Fire and Landscape Configuration Shape Forest Futures: Direct and Indirect Drivers of Seedling Diversity in a Human-Modified Tropical Rainforest

Fire and Landscape Configuration Shape Forest Futures: Direct and Indirect Drivers of Seedling Diversity in a Human-Modified Tropical Rainforest | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fire and Landscape Configuration Shape Forest Futures: Direct and Indirect Drivers of Seedling Diversity in a Human-Modified Tropical Rainforest Sergio Nicasio-Arzeta, Susana Maza-Villalobos, Aline Pingarroni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7430288/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Context The seedling community plays a crucial role in forest regeneration within fragmented rainforests. The characteristics of patches, including their size, shape, vegetation, and disturbance, as well as the composition and configuration of the surrounding landscape, have a significant effect on seedling communities. However, the response of seedling communities to patch and landscape structures varies, and the direct and indirect effects on seedling composition and diversity remain unclear. Objectives Considering the hierarchical relationship between landscapes, patches, and seedlings, we investigated the direct and indirect effects of patch and landscape structures on seedling communities across 16 forest patches in a highly fragmented rainforest in southeastern Mexico. Methods We adopted a multi-scale approach to assess how landscape composition and configuration influence patch structure. Subsequently, piecewise structural equation models were used to evaluate the direct and indirect (cascading) effects of patch and landscape structures on seedling abundance, composition, and α- and β-diversity. Results Our findings revealed that fire disturbance had cascading negative effects on seedling composition and β-diversity, increasing the abundance of light-demanding seedlings and thereby promoting the growth of early successional species. By contrast, patch aggregation had a cascading positive effect on seedling richness by enhancing the abundance of shade-tolerant seedlings, thereby supporting seed dispersal. Conclusions These results support the notion that the effects of patch disturbance and landscape configuration patterns on biodiversity are more pronounced in highly fragmented landscapes where spatial configuration can play a key role in enhancing regeneration resilience. alpha-diversity beta-diversity disturbance fragmentation landscape configuration structural equation models Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Deforestation worldwide has reduced manyto small rainforest patches (< 20 ha), which may dominate tropical landscapes in the following decades (Taubert et al. 2018 ). In this sense, it is fundamental to understand the abundance and diversity patterns of tree seedlings within patches, because tree species determine the function, structure, and diversity of rainforests (Brokaw and Scheiner 1989 ; Whitmore 1989 ; Dalling et al. 1998 ). The abundance, richness (α-diversity), and spatial heterogeneity (β-diversity) of tree seedlings are shaped by seed production and dispersal, as well as seedling mortality by density dependence and predation/trampling by herbivorous mammals (Nathan and Muller-Landau 2000 ; Harms et al. 2000 ; Comita et al. 2007 ; Young et al. 2016 ). Forest fragmentation causes changes in both the size and shape of patches, resulting in edge effects that positively impact light-demanding tree species and negatively impact shade-tolerant species, leading to a reduction in diversity (Benitez-Malvido 1998 ; Santo-Silva et al. 2013 ; Benítez-Malvido et al. 2018 ), impoverishing α- and β-diversity (Krishnadas et al., 2020 ; Liu et al., 2019 ; Tabarelli et al., 2012 ). Seedling communities can also be affected by patch vegetation. The tree basal area plays a key role in light availability, microclimate regulation, nutrient cycling, and seed production (Hardwick et al. 2015 ; Lohbeck et al. 2015 ; Minor and Kobe 2019 ; Wies et al. 2021 ), which regulates the abundance of light-demanding and shade-tolerant seedlings (Whitmore 1989 ; Denslow and Guzman G. 2000). In addition, patches with a high basal area harbor more forest-dependent birds and mammals, which favor seedling α- and β-diversity throughout seed dispersal (Zurita and Bellocq 2010 ; Wearn et al. 2017 ; Cudney-Valenzuela et al. 2021 ; Benítez-Malvido et al. 2022 ). Additionally, seedling abundance and composition can be influenced by tree diversity (Terborgh et al. 2001 ; Comita et al. 2007 ), in which seed input is limited to local adult trees (Wandrag et al. 2017 ). Furthermore, agricultural land use limits tropical forest regeneration (Guariguata and Ostertag 2001 ; Fontúrbel et al. 2015 ). The size, duration,, and severity of forest disturbances produced caused by agricultural and cattle management can limit seed rain and seed banks, reduce soil fertility, increase temperature and water deficits, and reduce the increase the abundance and diversity of seedlings (Benitez-Malvido 2006 ; Zermeño-Hernández et al. 2015 ). Such negative effects are more significant in landscapes dominated by cattle pastures (Zermeño-Hernández et al. 2016 ), where cattle grazing and trampling limit seedling establishment. Moreover, it disturbs soil fertility and reduces the diversity and basal area of the remaining forests (Barlow et al. 2002 ). Additionally, it is well documented that landscape composition (i.e., the proportion of primary forest and matrix cover within the landscape) and configuration (i.e., the number/aggregation of patches as well as the edge contrast between patches and matrix) significantly affect seedling communities. Landscape structure can influence seedling abundance, composition, and diversity by sustaining seed dispersal in landscapes with high forest cover and high patch density/aggregation, (Jesus et al. 2012 ; San-José et al. 2019 ) or by mitigating edge effects and facilitating the arrival of seed/seedling predators in landscapes with low edge contrast (Arasa-Gisbert et al. 2021 ; Nicasio-Arzeta et al. 2021 ). In highly deforested rainforests, landscape fragmentation has a stronger impact on understory vegetation than habitat loss (Nicasio-Arzeta et al. 2021 ; Arasa-Gisbert et al. 2022 ). The "fragmentation threshold hypothesis" postulates that patch-scale factors and landscape configuration have a stronger effect on biodiversity within landscapes with < 30% of forest cover (Andren 1994 ). Consequently, the synergistic effects of patch structure and landscape configuration may drive the tree seedling composition and diversity in highly fragmented rainforests. The seedling community and patch structure are expected to be influenced by landscape structure at multiple spatial scales, as the scale of the effect varies among biological variables (i.e., structure, abundance, composition, and richness), habitat specialization (i.e., light-demanding/shade-tolerant species), and the extent of disturbance(Miguet et al. 2016 ; Martin 2018 ; Suárez-Castro et al. 2018 ). This suggests that landscape structure may have cascading effects on seedling communities, a phenomenon that has been observed elsewhere (Hernández-Ruedas et al. 2018 ). These cascading effects can potentially operate through patch structural attributes. The proportion of forest cover in the landscape was directly related to patch size (Fahrig 2013 ; Hernando et al. 2017 ) and basal tree area/diversity (Laurance et al. 2000 ; Wies et al. 2021 ; Brindis-Badillo et al. 2022 ). Additionally, tree diversity is affected by the surrounding secondary forests and aggregation of forest patches (Nascimento et al. 2006 ; Laurance et al. 2007 ; Jesus et al. 2012 ). Forest disturbances caused by fires and disturbance intensity can also be modulated by edge contrast, forest cover, and patch density. Edge exposure increases the fuel availability and fire ignition (Román-Cuesta and Martínez-Vilalta 2006 ; Avila-Flores et al. 2010 ; Farfán Gutiérrez et al. 2018 ). Conversely, cattle presence is limited when forest cover or patch numbers are high, as they increase the occurrence of predators (Trolle 2003 ; Desbiez et al. 2009 ; de Souza et al. 2018 ). Although patches and landscape structures can potentially have direct or cascading effects on seedling communities, their estimation remains unexplored. This study aimed to identify the direct and cascading effects of patch and landscape structure on seedling abundance, composition, and diversity in fragmented rainforests. We hypothesized that patch structure and landscape configuration would have direct or cascading effects on seedling composition and diversity. Additionally, we predicted that seedling composition and β-diversity are more significantly influenced by patch structure, as these community attributes are sensitive to fluctuations in species abundance with contrasting life-history traits (Comita et al. 2007 ; Santo-Silva et al. 2015 ). Consequently, we expect similar species composition and greater β-diversity within patches with lower abundance of light-demanding seedlings and higher abundance of shade-tolerant seedlings. Additionally, we hypothesized that α-diversity of tree seedlings would be favored in landscapes with high forest cover or high patch aggregation, as species richness is strongly associated with seed dispersal (San-José et al. 2019 ; Arasa-Gisbert et al. 2022 ; Benítez-Malvido et al. 2022 ). These effects may occur through cascading effects of landscape structure by influencing patch structure (Fig. 1 , hypothetical pathway 1), or through cascading effects of patch structure by mediating abundance fluctuations (Fig. 1 , hypothetical path 2). Structural equation models were used to assess the direct and indirect effects of the patch and landscape components (Online Resource 1). 2. Material and methods 2.1. Study site. The study was conducted in the Marqués de Comillas region of the Lacandona rainforest in southeastern Mexico (Fig. 2 a). Most cleared areas in the study site are utilized for cattle ranching (Carabias et al. 2015 ), while the remaining forest patches shade the cattle. The latter function implies that some landowners remove understory vegetation to facilitate the movement of cattle and individuals across these patches. Fire-based land clearing is another prevalent practice in the area, and occasionally uncontrolled fires extend into patches, partially burning trees. Additionally, patches near Río Lacantún experience periodic flooding during the rainy season. Sixteen forest patches were selected for this study (Fig. 2 b). Land tenure in Marqués de Comillas is private, and land ownership is distributed among inhabitants (Kolb and Galicia 2018 ; Berget et al. 2021 ). Consequently, the study was conducted exclusively in patches where prior permission was obtained from owners. The patches were distributed across alluvial terraces, ensuring relative consistency in the diversity and composition of tree assemblages, soil types, and abiotic factors (Navarrete-Segueda et al. 2018 ; Lohbeck et al. 2022 ). 2.2. Seedling sampling. From February to June 2014, patches were sampled by positioning 1-ha blocks at their center. Each block contained 10 1-m2 plots randomly arranged in groups of two or three plots along five equidistant transects (20 m apart; Fig. 2 d). For the 1-ha forest patches, plots were never positioned at the edge to maintain a 20-m buffer zone of vegetation that protected them from strong edge effects (Fig. 2 d). Within each 1-m2 plot, all tree seedlings (10–100 cm height) were counted and identified to the lowest possible taxonomic level with the assistance of a local parataxonomist and field guide(Martínez et al. 1994 ; Sousa 2009 ). When field identification was not possible, samples were collected for identification at several herbaria sites (MEXU, ECO-SC-H). This study did not involve extraction or damage to endangered species. The plant nomenclature followed the Missouri Botanical Garden database, Tropicos (Missouri Botanical Garden 2019 ). Once identified, seedling species were classified by dispersal syndrome (Ibarra-Manríquez et al. 2001 ; Ibarra-Manríquez and Cornejo-Tenorio 2010 ) and light requirements for establishment and growth into light-demanding species, shade-tolerant species, and intermediate species (Nicotra et al. 1999 ; Rose 2000 ; Benítez-Malvido et al. 2001 ; Kitajima et al. 2013 ). A total of 1378 seedlings belonging to 27 families, 38 genera, and 67 species were recorded. Only animal-dispersed species were considered for further analysis, as they comprise up to 90% of the rainforest seed rain (Jordano 2000 ; San-José et al. 2019 ). The animal-dispersed seedlings accounted for 94.7% (1305) of the total recorded seedlings, representing 24 families, 30 genera, and 56 species. From this subset of species, 668 seedlings (52.72%) from 27 shade-tolerant tree species were identified, followed by 527 seedlings (40.38%) from 18 light-demanding species, and 90 seedlings (6.9%) from 11 intermediate or indeterminate species (Online Resource 2). 2.3. Seedling community. We assessed sampling completeness using Chao and Shen's sample-coverage estimator (Chao and Lee 1992 ). We combined data from the ten sampling plots within each patch. Subsequently, we estimated the proportion of individuals belonging to the species represented in the sample. The sample coverage among patches was 91.07 ± 7.47%, indicating that our sampling effort was sufficient to estimate diversity (Chao and Jost 2012 ). We aggregated the seedlings from the 10 1-m² plots to obtain an abundance of light-demanding and shade-tolerant seedlings for each patch (seedlings per 10 m²). Additionally, we employed Nonmetric Multidimensional Scaling (NMDS) to estimate species compositional similarity among patches (Laurance et al. 2007 ; Santo-Silva et al. 2015 ; Liu et al. 2019 ). We utilized the metaMDS function based on the Bray-Curtis dissimilarity index of the vegan package and complete seedling dataset (Oksanen et al. 2019 ). We employed the NMDS scores of the first two axes as response variables. We employed diversity decomposition of the effective numbers of species or Hill numbers (Jost 2006 , 2007 ). Hill numbers (qD) are expressed in units of species, which allows for the characterization of the distribution of species abundance and provides comprehensive information about community diversity (Jost 2006 ; Chao et al. 2012 ). We calculated the Hill numbers of all species (species richness or 0D), typical species (exponential of Shannon's entropy index or 1D), and dominant species (inverse of Simpson's index or 2D). The 0D is not sensitive to individual abundances (Jost 2007 ; Tuomisto 2010 ), enabling the inclusion of rare species in assessments, whereas 1D and 2D assign a high weight to the equally abundant and the most abundant species, respectively (Jost 2007 , 2010 ). We followed Jost ( 2007 ) and Tuomisto ( 2010 ) to calculate the gamma (γ), alpha (α), and beta (β) diversity (Online Resource 3). The α-diversity values are expressed in species/m 2 . In contrast, β-diversity represents the effective number of completely distinct assemblages, ranging from one (when the assemblages of all 1-m² plots are identical) to 10 (when the assemblages of the 10 1-m2 plots are entirely dissimilar; Table 1 ). The package vegan in R was used for the entire procedure (Oksanen et al. 2019 ). Table 1 Summary of response and explanatory variables of the study Variable Component Name Range Label Seedling Abundance Light-demanding seedlings 0–109 S LD Shade-tolerant seedlings 4–130 S ST Diversity α-diversity of all species 1.60–4.11 0 α β-diversity of all species 3.33–6.12 0 β α-diversity of typical species 1.45–3.60 1 α β-diversity of typical species 1.25–3.90 1 β α-diversity of dominant species 1.34–2.98 2 α β-diversity of dominant species 0.94–3.11 2 β Patch Spatial Size 1.96–71.13 P SZ Shape 1.40–5.30 P SI Vegetation Tree basal area 20.04–186 T BA Tree abundance 33–74 T A Number of adult tree species 10–27 T 0D Number of typical adult tree species 6.12–20.1 T 1D Number of dominant adult tree species 4.3–16.3 T 2D Disturbance Fire index -5.28–5.57 D F Management index -5.99–5.9 D M Landscape Composition Forest cover (1200 m radius) 1.83–3.49 FC 1200 Configuration Patch density (600 m radius) 0.89–6.2 PD 600 Patch density (900 m radius) 1.97–5.9 PD 900 Patch aggregation (600 m radius) 89.56–97.56 AI 600 2.4. Patch structure. We used a multispectral SPOT-5 satellite image with a 10 × 10 m pixel resolution, recorded in March 2013, to conduct supervised classification using GRASS GIS software (GRASS Development Team 2017 ). A forest/non-forest classification was developed, and sampling points were employed to evaluate classification accuracy. The overall classification accuracy was 79%. The resulting raster file was exported into a shapefile to calculate the patch perimeter in meters (m) and patch area in m 2 using the rgeos package (Bivand & Rundel, 2019). These data were later used to assess patch size in hectares and to evaluate patch shape as a dimensionless index that measures patch complexity against a square of the same size (Patton 1975 ). We controlled patch size and shape effects by using the residuals of a linear model between patch shape and size ( R 2 = 0.63; F 1,14 = 26.32; P = 0.0001). For each patch, we assessed the tree community by measuring the diameter at breast height (DBH 130 cm from the ground) of all trees with DBH ≥ 10 cm across ten 50 × 2 m transects (Gentry 1982 ). To reduce forest diversity and structural biases caused by edge effects, we positioned all transects at least 20 m from the patch edges, when feasible (Laurance et al. 2007 ). In each transect, we counted, measured the diameter, and identified every tree at the species level (Missouri Botanical Garden, 2019 ), and then computed tree abundance (trees per 0.1 ha), basal area (m² per ha), richness, Shannon's exponential entropy index, and the inverse of Simpson's index (species per 0.1 ha). In addition, we collected data on disturbance factors within patches through a questionnaire administered to landowners and field observations throughout the study period. We used the questionnaire results to construct a binary base of the following patch disturbance factors (Online Resource 4): floods during the rainy season, fire events during the last 10 years, presence of cattle, slashing of understory vegetation, and predominant land use in the adjacent matrix. Subsequently, we employed a logistic PCA (Landgraf and Lee 2020 ) to generate disturbance indices from the binary data. We previously estimated the number of dimensions ( k ) and tuning parameter ( m ) through cross-validation of the negative log-likelihood. We obtained k = 3 and m = 3 for the analysis (Online Resource 5). The final model accounted for 77.8% of the explained deviance. We used the logisticPCA package for the entire procedure (Landgraf and Lee 2015 , 2020 ). The Spearman correlation between disturbance factors and PCA components, slashing of understory vegetation, adjacent land use, and presence of cattle were strongly associated with the first component. In contrast, flooding and fire events are associated with the second component (Online Resource 6). Consequently, we employed the first component as the management disturbance index (D M ) and the second component as the fire disturbance index (D F ). 