Effects of Anthropogenic Activity on Aboveground Biomass and Carbon Stock in Upland and Seasonally Flooded Forests in Sierra Del Divisor National  Park, Perú

Fernando Pérez Grandez; Santiago Casas Luna; Marcel La Rosa-Sánchez; Bruno Padilla-Torres; Jehoshua Macedo-Bedoya; Doris Gómez-Ticerán; Fernando Camones-Gonzales; Abel Salinas-Inga; Franco Ángeles-Álvarez; Marco Carbajal-Bellido; Olga Solano Dávila; Manuel Perez-Mozombite; Octavio Monroy-Vilchis; Martha Zarco-González; Yakov Quinteros-Gómez

doi:10.20944/preprints202608.1464.v1

Submitted:

20 August 2026

Posted:

20 August 2026

You are already at the latest version

Abstract

Tropical Amazonian forests serve as critical carbon reservoirs, although their storage capacity varies significantly depending on the topographic, hydrologic, and successional heterogeneity of the landscape. This study quantified aboveground biomass (AGB) and carbon stocks across four vegetation units in the Tacshitea sector, Sierra del Divisor Na-tional Park (Peru): semi-dense flooded forest (Z1), hill forest (Z2), hill forest associated with secondary growth (Z3), and Mauritia flexuosa palm forest (Z4). We established 100 plots of 20 × 20 m (1 ha per zone), assessing all individuals with diameter at breast height (DBH) ≥ 10 cm using allometric equations validated for Amazonian ecosystems. A total of 2,363 individuals belonging to 158 species from 41 families were recorded. AGB varied sig-nificantly among vegetation units (Kruskal-Wallis, p < 0.001), with the highest values in Z1 (414.09 Mg ha⁻¹; 194.62 Mg C ha⁻¹) and Z2 (332.01 Mg ha⁻¹; 156.05 Mg C ha⁻¹), whereas Z3 and Z4 recorded substantially lower values (145.27–153.18 Mg ha⁻¹; 68.28–71.99 Mg C ha⁻¹). The Fabaceae family dominated carbon storage at the landscape scale (33.82%). Floristic diversity showed positive but weak associations with carbon storage (R² = 0.045–0.056), and this relationship lost significance when controlling for spatial structure using linear mixed-effects models (p = 0.409), indicating that structural attributes—particularly basal area and wood density—are more robust predictors of storage capacity than taxo-nomic richness per se. Beta diversity analysis revealed that floristic differentiation among zones was dominated by species turnover, explaining 26.5% of the variation by vege-tation unit (PERMANOVA, F = 11.55; p = 0.001). These results support the relevance of Sierra del Divisor National Park as a priority carbon sink in the western Peruvian Amazon and underscore the necessity of conservation strategies that integrate the structural heterogeneity of the forest mosaic.

Keywords:

biomass estimation

;

carbon sequestration

;

non-destructive sampling

;

western Amazonia

;

wood density

Subject:

Biology and Life Sciences - Ecology, Evolution, Behavior and Systematics

1. Introduction

Tropical forests, which cover 7–10% of Earth's land surface, represent the biome with the highest primary productivity and regulate the major biogeochemical cycles [1]. These ecosystems function as important carbon reservoirs, storing approximately 25% of terrestrial carbon in their aboveground and belowground biomass and soil horizons [2]. Furthermore, they contribute to climate system regulation through annual sequestration of 1.0–1.4 peta-grams of carbon, which attenuates global warming [3]. However, the Amazon basin shows progressive weakening in its carbon sink capacity, associated with increased tree mortality linked to climatic anomalies and landscape fragmentation, compromising ecosystem resilience [4].

Sierra del Divisor National Park (SDNP) stands as the third most extensive protected area in Peruvian Amazonia, encompassing 1,354,485 hectares distributed across the regions of Ucayali and Loreto [5]. Its volcanic mountain massifs exceed 900 meters, constituting the headwaters of ten major river basins [6]. The landscape hydrology of these ecosystems delineates seasonally inundated territories encompassing blackwater floodplain systems (tahuampas) associated with adjacent mountainous massifs, as well as extensive peatland networks characterized by organic substrates undergoing slow decomposition that exceed 5 m in depth, functioning as substantial belowground carbon reservoirs [5,7]. The pronounced topographic and hydrological heterogeneity inherent to these systems drives marked spatial variability in canopy structure and wood density — two critical biophysical attributes that directly govern the distribution of aboveground biomass across landscape scales [8].

Understanding variation in biomass storage capacity is fundamental, given that forest ecosystems span a continuum of successional states shaped by both natural landscape heterogeneity and post-disturbance regeneration dynamics. These processes collectively determine the structural complexity of forest stands and their capacity to accumulate and retain carbon over time [9]. In this context, empirically derived allometric models represent the methodological standard for estimating aboveground biomass at the individual tree level, offering a non-destructive approach that preserves stand structural integrity while enabling robust carbon quantification across ecologically diverse forest types [10,11].

To evaluate the role of the SDNP in the global carbon cycle this study quantified aboveground biomass and carbon stocks across four structurally distinct vegetation units within the Tacshitea sector, employing non-destructive allometric methods. Three working hypotheses were formulated to guide the analysis: (H1) aboveground biomass and carbon stocks differ significantly among vegetation units, with hill forests exhibiting greater storage capacity attributable to their higher structural complexity; (H2) hill forests associated with secondary successional dynamics present lower carbon density relative to mature forests, reflecting differences in stand development stage; and (H3) floristic diversity and canopy structural attributes are positively associated with aboveground biomass and carbon storage capacity across vegetation units.

2. Materials and Methods

2.1. Study Area

Sierra del Divisor National Park (SDNP), established in 2015, encompasses 1,354,485 ha in the departments of Ucayali and Loreto. It was created to protect a representative sample of the steep mountain region within the Amazonian humid tropical forest, with elevations near 1,000 m.a.s.l. This hill system, of volcanic origin, harbors a complex network of river basins that drain toward the Ucayali River in Peru and the Yuruá River in Brazil [5,12]. Among its principal functions are the protection of biological, geomorphological, and cultural diversity of indigenous peoples in situations of isolation and initial contact (PIACI), such as the Isconahua, Mayoruna, and Kapanawa peoples.

SDNP plays a key role in transboundary ecological connectivity, integrating a binational biological corridor of more than three million hectares [5]. Geologically, it constitutes one of the oldest formations in Amazonia, with landscapes of high geomorphological heterogeneity and difficult access, which has favored ecological integrity [13] and the presence of endemic and restricted-distribution species [14].

According to Holdridge's (1947) classification [15], SDNP corresponds to Tropical Humid Forest (bh-T), with annual precipitation of 1,600–2,000 mm, seasonality between October and April, and mean temperature of 25 °C [16,17].

Fieldwork was conducted between March and October 2025 at the Tacshitea Control and Surveillance Post (PCP) (7°53'54.39"S, 74°29'55.68"W; Figure 1), located on the banks of the river of the same name, a water body that potentially contributes to floristic differentiation patterns in western Amazonia [18,19]. Authorized anthropogenic activities in the area are restricted to scientific research and controlled ecotourism, safeguarding the subsistence rights of local communities [5].

2.2. Field Sampling

One hundred plots of 20 × 20 m (0.04 ha each) were established and distributed across four sampling zones in the Tacshitea sector (25 plots per zone; 1 ha sampled per zone), representative of the environmental heterogeneity of the area: semi-dense flooded forest (Z1), hill forest (Z2), hill forest associated with secondary growth (Z3), and palm forest dominated by Mauritia flexuosa (Z4). Selection was based on their physiographic, hydrological, floristic, and structural differences.

Plots were established with a minimum spacing of 40 m between them to reduce local spatial dependence, and zones were separated by at least 1,200 m. Biomass, carbon, and structural attribute values are presented standardized per hectare to facilitate comparisons; however, plots should be interpreted as spatial subsamples within each vegetation unit, not as independent geographical replicates. Therefore, statistical comparisons describe differences among the evaluated units in the Tacshitea sector and are not intended to be extrapolated to the entire SDNP.

The four zones encompass a gradient of ecological conditions, successional states, and hydrological regimes (Figure 2). Z1 (blackwater floodplain) and Z4 (palm swamp) correspond to hydromorphic systems with recurrent flooding; Z2 is a mature terra firme formation; and Z3 represents a secondary forest whose original disturbance is associated with subsistence agriculture (cassava, plantain, citrus) abandoned approximately ten years before sampling, allowing evaluation of structural recovery and post-disturbance carbon accumulation. Low recent human intervention in Z1 and Z4 is partly due to their difficult accessibility and flooding regime.

In each plot, all tree, shrub, and palm individuals with DBH ≥ 10 cm were evaluated, recording DBH and total height estimated with a SUUNTO clinometer [20]. Botanical samples were collected for taxonomic identification by a specialist using specialized literature and comparison with herbarium specimens [21]. Nomenclature was standardized with Plants of the World Online (POWO; https://powo.science.kew.org) and family classification followed the Angiosperm Phylogeny Website (APG; https://www.mobot.org/mobot/research/apweb/).

2.3. Calculation of AGB and Carbon Stock

To quantify aboveground biomass (AGB) and estimate carbon stocks, an empirical methodological approach based on dendrometric dimensions was followed using allometric equations validated for Amazonian ecosystems.

For each tree individual, basal area (BA) was calculated from diameter at breast height (DBH) using the following equation:

BA = (π × DBH²)/4000

Wood volume (V) was estimated by integrating basal area, total height (H), and a form factor of 0.7, according to the methodology proposed by Brown (1997) [22]:

V = BA × H × 0.7

Aboveground biomass (AGB) of woody plants was calculated by multiplying volume by wood-specific gravity (ρ), diameter at breast height (DBH), and total height (H) [10]:

AGB = (0.0673 * (ρ * (DBH²) * H) ^0.976)/1000

Wood density (ρ) for each taxon was assigned using the Global Wood Density Database [23].

For AGB estimation in palms (family Arecaceae), specific allometric models developed by Goodman et al. (2013) were applied [24].

Total carbon content was estimated by multiplying total aboveground biomass (Mg) by a carbon conversion factor (CF) of 0.47, as recommended by the IPCC [25] for tropical forests. Results were expressed as carbon stocks (Mg ha⁻¹).

2.4. Data Analysis

Forest structure was characterized by using descriptive statistics (mean ± SD) at the plot level. To evaluate differences among vegetation units, the Kruskal-Wallis test was employed, given that data did not meet the assumptions of normality (Shapiro-Wilk) or homogeneity of variances (Levene), followed by pairwise comparisons using Dunn's post-hoc test with Bonferroni correction. Results should be interpreted as comparisons among the units evaluated in the Tacshitea sector, without extrapolation to the entire SDNP. Basal area, aboveground biomass, and carbon values were expressed per hectare (1 ha sampled per zone).

Vertical stratification was evaluated using the Pretzsch index, a modification of the Shannon index applied to altitudinal structure. Individuals were classified into three strata according to the maximum height recorded per zone (Figure 3): upper (80–100%), middle (50–80%), and lower (0–50%) [26,27].

Floristic composition was assessed using the Importance Value Index (IVI) [28]. Alpha diversity was estimated using Shannon's (H′), Simpson's (1 − D), Pielou's evenness (J′), Fisher's α, and Margalef (DMg) indices. Species richness was compared among zones using rarefaction curves and abundance-based interpolation/extrapolation (q = 0). Asymptotic richness was estimated with Chao1, and sampling completeness was calculated as the ratio of observed to estimated richness using the iNEXT package [29].

Beta diversity was quantified using Bray–Curtis, Jaccard, and Sørensen indices. Sørensen dissimilarity was partitioned into turnover (Simpson dissimilarity) and nestedness components following Baselga (2010) [30] using the betapart package [31]. A SIMPER analysis [32] identified the species contributing most to compositional dissimilarity among zone pairs. All analyses and figures were produced in R v4.6.0 [33] using the tidyverse [34], vegan [35], iNEXT, betapart, and dunn.test packages.

