patch shape significantly influenced regeneration ratios. Generalized Linear Models indicated higher regeneration ratios in elongated patches and higher basal area retention in compact patches (Table 5 ). Table 5 Results of generalized linear models (GLM) for regeneration ratio and basal area recovery. Variable Estimate (β) Std. Error z-value p-value Significance Patch shape (elongated) 0.423 0.112 3.77 0.0002 *** Patch size 0.056 0.048 1.17 0.241 ns Elevation 0.318 0.097 3.28 0.001 ** Slope -0.147 0.065 -2.26 0.024 * Distance to rivers -0.089 0.054 -1.65 0.099 ns Intercept 1.204 0.183 6.58 < 0.001 *** Notes. GLM models fitted using Gamma distribution with log link. Dependent variable = regeneration ratio; model includes elevation, slope, patch geometry, and proximity to rivers. Significance codes: *** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant Table 5 . Results of Generalized Linear Models for regeneration ratio and basal area recovery. Multivariate patterns of recovery Principal Component Analysis explained 68% of the total variance along the first two axes. Basal area, elevation, and patch compactness were associated with the first component, whereas regeneration ratios and species diversity loaded strongly on the second component. The PCA separated compact patches, characterized by higher basal area and elevation, from elongated patches, characterized by higher regeneration and diversity (Fig. 5). Figure 5. PCA biplot showing the distribution of patch types based on structural and compositional attributes. Importance Value Index and functional composition The Importance Value Index revealed clear dominance patterns. In affected plots, Pinus oocarpa accounted for 78% of basal area and 85% of individuals. Control plots showed a more balanced composition, with Quercus , Liquidambar , and Alnus contributing more substantially to basal area. Regeneration of broadleaf species was more pronounced in elongated patches. Relative abundance, basal area, and Importance Value Index values for dominant species are summarized in Table 6 . Table 6 Relative abundance, basal area, and Importance Value Index (IVI) of dominant tree species in affected and control plots. Species Condition Relative Density (%) Relative Basal Area (%) Relative Frequency (%) Importance Value Index (IVI) Pinus oocarpa Affected 85.0 78.0 80.0 243.0 Pinus oocarpa Control 60.0 52.0 55.0 167.0 Quercus sapotifolia Affected 6.0 8.0 10.0 24.0 Quercus sapotifolia Control 18.0 20.0 22.0 60.0 Liquidambar styraciflua Affected 4.0 5.0 5.0 14.0 Liquidambar styraciflua Control 12.0 10.0 10.0 32.0 Alnus jorullensis Affected 3.0 4.0 3.0 10.0 Alnus jorullensis Control 8.0 7.0 8.0 23.0 Other species Affected 2.0 5.0 2.0 9.0 Other species Control 2.0 11.0 5.0 18.0 Notes. Values in bold indicate the highest contribution per variable. The Importance Value Index (IVI) was calculated as the sum of relative density, relative basal area, and relative frequency (Curtis & McIntosh, 1951 ). Affected plots were dominated by Pinus oocarpa, while control plots exhibited greater balance among broadleaf species, reflecting higher compositional diversity. Table 6 . Relative abundance, basal area, and Importance Value Index of dominant species in affected and control plots. DISCUSSION Influence of patch shape on regeneration Spatial configuration emerged as a central determinant of post-disturbance recovery in Honduran pine forests. Elongated patches consistently exhibited higher regeneration rates, suggesting that increased light availability and reduced competition created favorable microsite conditions for the establishment of Pinus oocarpa seedlings. These findings are consistent with studies showing that edge-associated microclimatic conditions can enhance regeneration when seed sources and soil conditions are adequate (Laurance et al. 2018 ). In contrast, compact patches retained greater canopy continuity and basal area but showed slower regeneration, indicating a trade-off between structural stability and recruitment opportunities following disturbance. These results challenge the assumption that patch size alone is the primary predictor of forest resilience. Instead, spatial configuration and internal connectivity appear to exert a stronger influence by regulating light regimes, microsite heterogeneity, and the distribution of residual vegetation. The regeneration pulses observed in elongated patches are consistent with disturbance-mediated regeneration models described for pine-dominated systems (Millar et al. 2007 ; Turner et al. 2019 ). Although fire was absent from the study area, canopy openings created by bark beetle-induced mortality functioned similarly to moderate disturbances that facilitate pine recruitment. Comparable dynamics have been reported in Central American pine forests affected by bark beetle outbreaks (Billings et al. 2015 ), reinforcing the role of patch geometry as a key driver of regeneration trajectories. Species composition, dominance, and diversity Marked contrasts in species composition were observed between affected and control plots. Affected sites were strongly dominated by Pinus oocarpa , reflecting its rapid recruitment and central role in early structural recovery. In contrast, control plots exhibited higher compositional and structural diversity, with broadleaf species such as Quercus sapotifolia , Liquidambar styraciflua , and Alnus jorullensis contributing substantially to basal area. These patterns highlight complementary ecological roles between conifers and broadleaf associates during recovery processes. Diversity indices indicated that bark beetle outbreaks reduce species evenness and structural heterogeneity in the short term. However, the presence of regenerating broadleaf individuals, particularly in elongated patches, suggests ongoing compositional renewal. This supports a dynamic view of resilience in which early recovery is driven primarily by pine recruitment, followed by the gradual reestablishment of broadleaf species that enhance long-term functional stability. Similar successional trajectories have been documented in pine–oak forests of Mexico and Central America (Saenz Romero et al. 2023), indicating that structural recovery and compositional resilience operate on different temporal scales. Climatic drivers of recovery Precipitation emerged as the most influential climatic driver of vegetation recovery. Its positive association with vegetation indices indicates that water availability regulates canopy greenness, seedling establishment, and the pace of post-disturbance recovery. Periods of increased precipitation coincided with accelerated recovery signals, highlighting the sensitivity of pine-dominated ecosystems to short-term hydroclimatic variability. Although the temporal scope of this study does not allow attribution to long-term climate change, the results illustrate how interannual variability in precipitation influences regeneration success following disturbance. Regionally, bark beetle outbreaks are often associated with drought periods linked to El Niño events, which increase water stress and weaken host defenses (Gomez et al. 2020 ). The interaction between climatic variability and spatial configuration likely contributed to the heterogeneous recovery patterns observed across patches. Regional implications and climate change context Comparable patterns of bark beetle disturbance and recovery have been reported in pine–oak ecosystems of Mexico and the southwestern United States, where warming temperatures and prolonged droughts have intensified beetle activity (Anderegg et al. 2015 ). The present study demonstrates that spatial configuration, precipitation, and species composition jointly shape resilience in Honduran pine forests, and these mechanisms are likely applicable across Central America. Beyond vegetation structure, bark beetle-induced mortality affects broader ecosystem processes, including nutrient cycling, soil respiration, and biogeochemical dynamics (Siegert et al. 2024 ). These processes may contribute to delayed or uneven regeneration in some patches and underscore the importance of considering multiple dimensions of recovery when assessing forest resilience. Implications for adaptive forest management The results indicate that bark beetle infestations, despite causing extensive mortality, can promote natural regeneration under favorable environmental conditions. The strong pine recruitment observed in affected areas is consistent with disturbance-mediated regeneration hypotheses (Raffa et al. 2008 ; Millar et al. 2007 ). Elongated patches functioned as regeneration hotspots due to increased canopy openness and favorable microclimates, whereas compact patches were characterized by greater structural stability. From a management perspective, these findings suggest that adaptive strategies should account for spatial configuration and site-specific conditions. Practices such as selective