Influence of a disturbance gradient on natural regeneration and understory diversity in semi-deciduous dense forests of Cameroon

Influence of a disturbance gradient on natural regeneration and understory diversity in semi-deciduous dense forests of Cameroon | 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 Influence of a disturbance gradient on natural regeneration and understory diversity in semi-deciduous dense forests of Cameroon André Paul Ebanga, Cédric Djomo Chimi, Jules Christian Zekeng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8231302/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study evaluates the differential effects of anthropogenic disturbances on natural regeneration patterns in Angossas Communal Forest (ACF) through a comprehensive inventory of woody vegetation along 2000 × 20 m linear transects covering 40 ha. We assessed disturbance levels, characterized land-use types, and stratified woody plants by diameter classes to examine understory diversity. Tree stumps were systematically documented to evaluate species regeneration capacity. Results demonstrate that the understory accounts for 80.85% of total stem density, with saplings (66.14%) constituting the dominant regeneration component. Ecological attributes varied significantly along the disturbance gradient (p < 0.05), showing peak sapling density in highly disturbed areas (875 stems. ha − 1 ) versus optimal seedling density in lightly disturbed zones (1467 stems. ha − 1 ). Mature secondary forests (MSF) with minimal disturbance exhibited the highest understory density for saplings (5267 ± 2838 stems. ha − 1 ) and maintained moderate seedlings density (1150 ± 780 stems. ha − 1 ), while displaying maximum diversity values (Shannon index: 2.7–2.9) and species richness, albeit with low floristic similarity among strata (10%). Vegetative regeneration was particularly active, with 78.78% of stumps producing an average of 5.00 ± 3.92 sprouts per stump, showing significant correlations with stump diameter and height (p < 0.05). These findings reveal partial ecosystem resilience through vigorous vegetative regeneration, while highlighting critical vulnerabilities in recruitment success. The study advocates for an integrated management approach combining conservation measures, sustainable harvesting practices, and active restoration strategies that capitalize on natural regeneration processes. We propose developing an adaptive management plan that incorporates ecological gradient mapping to optimize long-term forest resilience while addressing local community needs. Natural regeneration Understory vegetation Disturbance effects Sustainable agricultural practices Ecological vulnerability Forest conservation Ecosystem resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 I. Introduction Naturel regeneration is crucial for the persistence of forest ecosystems in the face of global change [ 1 , 2 ]. It serves as a key indicator of vegetation structure [ 3 ], and ensures forest dynamics and stability [ 4 ]. The understory, which harbors 60% of plant diversity [ 5 ], facilitates forest renewal through complex mechanisms such as seed rain, seed banks, and seedling-sapling interactions [ 6 , 7 ]. Understory growth depends on light availability and other resources, promoting species conservation and forest recovery [ 8 , 9 ]. However, persistent disturbances reduce understory density [ 7 ] and disrupt ecological processes, leading to substantial biodiversity loss [ 10 , 11 ]. Given escalating land-use pressures and the critical role of forests, the regenerative potential of tropical forests has gathered increasing attention [ 12 ]. Understanding the differential effects of disturbances on the understory is essential for optimizing forest management and conservation strategies. Studies have highlighted the diversity and ecological significance of the forest understory [ 5 , 13 ]. This layer facilitates the establishment of native flora, enhancing ecosystem stability and species heterogeneity [ 14 , 15 ]. At the landscape level, such diversity reflects vegetation resilience and its capacity to sustain biodiversity. Regeneration relies on key mechanisms, including seed rain, seed banks, and seedling-sapling-resprout interactions [ 6 , 7 ]. However, this critical process is increasingly compromised by persistent disturbances, threatening ecosystem resilience and functionality. While disturbances naturally shape forest structure and composition [ 16 , 17 ], persistent anthropogenic disturbances including fuelwood extraction, overharvesting of non-timber forest products (NTFPs), and slash-and-burn agriculture - lead to biomass depletion and disrupt critical ecological processes [ 18 , 19 ]. These disturbances drive biodiversity loss [ 20 ], ), modify juvenile banks in ways that conflict with species' ecological requirements [ 5 ] and inhibit natural succession, thereby severely constraining forest regeneration [ 7 , 18 ]. Their deleterious impacts on forest integrity and regeneration dynamics are well-documented in the scientific literature [ 21 – 23 ]. Anthropogenic disturbances significantly damage woody vegetation [ 24 ], triggering complex natural regeneration processes. This regeneration represents the most suitable mechanism for restoring ecosystem resilience [ 25 , 26 ]. ). It maintains species composition and facilitates post-disturbance recovery [ 27 ]. However, disturbances profoundly alter forest structure [ 9 , 28 , 29 ], promoting pioneer species through canopy fragmentation [ 27 , 30 , 31 ]. This dynamic may lead to long-term biomass overcompensation [ 32 ], albeit through variable ecological trajectories. Forest ecosystems exhibit differential responses to disturbance regimes. Recurrent disturbances favor ruderal species with competitive pioneer traits, while disadvantaging climax species that require stable environmental conditions [ 33 ]. For instance, intensive clear-cutting or repeated fires lead to vegetation homogenization, impairing tree recruitment and sapling development [ 28 , 34 ]. Conversely, moderate disturbances, such as selective logging, can enhance spatial heterogeneity and create ecological niches for specialist species [ 35 – 37 ]. In contrast, minimally disturbed systems exhibit asymmetric competition dominated by mature trees, which significantly suppresses understory growth and development through shading effects [ 38 , 39 ]. While anthropogenic disturbances invariably alter forest structure and functioning, their long-term impacts ultimately depend on species' regenerative capacity. Woody plants regenerate through two pathways: seedling recruitment (sexual) and vegetative resprouting (asexual), both of which are vital for forest dynamics [ 40 , 41 ]. However, disturbance impacts on these mechanisms remain poorly understood. Vegetative regeneration enhances post-disturbance recovery [ 42 ] and species persistence [ 43 ], but the effect of disturbance on ecosystem services and resilience require further research [ 44 , 45 ]. Understanding these processes is critical for conservation [ 20 , 24 ], particularly in understudied Cameroonian forests. The Angossas Communal Forest (ACF) exemplifies the current state of Congo Basin forests through widespread illegal logging, unsustainable resource harvesting, and land-use changes [ 46 , 47 ]. These anthropogenic pressures, exacerbated by population growth, disrupt natural regeneration mechanisms [ 48 , 49 ], threatening forest stability where regeneration is typically characterized by low density and diversity [ 7 , 50 ]. Therefore, this study aims to assess the differential effects of anthropogenic disturbances on forest regeneration and the response of damaged trees, with the ultimate goal of developing sustainable management strategies to enhance the resilience of ACF. Specifically, this study will: 1. Characterize understory vegetation diversity and regeneration patterns across disturbance gradients and land-use types; 2. Evaluate the vegetative reproduction capacity of damaged trees by analyzing individual predictive factors and basal diameter effects on stump resprouting; 3. Investigate functional relationships between stump characteristics and their vegetative reproduction potential. II. Materials and Methods II.1. Study area The study was conducted in the Angossas Communal Forest (ACF), a semi-deciduous dense forest within the Guineo-Congolian domain [ 51 ]. Located in the Mbaonz municipality (Haut-Nyong Department, East Region of Cameroon), the site extends geographically between 4°4'60"N and 12°58'60"E (Fig. 1 ) and covers 22,120 ha, divided into two distinct blocks. The topography features gently rolling terrain with an average elevation of 600 m. Slopes ranging from 0–5% indicate low erosion susceptibility and favorable plant growth conditions. The equatorial climate exhibits four unevenly distributed seasons, with mean annual rainfall of 1,600 mm and temperatures averaging 22–25°C. Soils are predominantly ferrallitic and hydromorphic, although their fertility is increasingly compromised by the intensification of slash-and-burn shifting agriculture [ 52 ]. Despite this, the area remains a biodiversity hotspot where illegal timber extraction and non-timber forest product (NTFP) harvesting constitute major anthropogenic pressures. II.2. Sampling Design and Methodology The sampling unit consisted of linear transects, a method recommended for studies of natural regeneration [ 53 ]. This approach allows for rapid and straightforward estimation of tree diversity and density while facilitating the assessment of vegetation variations along environmental gradients or across different habitats [ 54 ]. Ten transects of 2,000 m × 20 m (covering 4 hectares per transect) were established, spaced 1 km apart. Each transect was subdivided into 100 m segments to improve the recording of individual plants [ 55 ]. To accurately evaluate regeneration, nested quadrats of 10 m × 10 m (100 m²) and 5 m × 5 m (25 m²) were placed at 100 m intervals along the main transect. A total area of 40 ha was surveyed, representing a sampling intensity of 0.18%. II.3. Disturbance Assessment and Inventory Data Collection Field data on forest structure and disturbance levels were systematically collected along the established transects. At 100 m intervals, forest condition was assessed through physiognomic observations and classified into four disturbance categories following Tchouto et al. [ 3 ]: Category A: Severely degraded (> 50% canopy openness); Category B: Moderately disturbed (25–50% degradation, fragmented canopy with retained forest dominance); Category C: Lightly disturbed (< 25% human-induced canopy gaps) and Category D: Relatively intact (dense canopy, no anthropogenic degradation except hunting and NTFP collection). Land Use Types (LUTs) were characterized using integrated physiognomic and ecological criteria [ 56 ], including: dominant species composition, canopy closure status (open/closed), topographic features and understory vegetation density. Tree identification was carried out based on their diagnostic characteristics, along with diameter measurements (DBH). Large trees with a diameter > 10 cm were measured at 1.30 m above ground level. Saplings (defined as those with 5 cm ≤ DBH < 10 cm) were surveyed in 100 m² quadrats, while seedlings (DBH < 5 cm) were recorded in 25 m² quadrats [ 57 ]. For trees with irregular stem shapes, the diameter was measured 30 cm above protrusions [ 58 ]. II.4. Stump Identification and Parameter Measurement Stumps (both dead and living, with or without sprouts) were systematically identified along each transect based on foliage of resprouts, wood characteristics and bark features [ 37 ]. The decomposition state of each stump was recorded, and the cause of damage was identified and documented [ 24 ]. Basal diameter (BD) was measured at 20 cm below the cutting level, as most stumps were insufficiently tall for measurement at 1.30 m. Conversely, stump height was measured from the root collar to the cutting point. A total of six attributes were recorded: (1) species name (sprouted or non-sprouted stumps); (2) stump diameter; (3) stump height; (4) number of living or dead sprouts; (5) basal diameter and height of the dominant sprout; (6) degree of stump decomposition [ 42 ]. II.5. Data Analysis II.5.1. Characterization of Plant Diversity and Regeneration Along Disturbance and Land-Use Gradients The density of small and young trees was estimated for each land-use type along the disturbance gradient. Species richness within a transect was calculated as the total number of distinct species observed in a given stratum. Diversity indices such as the Shannon-Weaver index, Simpson index, Pielou’s evenness, and Fisher’s alpha, were computed based on species presence and abundance. These indices account for species number, relative abundance, and dominance at the given scale [ 57 ]. Species similarity between different strata and land-use types was quantified using the Jaccard index (1-Jaccard) (1-Jaccard) [ 59 ]. The natural regeneration index (NRI) for each transect was calculated, and a mean value was derived. This index was obtained as the ratio of seedlings (DBH ≤ 10 cm) to large trees [ 60 ]. II.5.2. Vegetative Reproduction of Damaged Trees, Predictive Factors, and Relationships Between Stump Characteristics and Sprouting Potential The total number of stumps was recorded each species, with density extrapolated to a per-hectare basis. The vegetative reproduction (VR) rate was calculated per species and then at the landscape scale. The stump section was examined to determine the disturbance type (natural or anthropogenic). The mean number of