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Article

Relationship Between Forest Structure and Soil Characteristics with Flooded and Non-Flooded Rainforests of Northern Amazonia (Brazil)

by
Edyrlli Naele Barbosa Pimentel
1,
Lucas Botelho Jerônimo
1,
Manoel Tavares de Paula
1,
María Vanessa Lencinas
2,
Guillermo Martínez Pastur
2,* and
Gerardo Rubio
3
1
Biodiversity and Agroecology Laboratory, Universidade do Estado do Pará, Enéias Pinheiro 2626, Marco, Belém 66095-015, PA, Brazil
2
Laboratorio de Recursos Agroforestales, Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Houssay 200, Tierra del Fuego, Ushuaia 9410, Argentina
3
Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA), Universidad de Buenos Aires (UBA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martin 4453, Buenos Aires 1417, Argentina
*
Author to whom correspondence should be addressed.
Forests 2025, 16(5), 793; https://doi.org/10.3390/f16050793
Submission received: 1 April 2025 / Revised: 2 May 2025 / Accepted: 7 May 2025 / Published: 9 May 2025
(This article belongs to the Special Issue Soil Organic Matter Dynamics and Microbial Roles in Biogeochemical Cycles in Forestry)
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Abstract

:
Environmental variability modifies forest structure through interactions among soil properties, topography, and climate. These factors influence the occurrence of contrasting forest types in northern Amazonia (Brazil), such as forests in highlands (Terra Firme) and forests under regular flooding (Várzea). Flooding regimes influence soil formation and modify soil geochemistry, nutrient distribution, and organic matter accumulation, shaping forest structure and composition. The objective was to determine the relationships between structure and soil characteristics in non-flooded and flooded tropical forests. We compared forest structure and soil characteristics at both conditions (n = 2 treatments × 20 replicas = 40 plots) using univariate and multivariate analyses. We found significant differences in most of the studied variables between forest types, both chemical and physical properties. Our results showed that flooding defines forest structure and composition (e.g., tree density, height, and volume) and influences soil nutrient characteristics. Floodplain forests exhibited higher soil nutrient concentration and organic carbon content, likely due to periodic litter accumulation, sediments, and reduced decomposition rates. In contrast, non-flooded forests were characterized by lower nutrient levels, higher sand content, and greater forest structure values (e.g., height, basal area, and volume). These insights contribute to understanding the functioning of both forest ecosystems.

1. Introduction

Environmental variability defines main forest structure characteristics, including growth rates, biodiversity values, and ecosystem processes [1,2,3]. The main environmental factors influencing forest structure include soil properties associated with water and nutrient supply, landscape elements like topographical positioning and hydrology, and climate conditions such as precipitation and temperature regimes [4,5,6]. These factors are closely interconnected, where one factor influences the others, favoring or limiting the vegetation development [7]. In this context, biomes are composed of a mixture of interconnected ecosystems influenced by these factors, e.g., floodplain forests and dry forests exemplify the interactions between soil and landscape characteristics [8,9]. For example, intra- and inter-annual variation of flooding regimes in floodplain forests directly influence forest dynamics through their soil biogeochemical changes and the dispersion of species between watercourses and environments [10,11].
The Amazon biome is one example of floodplain–dry forest mixed habitats and spans through nine Latin American countries, where half of its area belongs to Brazil [12]. This biome is the most biodiverse ecosystem on Earth, hosting more than 10% of all known species, and contributing to global carbon and water cycles [13,14]. The frequency and intensity of flooding influence over the main soil characteristics, generating a mosaic of contrasting forest types [15], which can be divided in frequently flooded areas locally called “Várzea” (VF) forests and non-flooded areas locally called “Terra Firme” (TF) forests, which are mainly located in highlands that remain above flood levels [16,17]. The flooded forests usually exhibit nutrient limitations and intense weather, resulting in high endemism and low species richness. These nutrient limitations are related to soil water dynamics and the edaphic conditions that affect tree physiology, e.g., flooded soils often have higher organic matter content than aerobic soils, which is due to chemical, metabolic, and physical mechanisms [18,19,20]. The heterogeneity of floodplain soils results in a wide variation in fertility (richness or poverty of soils), e.g., some floodplain soils can be eutrophic (rich) with a high concentration of nutrients for plants, while dystrophic soils have a low concentration of essential nutrients for cultivated plants and possible aluminum toxicity [21,22]. Many tree species in these areas are ecologically specialized, leading to distinct floristic compositions compared to non-flooded areas (TF) [23], with a unique evolutionary signature of their phylogenetic composition [24,25]. Seasonal flooding in lowlands (VF) results in the predominance of species adapted to long-term flooding [26]. In contrast, forests along streams and small rivers embedded within uplands (TF), where the water table is shallow, presented higher diversity of species adapted to heterogeneous soil composition, facing different selective pressures from those found in large-river floodplains. This results in a distinct species composition along the landscape [17,24]. The complex interaction between hydrological factors and soils influences the organization of the resulting forest biomes, according to the species’ adaptability [27,28], e.g., producing adventitious and aerenchyma roots, stem hypertrophy, lenticels, and reducing photosynthesis in response to water stress [26,29].
In this context, flooding and waterlogging create environmental conditions that differ significantly from those in non-flooded areas [30], e.g., soil organic matter tends to accumulate in flooded environments mainly because these areas act as sinks from nearby watersheds and significantly restrict their mineralization [31]. Another factor of great influence on soil characteristics is the topography, e.g., the flooding regime is primarily influenced by its proximity to the watercourse and its relative altitude [32,33]. These variations in soil formation processes across the Amazon biome have generated heterogeneous nutrient distributions [34], resulting in noticeable differences in the availability of nutrient levels for trees [35], which differ in the mechanisms by which they stabilize and store soil organic carbon [36]. In general, forest soil constitutes a significant global carbon reservoir [37,38,39]. The Amazon rainforest becomes a key biome in this context because it is one of the largest reservoirs of stored carbon in the world [40], making it necessary to deeply understand the relationships between soil characteristics and forest structure [41,42,43]. The main benefits of knowing the relationship between soil characteristics and forest structure are related to a deep understanding of forest natural dynamics, the autoecology of endangered species, carbon allocation, and resilience to face climatic extreme events [44,45,46]. For this, the objective was to determine potential relationships between forest structure and soil characteristics with flooding regime, e.g., flooded (VF) and non-flooded (TF) rainforests of northern Amazonia (Brazil). We tested the following hypotheses: (i) There are substantial differences in soils and forest structure of both forest types (VF and TF) derived from flooding regimes; (ii) positive synergies exist between soil characteristics and forest structure, where forest productivity can be enhanced in soils with higher nutrient availability and favorable soil physical conditions; and (iii) flooding regimes can limiting forest structure development, where elevation influence over soils characteristics and nutrient availability.