3. Results

3.1. Floristic Composition and Diversity

3.1.1. Floristic Composition and Importance Value Index (IVI)

Global IVI identified Inga edulis as the species with the highest relative importance (25.9), followed by Jacaranda copaia (17.3), Ocotea obovata (15.4), Bauhinia brachycalyx (13.9), Pouteria caimito (13.4), and Aniba hostmanniana (12.9), concentrating the highest values among the 20 taxa with the greatest IVI recorded (Figure 4).

IVI by zone revealed differences in dominant composition among sampling units (Figure 5). In Z1, Bauhinia brachycalyx dominated (34.93), followed by Dialium guianense (18.03) and Mauritia flexuosa (16.66). In Z2, Jacaranda copaia stood out (43.5), while in Z3 and Z4 the dominant species was Ocotea obovata (IVI = 31.52 and 40.51, respectively). Inga edulis ranked among the species with highest IVI in three zones: Z2 (36.7), Z3 (30.45), and Z4 (36.66).

3.1.2. Alpha Diversity

A total of 2,363 individuals (DBH ≥ 10 cm) belonging to 41 families and 158 species were recorded (Table 1). Z1 presented the highest richness (99 spp.), followed by Z3 (73), Z2 (59), and Z4 (39). The best-represented families were Fabaceae (35.5%), Lauraceae (25.6%), and Arecaceae (9.8%).

Alpha diversity indices showed differences among zones (Table 2). Z1 recorded the highest values for Shannon (H' = 3.636), Simpson (1-D = 0.946), Fisher (α = 34.492), and Margalef (DMg = 15.427), with 574 individuals. Z3 presented H' = 3.425 and 1-D = 0.939 (503 ind.); Z2, H' = 3.209 and 1-D = 0.938 (628 ind.); and Z4, despite concentrating the highest abundance (658 ind.), recorded the lowest diversity (H' = 2.759; 1-D = 0.903). Pielou's evenness (J') ranged between 0.753 (Z4) and 0.798 (Z3).

Rarefaction curves confirmed the gradient Z1 > Z3 > Z2 > Z4 (Figure 6). Chao1 estimators projected 133.7, 104.9, 68.8, and 45.1 species for Z1, Z3, Z2, and Z4, respectively. Sampling completeness was higher in Z4 (86.4%) and Z2 (85.7%), while Z1 (74.0%) and Z3 (69.6%) did not reach the asymptote by the end of the sampling effort.

3.1.3. Diversity and Floristic Differentiation

Baselga's (2010) [30] partitioning showed that floristic dissimilarity among zones was dominated by species turnover (Figure 7). The Z1–Z3(β_Sørensen ≈ 0.64) and Z1–Z4 (β_Sørensen ≈ 0.62) comparisons presented the highest values of total dissimilarity; in both cases turnover was the predominant component, although Z1–Z4 also showed a contribution from nestedness. The Z3–Z4 comparison recorded the lowest total dissimilarity and the highest relative contribution of nestednes.

Classic indices confirmed the marked floristic differentiation (Table 3), with Z1–Z3 being the most dissimilar pair (Jaccard = 0.7801; Bray-Curtis = 0.7493; Sørensen = 0.6395) and Z3–Z4 the most similar (Bray-Curtis = 0.6546; Jaccard = 0.4933; Sørensen = 0.6607).

SIMPER analysis (Figure 8) identified Ocotea obovata, Bauhinia brachycalyx, Inga edulis, Cecropia engleriana, Jacaranda copaia, and Mauritia flexuosa as the species with greatest contribution to floristic differences among zones. In comparisons with Z1, Jacaranda copaia and Bauhinia brachycalyx stood out; in comparisons with Z4, Ocotea obovata; and in Z3–Z4 the contributions were distributed without marked dominance.

PERMANOVA (Bray-Curtis) revealed significant differences in floristic composition among zones (F = 11.55; R² = 0.265; p = 0.001; Table 4). PERMDISP revealed heterogeneity in multivariate dispersion (F = 16.68; p = 0.001), attributable mainly to Z4, whose dispersion differed significantly from the other zones (p < 0.001), while Z1, Z2, and Z3 showed no differences among themselves (Appendix 2).

NMDS ordination (Bray-Curtis; stress = 0.271) showed partial separation of plots by zone (Figure 9). Although the stress value indicates limited two-dimensional representation, the pattern is consistent with compositional differences detected by PERMANOVA.

3.2. Forest Structure

3.2.1. Vertical Structure and Stratification

The Pretzsch index evidenced dominance of the lower stratum (0–50% of maximum height) in all zones (Figure 10; Kruskal-Wallis, p < 0.05). Z2 concentrated the highest proportion in this stratum (85.4%, N = 536; mean height = 15.5 ± 5.7 m), followed by Z1 (78.7%, N = 452; 11.9 ± 3.9 m).

In the middle stratum (50–80%), Z4 showed the highest representation (24.5%, N = 161; 20.7 ± 2.7 m), followed by Z3 (23.5%, N = 118; 21.4 ± 2.7 m). Z2 had the lowest participation at this level (14.3%, N = 90), although with individuals up to 39.5 m, evidencing marked discontinuity toward the canopy.

The upper stratum (80–100%) was underrepresented in all zones. Z2 recorded the lowest incidence (0.3%, N = 2) but the highest height values (51.5 ± 2.1 m; maximum 53 m). Z3 retained the highest relative proportion in the canopy (4.4%, N = 22; 30.2 ± 2.6 m), with more balanced structural dynamics. Mean heights in the lower stratum of Z3 (11.6 m) and Z4 (11.7 m) indicate more homogeneous regeneration structure compared to the vertical heterogeneity of Z1 and Z2.

Dunn's post-hoc tests (Table 5) revealed significant differences among zones in all three strata. For height, comparisons Z1–Z3 and Z1–Z4 (p < 0.001) stood out in the upper stratum. In the middle and lower strata, most comparisons for richness and abundance were significant (p < 0.05), particularly those involving Z3. Collectively, these results indicate differentiated vertical distribution among the four zones, with contrasting patterns in the composition of each stratum (Table 5).

3.2.2. Diameter and Height Distribution

Diameter distribution showed the inverted J pattern in all four zones, with the highest concentration in the 10–20 cm class (53.5–67.0% of total; Figure 11). In Z3 and Z4, more than 90% of individuals had DBH < 30 cm, while Z1 and Z2 showed greater representation in middle and high classes, including individuals > 80 cm DBH.

Height distribution showed concentration in the 12–19.9 m class in all zones (41.2–50.8%; Figure 12). Z1 presented the most balanced distribution between the 5–11.9 m classes (32.6%) and 12–19.9 m (43.7%). The 20–29.9 m classes were more represented in Z2 (28.0%) and Z1 (17.6%), and the ≥ 30 m class was most frequent in Z2 (8.8%).

Overall, Z1 and Z2 showed greater representation in higher diameter and height classes compared to Z3 and Z4, where smaller individuals predominate (Figure 11 and Figure 12).

3.2.3. Dasometric Variables

Dasometric variables showed consistent variation among zones (Table 6). Z1 and Z2 presented higher values of DBH (24.45 ± 20.82 and 23.2 ± 17.69 cm), height (14.56 ± 6.49 and 17.76 ± 7.74 m), basal area (46.45 and 41.96 m² ha⁻¹), and volume (739.66 and 637.36 m³ ha⁻¹) compared to Z3 and Z4, which recorded considerably lower and similar values between them (DBH ≈ 19 cm; volume < 265 m³ ha⁻¹).

Kruskal-Wallis and Dunn-Bonferroni tests confirmed significant differences among zones for all structural variables (Figure 13; Appendices 3, 4). DBH and basal area grouped Z1 and Z2 as statistically similar and superior to Z3 and Z4. Height in Z2 was significantly greater than all other zones, with no differences among Z1, Z3, and Z4. Volume followed a similar pattern, although with additional differences between Z1 and Z2, and between Z1 and Z4.

3.3. Aboveground Biomass and Carbon Storage

3.3.1. Biomass and Carbon by Zone

Aboveground biomass and carbon stocks showed clear variation among the evaluated vegetation units (Figure 14). At the hectare level, Zone 1 recorded the highest values for both biomass (414.09 Mg ha⁻¹) and carbon (194.62 Mg C ha⁻¹), followed by Zone 2, with 332.01 Mg ha⁻¹ of biomass and 156.05 Mg C ha⁻¹. In contrast, Zones 3 and 4 presented considerably lower values, with biomass between 145.27 and 153.18 Mg ha⁻¹ and carbon between 68.28 and 71.99 Mg C ha⁻¹, respectively.

At the individual level, biomass and carbon values show differences among zones, with higher mean values in Z1 and Z2 compared to Z3 and Z4 (Table 7). The variability of values within each zone is high across all units, with greater amplitude in Z1 and Z2.

3.3.2. Taxonomic Contribution to Carbon Storage

At the global level, Fabaceae dominated carbon storage (166.03 Mg C; 33.82%), followed by Lecythidaceae (13.11%), Lauraceae (9.03%), Arecaceae (6.97%), Sapotaceae (6.69%), Bignoniaceae (6.09%), and Malvaceae (5.20%). The remaining families contributed less than 4% individually (Figure 15).

Family contribution varied among zones (Figure 16). Fabaceae was the dominant family in all zones, although with different secondary composition: in Z1 it was accompanied by Lecythidaceae (23.59%), Malvaceae (12.04%), and Arecaceae (10.56%); in Z2, Bignoniaceae (17.82%) and Lauraceae (15.29%); in Z3, Lecythidaceae (22.08%), Lauraceae (15.18%), and Sapotaceae (14.99%); and in Z4, Arecaceae (18.39%), Lauraceae (16.74%), Urticaceae (12.13%), and Sapotaceae (10.46%).

3.3.3. Structural Distribution of Carbon

Individuals in the upper stratum (80–100%) recorded the highest individual carbon contributions (medians of 1.0–10.0 Mg C tree⁻¹ in Z1 and Z3), while those in the lower stratum (0–40%), despite dominating in abundance, presented medians < 0.1 Mg C (Figure 17).

Dunn's tests (Table 8) revealed significant differences in carbon stocks among zones in the middle and lower strata (p < 0.05 for most pairwise comparisons). The only exception was the Z3–Z4 comparison, which showed no significant differences in either stratum. In contrast, no significant differences were detected among zones in the upper stratum (p > 0.05 for all pairwise comparisons), suggesting a relatively homogeneous contribution of canopy trees to carbon storage.

3.4. Diversity–Structure–Carbon Relationships

Spearman's correlation matrix showed positive but low-magnitude relationships between diversity indices and carbon at the plot level (ρ = 0.13 for Shannon, Simpson, and richness; Table 9). Correlations among diversity metrics were, as expected, very high among themselves (ρ = 1.0).

Simple linear regression models indicated positive and significant associations between each diversity metric and carbon (Table 10): Shannon (β = 3.95; p = 0.034; R² = 0.045), Simpson (β = 81.34; p = 0.023; R² = 0.051), and richness (β = 0.066; p = 0.018; R² = 0.056). However, the low proportion of variance explained in all cases indicates limited predictive capacity at the plot level.

In the linear mixed model with zone as a random effect, the Shannon–carbon relationship remained positive but lost significance (p = 0.409; Table 11), suggesting that the spatial structure of sampling influences this association.

3.5. Zonal Distribution, Conservation Status, Ecological Importance, and Carbon Stock of Key Taxa

Taxa with the highest threat categories presented restricted distributions, low abundance and IVI, and modest carbon contributions (Table 12). Virola surinamensis (EN) was recorded only in Z2 (N = 2; IVI = 0.354; 0.066 Mg C); Dipteryx micrantha and Cinchona officinalis were limited to Z1, with IVI < 0.60 and contributions of 1.156 and 2.029 Mg C, respectively. Aniba perutilis stood out for the highest global IVI among the evaluated taxa (12.878), although with restricted distribution to Z2–Z3 and low carbon contribution (0.723 Mg C).