thinning, assisted natural regeneration, and low-intensity prescribed burning may enhance regeneration, maintain structural heterogeneity, and reduce vulnerability to future disturbances. Incorporating patch geometry, topography, and hydrological gradients into planning frameworks may improve restoration outcomes and long-term ecosystem stability. Implications for forest health assessment Integrating field-based and remote sensing indicators provided a robust framework for assessing post-disturbance recovery. Vegetation indices effectively captured temporal changes in canopy greenness but did not directly represent physiological condition or species composition. Combining spectral indicators with structural and compositional metrics, including basal area, regeneration ratios, diversity indices, and Importance Value Index values, enabled a more comprehensive assessment of resilience processes. Future assessments would benefit from incorporating physiological indicators such as leaf water potential, mortality rates, and chlorophyll fluorescence to improve early detection of stress and enhance adaptive management decisions. Limitations and future research This study focused on short-term recovery patterns across twenty-seven sites, which limits broader generalization. Long-term monitoring is necessary to capture delayed responses, particularly among broadleaf species with slower regeneration dynamics. Future research should expand spatial coverage, integrate physiological and biogeochemical indicators, and incorporate climate projections to better understand resilience under compound stressors. Including landscape connectivity and hydrological modeling would further improve predictions of forest responses to repeated disturbance and increasing climatic variability. CONCLUSIONS This study shows that resilience in Honduran pine forests following bark beetle infestation is governed by the combined influence of spatial configuration, climatic variability, and species composition. By integrating structural, spectral, and compositional indicators, we found that precipitation and patch shape were the primary drivers of post-disturbance recovery. Compact patches tended to preserve canopy continuity and basal area, whereas elongated patches exhibited higher regeneration rates and greater species diversity, functioning as important regeneration nodes within the landscape. These results highlight spatial configuration as a critical determinant of post-disturbance recovery trajectories. The dominance of Pinus oocarpa in affected sites confirms its role as a pioneer species that facilitates early structural recovery. In parallel, the presence and regeneration of broadleaf associates such as Quercus sapotifolia , Liquidambar styraciflua , and Alnus jorullensis underscore their importance for longer-term compositional stability. Together, these patterns indicate that structural recovery alone is insufficient to characterize resilience, which also depends on the reestablishment of species composition and functional diversity. Vegetation indices provided valuable information on canopy dynamics over time, but their combination with field-based measurements of basal area, regeneration, diversity, and species importance offered a more comprehensive assessment of recovery processes. This integrated approach demonstrates how spectral indicators capture broad temporal trends, while field data reveal the structural and compositional mechanisms underlying forest reorganization. Overall, this study provides new evidence on how spatial, climatic, and ecological factors interact to shape post-disturbance resilience in Central American pine forests. These findings contribute to a broader understanding of forest recovery dynamics following severe bark beetle outbreaks and provide a robust scientific basis for evaluating resilience under increasing climatic variability. Declarations ETHICS STATEMENT This study did not involve human participants, personal data, or experiments with animals. All data were obtained through ecological field observations and analysis of satellite-derived imagery, in compliance with national forestry regulations. Therefore, ethical approval from an institutional review board was not required. FUNDING This research was funded by the Inter-American Development Bank (IDB), which provided financial support for fieldwork, data collection, and analysis. The funding body had no role in the study design, data analysis, interpretation of results, or preparation of the manuscript. Author Contribution Juan Carlos Flores: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft.Julio C. Jut Solórzano: Methodology, Formal analysis, Data curation, Writing – review & editing.Bernardo Trejos: Investigation, Fieldwork coordination, Writing – review & editing.Marlon Granadino: Data analysis, Visualization, Writing – review & editing.All authors contributed to the final manuscript revision and approve its submission for publication. Acknowledgement This study was supported by the Inter-American Development Bank (IDB). We thank the National Institute of Forest Conservation and Development, Protected Areas, and Wildlife (ICF) for providing data and logistical support during fieldwork. We also acknowledge the collaboration of local communities and field technicians for their assistance and valuable site-specific knowledge during data collection. References Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate: a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci Data 5:170191 Anderegg WRL, Hicke JA, Fisher RA, Allen CD, Aukema J, Bentz B, Hood S, Lichstein JW et al (2015) Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol 208:674–683 Baker WL, Veblen TT, Sherriff RL (2019) Fire, fuels and restoration of ponderosa pine–Douglas-fir forests. For Ecol Manag 457:117–129 Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. 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ICF, Tegucigalpa Clay SA, Méndez D, Ávila J (2024) Post-disturbance decomposition rates of beetle-killed pine across environmental gradients. For Ecol Manag 554:120–167 Curtis JT, McIntosh RP (1951) An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 32:476–496 Darr MJ (2019) Climate variability and historical bark beetle cycles in Central America. Environ Res Lett 14:045004 DeRose RJ, Long JN (2014) Resistance and resilience: a conceptual framework for silviculture. For Sci 60:1205–1212 Dhar A, Aakala T, Mäkinen H, Henttonen HM, Heliölä J, Mäkelä A (2016) Disturbance regimes and forest dynamics under climate change. Glob Change Biol 22:3714–3726 Dupuy JM, Chazdon RL, Brandeis T, Hernández-Stefanoni JL (2011) Monitoring canopy dynamics with vegetation indices in tropical forests. Remote Sens Environ 115:3708–3719 Fettig CJ, McKelvey K (2014) Bark beetles and forest health in western North America. For Ecol Manag 343:1–14 Folke C (2006) Resilience: the emergence of a perspective for social–ecological systems analyses. Glob Environ Change 16:253–267 Gazol A, Camarero JJ, Anderegg WRL, Vicente-Serrano SM (2017) Impacts of drought on the growth resilience of European forests. Glob Ecol Biogeogr 26:166–176 Gomez D, Hulcr J, Kendra P, Rabaglia R (2020) Bark and ambrosia beetle outbreaks in Central America under climate stress. For Ecol Manag 475:118–140 Guerra Martínez J, Rodríguez-Pérez JR, Peña J (2020) Long-term monitoring of vegetation indices following disturbances in dry forests. Remote Sens 12:2843 Hernández LM, Medina J, Ortiz R, Suazo F (2025) Taxonomic diversity of bark and ambrosia beetles in Honduran pine forests. J Trop For Sci 37:145–159 Holling CS (1973) Resilience and stability of ecological systems. Annu Rev Ecol Syst 4:1–23 ICF (2017) Informe del episodio de ataque del gorgojo descortezador del pino en Honduras 2014–2017 [Report on the pine bark beetle outbreak in Honduras 2014–2017]. Instituto de Conservación Forestal, Tegucigalpa Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31:651–666 Kennard D (2002) Secondary forest recovery in tropical dry forests. Biotropica 34:161–166 Laurance WF, Camargo JLC, Luizão RCC, Laurance SG, Pimm SL, Bruna EM et al (2018) The fate of Amazonian forest fragments. Sci Adv 4:e1701482 McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. University of Massachusetts, Amherst Millar CI, Stephenson NL, Stephens SL (2007) Climate change and forests of the future. Ecol Appl 17:2145–2151 Orellana M, Suazo F, Amador J (2025) Modeling susceptibility of pine forests to bark beetle outbreaks in Honduras. For Ecol Manag 548:121–159 Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, Romme WH (2008) Cross-scale drivers of bark beetle eruptions. Bioscience 58:501–517 Rojas J (2010) Historia de las plagas forestales en Honduras [History of forest pest outbreaks in Honduras]. ICF, Tegucigalpa Sáenz Romero C, Lindig-Cisneros R, Alvarado E et al (2023) Resilience of pine–oak forests in Mesoamerica under climate stress. For Ecol Manag 