2. Materials and Methods

2.1. Study Area

The study area was located at Gunma Ecological Park, covering 540 ha of forest in the municipality of Belém (Pará State, Brazil) (1°13′02″ SL, 48°17′42″ WL) (Figure 1). The vegetation is mainly defined as dense ombrophilous Terra Firme forests, as well as secondary forests, igapó forests, and Várzea forests [47]. In Terra Firme, the main species were Schweilera coriacea (D.C.) S.A. Mori, Eschweilera collina Eyma, Pourouma mollis Trecul, and Dendrobangia boliviana Rusby, while in the Várzea environment, the vegetation is predominantly formed by Euterpe oleracea Mart., Pentaclethra macroloba (Willd.) Kuntze, Pterocarpus santalinoides L’Hér. ex D.C., and Virola surinamensis (Rol. ex Rottb.) Warb [47]. This area is part of the Amazon Plain, with little slope, even close to watercourses, and has yellow latosol type soils in Terra Firme areas [48], and in Várzea areas, the soils are fluvial Neosol with an organic horizon [49] equivalent to Ferralsols and Fluvisols according to the world reference base for soil resources [50]. The studied forest is a remnant patch representative of the high ecological value ecosystem with high endemism of the northern region, considered relevant for the conservation of many species [51,52]. The studied stands were not harvested at least during the last 50 years. This ecological reserve included the two main forest types described before for the Amazon biome: (i) Terra Firme forests (TFs) located in the highlands, and Várzea forests (VFs), which are frequently flooded lowlands [16]. This area was characterized by a humid tropical climate, with rainfall ranging from 2500 to 3000 mm year−1 evenly distributed throughout the year and an average temperature of 26 °C [52].

2.2. Forest Inventory

Our sampling effort was designed to compare both forest treatments (VF and TF) (2 treatments × 20 replicas = 40 forest inventory plots). Sampling areas were previously identified using Sentinel-2 images in a geographical information system (GIS), where human infrastructure (urbanization and paths) and altitude (m a.s.l.) were identified [53]. All the trees (>10 cm diameter at breast height, DBH) were measured inside each 500 m2 plot (20 × 25 m), identifying their species. The plot area was selected to obtain a balance between forest structure and characterization of soil properties (e.g., soil properties greatly changed according to micro-topography) [54]; however, different studies suggest bigger sizes to characterize the biodiversity [55]. DBH (cm) was measured using a tape measure, while tree height (H, m) was estimated through a 1 m reference rod. Based on these measurements, dominant height (DH, m), mean height (MH, m), basal area (BA, m2 ha−1), tree volume (TV, m3 ha−1), and tree density (TD, ind ha−1) were determined for each plot. Tree volume was estimated using the methodology proposed by Heinsdijk and Bastos [56] for the studied tree species.

2.3. Soil Sampling

Soil sampling was conducted at each forest inventory plot, using three composite soil sub-samples taken at 0–20 cm depth after removing the litter (e.g., fallen leaves, twigs, bark, and other plant debris in various stages of decomposition). The sub-samples were taken covering the influence area of the center of the forest inventory plot, despite the closeness of trees or overstory cover. The sampling in the flooded areas of Várzea forests (VFs) was conducted when the water did not cover the forest floor, at the beginning of the dry period in the state of Pará, which is in July. During this period, rainfall is consistently less, so floodplains are less flooded; however, they are still influenced by river flooding. Soil composite sampling was air dried at laboratory conditions, where the following soil nutrient content were measured: (i) organic carbon (C) was measured using the Walkley–Black (WB) dichromate method, (ii) total nitrogen (N) was measured using the Kjeldhal method, (iii) phosphorus (P), (iv) aluminum (Al), (v) calcium (Ca), (vi) magnesium (Mg), (vii) sodium (Na), (viii) potassium (K), (ix) iron (Fe), (x) manganese (Mn), (xi) zinc (Zn), and (xii) copper (Cu). Ca and Mg were measured via complexometric titration with EDTA, while Al was measured via complexometric titration with sodium hydroxide. P, Na, K, Fe, Mn, Zn, and Cu were measured using the Melich-1 method. C and N are total (expressed in %), while the rest of the nutrients are extractable (expressed in ppm). Also, the following soil properties were measured: (xiii) Soil acidity with 1:2.5 soil/water ratio (pH) was measured using potentiometry, (xiv) cation exchange capacity at pH 7.0 (CEC, Cmolc kg−1), (xv) potential acidity (H + Al) (ACI, Cmolc kg−1) was measured using complexometric titration with sodium hydroxide, and (xvi) sum of exchangeable bases (SB, Cmolc kg−1). The employed soil analytical methods followed the procedures outlined by Teixeira et al. [57]. Finally, the soil texture (percentage of sand, silt, and clay) was determined for each plot using the pipette method for particle size analysis [58].