Taxa with the broadest distributional range showed more consistent carbon contributions. Platymiscium stipulare and Euterpe cf. edulis (both VU–IUCN) were present in all four zones (34 and 22 individuals, respectively); P. stipulare combined a relatively high IVI (5.605) with 9.365 Mg C total. Meanwhile, Ceiba pentandra, represented by a single individual in Z1, accumulated 19.146 Mg C—the highest individual value recorded—evidencing the influence of large individuals in carbon storage.

4. Discussion

4.1. Forest Structure and Carbon

Aboveground biomass showed marked variation among vegetation units. Primary forest zones recorded elevated stocks: Z1 with 414.09 Mg ha⁻¹ and basal area of 46.45 m² ha⁻¹, and Z2 with 332.01 Mg ha⁻¹ and 41.96 m² ha⁻¹ (Table 6; Figure 14), differences that may be associated with topography and local hydrological conditions [37,38]. In Z1, recurrent flooding could limit vertical development and promote compensatory lateral growth resulting in larger basal areas, while soil stability in Z2 would favor taller stems with greater vertically concentrated biomass [39].

At the taxonomic level, carbon storage exhibited an asymmetric distribution dominated by a few families. Fabaceae accounted for 33.82% of total carbon stocks (Figure 15), a pattern consistent with the Amazonian hyperdominance reported by ter Steege et al. (2013) [40]. The prominence of this family has been associated with functional traits such as nitrogen fixation and high wood density, which may confer competitive advantages in oligotrophic soils [41].

The composition analysis further evidences a decoupling between ecological importance and biomass retention capacity. Species with high numerical dominance, such as Bauhinia brachycalyx in Z1 (IVI = 34.93), do not constitute the largest carbon sinks in their unit; conversely, species with moderate IVI but high wood density, such as Eschweilera albiflora (0.86 g cm⁻³), accumulate substantial fractions of local biomass (88.73 Mg). At the individual scale, a single Ceiba pentandra specimen in Z1 stored 19.146 Mg C (Table 12). These results indicate that forest physical structure and woody functional traits determine carbon reserves more directly than species richness or abundance per se [42,43].

4.2. Species Richness and Carbon

Linear regression models (Table 10) showed a positive but low-magnitude association between diversity metrics and carbon at the plot scale. Although Shannon, Simpson, and richness were significant (R² = 0.045–0.056), the incorporation of vegetation unit as a random effect reduced Shannon's significance to p = 0.409 (Table 11), suggesting that spatially structured environmental variability conditions the diversity–carbon relationship. Consequently, taxonomic richness alone does not constitute a robust predictor of forest biomass storage, a result congruent with previous observations in neotropical forests [44].

Analysis among vegetation units confirms the absence of a direct relationship between diversity and biomass at landscape scale. Z1 concentrated the highest richness (99 spp.; Fisher's α = 34.492) and the largest biomass stock (414.09 Mg ha⁻¹), but this pattern was not replicated in other zones: Z3, with the second highest diversity (73 spp.; H' = 3.425), recorded the lowest biomass (145.27 Mg ha⁻¹), while Z2 accumulated a substantial stock (156.05 Mg C ha⁻¹) despite its lower richness (59 spp.; H' = 3.209). These results support the mass ratio hypothesis, according to which carbon capture depends on the attributes of the most abundant species and not on total richness [45,46].

This disconnection reflects the influence of forest history and growth strategies. In Z3, agricultural abandonment favored colonization by pioneer species such as Cecropia engleriana, which increase diversity (J' = 0.798) but whose low wood density (0.49 g cm⁻³) limits biomass accumulation [47]. Similarly, the high diversity of Z1 includes an important proportion of rare species (Figure 6) that, although contributing to ecosystem stability against climatic fluctuations [48], have marginal volumetric contribution compared to the small group of dominant species.

4.3. Vertical Stratification and Carbon

The Pretzsch index revealed an asymmetric vertical structure across all zones (Figure 10), with the lower stratum (0–50% of maximum height) containing the highest proportion of individuals, reaching 85.4% in Z2 and 78.7% in Z1. In contrast, the upper stratum (80–100%) was poorly represented in Z2 (0.3%), despite this zone exhibiting the tallest trees in the inventory (51.5 ± 2.1 m). This pattern may reflect the legacy of selective logging, which typically removes large canopy trees while leaving lower strata relatively unaffected [49,50].

At the individual level, the upper stratum functions as the principal carbon reservoir, with medians of 1.0–10.0 Mg C per tree in Z1 and Z3, compared to contributions below 0.1 Mg C in the lower stratum (Figure 17). The high stock of Z2 (156.05 Mg C ha⁻¹) despite its few emergent trees suggests structural compensation by the middle stratum (50–80%): its higher density closes the canopy, reduces direct radiation, and stabilizes the microclimate, preventing forest degradation and liana proliferation [51,52].

Carbon distribution among strata also reflected differences in environmental conditions across zones (Table 8). In the lower and middle strata, carbon stocks differed significantly among most zones (p < 0.05), with the exception of the Z3–Z4 comparison. This pattern suggests that local environmental factors, including hydrological conditions and successional status, may influence biomass accumulation in the understory [53]. In contrast, no significant differences were detected among zones in the upper stratum (p > 0.05), indicating a relatively consistent contribution of canopy and emergent trees to carbon storage across the study area. This result may reflect the ability of dominant trees to maintain high biomass accumulation despite variation in soil and hydrological conditions [54].

4.4. Species Turnover and Spatial Structure

Multivariate analysis evidences that floristic variation at landscape scale responds to continuous species turnover. PERMANOVA confirms significant differences in composition among zones (F = 11.55; p = 0.001; R² = 0.265; Table 4), and although NMDS ordination presents marginal stress (0.271), groups plots coherently with topographic, hydrological, and land-use history differences of each zone (Figure 9).

PERMDISP (Appendix 2) revealed heterogeneity in internal variability among zones (p = 0.001), with Z4 being the most internally homogeneous. Permanent flooding and consequent soil anoxia would act as a strict environmental filter, allowing only the establishment of tolerant taxa and homogenizing the community [55].

Beta diversity partitioning confirms that differences among zones are mainly due to turnover and not nestedness (Figure 7). High dissimilarity between Z1 and Z3 (βSørensen ≈ 0.64) reflects practically exclusive communities, product of opposite environmental regimes—recurrent flooding versus post-agricultural regeneration. Conversely, lower dissimilarity between Z3 and Z4, driven by nestedness, suggests that disturbed or water-stressed ecosystems function as impoverished versions of more complex forests [56].

Dissimilarity indices and SIMPER corroborate the existence of discrete biological barriers among zones (Table 3; Figure 8). Z1 presents the most singular composition (Bray-Curtis = 0.7493 versus Z3), with floristic identities defined by key species: Bauhinia brachycalyx in Z1, Jacaranda copaia in Z2, Cecropia engleriana in Z3, and Ocotea obovata in Z4. These gradients demonstrate that both spatial heterogeneity of the ecosystem and its biomass retention capacity depend on the replacement of these species in response to different levels of environmental stress [57].

4.5. Conservation Status, Ecological Importance, and Carbon

The comparison among threat status, dominance (IVI), and carbon storage indicates that these variables operate independently. The most threatened species typically present restricted distributions and scarce individuals, with minimal contributions to total biomass. Virola surinamensis (EN) was recorded exclusively in Z2 with two individuals (IVI = 0.354; 0.066 Mg C), and both Dipteryx micrantha and Cinchona officinalis (VU) were limited to Z1, contributing 1.156 and 2.029 Mg C, respectively. Although their impact on carbon is reduced, maintaining these populations is indispensable for preserving genetic diversity and ecosystem functioning [58].

Community dominance also does not guarantee high carbon retention. Aniba perutilis (VU) recorded the highest IVI of the subgroup (12.878) with consistent presence in Z2 and Z3 (N = 10), but accumulated only 0.723 Mg C. In contrast, Platymiscium stipulare (VU), with greater plasticity across the entire hydrological gradient (Z1–Z4; N = 34) and moderate IVI (5.605), accumulated 9.365 Mg C. These differences confirm that spatial dominance and efficiency in accumulating dense wood are independent ecological processes.

Greatest carbon retention falls on large emergent trees, regardless of their threat category. Ceiba pentandra (LC), with a single individual in Z1, stored 19.146 Mg C—the largest individual deposit in the study—despite moderate global IVI (4.111), confirming that tree diameter and physical structure determine sink capacity more than species abundance [59].

This independence among conservation, ecological dominance, and biomass demands a mixed management strategy in SDNP: strict protection of emergent trees and high wood-density species for climate mitigation, and preservation of habitat heterogeneity that sustains threatened species with low population density. Long-term ecosystem viability depends on joint application of both directives [60].

5. Conclusions

The present study confirms that Sierra del Divisor National Park functions as a critical carbon sink in western Amazonia, with differentiated storage according to vegetation units (414.09 Mg ha⁻¹ in flooded forest, 332.01 Mg ha⁻¹ in hill forest, versus 145–153 Mg ha⁻¹ in secondary and hydrophytic forest). Crucially, species richness constitutes a weak predictor of biomass storage (R² = 0.045–0.056), while structural attributes basal area, wood density, and large individuals operate as more robust determinants, a pattern that rejects the hypothesis that diversity and carbon co-vary directly. The family Fabaceae hyperdominates global storage (33.82%), and species turnover explains floristic differences among zones. To maximize climate mitigation potential, intangibility of emergent trees, preservation of environmental heterogeneity that sustains threatened species, and aDBHtive management of regenerated areas are required, thereby consolidating a conservation strategy that balances carbon sequestration and biodiversity objectives.

Author Contributions

Conceptualization, Fernando Pérez Grandez, Santiago Casas Luna, Franco Ángeles-Álvarez, Marco Carbajal-Bellido and Yakov Quinteros-Gómez; Methodology, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Manuel Perez-Mozombite and Yakov Quinteros-Gómez; Software, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Doris Gómez-Ticerán, Olga Solano Dávila and Yakov Quinteros-Gómez; Validation, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Fernando Camones-Gonzales, Abel Salinas-Inga, Franco Ángeles-Álvarez and Yakov Quinteros-Gómez; Formal analysis, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Fernando Camones-Gonzales, Abel Salinas-Inga, Franco Ángeles-Álvarez, Marco Carbajal-Bellido, Olga Solano Dávila, Manuel Perez-Mozombite, Octavio Monroy-Vilchis, Martha Zarco-González and Yakov Quinteros-Gómez; Investigation, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Fernando Camones-Gonzales, Abel Salinas-Inga, Franco Ángeles-Álvarez, Marco Carbajal-Bellido, Olga Solano Dávila, Manuel Perez-Mozombite, Octavio Monroy-Vilchis, Martha Zarco-González and Yakov Quinteros-Gómez; Resources, Fernando Pérez Grandez, Santiago Casas Luna, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Abel Salinas-Inga, Olga Solano Dávila and Yakov Quinteros-Gómez; Data curation, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Abel Salinas-Inga, Marco Carbajal-Bellido, Olga Solano Dávila, Manuel Perez-Mozombite, Martha Zarco-González and Yakov Quinteros-Gómez; Writing – original draft, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Fernando Camones-Gonzales, Abel Salinas-Inga, Franco Ángeles-Álvarez, Marco Carbajal-Bellido, Olga Solano Dávila, Manuel Perez-Mozombite, Octavio Monroy-Vilchis, Martha Zarco-González and Yakov Quinteros-Gómez; Writing – review & editing, Fernando Pérez Grandez, Santiago Casas Luna, Marcel La Rosa-Sánchez, Bruno Padilla-Torres, Jehoshua Macedo-Bedoya, Doris Gómez-Ticerán, Fernando Camones-Gonzales, Abel Salinas-Inga, Franco Ángeles-Álvarez, Marco Carbajal-Bellido, Olga Solano Dávila, Manuel Perez-Mozombite, Octavio Monroy-Vilchis, Martha Zarco-González and Yakov Quinteros-Gómez; Visualization, Yakov Quinteros-Gómez; Supervision, Yakov Quinteros-Gómez; Project administration, Fernando Pérez Grandez and Yakov Quinteros-Gómez; Funding acquisition, Yakov Quinteros-Gómez.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by PROCIENCIA under Contract Nº PE501087942-2024-PROCIENCIA, corresponding to the project: "Áreas Naturales Protegidas y su vulnerabilidad ante el cambio climático: almacenamiento de Carbon y conservación de los 3 grandes depredadores terrestres de Perú", under the 2024 Basic Research Projects Call.