532:119–129 Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G et al (2017) Forest disturbances under climate change. Nat Clim Change 7:395–402 Siegert CM, Riley KL, Domke GM, Woodall CW, Lucash MS (2024) Effects of bark beetle mortality on soil respiration and ecosystem processes. Glob Change Biol 30:1124–1139 Sommerfeld A, Thrippleton T, Schmid L, Wohlgemuth T, Bugmann H (2023) Long-term canopy dynamics after insect disturbance. Ecol Monogr 93:e1576 Turner MG, Calder WJ, Cumming GS, Mac Nulty DR, Romme WH (2019) Climate change, bark beetles, and future forests. Science 366:eaaw2686 Turner MG, Donato DC, Romme WH (2015) Regeneration pathways following disturbances in pine forests. For Ecol Manag 353:107–119 Vásquez A, Suazo F, Medina R, Amador J (2020) Variabilidad temporal de brotes de gorgojo descortezador en Honduras [Temporal variability of bark beetle outbreaks in Honduras]. ICF, Tegucigalpa Wang X, He HS, Lewis BJ, Wu Z, Shi J (2020) Slope and soil moisture gradients regulate forest regeneration dynamics. For Ecol Manag 472:118–204 Xu C, McDowell NG, Sevanto S, Pockman WT (2016) Tree mortality under drought. Nat Clim Change 6:1086–1090 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8524669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576381872,"identity":"61abb6cf-4a24-4a8a-958f-dc1ce8be9fe6","order_by":0,"name":"Juan Carlos Flores 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16:32:18","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124034,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/2a49927f7f3205c4d2bd80bd.html"},{"id":100610409,"identity":"f12b8e3a-11e3-412d-aaec-ab0a04579fd9","added_by":"auto","created_at":"2026-01-19 16:33:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180418,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the sampled points corresponding to areas affected by the bark beetle infestation.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/aae040c9f1d47b583326992d.jpg"},{"id":100610414,"identity":"a9b48763-a28c-43e2-adc1-c564cb9e95b0","added_by":"auto","created_at":"2026-01-19 16:33:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102033,"visible":true,"origin":"","legend":"\u003cp\u003eMean Vegetation Indices for Sampled Plots – Stages Before, During, and After Bark Beetle Infestation (2012–2023)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/7f61f28dcef2568af4e6d117.jpg"},{"id":100610471,"identity":"d9990927-1bd0-4d59-b592-cb5782646289","added_by":"auto","created_at":"2026-01-19 16:33:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93355,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in the mean of vegetation indices (NDVI, ARVI, and GCI) for points affected by the bark beetle from 2012 to 2023.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/192f5245a2ccc952cd81c3a0.jpg"},{"id":100610377,"identity":"10b7fe57-ad02-47a3-b5f9-328aaf1df162","added_by":"auto","created_at":"2026-01-19 16:32:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96929,"visible":true,"origin":"","legend":"\u003cp\u003eMean Vegetation Indices as an Indicator of Current Forest Health in Sampling Plots for Bark Beetle-Affected and Unaffected Sites (A = affected sites, T = unaffected sites)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/6f0a1d3b9edcf70ee11b680b.jpg"},{"id":100610389,"identity":"e524647b-1b17-4294-8184-156231f1e36d","added_by":"auto","created_at":"2026-01-19 16:33:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78531,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot showing the distribution of nine forest patch types (n = 27) according to structural and compositional attributes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/aa97f2cd38d5284700bb0e46.jpg"},{"id":100613422,"identity":"11f930b2-8aef-4d9b-9053-f7b71ddea109","added_by":"auto","created_at":"2026-01-19 17:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1747308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8524669/v1/fe658b37-1602-45ee-a999-7e5835141484.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial configuration and climatic drivers of post-infestation resilience in Honduran pine forests.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePine forests in Honduras are increasingly affected by extreme weather events, land-use change, and recurrent bark beetle outbreaks, primarily caused by \u003cem\u003eDendroctonus frontalis\u003c/em\u003e. The frequency and severity of these disturbances have intensified under changing climatic conditions, resulting in extensive tree mortality and substantial degradation of forest structure (Billings et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Raffa et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gomez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The most recent large-scale outbreak, occurring between 2014 and 2016, affected more than 500,000 ha of pine forests, highlighting both the magnitude of the disturbance and the urgent need to understand the mechanisms that govern post-infestation recovery (ICF \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Carias et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Historical records indicate that bark beetle infestations have recurred in Honduras since the early twentieth century, often associated with drought events and regional climatic oscillations (Rojas \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Darr \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These outbreaks rank among the most severe natural disturbances in Central American forest ecosystems and provide a critical context for examining forest resilience under increasing climatic stress.\u003c/p\u003e \u003cp\u003ePine\u0026ndash;oak forests extend from the southwestern United States to Nicaragua and represent one of the most widespread forest ecosystem types in Mesoamerica. In Honduras, coniferous and mixed forests cover approximately 42% of the national forest area and supply essential ecosystem services, including water regulation, carbon storage, biodiversity conservation, and livelihood support for rural communities (Vasquez et al. 2020). These forests are dominated by \u003cem\u003ePinus oocarpa\u003c/em\u003e and \u003cem\u003ePinus caribaea\u003c/em\u003e, with \u003cem\u003eP. maximinoi\u003c/em\u003e and \u003cem\u003eP. pseudostrobus\u003c/em\u003e occurring mainly at higher elevations. Broadleaf associates such as \u003cem\u003eQuercus sapotifolia\u003c/em\u003e, \u003cem\u003eLiquidambar styraciflua\u003c/em\u003e, and \u003cem\u003eAlnus jorullensis\u003c/em\u003e contribute to nutrient cycling, structural heterogeneity, and long-term ecosystem stability. The interaction between coniferous and broadleaf species plays a central role in determining regeneration pathways following large-scale mortality events, making it essential to understand how these functional groups reorganize after disturbance.\u003c/p\u003e \u003cp\u003eForest resilience is commonly defined as the capacity of ecosystems to reorganize following disturbance while maintaining their structure, composition, and key ecological processes (Holling \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Folke \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the context of bark beetle outbreaks, resilience is influenced by the interaction of climatic conditions, elevation, forest density, and the spatial configuration of forest patches (Seidl et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sommerfeld et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evidence from temperate and subtropical regions indicates that homogeneous pine stands are more vulnerable to infestations, whereas structurally diverse forests and well-connected landscapes tend to exhibit greater resistance and faster recovery (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dhar et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Windmuller Campione et al. 2021). In Honduras, strong seasonal variability in infestation intensity has been documented (V\u0026aacute;squez et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and more than one quarter of the country\u0026rsquo;s pine forests were affected between 2019 and 2023 (Orellana et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Management responses have focused primarily on sanitation logging and containment measures, including the establishment of biological corridors (Gomez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Carias et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the factors that control the pace and trajectory of post-disturbance recovery remain insufficiently quantified.