2.4. Statistical Analyses

We employed different comparisons to study the relationships among forest structure and soil variables, including: (i) One-way analysis of variance (ANOVA) was used to compare forest structure, nutrient contents, and soil properties between forest types (VFs = Várzea forests, TFs = Terra Firme forests) through the Fisher test and their significance [59,60]. (ii) Principal Component Analysis (PCA) was performed [61] to detect similarity among plots of forest types using a data matrix of five forest structure variables (TD, DH, DBH, BA, and TV) and the 40 forest inventory plots. MH was discarded due to high redundancy with DH. Due to differences in units and dimensions, the correlation coefficients among columns were used for the final cross-product matrix. Furthermore, a Monte Carlo permutation test (n = 999) was conducted to assess the significance of the PCA axes [62]. (iii) A Detrended Correspondence Analysis (DCA) [63] using texture data (sand, silt, and clay) was used to characterize the forest inventory plots according to their altitude (4, 5, 6, 9–18, 36–43 m a.s.l.), generating a soil texture triangle. (iv) A Redundancy Analysis (RDA) [64] was performed to evaluate the multivariate relationships between forest structure and soil variables. We used the same five variables of forest structure employed in PCA as response variables and soil characteristics as environmental variables, with soil texture (sand, silt, and clay proportions) and nutrients analyzed separately. Finally, (v) another two independent RDAs were conducted using the same response and environmental variables, but splitting the whole database into two different subsets according to the forest type (VF and TF). RDA was chosen due to the short length of the gradient for these data (less than 2.0). We performed PCA and DCA using PC-Ord 5.0 software [65] and RDA using CANOCO software v5.1 [66,67].

3. Results

We found significant differences between treatments for all the studied forest structure variables (Table 1). Dominant and mean height (DH and MH) were higher in the non-flooded forests (TF), presenting trees with higher DBH, BA, and TV. Tree density (TD) was significantly lower than in the flooded areas (VF), which present lower values of the other variables (smaller tree size). PCA comparing forest structure variables (Figure 2) allowed us to detect differences between both treatments (VF and TF); however, four plots overlap. This overlapping showed that the forest structure occurred gradually across the landscape and between the studied treatments, where some plots presented intermediate characteristics. The non-flooded areas (TF) displayed a more homogeneous distribution compared to flooded areas (VF), which presented a wide dispersion for the studied axes that explained 62.8% and 27.1% of the variance. The weight of forest variables on the PCA changed according to the axes (Table A1), where TV > DH > BA > DBH > TD for axis 1, and TD > BA > DBH > TV > DH for axis 2.
Nutrient content in soils presented significant differences between forest types (VF and TF), except for iron (Fe) (Table 2). Nutrients were significantly higher in flooded areas (VF), where some of them showed higher variations between treatments, e.g., C = ×8.3, N = ×7.0, P = ×2.1, Al = ×1.9, Ca = ×7.2, Mg = ×2.0, Na = ×13.3, K = ×.1, Mn = ×321.2, Zn = ×10.6, and Cu = ×2.0. Soil physicochemical properties also presented significant differences when both forest types were compared (VF and TF), except for the pH (Table 3). The flooded treatments had higher cation exchange capacity (×3.2 higher), with a strong positive correlation with the organic matter values (r = 0.91). Potential acidity and the sum of exchangeable bases were also higher in flooded forests (VF) compared to non-flooded areas (TF) (×3.2 and ×2.9, respectively). Texture also displayed significant differences between forest types, where sand was higher in non-flooded areas (71.2% vs. 31.7%) and silt/clay was higher in flooded areas (40.1% and 28.2% vs. 12.5% and 16.3%, respectively). The difference between forest types is mainly related to altitude, which also presented significant differences (Table 3), where flooded areas (VF) had on average 7.0 m a.s.l. compared to the 41.8 m a.s.l. of the non-flooded areas (TF). In the soil texture triangle obtained through the DCA, it is possible to see the internal homogeneity of each elevation category, where plots showed a more homogeneous distribution at higher altitudes (Figure A1).
The multivariate relation between forest structure and soil variables indicated that total over bark volume of the stands (TV) was higher in sandy soils, while higher values of tree density (TD) were associated with silty and clayey soils (Figure 3). Likewise, higher nutrient contents were observed in almost all flooded forests (VF) compared to non-flooded forests (TF) (Figure 3). Axes 1 and 2 of RDA (Monte Carlo p-value = 0.002 for all canonical axes) for the whole set of plots explained 100% of the variance with texture and 99.9% of the variance with nutrient variables. Sand was the most important variable for the texture group (p = 0.002), while Zn, Al, and Mg were the most relevant nutrients (p = 0.002, 0.048, and 0.050, respectively). The detailed list of conditional effects of environmental variables for these RDA analyses is shown in Table A2.
Analyzing forest types (VF and TF) separately revealed some differences in the individual effect of texture and nutrient contents (Figure 4). In flooded areas (VF), plots at higher elevation were sandier and showed slightly higher total over bark volume in the stands. Contrarily, nutrients did not display a clear correlation with elevation in the flooded forests (Figure 4A). Regarding the non-flooded forests (TF), there was no clear grouping of plots either for texture or nutrients (Figure 4B). In flooded areas, Axes 1 and 2 of the RDA (Monte Carlo p-value = 0.290 for all canonical axes) explained 100% variance with texture variables and 99.7% variance with nutrient variables (Figure 4A). While in non-flooded forests, Axes 1 and 2 of the RDA (Monte Carlo p-value = 0.384 for all canonical axes) also explained 100% variance with texture variables and 99.4% variance with nutrient variables (Figure 4B). The detailed list of conditional effects of environmental variables for these RDAs is shown in Table A3. Finally, we tested the correlation forest structure, soil nutrient contents, soil physicochemical properties, and topography for the entire forest inventory database (VF and TF plots). All the variables presented significant correlation values with at least one forest structure variable, except Fe, which is not correlated with the studied ones (Table A4).