Conflicts of Interest

The author(s) declared no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix A

Appendix A.1

List of recorded species, origin, growth habit, abundance across zones, wood density (derived from the Global Wood Density Database; Zanne et al., 2020), aboveground biomass, and carbon stocks (DBH ≥ 10 cm) across four study zones in the Tacshitea sector, Parque Nacional Sierra del Divisor. Origin: N = Native, I = Introduced. Habit: T = Tree, P = Palm, S = Shrub. Zones: Z1 = semi-dense flooded forest (tahuampa); Z2 = hill forest; Z3 = hill forest with secondary growth (purma); Z4 = hydromorphic forest (aguajal). *Palms biomass was estimated using species-specific allometric models (Goodman et al., 2013) independent of wood density values.

	Family	Origin	Stratum				Wood Density (g cm^-3)	Biomass (Mg ha^-1)	Carbon Stock (Mg ha^-1)
	Family	Origin	Z1	Z2	Z3	Z4	Wood Density (g cm^-3)	Biomass (Mg ha^-1)	Carbon Stock (Mg ha^-1)
Anacardiaceae	Anacardium giganteum Hancock ex Engl.	N	0	1	0	0	0.45	0.020	0.010
Anacardiaceae	Spondias mombin L.	N	2	0	0	0	0.39	0.337	0.158
Annonaceae	Duguetia hadrantha (Diels) R.E. Fr.	N	1	0	0	0	0.72	0.144	0.067
	Duguetia quitarensis Benth.	N	0	4	11	10	0.80	2.759	1.297
	Guatteria chlorantha Diels	N	12	0	0	0	0.54	4.031	1.894
	Guatteria citriodora Ducke	N	0	0	1	0	0.56	0.036	0.017
	Guatteria guianensis (Aubl.) R.E. Fr.	N	0	0	3	0	0.56	0.394	0.185
	Guatteria modesta Diels	N	9	11	22	51	0.54	19.166	9.008
	Guatteria punctata (Aubl.) R.A. Howard	N	0	0	7	0	0.56	0.320	0.150
	Guatteria ramiflora (D.R. Simpson) Erkens & Maas	N	0	0	5	0	0.56	0.942	0.443
	Oxandra xylopioides Diels	N	1	3	0	0	0.77	1.231	0.579
	Unonopsis floribunda Diels	N	3	0	0	0	0.42	0.203	0.095
	Xylopia aromatica (Lam.) Mart.	N	0	0	1	0	0.59	0.443	0.208
Apocynaceae	Aspidosperma parvifolium A.DC.	N	0	9	0	0	0.78	7.063	3.319
	Couma macrocarpa Barb.Rodr.	N	1	0	4	6	0.49	1.701	0.799
	Himatanthus articulatus (Vahl) Woodson	N	1	0	0	0	0.59	0.214	0.101
Araliaceae	Dendropanax tessmannii (Harms) Harms	N	2	0	0	0	0.42	0.253	0.119
Arecaceae	Astrocaryum murumuru Mart.	N	21	0	0	0	*	4.676	2.198
	Attalea butyracea (Mutis ex L.f.) Wess.Boer	N	4	0	0	0	*	1.435	0.675
	Attalea phalerata Mart. ex Spreng.	N	28	0	1	1	*	14.750	6.933
	Euterpe edulis Mart.	N	9	9	3	2	*	2.090	0.983
	Iriartea deltoidea Ruiz & Pav.	N	10	21	4	2	*	4.765	2.240
	Mauritia flexuosa L.f.	N	36	0	0	39	*	38.297	18.000
	Oenocarpus bataua Mart.	N	2	0	3	5	*	6.359	2.989
	Socratea exorrhiza (Mart.) H.Wendl.	N	1	0	0	6	*	0.424	0.199
Bignoniaceae	Jacaranda copaia (Aubl.) D.Don	N	0	87	19	24	0.35	63.585	29.885
Burseraceae	Dacryodes nitens Cuatrec.	N	0	2	0	0	0.49	2.156	1.013
	Protium altsonii Sandwith	N	1	1	0	0	0.68	0.215	0.101
	Protium grandifolium Engl.	N	0	34	11	9	0.64	16.043	7.540
	Protium opacum Swart	N	0	0	1	0	0.57	0.205	0.096
	Tetragastris altissima (Aubl.) Swart	N	0	0	1	0	0.72	0.324	0.152
Cannabaceae	Ampelocera edentula Kuhlm.	N	0	1	0	0	0.70	0.620	0.291
Capparaceae	Preslianthus pittieri (Standl.) Iltis & Cornejo	N	1	0	0	0	0.50	0.066	0.031
Caricaceae	Jacaratia spinosa (Aubl.) A.DC.	N	3	0	0	0	0.27	0.117	0.055
Celastraceae	Peritassa cf. huanucana (Loes.) A. C. Sm.	N	2	0	0	0	0.71	0.663	0.311
Clusiaceae	Garcinia madruno (Kunth) Hammel	N	1	0	1	1	0.64	0.128	0.060
Clusiaceae	Tovomita umbellata Benth.	N	0	0	1	1	0.86	2.169	1.019
Combretaceae	Buchenavia grandis Ducke	N	2	0	0	0	0.76	0.804	0.378
	Buchenavia tomentosa Eichler	N	2	0	0	0	0.71	2.884	1.355
	Terminalia catappa L.	I	3	1	1	1	0.48	2.178	1.024
Cordiaceae	Cordia cymosa (Donn. Sm.) Standl.	N	0	0	1	0	0.51	0.109	0.051
Cordiaceae	Cordia nodosa Lam.	N	1	0	0	0	0.41	0.078	0.037
Dichapetalaceae	Tapura guianensis Aubl.	N	1	0	0	0	0.58	0.753	0.354
Elaeocarpaceae	Sloanea guianensis (Aubl.) Benth.	N	1	0	0	0	0.83	0.133	0.063
Euphorbiaceae	Alchornea glandulosa Poepp.	N	0	0	1	0	0.34	0.031	0.015
	Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.	N	6	1	0	0	0.49	7.580	3.563
	Hura crepitans L.	N	2	0	0	0	0.37	1.614	0.759
	Sapium glandulosum (L.) Morong	N	5	0	5	7	0.42	2.478	1.164
Fabaceae	Andira inermis (W.Wright) DC.	N	4	3	4	3	0.66	11.616	5.459
	Bauhinia brachycalyx Ducke	N	99	25	7	7	0.64	56.209	26.418
	Bauhinia longifolia (Bong.) Steud.	N	0	0	3	5	0.67	0.410	0.193
	Dialium guianense (Aubl.) Sandwith	N	35	0	2	3	0.89	36.683	17.241
	Dipteryx micrantha Harms	N	2	0	0	0	0.87	2.460	1.156
	Enterolobium schomburgkii (Benth.) Benth.	N	3	2	2	3	0.72	44.215	20.781
	Hymenaea courbaril L.	N	1	29	4	4	0.81	43.960	20.661
	Inga alba (Sw.) Willd.	N	2	0	0	0	0.64	0.272	0.128
	Inga auristellae Harms	N	0	0	10	0	0.58	0.847	0.398
	Inga capitata Desv.	N	0	5	0	0	0.59	0.805	0.378
	Inga coruscans Humb. & Bonpl. ex Willd.	N	0	0	4	0	0.72	0.314	0.148
	Inga edulis Mart.	N	32	60	64	104	0.59	120.795	56.774
	Inga gracilifolia Ducke	N	1	1	0	0	0.49	0.238	0.112
	Inga tocacheana D. R. Simpson	N	2	6	0	0	0.58	2.771	1.302
	Macrolobium ischnocalyx Harms	N	1	0	0	0	0.60	0.323	0.152
	Macrolobium limbatum Spruce ex Benth.	N	0	0	1	0	0.58	0.252	0.119
	Ormosia coccinea (Aubl.) Jacks.	N	0	0	1	1	0.63	3.246	1.525
	Parkia igneiflora Ducke	N	0	0	1	0	0.47	1.556	0.731
	Platymiscium stipulare Benth.	N	28	10	2	4	0.80	19.925	9.365
	Schizolobium parahyba (Vell.) S.F.Blake	N	12	0	0	0	0.35	6.321	2.971
	Zygia vasquezii L. Rico	N	1	0	0	0	0.81	0.040	0.019
Hypericaceae	Vismia baccifera (L.) Triana & Planch.	N	1	0	3	6	0.43	1.446	0.680
Lauraceae	Aniba hostmanniana (Nees) Mez	N	9	42	34	57	0.62	38.790	18.231
	Aniba perutilis Hemsl.	N	0	2	8	0	0.50	1.538	0.723
	Aniba puchury-minor (Mart.) Mez	N	0	3	8	0	0.53	0.754	0.354
	Aniba taubertiana Mez	N	0	0	3	0	0.67	0.649	0.305
	Beilschmiedia aff. sulcata (Ruiz & Pav.) Kosterm.	N	0	2	1	0	0.57	0.531	0.250
	Endlicheria formosa A.C. Sm.	N	1	0	0	0	0.42	0.117	0.055
	Licaria armeniaca (Nees) Kosterm.	N	0	7	0	0	0.55	3.656	1.718
	Nectandra cf. turbacensis (Kunth) Nees	N	0	2	0	0	0.54	0.944	0.444
	Ocotea aciphylla (Nees) Mez	N	4	54	0	0	0.51	24.106	11.330
	Ocotea obovata (Ruiz & Pav.) Mez	N	0	2	79	126	0.53	22.815	10.723
	Ocotea splendens (Meisn.) Baill.	N	0	1	3	0	0.45	0.452	0.212
Lecythidaceae	Cariniana estrellensis (Raddi) Kuntze	N	0	0	2	1	0.64	23.779	11.176
	Couroupita guianensis Aubl.	N	3	0	0	0	0.43	2.997	1.409
	Eschweilera albiflora (DC.) Miers	N	8	1	0	0	0.86	89.010	41.835
	Eschweilera juruensis R.Knuth	N	0	0	4	2	0.96	19.750	9.283
	Gustavia macarenensis Philipson	N	1	0	0	0	0.67	1.442	0.678
Linaceae	Hebepetalum humiriifolium (Planch.) Benth.	N	0	0	1	0	0.88	0.180	0.085
Malvaceae	Apeiba aspera Aubl.	N	19	4	6	14	0.31	5.456	2.565
	Apeiba membranacea Spruce ex Benth.	N	0	0	2	0	0.28	0.067	0.032
	Cavanillesia umbellata Ruiz & Pav.	N	3	0	1	0	0.15	0.215	0.101
	Ceiba pentandra (L.) Gaertn.	N	1	0	0	0	0.35	40.736	19.146
	Guazuma ulmifolia Lam.	N	1	0	0	0	0.51	0.052	0.024
	Matisia ochrocalyx K. Schum.	N	1	0	0	0	0.58	0.440	0.207
	Pachira aquatica Aubl.	N	0	1	2	4	0.63	0.684	0.322
	Pachira insignis (Sw.) Sw. ex Savigny	N	2	0	0	0	0.38	0.571	0.269
	Phragmotheca mammosa W.S.Alverson	N	1	0	0	0	0.50	0.296	0.139
	Quararibea guianensis Aubl.	N	2	0	0	0	0.58	0.144	0.068
	Septotheca tessmannii Ulbr.	N	2	0	0	0	0.59	1.825	0.858
	Theobroma obovatum Klotzsch ex Bernoulli	N	4	2	1	0	0.47	0.852	0.400
	Theobroma speciosum Willd. ex Spreng.	N	0	0	1	0	0.47	0.071	0.033
	Theobroma sylvestre (Aubl.) Mart.	N	2	14	4	0	0.67	2.897	1.361
Melastomataceae	Miconia longifolia (Aubl.) DC.	N	0	1	0	0	0.75	0.045	0.021
Melastomataceae	Mouriri myrtifolia Spruce ex Triana	N	1	0	0	0	0.84	0.061	0.029
Meliaceae	Carapa aff. guianensis Aubl.	N	2	0	0	0	0.53	0.190	0.089
	Guarea macrophylla M.Vahl	N	2	0	0	0	0.65	0.562	0.264
	Ruagea tomentosa Cuatrec.	N	0	1	0	0	0.47	0.159	0.075
	Trichilia micrantha Benth.	N	1	0	0	0	0.64	0.076	0.036
	Trichilia quadrijuga Kunth	N	4	11	1	0	0.55	3.051	1.434
Metteniusaceae	Calatola costaricensis Standl.	N	1	0	0	0	0.48	0.072	0.034
Moraceae	Brosimum lactescens (S. Moore) C. C. Berg	N	1	0	0	0	0.8	0.187	0.088
	Brosimum utile Oken	N	1	0	0	0	0.43	0.023	0.011
	Castilla ulei Warb.	N	0	1	0	0	0.82	0.450	0.211
	Ficus insipida Willd.	N	1	1	1	2	0.38	6.948	3.265
	Ficus pertusa L.fil.	N	3	0	0	0	0.42	1.017	0.478
	Naucleopsis ulei (Warb.) Ducke	N	2	0	1	0	0.67	4.903	2.304
	Perebea angustifolia (Poepp. & Endl.) C.C.Berg	N	5	47	13	9	0.52	19.091	8.973
	Perebea guianensis Aubl.	N	2	0	0	0	0.56	0.802	0.377
Myristicaceae	Iryanthera paradoxa (Schwacke) Warb.	N	1	2	0	0	0.60	1.065	0.501
	Iryanthera paraensis Huber	N	0	6	1	1	0.65	3.609	1.696
	Iryanthera ulei Warb.	N	1	0	0	0	0.59	2.523	1.186
	Otoba parvifolia (Markgr.) A.H. Gentry	N	1	0	0	0	0.42	0.108	0.051
	Virola calophylla (Spruce) Warb.	N	11	0	0	0	0.47	2.008	0.944
	Virola duckei A.C. Sm.	N	0	1	0	0	0.47	0.130	0.061
	Virola elongata (Benth.) Warb.	N	5	2	0	0	0.52	2.171	1.020
	Virola pavonis (A. DC.) A.C. Sm.	N	1	0	0	0	0.61	0.262	0.123
	Virola surinamensis (Rol. ex Rottb.) Warb.	N	0	2	0	0	0.41	0.141	0.066
Myrtaceae	Myrcia bracteata (Rich.) DC.	N	0	0	2	0	0.81	0.319	0.150
Myrtaceae	Myrciaria floribunda (H.West ex Willd.) O.Berg	N	2	0	0	0	0.79	0.521	0.245
Olacaceae	Heisteria acuminata (Bonpl.) Engl	N	1	0	0	0	0.57	0.293	0.137
Piperaceae	Piper reticulatum L.	N	1	0	0	0	0.33	0.179	0.084
Polygonaceae	Triplaris peruviana Fisch. & C.A.Mey. ex C.A.Mey.	N	2	0	0	0	0.52	0.171	0.080
Rubiaceae	Calycophyllum acreanum Ducke	N	0	1	0	0	0.80	0.066	0.031
	Chimarrhis glabriflora Ducke	N	0	2	0	0	0.71	1.203	0.565
	Cinchona officinalis L.	N	2	0	0	0	0.54	4.317	2.029
	Duroia hirsuta (Poepp.) K. Schum.	N	2	0	0	0	0.77	0.453	0.213
	Hippotis triflora Ruiz & Pav.	N	0	1	1	0	0.50	0.618	0.290
	Ladenbergia graciliflora K. Schum.	N	1	0	0	0	0.49	0.030	0.014
	Ladenbergia macrocarpa (Vahl) Klotzsch	N	0	2	0	0	0.49	0.308	0.145
Salicaceae	Casearia pitumba Sleumer	N	0	0	1	0	0.73	0.158	0.074
Salicaceae	Hasseltia floribunda Kunth	N	1	0	0	0	0.52	0.089	0.042
Sapindaceae	Cupania latifolia Kunth	N	0	0	5	0	0.61	0.964	0.453
Sapotaceae	Manilkara bidentata (A.DC.) A.Chev.	N	4	0	0	0	0.87	1.533	0.721
	Pouteria caimito (Ruiz & Pav.) Radlk.	N	21	45	31	37	0.80	65.967	31.005
	Pouteria guianensis Aubl.	N	0	0	3	0	1.01	0.252	0.119
	Pouteria reticulata (Engl.) Eyma	N	0	0	3	0	0.82	1.107	0.520
	Pouteria torta subsp. glabra T.D. Penn.	N	0	0	2	0	0.90	0.347	0.163
	Pouteria trilocularis Cronquist	N	0	2	4	0	0.67	0.649	0.305
Simaroubaceae	Simarouba amara Aubl.	N	4	5	4	8	0.38	4.506	2.118
Siparunaceae	Siparuna cuspidata (Tul.) A. DC.	N	1	0	0	0	0.62	0.337	0.158
Swartziaceae	Swartzia brachyrhachis var. peruviana R.S.Cowan	N	2	11	3	4	0.90	15.425	7.250
Urticaceae	Cecropia engleriana Snethl.	N	1	9	27	69	0.49	28.574	13.430
	Cecropia membranacea Trecul	N	0	0	14	0	0.36	2.415	1.135
	Pourouma cecropiifolia Mart.	N	0	6	10	18	0.36	4.164	1.957
Violaceae	Rinorea lindeniana (Tul.) Kuntze	N	1	0	0	0	0.67	0.053	0.025
	Rinorea neglecta Sandwith	N	1	0	0	0	0.67	0.028	0.013
	Rinorea viridifolia Rusby	N	19	6	2	0	0.52	2.349	1.104