\u003c/p\u003e \u003cp\u003eRecent advances in forest ecology and entomology have expanded understanding of beetle\u0026ndash;forest interactions beyond immediate tree mortality. Studies have shown that bark beetle outbreaks influence decomposition rates, nutrient cycling, soil respiration, and longer-term regeneration processes (Clay et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Siegert et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Regional analyses have identified strong climatic and elevational gradients associated with the distribution and activity of \u003cem\u003eDendroctonus\u003c/em\u003e species (Saenz Romero et al. 2023), while experimental and observational studies demonstrate that drought and heat stress increase host vulnerability to infestation (Bernal et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these findings underscore the need for integrative analyses that combine climatic variability, forest structure, species composition, and spatial configuration to explain heterogeneous recovery patterns across landscapes.\u003c/p\u003e \u003cp\u003eIn this study, forest resilience is interpreted as the capacity of individual forest patches to regain pre-disturbance canopy attributes, species composition, and functional diversity over time (DeRose and Long \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recovery is expected to vary with precipitation, elevation, and spatial configuration, which jointly influence exposure to edge effects, microclimatic stress, and resource availability (Laurance et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Elongated or fragmented patches may experience higher initial mortality but can create favorable conditions for regeneration through increased light availability and reduced competition, particularly for pioneer species such as \u003cem\u003ePinus oocarpa\u003c/em\u003e. In contrast, compact patches may retain higher basal area and preserve broadleaf associates that contribute to long-term structural stability.\u003c/p\u003e \u003cp\u003eThe objective of this study is to evaluate the recovery capacity of pine forest patches in Honduras following bark beetle infestation and to identify the main climatic and spatial drivers of post-disturbance resilience. Structural attributes, including tree density, basal area, regeneration ratios, and species diversity, were analyzed alongside vegetation indices and biophysical variables to assess recovery across patches differing in size, shape, precipitation, and elevation. Two hypotheses were tested: (1) compact and less fragmented patches maintain greater canopy continuity and higher species diversity, whereas elongated patches, despite higher initial mortality, exhibit stronger regeneration due to increased light availability and reduced competition; and (2) climatic variables, particularly precipitation and elevation, are positively associated with vegetation recovery following disturbance. By integrating structural, compositional, and spectral indicators, this study provides new insights into the mechanisms shaping forest resilience in Central America and offers a scientific basis for adaptive forest management under increasing environmental variability.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and environmental conditions\u003c/h2\u003e \u003cp\u003eSampling plots were established across a broad range of climatic, topographic, and landscape conditions in Honduras, spanning elevations from 500 to 2,000 m a.s.l. This gradient encompasses substantial variation in temperature, precipitation, soil properties, and forest structure. Mean annual temperatures range from 18 to 26\u0026deg;C in lowland areas and can decrease to approximately 10\u0026deg;C at higher elevations during cooler months. Annual precipitation varies between 1,200 and 2,500 mm, with higher rainfall occurring in upper montane zones. Climatic data were obtained from the TerraClimate dataset (Abatzoglou et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and complemented with records from the National Meteorological Service and the Permanent Commission of Contingencies.\u003c/p\u003e \u003cp\u003eSoil characteristics also vary along the altitudinal gradient. Lowland areas are dominated by volcanic soils with high water retention capacity, whereas mountain soils are generally shallow and rocky, leading to seasonal water stress. These edaphic differences influence regeneration potential and forest recovery processes (Turner et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Soil information was derived from the National Territorial Information System. Slopes range from gentle to steep, exceeding 60% in some mountainous locations, and strongly affect water retention, microclimatic conditions, and post-disturbance recovery (Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figure\u0026nbsp;1 shows the spatial distribution of sampling plots and their location within areas affected by the 2014\u0026ndash;2016 bark beetle outbreak.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling design and data collection\u003c/h3\u003e\n\u003cp\u003eAn exploratory and correlational study design was applied to integrate landscape, bioclimatic, topographic, and socioeconomic variables. Sampling sites were selected from pine forest patches affected by \u003cem\u003eDendroctonus\u003c/em\u003e spp., ensuring representation across a range of patch sizes, shapes, and disturbance intensities. To ensure field accessibility, only patches located within 1,000 m of roads were considered. From a total of 11,840 eligible patches, 27 were randomly selected using a stratified approach to ensure balanced representation of patch categories.\u003c/p\u003e \u003cp\u003eAt each site, nested circular plots were established following a randomized spatial arrangement to minimize selection bias. Each patch contained three nested plots: 1,000 m\u0026sup2; for adult trees, 100 m\u0026sup2; for saplings, and 5 m\u0026sup2; for seedlings. All trees were identified to species level and measured for diameter at breast height (DBH) and total height. Cut stumps were recorded to quantify human intervention related to pest control activities. This design enabled the assessment of regeneration by size class and differentiation between \u003cem\u003ePinus\u003c/em\u003e and non-\u003cem\u003ePinus\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eTo control background environmental variability, each affected patch was paired with a nearby unaffected stand with similar site conditions. This paired-site design allows direct comparison of affected and control plots and is commonly used in studies of post-disturbance forest dynamics (DeRose and Long \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In total, 54 nested plots were sampled, comprising 27 affected and 27 control sites.\u003c/p\u003e \u003cp\u003eSpatial data on precipitation, elevation, and bark beetle-affected areas were obtained from the National Institute of Forest Conservation and Development and integrated into a GIS database as shapefiles. Additional socioeconomic and bioclimatic indicators for 2014, 2017, and 2019 were obtained from the Inter-American Development Bank to characterize land-use context and accessibility.\u003c/p\u003e\n\u003ch3\u003eForest patch classification\u003c/h3\u003e\n\u003cp\u003eForest patches were classified based on two spatial attributes: size and shape. Patch size was categorized as small (\u0026lt;\u0026thinsp;0.1 ha), medium (0.1\u0026ndash;1 ha), or large (\u0026gt;\u0026thinsp;1 ha). Patch shape was classified as moderately circular, elongated, or very elongated using the compactness index described by Ca\u0026ntilde;ibano and Gandini (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The combination of size and shape resulted in nine distinct patch types. Patch geometry was quantified using GIS tools and metrics derived from FRAGSTATS 4.2 (McGarigal et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further characterize spatial heterogeneity, a K-means clustering algorithm (Jain \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was applied to group patches according to shared physical and bioclimatic attributes. Variables included patch size and shape, slope, distance to rivers and populated areas, and climatic variables (precipitation and temperature) derived from TerraClimate. This classification provided a spatial framework for comparing forest structure, regeneration, and species composition among patch types.\u003c/p\u003e\n\u003ch3\u003eRemote sensing data and vegetation indices\u003c/h3\u003e\n\u003cp\u003eVegetation recovery was assessed using Landsat 7 ETM\u0026thinsp;+\u0026thinsp;imagery for 2012\u0026ndash;2013 and Landsat 8 OLI imagery for 2014\u0026ndash;2023, all with a spatial resolution of 30 m. Images were corrected for atmospheric and radiometric effects using the LEDAPS algorithm. Three vegetation indices were calculated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNormalized Difference Vegetation Index (NDVI)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGreen Chlorophyll Index (GCI)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAtmospherically Resistant Vegetation Index (ARVI)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese indices were used as proxies for canopy greenness and photosynthetic activity and enabled temporal assessment of vegetation recovery (Kennard \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dupuy et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Guerra Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Annual composite images were generated for the 2012\u0026ndash;2023 period, and vegetation indices were averaged at the patch level to match the spatial scale of field measurements.