2.5. Landscape structure We calculated five landscape metrics (Online Resource 7) that influence patch size, vegetation, and disturbance in the tropics and seedling community. The composition metrics comprised the proportion of primary forest cover (FC) and secondary forest cover (SF), whereas the configuration metrics comprised patch density (PD), patch aggregation (AI), and edge contrast (EC). We calculated by assigning quality values to each matrix cover. These values serve as indicators of the capacity of the matrix cover to mitigate edge effects and facilitate the movement of terrestrial mammals. The quality values were (Garmendia et al. 2013 ; Galán-Acedo et al. 2019 ): 1 (water bodies, representing the lowest suitability), 2 (anthropogenic cover), 3 (cattle pasture), 4 (arboreal crops), 5 (floodplains), 6 (secondary forest), and 7 (old-growth forest, representing the highest suitability). We estimated the area-weighted mean of the EC index to obtain a more robust and realistic representation of the landscape structure effects(Li and Archer 1997 ). We measured these landscape metrics within 13 circular buffers (300–1500 m radius at 100 m intervals) from the center of each focal patch (Fig. 2 c). Radii were based on the dispersal distances of seed dispersers and terrestrial mammals, landscape size to assess seedling communities and disturbances, and spatial scales at which landscape structure affects forest diversity and structure in the study region (Zermeño-Hernández et al. 2016 ; San-José et al. 2019 ; Wies et al. 2021 ; Nicasio-Arzeta et al. 2021 ). Independence between sampling sites, landscape metrics, and buffer sizes can be found in Nicasio-Arzeta et al. ( 2021 ). 2.6. Statistical analysis We utilized linear models to estimate the scale of the effect of each landscape metric on patch structure. We fitted patch size, shape, tree basal area, diversity, and disturbance by fire and management with a single landscape metric for each buffer and obtained the coefficient of determination ( R² ). Subsequently, we plotted the resulting 13 R 2 values and selected the one with the strongest response as the scale of the effect for that landscape metric. We repeated this procedure for each patch variable and the landscape metric. Subsequently, we evaluated the effects of landscape metrics and fire disturbance on each patch structure variable by using multiple linear models. We employed only landscape metrics at the scale of the effect identified in the previous step (Online Resource 8). All models were additive because of the limited sample size of the 16 forest patches. We employed the dredge function of the MuMIn package (Barton 2018 ) to create all possible combinations of explanatory variables and the null model (only the intercept) to assess the relative importance and effects of landscape metrics and fire disturbance using an information theory approach and multimodel inference (Burnham and Anderson 2002 ). We used each model's accumulated sum of Akaike weights (wi) to select a subset of models with ∑ w i ≤ 0.95. Then, we employed w i of the model’s subset to calculate each explanatory variable's relative importance and the model-averaged parameter estimates. We considered influential variables for which the confidence interval did not include zero in the averaged parameters. All statistical analyses in the R 3.5.2 statistical computing environment (R Development Core Team 2025 ). We selected the significant landscape metrics and disturbance factors of each patch structural variable for further analysis (Online Resource 9). We analyzed the direct and indirect effects of patch and landscape structures on seedling abundance, composition, and diversity using structural equation models (SEM). SEM is a statistical method used to test causal relationships (Fan et al. 2016 ; Tarka 2018 ). We proposed a conceptual model of multiple casualties to explain seedling abundance, diversity, and composition in relation to the patch components (size, shape, basal area, tree diversity, and disturbance by fire and management), which served as exogenous predictors (Online Resource 1). Indirect paths included the abundance of light-demanding and shade-tolerant seedlings, representing a hypothesis regarding cascading effects mediated by abundance fluctuations. We previously assessed the association between the explanatory variables to avoid variance inflation. We found that patch structural variables were strongly correlated (Online Resource 10); therefore, only tree basal area and diversity were used for further analysis. The SEM was fitted using a piecewiseSEM package (Lefcheck 2016 ). Multivariate normality of seedling ( γ 1,p = 217.39; P = 0.54 and γ 2,p = -1.95; P = 0.052) and patch data ( γ 1,p = 67.41; P = 0.14 and γ 2,p = -1.03; P = 0.30) was validated. We applied a logarithmic transformation to the patch size and abundance of light-demanding seedlings and a square root transformation to the abundance of shade-tolerant seedlings to meet the normality assumptions. Owing to sample size limitations, we performed simple model structures (few variables). Hence, we tested different combinations of patch structures (D M , T BA , T D , P SZ , and P SI ) using alternative models instead of all together. We employed the fire disturbance index and selected landscape metrics (D F , FC 1200 , PD 600 , PD 900 , and AI 600 ) to explain the variation in the exogenous variables. Alternative models consist of combinations of one and two exogenous predictors. This produced 15 alternative models for each diversity order (all species: 0 α, 0 β; typical species: 1 α, 1 β; dominant species: 2 α, 2 β) and composition indices (MDS1, MDS2). The model selection procedure consisted of rejecting all models with a lack of fit ( P < 0.05); then, we excluded the models that had no significant links to seedling abundance, composition, or diversity variables we attempted to explain. Finally, the best-fitting models were selected based on the lowest AICc values. 3. Results Our candidate models were well fitted, indicating that the conceptual model adequately described the data (Fig. 1 ). The best-fitted models (those with the lowest AICc; Online Resources 11 and 12) included patch size and aggregation for diversity of all species (Fig. 3 b), whereas tree basal area explained seedling composition (Fig. 3 a) and the diversity of typical and dominant species (Fig. 3 c and d). For the latter models, the tree basal area decreased in patches affected by previous fire events (B = -0.63), and the abundance of light-demanding seedlings was reduced by the tree basal area (B = -0.54). The latter indicated that fire disturbance indirectly promoted the abundance of light-demanding seedlings (B = -0.63 × 0.54 = 0.34). The seedling composition model explained 39% of the basal area, 30% of the light-demanding seedling abundance, 12% of the shade-tolerant seedling abundance, and 64% and 56% of the first and second NMDS axes, respectively (Fig. 3 a). The first axis was negatively influenced by light-demanding seedlings (B = -0.55), whereas the second NMDS axis was negatively affected by the abundance of shade-tolerant seedlings (B = -0.76). The indirect effect of fire disturbance on the first composition axis was lower (i.e., -0.63 × -0.55 × 0.54 = -0.18) than the indirect effect of basal area (i.e., -0.55 × 0.54 = 0.29). Seedling composition differed between fire-disturbed and non-disturbed patches ( F 1,14 = 2.64; R 2 = 0.16; P = 0.01), where the abundance of light-demanding seedlings was higher in fire-disturbed patches than in their non-disturbed counterparts (Fig. 4 ). The typical species model (Fig. 3 c) explained 40% of the α-diversity and was positively influenced only by the abundance of shade-tolerant seedlings (B = 0.54). In contrast, the 68% explanation of β-diversity variation was strongly attributed to the negative effect of light-demanding seedlings (B = -1) and indirectly promoted by tree basal area (i.e., -1 × 0.54 = 0.54). This also holds true for the dominant species model (Fig. 3 d), which explained 69% of the β-diversity through the negative effects of light-demanding (B = -0.94) and shade-tolerant (B = -0.43) seedling abundances. Tree basal area also positively affected the β-diversity of dominant species (i.e., -0.94 × 0.54 = 0.50). Finally, fire disturbance had overall indirect effects on the decline in β-diversity of typical (B = -1 × -0.54 × -0.63 = 0.34) and dominant species (B = -0.94 × -0.54 × -0.63 = 0.32). Finally, the model for all species explained 40% of the patch size, 10% of light-demanding seedlings, 51% of shade-tolerant seedlings, 50% of α-diversity, and 17% of β-diversity (Fig. 3 b). Patch aggregation had direct effects on patch size (B = 0.64) and shade-tolerant seedlings (B = 0.63), which were also affected by light-demanding seedlings (B = -0.58). In turn, shade-tolerant seedlings positively affected α-diversity (B = 0.56), which was indirectly reduced by the abundance of light-demanding seedlings (B = -0.58 × 0.56 = -0.32). Patch aggregation had cascading effects on the increase in seedling α-diversity (B = -0.63 × 0.54 = 0.34). 4. Discussion Our findings confirm that landscape configuration and disturbance factors within patches, such as fire incidence, drive species composition and α- and β-diversity of typical and dominant seedling species. As anticipated, patches with low-disturbance regimes and higher basal areas exhibited a reduced abundance of light-demanding seedlings species, leading to a more diversified seedling community. Furthermore, landscapes with more aggregated patches increased the abundance of shade-tolerant seedling species, resulting in cascading effects that favored α-diversity of species within the regenerative community. Thus, the cascading effects of patch disturbance and landscape configuration shaped the species composition and α- and β-diversity, respectively. Fire disturbance was the primary local factor exerting significant cascading effects that modulated the abundance, composition, and diversity of the regenerative seedling community. Fire is an atypical disturbance in tropical rainforests, where most tree species are considered fire sensitive (Trejo 2008 ). Nevertheless, land use changes to pastures and farmlands, forest fragmentation, and extreme weather events increase fire frequency, severity, and extent (Cochrane and Laurance 2002 , 2008 ). In this study, fire occurrence, which was present in approximately 50% of the forest patches, significantly reduced the basal area of adult trees, leading to an increased abundance of light-demanding species in the understory. This observation is consistent with other studies, which have shown that surface fires reduce the basal area by removing medium- and large-sized trees and lianas (Nepstad et al. 1999 ; Barlow et al. 2003a ; Cochrane and Laurance 2008 ). The consequent canopy openness, which can be four times greater than that in unburned forests, increases the light incidence on the forest floor (Barlow et al. 2003a ). These microclimate alterations favor the dominance of disturbance-tolerant tree species in the understory (Cochrane and Laurance 2002 ), as observed in this study. In turn, the abundance of light-demanding species directly affected seedling composition and turnover of equally common and abundant species. Consequently, patches with fire events in the preceding 10 years exhibited a distinct seedling community compared with those without fires. Patches with fire events were predominantly characterized by light-demanding species, such as Inga punctata . Some tropical rainforest tree species, such as Swietenia macrophylla in southern Mexico, respond positively to fires. However, most tree species in tropical forests are fire sensitive (Barlow et al. 2003b ; Brando et al. 2012 ). Therefore, the presence of species such as Brosimum alicastrum and Ampelocera hottlei found exclusively in unburned patches may be threatened by the occurrence of fires. An assemblage of species can be determined by their species abundance, frequency, clade of membership (e.g., family, order, class), as well as their functional attributes (e.g., shade-tolerant species, light-demanding species, anemochorous species, zoochorous species, nitrogen-fixing species). In our study, a lower basal area was associated with an increased abundance of light-demanding species, suggesting that disturbed fragments (characterized by a lower abundance of large trees) facilitate the establishment and survival of these species by increasing light availability (Denslow 1996 ). These conditions promote the establishment of a subset of ecologically redundant species that lack phylogenetic relationships, resulting in impoverished species assemblages that are dominated by a few species (Oliveira et al. 2008 ; Tabarelli et al. 2012 ). These assemblages indicate a process of biotic homogenization, characterized by low dissimilarity between communities, as well as a reduction in β-diversity (i.e., less species turnover) at local and landscape scales (Olden and Rooney 2006 ; Tabarelli et al. 2012 ; Solar et al. 2015 ). Basal area influences the abundance and composition of seedlings through seed production (Chapman et al. 1992 ), light availability, water and nutrient availability in the soil (Denslow 1987 ; Martinez-Ramos and Soto-Castro 1993 ; Turner 2004 ), and conspecific seedling mortality (Wright 2002 ). This is consistent with our observations, which show that a greater abundance of light-demanding seedlings reduces both the composition of seedlings and the β-diversity of typical and dominant species, which are variables sensitive to abundance fluctuations (Jost, 2007 ; Roden et al., 2018). Furthermore, the absence of direct effects of landscape structure supports the hypothesis that generalist species and abundances are primarily affected by small-scale factors (Miguet et al., 2016 ). Conversely, our SEMs images show that landscapes with a high aggregation index (AI > 95%) support 26–28% more seedling species per 10 m² compared to landscapes with more dispersed patches (AI < 92%; Fig. 3 b). Importantly, this increase in α-diversity occurs without reducing compositional heterogeneity: β-diversity remains high because clustered patches disproportionately boost the abundance of shade-tolerant, animal-dispersed species, while limiting the dominance of disturbance-tolerant pioneers (Arroyo-Rodríguez et al. 2013 ; Nicasio-Arzeta et al. 2021 ). This supports the hypotheses of Miguet et al. ( 2016 ), who proposed that landscape effects are stronger for specialist species and those with smaller populations. Shade-tolerant species show more habitat specialization and tend to have lower population densities than light-demanding species. (Denslow 1996 ; Turner 2004 ). Moreover, their abundance is regulated by conspecific mortality and the foraging of fruits, seeds, and seedlings by terrestrial mammals (Dirzo and Miranda 1990 ; Comita et al. 2007 , 2010 ; Camargo-Sanabria et al. 2014 ). Seed dispersal and predation, in conjunction with density-dependent seedling mortality, promote seedling diversity (Harms et al. 2000 ; Wright 2002 ; Comita et al. 2010 ), thus elucidating the positive effect on the α diversity of all seedling species. This suggests that this configurational pattern facilitates the arrival of seed dispersers and predators. Notably, the abundance of shade-tolerant seedlings was also influenced by the abundance of light-demanding seedlings, which indicated that the latter competed for resources. Competitive exclusion can significantly lower α diversity in forests with high light levels and low herbivore pressure (Wright, 2002 ). This finding supports an indirect effect of light-demanding seedling abundance on species richness. These results emphasize the role of patch aggregation as a form of "spatial insurance" that landscape configuration can provide in fragmented habitats (Villard and Metzger 2014 ; Suárez-Castro et al. 2022 ). In our study area, this, in turn, helped reduce fire-induced diversity loss and supported the propagule pool necessary for long-term forest regeneration. As fire frequency in Mesoamerican rainforests is expected to increase owing to drier climates and ongoing pasture expansion (Armenteras et al. 2021 ), maintaining or restoring patch aggregation becomes a crucial strategy for managers. Fencerow enrichment planting or assisted natural regeneration of intervening secondary forests can significantly reduce the average distance between seed sources and recipient patches (Brancalion et al. 2019 ). This, in turn, helps mitigate the homogenizing impact of understory fires on seedling assemblages. Practically speaking, prioritizing the clustering of remnant forests during land allocation decisions should go hand in hand with fire prevention measures (Cochrane 2009 ). Together, they form a cost-effective insurance policy for preserving carbon storage, timber potential, and non-timber forest products that support local livelihoods. Our findings highlight critical implications for the agricultural management of fragmented rainforests. Fire disturbance significantly decreases forest resilience by homogenizing seedling assemblages and favoring species that are more tolerant to disturbance. Effective management should prioritize mitigating fire disturbances, sustain forest cover and tree basal areas, and minimizing patch isolation. Integrating these insights into conservation planning requires a multidimensional approach that bridges the gap between local patch management and broader landscape configuration. This alignment is crucial for safeguarding the ecological integrity and long-term sustainability of fragmented tropical rainforests amid escalating anthropogenic pressures. Declarations Funding JBM received grant projects from Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT), Dirección General de Asuntos del Personal Académico UNAM [IN214014, IN202117, and IN201620], and from Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI), México [CB2005-C01-51043, CB2006-56799 and CB2007-7912]. Competing interests The authors have no relevant financial or non-financial interests to disclose. CRediT authorship contribution statement S.N.A. conceptualization; S.N.A. methodology and project administration; J.B.M. supervision, funding acquisition, and resources; S.N.A. investigation, data curation, formal analysis, visualization, and validation; S.N.A. and S.M.V. wrote the original draft; I.Z.H., A.P., and J.B.M. writing – review and editing. All the authors have read and approved the final manuscript. Data availability The datasets generated and/or analyzed during the current study are available from the first author upon reasonable request. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation for this study, the authors used Grammarly and Paperpal to improve language readability and grammar. After using these tools, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article. Acknowledgement We extend our gratitude to the residents of Quiringüicharo for their warm welcome and assistance. We appreciate J. Manuel Lobato-García's technical and logistical aid in our field research. We thank Gilberto Jamangapé and Rafael Lombera for their expertise in plant identification. Estación Chajul facilitated our work by providing accommodation and logistical support. SNA was a doctoral student from Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM), and received from Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) fellowship 317569. References Andren H (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 355–366. https://doi.org/10.2307/3545823 Arasa-Gisbert R, Arroyo-Rodríguez V, Galán-Acedo C, et al (2021) Tree recruitment failure in old-growth forest patches across human-modified rainforests. Journal of Ecology 1–13. https://doi.org/10.1111/1365-2745.13643 Arasa-Gisbert R, Arroyo-Rodríguez V, Meave JA, et al (2022) Forest loss and treeless matrices cause the functional impoverishment of sapling communities in old-growth forest patches across tropical regions. Journal of Applied Ecology 069: https://doi.org/10.1111/1365-2664.14197 Armenteras D, Dávalos LM, Barreto JS, et al (2021) Fire-induced loss of the world’s most biodiverse forests in Latin America. Sci Adv 7:3357–3370. https://doi.org/10.1126/sciadv.abd3357 Arroyo-Rodríguez V, Rös M, Escobar F, et al (2013) Plant β-diversity in fragmented rain forests: Testing floristic homogenization and differentiation hypotheses. Journal of Ecology 101:1449–1458. https://doi.org/10.1111/1365-2745.12153 Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, et al (2010) Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective. Chin Geogr Sci 20:491–497. https://doi.org/10.1007/s11769-010-0437-x Barlow J, Haugaasen T, Peres CA (2002) Effects of ground fires on understorey bird assemblages in Amazonian forests. Biol Conserv 105:157–169. https://doi.org/10.1016/S0006-3207(01)00177-X Barlow J, Peres CA, Lagan BO, Haugaasen T (2003a) Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol Lett 6:6–8. https://doi.org/10.1046/j.1461-0248.2003.00394.x Barlow J, Peres CA, Lagan BO, Haugaasen T (2003b) Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol Lett 6:6–8 Barton K (2018) MuMIn: Multi-Model Inference. R package version 1421 Benitez-Malvido J (1998) Impact of forest fragmentation on seedling abundance in a tropical rain forest. Conservation Biology 12:380–389. https://doi.org/10.1046/j.1523-1739.1998.96295.x Benitez-Malvido J (2006) Effect of low vegetation on the recruitment of plants in successional habitat types. Biotropica 38:171–182. https://doi.org/10.1111/j.1744-7429.2006.00137.x Benítez-Malvido J, Álvarez-Añorve MY, Ávila-Cabadilla LD, et al (2022) Phylogenetic and functional structure of tree communities at different life stages in tropical rain forest fragments. Glob Ecol Conserv 36:1–15. https://doi.org/10.1016/j.gecco.2022.e02113 Benítez-Malvido J, Lázaro A, Ferraz IDK (2018) Effect of distance to edge and edge interaction on seedling regeneration and biotic damage in tropical rainforest fragments: A long-term experiment. Journal of Ecology 106:2204–2217. https://doi.org/10.1111/1365-2745.13003 Benítez-Malvido J, Martínez-Ramos M, Ceccon E (2001) Seed rain vs. seed bank, and the effect of vegetation cover on the recruitment of tree seedlings in tropical successional vegetation. In: Gottsberger G, Liede S (eds) Life Forms and Dynamics in Tropical Forests. Schweizerbart Science Publishers, Stuttgart, Germany, pp 1805–203 Berget C, Verschoor G, García-Frapolli E, et al (2021) Landscapes on the move: Land-use change history in a mexican agroforest frontier. Land (Basel) 10:. https://doi.org/10.3390/land10101066 Brancalion PHS, Niamir A, Broadbent E, et al (2019) Global restoration opportunities in tropical rainforest landscapes. Sci Adv 5:3223–3226. https://doi.org/10.1126/sciadv.aav3223 Brando PM, Nepstad DC, Balch JK, et al (2012) Fire-induced tree mortality in a neotropical forest: The roles of bark traits, tree size, wood density and fire behavior. Glob Chang Biol 18:630–641. https://doi.org/10.1111/j.1365-2486.2011.02533.x Brindis-Badillo DA, Arroyo-Rodríguez V, Mendoza E, et al (2022) Conserving dominant trees in human-modified landscapes at the Lacandon tropical rainforest. Biol Conserv 270:109548. https://doi.org/10.1016/J.BIOCON.2022.109548 Brokaw N, Scheiner SM (1989) Species composition in gaps and structure of a tropical forest. Ecology 70:538–541. https://doi.org/10.2307/1940196 Burnham KP, Anderson DR (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Springer-Verlag Camargo-Sanabria AA, Mendoza E, Guevara R, et al (2014) Experimental defaunation of terrestrial mammalian herbivores alters tropical rainforest understorey diversity. Proceedings of the Royal Society B: Biological Sciences 282:. https://doi.org/10.1098/rspb.2014.2580 Carabias J, De la Maza J, Cadena R (2015) Conservación y desarrollo sustentable en la Selva Lacandona: 25 años de actividades y experiencia. Natura y Ecosistemas Mexicanos, Mexico City Chao A, Chiu CH, Hsieh TC (2012) Proposing a resolution to debates on diversity partitioning. Ecology 93:2037–2051. https://doi.org/10.1890/11-1817.1 Chao A, Jost L (2012) Coverage-based rarefaction and extrapolation: Standardizing samples by completeness rather than size. Ecology 93:2533–2547. https://doi.org/10.1890/11-1952.1 Chao A, Lee SM (1992) Estimating the number of classes via sample coverage. J Am Stat Assoc 87:210–217. https://doi.org/10.1080/01621459.1992.10475194 Chapman CA, Chapman LJ, Wangham R, et al (1992) Estimators of Fruit Abundance of Tropical Trees. Biotropica 24:527–531. https://doi.org/10.2307/2389015 Cochrane MA (2009) Fire in the tropics. In: Cochrane MA (ed) Tropical Fire Ecology. Climate Change, Land Use and Ecosystem Dynamics. Praxis Publishing Ltd., Chichester, UK, pp 1–23 Cochrane MA, Laurance WF (2008) Synergisms among fire, land use, and climate change in the Amazon. Ambio 37:522–527. https://doi.org/10.1579/0044-7447-37.7.522 Cochrane MA, Laurance WF (2002) Fire as a large-scale edge effect in Amazonian forests. J Trop Ecol 18:311–325. https://doi.org/10.1017/S0266467402002237 Comita LS, Aguilar S, Pérez R, et al (2007) Patterns of woody plant species abundance and diversity in the seedling layer of a tropical forest. Journal of Vegetation Science 18:163. https://doi.org/10.1658/1100-9233 (2007)18[163:powpsa]2.0.co;2 Comita LS, Muller-Landau HC, Aguilar S, Hubbell SP (2010) Asymmetrie density dependence shapes species abundances in a tropical tree community. Science (1979) 329:330–332. https://doi.org/10.1126/science.1190772 Cudney-Valenzuela SJ, Arroyo-Rodríguez V, Andresen E, et al (2021) Does patch quality drive arboreal mammal assemblages in fragmented rainforests? Perspect Ecol Conserv 19:61–68. https://doi.org/10.1016/j.pecon.2020.12.004 Dalling JW, Hubbell SP, Silvera K (1998) Seed dispersal, seedling establishment and gap partitioning among tropical pioneer trees. Journal of Ecology 86:674–689. https://doi.org/10.1046/j.1365-2745.1998.00298.x de Souza JC, da Silva RM, Gonçalves MPR, et al (2018) Habitat use, ranching, and human-wildlife conflict within a fragmented landscape in the Pantanal, Brazil. Biol Conserv 217:349–357. https://doi.org/10.1016/j.biocon.2017.11.019 Denslow JS (1987) Tropical Rainforest Gaps and Tree Species Diversity. Annu Rev Ecol Syst 18:431–451. https://doi.org/10.1146/annurev.es.18.110187.002243 Denslow JS (1996) Functional Group Diversity and Responses to Disturbance. In: Biodiversity and Ecosystem Processes in Tropical Forests. Springer, Berlin, Heidelberg, pp 127–151 Denslow JS, Guzman G. S (2000) Variation in stand structure, light and seedling abundance across a tropical moist forest chronosequence, Panama. Journal of Vegetation Science 11:201–212. https://doi.org/10.2307/3236800 Desbiez AL, Bodmer RE, Aparecida S (2009) Wildlife habitat selection and sustainable resources management in a Neotropical wetland. Int J Biodivers Conserv 1:11–20 Dirzo R, Miranda A (1990) Contemporary Neotropical Defaunation and Forest Structure, Function, and Diversity-A Sequel to John Terborgh*. Conservation Biology 4:444–447. https://doi.org/10.1111/j.1523-1739.1990.tb00320.x Fahrig L (2013) Rethinking patch size and isolation effects: The habitat amount hypothesis. J Biogeogr 40:1649–1663. https://doi.org/10.1111/jbi.12130 Fan Y, Chen J, Shirkey G, et al (2016) Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol Process 5:19 Farfán Gutiérrez M, Pérez-Salicrup DR, Flamenco-Sandoval A, et al (2018) Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques 24:. https://doi.org/10.21829/myb.2018.2431591 Fontúrbel FE, Candia AB, Malebrán J, et al (2015) Meta-analysis of anthropogenic habitat disturbance effects on animal-mediated seed dispersal. Glob Chang Biol 21:3951–3960. https://doi.org/10.1111/gcb.13025 Galán-Acedo C, Arroyo-Rodríguez V, Estrada A, Ramos-Fernández G (2019) Forest cover and matrix functionality drive the abundance and reproductive success of an endangered primate in two fragmented rainforests. Landsc Ecol 34:147–158. https://doi.org/10.1007/s10980-018-0753-6 Garmendia A, Arroyo-Rodríguez V, Estrada A, et al (2013) Landscape and patch attributes impacting medium- and large-sized terrestrial mammals in a fragmented rain forest. J Trop Ecol 29:331–344. https://doi.org/10.1017/S0266467413000370 Gentry AH (1982) Patterns of Neotropical Plant Species Diversity. In: Hecht MK, Wallace B, Prance GT (eds) Evolutionary Biology. Springer US, pp 1–84 GRASS Development Team (2017) Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.2. http://grass.osgeo.org Guariguata MR, Ostertag R (2001) Neotropical secondary forest succession: changes in structural and functional characteristics. For Ecol Manage 148:185–206. https://doi.org/10.1016/S0378-1127(00)00535-1 Hardwick SR, Toumi R, Pfeifer M, et al (2015) The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: Forest disturbance drives changes in microclimate. Agric For Meteorol 201:187–195. https://doi.org/10.1016/j.agrformet.2014.11.010 Harms KEE, Wright SJJ, Calderón O, et al (2000) Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404:493–5. https://doi.org/10.1038/35006630 Hernández-Ruedas MA, Arroyo-Rodríguez V, Morante-Filho JC, et al (2018) Fragmentation and matrix contrast favor understory plants through negative cascading effects on a strong competitor palm. Ecological Applications 28:1546–1553. https://doi.org/10.1002/eap.1740 Hernando A, Velázquez J, Valbuena R, et al (2017) Influence of the resolution of forest cover maps in evaluating fragmentation and connectivity to assess habitat conservation status. Ecol Indic 79:295–302. https://doi.org/10.1016/j.ecolind.2017.04.031 Ibarra-Manríquez G, Cornejo-Tenorio G (2010) Diversidad de frutos de los árboles del bosque tropical perennifolio De México. Acta Bot Mex 104:51–104 Ibarra-Manríquez G, Martínez Ramos M, Oyama K (2001) Seedling functional types in a lowland rain forest in Mexico. Am J Bot 88:1801–1812. https://doi.org/10.2307/3558356 Jesus FM, Pivello VR, Meirelles ST, et al (2012) The importance of landscape structure for seed dispersal in rain forest fragments. Journal of Vegetation Science 23:1126–1136. https://doi.org/10.1111/j.1654-1103.2012.01418.x Jordano P (2000) Fruits and frugivory. In: Fenner M (ed) Seeds: the ecology of regeneration in plant communities, 2nd edn. CABI Publishing, Wallingford, U.K., pp 125–166 Jost L (2006) Entropy and diversity. Oikos 113:363–375. https://doi.org/10.1111/j.2006.0030-1299.14714.x Jost L (2007) Partitioning diversity into independent alpha and beta components. Ecology 88:2427–2439. https://doi.org/10.1890/06-1736.1 Jost L (2010) The relation between evenness and diversity. Diversity (Basel) 2:207–232. https://doi.org/10.3390/d2020207 Kitajima K, Cordero RA, Wright SJ (2013) Leaf life span spectrum of tropical woody seedlings: Effects of light and ontogeny and consequences for survival. Ann Bot 112:685–699. https://doi.org/10.1093/aob/mct036 Kolb M, Galicia L (2018) Scenarios and story lines: drivers of land use change in southern Mexico. Environ Dev Sustain 20:681–702. https://doi.org/10.1007/s10668-016-9905-5 Krishnadas M, Agarwal K, Comita LS (2020) Edge effects alter the role of fungi and insects in mediating functional composition and diversity of seedling recruits in a fragmented tropical forest. Ann Bot 126:1181–1191. https://doi.org/10.1093/aob/mcaa138 Landgraf AJ, Lee Y (2020) Dimensionality reduction for binary data through the projection of natural parameters. J Multivar Anal 180:. https://doi.org/10.1016/j.jmva.2020.104668 Landgraf AJ, Lee Y (2015) Dimensionality Reduction for Binary Data through the Projection of Natural Parameters Laurance WF, Delamonica P, Laurance SG, et al (2000) Rainforest fragmentation kills big trees. Nature 404:836. https://doi.org/10.1038/35009032 Laurance WF, Nascimento HEMM, Laurance SG, et al (2007) Habitat fragmentation, variable edge effects, and the landscape-divergence hypothesis. PLoS One 2:e1017. https://doi.org/10.1371/journal.pone.0001017 Lefcheck JS (2016) piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol Evol 7:573–579. https://doi.org/10.1111/2041-210X.12512 Li BL, Archer S (1997) Weighted mean patch size: A robust index for quantifying landscape structure. Ecol Modell 102:353–361. https://doi.org/10.1016/S0304-3800(97)00071-9 Liu J, Coomes DA, Hu G, et al (2019) Larger fragments have more late-successional species of woody plants than smaller fragments after 50 years of secondary succession. Journal of Ecology 107:582–594. https://doi.org/10.1111/1365-2745.13071 Lohbeck M, DeVries B, Bongers F, et al (2022) Mexican agricultural frontier communities differ in forest dynamics with consequences for conservation and restoration. Remote Sens Ecol Conserv 37:10–14. https://doi.org/10.1002/RSE2.262 Lohbeck M, Poorter L, Martínez-Ramos M, et al (2015) Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 96:1242–1252. https://doi.org/10.1890/14-0472.1 Martin AE (2018) The spatial scale of a species’ response to the landscape context depends on which biological response you measure. Current Landscape Ecology Reports 3:23–33. https://doi.org/10.1007/s40823-018-0030-z Martínez E, Ramos-A. CH, Chiang F (1994) Lista florística de la Lacandona, Chiapas. Bot Sci 0:99–177. https://doi.org/10.17129/botsci.1430 Martinez-Ramos M, Soto-Castro A (1993) Seed rain and advanced regeneration in a tropical rain forest. Vegetatio 107:299–318. https://doi.org/10.1007/BF00052231 Miguet P, Jackson HB, Jackson ND, et al (2016) What determines the spatial extent of landscape effects on species? Landsc Ecol 31:1177–1194. https://doi.org/10.1007/s10980-015-0314-1 Minor DM, Kobe RK (2019) Fruit production is influenced by tree size and size-asymmetric crowding in a wet tropical forest. Ecol Evol 9:1458–1472. https://doi.org/10.1002/ece3.4867 Missouri Botanical Garden (2019) Tropicos.org. https://www.tropicos.org/ . Accessed 3 Jun 2019 Nascimento HEM, Andrade ACS, Camargo JLC, et al (2006) Effects of the Surrounding Matrix on Tree Recruitment in Amazonian Forest Fragments. Conservation Biology 20:853–860. https://doi.org/10.1111/j.1523-1739.2006.00344.x Nathan R, Muller-Landau HC (2000) Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol Evol 15:278–285. https://doi.org/10.1016/S0169-5347(00)01874-7 Navarrete-Segueda A, Martínez-Ramos M, Ibarra-Manríquez G, et al (2018) Variation of main terrestrial carbon stocks at the landscape-scale are shaped by soil in a tropical rainforest. Geoderma 313:57–68. https://doi.org/10.1016/j.geoderma.2017.10.023 Nepstad DC, Veríssimo A, Alencar A, et al (1999) Large-scale impoverishment of amazonian forests by logging and fire. Nature 398:505–508. https://doi.org/10.1038/19066 Nicasio-Arzeta S, Zermeño-Hernández IE, Maza-Villalobos S, Benítez-Malvido J (2021) Landscape structure shapes the diversity of tree seedlings at multiple spatial scales in a fragmented tropical rainforest. PLoS One 16:e0253284. https://doi.org/10.1371/journal.pone.0253284 Nicotra AB, Chazdon RL, Iriarte SVB (1999) Spatial heterogeneity of light and woody seedling regeneration in tropical wet forests. Ecology 80:1908–1926. https://doi.org/10.1890/0012-9658(1999)080 [1908:SHOLAW]2.0.CO;2 Oksanen J, Blanchet FG, Friendly M, et al (2019) vegan: Community Ecology Package. https://cran.r-project.org/web/packages/vegan/index.html Olden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Global Ecology and Biogeography 15:113–120. https://doi.org/10.1111/j.1466-822X.2006.00214.x Oliveira MA, Santos AMM, Tabarelli M (2008) Profound impoverishment of the large-tree stand in a hyper-fragmented landscape of the Atlantic forest. For Ecol Manage 256:1910–1917. https://doi.org/10.1016/j.foreco.2008.07.014 Patton DR (1975) A diversity index for quantifying habitat “edge.” Wildl Soc Bull 171–173 R Development Core Team (2025) R: A Language and Environment for Statistical Computing. https://www.r-project.org/ Román-Cuesta RM, Martínez-Vilalta J (2006) Effectiveness of protected areas in mitigating fire within their boundaries: Case study of Chiapas, Mexico. Conservation Biology 20:1074–1086. https://doi.org/10.1111/j.1523-1739.2006.00478.x Rose S (2000) Seeds, seedlings, and gaps - size matters. a study in the tropical rain forest of Guyana. In: Programme T-G, Rose S (eds) Trobenpos-Guyana Series 9. Trobenpos-Guyana Programme, Georgetown, Guyana, pp 1–184 San-José M, Arroyo-Rodríguez V, Jordano P, et al (2019) The scale of landscape effect on seed dispersal depends on both response variables and landscape predictor. Landsc Ecol 1–12. https://doi.org/10.1007/s10980-019-00821-y Santo-Silva EE, Almeida WR, Melo FPL, et al (2013) The nature of seedling assemblages in a fragmented tropical landscape: Implications for forest regeneration. Biotropica 45:386–394. https://doi.org/10.1111/btp.12013 Santo-Silva EE, Withey KD, Almeida WR, et al (2015) Seedling assemblages and the alternative successional pathways experienced by Atlantic forest fragments. Plant Ecol Divers 8:483–492. https://doi.org/10.1080/17550874.2015.1048486 Solar RR, Barlow J, Ferreira J, et al (2015) How pervasive is biotic homogenization in human-modified tropical forest landscapes? Ecol Lett 18:1108–1118. https://doi.org/10.1111/ele.12494 Sousa M (2009) Adiciones al género Inga para la flora Mesoamericana. Acta Bot Mex 89:25–41 Suárez-Castro AF, Maron M, Mitchell MGE, Rhodes JR (2022) Disentangling direct and indirect effects of landscape structure on urban bird richness and functional diversity. Ecological Applications 32:e2713. https://doi.org/10.1002/EAP.2713 ;PAGEGROUP:STRING:PUBLICATION Suárez-Castro AF, Simmonds JS, Mitchell MGE, et al (2018) The Scale-Dependent Role of Biological Traits in Landscape Ecology: A Review. Current Landscape Ecology Reports 3:12–22. https://doi.org/10.1007/s40823-018-0031-y Tabarelli M, Peres CA, Melo FPL (2012) The “few winners and many losers” paradigm revisited: Emerging prospects for tropical forest biodiversity. Biol Conserv 155:136–140. https://doi.org/10.1016/j.biocon.2012.06.020 Tarka P (2018) An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Qual Quant 52:313–354. https://doi.org/10.1007/s11135-017-0469-8 Taubert F, Fischer R, Groeneveld J, et al (2018) Global patterns of tropical forest fragmentation. Nature 554:519–522. https://doi.org/10.1038/nature25508 Terborgh J, Lopez L, Nuñez P V., et al (2001) Ecological meltdown in predator-free forest fragments. Science (1979) 294:1923–1926. https://doi.org/10.1126/science.1064397 Trejo DAR (2008) Fire Regimes, Fire Ecology, and Fire Management in Mexico. AMBIO: A Journal of the Human Environment 37:548–556. https://doi.org/10.1579/0044-7447-37.7.548 Trolle M (2003) Mammal survey in the southeastern Pantanal, Brazil. Biodivers Conserv 12:823–836. https://doi.org/10.1023/A:1022489426920 Tuomisto H (2010) A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33:2–22. https://doi.org/10.1111/j.1600-0587.2009.05880.x Turner IM (2004) The Ecology of Trees in the Tropical Rain Forest. Cambridge University Press, Singapore Villard MA, Metzger JP (2014) Beyond the fragmentation debate: A conceptual model to predict when habitat configuration really matters. Journal of Applied Ecology 51:309–318. https://doi.org/10.1111/1365-2664.12190 ;JOURNAL:JOURNAL:13652664;PAGE:STRING:ARTICLE/CHAPTER Wandrag EM, Dunham AE, Duncan RP, Rogers HS (2017) Seed dispersal increases local species richness and reduces spatial turnover of tropical tree seedlings. Proc Natl Acad Sci U S A 114:10689–10694. https://doi.org/10.1073/pnas.1709584114 Wearn OR, Rowcliffe JM, Carbone C, et al (2017) Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach. Biol Conserv 212:162–171. https://doi.org/10.1016/j.biocon.2017.05.007 Whitmore TC (1989) Canopy gaps and the two major groups of forest. Ecology 70:536–538. https://doi.org/10.2307/1940195 Wies G, Nicasio Arzeta S, Martinez Ramos M (2021) Critical ecological thresholds for conservation of tropical rainforest in Human Modified Landscapes. Biol Conserv 255:109023. https://doi.org/10.1016/j.biocon.2021.109023 Wright SJ (2002) Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130:1–14. https://doi.org/10.1007/s004420100809 Young HS, McCauley DJ, Galetti M, Dirzo R (2016) Patterns, Causes, and Consequences of Anthropocene Defaunation. Annu Rev Ecol Evol Syst 47:333–358. https://doi.org/10.1146/annurev-ecolsys-112414-054142 Zermeño-Hernández I, Méndez-Toribio M, Siebe C, et al (2015) Ecological disturbance regimes caused by agricultural land uses and their effects on tropical forest regeneration. Appl Veg Sci 18:443–455. https://doi.org/10.1111/avsc.12161 Zermeño-Hernández I, Pingarroni A, Martínez-Ramos M (2016) Agricultural land-use diversity and forest regeneration potential in human- modified tropical landscapes. Agric Ecosyst Environ 230:210–220. https://doi.org/10.1016/j.agee.2016.06.007 Zurita GA, Bellocq MI (2010) Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc Ecol 25:147–158. https://doi.org/10.1007/s10980-009-9410-4 Additional Declarations No competing interests reported. Supplementary Files OnlineResource1.docx OnlineResource8.docx OnlineResource2.docx OnlineResource10.docx OnlineResource6.docx OnlineResource4.docx OnlineResource3.docx OnlineResource9.docx OnlineResource7.docx OnlineResource5.docx OnlineResource12.docx OnlineResource11.