4. Discussion

We found significant differences in the forest structure main characteristics of the two studied forest types (flooded and non-flooded) for the analyzed parameters. These differences in forest structure were previously described for all Amazonia, which influences biodiversity and many ecological processes [14,23,29,68]. Tree density showed one of the greatest differences between forest types. The large disparity in the tree density may be influenced by the species, where some trees presented a higher number of codominant stems, e.g., Silva and Neto [69] recommended counting each stem as a separate individual for all species, which contributes to the dominance of some species in the flooded areas (e.g., VF). These changes in tree density also influenced other forest structure parameters (e.g., basal area and total over bark volume). This increment in tree biomass was higher in non-flooded areas (TF) than in flooded areas (VF), despite the higher tree density being observed in this area. This can also be linked to the presence of some species with multiple stems (e.g., Euterpe oleracea), which had low DBH but many stems. A study conducted in Amazonian forests corroborates that TV in floodplain forests is lower than in non-flooded forests where this species is growing [70]. In the same way, another study found a higher degree of stem branching associated with greater basal area and lower height in the floodplain compared with non-flooded areas in the western Brazilian Amazon [16].
The changes in forest structure were influenced by tree species composition, affecting microclimatic conditions, which can be related to soil restrictions on tree growth [71]. For example, the basal area of the stands was lower in flooded Várzea forests than in non-flooded Terra Firme forests analyzed in the state of Pará (Brazil), despite the flooded areas showing greater soil fertility [72], as was also found in our study. Regarding the influence of soil chemical properties on basal area values, the relationship between forest dynamics, structure, and soil fertility remains unclear. Some studies find interactions between soil fertility and biomass, while others did not find any clear relationship [73]. The flooding limits the soil nutrient uptake by trees [74,75] and can influence tree growth and site quality of the stands [76,77]. In our results, the flooded areas (VF) exhibited lower values of dominant and average height than non-flooded areas (TF), which can be explained by the disturbances caused by flooding to the soil properties and their geomorphic dynamics [78].
The hydromorphic nature of soils in flooded areas resulted in significant differences in their chemical properties compared to soils developed in highlands. Moreover, soil parameters in flooded areas (VF) showed considerably higher variability than in non-flooded areas (TF), primarily associated with texture (granulometric composition) and location within the flood gradient, as was indicated by its altitude [79]. The higher levels of soil organic carbon in flooded (VF) compared to non-flooded (TF) areas may be associated with the limited mineralization due to seasonal flooding [80] and the activity of anaerobic organisms [81]. These high values of soil organic carbon of our study found in flooded areas (VF) are consistent with those reported elsewhere for the Amazonian floodplain in Northeast Brazil, where authors found organosols with similar soil organic carbon contents (7.0–425.0 mg kg−1) [82]. Associated with the described constraints, both forest types (VF and TF) exhibited low soil phosphorus values, which were similar [83] or lower [84,85] than those reported for other flooded areas in the Amazon. The substantial heterogeneity of the Amazon rainforest associated with flooding regimes indicates that not all local rainforest types are necessarily P-limited, as their productivity is supported by complex nutrient cycling mechanisms such as a quickly organic matter mineralization [86,87,88].
The other nutrients also showed differences between the studied forest types, except iron (Fe), which was pointed out as one nutrient that presents differences due to flooding in Amazonian forests [89,90]. In example, potassium (K) values were higher (122 mg kg−1) than those found in soils of flooded areas (91 mg kg−1) [90] and in non-flooded forest soils of Pará state in Brazil (86 mg kg−1) [91] but lower than those found in central Amazon flooded Várzea forest soils (155 mg kg−1) [92]. In the non-flooded areas (TF), we found lower values of K, calcium (Ca), and magnesium (Mg) (70 mg kg−1, 26 mg kg−1, and 19 mg kg−1, respectively) and higher values for manganese (Mn, 50 mg kg−1) [92]. These authors find higher concentrations of these nutrients in Várzea flooded forest soils (K: 155 mg kg−1, Ca: 1436 mg kg−1, Mg: 312 mg kg−1, and Mn: 163 mg kg−1). In our study, nutrients such as K, Ca, and Mg have increased their concentration due to the flooding influence (VF), but they can be released into the soil water solution under some specific anaerobic conditions [90,93], generating greater cation exchange capacity. Contrary, in non-flooded areas (TF), low concentrations of Ca, Mg, and K are associated with the presence of negative charges in the colloids of soils [94]. Flooding and drainage lead to changes in the soil redox potential and pH, which result in significant changes in the bioavailability of heavy metals [95]. Furthermore, the Fe can change their availability in soil due to oxide formation and the combination with other metals, reducing their translocation [96]. In general, Amazon soils located at higher landscape positions generally have lower values of K, Ca, and Mg [97]. As was expected, higher aluminum (Al) concentration in soils was found in the study area, particularly in flooded soils (VF). However, the remarkably high soil organic carbon content in flooded areas (VF) could mitigate the harmful toxicity of this nutrient [98,99,100]. The strong soil acidity observed in both areas aligns with findings from a comparison between soils from the same forest types (VF and TF), which found no significant differences in soil pH, ranging from 4.0 to 5.3 [101].
In our study, we also found differences in physical parameters of soils between forest types, including a greater amount of silt in flooded forests (VF) compared to non-flooded areas, where a higher proportion of sand was found. These differences in soil texture influence nutrient concentration and availability for tree growth and development [102,103]. The multivariate analyses (e.g., RDA) corroborated these results, showing that flooded forest plots clustered more towards clay and silt soils, while non-flooded forest plots clustered together. The predominance of soils with higher contents of silt and clay in floodplain areas is due to the limitation of the pedogenesis process caused by constant flooding, leading to the formation of young soils [89]. In contrast, non-flooded soils, which are generally older and more weathered, tend to be sandier and well-developed [104]. Some studies pointed out that higher silt and clay contents in soils, as well as the clay mineralogy, are the main factors affecting the maximal C and N storage levels of soils [105]. Mineral types are considered the main drivers of the stabilizing agents of organic materials [106], which can influence the forest productivity [107]. In the multivariate analyses, we can also see that soil physical parameters presented some clear relationships with the forest structure, e.g., total over bark volume of the stands was more closely related to sandy soils, while tree density was more associated with clayey and silty soils. This is clear evidence that higher tree density in clay and silty soils may be related to greater water and nutrient retention in soils [108].