Appendix A.2. Pairwise Tests of Multivariate Dispersion (PERMDISP) Among Sampling Zones

Comparison	diff	lwr	upr	p adj
Z2-Z1	-0.0569	-0.1209	0.0072	0.1005
Z3-Z1	-0.0155	-0.0796	0.0486	0.9215
Z4-Z1	-0.1573	-0.2214	-0.0932	0
Z3-Z2	0.0414	-0.0227	0.1055	0.3353
Z4-Z2	-0.1004	-0.1645	-0.0363	0.0005
Z4-Z3	-0.1418	-0.2059	-0.0777	0
Note. Tukey HSD test based on distances to group centroids.

Appendix A.3. Kruskal-Wallis Test Results for Dasometric Variables Between Zones

Nivel	Variable (Unit)	N total	Median by Zone	χ²	df	p-valor	Sig.
Individual	DBH (cm)	2363	Z1=17.825; Z2=19.099; Z3=16.234; Z4=16.87	38.913	3	0	***
Individual	Height (m)	2363	Z1=14; Z2=16; Z3=14; Z4=14	85.555	3	0	***
Individual	Basal_Area (m²)	2363	Z1=0.025; Z2=0.029; Z3=0.021; Z4=0.022	38.852	3	0	***
Individual	Volume (m³)	2363	Z1=0.245; Z2=0.317; Z3=0.18; Z4=0.204	52.33	3	0	***
Individual	Biomass (Mg)	2362	Z1=0.159; Z2=0.178; Z3=0.102; Z4=0.109	64.076	3	0	***
Individual	Carbon (Mg C)	2362	Z1=0.075; Z2=0.084; Z3=0.048; Z4=0.051	64.076	3	0	***
Plot	Richness (spp Plot ⁻¹)	100	Z1=12; Z2=12; Z3=11; Z4=11	1.086	3	0.7805	ns
Plot	Abundance (ind Plot ⁻¹)	100	Z1=22; Z2=24; Z3=20; Z4=27	7.462	3	0.0586	ns
Plot	Biomass (Mg Plot ⁻¹)	100	Z1=8.504; Z2=10.275; Z3=3.561; Z4=4.611	25.809	3	0	***
Plot	Carbon (Mg C Plot ⁻¹)	100	Z1=3.997; Z2=4.829; Z3=1.674; Z4=2.167	25.809	3	0	***
Plot	AB (m² Plot ⁻¹)	100	Z1=1.346; Z2=1.251; Z3=0.753; Z4=0.759	23.069	3	0	***
Plot	Volume (m³ Plot ⁻¹)	100	Z1=15.784; Z2=19.392; Z3=6.516; Z4=8.542	29.398	3	0	***
Note. χ² = Kruskal-Wallis statistic; df = degrees of freedom. * p < 0.001; p < 0.01; * p < 0.05; ns = not significant (α = 0.05). Individual-level variables: n per zone varies. Subplot-level: n = 25 per zone.

Appendix A.4. Dunn Post-Hoc Test Results (Bonferroni Correction) for Significant Variables

Level	Variable	Comparison	Z	Adjusted p-value	Significance
Individual	DBH	Z1 - Z2	-0.293	1	ns
Individual	DBH	Z1 - Z3	4.684	0	***
Individual	DBH	Z2 - Z3	5.064	0	***
Individual	DBH	Z1 - Z4	3.639	0.0008	***
Individual	DBH	Z2 - Z4	4.029	0.0002	***
Individual	DBH	Z3 - Z4	-1.321	0.5595	ns
Individual	Height	Z1 - Z2	-7.504	0	***
Individual	Height	Z1 - Z3	-0.445	1	ns
Individual	Height	Z2 - Z3	6.788	0	***
Individual	Height	Z1 - Z4	0.196	1	ns
Individual	Height	Z2 - Z4	7.967	0	***
Individual	Height	Z3 - Z4	0.647	1	ns
Individual	Basal_Area	Z1 - Z2	-0.323	1	ns
Individual	Basal_Area	Z1 - Z3	4.651	0	***
Individual	Basal_Area	Z2 - Z3	5.058	0	***
Individual	Basal_Area	Z1 - Z4	3.64	0.0008	***
Individual	Basal_Area	Z2 - Z4	4.061	0.0001	***
Individual	Basal_Area	Z3 - Z4	-1.286	0.5957	ns
Individual	Volume	Z1 - Z2	-2.758	0.0175	*
Individual	Volume	Z1 - Z3	3.631	0.0008	***
Individual	Volume	Z2 - Z3	6.368	0	***
Individual	Volume	Z1 - Z4	2.872	0.0122	*
Individual	Volume	Z2 - Z4	5.795	0	***
Individual	Volume	Z3 - Z4	-0.975	0.9888	ns
Individual	Biomass	Z1 - Z2	-2.112	0.104	ns
Individual	Biomass	Z1 - Z3	4.674	0	***
Individual	Biomass	Z2 - Z3	6.808	0	***
Individual	Biomass	Z1 - Z4	3.943	0.0002	***
Individual	Biomass	Z2 - Z4	6.221	0	***
Individual	Biomass	Z3 - Z4	-1.018	0.9254	ns
Individual	Carbon	Z1 - Z2	-2.112	0.104	ns
Individual	Carbon	Z1 - Z3	4.674	0	***
Individual	Carbon	Z2 - Z3	6.808	0	***
Individual	Carbon	Z1 - Z4	3.943	0.0002	***
Individual	Carbon	Z2 - Z4	6.221	0	***
Individual	Carbon	Z3 - Z4	-1.018	0.9254	ns
Plot	Biomass	Z1 - Z2	-1.243	0.6416	ns
Plot	Biomass	Z1 - Z3	2.954	0.0094	**
Plot	Biomass	Z2 - Z3	4.197	0.0001	***
Plot	Biomass	Z1 - Z4	2.764	0.0171	*
Plot	Biomass	Z2 - Z4	4.007	0.0002	***
Plot	Biomass	Z3 - Z4	-0.19	1	ns
Plot	Carbon	Z1 - Z2	-1.243	0.6416	ns
Plot	Carbon	Z1 - Z3	2.954	0.0094	**
Plot	Carbon	Z2 - Z3	4.197	0.0001	***
Plot	Carbon	Z1 - Z4	2.764	0.0171	*
Plot	Carbon	Z2 - Z4	4.007	0.0002	***
Plot	Carbon	Z3 - Z4	-0.19	1	ns
Plot	AB	Z1 - Z2	-0.595	1	ns
Plot	AB	Z1 - Z3	3.461	0.0016	**
Plot	AB	Z2 - Z3	4.056	0.0001	***
Plot	AB	Z1 - Z4	2.564	0.031	*
Plot	AB	Z2 - Z4	3.159	0.0048	**
Plot	AB	Z3 - Z4	-0.897	1	ns
Plot	Volume	Z1 - Z2	-1.37	0.5123	ns
Plot	Volume	Z1 - Z3	3.154	0.0048	**
Plot	Volume	Z2 - Z3	4.524	0	***
Plot	Volume	Z1 - Z4	2.886	0.0117	*
Plot	Volume	Z2 - Z4	4.256	0.0001	***
Plot	Volume	Z3 - Z4	-0.268	1	ns
Note. Only variables with significant Kruskal-Wallis are included. Z = standardized statistic; p adjusted = Bonferroni-corrected p-value.