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 May, 2026 Reviews received at journal 21 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 22 Aug, 2025 Submission checks completed at journal 22 Aug, 2025 First submitted to journal 21 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7430288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510404732,"identity":"798ff34b-00f5-40ee-9415-5a501d1215d5","order_by":0,"name":"Sergio Nicasio-Arzeta","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Nicasio-Arzeta","suffix":""},{"id":510404734,"identity":"248ecad7-19f7-463c-bdcd-c4c3c84a871d","order_by":1,"name":"Susana Maza-Villalobos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYPACG9K1pJGu5TAJauVnZCd+/FFzXo6/vf2ZdAXDtsQGQloMbuRulpA4dttY4swZM8kzDLeNCdpiIJG7QcKA7XbiBokcNskGhttyRDgsd/OPhH/n6jdIpD8DaeEh7JkbudskDrYdSDCQSDAjzhaDM2+3WTb2JRvOOHPG2LLBgAi/yLfnbr7545udPDDEHt5sqLhNOMTQLSVR/SgYBaNgFIwC7AAAhF87m2I666cAAAAASUVORK5CYII=","orcid":"","institution":"El Colegio de la Frontera Sur, Unidad San Cristóbal de Las Casas","correspondingAuthor":true,"prefix":"","firstName":"Susana","middleName":"","lastName":"Maza-Villalobos","suffix":""},{"id":510404736,"identity":"7230d431-e7b2-4fc1-b9cc-25d5f2cbc137","order_by":2,"name":"Aline Pingarroni","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"Aline","middleName":"","lastName":"Pingarroni","suffix":""},{"id":510404738,"identity":"8824822d-f436-46cb-9890-452ce1ea08a4","order_by":3,"name":"Isela Zermeño-Hernández","email":"","orcid":"","institution":"Universidad Michoacana de San Nicolás de Hidalgo","correspondingAuthor":false,"prefix":"","firstName":"Isela","middleName":"","lastName":"Zermeño-Hernández","suffix":""},{"id":510404739,"identity":"d85821d3-2749-4311-a124-932b90cdc88a","order_by":4,"name":"Julieta Benítez-Malvido","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"Julieta","middleName":"","lastName":"Benítez-Malvido","suffix":""}],"badges":[],"createdAt":"2025-08-22 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7430288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7430288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91017084,"identity":"5770dedd-8688-48c5-98a8-9a67898f0691","added_by":"auto","created_at":"2025-09-10 17:09:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39250,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model employed to assess the association among landscape (i.e., composition and configuration) and patch structure (i.e., size, shape, tree basal area, tree diversity, and disturbance), and seedling community (i.e., abundance and diversity). The hypothesis evaluated in this study are presented in two hypothetical pathways: direct effects of landscape/patch and cascading effects whether by landscape structure through patches (1) or by patch structure through seedling abundances (2).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/58c2b2d8be2788daeb7044a9.png"},{"id":91016627,"identity":"94b75e98-3aed-4b9e-acee-e31c3f18d4c5","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":332277,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area in the Lacandona rain forest in Chiapas, southeastern Mexico (a). We show the location of the 16 study forest fragments (black) in the Marqués de Comillas Region (b). The 13 buffer sizes (300-1500ha) around the geographic center of the focal patch (c) and the seedling sampling procedure (d) are also indicated.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/3a3b7b0e3ab507f27205bc28.png"},{"id":91016637,"identity":"52c053a5-0e4e-45d9-9465-1da934743c75","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94313,"visible":true,"origin":"","legend":"\u003cp\u003eBest-fitted structural equation models for seedling (a) composition (\u003cem\u003edf\u003c/em\u003e =12), and α- and β-diversity of (b) all species (\u003cem\u003edf\u003c/em\u003e =10), (c) typical species (\u003cem\u003edf\u003c/em\u003e =10) and (d) dominant species (\u003cem\u003edf\u003c/em\u003e =10) in 16 forest patches in the Lacandona rainforest, southeast Mexico. Arrow thickness is proportional to the standardized path-coefficient value. The coefficients of determination of linear models (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) are reported. Patch size (P\u003csub\u003eSZ\u003c/sub\u003e), tree basal area (T\u003csub\u003eBA\u003c/sub\u003e), disturbance by fire (D\u003csub\u003eF\u003c/sub\u003e), patch aggregation (AI), as well as the abundances of light-demanding (LD), and shade-tolerant seedlings (ST), compositional axes from the NMDS analysis (MDS1 and MDS2), and α- and β-diversity of all species (\u003csup\u003e0\u003c/sup\u003eα, \u003csup\u003e0\u003c/sup\u003eβ) typical species (\u003csup\u003e1\u003c/sup\u003eα, \u003csup\u003e1\u003c/sup\u003eβ) and dominant species (\u003csup\u003e2\u003c/sup\u003eα, \u003csup\u003e2\u003c/sup\u003eβ) are shown.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/82e932ec97539c88defd25f9.png"},{"id":91017094,"identity":"142cc06a-919d-46e3-b246-8f24f969c919","added_by":"auto","created_at":"2025-09-10 17:09:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":310813,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS ordination of seedling assemblages of 16 forest patches (grey numbers) in the Lacandona rainforest, southeast Mexico. Abbreviations are seedling species (Online Resource 2).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/c44a2048b10302dc2e297fac.png"},{"id":91017987,"identity":"904717ea-ddc4-41c7-9eed-eaa87836b952","added_by":"auto","created_at":"2025-09-10 17:25:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1485437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/0fab3351-fefd-479d-9126-76d3aa091c28.pdf"},{"id":91016630,"identity":"3e261461-2dc9-4032-9c03-f7fa021501b6","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":119113,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/8dac500aaf504142163a8a83.docx"},{"id":91016623,"identity":"58e7d46f-182b-4456-aa9e-e4cedb10e181","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16030,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/7121a88722de8713ca97577b.docx"},{"id":91016640,"identity":"2c71c574-75a8-43dc-945a-116dd894b242","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28541,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/20d63727492294448e3bbfbf.docx"},{"id":91016652,"identity":"c50d5045-31ed-4eae-8084-033635649929","added_by":"auto","created_at":"2025-09-10 17:01:11","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17116,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource10.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/22bd3f722e740b801cea43e1.docx"},{"id":91017087,"identity":"8e043860-d03b-44f7-8a30-32d13a4f0f64","added_by":"auto","created_at":"2025-09-10 17:09:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16790,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource6.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/e4192074fb3d44215cdac05d.docx"},{"id":91016624,"identity":"1b49fcff-07d4-44ff-907a-13ab9123c8f7","added_by":"auto","created_at":"2025-09-10 17:01:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17672,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/64562ea9362ab8ed8a09e96d.docx"},{"id":91016664,"identity":"3e0dfe74-0611-46e4-a3f4-40298cf08774","added_by":"auto","created_at":"2025-09-10 17:01:11","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":19734,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/786e23361d4c5f38e28a644a.docx"},{"id":91017095,"identity":"87fbaa13-d65e-480b-aa9c-a39827d1a449","added_by":"auto","created_at":"2025-09-10 17:09:11","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17072,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource9.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/2ec0efd470ccccbf66f43592.docx"},{"id":91016663,"identity":"c46a25ac-34b3-4d42-9c44-f01619feed72","added_by":"auto","created_at":"2025-09-10 17:01:11","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":17021,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource7.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/0ceffc8e8981547c5ddd16ca.docx"},{"id":91017092,"identity":"ee5705c5-c387-47ec-9ad8-5fbf2ebd65cc","added_by":"auto","created_at":"2025-09-10 17:09:11","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":41519,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/3d1e048b8e5f086c998610ff.docx"},{"id":91017417,"identity":"88c2ca54-d02c-47f0-a737-6d10edf87a37","added_by":"auto","created_at":"2025-09-10 17:17:10","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":24657,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/cfa2e18bec1b57a4072878e2.docx"},{"id":91016650,"identity":"39588559-dabe-4beb-bdce-d084dd2ab045","added_by":"auto","created_at":"2025-09-10 17:01:11","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":22021,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7430288/v1/7095a0be6c12a1602e78cea5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fire and Landscape Configuration Shape Forest Futures: Direct and Indirect Drivers of Seedling Diversity in a Human-Modified Tropical Rainforest","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeforestation worldwide has reduced manyto small rainforest patches (\u0026lt;\u0026thinsp;20 ha), which may dominate tropical landscapes in the following decades (Taubert et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this sense, it is fundamental to understand the abundance and diversity patterns of tree seedlings within patches, because tree species determine the function, structure, and diversity of rainforests (Brokaw and Scheiner \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Whitmore \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Dalling et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe abundance, richness (α-diversity), and spatial heterogeneity (β-diversity) of tree seedlings are shaped by seed production and dispersal, as well as seedling mortality by density dependence and predation/trampling by herbivorous mammals (Nathan and Muller-Landau \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Harms et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Comita et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Young et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Forest fragmentation causes changes in both the size and shape of patches, resulting in edge effects that positively impact light-demanding tree species and negatively impact shade-tolerant species, leading to a reduction in diversity (Benitez-Malvido \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Santo-Silva et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ben\u0026iacute;tez-Malvido et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), impoverishing α- and β-diversity (Krishnadas et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tabarelli et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Seedling communities can also be affected by patch vegetation.\u003c/p\u003e\u003cp\u003eThe tree basal area plays a key role in light availability, microclimate regulation, nutrient cycling, and seed production (Hardwick et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lohbeck et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Minor and Kobe \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wies et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which regulates the abundance of light-demanding and shade-tolerant seedlings (Whitmore \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Denslow and Guzman G. 2000). In addition, patches with a high basal area harbor more forest-dependent birds and mammals, which favor seedling α- and β-diversity throughout seed dispersal (Zurita and Bellocq \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wearn et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cudney-Valenzuela et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ben\u0026iacute;tez-Malvido et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, seedling abundance and composition can be influenced by tree diversity (Terborgh et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Comita et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), in which seed input is limited to local adult trees (Wandrag et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, agricultural land use limits tropical forest regeneration (Guariguata and Ostertag \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Font\u0026uacute;rbel et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The size, duration,, and severity of forest disturbances produced caused by agricultural and cattle management can limit seed rain and seed banks, reduce soil fertility, increase temperature and water deficits, and reduce the increase the abundance and diversity of seedlings (Benitez-Malvido \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zerme\u0026ntilde;o-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Such negative effects are more significant in landscapes dominated by cattle pastures (Zerme\u0026ntilde;o-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where cattle grazing and trampling limit seedling establishment. Moreover, it disturbs soil fertility and reduces the diversity and basal area of the remaining forests (Barlow et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, it is well documented that landscape composition (i.e., the proportion of primary forest and matrix cover within the landscape) and configuration (i.e., the number/aggregation of patches as well as the edge contrast between patches and matrix) significantly affect seedling communities. Landscape structure can influence seedling abundance, composition, and diversity by sustaining seed dispersal in landscapes with high forest cover and high patch density/aggregation, (Jesus et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; San-Jos\u0026eacute; et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or by mitigating edge effects and facilitating the arrival of seed/seedling predators in landscapes with low edge contrast (Arasa-Gisbert et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nicasio-Arzeta et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In highly deforested rainforests, landscape fragmentation has a stronger impact on understory vegetation than habitat loss (Nicasio-Arzeta et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Arasa-Gisbert et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The \"fragmentation threshold hypothesis\" postulates that patch-scale factors and landscape configuration have a stronger effect on biodiversity within landscapes with \u0026lt;\u0026thinsp;30% of forest cover (Andren \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Consequently, the synergistic effects of patch structure and landscape configuration may drive the tree seedling composition and diversity in highly fragmented rainforests.\u003c/p\u003e\u003cp\u003eThe seedling community and patch structure are expected to be influenced by landscape structure at multiple spatial scales, as the scale of the effect varies among biological variables (i.e., structure, abundance, composition, and richness), habitat specialization (i.e., light-demanding/shade-tolerant species), and the extent of disturbance(Miguet et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martin \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Su\u0026aacute;rez-Castro et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This suggests that landscape structure may have cascading effects on seedling communities, a phenomenon that has been observed elsewhere (Hern\u0026aacute;ndez-Ruedas et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These cascading effects can potentially operate through patch structural attributes. The proportion of forest cover in the landscape was directly related to patch size (Fahrig \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hernando et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and basal tree area/diversity (Laurance et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wies et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brindis-Badillo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, tree diversity is affected by the surrounding secondary forests and aggregation of forest patches (Nascimento et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Laurance et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jesus et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Forest disturbances caused by fires and disturbance intensity can also be modulated by edge contrast, forest cover, and patch density. Edge exposure increases the fuel availability and fire ignition (Rom\u0026aacute;n-Cuesta and Mart\u0026iacute;nez-Vilalta \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Avila-Flores et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Farf\u0026aacute;n Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, cattle presence is limited when forest cover or patch numbers are high, as they increase the occurrence of predators (Trolle \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Desbiez et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; de Souza et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although patches and landscape structures can potentially have direct or cascading effects on seedling communities, their estimation remains unexplored.\u003c/p\u003e\u003cp\u003eThis study aimed to identify the direct and cascading effects of patch and landscape structure on seedling abundance, composition, and diversity in fragmented rainforests. We hypothesized that patch structure and landscape configuration would have direct or cascading effects on seedling composition and diversity. Additionally, we predicted that seedling composition and β-diversity are more significantly influenced by patch structure, as these community attributes are sensitive to fluctuations in species abundance with contrasting life-history traits (Comita et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Santo-Silva et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, we expect similar species composition and greater β-diversity within patches with lower abundance of light-demanding seedlings and higher abundance of shade-tolerant seedlings. Additionally, we hypothesized that α-diversity of tree seedlings would be favored in landscapes with high forest cover or high patch aggregation, as species richness is strongly associated with seed dispersal (San-Jos\u0026eacute; et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arasa-Gisbert et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ben\u0026iacute;tez-Malvido et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These effects may occur through cascading effects of landscape structure by influencing patch structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, hypothetical pathway 1), or through cascading effects of patch structure by mediating abundance fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, hypothetical path 2). Structural equation models were used to assess the direct and indirect effects of the patch and landscape components (Online Resource 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study site.\u003c/h2\u003e\u003cp\u003eThe study was conducted in the Marqu\u0026eacute;s de Comillas region of the Lacandona rainforest in southeastern Mexico (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Most cleared areas in the study site are utilized for cattle ranching (Carabias et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while the remaining forest patches shade the cattle. The latter function implies that some landowners remove understory vegetation to facilitate the movement of cattle and individuals across these patches. Fire-based land clearing is another prevalent practice in the area, and occasionally uncontrolled fires extend into patches, partially burning trees. Additionally, patches near R\u0026iacute;o Lacant\u0026uacute;n experience periodic flooding during the rainy season.