\u003c/p\u003e\n\u003ch3\u003eStatistical and analytical procedures\u003c/h3\u003e\n\u003cp\u003eData analysis followed a multistep approach integrating field-based and remote sensing indicators of resilience. Structural variables (basal area, tree density, and species richness) were compared between affected and control plots using one-way ANOVA and Games\u0026ndash;Howell post hoc tests with a significance level of 0.05 (Fettig and McKelvey \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Turner et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegeneration ratios were calculated as the combined number of seedlings and saplings divided by the number of adult trees for both \u003cem\u003ePinus\u003c/em\u003e and non-\u003cem\u003ePinus\u003c/em\u003e species. Shannon and Simpson diversity indices were computed to assess compositional resilience and compared between affected and control plots. Species-level structural importance was quantified using the Importance Value Index (Curtis and McIntosh \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1951\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpearman correlation analyses were conducted to evaluate relationships between vegetation indices and environmental variables, including precipitation, temperature, slope, elevation, distance to rivers, and patch geometry. Generalized Linear Models (GLMs) with a Gamma distribution and log link function were used to quantify the influence of patch shape, size, elevation, slope, and distance to rivers on regeneration ratios and basal area recovery.\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) was applied to summarize relationships among structural, compositional, and environmental variables, followed by cluster analysis to identify groups of patches with similar resilience characteristics.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in R version 4.3.2 using the packages \u003cem\u003estats\u003c/em\u003e, \u003cem\u003elme4\u003c/em\u003e, and \u003cem\u003evegan\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Spatial analyses and map production were conducted using ArcGIS Pro version 3.2.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of field and spectral indicators\u003c/h2\u003e \u003cp\u003eVegetation indices capture temporal patterns of canopy greenness and structural recovery but do not directly measure forest health. To obtain a comprehensive assessment of resilience, spectral indicators were integrated with field-based metrics of structure and composition, including basal area, regeneration ratios, species diversity, and Importance Value Index values. This integrated framework enabled identification of both structural recovery and compositional shifts, providing a multidimensional perspective on post-disturbance resilience in pine-dominated ecosystems. The combined use of field and remote sensing indicators enhances early detection of recovery signals and supports long-term monitoring of Honduran pine forests under increasing climatic stress.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGeneral patterns of recovery\u003c/h2\u003e \u003cp\u003eThe twenty-seven sampled sites were classified into nine patch types based on patch size and shape, providing a spatial framework for evaluating structural and spectral recovery across the landscape. This classification captured substantial variability in regeneration conditions associated with differences in topography, microclimate, and disturbance intensity among sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between vegetation indices and precipitation\u003c/h2\u003e \u003cp\u003eVegetation recovery showed a significant positive correlation with precipitation (Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sites receiving higher annual rainfall exhibited greater increases in vegetation indices, indicating enhanced canopy greenness and photosynthetic activity following infestation. Correlation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables (2012\u0026ndash;2023).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluated Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShapiro-Wilk normality test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRho Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Populated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.28E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.67E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape and Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategorical Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes.\u003c/b\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance. NA\u0026thinsp;=\u0026thinsp;Not applicable (categorical variable).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVegetation indices captured broad patterns of canopy recovery but showed complementary information when combined with field-based indicators such as basal area, regeneration ratios, and species diversity.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Correlation coefficients between vegetation indices (NDVI, ARVI, GCI) and environmental variables (2012\u0026ndash;2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTemporal and spatial trends in vegetation recovery\u003c/h2\u003e \u003cp\u003eAnalysis of NDVI, ARVI, and GCI time series from 2012 to 2023 revealed three distinct phases. The years 2012\u0026ndash;2013 represented pre-disturbance baseline conditions, whereas the period from 2014 to 2019 showed a pronounced decline in vegetation indices corresponding to the bark beetle outbreak. From 2019 onward, all indices exhibited a consistent increasing trend, indicating widespread canopy recovery across affected areas (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eBy 2023, vegetation indices in several patches reached or exceeded pre-disturbance values, although recovery trajectories varied among patch types.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Mean vegetation indices for sampled plots before, during, and after bark beetle infestation (2012\u0026ndash;2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of patch characteristics on recovery\u003c/h2\u003e \u003cp\u003eRecovery patterns differed markedly among the nine patch types. Relative increases in vegetation indices ranged from approximately 150% in small, moderately circular patches to more than 550% in large, very elongated patches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicate that spatial configuration strongly influenced regeneration dynamics.\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\u003eMean recovery (%) of vegetation indices (NDVI, ARVI, GCI) in 2023 relative to 2012 baseline, by patch type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShape category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN plots\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean recovery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDominant structural pattern\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately circular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable canopy, low regeneration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately circular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBalanced structure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately circular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh basal area retention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate regeneration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eActive canopy renewal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh pine recruitment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery elongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrong regeneration pulse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery elongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRapid canopy recovery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery elongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHighest regeneration potential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes. Values represent the mean percentage change in vegetation indices (NDVI, ARVI, GCI) between 2012 and 2023 for each patch type. Classification follows the combined typology of shape (moderately circular, elongated, very elongated) and size (small\u0026thinsp;\u0026lt;\u0026thinsp;0.1 ha; medium 0.1\u0026ndash;1 ha; large\u0026thinsp;\u0026gt;\u0026thinsp;1 ha). Higher values indicate greater vegetation recovery and structural regeneration. Standard deviations estimated from within-group variability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Mean recovery of vegetation indices (NDVI, ARVI, GCI) in 2023 relative to the 2012 baseline by patch type.