sprouts per stump was used to estimate the VR potential, defined as the ratio of sprouts per stump to the total number of recorded stumps [ 24 ]. II.6. Statistical Analyses Multiple statistical approaches were employed to analyze the data. After verifying data non-normality using homogeneity tests, analysis of variance (ANOVA) was performed to assess the effect of land-use types on ecological variables across the disturbance gradient, and examine relationships between tree reproductive capacity and biotic factors. Permutational Multivariate Analysis of Variance (PERMANOVA) was used to evaluate differential disturbance effects on understory vegetation [ 61 , 62 ]. Non-metric multidimensional scaling (NMDS) was used to examine the distribution of floristic composition along disturbance and land use types, in conjunction with agglomerative hierarchical clustering. Spearman’s rank correlations quantified associations between stump attributes and sprouting potential, while linear regression models analyzed the influence of stump parameters on regeneration potential [ 63 ]. II.6. Results II.6.1. Vegetation diversity and regeneration, differential effects of disturbance and Land-Use types in the understory The understory accounted for 80.85% of total recorded tree density, with a marked predominance of juvenile trees (66.14%; 2108 ± 1516.25 stems. ha − 1 ) over seedlings (33.86%; 1,079 ± 155 stems. ha − 1 ). This dominance was confirmed by the Natural Regeneration Index (NRI: 5.26 ± 1.01) and Pearson's coefficient (R = 0.003). PERMANOVA revealed significant variation in vegetation attributes along the disturbance gradient (F = 1,176, p < 0.001 for Saplings; F = 89.46, p < 0.0001 for seedlings). Saplings exhibited significantly higher density in heavily disturbed zones (A: 430.76 ± 14; B: 875.47 ± 39.65 stems. ha − 1 ; F = 2.08, p = 0.0004) compared to relatively undisturbed areas (C/D: 3750.99 ± 15.03; 3000 ± 15.13 stems. ha − 1 ). In contrast, seedlings reached peak density in less disturbed zones (C: 1466.66 ± 101. stems. ha − 1 ; F = 12.24, p = 0.0045; Table 1 ). Species dominance varied significantly with disturbance intensity (F = 628.1, p = 0.0011 for Saplings; F = 628.1, p = 0.002 for seedlings). Diversity indices peaked in zones C/D for saplings (Shannon: 2.91/2.74 and Simpson index: 0.91/0.89; F = 327.3, p < 0.001), while seedlings showed reduced diversity, though still highest in zone C (Shannon = 0.72; Simpson = 0.45; F = 705.8-248.2, p < 0.05; Table 1 ). Total species richness reached 99 ± 0.38 species, with maximum values in zone D (20.91 ± 10.40 species; p < 0.001 Fig. 2 I). Seedlings exhibited lower species richness (1.33 ± 0.51 species, p < 0.000), but values remained significantly higher in less disturbed C/D zones (Fig. 2 II, p 50% canopy openness); Category B: Moderately disturbed (25–50% degradation,); Category C: Lightly disturbed (< 25% human-induced canopy gaps) and Category D: Relatively intact. Variables Juvenile trees Saplings A B C D P-value A B C D p-value Density (ind./ha) 430.76 ± 14 a 875.47 ± 39.65 a 3750.99 ± 15.03 b 3000 ± 15.13 c 0.0004 933.33 ± 46.88 a 600 ± 21.08 a 1466.66 ± 101.84 a 533.33 ± 21.55 a 0.004 Dominance 0.32 ± 0.20 a 0.21 ± 0.02 b 0.08 ± 0.01c 0.17 ± 0.01 b 0.0011 0.70 ± 0.25 ab 0.75 ± 0.27 b 0.55 ± 0.22 a 0.83 ± 0.25 b 0.002 Shannon 1.26 ± 0.39 a 1.87 ± 0.65 b 2.91 ± 0.59 c 2.74 ± 0.70 d 0.0009 0.42 ± 0.03 ab 0.34 ± 0.07 b 0.72 ± 0.41 a 0.35 ± 0.03 b 0.001 Simpson 0.67 ± 0.20 a 0.79 ± 0.21 b 0.91 ± 0.13c 0.89 ± 0.13c 0.0002 0.29 ± 0.25 ab 0.25 ± 0.02 b 0.45 ± 0.22 a 0.17 ± 0.05 b 0.0124 Piélou 0.91 ± 0.26 a 0.93 ± 0.03 a 0.94 ± 0.13 a 0.94 ± 0.12 a 0.0021 0.61 ± 0.05 ab 0.5 ± 0.05 a 0.76 ± 0.36 b 0.33 ± 0.05 ab 0.0032 Alpha-Fisher 5.40 ± 6.18 a 13.89 ± 4.89 a 31.85 ± 12.99 b 31.14 ± 16.7 b 0.0003 1.75 ± 1.05 a 0a 1.73 ± 0.85 a 0a 0.052 Total density = 2108 ± 1516.25 Total density = 1079 ± 155 IRN = 5.26 ± 1.01 ; R = 0.003 F = 1176 ; p-value = 0.0001 ; N = 999 F = 89.46 ; p-value = 0.00001; N = 999 II.6.2. Characterization of Understory Diversity and Density Across Land-Use Types Results demonstrate significant variations in ecological parameters and understory diversity (Fig. 3 ; p < 0.05). Seedling density showed marked differences among land-use types (LUTs) (CV = 10.69%, p = 0.002), reaching its maximum in Mature Secondary Forests (MSF: 1,150 ± 780 trees ha⁻¹), a value significantly higher than in fallows (600 ± 284 trees ha⁻¹; χ² = 11, df = 3, p = 0.005). This density was strongly influenced by disturbance level (A: χ² = 14.03, df = 4, p = 0.0021). Sapling density varied considerably and significantly among LUTs (FCC; 55.21%, p = 0.001), being significantly influenced by disturbance intensity. The highest densities occurred in MSF (χ² = 11, df = 4, p < 0.001; 5,267 ± 2,838 trees ha⁻¹), which were less disturbed areas (C/D), followed by fallows (733.30 ± 372 trees ha⁻¹). Highly disturbed areas, including croplands (CL), swamps, and Young Secondary Forests (YSF), revealed lower densities for seedlings (A, B: 400 trees ha⁻¹; FCC = 10.69; p = 0.002) but higher densities for saplings (733.3 ± 372 trees ha⁻¹; χ² = 12.5, df = 1, p < 0.001). Species richness varied significantly among LUTs (seedlings: FCC = 23.75%, p < 0.004; saplings: FCC = 20.74%, p = 0.007), consistently peaking in MSF (saplings: 30.75 ± 13.34 species; seedlings: 2.5 ± 1.19 species; p < 0.05). Species dominance showed contrasting patterns: non-significant for seedlings (p = 0.054) despite notable variability (CV = 13.25%), but significant for saplings (p = 0.041) with high variability (CV = 36.39%) and maximum values in less disturbed MSF (C/D: 30.75 ± 13.34 species; Table 2 ). The differences in understory species composition and dominance among land use types (Fig. 3 a), and across the disturbance gradient (Fig. 3 b) were confirmed by the non-metric multidimensional scaling analysis (NMDS). Species similarity was low between seedlings and adult trees (Jaccard = 0.10), unlike saplings, which showed greater similarity to adults (Jaccard = 0.71 and 0.14 for adults and saplings, respectively; Table 3 ). Table 2 Diversity indices and density metrics of understory vegetation by land-use type and disturbance level. land-use type: MSF = Mature secondary forest; YFS = Young secondary forest; FSF = Seasonally flooded swamp; FCC = Food crop cultivation. CV = Variation of coefficient (%). Variables Seedlings MSF YFS Swamp FSF FCC Fallow CV (%) p-value Disturbance level C B C D A A Density 1110 ± 78 400 400 160 400 600 ± 184 10.69 0.002 Species Richess 2.5 ± 1.19 1 1 2 1 1.50 ± 0.71 23.75 0.004 Dominance 0.58 ± 0.29 1 1 0.5 1 0.75 ± 0.35 13.25 0.054 Shannon-index 0.77 ± 0.53 0 0 0.5 0 0.35 ± 0.49 60.35 0.005 Simpson-index 0.46 ± 0.30 0 0 0.69 0 0.25 ± 0.35 2.15 0.001 Saplings Density 5267 ± 28 425 ± 305 200 250 ± 191 733.30 ± 372 400 55.21 0.001 Disturbance C/D B C/D C A A Species Richess 30.75 ± 13.34 3.88 ± 2.59 2 2.25 ± 1.5 6.33 ± 2.87 4 20.74 0.007 Dominance 5.52 ± 2.60 0.1 ± 0.30 0.5 0.65 ± 0.40 19.70 ± 6.5 0.25 36.39 0.041 Shannon-index 3.15 ± 0.47 1.12 ± 0.76 0.69 0.61 ± 0.71 1.73 ± 0.41 1.38 27.04 0.031 Simpson-index 0.94 ± 0.02 0.57 ± 0.36 0.7 0.35 ± 0.40 0.81 ± 0.08 0.75 20.87 0.073 Table 3 Jaccard similarity index (%) between regeneration stages (seedlings and saplings) and the upper canopy layer Seedlings Saplings Larges trees Seedlings 0 Saplings 0.14 0 Larges trees 0.10 0.71 0 Hierarchical clustering distinguished two main groups among land use types (Fig. 3 a). Group 1, composed solely of mature secondary forests, was characterized by high species abundance and exhibited unique traits within the study area. Group 2 showed strong homogeneity, with marked similarities between young secondary forests and crop fields, and ecological proximity between swamps and periodically flooded swamps. Fallows, distinguished by high heterogeneity, were predominantly composed of fast-growing pioneer species. In contrast, the ten most abundant understory species were divided into four groups (Fig. 3 b). Group 1, the largest, included six species: Afraegle paniculata , Enanthia chlorantha , Hexalobus crispiflorus , Xylopia sp., and Nesogordonia papaverifera . These species are shade-tolerant but require small gaps for growth and are typical of relatively undisturbed mature and young secondary forests. Group 2, represented by Oddoniodendron normandii , displayed unique ecological characteristics and high heterogeneity. Its presence indicates a state of mature forest. Groups 3 and 4 shared similar ecological and adaptive traits, including Annona sp. And Coffea sp. (Group3), and Amphimas pterocarpoides and Chrysophyllum boukokoense (Group 4). These species are highly shade-tolerant and require high atmospheric humidity. II.6.2. Vegetative Reproduction capacity of damaged trees, individual predictive factors, and basal stump diameter effects II.6.2.1. Vegetative Reproduction capacity of damaged trees A total of 147 woody stumps were recorded, representing 33 species, 27 genera, and 26 botanical families, with a mean density of 37 stumps. ha⁻¹. Among these, 9.52% could not be identified due to an advanced decomposition state. Stump origins showed predominance of anthropogenic impacts (55.88%) compared to windthrows (25%) and relatively undisturbed areas (22.22%; Appendix 1). Overall vegetative reproduction capacity was high, with 78.79% of individuals producing sprouts versus 21.21% showing no regeneration (predominantly dead and highly decomposed stumps). Mean sprout count per stump was 5.00 ± 3.92, with notable interspecific variation ranging from 6 for Santiria trimera to 14 ± 4.22 sprouts for Uapaca paludosa (Table 4 ). Five species demonstrated particularly high vegetative reproduction potential (≥ 5%), collectively representing 43 stumps (29.25%). These high-capacity reproducers were primarily shade-tolerant taxa affected by anthropogenic disturbances, with particular concentration in food crop areas and young fallows (Table 3 ). The species were: Uapaca paludosa (9.75%; Phyllanthaceae), Lannea welwitschii (6.80%; Anacardiaceae), Pycnanthus angolensis (Myristicaceae), Celtis zenkeri (5.95%; Cannabaceae), Celtis mildbraedii (5.78%; Cannabaceae), and Anonidium mannii (5.44%; Annonaceae). Table 4 Species with a Reproduction Potential (≥ 2%), their corresponding numbers of stems and suckers Familly Species Number of stumps Number of sprouts Reproduction Potential (%) Anacardiaceae Lannea Welwitschii 1 10 6.80 Trichoscypha acuminata 1 3 2.04 Annonaceae Anonidium mannii 7 3.5 ± 2 2.38 Burseraceae Santiria trimera 1 6 4.08 Cannabaceae Celtis adolfi-friderici 2 4 2.72 Celtis mildbraedii 2 8.5 ± 2.5 5.78 Celtis zenkeiri 6 8.75 ± 2.25 5.95 Euphorbiaceae Macaranga hurifolia 7 4.67 ± 1.78 3.17 Ricinodendron heudelotii 1 4 2.72 Lecythidaceae Petersianthus macrocarpus 18 6.4 ± 0.78 4.35 Irvingiaceae Desbordesia glaucescens 5 6.2 ± 1.56 4.22 Meliaceae Trichilia drageana 3 3.33 ± 1.78 2.67 Trichilia welwitschii 6 4 ± 1.33 2.72 Myristicaceae Pycnanthus angolensis 23 3.57 ± 0.37 6.80 Olacaceae Aptandra zenkeri 3 3.5 ± 0.12 2,38 Passifloraceae Barteria fistulosa 3 3.33 ± 1.55 2.27 Phyllanthaceae Uapaca paludosa 4 14 ± 4.22 9.75 Urticaceae Myrianthus arboreus 2 5 ± 1.52 3.40 II.6.2.2. Predictive factors and effect of basal diameter of damaged tree stumps The analysis of variance (ANOVA) revealed the influence of biotic variables on sprout number (Table 5 ). Sprout number differed significantly with stump condition (coefficient = 13.903; p = 0.003). The distribution of physiological states showed that 50% of stumps were in good vitality, while 26.47% revealed an intermediate state (alive and/or partially decomposed) and 17.64% were in an advanced state of decay. In contrast to the physiological condition, the other biotic variables studied showed no significant influence on sprout production (p > 0.05). No notable differences were observed regarding taxonomic affiliation (families: coefficient = 22.34; df = 33; p > 0.05; species: coefficient = 33; df = 33; p > 0.05) or disturbance origin (p > 0.05). Table 5 Relationships between biotic variables and the reproductive capacity of damaged tree stumps Dependent variable Predictive variables Coefficient df p-value Number of sprouts Species 33 33 0.467 Familly 22.34 33 0.438 Stump condition 13.903 3 0.003 Disturbance 11.12 8 0.194 Spearman correlation analysis revealed significant relationships between stump attributes and regeneration performance (p < 0.05; Fig. 5 ). The number of sprouts showed significant positive correlations with both sprout diameter (r = 0.235; p = 0.03) and stump height (r = 0.654; p = 0.01; Appendix 2). Reproduction potential was strongly correlated with the diameter (r = 0.452; p = 0.02) and height (r = 0.32; p = 0.02) of the most developed sprout (Appendix 2). While no overall correlation was found with stump diameter (p > 0.05), a complete reproduction failure in large in large-diameter stumps (≥ 40 cm: Dichostemma glaucescens, Mitragyna ciliata, Musanga cecropioides ) was observed. Optimal reproduction occurred in intermediate-diameter stumps (8–28 cm), particularly in Uapaca paludosa (16.75 cm; 14 sprouts), Celtis mildbraedii (18 cm; 8.5 sprouts), Celtis zenkeiri (27.83 cm; 8.75 sprouts), Lannea welwitschii (25 cm; 10 sprouts), and Santiria trimera (8 cm; 6 sprouts) (Appendix 3). Stump density showed no significant correlation with regeneration potential (p > 0.05). Notably, species with fewer stumps ( L. welwitschii ; n = 1, C. mildbraedii ; n = 2, C. zenkeri ; n = 6, U. paludosa ; n = 4) produced the highest sprout counts, while those with abundant stumps ( Petersianthus macrocarpus ; n = 18 and Pycnanthus angolensis ; n = 23) showed limited regeneration (6 and 5 sprouts, respectively; Appendix 1). II.6.3. Relationship between stump attributes and Reproductive Potential Statistical modeling revealed significant influences of stump characteristics on total sprout production (p˂0.05). The analysis demonstrated a significant positive relationship between stump height and sprout abundance in damaged trees (adj. R² = 0.158, slope = 0.398; p = 0.019; Fig. 6 b). In contrast, mean stump diameter showed no significant association with sprout production, although a negative trend was observed (adj. R² = 0.052, slope = -0.227; p = 0.274; Fig. 6 a). Similarly, non-significant relationships were found between sprout abundance and: (1) height of the dominant sprout (adj. R² = 0.045, slope = 0.212; p = 0.237; Fig. 6 c), (2) stump density (adj. R² = 0.042, slope = 0.206; p = 0.324; Fig. 5 d), and (3) density of the stump (adj. R = 0.052211, slope = 0,32154; p = 0,540; Fig. 6 e). II.7. Discussion II.7.1. Vegetation diversity and regeneration, differential effects of disturbance and Land-Use types in the understory Semi-deciduous tropical forests exhibit significant understory dominance [ 5 ]. In the study area, this stratum constituted the predominant component of the forest structure, accounting for 80.85% of all trees (3187 ± 1671.25 stems. ha-1), even though this proportion remains lower than values reported for other humid forests [ 5 , 64 , 65 ]. Indeed, biotic and abiotic factors can influence the distribution, survival, recruitment, and mortality of Saplings [ 5 , 7 ], which may explain the observed variations. The predominance of young trees (66.14%) underscores their critical role in forest regeneration dynamics [ 5 ]. However, the relatively low proportion of small trees (33.86%) highlights the adverse effects of anthropogenic and natural disturbances [ 23 ], raising concerns regarding the long-term sustainability of forest regeneration mechanisms. Implementing adaptive management strategies, such as restricting agricultural burning and promoting agroforestry systems is essential to preserve understory biodiversity. Such approaches should integrate ecological conservation with restoration efforts [ 23 , 66 , 67 ]. The results indicated a high understory density in highly disturbed areas (430.76 ± 14 stems/ha) and moderately disturbed areas (875.47 ± 39.65 stems/ha). According to Watson et al. [ 31 ], disturbances alter forest structure by promoting increased light penetration, availability of mineral elements, and other nutrients, which stimulates understory regeneration and facilitates the establishment of pioneer species [ 32 , 57 ]. Their impacts on biodiversity vary depending on type and frequency [ 35 ], which explains why small trees in the region exhibit maximum density in less disturbed areas (1,466.66 ± 101.84 stems. ha-1). Indeed, understory density and composition are correlated with canopy openness, mediated by environmental variables [ 68 , 69 ]. These modifications may lead to a selection effect, thereby altering original diversity [ 70 ]. II.7.2. Effects of land-use types on understory diversity and density The analysis of understory vegetation in this study revealed greater species variation along a disturbance gradient. Species diversity was very low in highly disturbed, stressed vegetation compared to intermediate vegetation (Shannon = 2.91 for saplings; 0.72 for small stems), highlighting the link between ecological stability and biodiversity [ 15 ]. These findings align with those of Mounmemi et al. [ 71 ], who emphasized that low-intensity logging has little negative impact on species diversity and richness. Furthermore, secondary succession is more favorable for the recovery of forests that have undergone low-intensity logging [ 71 ]. This is supported by the number of understory species in low-disturbance environments (20.91 species) compared to 15 species in highly disturbed environments, confirming that intact forests act as biodiversity reservoirs [ 21 ]. These differences observed across the disturbance gradient reflect the mechanisms described by Matula et al. [ 72 ], notably the post-disturbance explosion of sapling regeneration and the difficult persistence of established small trees. Differential management is essential, aiming to protect intact areas and implement assisted regeneration in degraded zones [ 6 ]. Results reveal significant variations in stem density and species richness across land-use types, confirming the impact of anthropogenic disturbances on forest regeneration [ 2 , 7 ]. Less disturbed mature secondary forests exhibited a higher density of small stems (5,267 ± 2,838 stems/ha) compared to 1,150 ± 780 stems/ha for saplings, reflecting optimal regeneration under a stable canopy [ 5 , 6 ]. This stability provides favorable microclimatic conditions for recruitment [ 8 ]. In contrast, highly disturbed young secondary forests showed low tree species density (400 stems/ha), confirming the impact of anthropogenic disturbances on forest regeneration [ 2 , 7 ]. These findings align with similar studies which reported that the structure and diversity of the understory in degraded tropical areas are strongly shaped by human activities [ 23 , 73 ]. Bentsi-Enchill et al. [ 74 ] attributed such patterns to disturbance factors like logging, fuelwood extraction, and slash-and-burn agriculture in a similar context. Fuelwood extraction and slash-and-burn agriculture are common in the study area and likely contributed to the reduced density of the understory vegetation. This explains why fallows and subsistence crop fields exhibited limited regeneration (600 − 400 stems/ha), characterized by a depleted soil seed bank and substantial competition with ruderal species [ 7 ]. These results are consistent with previous studies highlighting the detrimental effects of unsustainable agricultural practices on natural regeneration [ 5 , 50 , 75 ], where human activities reduce seed availability through land clearance [ 31 ]. Furthermore, human-induced bushfires exacerbate this trend by lowering seedling survival rates [ 49 ]. Species richness was highest in mature secondary forests (30 species) compared to 6 species in croplands, where disturbances promote the dominance of a few generalists’ species [ 50 ]. While degraded croplands have been shown to favor certain pioneer species [ 5 ], their expansion risks impairing forest succession [ 17 ]. The pronounced species richness for small stems in subsistence crop fields (19.70) aligns with heavily exploited areas, where overall diversity is impoverished. This phenomenon has been observed in other studies[ 50 , 76 ], which highlighted that repeated disturbances disproportionately benefit a limited number of generalist species at the expense of specialists. Repeated disturbances disproportionately benefit a few generalists at the expense of specialists. Thus, balanced management (e.g., selective harvesting) and targeted restoration are critical to sustaining ecological resilience. II.7.3. Vegetative Reproduction capacity of damaged trees, individual predictive factors, and basal stump diameter effects The study of stump sprouting reveals a major adaptive mechanism in response to environmental disturbances, representing a recurrent biological phenomenon in woody plants [ 77 ]. Results indicated that 78.79% of recorded tree stumps produced sprouts, demonstrating a remarkable capacity for post-disturbance reproduction in the Semi-Deciduous Forest [ 7 ]. Although this phenomenon primarily constitutes a survival strategy rather than true regeneration [ 37 ], it plays a major ecological role in tropical forest ecosystems. These results can be explained by several interdependent factors, including local edapho-climatic conditions, particularly the rainfall regime characteristic of tropical zones [ 78 ]; post-disturbance micro-environmental modifications, such as increased light availability [ 8 ]; and thermal fluctuations that promote meristem activation [ 79 ]. Our findings are consistent with the 79% recorded in the semi-deciduous forests of South-West Cameroon and 75% in Asia [ 37 ], but lower than the 97.5% observed in Ugandan forests [ 24 ]. The high reproductive capacity recorded in the tropical zone confirms the universality of this adaptive mechanism, providing forest ecosystems with two key strategies: (1) enhanced competition through rapid recolonization of ecological niches, and (2) improved resilience to anthropogenic disturbances [ 42 ]. However, while this regeneration mode maintains forest cover effectiveness, it leads to a depletion of the original species diversity and increased dominance of pioneer species [ 80 ]. These findings underscore the need for differentiated management that combines the protection of natural sprouts, the conservation of stumps of climax species, and assisted regeneration for species with low sprouting capacity. This integrated approach would reconcile ecological resilience and biodiversity maintenance in disturbed ecosystems. II.7.4. Relationship between Stump Attributes and Regeneration Potential The study reveals a positive correlation between the number of sprouts, stump height, and sprout diameter. In contrast to previous studies [ 24 , 42 ], no significant relationship was found between stump diameter and the number of sprouts. Stumps of large diameter (≥ 40 cm; e.g., Dichostemma glaucescens , Mitragyna ciliata , Musanga cecropioides ) exhibited low regeneration, whereas the best performance was observed for intermediate diameters. Certain species ( Musanga cecropioides ) preferentially regenerate via seeds[ 81 ], while others ( Uapaca paludosa ) favor vegetative reproduction. Although seed regeneration dominates after a disturbance [ 81 ], our study confirms the complementary importance of stump sprouts, particularly under recurrent anthropogenic pressures. The low stump mortality rate (9.52%) indicates a regenerative capacity that ensures the partial replacement of felled individuals. Furthermore, the number of sprouts increases with stump height and the height of the dominant sprout, consistent with the findings of Mwavu & Witkowski [ 24 ]. This underscores the activation of dormant buds, maximizing their chances of survival. This morphological plasticity enables the rapid reconstitution of the photosynthetic apparatus after cutting or mechanical damage [ 82 ] The growth and survival of sprouts are crucial for the resilience of disturbed forests [ 73 ]. However, excessive disturbance (e.g., logging, shifting cultivation) that exceeds a critical threshold could compromise this regeneration [ 23 ]. ). Sustainable management should therefore limit the frequency and intensity of cutting to preserve this potential. III.7.5. Implications for Sustainable Management and Conservation The findings highlight the need for adaptive and differentiated forest management strategies that enhance natural regeneration while minimizing disturbance impacts. Protecting less disturbed stands as biodiversity reservoirs and promoting assisted regeneration in degraded zones are essential to maintain ecological resilience [ 6 , 23 ]. Integrating agroforestry and restricting slash-and-burn agriculture could reduce pressure on forest resources (Arroyo-Rodríguez et al. 2020). Furthermore, conserving stumps of climax species and managing natural sprouts can sustain forest structure and genetic diversity [ 50 ]. Furthermore, conserving stumps of climax species and managing natural sprouts can sustain forest structure and genetic diversity [ 80 ]. Such measures support a landscape-level approach linking conservation, restoration, and community-based management, ensuring the long-term sustainability of semi-deciduous tropical forests. Conclusion This study assessed the differential effects of anthropogenic disturbances on forest regeneration and the response of damaged trees. The study reveals the complex dynamics of natural regeneration in the forest of Angossas, highlighting several key findings. The results demonstrate a predominance of understory vegetation (80.85%), with seedlings constituting 33.86% of individuals, showing differential regeneration patterns along disturbance gradients. Saplings thrive in disturbed areas (875 stems. ha-1) with higher diversity (Shannon index = 2.9), while saplings reach maximum diversity in intact zones. Land-use types significantly influence understory structure and composition, with an alarming 60% reduction in seedlings in disturbed areas. Although resilient, mature secondary forests (MSF) remain vulnerable to fragmentation, confirming that anthropogenic disturbances substantially reduce understory density and diversity. The low floristic similarity between strata (only 10% shared species) confirms an ecological discontinuity between understory and canopy layers. This disparity underscores the increased vulnerability of juvenile trees to human pressures, signaling an urgent need for targeted conservation measures. Damaged trees exhibit remarkable vegetative reproduction capacity (78.8% of stumps producing sprouts), although this varies by species and micro-environmental conditions. The abundance of sprouts and their rapid growth compared to sexually regenerated saplings make this a crucial resilience mechanism for maintaining degraded forest ecosystems. These findings strongly advocate for the systematic integration of vegetative reproduction into forest management strategies. These results call for sustainable forest management approaches focusing on strict protection of mature secondary forests as biodiversity reservoirs and sources of natural regeneration; adaptive silvicultural practices promoting vegetative regeneration (conservation