\u003c/p\u003e\u003cp\u003eSixteen forest patches were selected for this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Land tenure in Marqu\u0026eacute;s de Comillas is private, and land ownership is distributed among inhabitants (Kolb and Galicia \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Berget et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, the study was conducted exclusively in patches where prior permission was obtained from owners. The patches were distributed across alluvial terraces, ensuring relative consistency in the diversity and composition of tree assemblages, soil types, and abiotic factors (Navarrete-Segueda et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lohbeck et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Seedling sampling.\u003c/h2\u003e\u003cp\u003eFrom February to June 2014, patches were sampled by positioning 1-ha blocks at their center. Each block contained 10 1-m2 plots randomly arranged in groups of two or three plots along five equidistant transects (20 m apart; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). For the 1-ha forest patches, plots were never positioned at the edge to maintain a 20-m buffer zone of vegetation that protected them from strong edge effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Within each 1-m2 plot, all tree seedlings (10\u0026ndash;100 cm height) were counted and identified to the lowest possible taxonomic level with the assistance of a local parataxonomist and field guide(Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sousa \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). When field identification was not possible, samples were collected for identification at several herbaria sites (MEXU, ECO-SC-H). This study did not involve extraction or damage to endangered species. The plant nomenclature followed the Missouri Botanical Garden database, Tropicos (Missouri Botanical Garden \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Once identified, seedling species were classified by dispersal syndrome (Ibarra-Manr\u0026iacute;quez et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ibarra-Manr\u0026iacute;quez and Cornejo-Tenorio \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and light requirements for establishment and growth into light-demanding species, shade-tolerant species, and intermediate species (Nicotra et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rose \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ben\u0026iacute;tez-Malvido et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kitajima et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA total of 1378 seedlings belonging to 27 families, 38 genera, and 67 species were recorded. Only animal-dispersed species were considered for further analysis, as they comprise up to 90% of the rainforest seed rain (Jordano \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; San-Jos\u0026eacute; et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The animal-dispersed seedlings accounted for 94.7% (1305) of the total recorded seedlings, representing 24 families, 30 genera, and 56 species. From this subset of species, 668 seedlings (52.72%) from 27 shade-tolerant tree species were identified, followed by 527 seedlings (40.38%) from 18 light-demanding species, and 90 seedlings (6.9%) from 11 intermediate or indeterminate species (Online Resource 2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Seedling community.\u003c/h2\u003e\u003cp\u003eWe assessed sampling completeness using Chao and Shen's sample-coverage estimator (Chao and Lee \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). We combined data from the ten sampling plots within each patch. Subsequently, we estimated the proportion of individuals belonging to the species represented in the sample. The sample coverage among patches was 91.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47%, indicating that our sampling effort was sufficient to estimate diversity (Chao and Jost \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe aggregated the seedlings from the 10 1-m\u0026sup2; plots to obtain an abundance of light-demanding and shade-tolerant seedlings for each patch (seedlings per 10 m\u0026sup2;). Additionally, we employed Nonmetric Multidimensional Scaling (NMDS) to estimate species compositional similarity among patches (Laurance et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Santo-Silva et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We utilized the metaMDS function based on the Bray-Curtis dissimilarity index of the vegan package and complete seedling dataset (Oksanen et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We employed the NMDS scores of the first two axes as response variables.\u003c/p\u003e\u003cp\u003eWe employed diversity decomposition of the effective numbers of species or Hill numbers (Jost \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Hill numbers (qD) are expressed in units of species, which allows for the characterization of the distribution of species abundance and provides comprehensive information about community diversity (Jost \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chao et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We calculated the Hill numbers of all species (species richness or 0D), typical species (exponential of Shannon's entropy index or 1D), and dominant species (inverse of Simpson's index or 2D). The 0D is not sensitive to individual abundances (Jost \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tuomisto \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), enabling the inclusion of rare species in assessments, whereas 1D and 2D assign a high weight to the equally abundant and the most abundant species, respectively (Jost \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe followed Jost (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Tuomisto (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to calculate the gamma (γ), alpha (α), and beta (β) diversity (Online Resource 3). The α-diversity values are expressed in species/m\u003csup\u003e2\u003c/sup\u003e. In contrast, β-diversity represents the effective number of completely distinct assemblages, ranging from one (when the assemblages of all 1-m\u0026sup2; plots are identical) to 10 (when the assemblages of the 10 1-m2 plots are entirely dissimilar; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The package \u003cem\u003evegan\u003c/em\u003e in R was used for the entire procedure (Oksanen et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\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\u003eSummary of response and explanatory variables of the study\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLabel\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAbundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLight-demanding seedlings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS\u003csub\u003eLD\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShade-tolerant seedlings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eDiversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-diversity of all species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60\u0026ndash;4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e0\u003c/sup\u003eα\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ-diversity of all species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.33\u0026ndash;6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e0\u003c/sup\u003eβ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-diversity of typical species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u0026ndash;3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eα\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ-diversity of typical species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25\u0026ndash;3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eβ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-diversity of dominant species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u0026ndash;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eα\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ-diversity of dominant species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u0026ndash;3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eβ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003ePatch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpatial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.96\u0026ndash;71.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003csub\u003eSZ\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u0026ndash;5.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003csub\u003eSI\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTree basal area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.04\u0026ndash;186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003eBA\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTree abundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of adult tree species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u0026ndash;27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003e0D\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of typical adult tree species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.12\u0026ndash;20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003e1D\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of dominant adult tree species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3\u0026ndash;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003e2D\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDisturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFire index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.28\u0026ndash;5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD\u003csub\u003eF\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eManagement index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.99\u0026ndash;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD\u003csub\u003eM\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLandscape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComposition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForest cover (1200 m radius)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.83\u0026ndash;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFC\u003csub\u003e1200\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eConfiguration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatch density (600 m radius)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u0026ndash;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePD\u003csub\u003e600\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatch density (900 m radius)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.97\u0026ndash;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePD\u003csub\u003e900\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatch aggregation (600 m radius)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.56\u0026ndash;97.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAI\u003csub\u003e600\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Patch structure.\u003c/h2\u003e\u003cp\u003eWe used a multispectral SPOT-5 satellite image with a 10 \u0026times; 10 m pixel resolution, recorded in March 2013, to conduct supervised classification using GRASS GIS software (GRASS Development Team \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A forest/non-forest classification was developed, and sampling points were employed to evaluate classification accuracy. The overall classification accuracy was 79%. The resulting raster file was exported into a shapefile to calculate the patch perimeter in meters (m) and patch area in m\u003csup\u003e2\u003c/sup\u003e using the \u003cem\u003ergeos\u003c/em\u003e package (Bivand \u0026amp; Rundel, 2019). These data were later used to assess patch size in hectares and to evaluate patch shape as a dimensionless index that measures patch complexity against a square of the same size (Patton \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). We controlled patch size and shape effects by using the residuals of a linear model between patch shape and size (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.63; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e1,14\u003c/sub\u003e = 26.32; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eFor each patch, we assessed the tree community by measuring the diameter at breast height (DBH 130 cm from the ground) of all trees with DBH\u0026thinsp;\u0026ge;\u0026thinsp;10 cm across ten 50 \u0026times; 2 m transects (Gentry \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). To reduce forest diversity and structural biases caused by edge effects, we positioned all transects at least 20 m from the patch edges, when feasible (Laurance et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In each transect, we counted, measured the diameter, and identified every tree at the species level (Missouri Botanical Garden, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and then computed tree abundance (trees per 0.1 ha), basal area (m\u0026sup2; per ha), richness, Shannon's exponential entropy index, and the inverse of Simpson's index (species per 0.1 ha).\u003c/p\u003e\u003cp\u003eIn addition, we collected data on disturbance factors within patches through a questionnaire administered to landowners and field observations throughout the study period. We used the questionnaire results to construct a binary base of the following patch disturbance factors (Online Resource 4): floods during the rainy season, fire events during the last 10 years, presence of cattle, slashing of understory vegetation, and predominant land use in the adjacent matrix. Subsequently, we employed a logistic PCA (Landgraf and Lee \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to generate disturbance indices from the binary data. We previously estimated the number of dimensions (\u003cem\u003ek\u003c/em\u003e) and tuning parameter (\u003cem\u003em\u003c/em\u003e) through cross-validation of the negative log-likelihood. We obtained \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 and \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 for the analysis (Online Resource 5). The final model accounted for 77.8% of the explained deviance. We used the \u003cem\u003elogisticPCA\u003c/em\u003e package for the entire procedure (Landgraf and Lee \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Spearman correlation between disturbance factors and PCA components, slashing of understory vegetation, adjacent land use, and presence of cattle were strongly associated with the first component. In contrast, flooding and fire events are associated with the second component (Online Resource 6). Consequently, we employed the first component as the management disturbance index (D\u003csub\u003eM\u003c/sub\u003e) and the second component as the fire disturbance index (D\u003csub\u003eF\u003c/sub\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Landscape structure\u003c/h2\u003e\u003cp\u003eWe calculated five landscape metrics (Online Resource 7) that influence patch size, vegetation, and disturbance in the tropics and seedling community. The composition metrics comprised the proportion of primary forest cover (FC) and secondary forest cover (SF), whereas the configuration metrics comprised patch density (PD), patch aggregation (AI), and edge contrast (EC). We calculated by assigning quality values to each matrix cover. These values serve as indicators of the capacity of the matrix cover to mitigate edge effects and facilitate the movement of terrestrial mammals. The quality values were (Garmendia et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gal\u0026aacute;n-Acedo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e): 1 (water bodies, representing the lowest suitability), 2 (anthropogenic cover), 3 (cattle pasture), 4 (arboreal crops), 5 (floodplains), 6 (secondary forest), and 7 (old-growth forest, representing the highest suitability). We estimated the area-weighted mean of the EC index to obtain a more robust and realistic representation of the landscape structure effects(Li and Archer \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe measured these landscape metrics within 13 circular buffers (300\u0026ndash;1500 m radius at 100 m intervals) from the center of each focal patch (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Radii were based on the dispersal distances of seed dispersers and terrestrial mammals, landscape size to assess seedling communities and disturbances, and spatial scales at which landscape structure affects forest diversity and structure in the study region (Zerme\u0026ntilde;o-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; San-Jos\u0026eacute; et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wies et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nicasio-Arzeta et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Independence between sampling sites, landscape metrics, and buffer sizes can be found in Nicasio-Arzeta et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e\u003cp\u003eWe utilized linear models to estimate the scale of the effect of each landscape metric on patch structure. We fitted patch size, shape, tree basal area, diversity, and disturbance by fire and management with a single landscape metric for each buffer and obtained the coefficient of determination (\u003cem\u003eR\u0026sup2;\u003c/em\u003e). Subsequently, we plotted the resulting 13 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values and selected the one with the strongest response as the scale of the effect for that landscape metric. We repeated this procedure for each patch variable and the landscape metric. Subsequently, we evaluated the effects of landscape metrics and fire disturbance on each patch structure variable by using multiple linear models. We employed only landscape metrics at the scale of the effect identified in the previous step (Online Resource 8). All models were additive because of the limited sample size of the 16 forest patches.\u003c/p\u003e\u003cp\u003eWe employed the dredge function of the \u003cem\u003eMuMIn\u003c/em\u003e package (Barton \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to create all possible combinations of explanatory variables and the null model (only the intercept) to assess the relative importance and effects of landscape metrics and fire disturbance using an information theory approach and multimodel inference (Burnham and Anderson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). We used each model's accumulated sum of Akaike weights (wi) to select a subset of models with \u0026sum;\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u0026le; 0.95. Then, we employed \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e of the model\u0026rsquo;s subset to calculate each explanatory variable's relative importance and the model-averaged parameter estimates. We considered influential variables for which the confidence interval did not include zero in the averaged parameters. All statistical analyses in the R 3.5.2 statistical computing environment (R Development Core Team \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We selected the significant landscape metrics and disturbance factors of each patch structural variable for further analysis (Online Resource 9).\u003c/p\u003e\u003cp\u003eWe analyzed the direct and indirect effects of patch and landscape structures on seedling abundance, composition, and diversity using structural equation models (SEM). SEM is a statistical method used to test causal relationships (Fan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tarka \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We proposed a conceptual model of multiple casualties to explain seedling abundance, diversity, and composition in relation to the patch components (size, shape, basal area, tree diversity, and disturbance by fire and management), which served as exogenous predictors (Online Resource 1). Indirect paths included the abundance of light-demanding and shade-tolerant seedlings, representing a hypothesis regarding cascading effects mediated by abundance fluctuations. We previously assessed the association between the explanatory variables to avoid variance inflation. We found that patch structural variables were strongly correlated (Online Resource 10); therefore, only tree basal area and diversity were used for further analysis.