Applying the Soil Management Assessment Framework (SMAF) to Assess Mangrove Soil Quality

Abstract

Soil quality (SQ) refers to its capacity to perform its functions. Thus, the SQ index (SQI) is a potentially useful tool for monitoring soil changes induced by mangrove restoration initiatives. Although the soil management assessment framework (SMAF) is a well-developed tool for SQ assessments in diverse ecosystems, it has never been tested on mangrove soils. In this study, we tested the SMAF to evaluate the shifts in the SQ of mangroves in a reforestation initiative using three- and seven-year plantations, which were compared with degraded and mature mangroves. A minimum dataset, composed of the pH and available P as chemical indicators, bulk density as a physical indicator, and soil organic carbon as a biological indicator, was used to calculate the SQI. The SMAF scores facilitated the monitoring of improvement in the mangrove SQ with vegetation development, mainly driven by physical and biological indicators. The SMAF may be a useful tool for monitoring SQ in mangroves under protection and recovery initiatives. Nevertheless, we suggest the inclusion of additional biological and chemical indicators in the minimum dataset for future studies to better represent specific processes and functions (e.g., microbial redox reactions and contaminant immobilization) that can alter the SQ of mangroves.

1. Introduction

Different forest biomes at a global scale provide significant ecosystem service (ES) diversity. Recognizing their importance and the mechanisms controlling their occurrence are pivotal for sustainable decision-making [1]. Mangroves are estuarine ecosystems that provide a wide diversity of ESs, such as regulation, support, and culture for human livelihood [2,3,4,5]. Despite this recognition, mangroves are one of the ecosystems most threatened by human activities, i.e., mainly aquaculture, sewage and industrial disposal, and deforestation [6,7]. Mangrove degradation is mainly related to the total or partial suppression of mangrove vegetation, which has declined by 30–50% over the past half century, triggering a loss in soil quality, which in turn affects ES provisions (e.g., carbon accumulation and metal immobilization) [8,9].

Additionally, many of the ESs provided by mangroves are directly associated with soil processes and soil quality (SQ) [10,11,12]. Accordingly, SQ can be conceptualized as a soil’s capacity to perform its functions, such as sustaining its productivity, improving water quality, and providing ESs. This ability to perform specific functions is associated with the inherent characteristics of each soil type [13,14]. As tidal activity influences mangroves, this flooded environment has soil characterized by intrinsic geochemical characteristics, such as a high salinity, low oxygen diffusion, and predominance of anaerobic metabolism [15,16]. Moreover, the geochemical features of mangrove soils lead to low organic matter decomposition rates and iron sulfide formation, which favor the sequestration of large amounts of carbon (reaching ~five-fold that of terrestrial ecosystems) and contaminant immobilization in the soil [9,17]. In this sense, evaluating SQ is pivotal for ensuring the maintenance of ESs.

However, SQ cannot be measured directly in the field or laboratory, but can be indirectly inferred by soil indicators (e.g., soil chemical, physical, and biological properties) sensitive to changes in soil functions [13,14,18,19,20]. The use of a soil quality index (SQI) may be a strategic tool in providing useful information that can promote sustainability in highly threatened environments [18].

The SQI approach has been used in mangrove ecosystems to understand the magnitude of the effects of land-use changes; for example, the removal of mangrove forests for rice cultivation [21] and the clearing of mangrove vegetation [22]. These previous studies focused on developing region-specific SQIs. From this perspective, there is a need for standardized SQI studies that can be replicated for comparisons and quantifications of the environmental impacts on mangrove soils. In this study, we innovatively tested mangrove soils using the soil management assessment framework (SMAF), a widely used tool for assessing the SQI in agricultural soils [18]; however, to the best of our knowledge, this tool has never been tested on mangrove soils.

The SMAF uses integrative measurements related to ecosystem processes and functions, which are reflected in the SQI based on the chemical, physical, and biological properties of soils [19,23,24]. It is a cost-effective framework that uses selected indicators and a reduced number of measurements (i.e., a minimum dataset) to reliably detect the changes in SQI [14].

Although the SMAF was developed for North American soils [18], it is suitable for assessing the SQI of tropical soils [25,26,27,28]. A recent study also showed that is useful for human-made soils [29]. The SMAF is a three-step framework that includes (1) indicator selection, (2) indicator interpretation, and (3) integration into an overall SQI [18]. The first step includes the chemical, physical, and biological indicators to accurately assess the SQ. In the second step, the SMAF implements non-linear scoring curves to interpret 13 indicators (i.e., the pH, soil aggregation stability, bulk density (BD), available plant water, water-filler pore space, electrical conductivity, sodium adsorption rate, extractable phosphorus and potassium, microbial biomass, soil organic carbon (SOC), potentially mineralizable nitrogen, and β-glucosidase). In the third step, within these individual scores, the SMAF integrates them into an overall SQI ranging from 0 to 1, which represents the functioning rate of the soil compared with its potential capacity.