of climax species stumps, selective logging); active restoration of degraded areas through targeted measures (seed bank enrichment, invasive species control). Particular attention should be paid to maintaining structural heterogeneity and limiting agricultural fires. This approach, building on the identified natural processes, will help reconcile ecosystem resilience, biodiversity conservation, and local needs. Declarations Funding Funding This work receives financial support from the Conservation Action Research Network Program 2022 and the Rufford Small Grant (3884-B). Ethics declarations Consent to participate Not applicable. No human participants were involved in this study. Consent to publish All authors consent to the publication of this manuscript. Declaration of Competing Interest The authors declare that they have no competing interests. Author Contribution **Conceptualization** : APE, CDC, CJZ; **Methodology** : APE, CDC, JCZ; **Formal analysis and investigation:** APE; **Writing - original draft preparation** : APE, CDC, KLM, GY, LFME; **Writing - review and editing** : APE, CDC, JCZ, RLN, KLM; **Funding acquisition** : APE, CDC, JCZ; **Resources:** APE, CDC, JCZ; **Supervision** : JLF, MMM. Acknowledgement AcknowledgmentsWe extend our sincere gratitude to the Conservation Action Research Network Program 2022 and the Rufford Small Grant (No. 3884-B) for the decisive financial support that enabled data collection for this study. We warmly thank the Conservation and Sustainable Natural Resources Management Network (CSNRM-Net) for their valuable technical and logistical support, which proved crucial in implementing this research project. We also express our deep appreciation to the administrative and traditional authorities of the Mboanz district, as well as the local communities, for their essential hospitality and collaboration throughout this study. Special recognition is extended to Dr. Gauthier Ligot for his significant contribution to improving the quality of the manuscript. Finally, we express our utmost gratitude to our field guides, whose expertise and dedication were indispensable to the success of the inventory campaigns. Data Availability The data supporting the findings of this study will be made available by the corresponding author upon reasonable request. References White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens Environ. 2017;194:303–21. https://doi.org/10.1016/j.rse.2017.03.035 . 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Supplementary Files Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviews received at journal 27 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Editor invited by journal 01 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 28 Nov, 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. 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15:31:08","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":274470,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/4891de165d35ed6824aebf29.html"},{"id":97893400,"identity":"bb4a43c5-ed93-4e43-8f44-146672930d49","added_by":"auto","created_at":"2025-12-10 15:30:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366260,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/d96a47987b2daf3a9e6a4931.png"},{"id":97684799,"identity":"32924b6d-e8e3-403a-90c0-10f363372436","added_by":"auto","created_at":"2025-12-08 10:08:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":305573,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in species richness within the understory vegetation according to disturbance intensity level: (I) saplings; (II) seedlings\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/b146113de834bf2aa69c7002.png"},{"id":97684790,"identity":"34358f65-8786-4734-ad03-8e26acf08404","added_by":"auto","created_at":"2025-12-08 10:08:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299295,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) ordinations showing variations in understory species composition across disturbance levels and Land use types. a. Land use types; b. Disturbance gradients. Legend. Land use types: MSF. Mature secondry forest ; YSF. Young secondry forest; SW. Swamp; FSF. Seasonally flooded swamp; FCC. Food crop cultivation. Disturbance gradients: A. Severely degraded (\u0026gt;50% canopy openness); B. Moderately disturbed (25-50% degradation); C. Lightly disturbed (\u0026lt;25% human-induced canopy gaps); D. Relatively intact.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/b715f9d4b4b205a8a88f6e32.png"},{"id":97684716,"identity":"764fe2ec-3965-48b9-9f2e-324a4803845d","added_by":"auto","created_at":"2025-12-08 10:08:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320796,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical Classification of Land Use Type and dominance understory species. (a). Land use types; (b). Understory species.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/323db4974fe306ddae313c7c.png"},{"id":97684724,"identity":"fa81d732-f436-43e8-813b-fb5449b8d949","added_by":"auto","created_at":"2025-12-08 10:08:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":508053,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation (p \u0026lt; 0.05) between sprouting potential and stump attributes. Positive correlations are shown in blue, and negative correlations in red. Color intensity and circle size are proportional to the correlation coefficient. On the right side of the matrix, the legend color indicates the correlation coefficient and the corresponding colors.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/af89a922c67a71639a7edaa8.png"},{"id":97684718,"identity":"f4080612-c87d-4ae0-ad78-ee58c01484f8","added_by":"auto","created_at":"2025-12-08 10:08:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":659866,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis between stump attributes and sprout production. a) Stump diameter, b) Height of the tallest sprout, c) Stump height, d) Height of the main sprout, e) Stump density\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/6c30b590b4af23ebd014e009.png"},{"id":97902527,"identity":"c234055d-5167-477e-a7f6-bc971652f1f8","added_by":"auto","created_at":"2025-12-10 15:52:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4153014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/86fba74f-6763-48af-8595-d3bb3b320c2d.pdf"},{"id":97893304,"identity":"d82a8afd-d358-4e70-b09b-ce5786f61247","added_by":"auto","created_at":"2025-12-10 15:29:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25439,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231302/v1/5e260c1054bcfecaeee37d09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of a disturbance gradient on natural regeneration and understory diversity in semi-deciduous dense forests of Cameroon","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eNaturel regeneration is crucial for the persistence of forest ecosystems in the face of global change [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It serves as a key indicator of vegetation structure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and ensures forest dynamics and stability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The understory, which harbors 60% of plant diversity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], facilitates forest renewal through complex mechanisms such as seed rain, seed banks, and seedling-sapling interactions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Understory growth depends on light availability and other resources, promoting species conservation and forest recovery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, persistent disturbances reduce understory density [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and disrupt ecological processes, leading to substantial biodiversity loss [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given escalating land-use pressures and the critical role of forests, the regenerative potential of tropical forests has gathered increasing attention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Understanding the differential effects of disturbances on the understory is essential for optimizing forest management and conservation strategies.\u003c/p\u003e\u003cp\u003eStudies have highlighted the diversity and ecological significance of the forest understory [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This layer facilitates the establishment of native flora, enhancing ecosystem stability and species heterogeneity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At the landscape level, such diversity reflects vegetation resilience and its capacity to sustain biodiversity. Regeneration relies on key mechanisms, including seed rain, seed banks, and seedling-sapling-resprout interactions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, this critical process is increasingly compromised by persistent disturbances, threatening ecosystem resilience and functionality.\u003c/p\u003e\u003cp\u003eWhile disturbances naturally shape forest structure and composition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], persistent anthropogenic disturbances including fuelwood extraction, overharvesting of non-timber forest products (NTFPs), and slash-and-burn agriculture - lead to biomass depletion and disrupt critical ecological processes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These disturbances drive biodiversity loss [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], ), modify juvenile banks in ways that conflict with species' ecological requirements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and inhibit natural succession, thereby severely constraining forest regeneration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Their deleterious impacts on forest integrity and regeneration dynamics are well-documented in the scientific literature [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnthropogenic disturbances significantly damage woody vegetation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], triggering complex natural regeneration processes. This regeneration represents the most suitable mechanism for restoring ecosystem resilience [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. ). It maintains species composition and facilitates post-disturbance recovery [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, disturbances profoundly alter forest structure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], promoting pioneer species through canopy fragmentation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This dynamic may lead to long-term biomass overcompensation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], albeit through variable ecological trajectories.\u003c/p\u003e\u003cp\u003eForest ecosystems exhibit differential responses to disturbance regimes. Recurrent disturbances favor ruderal species with competitive pioneer traits, while disadvantaging climax species that require stable environmental conditions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For instance, intensive clear-cutting or repeated fires lead to vegetation homogenization, impairing tree recruitment and sapling development [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, moderate disturbances, such as selective logging, can enhance spatial heterogeneity and create ecological niches for specialist species [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, minimally disturbed systems exhibit asymmetric competition dominated by mature trees, which significantly suppresses understory growth and development through shading effects [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While anthropogenic disturbances invariably alter forest structure and functioning, their long-term impacts ultimately depend on species' regenerative capacity.\u003c/p\u003e\u003cp\u003eWoody plants regenerate through two pathways: seedling recruitment (sexual) and vegetative resprouting (asexual), both of which are vital for forest dynamics [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, disturbance impacts on these mechanisms remain poorly understood. Vegetative regeneration enhances post-disturbance recovery [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and species persistence [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], but the effect of disturbance on ecosystem services and resilience require further research [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Understanding these processes is critical for conservation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], particularly in understudied Cameroonian forests.\u003c/p\u003e\u003cp\u003eThe Angossas Communal Forest (ACF) exemplifies the current state of Congo Basin forests through widespread illegal logging, unsustainable resource harvesting, and land-use changes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These anthropogenic pressures, exacerbated by population growth, disrupt natural regeneration mechanisms [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], threatening forest stability where regeneration is typically characterized by low density and diversity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Therefore, this study aims to assess the differential effects of anthropogenic disturbances on forest regeneration and the response of damaged trees, with the ultimate goal of developing sustainable management strategies to enhance the resilience of ACF. Specifically, this study will: 1. Characterize understory vegetation diversity and regeneration patterns across disturbance gradients and land-use types; 2. Evaluate the vegetative reproduction capacity of damaged trees by analyzing individual predictive factors and basal diameter effects on stump resprouting; 3. Investigate functional relationships between stump characteristics and their vegetative reproduction potential.