Appendix A.5. Top 5 Species by Importance Value Index (IVI) per Zone with Biomass and Carbon Contribution

Zone	Rank	Species	Family	IVI (300)	Biomass (Mg)	Carbon Stock (Mg C)
Z1	1	Bauhinia brachycalyx	Fabaceae	34.93	68.43	32.16
	2	Dialium guianense	Fabaceae	18.03	47.5	22.32
	3	Mauritia flexuosa	Arecaceae	16.66	38.3	18
	4	Attalea phalerata	Arecaceae	14.98	14.75	6.93
	5	Eschweilera albiflora	Lecythidaceae	14.56	119.23	56.04
Z2	1	Jacaranda copaia	Bignoniaceae	43.5	77.49	36.42
	2	Inga edulis	Fabaceae	36.7	152.89	71.86
	3	Ocotea aciphylla	Lauraceae	21.62	29.33	13.79
	4	Pouteria caimito	Sapotaceae	18.5	81.43	38.27
	5	Hymenaea courbaril	Fabaceae	17.51	55.34	26.01
Z3	1	Ocotea obovata	Lauraceae	31.52	26.52	12.46
	2	Inga edulis	Fabaceae	30.45	152.89	71.86
	3	Aniba hostmanniana	Lauraceae	20.69	46.91	22.05
	4	Pouteria caimito	Sapotaceae	19.97	81.43	38.27
	5	Cecropia engleriana	Urticaceae	15.24	34.07	16.01
Z4	1	Ocotea obovata	Lauraceae	40.51	26.52	12.46
	2	Inga edulis Mart.	Fabaceae	36.66	152.89	71.86
	3	Cecropia engleriana	Urticaceae	28.98	34.07	16.01
	4	Mauritia flexuosa	Arecaceae	25.55	38.3	18
	5	Aniba hostmanniana	Lauraceae	22.85	46.91	22.05
Note. IVI (300) = relative abundance + relative frequency + relative dominance. Biomass and carbon: cumulative totals across all zones. Species names in italics.