5. Conclusions

This study found differences in forest structure and soil characteristics between flooded (Várzea) and non-flooded (Terra Firme) forests in northern Amazonia, Brazil. Our results showed that the flooding regime defines the forest composition and dynamics (e.g., tree density, height, and volume) and influences soil nutrient characteristics. Floodplain forests exhibited higher soil nutrient concentrations and organic carbon content, likely due to the periodic accumulation of litter, deposition of sediments, and reduced decomposition rates under anaerobic conditions. In contrast, non-flooded forests located in upper lands were characterized by lower nutrient levels, higher sand content, and greater values of some forest structure variables (e.g., height, volume, and basal area). This suggests that higher nutrient availability is not the most important primary factor driving forest productivity, where the flooding regime can limit forest growth. We observed greater differences in soil and forest structure variables within the floodplain forest plots compared to non-flooded forest plots, supporting our hypothesis that elevation influences soil processes and, consequently, forest structure and composition. These findings can contribute to a deeper understanding of the Amazon rainforest ecology and highlight the importance of considering hydrological and edaphic factors in the forest dynamics and characterization.

Author Contributions

Conceptualization, E.N.B.P., M.T.d.P. and G.R.; methodology, G.M.P., M.V.L. and G.R.; software, E.N.B.P. and L.B.J.; validation, E.N.B.P.; formal analysis, E.N.B.P., G.R. and M.V.L.; investigation, E.N.B.P. and G.M.P.; resources, G.M.P.; data curation, E.N.B.P.; writing-original draft preparation, E.N.B.P. and G.R.; writing, review and editing, G.M.P., M.V.L., L.B.J. and M.T.d.P.; visualization, E.N.B.P.; supervision and project administration, M.T.d.P. and G.R.; funding acquisition, M.T.d.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was conducted with the financial support of the CAPES Doctoral Sandwich Program scholarship (PDSE) of Brazil.

Data Availability Statement

Availability of data and material: At the CONICET (Argentina) repository and can be requested to the authors for further analyses.