\u003c/p\u003e\u003cp\u003eThe SEM was fitted using a \u003cem\u003epiecewiseSEM\u003c/em\u003e package (Lefcheck \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Multivariate normality of seedling (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1,p\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;217.39; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54 and \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2,p\u003c/em\u003e\u003c/sub\u003e = -1.95; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052) and patch data (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1,p\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;67.41; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14 and \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2,p\u003c/em\u003e\u003c/sub\u003e = -1.03; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30) was validated. We applied a logarithmic transformation to the patch size and abundance of light-demanding seedlings and a square root transformation to the abundance of shade-tolerant seedlings to meet the normality assumptions. Owing to sample size limitations, we performed simple model structures (few variables). Hence, we tested different combinations of patch structures (D\u003csub\u003eM\u003c/sub\u003e, T\u003csub\u003eBA\u003c/sub\u003e, T\u003csub\u003eD\u003c/sub\u003e, P\u003csub\u003eSZ\u003c/sub\u003e, and P\u003csub\u003eSI\u003c/sub\u003e) using alternative models instead of all together. We employed the fire disturbance index and selected landscape metrics (D\u003csub\u003eF\u003c/sub\u003e, FC\u003csub\u003e1200\u003c/sub\u003e, PD\u003csub\u003e600\u003c/sub\u003e, PD\u003csub\u003e900\u003c/sub\u003e, and AI\u003csub\u003e600\u003c/sub\u003e) to explain the variation in the exogenous variables. Alternative models consist of combinations of one and two exogenous predictors. This produced 15 alternative models for each diversity order (all species: \u003csup\u003e0\u003c/sup\u003eα, \u003csup\u003e0\u003c/sup\u003eβ; typical species: \u003csup\u003e1\u003c/sup\u003eα, \u003csup\u003e1\u003c/sup\u003eβ; dominant species: \u003csup\u003e2\u003c/sup\u003eα, \u003csup\u003e2\u003c/sup\u003eβ) and composition indices (MDS1, MDS2). The model selection procedure consisted of rejecting all models with a lack of fit (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); then, we excluded the models that had no significant links to seedling abundance, composition, or diversity variables we attempted to explain. Finally, the best-fitting models were selected based on the lowest AICc values.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOur candidate models were well fitted, indicating that the conceptual model adequately described the data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The best-fitted models (those with the lowest AICc; Online Resources 11 and 12) included patch size and aggregation for diversity of all species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), whereas tree basal area explained seedling composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and the diversity of typical and dominant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and d). For the latter models, the tree basal area decreased in patches affected by previous fire events (B = -0.63), and the abundance of light-demanding seedlings was reduced by the tree basal area (B = -0.54). The latter indicated that fire disturbance indirectly promoted the abundance of light-demanding seedlings (B = -0.63 \u0026times; 0.54\u0026thinsp;=\u0026thinsp;0.34).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe seedling composition model explained 39% of the basal area, 30% of the light-demanding seedling abundance, 12% of the shade-tolerant seedling abundance, and 64% and 56% of the first and second NMDS axes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The first axis was negatively influenced by light-demanding seedlings (B = -0.55), whereas the second NMDS axis was negatively affected by the abundance of shade-tolerant seedlings (B = -0.76). The indirect effect of fire disturbance on the first composition axis was lower (i.e., -0.63 \u0026times; -0.55 \u0026times; 0.54 = -0.18) than the indirect effect of basal area (i.e., -0.55 \u0026times; 0.54\u0026thinsp;=\u0026thinsp;0.29). Seedling composition differed between fire-disturbed and non-disturbed patches (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e1,14\u003c/em\u003e\u003c/sub\u003e = 2.64; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.16; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), where the abundance of light-demanding seedlings was higher in fire-disturbed patches than in their non-disturbed counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe typical species model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) explained 40% of the α-diversity and was positively influenced only by the abundance of shade-tolerant seedlings (B\u0026thinsp;=\u0026thinsp;0.54). In contrast, the 68% explanation of β-diversity variation was strongly attributed to the negative effect of light-demanding seedlings (B = -1) and indirectly promoted by tree basal area (i.e., -1 \u0026times; 0.54\u0026thinsp;=\u0026thinsp;0.54). This also holds true for the dominant species model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), which explained 69% of the β-diversity through the negative effects of light-demanding (B = -0.94) and shade-tolerant (B = -0.43) seedling abundances. Tree basal area also positively affected the β-diversity of dominant species (i.e., -0.94 \u0026times; 0.54\u0026thinsp;=\u0026thinsp;0.50). Finally, fire disturbance had overall indirect effects on the decline in β-diversity of typical (B = -1 \u0026times; -0.54 \u0026times; -0.63\u0026thinsp;=\u0026thinsp;0.34) and dominant species (B = -0.94 \u0026times; -0.54 \u0026times; -0.63\u0026thinsp;=\u0026thinsp;0.32).\u003c/p\u003e\u003cp\u003eFinally, the model for all species explained 40% of the patch size, 10% of light-demanding seedlings, 51% of shade-tolerant seedlings, 50% of α-diversity, and 17% of β-diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Patch aggregation had direct effects on patch size (B\u0026thinsp;=\u0026thinsp;0.64) and shade-tolerant seedlings (B\u0026thinsp;=\u0026thinsp;0.63), which were also affected by light-demanding seedlings (B = -0.58). In turn, shade-tolerant seedlings positively affected α-diversity (B\u0026thinsp;=\u0026thinsp;0.56), which was indirectly reduced by the abundance of light-demanding seedlings (B = -0.58 \u0026times; 0.56 = -0.32). Patch aggregation had cascading effects on the increase in seedling α-diversity (B = -0.63 \u0026times; 0.54\u0026thinsp;=\u0026thinsp;0.34).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur findings confirm that landscape configuration and disturbance factors within patches, such as fire incidence, drive species composition and α- and β-diversity of typical and dominant seedling species. As anticipated, patches with low-disturbance regimes and higher basal areas exhibited a reduced abundance of light-demanding seedlings species, leading to a more diversified seedling community. Furthermore, landscapes with more aggregated patches increased the abundance of shade-tolerant seedling species, resulting in cascading effects that favored α-diversity of species within the regenerative community. Thus, the cascading effects of patch disturbance and landscape configuration shaped the species composition and α- and β-diversity, respectively.\u003c/p\u003e\u003cp\u003eFire disturbance was the primary local factor exerting significant cascading effects that modulated the abundance, composition, and diversity of the regenerative seedling community. Fire is an atypical disturbance in tropical rainforests, where most tree species are considered fire sensitive (Trejo \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Nevertheless, land use changes to pastures and farmlands, forest fragmentation, and extreme weather events increase fire frequency, severity, and extent (Cochrane and Laurance \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this study, fire occurrence, which was present in approximately 50% of the forest patches, significantly reduced the basal area of adult trees, leading to an increased abundance of light-demanding species in the understory. This observation is consistent with other studies, which have shown that surface fires reduce the basal area by removing medium- and large-sized trees and lianas (Nepstad et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Barlow et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e; Cochrane and Laurance \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The consequent canopy openness, which can be four times greater than that in unburned forests, increases the light incidence on the forest floor (Barlow et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese microclimate alterations favor the dominance of disturbance-tolerant tree species in the understory (Cochrane and Laurance \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), as observed in this study. In turn, the abundance of light-demanding species directly affected seedling composition and turnover of equally common and abundant species. Consequently, patches with fire events in the preceding 10 years exhibited a distinct seedling community compared with those without fires. Patches with fire events were predominantly characterized by light-demanding species, such as \u003cem\u003eInga punctata\u003c/em\u003e. Some tropical rainforest tree species, such as \u003cem\u003eSwietenia macrophylla\u003c/em\u003e in southern Mexico, respond positively to fires. However, most tree species in tropical forests are fire sensitive (Barlow et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003b\u003c/span\u003e; Brando et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the presence of species such as \u003cem\u003eBrosimum alicastrum\u003c/em\u003e and \u003cem\u003eAmpelocera hottlei\u003c/em\u003e found exclusively in unburned patches may be threatened by the occurrence of fires.\u003c/p\u003e\u003cp\u003eAn assemblage of species can be determined by their species abundance, frequency, clade of membership (e.g., family, order, class), as well as their functional attributes (e.g., shade-tolerant species, light-demanding species, anemochorous species, zoochorous species, nitrogen-fixing species). In our study, a lower basal area was associated with an increased abundance of light-demanding species, suggesting that disturbed fragments (characterized by a lower abundance of large trees) facilitate the establishment and survival of these species by increasing light availability (Denslow \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). These conditions promote the establishment of a subset of ecologically redundant species that lack phylogenetic relationships, resulting in impoverished species assemblages that are dominated by a few species (Oliveira et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tabarelli et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These assemblages indicate a process of biotic homogenization, characterized by low dissimilarity between communities, as well as a reduction in β-diversity (i.e., less species turnover) at local and landscape scales (Olden and Rooney \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tabarelli et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Solar et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Basal area influences the abundance and composition of seedlings through seed production (Chapman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), light availability, water and nutrient availability in the soil (Denslow \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Martinez-Ramos and Soto-Castro \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Turner \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and conspecific seedling mortality (Wright \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This is consistent with our observations, which show that a greater abundance of light-demanding seedlings reduces both the composition of seedlings and the β-diversity of typical and dominant species, which are variables sensitive to abundance fluctuations (Jost, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Roden et al., 2018). Furthermore, the absence of direct effects of landscape structure supports the hypothesis that generalist species and abundances are primarily affected by small-scale factors (Miguet et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, our SEMs images show that landscapes with a high aggregation index (AI\u0026thinsp;\u0026gt;\u0026thinsp;95%) support 26\u0026ndash;28% more seedling species per 10 m\u0026sup2; compared to landscapes with more dispersed patches (AI\u0026thinsp;\u0026lt;\u0026thinsp;92%; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Importantly, this increase in α-diversity occurs without reducing compositional heterogeneity: β-diversity remains high because clustered patches disproportionately boost the abundance of shade-tolerant, animal-dispersed species, while limiting the dominance of disturbance-tolerant pioneers (Arroyo-Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nicasio-Arzeta et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This supports the hypotheses of Miguet et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who proposed that landscape effects are stronger for specialist species and those with smaller populations. Shade-tolerant species show more habitat specialization and tend to have lower population densities than light-demanding species. (Denslow \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Turner \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, their abundance is regulated by conspecific mortality and the foraging of fruits, seeds, and seedlings by terrestrial mammals (Dirzo and Miranda \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Comita et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Camargo-Sanabria et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Seed dispersal and predation, in conjunction with density-dependent seedling mortality, promote seedling diversity (Harms et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wright \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Comita et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thus elucidating the positive effect on the α diversity of all seedling species. This suggests that this configurational pattern facilitates the arrival of seed dispersers and predators. Notably, the abundance of shade-tolerant seedlings was also influenced by the abundance of light-demanding seedlings, which indicated that the latter competed for resources. Competitive exclusion can significantly lower α diversity in forests with high light levels and low herbivore pressure (Wright, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This finding supports an indirect effect of light-demanding seedling abundance on species richness. These results emphasize the role of patch aggregation as a form of \"spatial insurance\" that landscape configuration can provide in fragmented habitats (Villard and Metzger \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Su\u0026aacute;rez-Castro et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In our study area, this, in turn, helped reduce fire-induced diversity loss and supported the propagule pool necessary for long-term forest regeneration.\u003c/p\u003e\u003cp\u003eAs fire frequency in Mesoamerican rainforests is expected to increase owing to drier climates and ongoing pasture expansion (Armenteras et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), maintaining or restoring patch aggregation becomes a crucial strategy for managers. Fencerow enrichment planting or assisted natural regeneration of intervening secondary forests can significantly reduce the average distance between seed sources and recipient patches (Brancalion et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This, in turn, helps mitigate the homogenizing impact of understory fires on seedling assemblages. Practically speaking, prioritizing the clustering of remnant forests during land allocation decisions should go hand in hand with fire prevention measures (Cochrane \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Together, they form a cost-effective insurance policy for preserving carbon storage, timber potential, and non-timber forest products that support local livelihoods.\u003c/p\u003e\u003cp\u003eOur findings highlight critical implications for the agricultural management of fragmented rainforests. Fire disturbance significantly decreases forest resilience by homogenizing seedling assemblages and favoring species that are more tolerant to disturbance. Effective management should prioritize mitigating fire disturbances, sustain forest cover and tree basal areas, and minimizing patch isolation. Integrating these insights into conservation planning requires a multidimensional approach that bridges the gap between local patch management and broader landscape configuration. This alignment is crucial for safeguarding the ecological integrity and long-term sustainability of fragmented tropical rainforests amid escalating anthropogenic pressures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJBM received grant projects from Programa de Apoyo a Proyectos de Investigaci\u0026oacute;n e Innovaci\u0026oacute;n Tecnol\u0026oacute;gica (PAPIIT), Direcci\u0026oacute;n General de Asuntos del Personal Acad\u0026eacute;mico UNAM [IN214014, IN202117, and IN201620], and from Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI), M\u0026eacute;xico [CB2005-C01-51043, CB2006-56799 and CB2007-7912].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRediT authorship contribution statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eS.N.A. conceptualization; S.N.A. methodology and project administration; J.B.M. supervision, funding acquisition, and resources; S.N.A. investigation, data curation, formal analysis, visualization, and validation; S.N.A. and S.M.V. wrote the original draft; I.Z.H., A.P., and J.B.M. writing \u0026ndash; review and editing. All the authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the first author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation for this study, the authors used Grammarly and Paperpal to improve language readability and grammar. After using these tools, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the residents of Quiring\u0026uuml;icharo for their warm welcome and assistance. We appreciate J. Manuel Lobato-Garc\u0026iacute;a\u0026apos;s technical and logistical aid in our field research. We thank Gilberto Jamangap\u0026eacute; and Rafael Lombera for their expertise in plant identification. Estaci\u0026oacute;n Chajul facilitated our work by providing accommodation and logistical support. SNA was a doctoral student from Programa de Doctorado en Ciencias Biom\u0026eacute;dicas, Universidad Nacional Aut\u0026oacute;noma de M\u0026eacute;xico (UNAM), and received from Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI) fellowship 317569.