References

Malhi, Y.; Grace, J. Tropical forests and atmospheric carbon dioxide. Trends Ecol. Evol. 2000, 15, 332–337. [Google Scholar] [CrossRef]
Saatchi, S. S.; Harris, N. L.; Brown, S.; Lefsky, M.; Mitchard, E. T.; Salas, W.; Zutta, B. R.; Buermann, W.; Lewis, S. L.; Hagen, S.; Petrova, S.; White, L.; Silman, M.; Morel, A. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. USA 2011, 108, 9899–9904. [Google Scholar] [CrossRef] [PubMed]
Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Hayes, D.; et al. A large and persistent carbon sink in the world's forests. Science 2011, 333, 988–993. [Google Scholar] [CrossRef] [PubMed]
Hubau, W.; Lewis, S. L.; Phillips, O. L.; Affum-Baffoe, K.; Beeckman, H.; Cuní-Sanchez, A.; Daniels, A. K.; Ewango, C. E. N.; Fauset, S.; Mukinzi, J. M.; Sheil, D.; Sonké, B.; Sullivan, M. J. P.; Sunderland, T. C. H.; Taedoumg, H.; Thomas, S. C.; White, L. J. T.; Abernethy, K. A.; Adu-Bredu, S.; Amani, C. A.; Zemagho, L.; et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 2020, 579, 80–87. [Google Scholar] [CrossRef] [PubMed]
Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP). Plan Maestro del Parque Nacional Sierra del Divisor, periodo 2023–2027 (Resolución de Presidencia N.° 043-2023-SERNANP); Ministerio del Ambiente: Lima, Perú, 2023; Available online: https://www.gob.pe/institucion/sernanp/normas-legales/3869794-043-2023-sernanp.
Antoine, P.-O.; Abello, M. A.; Adnet, S.; Altamirano Sierra, A. J.; Baby, P.; Billet, G.; Boivin, M.; Calderón, Y.; Candela, A.; Chabain, J.; Corfu, F.; Croft, D. A.; Ganerød, M.; Jaramillo, C.; Klaus, S.; Marivaux, L.; Navarrete, R. E.; Orliac, M. J.; Parra, F.; Salas-Gismondi, R.; et al. A 60-million-year Cenozoic history of western Amazonian ecosystems in Contamana, eastern Peru. Gondwana Res. Int. Geosci. J. 2016, 31, 30–59. [Google Scholar] [CrossRef]
Draper, F. C.; Roucoux, K. H.; Lawson, I. T.; Mitchard, E. T. A.; Honorio Coronado, E. N.; Lähteenoja, O.; Torres Montenegro, L.; Valderrama Sandoval, E.; Zaráte, R.; Baker, T. R. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 2014, 9, 124017. [Google Scholar] [CrossRef]
Feldpausch, T. R.; Lloyd, J.; Lewis, S. L.; Brienen, R. J. W.; Gloor, M.; Monteagudo Mendoza, A.; Lopez-Gonzalez, G.; Banin, L.; Abu Salim, K.; Affum-Baffoe, K.; Alexiades, M.; Almeida, S.; Amaral, I.; Andrade, A.; Aragão, L. E. O. C.; Araujo Murakami, A.; Arets, E. J. M. M.; Arroyo, L.; Phillips, O. L.; et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 2012, 9, 3381–3403. [Google Scholar] [CrossRef]
Bax, V.; Francesconi, W.; Quintero, M. Spatial modeling of deforestation processes in the Central Peruvian Amazon. J. Nat. Conserv. 2016, 29, 79–88. [Google Scholar] [CrossRef]
Chave, J.; Réjou-Méchain, M.; Búrquez, A.; Chidumayo, E.; Colgan, M. S.; Delitti, W. B. C.; Duque, A.; Eid, T.; Fearnside, P. M.; Goodman, R. C.; Henry, M.; Martínez-Yrízar, A.; Mugasha, W. A.; Muller-Landau, H. C.; Mencuccini, M.; Nelson, B. W.; Ngomanda, A.; Nogueira, E. M.; Ortiz-Malavassi, E.; Vieilledent, G.; et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 2014, 20, 3177–3190. [Google Scholar] [CrossRef] [PubMed]
Clark, D.A.; Brown, S.; Kicklighter, D.W.; Chambers, J.Q.; Thomlinson, J.R.; Ni, J. Measuring net primary production in forests: Concepts and field methods. Ecol. Appl. 2001, 11, 356–370. [Google Scholar] [CrossRef]
Hoorn, C.; Wesselingh, F. P.; Ter Steege, H.; Bermudez, M. A.; Mora, A.; Sevink, J.; Sanmartín, I.; Sanchez-Meseguer, A.; Anderson, C. L.; Figueiredo, J. P.; Jaramillo, C.; Riff, D.; Negri, F. R.; Hooghiemstra, H.; Lundberg, J.; Stadler, T.; Särkinen, T.; Antonelli, A. Amazonia Through Time: Andean Uplift, Climate Change, Landscape Evolution, and Biodiversity. Science 2010, 330, 927–931. [Google Scholar] [CrossRef] [PubMed]
Watson, J. E. M.; Evans, T.; Venter, O.; Williams, B.; Tulloch, A.; Stewart, C.; Thompson, I.; Ray, J. C.; Murray, K.; Salazar, A.; McAlpine, C.; Potapov, P.; Walston, J.; Robinson, J. G.; Painter, M.; Wilkie, D.; Filardi, C.; Laurance, W. F.; Houghton, R. A.; Lindenmayer, D.; et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2018, 2, 599–610. [Google Scholar] [CrossRef] [PubMed]
Antonelli, A.; Zizka, A.; Carvalho, F. A.; Scharn, R.; Bacon, C. D.; Silvestro, D.; Condamine, F. L. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. 2018, 115, 6034–6039. [Google Scholar] [CrossRef] [PubMed]
Holdridge, L. R. Determination of World Plant Formations From Simple Climatic Data. Science 1947, 105, 367–368. [Google Scholar] [CrossRef] [PubMed]
Bardales, R.; Hyde, M.; Gallo, J.; Boron, V. National parks and conservation concessions: a comparison between mammal populations in two types of tropical protected areas in Ucayali, Peru. J. Trop. Ecol. 2023, 39, 1–7. [Google Scholar] [CrossRef]
Salati, E.; Vose, P. B. Amazon Basin: A System in Equilibrium. Science 1984, 225, 129–138. [Google Scholar] [CrossRef] [PubMed]
Gentry, A.; Ortiz, R. Patrones de composición florística en la Amazonia peruana. In Amazonia peruana, vegetación húmeda tropical en el llano subandino; Kalliola, R., Puhakka, M., Danjoy, W., Eds.; PAUT y ONERN: Lima, Perú, 1993; pp. 155–166. Available online: http://www.mobot.org/MOBOT/Research/curators/pdf/Gentry-Ortiz-1993.pdf.
Rodrigues Bijos, N.; Ulysses Orlando Eugênio, C.; de Roure Bandeira Mello, T.; Ferreira de Souza, G.; Rodrigues Munhoz, C. B. Plant species composition, richness, and diversity in the palm swamps ( veredas ) of Central Brazil. Flora 2017, 236–237, 94–99. [Google Scholar] [CrossRef]
Lamprecht, Hans. Silvicultura en los trópicos. Los ecosistemas forestales en los bosques tropicales y sus especies arbóreas. Posibilidades para un aprovechamiento sostenido [online]; GTZ. Deutsche Gesellschaft für Technische Zusammenarbeit: Eschborn, 1990; Available online: https://bibliotecadigital.infor.cl/handle/20.500.12220/1232.
Curtis, J. T.; McIntosh, R. P. An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 1951, 32, 476–496. [Google Scholar] [CrossRef]
Brown, S. Estimating Biomass and Biomass Change of Tropical Forests: A Primer. In FAO Forestry Paper 134; Food and Agriculture Organization: Rome, Italy, 1997. [Google Scholar]
Zanne, A.E.; Lopez-Gonzalez, G.; Coomes, D.A.; Ilic, J.; Chave, J.; et al. Data from: Towards a worldwide wood economics spectrum [Dataset]. Dryad 2020. [Google Scholar] [CrossRef]
Goodman, R.C.; Phillips, O.L.; Baker, T.R. Amazon palm biomass and allometry. For. Ecol. Manag. 2013. [Google Scholar] [CrossRef]
IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In Agriculture, Forestry and Other Land Use; Intergovernmental Panel on Climate Change, IGES: Hayama, Japan, 2006; Volume 4. [Google Scholar]
Aguirre Calderón, O. A. Índices para la caracterización de la estructura del estrato arbóreo de ecosistemas forestales. Rev. Cienc. For. En. México 2002, 27, 5–27. [Google Scholar]
Pretzsch, H. Forest Dynamics, Growth and Yield: From Measurement to Model; Springer: Berlin, Germany, 2009. [Google Scholar] [CrossRef]
Curtis, J. T.; McIntosh, R. P. An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 1951, 32, 476–496. [Google Scholar] [CrossRef]
Chao, A.; Gotelli, N. J.; Hsieh, T. C.; Sander, E. L.; Ma, K. H.; Colwell, R. K.; Ellison, A. M. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 2014, 84, 45–67. [Google Scholar] [CrossRef]
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 2010, 19, 134–143. [Google Scholar] [CrossRef]
Baselga, A.; Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 2012, 3, 808–812. [Google Scholar] [CrossRef]
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 1993, 18, 117–143. [Google Scholar] [CrossRef]
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2026; Available online: https://www.r-project.org/.
Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; McGowan, L. D.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; Kuhn, M.; Pedersen, T. L.; Miller, E.; Bache, S. M.; Müller, K.; Ooms, J.; Robinson, D.; Seidel, D. P.; Spinu, V.; Yutani, H.; et al. Welcome to the tidyverse. J. Open Source Softw. 2019, 4, 1686. [Google Scholar] [CrossRef]
Oksanen, J.; Simpson, G.L.; Blanchet, F.G.; Kindt, R.; Weedon, J.; et al. vegan: Community Ecology Package, R package version 2.6-4. 2022. Available online: https://cran.r-project.org/package=vegan.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001, 26, 32–46. [Google Scholar] [CrossRef]
de Castilho, C. V.; Magnusson, W. E.; De Araújo, R. N. O.; Luizão, R. C.; Luizão, F. J.; Lima, A. P.; Higuchi, N. Variation in aboveground tree live biomass in a central Amazonian Forest: Effects of soil and topography. For. Ecol. Manag. 2006, 234, 85–96. [Google Scholar] [CrossRef]
Schietti, J.; Emilio, T.; Rennó, C. D.; Drucker, D. P.; Costa, F. R.; Nogueira, A.; Baccaro, F. B.; Figueiredo, F.; Castilho, C. V.; Kinupp, V.; Guillaumet, J.; Garcia, A. R. M.; Lima, A. P.; Magnusson, W. E. Vertical distance from drainage drives floristic composition changes in an Amazonian rainforest. Plant Ecol. Divers. 2013, 7, 241–253. [Google Scholar] [CrossRef]
Baker, T.R.; Phillips, O.L.; Malhi, Y.; Almeida, S.; Arroyo, L.; Di Fiore, A.; Erwin, T.; Killeen, T.J.; Laurance, S.G.; Laurance, W.F.; Lewis, S.L.; Lloyd, J.; Monteagudo, A.; Neill, D.A.; Patiño, S.; Pitman, N.C.A.; M. Silva, J.N.; Vásquez Martínez, R. Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob. Change Biol. 2004, 10, 545–562. [Google Scholar] [CrossRef]
Ter Steege, H.; Pitman, N. C. A.; Sabatier, D.; Baraloto, C.; Salomão, R. P.; Guevara, J. E.; Phillips, O. L.; Castilho, C. V.; Magnusson, W. E.; Molino, J.; Monteagudo, A.; Vargas, P. N.; Montero, J. C.; Feldpausch, T. R.; Coronado, E. N. H.; Killeen, T. J.; Mostacedo, B.; Vasquez, R.; Assis, R. L.; Silman, M. R.; et al. Hyperdominance in the Amazonian Tree Flora. Science 2013, 342, 1243092. [Google Scholar] [CrossRef] [PubMed]
de Souza, F. C.; Dexter, K. G.; Phillips, O. L.; Brienen, R. J. W.; Chave, J.; Galbraith, D. R.; Gonzalez, G. L.; Mendoza, A. M.; Pennington, R. T.; Poorter, L.; Alexiades, M.; Álvarez-Dávila, E.; Andrade, A.; Aragão, L. E.; Araujo-Murakami, A.; Arets, E. J. M. M.; C, G. A. A.; Baraloto, C.; Barroso, J. G.; Baker, T. R.; et al. Evolutionary heritage influences Amazon tree ecology. Proc. R. Soc. B Biol. Sci. 2016, 283, 20161587. [Google Scholar] [CrossRef] [PubMed]
Slik, J. W. F.; Paoli, G.; McGuire, K.; Amaral, I.; Barroso, J.; Bastian, M.; Blanc, L.; Bongers, F.; Boundja, P.; Clark, C.; Collins, M.; Dauby, G.; Ding, Y.; Doucet, J.; Eler, E.; Ferreira, L.; Forshed, O.; Fredriksson, G.; Gillet, J.; Zweifel, N.; et al. Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Glob. Ecol. Biogeogr. 2013b, 22, 1261–1271. [Google Scholar] [CrossRef]
Yuan, Z.; Wang, S.; Ali, A.; Gazol, A.; Ruiz-Benito, P.; Wang, X.; Lin, F.; Ye, J.; Hao, Z.; Loreau, M. Aboveground carbon storage is driven by functional trait composition and stand structural attributes rather than biodiversity in temperate mixed forests recovering from disturbances. Ann. For. Sci. 2018, 75. [Google Scholar] [CrossRef]
Finegan, B.; Peña-Claros, M.; De Oliveira, A.; Ascarrunz, N.; Bret-Harte, M. S.; Carreño-Rocabado, G.; Casanoves, F.; Díaz, S.; Velepucha, P. E.; Fernandez, F.; Licona, J. C.; Lorenzo, L.; Negret, B. S.; Vaz, M.; Poorter, L. Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses. J. Ecol. 2014, 103, 191–201. [Google Scholar] [CrossRef]
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 1998, 86, 902–910. [Google Scholar] [CrossRef]
Prado-Junior, J. A.; Schiavini, I.; Vale, V. S.; Arantes, C. S.; Van Der Sande, M. T.; Lohbeck, M.; Poorter, L. Conservative species drive biomass productivity in tropical dry forests. J. Ecol. 2016, 104, 817–827. [Google Scholar] [CrossRef]
Lohbeck, M.; Poorter, L.; Martínez-Ramos, M.; Bongers, F. Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 2014, 96, 1242–1252. [Google Scholar] [CrossRef] [PubMed]
Mouillot, D.; Bellwood, D. R.; Baraloto, C.; Chave, J.; Galzin, R.; Harmelin-Vivien, M.; Kulbicki, M.; Lavergne, S.; Lavorel, S.; Mouquet, N.; Paine, C. E. T.; Renaud, J.; Thuiller, W. Rare Species Support Vulnerable Functions in High-Diversity Ecosystems. PLoS Biol. 2013, 11, e1001569. [Google Scholar] [CrossRef] [PubMed]
Asner, G. P.; Knapp, D. E.; Broadbent, E. N.; Oliveira, P. J. C.; Keller, M.; Silva, J. N. Selective Logging in the Brazilian Amazon. Science 2005, 310, 480–482. [Google Scholar] [CrossRef] [PubMed]
Longo, M.; Keller, M.; Santos, M. N.; Leitold, V.; Pinagé, E. R.; Baccini, A.; Saatchi, S.; Nogueira, E. M.; Batistella, M.; Morton, D. C. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 2016, 30, 1639–1660. [Google Scholar] [CrossRef]
Jucker, T.; Bouriaud, O.; Avacaritei, D.; Coomes, D. A. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol. Lett. 2014, 17, 1560–1569. [Google Scholar] [CrossRef] [PubMed]
Rutishauser, E.; Hérault, B.; Baraloto, C.; Blanc, L.; Descroix, L.; Sotta, E. D.; Ferreira, J.; Kanashiro, M.; Mazzei, L.; D'Oliveira, M. V.; De Oliveira, L. C.; Peña-Claros, M.; Putz, F. E.; Ruschel, A. R.; Rodney, K.; Roopsind, A.; Shenkin, A.; Da Silva, K. E.; De Souza, C. R.; Sist, P.; et al. Rapid tree carbon stock recovery in managed Amazonian forests. Curr. Biol. 2015, 25, 2738. [Google Scholar] [CrossRef]
Parolin, P. Submerged in darkness: adaptations to prolonged submergence by woody species of the Amazonian floodplains. Ann. Bot. 2008, 103, 359–376. [Google Scholar] [CrossRef] [PubMed]
Malhi, Y.; Doughty, C. E.; Goldsmith, G. R.; Metcalfe, D. B.; Girardin, C. A. J.; Marthews, T. R.; Del Aguila-Pasquel, J.; Aragão, L. E.; Araujo-Murakami, A.; Brando, P.; Da Costa, A. C. L.; Silva-Espejo, J. E.; Amézquita, F. F.; Galbraith, D. R.; Quesada, C. A.; Rocha, W.; Salinas-Revilla, N.; Silvério, D.; Meir, P.; Phillips, O. L. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Glob. Change Biol. 2015, 21, 2283–2295. [Google Scholar] [CrossRef] [PubMed]
Parolin, P.; De Simone, O.; Haase, K.; Waldhoff, D.; Rottenberger, S.; Kuhn, U.; Kesselmeier, J.; Kleiss, B.; Schmidt, W.; Piedade, M.T.F.; Junk, W.J. Central Amazonian floodplain forests: Tree adaptations in a pulsing system. Bot. Rev. 2004, 70, 357–380. [Google Scholar] [CrossRef]
Socolar, J. B.; Gilroy, J. J.; Kunin, W. E.; Edwards, D. P. How should Beta-Diversity inform biodiversity conservation? Trends Ecol. Evol. 2015, 31, 67–80. [Google Scholar] [CrossRef] [PubMed]
Anderson, M. J.; Crist, T. O.; Chase, J. M.; Vellend, M.; Inouye, B. D.; Freestone, A. L.; Sanders, N. J.; Cornell, H. V.; Comita, L. S.; Davies, K. F.; Harrison, S. P.; Kraft, N. J. B.; Stegen, J. C.; Swenson, N. G. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 2010, 14, 19–28. [Google Scholar] [CrossRef] [PubMed]
Putz, F. E.; Zuidema, P. A.; Synnott, T.; Peña-Claros, M.; Pinard, M. A.; Sheil, D.; Vanclay, J. K.; Sist, P.; Gourlet-Fleury, S.; Griscom, B.; Palmer, J.; Zagt, R. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conserv. Lett. 2012, 5, 296–303. [Google Scholar] [CrossRef]
Lindenmayer, D. B.; Laurance, W. F.; Franklin, J. F. Global Decline in Large Old Trees. Science 2012, 338, 1305–1306. [Google Scholar] [CrossRef] [PubMed]
Ricketts, T. H.; Soares-Filho, B.; Da Fonseca, G. A. B.; Nepstad, D.; Pfaff, A.; Petsonk, A.; Anderson, A.; Boucher, D.; Cattaneo, A.; Conte, M.; Creighton, K.; Linden, L.; Maretti, C.; Moutinho, P.; Ullman, R.; Victurine, R. Indigenous Lands, Protected Areas, and Slowing Climate Change. PLoS Biol. 2010, 8, e1000331. [Google Scholar] [CrossRef] [PubMed]

Figure 1. Location of the study area in Sierra del Divisor National Park (Peru) and sampling zones in the Tacshitea sector: semi-dense flooded forest (Z1), hill forest (Z2), hill forest associated with secondary growth (Z3) and Mauritia flexuosa palm forest (Z4).