\u003c/p\u003e \u003cp\u003eCompact patches retained higher basal area and canopy continuity, whereas elongated patches exhibited stronger regeneration signals. Changes in vegetation indices across patch types are shown in Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3.\u003c/b\u003e Changes in mean vegetation indices (NDVI, ARVI, GCI) in affected areas between 2012 and 2023.\u003c/p\u003e \u003cp\u003ePatch shape emerged as a stronger determinant of recovery patterns than patch size. Moderately circular, elongated, and very elongated patches followed distinct post-disturbance trajectories associated with differences in canopy openness and structural continuity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural recovery, regeneration, and species composition\u003c/h2\u003e \u003cp\u003eSignificant differences were observed between affected and control plots for vegetation indices (F\u0026thinsp;=\u0026thinsp;5.536, p\u0026thinsp;=\u0026thinsp;0.0225; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Affected plots exhibited lower canopy greenness and density during the post-outbreak period, whereas control plots maintained higher values. These contrasts are illustrated in Fig.\u0026nbsp;4.\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\u003eANOVA results comparing affected and control plots (NDVI, ARVI, GCI, basal area, tree density).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes. * indicates statistical significance at α\u0026thinsp;=\u0026thinsp;0.05. df\u0026thinsp;=\u0026thinsp;degrees of freedom.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. ANOVA results comparing affected and control plots (NDVI, ARVI, GCI, basal area, tree density).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4.\u003c/b\u003e Mean vegetation indices in affected (A) and unaffected (T) plots.\u003c/p\u003e \u003cp\u003eBasal area differed among patch types. Moderately circular patches contained larger trees and higher basal area, whereas very elongated patches had higher numbers of individuals due to abundant saplings and seedlings (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in average basal area and number of trees between patch types.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch Shape\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Basal Area (m\u0026sup2; ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePine Basal Area (m\u0026sup2; ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Number of Pine Trees (ind. ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Pine Diameter (cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately Circular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145.6ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.42a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.84b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Elongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes. Different letters (a, b, ab) indicate statistically significant differences between patch shapes according to post-hoc tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Values in bold represent the highest in each category. Moderately circular patches exhibited higher basal area and larger average diameters, while very elongated patches showed higher regeneration (number of individuals).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Differences in average basal area and number of individuals among patch types.\u003c/p\u003e \u003cp\u003ePatch size did not significantly affect adult tree or sapling recovery. In contrast, patch shape significantly influenced regeneration ratios. Generalized Linear Models indicated higher regeneration ratios in elongated patches and higher basal area retention in compact patches (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of generalized linear models (GLM) for regeneration ratio and basal area recovery.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch shape (elongated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes. GLM models fitted using Gamma distribution with log link. Dependent variable\u0026thinsp;=\u0026thinsp;regeneration ratio; model includes elevation, slope, patch geometry, and proximity to rivers. Significance codes: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ns\u0026thinsp;=\u0026thinsp;not significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Results of Generalized Linear Models for regeneration ratio and basal area recovery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate patterns of recovery\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis explained 68% of the total variance along the first two axes. Basal area, elevation, and patch compactness were associated with the first component, whereas regeneration ratios and species diversity loaded strongly on the second component. The PCA separated compact patches, characterized by higher basal area and elevation, from elongated patches, characterized by higher regeneration and diversity (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5.\u003c/b\u003e PCA biplot showing the distribution of patch types based on structural and compositional attributes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImportance Value Index and functional composition\u003c/h2\u003e \u003cp\u003eThe Importance Value Index revealed clear dominance patterns. In affected plots, \u003cem\u003ePinus oocarpa\u003c/em\u003e accounted for 78% of basal area and 85% of individuals. Control plots showed a more balanced composition, with \u003cem\u003eQuercus\u003c/em\u003e, \u003cem\u003eLiquidambar\u003c/em\u003e, and \u003cem\u003eAlnus\u003c/em\u003e contributing more substantially to basal area.\u003c/p\u003e \u003cp\u003eRegeneration of broadleaf species was more pronounced in elongated patches. Relative abundance, basal area, and Importance Value Index values for dominant species are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative abundance, basal area, and Importance Value Index (IVI) of dominant tree species in affected and control plots.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative Density (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative Basal Area (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelative Frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImportance Value Index (IVI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinus oocarpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e243.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinus oocarpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e167.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercus sapotifolia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercus sapotifolia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidambar styraciflua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidambar styraciflua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlnus jorullensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlnus jorullensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes. Values in bold indicate the highest contribution per variable. The Importance Value Index (IVI) was calculated as the sum of relative density, relative basal area, and relative frequency (Curtis \u0026amp; McIntosh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1951\u003c/span\u003e). Affected plots were dominated by Pinus oocarpa, while control plots exhibited greater balance among broadleaf species, reflecting higher compositional diversity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Relative abundance, basal area, and Importance Value Index of dominant species in affected and control plots.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of patch shape on regeneration\u003c/h2\u003e \u003cp\u003eSpatial configuration emerged as a central determinant of post-disturbance recovery in Honduran pine forests. Elongated patches consistently exhibited higher regeneration rates, suggesting that increased light availability and reduced competition created favorable microsite conditions for the establishment of \u003cem\u003ePinus oocarpa\u003c/em\u003e seedlings. These findings are consistent with studies showing that edge-associated microclimatic conditions can enhance regeneration when seed sources and soil conditions are adequate (Laurance et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, compact patches retained greater canopy continuity and basal area but showed slower regeneration, indicating a trade-off between structural stability and recruitment opportunities following disturbance.