Acknowledgments

We acknowledge the researchers and technicians who supported this research during field work and laboratory analyses. It was impossible to obtain these invaluable data without their disinterested and unconditional help.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Eigenvectors of PCA conducted for forest structure, scaled to unit length. Variable names are listed in Table 1.
Table A1. Eigenvectors of PCA conducted for forest structure, scaled to unit length. Variable names are listed in Table 1.
VariablesAxis 1Axis 2Axis 3Axis 4Axis 5
DH−0.4873−0.00790.82760.17400.2176
BA−0.47360.4073−0.4133−0.14940.6455
TD0.20990.77770.05680.5650−0.1696
DBH−0.4708−0.3791−0.37380.6777−0.1886
TV−0.52210.2924−0.0376−0.4109−0.6867
Forests 16 00793 g0a1
Figure A1. Detrended Correspondence Analysis (DCA) of soil texture triangle (proportions of sand, clay, and silt) in Várzea and Terra Firme plots, with plot differentiated in five elevation groups: less than 4 m a.s.l., 4–5 m a.s.l., 5–6 m a.s.l., 9–18 m a.s.l., and 36–43 m a.s.l. Plots from Várzea included elevations from less than 4 to 18 m a.s.l., while plots from Terra Firme (red circle) included elevations from 36 to 43 m a.s.l.
Figure A1. Detrended Correspondence Analysis (DCA) of soil texture triangle (proportions of sand, clay, and silt) in Várzea and Terra Firme plots, with plot differentiated in five elevation groups: less than 4 m a.s.l., 4–5 m a.s.l., 5–6 m a.s.l., 9–18 m a.s.l., and 36–43 m a.s.l. Plots from Várzea included elevations from less than 4 to 18 m a.s.l., while plots from Terra Firme (red circle) included elevations from 36 to 43 m a.s.l.
Forests 16 00793 g0a1
Table A2. Conditional effects of environmental variables used in RDAs for the whole set of studied plots. Lambda A is the additional variance the variable explains, considering the variables already included, where P is the significance obtained for F when the variable was added to the model. Variable names are listed in Table 2 and Table 3.
Table A2. Conditional effects of environmental variables used in RDAs for the whole set of studied plots. Lambda A is the additional variance the variable explains, considering the variables already included, where P is the significance obtained for F when the variable was added to the model. Variable names are listed in Table 2 and Table 3.
VariableLambda APF
TextureSand0.470.00234.25
Silt0.010.4780.52
NutrientsZn0.510.00239.62
Mg0.050.0503.86
Al0.040.0483.60
Fe0.020.1821.86
N0.010.2501.49
K0.010.5260.46
Cu0.010.3440.88
P<0.010.7540.16
Na<0.010.7740.12
C<0.010.7060.19
Mn<0.010.8200.08
Ca<0.010.9540.02
Table A3. Conditional effects of environmental variables used in RDAs for the subset of plots of Várzea and Terra Firme. Lambda A is the additional variance the variable explains, considering the variables already included, while P is the significance obtained for F when the variable was added to the model. Variable names are listed in Table 2 and Table 3.
Table A3. Conditional effects of environmental variables used in RDAs for the subset of plots of Várzea and Terra Firme. Lambda A is the additional variance the variable explains, considering the variables already included, while P is the significance obtained for F when the variable was added to the model. Variable names are listed in Table 2 and Table 3.
SubsetVariableLambda APF
VárzeaTextureSand0.140.0982.87
Silt<0.010.8400.06
NutrientsMg0.320.0088.37
P0.110.0963.47
Zn0.060.1901.77
N0.090.0883.42
Na0.050.2601.56
Al0.010.4960.56
Ca0.020.5140.48
Cu0.010.4900.53
C0.030.3200.97
K0.020.4880.61
Mn<0.010.7580.14
Fe0.010.8240.09
Terra FirmeTextureSand0.100.1582.07
Silt0.020.6180.26
NutrientsCu0.140.0963.00
P0.090.1702.01
Na0.040.4280.72
Fe0.040.3560.93
Al0.040.3420.95
Mg0.020.6600.26
C0.010.6600.20
Mn<0.010.7400.11
Ca0.010.7560.14
Zn<0.010.8240.06
K0.010.9480.02
N<0.010.9720.01
Table A4. Correlation among forest structure, soil nutrient contents, soil physicochemical properties, and topography for the entire forest inventory database (Várzea and Terra Firme plots). Pearson coefficient and p-value between brackets are presented. Variable names are listed in Table 1, Table 2 and Table 3.
Table A4. Correlation among forest structure, soil nutrient contents, soil physicochemical properties, and topography for the entire forest inventory database (Várzea and Terra Firme plots). Pearson coefficient and p-value between brackets are presented. Variable names are listed in Table 1, Table 2 and Table 3.
VariableTDDHMHDBHBATV
C0.469−0.581−0.699−0.699−0.36−0.455
(<0.01)(<0.01)(<0.01)(<0.01)(0.02)(<0.01)
N0.507−0.513−0.612−0.642−0.283−0.378
(<0.01)(<0.01)(<0.01)(<0.01)(0.08)(0.02)
P0.249−0.352−0.429−0.214−0.069−0.182
(0.12)(0.02)(<0.01)(0.18)(0.67)(0.26)
Al<0.001−0.142−0.341−0.118−0.114−0.168
(0.99)(0.38)(0.03)(0.47)(0.48)(0.30)
Ca0.399−0.451−0.467−0.645−0.335−0.374
(0.01)(<0.01)(<0.01)(<0.01)(0.03)(0.02)
Mg0.698−0.476−0.477−0.665−0.221−0.327
(<0.01)(<0.01)(<0.01)(<0.01)(0.17)(0.04)
Na0.311−0.576−0.623−0.547−0.329−0.446
(0.05)(<0.01)(<0.01)(<0.01)(0.04)(<0.01)
K0.434−0.362−0.532−0.575−0.249−0.332
(<0.01)(0.02)(<0.01)(<0.01)(0.122)(0.04)
Fe0.248−0.094−0.077−0.2230.1390.086
(0.12)(0.56)(0.64)(0.17)(0.39)(0.59)
Mn0.339−0.309−0.275−0.475−0.199−0.244
(0.03)(0.05)(0.08)(<0.01)(0.22)(0.13)
Zn0.556−0.591−0.649−0.740−0.372−0.460
(<0.01)(<0.01)(<0.01)(<0.01)(0.02)(<0.01)
Cu0.421−0.393−0.400−0.429−0.078−0.155
(<0.01)(0.01)(0.01)(<0.01)(0.63)(0.34)
pH0.1470.0410.145−0.1490.0200.068
(0.36)(0.80)(0.37)(0.36)(0.90)(0.68)
CEC0.515−0.508−0.663−0.730−0.329−0.420
(<0.01)(<0.01)(<0.01)(<0.01)(0.04)(<0.01)
ACI0.504−0.472−0.680−0.697−0.295−0.394
(<0.01)(<0.01)(<0.01)(<0.01)(0.06)(0.01)
SB0.458−0.504−0.529−0.689−0.348−0.408
(<0.01)(<0.01)(<0.01)(<0.01)(0.03)(<0.01)
SAND−0.4060.5740.6210.6390.4050.486
(<0.01)(<0.01)(<0.01)(<0.01)(<0.01)(<0.01)
SILT0.422−0.552−0.626−0.659−0.392−0.468
(<0.01)(<0.01)(<0.01)(<0.01)(0.01)(<0.01)
CLAY0.278−0.470−0.460−0.448−0.327−0.395
(0.08)(<0.01)(<0.01)(<0.01)(0.04)(0.01)
ELE−0.3990.7070.7630.6250.4100.545
(<0.01)(<0.01)(<0.01)(<0.01)(<0.01)(<0.01)