Figure 2. Sampling zones in the Tacshitea sector: (a) semi-dense floodplain forest (Z1); (b) hillslope forest (Z2); (c) hillslope forest associated with secondary growth (Z3); (d) Mauritia flexuosa palm forest (Z4).

Figure 3. Representation of vertical stratification in three levels. Own elaboration, based on Pretzsch (2009) [27].

Figure 4. Top 20 Species by Global Importance Value Index (IVI). IVI (300) = relative abundance + relative frequency + relative dominance. .

Figure 5. Top 10 species by Importance Value Index (IVI) in each sampling zone. IVI (300) = relative abundance + relative frequency + relative dominance.

Figure 6. Rarefaction and Interpolation/Extrapolation curves by zone based on species richness (q = 0). Solid lines represent rarefaction, dashed lines represent extrapolation, and circles indicate the observed sample size (n) for each zone, with shaded ribbons representing the 95% confidence interval.

Figure 7. Relationship between Turnover and Nestedness by pair of zones. Points represent the beta diversity components (Sørensen index) following Baselga (2010) [30]. Points toward the right indicate differences due to species turnover (Simpson index), while points upward indicate differences due to nestedness.

Figure 8. SIMPER — Species contributing to differences between zones. The horizontal bar charts show the top 10 species by their average contribution to the Bray-Curtis dissimilarity for each pairwise comparison between zones (Z1 to Z4).

Figure 9. Non-metric multidimensional scaling (NMDS) ordination of floristic composition among the four sampling zones based on Bray–Curtis dissimilarity. Each point represents a sampling plot, and shaded ellipses indicate the 95% confidence interval around group centroids. Stress = 0.271.

Figure 10. Vertical structure of the sampling zones based on Pretzsch strata. The tallest tree in each zone was considered 100% of relative height, and individuals were classified into Low (0–50%), Medium (50–80%), and High (80–100%) strata. Numbers above the bars indicate abundance and relative frequency (%). ADBHted from Aguirre Calderón (2002) and Pretzsch (2009) [26,27].

Figure 11. Diameter class distribution by zone. Relative abundance (%) of individuals across 10-cm diameter at breast height (DBH) classes in the four sampling zones. Bars represent the percentage of individuals within each diameter class relative to the total number of individuals recorded in each zone.

Figure 12. Height class distribution by zone. Relative abundance (%) of individuals across five height classes (<5 m, 5–11.9 m, 12–19.9 m, 20–29.9 m, and ≥30 m) in the four sampling zones. Bars represent the percentage of individuals within each height class relative to the total number of individuals recorded in each zone.

Figure 13. Comparison of forest structural variables among zones. Boxplots showing Diameter at Breast Height (DBH), tree height, basal area, and stem volume across the four sampling zones (Z1–Z4). Boxes represent the interquartile range (Q1–Q3), the horizontal line indicates the median, and diamonds represent the mean. Different letters indicate statistically significant differences among zones according to Dunn–Bonferroni post-hoc tests (α = 0.05). Log₁₀ transformation was applied to DBH, basal area, and stem volume due to right-skewed distributions.

Figure 14. Aboveground biomass and carbon stocks by vegetation unit. Total aboveground biomass (Mg ha⁻¹) and carbon stock (Mg C ha⁻¹) estimated for each sampled vegetation unit (Z1–Z4), based on all measured individuals within 1-ha plots. Bars represent total accumulated biomass and carbon per hectare in each unit.

Figure 15. Global carbon stock treemap by family (Top 15). The area of each rectangle is proportional to the total aboveground carbon stock (Mg C) contributed by each botanical family. The top 15 families are shown individually, while all remaining families are grouped into the “Others” category.

Figure 16. Top 10 plant families contributing to aboveground carbon stock by zone. Lollipop plots showing total aboveground carbon stock (Mg C) contributed by the ten most important botanical families within each study zone (Z1–Z4).

Figure 17. Carbon distribution by Pretzsch stratum and zone. The boxplots show carbon values on a log₁₀ scale, where boxes represent Q1–Q3, the horizontal line represents the median, and the white diamond indicates the mean for each vertical stratum (I — Upper, II — Middle, III — Lower).

Table 1. General characterization of the forest inventory by zone.

Zone	N plots	Area (ha)	N orders	N families	N species	N individuals
Z1	25	1	21	37	99	574
Z2	25	1	14	23	59	628
Z3	25	1	16	27	73	503
Z4	25	1	15	19	39	658
Total	100	4	24	41	158	2363

Table 2. Alpha diversity indices by zone.

Zone	S (Richness)	N (abund.)	H' Shannon	1-D Simpson	J' Pielou	α Fisher	DMg Margalef
Z1	99	574	3,636	0,946	0,791	34,492	15,427
Z2	59	628	3,209	0,938	0,787	15,955	9,003
Z3	73	503	3,425	0,939	0,798	23,469	11,574
Z4	39	658	2,759	0,903	0,753	9,075	5,856

Note. S = species richness; N = total abundance; H' = Shannon entropy; 1-D = Simpson diversity index; J' = Pielou evenness; α = Fisher's alpha; DMg = Margalef richness index.

Table 3. Pairwise beta diversity indices between zones.

Zone Pair	Bray-Curtis (sim)	Jaccard (sim)	Sørensen (sim)	Bray-Curtis (dis)	Jaccard (dis)	Sørensen (dis)
Z1 vs Z2	0.2945	0.2441	0.3924	0.7055	0.7559	0.6076
Z1 vs Z3	0.2507	0.2199	0.3605	0.7493	0.7801	0.6395
Z1 vs Z4	0.2776	0.2321	0.3768	0.7224	0.7679	0.6232
Z2 vs Z3	0.4527	0.3469	0.5152	0.5473	0.6531	0.4848
Z2 vs Z4	0.3966	0.3425	0.5102	0.6034	0.6575	0.4898
Z3 vs Z4	0.6546	0.4933	0.6607	0.3454	0.5067	0.3393

Note. sim = similarity; dis = dissimilarity (0 = no shared species; 1 = identical). Bray-Curtis: abundance-based. Jaccard and Sørensen: presence/absence.

Table 4. Permutational multivariate analysis of variance (PERMANOVA) of floristic composition between zones.

Source of variation	gl	SC	R²	F	p-valor
Model	3	8.1138	0.2652	11.5483	0.001
Residual	96	22.4831	0.7348
Total	99	30.5969	1

Note. gl = degrees of freedom; SC = sum of squares; F = F-statistic; R² = proportion of variance explained; 999 permutations. Distance: Bray-Curtis. Method: Anderson (2001) [36].

Table 5. Results of Dunn's post-hoc test for structural variables by Pretzsch stratum (Bonferroni, p < 0.05).

Variable	Stratum	Z1–Z2	Z1–Z3	Z2–Z3	Z1–Z4	Z2–Z4	Z3–Z4
Height	I (upper)	ns	***	*	***	*	ns
	II (middle)	ns	***	ns	ns	ns	**
	III (lower)	ns	***	ns	ns	ns	**
Richness	I (upper)	***	***	***	***	***	ns
	II (middle)	***	***	***	ns	***	***
	III (lower)	*	***	***	***	***	***
Abundance	I (upper)	***	***	***	***	***	ns
	II (middle)	***	*	**	***	***	***
	III (lower)	***	ns	***	***	ns	***

Note: Levels of statistical significance: ns = p > 0.05; * = p < 0.05; ** = p < 0.01; *** = p < 0.001. p-values adjusted using Dunn’s method.

Table 6. Summary of structural variables.

Parameter	Z1	Z2	Z3	Z4
DAP (cm)	24.45 ± 20.82	23.2 ± 17.69	19 ± 10.21	18.99 ± 8.32
Height (m)	14.56 ± 6.49	17.76 ± 7.74	14.67 ± 6.17	14.24 ± 5.41
AB ind⁻¹ (m²)	0.0809 ± 0.3015	0.0668 ± 0.2779	0.0365 ± 0.0571	0.0337 ± 0.0383
AB ha⁻¹ (m²)	46.45	41.96	18.37	22.2
Vol ind⁻¹ (m³)	1.289 ± 7.225	1.015 ± 4.318	0.486 ± 1.183	0.402 ± 0.74
Vol ha⁻¹ (m³)	739.66	637.36	244.43	264.54

Note. Individual-level values are presented as mean ± SD. Hectare-level values correspond to totals recorded within each 1-ha sampling zone. AB = basal area; Vol = volume.

Table 7. Summary of individual biomass and carbon stock by zone.

Parameter	Z1	Z2	Z3	Z4
Biomass ind⁻¹ (Mg)	0.7214 ± 3.8242	0.5287 ± 2.2704	0.2888 ± 0.7516	0.2328 ± 0.4852
Carbon ind⁻¹ (Mg C)	0.3391 ± 1.7974	0.2485 ± 1.0671	0.1357 ± 0.3533	0.1094 ± 0.228

Note. Individual-level values are presented as mean ± SD. Hectare-level values correspond to totals recorded within each 1-ha sampling zone.

Table 8. Results of Dunn's post-hoc test for carbon stock by Pretzsch stratum (Bonferroni, p < 0.05).

Variable	Stratum	Z1–Z2	Z1–Z3	Z2–Z3	Z1–Z4	Z2–Z4	Z3–Z4
Carbon	I (High 80–100%)	ns	ns	ns	ns	ns	ns
	II (Medium 40–80%)	*	***	***	***	***	ns
	III (Low 0–40%)	*	***	***	***	***	ns

Note: Statistical significance levels: ns = p > 0.05; * = p < 0.05; = p < 0.01; *** = p < 0.001. P-values adjusted using Dunn's method.

Table 9. Spearman correlation matrix between diversity metrics and carbon stock.

Shannon	Simpson	Richness	Carbon
1	1	1	0.13
1	1	1	0.13
1	1	1	0.13
0.13	0.13	0.13	1

Note. Spearman correlation coefficients among diversity metrics and carbon (Mg C per plot).

Table 10. Linear regression models between diversity metrics and carbon stock.

Model	Estimate	SE	p_value	R2
Carbon ~ Shannon	3.954869867	1.836312275	0.033716283	0.045191973
Carbon ~ Simpson	81.34326875	35.28662236	0.02326339	0.0514356
Carbon ~ Richness	0.065608689	0.027161346	0.017565969	0.056192397

Note. Simple linear models relating carbon stock (Mg C) and diversity metrics.

Table 11. Linear mixed-effects model explaining carbon stock variation.

Term	Estimate	SE	t_value	p_value
(Intercept)	-7.972775107	12.49851961	1.999999962	0.588830668
Shannon	3.954869867	3.818148737	1.999999963	0.409111914

Note. Mixed-effects model with Shannon diversity as fixed effect and Zone as random intercept.

Table 12. Conservation status, zonal distribution, and aboveground carbon stock of key species.

Specie	UICN	DS 043-2006-AG	CITES	Presence zones	Records (N)	Global IVI (300)	Total carbon (Mg)
Aniba perutilis	VU	VU	NR	Z2, Z3	10	12.878	0.723
Ceiba pentandra	LC	NT	NR	Z1	1	4.111	19.146
Cinchona officinalis	VU	NC	NR	Z1	2	0.577	2.029
Dipteryx micrantha	NT	VU	II	Z1	2	0.488	1.156
Euterpe cf. edulis	VU	NC	NR	Z1, Z2, Z3, Z4	22	2.731	0.983
Platymiscium stipulare	VU	NT	NR	Z1, Z2, Z3, Z4	34	5.605	9.365
Virola surinamensis	EN	EN	NR	Z2	2	0.354	0.066

Notes: VU = Vulnerable; EN = Endangered; NT = Near Threatened; LC = Least Concern; NC = Not Categorized; NR = Not Regulated. N = Number of recorded individuals. Carbon expressed in Mg. The areas correspond to the sampling units (Z1–Z4) where the species was confirmed to be present.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.