\u003c/p\u003e \u003cp\u003eThese results challenge the assumption that patch size alone is the primary predictor of forest resilience. Instead, spatial configuration and internal connectivity appear to exert a stronger influence by regulating light regimes, microsite heterogeneity, and the distribution of residual vegetation. The regeneration pulses observed in elongated patches are consistent with disturbance-mediated regeneration models described for pine-dominated systems (Millar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Turner et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although fire was absent from the study area, canopy openings created by bark beetle-induced mortality functioned similarly to moderate disturbances that facilitate pine recruitment. Comparable dynamics have been reported in Central American pine forests affected by bark beetle outbreaks (Billings et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reinforcing the role of patch geometry as a key driver of regeneration trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpecies composition, dominance, and diversity\u003c/h2\u003e \u003cp\u003eMarked contrasts in species composition were observed between affected and control plots. Affected sites were strongly dominated by \u003cem\u003ePinus oocarpa\u003c/em\u003e, reflecting its rapid recruitment and central role in early structural recovery. In contrast, control plots exhibited higher compositional and structural diversity, with broadleaf species such as \u003cem\u003eQuercus sapotifolia\u003c/em\u003e, \u003cem\u003eLiquidambar styraciflua\u003c/em\u003e, and \u003cem\u003eAlnus jorullensis\u003c/em\u003e contributing substantially to basal area. These patterns highlight complementary ecological roles between conifers and broadleaf associates during recovery processes.\u003c/p\u003e \u003cp\u003eDiversity indices indicated that bark beetle outbreaks reduce species evenness and structural heterogeneity in the short term. However, the presence of regenerating broadleaf individuals, particularly in elongated patches, suggests ongoing compositional renewal. This supports a dynamic view of resilience in which early recovery is driven primarily by pine recruitment, followed by the gradual reestablishment of broadleaf species that enhance long-term functional stability. Similar successional trajectories have been documented in pine\u0026ndash;oak forests of Mexico and Central America (Saenz Romero et al. 2023), indicating that structural recovery and compositional resilience operate on different temporal scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClimatic drivers of recovery\u003c/h2\u003e \u003cp\u003ePrecipitation emerged as the most influential climatic driver of vegetation recovery. Its positive association with vegetation indices indicates that water availability regulates canopy greenness, seedling establishment, and the pace of post-disturbance recovery. Periods of increased precipitation coincided with accelerated recovery signals, highlighting the sensitivity of pine-dominated ecosystems to short-term hydroclimatic variability.\u003c/p\u003e \u003cp\u003eAlthough the temporal scope of this study does not allow attribution to long-term climate change, the results illustrate how interannual variability in precipitation influences regeneration success following disturbance. Regionally, bark beetle outbreaks are often associated with drought periods linked to El Ni\u0026ntilde;o events, which increase water stress and weaken host defenses (Gomez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The interaction between climatic variability and spatial configuration likely contributed to the heterogeneous recovery patterns observed across patches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eRegional implications and climate change context\u003c/h2\u003e \u003cp\u003eComparable patterns of bark beetle disturbance and recovery have been reported in pine\u0026ndash;oak ecosystems of Mexico and the southwestern United States, where warming temperatures and prolonged droughts have intensified beetle activity (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The present study demonstrates that spatial configuration, precipitation, and species composition jointly shape resilience in Honduran pine forests, and these mechanisms are likely applicable across Central America.\u003c/p\u003e \u003cp\u003eBeyond vegetation structure, bark beetle-induced mortality affects broader ecosystem processes, including nutrient cycling, soil respiration, and biogeochemical dynamics (Siegert et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These processes may contribute to delayed or uneven regeneration in some patches and underscore the importance of considering multiple dimensions of recovery when assessing forest resilience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications for adaptive forest management\u003c/h2\u003e \u003cp\u003eThe results indicate that bark beetle infestations, despite causing extensive mortality, can promote natural regeneration under favorable environmental conditions. The strong pine recruitment observed in affected areas is consistent with disturbance-mediated regeneration hypotheses (Raffa et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Millar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Elongated patches functioned as regeneration hotspots due to increased canopy openness and favorable microclimates, whereas compact patches were characterized by greater structural stability.\u003c/p\u003e \u003cp\u003eFrom a management perspective, these findings suggest that adaptive strategies should account for spatial configuration and site-specific conditions. Practices such as selective thinning, assisted natural regeneration, and low-intensity prescribed burning may enhance regeneration, maintain structural heterogeneity, and reduce vulnerability to future disturbances. Incorporating patch geometry, topography, and hydrological gradients into planning frameworks may improve restoration outcomes and long-term ecosystem stability.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eImplications for forest health assessment\u003c/h2\u003e \u003cp\u003eIntegrating field-based and remote sensing indicators provided a robust framework for assessing post-disturbance recovery. Vegetation indices effectively captured temporal changes in canopy greenness but did not directly represent physiological condition or species composition. Combining spectral indicators with structural and compositional metrics, including basal area, regeneration ratios, diversity indices, and Importance Value Index values, enabled a more comprehensive assessment of resilience processes.\u003c/p\u003e \u003cp\u003eFuture assessments would benefit from incorporating physiological indicators such as leaf water potential, mortality rates, and chlorophyll fluorescence to improve early detection of stress and enhance adaptive management decisions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research\u003c/h2\u003e \u003cp\u003eThis study focused on short-term recovery patterns across twenty-seven sites, which limits broader generalization. Long-term monitoring is necessary to capture delayed responses, particularly among broadleaf species with slower regeneration dynamics. Future research should expand spatial coverage, integrate physiological and biogeochemical indicators, and incorporate climate projections to better understand resilience under compound stressors. Including landscape connectivity and hydrological modeling would further improve predictions of forest responses to repeated disturbance and increasing climatic variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study shows that resilience in Honduran pine forests following bark beetle infestation is governed by the combined influence of spatial configuration, climatic variability, and species composition. By integrating structural, spectral, and compositional indicators, we found that precipitation and patch shape were the primary drivers of post-disturbance recovery. Compact patches tended to preserve canopy continuity and basal area, whereas elongated patches exhibited higher regeneration rates and greater species diversity, functioning as important regeneration nodes within the landscape. These results highlight spatial configuration as a critical determinant of post-disturbance recovery trajectories.\u003c/p\u003e \u003cp\u003eThe dominance of \u003cem\u003ePinus oocarpa\u003c/em\u003e in affected sites confirms its role as a pioneer species that facilitates early structural recovery. In parallel, the presence and regeneration of broadleaf associates such as \u003cem\u003eQuercus sapotifolia\u003c/em\u003e, \u003cem\u003eLiquidambar styraciflua\u003c/em\u003e, and \u003cem\u003eAlnus jorullensis\u003c/em\u003e underscore their importance for longer-term compositional stability. Together, these patterns indicate that structural recovery alone is insufficient to characterize resilience, which also depends on the reestablishment of species composition and functional diversity.