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Forests 16 00793 g001
Figure 1. Location and altitude (m a.s.l.) of the study area at Gunma Ecological Park (Pará State, Brazil). TFs = Terra Firme forests; VFs = Várzea forests.
Figure 1. Location and altitude (m a.s.l.) of the study area at Gunma Ecological Park (Pará State, Brazil). TFs = Terra Firme forests; VFs = Várzea forests.
Forests 16 00793 g001
Forests 16 00793 g002
Figure 2. Principal Component Analysis (PCA) for forest structure variables, classified according to forest types under different flooded regimes (blue = Várzea forests, green = Terra Firme forests). TD = tree density (n ha−1); DH = dominant height (m); DBH = diameter at breast height (cm); BA = basal area (m2 ha−1); TV = total over bark volume (m3 ha−1).
Figure 2. Principal Component Analysis (PCA) for forest structure variables, classified according to forest types under different flooded regimes (blue = Várzea forests, green = Terra Firme forests). TD = tree density (n ha−1); DH = dominant height (m); DBH = diameter at breast height (cm); BA = basal area (m2 ha−1); TV = total over bark volume (m3 ha−1).
Forests 16 00793 g002
Forests 16 00793 g003
Figure 3. Redundancy Analysis (RDA) using forest structure (thin arrows) as response variables and soil characteristics (texture and nutrients, broad arrows) as environmental variables. Plots are colored according to the different flooded regimes: Blue dots = flooded Várzea forests; Green dots = non-flooded Terra Firme forests.
Figure 3. Redundancy Analysis (RDA) using forest structure (thin arrows) as response variables and soil characteristics (texture and nutrients, broad arrows) as environmental variables. Plots are colored according to the different flooded regimes: Blue dots = flooded Várzea forests; Green dots = non-flooded Terra Firme forests.
Forests 16 00793 g003
Forests 16 00793 g004
Figure 4. Redundancy Analysis (RDA) for the subsets of forest types under different flooded regimes: (A) flooded areas in Várzea forests, classified according to elevation levels; and (B) non-flooded areas in Terra Firme forests, classified according to homogeneous areas determined by satellite images. Both analyses used forest structure (thin arrows) as response variables and soil characteristics (texture and nutrients, broad arrows) as environmental variables.
Figure 4. Redundancy Analysis (RDA) for the subsets of forest types under different flooded regimes: (A) flooded areas in Várzea forests, classified according to elevation levels; and (B) non-flooded areas in Terra Firme forests, classified according to homogeneous areas determined by satellite images. Both analyses used forest structure (thin arrows) as response variables and soil characteristics (texture and nutrients, broad arrows) as environmental variables.
Forests 16 00793 g004
Table 1. ANOVAs comparing forest structure between the studied forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). TD = tree density (n ha−1); DH = dominant height (m); MH = mean height (m); DBH = diameter at breast height (cm); BA = basal area (m2 ha−1); TV = total over bark volume (m3 ha−1).
Table 1. ANOVAs comparing forest structure between the studied forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). TD = tree density (n ha−1); DH = dominant height (m); MH = mean height (m); DBH = diameter at breast height (cm); BA = basal area (m2 ha−1); TV = total over bark volume (m3 ha−1).
LevelTDDHMHDBHBATV
VF756
(6)		22.0
(1.2)		15.1
(0.4)		14.4
(4.6)		18.2
(1.0)		241.0
(17.3)
TF378
(1)		29.9
(0.8)		18.8
(0.4)		25.0
(0.6)		25.0
(0.1)		429.8
(2.0)
F
(p)		8.52
(<0.01)		31.85
(<0.01)		53.63
(<0.01)		25.76
(<0.01)		5.90
(0.01)		12.60
(<0.01)
For each level, the data shown are the mean (standard error). F = Fisher test; p = probability.