\u003c/p\u003e"},{"header":"II. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eII.1. Study area\u003c/h2\u003e\u003cp\u003eThe study was conducted in the Angossas Communal Forest (ACF), a semi-deciduous dense forest within the Guineo-Congolian domain [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Located in the Mbaonz municipality (Haut-Nyong Department, East Region of Cameroon), the site extends geographically between 4\u0026deg;4'60\"N and 12\u0026deg;58'60\"E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and covers 22,120 ha, divided into two distinct blocks. The topography features gently rolling terrain with an average elevation of 600 m. Slopes ranging from 0\u0026ndash;5% indicate low erosion susceptibility and favorable plant growth conditions. The equatorial climate exhibits four unevenly distributed seasons, with mean annual rainfall of 1,600 mm and temperatures averaging 22\u0026ndash;25\u0026deg;C. Soils are predominantly ferrallitic and hydromorphic, although their fertility is increasingly compromised by the intensification of slash-and-burn shifting agriculture [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Despite this, the area remains a biodiversity hotspot where illegal timber extraction and non-timber forest product (NTFP) harvesting constitute major anthropogenic pressures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eII.2. Sampling Design and Methodology\u003c/h2\u003e\u003cp\u003eThe sampling unit consisted of linear transects, a method recommended for studies of natural regeneration [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This approach allows for rapid and straightforward estimation of tree diversity and density while facilitating the assessment of vegetation variations along environmental gradients or across different habitats [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Ten transects of 2,000 m \u0026times; 20 m (covering 4 hectares per transect) were established, spaced 1 km apart. Each transect was subdivided into 100 m segments to improve the recording of individual plants [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. To accurately evaluate regeneration, nested quadrats of 10 m \u0026times; 10 m (100 m\u0026sup2;) and 5 m \u0026times; 5 m (25 m\u0026sup2;) were placed at 100 m intervals along the main transect. A total area of 40 ha was surveyed, representing a sampling intensity of 0.18%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eII.3. Disturbance Assessment and Inventory Data Collection\u003c/h2\u003e\u003cp\u003eField data on forest structure and disturbance levels were systematically collected along the established transects. At 100 m intervals, forest condition was assessed through physiognomic observations and classified into four disturbance categories following Tchouto et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]: Category A: Severely degraded (\u0026gt;\u0026thinsp;50% canopy openness); Category B: Moderately disturbed (25\u0026ndash;50% degradation, fragmented canopy with retained forest dominance); Category C: Lightly disturbed (\u0026lt;\u0026thinsp;25% human-induced canopy gaps) and Category D: Relatively intact (dense canopy, no anthropogenic degradation except hunting and NTFP collection). Land Use Types (LUTs) were characterized using integrated physiognomic and ecological criteria [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], including: dominant species composition, canopy closure status (open/closed), topographic features and understory vegetation density. Tree identification was carried out based on their diagnostic characteristics, along with diameter measurements (DBH). Large trees with a diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 cm were measured at 1.30 m above ground level. Saplings (defined as those with 5 cm\u0026thinsp;\u0026le;\u0026thinsp;DBH\u0026thinsp;\u0026lt;\u0026thinsp;10 cm) were surveyed in 100 m\u0026sup2; quadrats, while seedlings (DBH\u0026thinsp;\u0026lt;\u0026thinsp;5 cm) were recorded in 25 m\u0026sup2; quadrats [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. For trees with irregular stem shapes, the diameter was measured 30 cm above protrusions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eII.4. Stump Identification and Parameter Measurement\u003c/h2\u003e\u003cp\u003eStumps (both dead and living, with or without sprouts) were systematically identified along each transect based on foliage of resprouts, wood characteristics and bark features [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The decomposition state of each stump was recorded, and the cause of damage was identified and documented [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Basal diameter (BD) was measured at 20 cm below the cutting level, as most stumps were insufficiently tall for measurement at 1.30 m. Conversely, stump height was measured from the root collar to the cutting point. A total of six attributes were recorded: (1) species name (sprouted or non-sprouted stumps); (2) stump diameter; (3) stump height; (4) number of living or dead sprouts; (5) basal diameter and height of the dominant sprout; (6) degree of stump decomposition [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eII.5. Data Analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003eII.5.1. Characterization of Plant Diversity and Regeneration Along Disturbance and Land-Use Gradients\u003c/h2\u003e\u003cp\u003eThe density of small and young trees was estimated for each land-use type along the disturbance gradient. Species richness within a transect was calculated as the total number of distinct species observed in a given stratum. Diversity indices such as the Shannon-Weaver index, Simpson index, Pielou\u0026rsquo;s evenness, and Fisher\u0026rsquo;s alpha, were computed based on species presence and abundance. These indices account for species number, relative abundance, and dominance at the given scale [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Species similarity between different strata and land-use types was quantified using the Jaccard index (1-Jaccard) (1-Jaccard) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The natural regeneration index (NRI) for each transect was calculated, and a mean value was derived. This index was obtained as the ratio of seedlings (DBH\u0026thinsp;\u0026le;\u0026thinsp;10 cm) to large trees [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eII.5.2. Vegetative Reproduction of Damaged Trees, Predictive Factors, and Relationships Between Stump Characteristics and Sprouting Potential\u003c/h2\u003e\u003cp\u003eThe total number of stumps was recorded each species, with density extrapolated to a per-hectare basis. The vegetative reproduction (VR) rate was calculated per species and then at the landscape scale. The stump section was examined to determine the disturbance type (natural or anthropogenic). The mean number of sprouts per stump was used to estimate the VR potential, defined as the ratio of sprouts per stump to the total number of recorded stumps [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eII.6. Statistical Analyses\u003c/h2\u003e\u003cp\u003eMultiple statistical approaches were employed to analyze the data. After verifying data non-normality using homogeneity tests, analysis of variance (ANOVA) was performed to assess the effect of land-use types on ecological variables across the disturbance gradient, and examine relationships between tree reproductive capacity and biotic factors. Permutational Multivariate Analysis of Variance (PERMANOVA) was used to evaluate differential disturbance effects on understory vegetation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Non-metric multidimensional scaling (NMDS) was used to examine the distribution of floristic composition along disturbance and land use types, in conjunction with agglomerative hierarchical clustering. Spearman\u0026rsquo;s rank correlations quantified associations between stump attributes and sprouting potential, while linear regression models analyzed the influence of stump parameters on regeneration potential [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eII.6. Results\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eII.6.1. Vegetation diversity and regeneration, differential effects of disturbance and Land-Use types in the understory\u003c/h2\u003e\u003cp\u003eThe understory accounted for 80.85% of total recorded tree density, with a marked predominance of juvenile trees (66.14%; 2108\u0026thinsp;\u0026plusmn;\u0026thinsp;1516.25 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) over seedlings (33.86%; 1,079\u0026thinsp;\u0026plusmn;\u0026thinsp;155 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). This dominance was confirmed by the Natural Regeneration Index (NRI: 5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01) and Pearson's coefficient (R\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003ePERMANOVA revealed significant variation in vegetation attributes along the disturbance gradient (F\u0026thinsp;=\u0026thinsp;1,176, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for Saplings; F\u0026thinsp;=\u0026thinsp;89.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for seedlings). Saplings exhibited significantly higher density in heavily disturbed zones (A: 430.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14; B: 875.47\u0026thinsp;\u0026plusmn;\u0026thinsp;39.65 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; F\u0026thinsp;=\u0026thinsp;2.08, p\u0026thinsp;=\u0026thinsp;0.0004) compared to relatively undisturbed areas (C/D: 3750.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.03; 3000\u0026thinsp;\u0026plusmn;\u0026thinsp;15.13 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). In contrast, seedlings reached peak density in less disturbed zones (C: 1466.66\u0026thinsp;\u0026plusmn;\u0026thinsp;101. stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; F\u0026thinsp;=\u0026thinsp;12.24, p\u0026thinsp;=\u0026thinsp;0.0045; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecies dominance varied significantly with disturbance intensity (F\u0026thinsp;=\u0026thinsp;628.1, p\u0026thinsp;=\u0026thinsp;0.0011 for Saplings; F\u0026thinsp;=\u0026thinsp;628.1, p\u0026thinsp;=\u0026thinsp;0.002 for seedlings). Diversity indices peaked in zones C/D for saplings (Shannon: 2.91/2.74 and Simpson index: 0.91/0.89; F\u0026thinsp;=\u0026thinsp;327.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while seedlings showed reduced diversity, though still highest in zone C (Shannon\u0026thinsp;=\u0026thinsp;0.72; Simpson\u0026thinsp;=\u0026thinsp;0.45; F\u0026thinsp;=\u0026thinsp;705.8-248.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTotal species richness reached 99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 species, with maximum values in zone D (20.91\u0026thinsp;\u0026plusmn;\u0026thinsp;10.40 species; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Seedlings exhibited lower species richness (1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 species, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000), but values remained significantly higher in less disturbed C/D zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eII, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003ePermutational Multivariate Analysis of Variance (PERMANOVA; N\u0026thinsp;=\u0026thinsp;999 permutations) results showing understory disturbance effects. Disturbance level: A: Severely degraded (\u0026gt;\u0026thinsp;50% canopy openness); Category B: Moderately disturbed (25\u0026ndash;50% degradation,); Category C: Lightly disturbed (\u0026lt;\u0026thinsp;25% human-induced canopy gaps) and Category D: Relatively intact.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eJuvenile trees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eSaplings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity (ind./ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e430.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e875.47\u0026thinsp;\u0026plusmn;\u0026thinsp;39.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3750.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3000\u0026thinsp;\u0026plusmn;\u0026thinsp;15.13\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e933.33\u0026thinsp;\u0026plusmn;\u0026thinsp;46.88\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e600\u0026thinsp;\u0026plusmn;\u0026thinsp;21.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1466.66\u0026thinsp;\u0026plusmn;\u0026thinsp;101.84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e533.33\u0026thinsp;\u0026plusmn;\u0026thinsp;21.55\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDominance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShannon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.0124\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePi\u0026eacute;lou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.0032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha-Fisher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.99\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.14\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eTotal density\u0026thinsp;=\u0026thinsp;2108\u0026thinsp;\u0026plusmn;\u0026thinsp;1516.