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndren H (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 355\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3545823\u003c/span\u003e\u003cspan address=\"10.2307/3545823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArasa-Gisbert R, Arroyo-Rodr\u0026iacute;guez V, Gal\u0026aacute;n-Acedo C, et al (2021) Tree recruitment failure in old-growth forest patches across human-modified rainforests. Journal of Ecology 1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.13643\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.13643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArasa-Gisbert R, Arroyo-Rodr\u0026iacute;guez V, Meave JA, et al (2022) Forest loss and treeless matrices cause the functional impoverishment of sapling communities in old-growth forest patches across tropical regions. Journal of Applied Ecology 069: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.14197\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.14197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmenteras D, D\u0026aacute;valos LM, Barreto JS, et al (2021) Fire-induced loss of the world\u0026rsquo;s most biodiverse forests in Latin America. Sci Adv 7:3357\u0026ndash;3370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.abd3357\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abd3357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArroyo-Rodr\u0026iacute;guez V, R\u0026ouml;s M, Escobar F, et al (2013) Plant β-diversity in fragmented rain forests: Testing floristic homogenization and differentiation hypotheses. Journal of Ecology 101:1449\u0026ndash;1458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.12153\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.12153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAvila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, et al (2010) Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective. Chin Geogr Sci 20:491\u0026ndash;497. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11769-010-0437-x\u003c/span\u003e\u003cspan address=\"10.1007/s11769-010-0437-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarlow J, Haugaasen T, Peres CA (2002) Effects of ground fires on understorey bird assemblages in Amazonian forests. Biol Conserv 105:157\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0006-3207(01)00177-X\u003c/span\u003e\u003cspan address=\"10.1016/S0006-3207(01)00177-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarlow J, Peres CA, Lagan BO, Haugaasen T (2003a) Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol Lett 6:6\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1461-0248.2003.00394.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1461-0248.2003.00394.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarlow J, Peres CA, Lagan BO, Haugaasen T (2003b) Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol Lett 6:6\u0026ndash;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarton K (2018) MuMIn: Multi-Model Inference. R package version 1421\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenitez-Malvido J (1998) Impact of forest fragmentation on seedling abundance in a tropical rain forest. Conservation Biology 12:380\u0026ndash;389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1523-1739.1998.96295.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1523-1739.1998.96295.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenitez-Malvido J (2006) Effect of low vegetation on the recruitment of plants in successional habitat types. Biotropica 38:171\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7429.2006.00137.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7429.2006.00137.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez-Malvido J, \u0026Aacute;lvarez-A\u0026ntilde;orve MY, \u0026Aacute;vila-Cabadilla LD, et al (2022) Phylogenetic and functional structure of tree communities at different life stages in tropical rain forest fragments. Glob Ecol Conserv 36:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2022.e02113\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2022.e02113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez-Malvido J, L\u0026aacute;zaro A, Ferraz IDK (2018) Effect of distance to edge and edge interaction on seedling regeneration and biotic damage in tropical rainforest fragments: A long-term experiment. Journal of Ecology 106:2204\u0026ndash;2217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.13003\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.13003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez-Malvido J, Mart\u0026iacute;nez-Ramos M, Ceccon E (2001) Seed rain vs. seed bank, and the effect of vegetation cover on the recruitment of tree seedlings in tropical successional vegetation. In: Gottsberger G, Liede S (eds) Life Forms and Dynamics in Tropical Forests. Schweizerbart Science Publishers, Stuttgart, Germany, pp 1805\u0026ndash;203\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerget C, Verschoor G, Garc\u0026iacute;a-Frapolli E, et al (2021) Landscapes on the move: Land-use change history in a mexican agroforest frontier. Land (Basel) 10:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/land10101066\u003c/span\u003e\u003cspan address=\"10.3390/land10101066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrancalion PHS, Niamir A, Broadbent E, et al (2019) Global restoration opportunities in tropical rainforest landscapes. Sci Adv 5:3223\u0026ndash;3226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.aav3223\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.aav3223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrando PM, Nepstad DC, Balch JK, et al (2012) Fire-induced tree mortality in a neotropical forest: The roles of bark traits, tree size, wood density and fire behavior. Glob Chang Biol 18:630\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2011.02533.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2011.02533.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrindis-Badillo DA, Arroyo-Rodr\u0026iacute;guez V, Mendoza E, et al (2022) Conserving dominant trees in human-modified landscapes at the Lacandon tropical rainforest. Biol Conserv 270:109548. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.BIOCON.2022.109548\u003c/span\u003e\u003cspan address=\"10.1016/J.BIOCON.2022.109548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrokaw N, Scheiner SM (1989) Species composition in gaps and structure of a tropical forest. Ecology 70:538\u0026ndash;541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1940196\u003c/span\u003e\u003cspan address=\"10.2307/1940196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurnham KP, Anderson DR (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Springer-Verlag\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamargo-Sanabria AA, Mendoza E, Guevara R, et al (2014) Experimental defaunation of terrestrial mammalian herbivores alters tropical rainforest understorey diversity. Proceedings of the Royal Society B: Biological Sciences 282:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2014.2580\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2014.2580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarabias J, De la Maza J, Cadena R (2015) Conservaci\u0026oacute;n y desarrollo sustentable en la Selva Lacandona: 25 a\u0026ntilde;os de actividades y experiencia. Natura y Ecosistemas Mexicanos, Mexico City\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChao A, Chiu CH, Hsieh TC (2012) Proposing a resolution to debates on diversity partitioning. Ecology 93:2037\u0026ndash;2051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/11-1817.1\u003c/span\u003e\u003cspan address=\"10.1890/11-1817.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChao A, Jost L (2012) Coverage-based rarefaction and extrapolation: Standardizing samples by completeness rather than size. Ecology 93:2533\u0026ndash;2547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/11-1952.1\u003c/span\u003e\u003cspan address=\"10.1890/11-1952.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChao A, Lee SM (1992) Estimating the number of classes via sample coverage. J Am Stat Assoc 87:210\u0026ndash;217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01621459.1992.10475194\u003c/span\u003e\u003cspan address=\"10.1080/01621459.1992.10475194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChapman CA, Chapman LJ, Wangham R, et al (1992) Estimators of Fruit Abundance of Tropical Trees. Biotropica 24:527\u0026ndash;531. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2389015\u003c/span\u003e\u003cspan address=\"10.2307/2389015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCochrane MA (2009) Fire in the tropics. In: Cochrane MA (ed) Tropical Fire Ecology. Climate Change, Land Use and Ecosystem Dynamics. Praxis Publishing Ltd., Chichester, UK, pp 1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCochrane MA, Laurance WF (2008) Synergisms among fire, land use, and climate change in the Amazon. Ambio 37:522\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1579/0044-7447-37.7.522\u003c/span\u003e\u003cspan address=\"10.1579/0044-7447-37.7.522\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCochrane MA, Laurance WF (2002) Fire as a large-scale edge effect in Amazonian forests. J Trop Ecol 18:311\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0266467402002237\u003c/span\u003e\u003cspan address=\"10.1017/S0266467402002237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eComita LS, Aguilar S, P\u0026eacute;rez R, et al (2007) Patterns of woody plant species abundance and diversity in the seedling layer of a tropical forest. Journal of Vegetation Science 18:163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1658/1100-9233\u003c/span\u003e\u003cspan address=\"10.1658/1100-9233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2007)18[163:powpsa]2.0.co;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eComita LS, Muller-Landau HC, Aguilar S, Hubbell SP (2010) Asymmetrie density dependence shapes species abundances in a tropical tree community. Science (1979) 329:330\u0026ndash;332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1190772\u003c/span\u003e\u003cspan address=\"10.1126/science.1190772\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCudney-Valenzuela SJ, Arroyo-Rodr\u0026iacute;guez V, Andresen E, et al (2021) Does patch quality drive arboreal mammal assemblages in fragmented rainforests? Perspect Ecol Conserv 19:61\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pecon.2020.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.pecon.2020.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDalling JW, Hubbell SP, Silvera K (1998) Seed dispersal, seedling establishment and gap partitioning among tropical pioneer trees. Journal of Ecology 86:674\u0026ndash;689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-2745.1998.00298.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2745.1998.00298.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Souza JC, da Silva RM, Gon\u0026ccedil;alves MPR, et al (2018) Habitat use, ranching, and human-wildlife conflict within a fragmented landscape in the Pantanal, Brazil. Biol Conserv 217:349\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2017.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2017.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenslow JS (1987) Tropical Rainforest Gaps and Tree Species Diversity. Annu Rev Ecol Syst 18:431\u0026ndash;451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.es.18.110187.002243\u003c/span\u003e\u003cspan address=\"10.1146/annurev.es.18.110187.002243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenslow JS (1996) Functional Group Diversity and Responses to Disturbance. In: Biodiversity and Ecosystem Processes in Tropical Forests. Springer, Berlin, Heidelberg, pp 127\u0026ndash;151\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenslow JS, Guzman G. S (2000) Variation in stand structure, light and seedling abundance across a tropical moist forest chronosequence, Panama. Journal of Vegetation Science 11:201\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3236800\u003c/span\u003e\u003cspan address=\"10.2307/3236800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesbiez AL, Bodmer RE, Aparecida S (2009) Wildlife habitat selection and sustainable resources management in a Neotropical wetland. Int J Biodivers Conserv 1:11\u0026ndash;20\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDirzo R, Miranda A (1990) Contemporary Neotropical Defaunation and Forest Structure, Function, and Diversity-A Sequel to John Terborgh*. Conservation Biology 4:444\u0026ndash;447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.1990.tb00320.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.1990.tb00320.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFahrig L (2013) Rethinking patch size and isolation effects: The habitat amount hypothesis. J Biogeogr 40:1649\u0026ndash;1663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jbi.12130\u003c/span\u003e\u003cspan address=\"10.1111/jbi.12130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan Y, Chen J, Shirkey G, et al (2016) Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol Process 5:19\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarf\u0026aacute;n Guti\u0026eacute;rrez M, P\u0026eacute;rez-Salicrup DR, Flamenco-Sandoval A, et al (2018) Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques 24:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21829/myb.2018.2431591\u003c/span\u003e\u003cspan address=\"10.21829/myb.2018.2431591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFont\u0026uacute;rbel FE, Candia AB, Malebr\u0026aacute;n J, et al (2015) Meta-analysis of anthropogenic habitat disturbance effects on animal-mediated seed dispersal. Glob Chang Biol 21:3951\u0026ndash;3960. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.13025\u003c/span\u003e\u003cspan address=\"10.1111/gcb.13025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGal\u0026aacute;n-Acedo C, Arroyo-Rodr\u0026iacute;guez V, Estrada A, Ramos-Fern\u0026aacute;ndez G (2019) Forest cover and matrix functionality drive the abundance and reproductive success of an endangered primate in two fragmented rainforests. Landsc Ecol 34:147\u0026ndash;158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-018-0753-6\u003c/span\u003e\u003cspan address=\"10.1007/s10980-018-0753-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarmendia A, Arroyo-Rodr\u0026iacute;guez V, Estrada A, et al (2013) Landscape and patch attributes impacting medium- and large-sized terrestrial mammals in a fragmented rain forest. J Trop Ecol 29:331\u0026ndash;344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0266467413000370\u003c/span\u003e\u003cspan address=\"10.1017/S0266467413000370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentry AH (1982) Patterns of Neotropical Plant Species Diversity. In: Hecht MK, Wallace B, Prance GT (eds) Evolutionary Biology. Springer US, pp 1\u0026ndash;84\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGRASS Development Team (2017) Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://grass.osgeo.org\u003c/span\u003e\u003cspan address=\"http://grass.osgeo.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuariguata MR, Ostertag R (2001) Neotropical secondary forest succession: changes in structural and functional characteristics. For Ecol Manage 148:185\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0378-1127(00)00535-1\u003c/span\u003e\u003cspan address=\"10.1016/S0378-1127(00)00535-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHardwick SR, Toumi R, Pfeifer M, et al (2015) The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: Forest disturbance drives changes in microclimate. Agric For Meteorol 201:187\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2014.11.010\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2014.11.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarms KEE, Wright SJJ, Calder\u0026oacute;n O, et al (2000) Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404:493\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/35006630\u003c/span\u003e\u003cspan address=\"10.1038/35006630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Ruedas MA, Arroyo-Rodr\u0026iacute;guez V, Morante-Filho JC, et al (2018) Fragmentation and matrix contrast favor understory plants through negative cascading effects on a strong competitor palm. Ecological Applications 28:1546\u0026ndash;1553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eap.1740\u003c/span\u003e\u003cspan address=\"10.1002/eap.1740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHernando A, Vel\u0026aacute;zquez J, Valbuena R, et al (2017) Influence of the resolution of forest cover maps in evaluating fragmentation and connectivity to assess habitat conservation status. Ecol Indic 79:295\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2017.04.031\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2017.04.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIbarra-Manr\u0026iacute;quez G, Cornejo-Tenorio G (2010) Diversidad de frutos de los \u0026aacute;rboles del bosque tropical perennifolio De M\u0026eacute;xico. Acta Bot Mex 104:51\u0026ndash;104\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIbarra-Manr\u0026iacute;quez G, Mart\u0026iacute;nez Ramos M, Oyama K (2001) Seedling functional types in a lowland rain forest in Mexico. Am J Bot 88:1801\u0026ndash;1812. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3558356\u003c/span\u003e\u003cspan address=\"10.2307/3558356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJesus FM, Pivello VR, Meirelles ST, et al (2012) The importance of landscape structure for seed dispersal in rain forest fragments. Journal of Vegetation Science 23:1126\u0026ndash;1136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2012.01418.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2012.01418.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJordano P (2000) Fruits and frugivory. In: Fenner M (ed) Seeds: the ecology of regeneration in plant communities, 2nd edn. CABI Publishing, Wallingford, U.K., pp 125\u0026ndash;166\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJost L (2006) Entropy and diversity. Oikos 113:363\u0026ndash;375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2006.0030-1299.14714.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2006.0030-1299.14714.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJost L (2007) Partitioning diversity into independent alpha and beta components. Ecology 88:2427\u0026ndash;2439. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/06-1736.1\u003c/span\u003e\u003cspan address=\"10.1890/06-1736.