Table 2. ANOVAs comparing soil nutrient contents between forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). C = organic carbon (%); N = total nitrogen (%); P = phosphorus (ppm); Al = aluminum (ppm); Ca = calcium (ppm); Mg = magnesium (ppm); Na = sodium (ppm); K = potassium (ppm); Fe = iron (ppm); Mn = manganese (ppm); Zn = zinc (ppm); Cu = copper (ppm).
Table 2. ANOVAs comparing soil nutrient contents between forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). C = organic carbon (%); N = total nitrogen (%); P = phosphorus (ppm); Al = aluminum (ppm); Ca = calcium (ppm); Mg = magnesium (ppm); Na = sodium (ppm); K = potassium (ppm); Fe = iron (ppm); Mn = manganese (ppm); Zn = zinc (ppm); Cu = copper (ppm).
LevelCNPAlCaMgNaKFeMnZnCu
VF13.3
(1.5)		0.7
(0.1)		6.7
(1.1)		229.1
(33.0)		1054.1
(265.0)		155.0
(20.1)		256.3
(30.6)		122.1
(15.6)		1353.6
(176.3)		160.6
(66.2)		15.9
(2.6)		2.8
(0.3)
TF1.6
(0.1)		0.1
(<0.1)		3.2
(0.2)		117.7
(6.2)		147.3
(10.0)		77.8
(7.1)		19.2
(1.8)		17.2
(4.4)		1083.2
(37.7)		0.5
(0.1)		1.5
(0.2)		1.4
(0.3)
F
(p)		58.30 (<0.01)62.23 (<0.01)9.28
(<0.01)		11.01
(<0.01)		11.69
(<0.01)		13.16
(<0.01)		59.62
(<0.01)		42.04
(<0.01)		2.25
(0.12)		5.84
(<0.01)		31.15
(<0.01)		11.69
(<0.01)
The data present the mean ± standard error. F = Fisher test; p = probability.
Table 3. ANOVAs comparing soil physicochemical properties and topography between forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). pH = soil acidity; CEC = cation exchange capacity (Cmolc kg−1); ACI = potential acidity (Cmolc kg−1); SB = exchangeable bases (Cmolc kg−1); Sand (%); Silt (%); Clay (%); ELE = elevation (m a.s.l.).
Table 3. ANOVAs comparing soil physicochemical properties and topography between forest types under different flooded regimes (VFs = Várzea forests, TFs = Terra Firme forests). pH = soil acidity; CEC = cation exchange capacity (Cmolc kg−1); ACI = potential acidity (Cmolc kg−1); SB = exchangeable bases (Cmolc kg−1); Sand (%); Silt (%); Clay (%); ELE = elevation (m a.s.l.).
LevelpHCECACISBSandSiltClayELE
VF4.15
(0.06)		33.37
(3.71)		25.41
(2.54)		7.96
(1.50)		31.7
(3.8)		40.1
(2.6)		28.2
(2.3)		7.0
(0.9)
TF4.22
(0.04)		10.33
(0.69)		8.83
(0.65)		1.50
(0.07)		71.2
(0.9)		12.5
(0.7)		16.3
(0.6)		41.8
(0.4)
F
(p)		0.78
(0.38)		37.25 (<0.01)39.91
(<0.01)		18.47
(<0.01)		100.7
(<0.01)		105.1
(<0.01)		25.4
(<0.01)		1190.0
(<0.01)
The data present the mean ± standard error. F = Fisher test; p = probability.
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Barbosa Pimentel, E.N.; Botelho Jerônimo, L.; Tavares de Paula, M.; Lencinas, M.V.; Martínez Pastur, G.; Rubio, G. Relationship Between Forest Structure and Soil Characteristics with Flooded and Non-Flooded Rainforests of Northern Amazonia (Brazil). Forests 2025, 16, 793. https://doi.org/10.3390/f16050793

AMA Style

Barbosa Pimentel EN, Botelho Jerônimo L, Tavares de Paula M, Lencinas MV, Martínez Pastur G, Rubio G. Relationship Between Forest Structure and Soil Characteristics with Flooded and Non-Flooded Rainforests of Northern Amazonia (Brazil). Forests. 2025; 16(5):793. https://doi.org/10.3390/f16050793

Chicago/Turabian Style

Barbosa Pimentel, Edyrlli Naele, Lucas Botelho Jerônimo, Manoel Tavares de Paula, María Vanessa Lencinas, Guillermo Martínez Pastur, and Gerardo Rubio. 2025. "Relationship Between Forest Structure and Soil Characteristics with Flooded and Non-Flooded Rainforests of Northern Amazonia (Brazil)" Forests 16, no. 5: 793. https://doi.org/10.3390/f16050793

APA Style

Barbosa Pimentel, E. N., Botelho Jerônimo, L., Tavares de Paula, M., Lencinas, M. V., Martínez Pastur, G., & Rubio, G. (2025). Relationship Between Forest Structure and Soil Characteristics with Flooded and Non-Flooded Rainforests of Northern Amazonia (Brazil). Forests, 16(5), 793. https://doi.org/10.3390/f16050793

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