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eTotal density\u0026thinsp;=\u0026thinsp;1079\u0026thinsp;\u0026plusmn;\u0026thinsp;155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIRN\u0026thinsp;=\u0026thinsp;5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u0026nbsp;; R\u0026thinsp;=\u0026thinsp;0.003\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;1176\u0026nbsp;; p-value\u0026thinsp;=\u0026thinsp;0.0001\u0026nbsp;; N\u0026thinsp;=\u0026thinsp;999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;89.46\u0026nbsp;; p-value\u0026thinsp;=\u0026thinsp;0.00001; N\u0026thinsp;=\u0026thinsp;999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eII.6.2. Characterization of Understory Diversity and Density Across Land-Use Types\u003c/h2\u003e\u003cp\u003eResults demonstrate significant variations in ecological parameters and understory diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Seedling density showed marked differences among land-use types (LUTs) (CV\u0026thinsp;=\u0026thinsp;10.69%, p\u0026thinsp;=\u0026thinsp;0.002), reaching its maximum in Mature Secondary Forests (MSF: 1,150\u0026thinsp;\u0026plusmn;\u0026thinsp;780 trees ha⁻\u0026sup1;), a value significantly higher than in fallows (600\u0026thinsp;\u0026plusmn;\u0026thinsp;284 trees ha⁻\u0026sup1;; χ\u0026sup2; = 11, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.005). This density was strongly influenced by disturbance level (A: χ\u0026sup2; = 14.03, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.0021). Sapling density varied considerably and significantly among LUTs (FCC; 55.21%, p\u0026thinsp;=\u0026thinsp;0.001), being significantly influenced by disturbance intensity. The highest densities occurred in MSF (χ\u0026sup2; = 11, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 5,267\u0026thinsp;\u0026plusmn;\u0026thinsp;2,838 trees ha⁻\u0026sup1;), which were less disturbed areas (C/D), followed by fallows (733.30\u0026thinsp;\u0026plusmn;\u0026thinsp;372 trees ha⁻\u0026sup1;). Highly disturbed areas, including croplands (CL), swamps, and Young Secondary Forests (YSF), revealed lower densities for seedlings (A, B: 400 trees ha⁻\u0026sup1;; FCC\u0026thinsp;=\u0026thinsp;10.69; p\u0026thinsp;=\u0026thinsp;0.002) but higher densities for saplings (733.3\u0026thinsp;\u0026plusmn;\u0026thinsp;372 trees ha⁻\u0026sup1;; χ\u0026sup2; = 12.5, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eSpecies richness varied significantly among LUTs (seedlings: FCC\u0026thinsp;=\u0026thinsp;23.75%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.004; saplings: FCC\u0026thinsp;=\u0026thinsp;20.74%, p\u0026thinsp;=\u0026thinsp;0.007), consistently peaking in MSF (saplings: 30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;13.34 species; seedlings: 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19 species; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Species dominance showed contrasting patterns: non-significant for seedlings (p\u0026thinsp;=\u0026thinsp;0.054) despite notable variability (CV\u0026thinsp;=\u0026thinsp;13.25%), but significant for saplings (p\u0026thinsp;=\u0026thinsp;0.041) with high variability (CV\u0026thinsp;=\u0026thinsp;36.39%) and maximum values in less disturbed MSF (C/D: 30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;13.34 species; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The differences in understory species composition and dominance among land use types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), and across the disturbance gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) were confirmed by the non-metric multidimensional scaling analysis (NMDS).\u003c/p\u003e\u003cp\u003eSpecies similarity was low between seedlings and adult trees (Jaccard\u0026thinsp;=\u0026thinsp;0.10), unlike saplings, which showed greater similarity to adults (Jaccard\u0026thinsp;=\u0026thinsp;0.71 and 0.14 for adults and saplings, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiversity indices and density metrics of understory vegetation by land-use type and disturbance level. land-use type: MSF\u0026thinsp;=\u0026thinsp;Mature secondary forest; YFS\u0026thinsp;=\u0026thinsp;Young secondary forest; FSF\u0026thinsp;=\u0026thinsp;Seasonally flooded swamp; FCC\u0026thinsp;=\u0026thinsp;Food crop cultivation. CV\u0026thinsp;=\u0026thinsp;Variation of coefficient (%).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eSeedlings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSwamp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFSF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFCC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFallow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbance level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1110\u0026thinsp;\u0026plusmn;\u0026thinsp;78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e600\u0026thinsp;\u0026plusmn;\u0026thinsp;184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies Richess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDominance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShannon-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eSaplings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5267\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425\u0026thinsp;\u0026plusmn;\u0026thinsp;305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e250\u0026thinsp;\u0026plusmn;\u0026thinsp;191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e733.30\u0026thinsp;\u0026plusmn;\u0026thinsp;372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC/D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies Richess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;13.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDominance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShannon-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eJaccard similarity index (%) between regeneration stages (seedlings and saplings) and the upper canopy layer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedlings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSaplings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLarges trees\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeedlings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaplings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarges trees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHierarchical clustering distinguished two main groups among land use types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Group 1, composed solely of mature secondary forests, was characterized by high species abundance and exhibited unique traits within the study area. Group 2 showed strong homogeneity, with marked similarities between young secondary forests and crop fields, and ecological proximity between swamps and periodically flooded swamps. Fallows, distinguished by high heterogeneity, were predominantly composed of fast-growing pioneer species.\u003c/p\u003e\u003cp\u003eIn contrast, the ten most abundant understory species were divided into four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Group 1, the largest, included six species: \u003cem\u003eAfraegle paniculata\u003c/em\u003e, \u003cem\u003eEnanthia chlorantha\u003c/em\u003e, \u003cem\u003eHexalobus crispiflorus\u003c/em\u003e, \u003cem\u003eXylopia\u003c/em\u003e sp., and \u003cem\u003eNesogordonia papaverifera\u003c/em\u003e. These species are shade-tolerant but require small gaps for growth and are typical of relatively undisturbed mature and young secondary forests. Group 2, represented by \u003cem\u003eOddoniodendron normandii\u003c/em\u003e, displayed unique ecological characteristics and high heterogeneity. Its presence indicates a state of mature forest. Groups 3 and 4 shared similar ecological and adaptive traits, including \u003cem\u003eAnnona\u003c/em\u003e sp. And Coffea sp. (Group3), and \u003cem\u003eAmphimas pterocarpoides\u003c/em\u003e and \u003cem\u003eChrysophyllum boukokoense\u003c/em\u003e (Group 4). These species are highly shade-tolerant and require high atmospheric humidity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003eII.6.2. Vegetative Reproduction capacity of damaged trees, individual predictive factors, and basal stump diameter effects\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section4\"\u003e\u003ch2\u003eII.6.2.1. Vegetative Reproduction capacity of damaged trees\u003c/h2\u003e\u003cp\u003eA total of 147 woody stumps were recorded, representing 33 species, 27 genera, and 26 botanical families, with a mean density of 37 stumps. ha⁻\u0026sup1;. Among these, 9.52% could not be identified due to an advanced decomposition state. Stump origins showed predominance of anthropogenic impacts (55.88%) compared to windthrows (25%) and relatively undisturbed areas (22.22%; Appendix 1).\u003c/p\u003e\u003cp\u003eOverall vegetative reproduction capacity was high, with 78.79% of individuals producing sprouts versus 21.21% showing no regeneration (predominantly dead and highly decomposed stumps). Mean sprout count per stump was 5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92, with notable interspecific variation ranging from 6 for \u003cem\u003eSantiria trimera\u003c/em\u003e to 14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22 sprouts for \u003cem\u003eUapaca paludosa\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Five species demonstrated particularly high vegetative reproduction potential (\u0026ge;\u0026thinsp;5%), collectively representing 43 stumps (29.25%).\u003c/p\u003e\u003cp\u003eThese high-capacity reproducers were primarily shade-tolerant taxa affected by anthropogenic disturbances, with particular concentration in food crop areas and young fallows (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The species were: \u003cem\u003eUapaca paludosa\u003c/em\u003e (9.75%; Phyllanthaceae), \u003cem\u003eLannea welwitschii\u003c/em\u003e (6.80%; Anacardiaceae), \u003cem\u003ePycnanthus angolensis\u003c/em\u003e (Myristicaceae), \u003cem\u003eCeltis zenkeri\u003c/em\u003e (5.95%; Cannabaceae), \u003cem\u003eCeltis mildbraedii\u003c/em\u003e (5.78%; Cannabaceae), and \u003cem\u003eAnonidium mannii\u003c/em\u003e (5.44%; Annonaceae).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecies with a Reproduction Potential (\u0026ge;\u0026thinsp;2%), their corresponding numbers of stems and suckers\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\u003eFamilly\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of stumps\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of sprouts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReproduction Potential (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnacardiaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLannea Welwitschii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTrichoscypha acuminata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnonaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnonidium mannii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurseraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSantiria trimera\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCannabaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCeltis adolfi-friderici\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCeltis mildbraedii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCeltis zenkeiri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEuphorbiaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMacaranga hurifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRicinodendron heudelotii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLecythidaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePetersianthus macrocarpus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrvingiaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDesbordesia glaucescens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMeliaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTrichilia drageana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTrichilia welwitschii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyristicaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePycnanthus angolensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOlacaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAptandra zenkeri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePassifloraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBarteria fistulosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhyllanthaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUapaca paludosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrticaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMyrianthus arboreus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section4\"\u003e\u003ch2\u003eII.6.2.2. Predictive factors and effect of basal diameter of damaged tree stumps\u003c/h2\u003e\u003cp\u003eThe analysis of variance (ANOVA) revealed the influence of biotic variables on sprout number (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Sprout number differed significantly with stump condition (coefficient\u0026thinsp;=\u0026thinsp;13.903; p\u0026thinsp;=\u0026thinsp;0.003). The distribution of physiological states showed that 50% of stumps were in good vitality, while 26.47% revealed an intermediate state (alive and/or partially decomposed) and 17.64% were in an advanced state of decay. In contrast to the physiological condition, the other biotic variables studied showed no significant influence on sprout production (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). No notable differences were observed regarding taxonomic affiliation (families: coefficient\u0026thinsp;=\u0026thinsp;22.34; df\u0026thinsp;=\u0026thinsp;33; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; species: coefficient\u0026thinsp;=\u0026thinsp;33; df\u0026thinsp;=\u0026thinsp;33; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) or disturbance origin (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationships between biotic variables and the reproductive capacity of damaged tree stumps\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\u003eDependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictive variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNumber of sprouts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFamilly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStump condition\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e13.903\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpearman correlation analysis revealed significant relationships between stump attributes and regeneration performance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The number of sprouts showed significant positive correlations with both sprout diameter (r\u0026thinsp;=\u0026thinsp;0.235; p\u0026thinsp;=\u0026thinsp;0.03) and stump height (r\u0026thinsp;=\u0026thinsp;0.654; p\u0026thinsp;=\u0026thinsp;0.01; Appendix 2). Reproduction potential was strongly correlated with the diameter (r\u0026thinsp;=\u0026thinsp;0.452; p\u0026thinsp;=\u0026thinsp;0.02) and height (r\u0026thinsp;=\u0026thinsp;0.32; p\u0026thinsp;=\u0026thinsp;0.02) of the most developed sprout (Appendix 2). While no overall correlation was found with stump diameter (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), a complete reproduction failure in large in large-diameter stumps (\u0026ge;\u0026thinsp;40 cm: \u003cem\u003eDichostemma glaucescens, Mitragyna ciliata, Musanga cecropioides\u003c/em\u003e) was observed. Optimal reproduction occurred in intermediate-diameter stumps (8\u0026ndash;28 cm), particularly in \u003cem\u003eUapaca paludosa\u003c/em\u003e (16.75 cm; 14 sprouts), \u003cem\u003eCeltis mildbraedii\u003c/em\u003e (18 cm; 8.5 sprouts), \u003cem\u003eCeltis zenkeiri\u003c/em\u003e (27.83 cm; 8.75 sprouts), \u003cem\u003eLannea welwitschii\u003c/em\u003e (25 cm; 10 sprouts), and \u003cem\u003eSantiria trimera\u003c/em\u003e (8 cm; 6 sprouts) (Appendix 3). Stump density showed no significant correlation with regeneration potential (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, species with fewer stumps (\u003cem\u003eL. welwitschii\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eC. mildbraedii\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eC. zenkeri\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;6, \u003cem\u003eU. paludosa\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;4) produced the highest sprout counts, while those with abundant stumps (\u003cem\u003ePetersianthus macrocarpus\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;18 and \u003cem\u003ePycnanthus angolensis\u003c/em\u003e; n\u0026thinsp;=\u0026thinsp;23) showed limited regeneration (6 and 5 sprouts, respectively; Appendix 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003eII.6.3. Relationship between stump attributes and Reproductive Potential\u003c/h2\u003e\u003cp\u003eStatistical modeling revealed significant influences of stump characteristics on total sprout production (p˂0.05). The analysis demonstrated a significant positive relationship between stump height and sprout abundance in damaged trees (adj. R\u0026sup2; = 0.158, slope\u0026thinsp;=\u0026thinsp;0.398; p\u0026thinsp;=\u0026thinsp;0.019; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In contrast, mean stump diameter showed no significant association with sprout production, although a negative trend was observed (adj. R\u0026sup2; = 0.052, slope = -0.227; p\u0026thinsp;=\u0026thinsp;0.274; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Similarly, non-significant relationships were found between sprout abundance and: (1) height of the dominant sprout (adj. R\u0026sup2; = 0.045, slope\u0026thinsp;=\u0026thinsp;0.212; p\u0026thinsp;=\u0026thinsp;0.237; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), (2) stump density (adj. R\u0026sup2; = 0.042, slope\u0026thinsp;=\u0026thinsp;0.206; p\u0026thinsp;=\u0026thinsp;0.324; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), and (3) density of the stump (adj. R\u0026thinsp;=\u0026thinsp;0.052211, slope\u0026thinsp;=\u0026thinsp;0,32154; p\u0026thinsp;=\u0026thinsp;0,540; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eII.7. Discussion\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003eII.7.1. Vegetation diversity and regeneration, differential effects of disturbance and Land-Use types in the understory\u003c/h2\u003e\u003cp\u003eSemi-deciduous tropical forests exhibit significant understory dominance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the study area, this stratum constituted the predominant component of the forest structure, accounting for 80.85% of all trees (3187\u0026thinsp;\u0026plusmn;\u0026thinsp;1671.25 stems. ha-1), even though this proportion remains lower than values reported for other humid forests [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Indeed, biotic and abiotic factors can influence the distribution, survival, recruitment, and mortality of Saplings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which may explain the observed variations.\u003c/p\u003e\u003cp\u003eThe predominance of young trees (66.14%) underscores their critical role in forest regeneration dynamics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the relatively low proportion of small trees (33.86%) highlights the adverse effects of anthropogenic and natural disturbances [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], raising concerns regarding the long-term sustainability of forest regeneration mechanisms. Implementing adaptive management strategies, such as restricting agricultural burning and promoting agroforestry systems is essential to preserve understory biodiversity. Such approaches should integrate ecological conservation with restoration efforts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results indicated a high understory density in highly disturbed areas (430.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14 stems/ha) and moderately disturbed areas (875.47\u0026thinsp;\u0026plusmn;\u0026thinsp;39.65 stems/ha). According to Watson et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], disturbances alter forest structure by promoting increased light penetration, availability of mineral elements, and other nutrients, which stimulates understory regeneration and facilitates the establishment of pioneer species [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Their impacts on biodiversity vary depending on type and frequency [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which explains why small trees in the region exhibit maximum density in less disturbed areas (1,466.66\u0026thinsp;\u0026plusmn;\u0026thinsp;101.84 stems. ha-1). Indeed, understory density and composition are correlated with canopy openness, mediated by environmental variables [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. These modifications may lead to a selection effect, thereby altering original diversity [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003eII.7.2. Effects of land-use types on understory diversity and density\u003c/h2\u003e\u003cp\u003eThe analysis of understory vegetation in this study revealed greater species variation along a disturbance gradient. Species diversity was very low in highly disturbed, stressed vegetation compared to intermediate vegetation (Shannon\u0026thinsp;=\u0026thinsp;2.91 for saplings; 0.72 for small stems), highlighting the link between ecological stability and biodiversity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These findings align with those of Mounmemi et al. [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], who emphasized that low-intensity logging has little negative impact on species diversity and richness. Furthermore, secondary succession is more favorable for the recovery of forests that have undergone low-intensity logging [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This is supported by the number of understory species in low-disturbance environments (20.91 species) compared to 15 species in highly disturbed environments, confirming that intact forests act as biodiversity reservoirs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These differences observed across the disturbance gradient reflect the mechanisms described by Matula et al. [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], notably the post-disturbance explosion of sapling regeneration and the difficult persistence of established small trees. Differential management is essential, aiming to protect intact areas and implement assisted regeneration in degraded zones [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResults reveal significant variations in stem density and species richness across land-use types, confirming the impact of anthropogenic disturbances on forest regeneration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Less disturbed mature secondary forests exhibited a higher density of small stems (5,267\u0026thinsp;\u0026plusmn;\u0026thinsp;2,838 stems/ha) compared to 1,150\u0026thinsp;\u0026plusmn;\u0026thinsp;780 stems/ha for saplings, reflecting optimal regeneration under a stable canopy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This stability provides favorable microclimatic conditions for recruitment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, highly disturbed young secondary forests showed low tree species density (400 stems/ha), confirming the impact of anthropogenic disturbances on forest regeneration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings align with similar studies which reported that the structure and diversity of the understory in degraded tropical areas are strongly shaped by human activities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Bentsi-Enchill et al. [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] attributed such patterns to disturbance factors like logging, fuelwood extraction, and slash-and-burn agriculture in a similar context. Fuelwood extraction and slash-and-burn agriculture are common in the study area and likely contributed to the reduced density of the understory vegetation.\u003c/p\u003e\u003cp\u003eThis explains why fallows and subsistence crop fields exhibited limited regeneration (600\u0026thinsp;\u0026minus;\u0026thinsp;400 stems/ha), characterized by a depleted soil seed bank and substantial competition with ruderal species [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These results are consistent with previous studies highlighting the detrimental effects of unsustainable agricultural practices on natural regeneration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], where human activities reduce seed availability through land clearance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, human-induced bushfires exacerbate this trend by lowering seedling survival rates [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpecies richness was highest in mature secondary forests (30 species) compared to 6 species in croplands, where disturbances promote the dominance of a few generalists\u0026rsquo; species [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While degraded croplands have been shown to favor certain pioneer species [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], their expansion risks impairing forest succession [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The pronounced species richness for small stems in subsistence crop fields (19.70) aligns with heavily exploited areas, where overall diversity is impoverished. This phenomenon has been observed in other studies[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], which highlighted that repeated disturbances disproportionately benefit a limited number of generalist species at the expense of specialists. Repeated disturbances disproportionately benefit a few generalists at the expense of specialists. Thus, balanced management (e.g., selective harvesting) and targeted restoration are critical to sustaining ecological resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eII.7.3. Vegetative Reproduction capacity of damaged trees, individual predictive factors, and basal stump diameter effects\u003c/h2\u003e\u003cp\u003eThe study of stump sprouting reveals a major adaptive mechanism in response to environmental disturbances, representing a recurrent biological phenomenon in woody plants [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Results indicated that 78.79% of recorded tree stumps produced sprouts, demonstrating a remarkable capacity for post-disturbance reproduction in the Semi-Deciduous Forest [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although this phenomenon primarily constitutes a survival strategy rather than true regeneration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], it plays a major ecological role in tropical forest ecosystems. These results can be explained by several interdependent factors, including local edapho-climatic conditions, particularly the rainfall regime characteristic of tropical zones [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]; post-disturbance micro-environmental modifications, such as increased light availability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; and thermal fluctuations that promote meristem activation [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Our findings are consistent with the 79% recorded in the semi-deciduous forests of South-West Cameroon and 75% in Asia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], but lower than the 97.5% observed in Ugandan forests [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The high reproductive capacity recorded in the tropical zone confirms the universality of this adaptive mechanism, providing forest ecosystems with two key strategies: (1) enhanced competition through rapid recolonization of ecological niches, and (2) improved resilience to anthropogenic disturbances [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, while this regeneration mode maintains forest cover effectiveness, it leads to a depletion of the original species diversity and increased dominance of pioneer species [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. These findings underscore the need for differentiated management that combines the protection of natural sprouts, the conservation of stumps of climax species, and assisted regeneration for species with low sprouting capacity. This integrated approach would reconcile ecological resilience and biodiversity maintenance in disturbed ecosystems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eII.7.4. Relationship between Stump Attributes and Regeneration Potential\u003c/h2\u003e\u003cp\u003eThe study reveals a positive correlation between the number of sprouts, stump height, and sprout diameter. In contrast to previous studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], no significant relationship was found between stump diameter and the number of sprouts. Stumps of large diameter (\u0026ge;\u0026thinsp;40 cm; e.g., \u003cem\u003eDichostemma glaucescens\u003c/em\u003e, \u003cem\u003eMitragyna ciliata\u003c/em\u003e, \u003cem\u003eMusanga cecropioides\u003c/em\u003e) exhibited low regeneration, whereas the best performance was observed for intermediate diameters. Certain species (\u003cem\u003eMusanga cecropioides\u003c/em\u003e) preferentially regenerate via seeds[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], while others (\u003cem\u003eUapaca paludosa\u003c/em\u003e) favor vegetative reproduction.