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJost L (2010) The relation between evenness and diversity. Diversity (Basel) 2:207\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d2020207\u003c/span\u003e\u003cspan address=\"10.3390/d2020207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKitajima K, Cordero RA, Wright SJ (2013) Leaf life span spectrum of tropical woody seedlings: Effects of light and ontogeny and consequences for survival. Ann Bot 112:685\u0026ndash;699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mct036\u003c/span\u003e\u003cspan address=\"10.1093/aob/mct036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolb M, Galicia L (2018) Scenarios and story lines: drivers of land use change in southern Mexico. Environ Dev Sustain 20:681\u0026ndash;702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10668-016-9905-5\u003c/span\u003e\u003cspan address=\"10.1007/s10668-016-9905-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrishnadas M, Agarwal K, Comita LS (2020) Edge effects alter the role of fungi and insects in mediating functional composition and diversity of seedling recruits in a fragmented tropical forest. Ann Bot 126:1181\u0026ndash;1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcaa138\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcaa138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandgraf AJ, Lee Y (2020) Dimensionality reduction for binary data through the projection of natural parameters. J Multivar Anal 180:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmva.2020.104668\u003c/span\u003e\u003cspan address=\"10.1016/j.jmva.2020.104668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandgraf AJ, Lee Y (2015) Dimensionality Reduction for Binary Data through the Projection of Natural Parameters\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaurance WF, Delamonica P, Laurance SG, et al (2000) Rainforest fragmentation kills big trees. Nature 404:836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/35009032\u003c/span\u003e\u003cspan address=\"10.1038/35009032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaurance WF, Nascimento HEMM, Laurance SG, et al (2007) Habitat fragmentation, variable edge effects, and the landscape-divergence hypothesis. PLoS One 2:e1017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0001017\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0001017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLefcheck JS (2016) piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol Evol 7:573\u0026ndash;579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.12512\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.12512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi BL, Archer S (1997) Weighted mean patch size: A robust index for quantifying landscape structure. Ecol Modell 102:353\u0026ndash;361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0304-3800(97)00071-9\u003c/span\u003e\u003cspan address=\"10.1016/S0304-3800(97)00071-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Coomes DA, Hu G, et al (2019) Larger fragments have more late-successional species of woody plants than smaller fragments after 50 years of secondary succession. Journal of Ecology 107:582\u0026ndash;594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.13071\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.13071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLohbeck M, DeVries B, Bongers F, et al (2022) Mexican agricultural frontier communities differ in forest dynamics with consequences for conservation and restoration. Remote Sens Ecol Conserv 37:10\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/RSE2.262\u003c/span\u003e\u003cspan address=\"10.1002/RSE2.262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLohbeck M, Poorter L, Mart\u0026iacute;nez-Ramos M, et al (2015) Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 96:1242\u0026ndash;1252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/14-0472.1\u003c/span\u003e\u003cspan address=\"10.1890/14-0472.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin AE (2018) The spatial scale of a species\u0026rsquo; response to the landscape context depends on which biological response you measure. Current Landscape Ecology Reports 3:23\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40823-018-0030-z\u003c/span\u003e\u003cspan address=\"10.1007/s40823-018-0030-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez E, Ramos-A. CH, Chiang F (1994) Lista flor\u0026iacute;stica de la Lacandona, Chiapas. Bot Sci 0:99\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17129/botsci.1430\u003c/span\u003e\u003cspan address=\"10.17129/botsci.1430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartinez-Ramos M, Soto-Castro A (1993) Seed rain and advanced regeneration in a tropical rain forest. Vegetatio 107:299\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00052231\u003c/span\u003e\u003cspan address=\"10.1007/BF00052231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiguet P, Jackson HB, Jackson ND, et al (2016) What determines the spatial extent of landscape effects on species? Landsc Ecol 31:1177\u0026ndash;1194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-015-0314-1\u003c/span\u003e\u003cspan address=\"10.1007/s10980-015-0314-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinor DM, Kobe RK (2019) Fruit production is influenced by tree size and size-asymmetric crowding in a wet tropical forest. Ecol Evol 9:1458\u0026ndash;1472. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.4867\u003c/span\u003e\u003cspan address=\"10.1002/ece3.4867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMissouri Botanical Garden (2019) Tropicos.org. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tropicos.org/\u003c/span\u003e\u003cspan address=\"https://www.tropicos.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 3 Jun 2019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNascimento HEM, Andrade ACS, Camargo JLC, et al (2006) Effects of the Surrounding Matrix on Tree Recruitment in Amazonian Forest Fragments. Conservation Biology 20:853\u0026ndash;860. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.2006.00344.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.2006.00344.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNathan R, Muller-Landau HC (2000) Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol Evol 15:278\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-5347(00)01874-7\u003c/span\u003e\u003cspan address=\"10.1016/S0169-5347(00)01874-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNavarrete-Segueda A, Mart\u0026iacute;nez-Ramos M, Ibarra-Manr\u0026iacute;quez G, et al (2018) Variation of main terrestrial carbon stocks at the landscape-scale are shaped by soil in a tropical rainforest. Geoderma 313:57\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2017.10.023\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2017.10.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNepstad DC, Ver\u0026iacute;ssimo A, Alencar A, et al (1999) Large-scale impoverishment of amazonian forests by logging and fire. Nature 398:505\u0026ndash;508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/19066\u003c/span\u003e\u003cspan address=\"10.1038/19066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicasio-Arzeta S, Zerme\u0026ntilde;o-Hern\u0026aacute;ndez IE, Maza-Villalobos S, Ben\u0026iacute;tez-Malvido J (2021) Landscape structure shapes the diversity of tree seedlings at multiple spatial scales in a fragmented tropical rainforest. PLoS One 16:e0253284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0253284\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0253284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicotra AB, Chazdon RL, Iriarte SVB (1999) Spatial heterogeneity of light and woody seedling regeneration in tropical wet forests. Ecology 80:1908\u0026ndash;1926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(1999)080\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(1999)080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[1908:SHOLAW]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOksanen J, Blanchet FG, Friendly M, et al (2019) vegan: Community Ecology Package. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/vegan/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Global Ecology and Biogeography 15:113\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1466-822X.2006.00214.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1466-822X.2006.00214.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira MA, Santos AMM, Tabarelli M (2008) Profound impoverishment of the large-tree stand in a hyper-fragmented landscape of the Atlantic forest. For Ecol Manage 256:1910\u0026ndash;1917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2008.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2008.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatton DR (1975) A diversity index for quantifying habitat \u0026ldquo;edge.\u0026rdquo; Wildl Soc Bull 171\u0026ndash;173\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Development Core Team (2025) R: A Language and Environment for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRom\u0026aacute;n-Cuesta RM, Mart\u0026iacute;nez-Vilalta J (2006) Effectiveness of protected areas in mitigating fire within their boundaries: Case study of Chiapas, Mexico. Conservation Biology 20:1074\u0026ndash;1086. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.2006.00478.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.2006.00478.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose S (2000) Seeds, seedlings, and gaps - size matters. a study in the tropical rain forest of Guyana. In: Programme T-G, Rose S (eds) Trobenpos-Guyana Series 9. Trobenpos-Guyana Programme, Georgetown, Guyana, pp 1\u0026ndash;184\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSan-Jos\u0026eacute; M, Arroyo-Rodr\u0026iacute;guez V, Jordano P, et al (2019) The scale of landscape effect on seed dispersal depends on both response variables and landscape predictor. Landsc Ecol 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-019-00821-y\u003c/span\u003e\u003cspan address=\"10.1007/s10980-019-00821-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanto-Silva EE, Almeida WR, Melo FPL, et al (2013) The nature of seedling assemblages in a fragmented tropical landscape: Implications for forest regeneration. Biotropica 45:386\u0026ndash;394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/btp.12013\u003c/span\u003e\u003cspan address=\"10.1111/btp.12013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanto-Silva EE, Withey KD, Almeida WR, et al (2015) Seedling assemblages and the alternative successional pathways experienced by Atlantic forest fragments. Plant Ecol Divers 8:483\u0026ndash;492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17550874.2015.1048486\u003c/span\u003e\u003cspan address=\"10.1080/17550874.2015.1048486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolar RR, Barlow J, Ferreira J, et al (2015) How pervasive is biotic homogenization in human-modified tropical forest landscapes? Ecol Lett 18:1108\u0026ndash;1118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12494\u003c/span\u003e\u003cspan address=\"10.1111/ele.12494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSousa M (2009) Adiciones al g\u0026eacute;nero Inga para la flora Mesoamericana. Acta Bot Mex 89:25\u0026ndash;41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu\u0026aacute;rez-Castro AF, Maron M, Mitchell MGE, Rhodes JR (2022) Disentangling direct and indirect effects of landscape structure on urban bird richness and functional diversity. Ecological Applications 32:e2713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/EAP.2713\u003c/span\u003e\u003cspan address=\"10.1002/EAP.2713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e;PAGEGROUP:STRING:PUBLICATION\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu\u0026aacute;rez-Castro AF, Simmonds JS, Mitchell MGE, et al (2018) The Scale-Dependent Role of Biological Traits in Landscape Ecology: A Review. Current Landscape Ecology Reports 3:12\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40823-018-0031-y\u003c/span\u003e\u003cspan address=\"10.1007/s40823-018-0031-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabarelli M, Peres CA, Melo FPL (2012) The \u0026ldquo;few winners and many losers\u0026rdquo; paradigm revisited: Emerging prospects for tropical forest biodiversity. Biol Conserv 155:136\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2012.06.020\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2012.06.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarka P (2018) An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Qual Quant 52:313\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11135-017-0469-8\u003c/span\u003e\u003cspan address=\"10.1007/s11135-017-0469-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaubert F, Fischer R, Groeneveld J, et al (2018) Global patterns of tropical forest fragmentation. Nature 554:519\u0026ndash;522. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature25508\u003c/span\u003e\u003cspan address=\"10.1038/nature25508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTerborgh J, Lopez L, Nu\u0026ntilde;ez P V., et al (2001) Ecological meltdown in predator-free forest fragments. Science (1979) 294:1923\u0026ndash;1926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1064397\u003c/span\u003e\u003cspan address=\"10.1126/science.1064397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrejo DAR (2008) Fire Regimes, Fire Ecology, and Fire Management in Mexico. AMBIO: A Journal of the Human Environment 37:548\u0026ndash;556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1579/0044-7447-37.7.548\u003c/span\u003e\u003cspan address=\"10.1579/0044-7447-37.7.548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrolle M (2003) Mammal survey in the southeastern Pantanal, Brazil. Biodivers Conserv 12:823\u0026ndash;836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1022489426920\u003c/span\u003e\u003cspan address=\"10.1023/A:1022489426920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTuomisto H (2010) A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33:2\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0587.2009.05880.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0587.2009.05880.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurner IM (2004) The Ecology of Trees in the Tropical Rain Forest. Cambridge University Press, Singapore\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVillard MA, Metzger JP (2014) Beyond the fragmentation debate: A conceptual model to predict when habitat configuration really matters. Journal of Applied Ecology 51:309\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.12190\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.12190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e;JOURNAL:JOURNAL:13652664;PAGE:STRING:ARTICLE/CHAPTER\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWandrag EM, Dunham AE, Duncan RP, Rogers HS (2017) Seed dispersal increases local species richness and reduces spatial turnover of tropical tree seedlings. Proc Natl Acad Sci U S A 114:10689\u0026ndash;10694. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1709584114\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1709584114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWearn OR, Rowcliffe JM, Carbone C, et al (2017) Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach. Biol Conserv 212:162\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2017.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2017.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhitmore TC (1989) Canopy gaps and the two major groups of forest. Ecology 70:536\u0026ndash;538. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1940195\u003c/span\u003e\u003cspan address=\"10.2307/1940195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWies G, Nicasio Arzeta S, Martinez Ramos M (2021) Critical ecological thresholds for conservation of tropical rainforest in Human Modified Landscapes. Biol Conserv 255:109023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2021.109023\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2021.109023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright SJ (2002) Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s004420100809\u003c/span\u003e\u003cspan address=\"10.1007/s004420100809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoung HS, McCauley DJ, Galetti M, Dirzo R (2016) Patterns, Causes, and Consequences of Anthropocene Defaunation. Annu Rev Ecol Evol Syst 47:333\u0026ndash;358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-ecolsys-112414-054142\u003c/span\u003e\u003cspan address=\"10.1146/annurev-ecolsys-112414-054142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZerme\u0026ntilde;o-Hern\u0026aacute;ndez I, M\u0026eacute;ndez-Toribio M, Siebe C, et al (2015) Ecological disturbance regimes caused by agricultural land uses and their effects on tropical forest regeneration. Appl Veg Sci 18:443\u0026ndash;455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/avsc.12161\u003c/span\u003e\u003cspan address=\"10.1111/avsc.12161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZerme\u0026ntilde;o-Hern\u0026aacute;ndez I, Pingarroni A, Mart\u0026iacute;nez-Ramos M (2016) Agricultural land-use diversity and forest regeneration potential in human- modified tropical landscapes. Agric Ecosyst Environ 230:210\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2016.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2016.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZurita GA, Bellocq MI (2010) Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc Ecol 25:147\u0026ndash;158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-009-9410-4\u003c/span\u003e\u003cspan address=\"10.1007/s10980-009-9410-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"alpha-diversity, beta-diversity, disturbance, fragmentation, landscape configuration, structural equation models","lastPublishedDoi":"10.21203/rs.3.rs-7430288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7430288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e\u003cp\u003eThe seedling community plays a crucial role in forest regeneration within fragmented rainforests. The characteristics of patches, including their size, shape, vegetation, and disturbance, as well as the composition and configuration of the surrounding landscape, have a significant effect on seedling communities. However, the response of seedling communities to patch and landscape structures varies, and the direct and indirect effects on seedling composition and diversity remain unclear.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eConsidering the hierarchical relationship between landscapes, patches, and seedlings, we investigated the direct and indirect effects of patch and landscape structures on seedling communities across 16 forest patches in a highly fragmented rainforest in southeastern Mexico.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe adopted a multi-scale approach to assess how landscape composition and configuration influence patch structure. Subsequently, piecewise structural equation models were used to evaluate the direct and indirect (cascading) effects of patch and landscape structures on seedling abundance, composition, and α- and β-diversity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur findings revealed that fire disturbance had cascading negative effects on seedling composition and β-diversity, increasing the abundance of light-demanding seedlings and thereby promoting the growth of early successional species. By contrast, patch aggregation had a cascading positive effect on seedling richness by enhancing the abundance of shade-tolerant seedlings, thereby supporting seed dispersal.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese results support the notion that the effects of patch disturbance and landscape configuration patterns on biodiversity are more pronounced in highly fragmented landscapes where spatial configuration can play a key role in enhancing regeneration resilience.\u003c/p\u003e","manuscriptTitle":"Fire and Landscape Configuration Shape Forest Futures: Direct and Indirect Drivers of Seedling Diversity in a Human-Modified Tropical Rainforest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 17:01:05","doi":"10.21203/rs.3.rs-7430288/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"203992636278412460556470894850363826659","date":"2026-05-26T09:38:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-21T07:07:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52775951450151141012832169725442415238","date":"2026-05-03T07:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30836274864115060941492583047567718146","date":"2025-11-17T20:32:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68257318295500352653331661900200971298","date":"2025-09-19T11:51:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176081432038510323965774395086199222815","date":"2025-09-04T18:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T21:18:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T10:37:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-22T10:36:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Landscape Ecology","date":"2025-08-22T03:08:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b2b71a23-471a-43ca-82e4-8ba3895ec9d8","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"203992636278412460556470894850363826659","date":"2026-05-26T09:38:06+00:00","index":64,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-21T07:07:28+00:00","index":63,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T17:01:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 17:01:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7430288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7430288","identity":"rs-7430288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}