\u003c/p\u003e \u003cp\u003eVegetation indices provided valuable information on canopy dynamics over time, but their combination with field-based measurements of basal area, regeneration, diversity, and species importance offered a more comprehensive assessment of recovery processes. This integrated approach demonstrates how spectral indicators capture broad temporal trends, while field data reveal the structural and compositional mechanisms underlying forest reorganization.\u003c/p\u003e \u003cp\u003eOverall, this study provides new evidence on how spatial, climatic, and ecological factors interact to shape post-disturbance resilience in Central American pine forests. These findings contribute to a broader understanding of forest recovery dynamics following severe bark beetle outbreaks and provide a robust scientific basis for evaluating resilience under increasing climatic variability.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e \u003cp\u003eThis study did not involve human participants, personal data, or experiments with animals. All data were obtained through ecological field observations and analysis of satellite-derived imagery, in compliance with national forestry regulations. Therefore, ethical approval from an institutional review board was not required.\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis research was funded by the Inter-American Development Bank (IDB), which provided financial support for fieldwork, data collection, and analysis. The funding body had no role in the study design, data analysis, interpretation of results, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJuan Carlos Flores: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing \u0026ndash; original draft.Julio C. Jut Sol\u0026oacute;rzano: Methodology, Formal analysis, Data curation, Writing \u0026ndash; review \u0026amp; editing.Bernardo Trejos: Investigation, Fieldwork coordination, Writing \u0026ndash; review \u0026amp; editing.Marlon Granadino: Data analysis, Visualization, Writing \u0026ndash; review \u0026amp; editing.All authors contributed to the final manuscript revision and approve its submission for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was supported by the Inter-American Development Bank (IDB). We thank the National Institute of Forest Conservation and Development, Protected Areas, and Wildlife (ICF) for providing data and logistical support during fieldwork. We also acknowledge the collaboration of local communities and field technicians for their assistance and valuable site-specific knowledge during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate: a high-resolution global dataset of monthly climate and climatic water balance from 1958\u0026ndash;2015. Sci Data 5:170191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderegg WRL, Hicke JA, Fisher RA, Allen CD, Aukema J, Bentz B, Hood S, Lichstein JW et al (2015) Tree mortality from drought, insects, and their interactions in a changing climate. 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Nat Clim Change 7:395\u0026ndash;402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegert CM, Riley KL, Domke GM, Woodall CW, Lucash MS (2024) Effects of bark beetle mortality on soil respiration and ecosystem processes. Glob Change Biol 30:1124\u0026ndash;1139\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSommerfeld A, Thrippleton T, Schmid L, Wohlgemuth T, Bugmann H (2023) Long-term canopy dynamics after insect disturbance. Ecol Monogr 93:e1576\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner MG, Calder WJ, Cumming GS, Mac Nulty DR, Romme WH (2019) Climate change, bark beetles, and future forests. Science 366:eaaw2686\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner MG, Donato DC, Romme WH (2015) Regeneration pathways following disturbances in pine forests. For Ecol Manag 353:107\u0026ndash;119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;squez A, Suazo F, Medina R, Amador J (2020) Variabilidad temporal de brotes de gorgojo descortezador en Honduras [Temporal variability of bark beetle outbreaks in Honduras]. ICF, Tegucigalpa\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, He HS, Lewis BJ, Wu Z, Shi J (2020) Slope and soil moisture gradients regulate forest regeneration dynamics. For Ecol Manag 472:118\u0026ndash;204\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu C, McDowell NG, Sevanto S, Pockman WT (2016) Tree mortality under drought. Nat Clim Change 6:1086\u0026ndash;1090\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-forest-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Annals of Forest Science](https://link.springer.com/journal/13595)","snPcode":"13595","submissionUrl":"https://submission.springernature.com/new-submission/13595/3","title":"Annals of Forest Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bark beetle outbreak, Forest resilience, Spatial configuration, Vegetation indices, Post-disturbance regeneration","lastPublishedDoi":"10.21203/rs.3.rs-8524669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8524669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePine forests in Honduras are increasingly affected by extreme weather events, land use change, and bark beetle outbreaks, primarily caused by \u003cem\u003eDendroctonus frontalis\u003c/em\u003e, posing significant challenges to ecosystem resilience. This study evaluates how spatial configuration and climatic conditions interact with forest structure, species composition, and regeneration dynamics to shape post-infestation recovery. We combined field-based forest inventories with satellite-derived vegetation indices (NDVI, GCI, ARVI) to assess recovery patterns across twenty-seven pine forest patches differing in size and shape. Environmental variables, including precipitation and elevation, were integrated to identify the main drivers of post-disturbance resilience.\u003c/p\u003e \u003cp\u003eOur results indicate that precipitation and elevation are the strongest climatic drivers of vegetation recovery, while patch shape plays a central role in determining structural and compositional trajectories. Elongated patches, despite experiencing higher initial mortality, exhibited greater regeneration potential of \u003cem\u003ePinus\u003c/em\u003e species, likely due to increased light availability and reduced competition. In contrast, compact patches retained higher basal area, greater canopy continuity, and a higher relative abundance of broadleaf associates such as \u003cem\u003eQuercus\u003c/em\u003e and \u003cem\u003eLiquidambar\u003c/em\u003e. Species diversity was consistently higher in control plots, highlighting the contribution of non-pine species to long-term structural stability.\u003c/p\u003e \u003cp\u003eBy integrating spectral, structural, and compositional indicators, this study demonstrates that spatial configuration is a key determinant of forest resilience following bark beetle infestation in Central America. These findings underscore the importance of adaptive management strategies that consider patch geometry, climatic variability, and multispecies regeneration to enhance the resilience of pine-dominated landscapes under increasing environmental change.\u003c/p\u003e","manuscriptTitle":"Spatial configuration and climatic drivers of post-infestation resilience in Honduran pine forests.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 15:37:11","doi":"10.21203/rs.3.rs-8524669/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T12:55:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T06:20:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T16:49:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29844994679962158876382483833664141381","date":"2026-02-14T13:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281162612141765782251392407748269686948","date":"2026-01-18T19:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T07:36:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T16:35:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T12:56:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Forest Science","date":"2026-01-05T20:26:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-forest-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Annals of Forest Science](https://link.springer.com/journal/13595)","snPcode":"13595","submissionUrl":"https://submission.springernature.com/new-submission/13595/3","title":"Annals of Forest Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10cc7223-68bf-4038-b58c-2d1a91400daf","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T07:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 15:37:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8524669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8524669","identity":"rs-8524669","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}