\u003c/p\u003e\u003cp\u003eAlthough seed regeneration dominates after a disturbance [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], our study confirms the complementary importance of stump sprouts, particularly under recurrent anthropogenic pressures. The low stump mortality rate (9.52%) indicates a regenerative capacity that ensures the partial replacement of felled individuals. Furthermore, the number of sprouts increases with stump height and the height of the dominant sprout, consistent with the findings of Mwavu \u0026amp; Witkowski [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This underscores the activation of dormant buds, maximizing their chances of survival. This morphological plasticity enables the rapid reconstitution of the photosynthetic apparatus after cutting or mechanical damage [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe growth and survival of sprouts are crucial for the resilience of disturbed forests [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, excessive disturbance (e.g., logging, shifting cultivation) that exceeds a critical threshold could compromise this regeneration [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. ). Sustainable management should therefore limit the frequency and intensity of cutting to preserve this potential.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eIII.7.5. Implications for Sustainable Management and Conservation\u003c/h2\u003e\u003cp\u003eThe findings highlight the need for adaptive and differentiated forest management strategies that enhance natural regeneration while minimizing disturbance impacts. Protecting less disturbed stands as biodiversity reservoirs and promoting assisted regeneration in degraded zones are essential to maintain ecological resilience [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Integrating agroforestry and restricting slash-and-burn agriculture could reduce pressure on forest resources (Arroyo-Rodr\u0026iacute;guez et al. 2020). Furthermore, conserving stumps of climax species and managing natural sprouts can sustain forest structure and genetic diversity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, conserving stumps of climax species and managing natural sprouts can sustain forest structure and genetic diversity [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Such measures support a landscape-level approach linking conservation, restoration, and community-based management, ensuring the long-term sustainability of semi-deciduous tropical forests.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study assessed the differential effects of anthropogenic disturbances on forest regeneration and the response of damaged trees. The study reveals the complex dynamics of natural regeneration in the forest of Angossas, highlighting several key findings. The results demonstrate a predominance of understory vegetation (80.85%), with seedlings constituting 33.86% of individuals, showing differential regeneration patterns along disturbance gradients. Saplings thrive in disturbed areas (875 stems. ha-1) with higher diversity (Shannon index\u0026thinsp;=\u0026thinsp;2.9), while saplings reach maximum diversity in intact zones. Land-use types significantly influence understory structure and composition, with an alarming 60% reduction in seedlings in disturbed areas. Although resilient, mature secondary forests (MSF) remain vulnerable to fragmentation, confirming that anthropogenic disturbances substantially reduce understory density and diversity.\u003c/p\u003e\u003cp\u003eThe low floristic similarity between strata (only 10% shared species) confirms an ecological discontinuity between understory and canopy layers. This disparity underscores the increased vulnerability of juvenile trees to human pressures, signaling an urgent need for targeted conservation measures.\u003c/p\u003e\u003cp\u003eDamaged trees exhibit remarkable vegetative reproduction capacity (78.8% of stumps producing sprouts), although this varies by species and micro-environmental conditions. The abundance of sprouts and their rapid growth compared to sexually regenerated saplings make this a crucial resilience mechanism for maintaining degraded forest ecosystems. These findings strongly advocate for the systematic integration of vegetative reproduction into forest management strategies.\u003c/p\u003e\u003cp\u003eThese results call for sustainable forest management approaches focusing on strict protection of mature secondary forests as biodiversity reservoirs and sources of natural regeneration; adaptive silvicultural practices promoting vegetative regeneration (conservation of climax species stumps, selective logging); active restoration of degraded areas through targeted measures (seed bank enrichment, invasive species control). Particular attention should be paid to maintaining structural heterogeneity and limiting agricultural fires. This approach, building on the identified natural processes, will help reconcile ecosystem resilience, biodiversity conservation, and local needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eFunding This work receives financial support from the Conservation Action Research Network Program 2022 and the Rufford Small Grant (3884-B).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e\u003cb\u003eEthics declarations\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003eNot applicable. No human participants were involved in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**Conceptualization** : APE, CDC, CJZ; **Methodology** : APE, CDC, JCZ; **Formal analysis and investigation:** APE; **Writing - original draft preparation** : APE, CDC, KLM, GY, LFME; **Writing - review and editing** : APE, CDC, JCZ, RLN, KLM; **Funding acquisition** : APE, CDC, JCZ; **Resources:** APE, CDC, JCZ; **Supervision** : JLF, MMM.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgmentsWe extend our sincere gratitude to the Conservation Action Research Network Program 2022 and the Rufford Small Grant (No. 3884-B) for the decisive financial support that enabled data collection for this study. We warmly thank the Conservation and Sustainable Natural Resources Management Network (CSNRM-Net) for their valuable technical and logistical support, which proved crucial in implementing this research project. We also express our deep appreciation to the administrative and traditional authorities of the Mboanz district, as well as the local communities, for their essential hospitality and collaboration throughout this study. Special recognition is extended to Dr. Gauthier Ligot for his significant contribution to improving the quality of the manuscript. Finally, we express our utmost gratitude to our field guides, whose expertise and dedication were indispensable to the success of the inventory campaigns.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study will be made available by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhite JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. 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The effect of scientific evidence on conservation practitioners\u0026rsquo; management decisions. Conserv Biol. 2015;29(1):88\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cobi.12370\u003c/span\u003e\u003cspan address=\"10.1111/cobi.12370\" 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":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Natural regeneration, Understory vegetation, Disturbance effects, Sustainable agricultural practices, Ecological vulnerability, Forest conservation, Ecosystem resilience","lastPublishedDoi":"10.21203/rs.3.rs-8231302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8231302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the differential effects of anthropogenic disturbances on natural regeneration patterns in Angossas Communal Forest (ACF) through a comprehensive inventory of woody vegetation along 2000 \u0026times; 20 m linear transects covering 40 ha. We assessed disturbance levels, characterized land-use types, and stratified woody plants by diameter classes to examine understory diversity. Tree stumps were systematically documented to evaluate species regeneration capacity.\u003c/p\u003e\u003cp\u003eResults demonstrate that the understory accounts for 80.85% of total stem density, with saplings (66.14%) constituting the dominant regeneration component. Ecological attributes varied significantly along the disturbance gradient (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing peak sapling density in highly disturbed areas (875 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) versus optimal seedling density in lightly disturbed zones (1467 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Mature secondary forests (MSF) with minimal disturbance exhibited the highest understory density for saplings (5267\u0026thinsp;\u0026plusmn;\u0026thinsp;2838 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and maintained moderate seedlings density (1150\u0026thinsp;\u0026plusmn;\u0026thinsp;780 stems. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while displaying maximum diversity values (Shannon index: 2.7\u0026ndash;2.9) and species richness, albeit with low floristic similarity among strata (10%).\u003c/p\u003e\u003cp\u003eVegetative regeneration was particularly active, with 78.78% of stumps producing an average of 5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92 sprouts per stump, showing significant correlations with stump diameter and height (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings reveal partial ecosystem resilience through vigorous vegetative regeneration, while highlighting critical vulnerabilities in recruitment success.\u003c/p\u003e\u003cp\u003eThe study advocates for an integrated management approach combining conservation measures, sustainable harvesting practices, and active restoration strategies that capitalize on natural regeneration processes. We propose developing an adaptive management plan that incorporates ecological gradient mapping to optimize long-term forest resilience while addressing local community needs.\u003c/p\u003e","manuscriptTitle":"Influence of a disturbance gradient on natural regeneration and understory diversity in semi-deciduous dense forests of Cameroon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:07:25","doi":"10.21203/rs.3.rs-8231302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-22T11:21:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T08:59:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178661848893561764146889986074331458513","date":"2026-01-13T12:13:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T07:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259789768435805285960703617397878940223","date":"2025-12-24T00:40:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68982971920351414755185502118174248979","date":"2025-12-17T12:07:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T19:08:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T17:48:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T07:41:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T07:36:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Forests","date":"2025-11-28T14:17:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"303239aa-72fc-4b7a-beaa-2056048bd9ac","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T18:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:07:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8231302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8231302","identity":"rs-8231302","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}