Although SMAF has been applied worldwide, to the best of our knowledge, no studies have yet evaluated the sensitivity of SMAF scores for detecting changes in mangrove soils. We evaluated the applicability of the SMAF to assess the SQ changes in mangroves subjected to a reforestation initiative (i.e., plots at three- and seven-year-old plantations) and compared them with degraded and mature mangroves. Based on these analyses, there is an actual need for the recovery of coastal areas that provide a large range of ESs, which demand both public and private investors [30,31]. Quantitative proxies of the ecological evolution, as exposed by soil indicators, may be strategic tools to support the recovery of mangrove projects and monitor their evolution [28,29]. Therefore, we tested the following hypothesis: the development of mangrove forests increases SQ scores, and the SMAF can effectively detect changes in SQ.

2. Materials and Methods

2.1. Study Area

The study area was located in Ceará State, northeast Brazil (Figure 1). The region has a semi-arid climate (BSh, Köppen climate classification), with well-defined wet (February to May) and dry seasons (June to January), a mean annual precipitation <900 mm, and a mean annual temperature of 27 °C [32,33]. The mangrove soils in the study area are characterized by sand–clay textures originating from the sedimentary deposits of the Barreiras Formation, as well as influence from the surrounding dunes [34,35,36]. Additionally, mangroves experience a daily to diurnal mesotidal regime, ranging from 0.75 to 3.25 m [37].

Changes in the SQ were investigated in a mangrove reforestation initiative with three- (3Y) and seven (7Y)-year-old plantations, a degraded mangrove forest (DM), and a mature mangrove forest (MM; Figure 1). Each plot was separated by approximately 100 m. Additionally, the MM and DM covered an area of approximately 13,000 and 1000 m², respectively. The plantation areas of 3Y and 7Y were 3500 and 1000 m², respectively. The study plots were located within the Sabiaguaba Environmental Protection Area, which is a conservation unit created through municipality decrees in February 2006 for mangrove reforestation initiatives, sustainable use practices, and educational and tourism activities [38]. The MM plot was a well-developed forest free from disturbances for at least 30 years, composed of Avicennia germinans (L.) L., Laguncularia racemosa (L.) C. F., and Rhizophora mangle L. After the creation of the conservation unit, previously deforested areas were replanted with Rhizophora mangle propagules, as occurred in areas 3Y and 7Y. There was a total absence of vegetation in the DM plot owing to urban occupation and deforestation.

2.2. Soil Sampling

Four undisturbed soil cores (n = 16) were obtained during low tide within 1 × 1 m areas in each scenario (i.e., DM, 3Y, 7Y, and MM) using polyvinyl chloride tubes (0.05 m in diameter and 0.6 m in length) attached to a stainless-steel auger for flooded soils. To avoid chemical and biological alteration, the tubes were hermetically sealed and transported (vertically) under refrigeration (~4 °C) to the laboratory soon after sampling. Analyses were performed in triplicate using subsamples collected from the soil cores at depths of 0–30 cm.

2.3. Determination of Soil Quality Indicators

The soil pH values were obtained in situ using portable meters (HANNA, model HI98121, Hanna Instruments, Woonsocket, RI, USA) equipped with a glass electrode, which was previously calibrated with standard solutions (pH values of 4 and 7).

In the laboratory, the SOC content was determined via dry combustion using an elemental analyzer (LECO SE-144 DR). Soil samples for the SOC determination were treated with 1 mol L⁻¹ HCl for carbonate removal, dried at 45 °C until a constant weight was maintained, and then re-weighed [39]. The available P content in the mangrove soils was extracted using a Mehlich-1 instrument and quantified using calorimetry [40].

The undisturbed soil cores (i.e., collected with minimal compaction) were used to determine the soil BD. Thus, the soil BD was calculated using the mass of the soil solids and total soil volume (depth and tube diameter of 30 cm) [39].

2.4. Soil Quality Assessment Using SMAF

The SMAF was used as a tool to evaluate the effects that mangrove replanting had on the SQ compared to degraded and mature mangroves. The minimum dataset consisted of four soil indicators, i.e., the pH, available P, SOC, and BD.

The soil pH and available P were selected as the chemical indicators. The soil pH is an environmental physicochemical variable that indicates the acidity of mangrove soils [41]. Accordingly, soil pH values may reveal certain geochemical processes, such as acid drainage, which may be caused by the degradation and drainage of mangrove soils [42,43,44]. Additionally, pH measurements can be easily obtained in situ using portable meters, which facilitate replicability. The SOC was used as a biological indicator because carbon plays a key role in the biological activity of mangrove soils [18,45]. Phosphorous is a limiting nutrient in mangrove soils; therefore, it was selected as a key indicator to provide information on soil nutrient availability [46]. The soil BD provides information on soil compaction and aeration; it is also a necessary variable for soil carbon stock calculations [18,22,47].

The biological, physical, and chemical scores calculated by the SMAF scoring curves were based on site-specific algorithms for several factor classes, including the inherent soil characteristics (i.e., the soil texture, mineralogy, and weathering class), climate, topography (slope), crop system, and analytical methods. To calibrate these curves (i.e., establish the upper and lower limits or optimal values on the curves), different codes were selected in the SMAF spreadsheet [27]. Thus, we created SMAF algorithms according to the conditions of this study; Table 1 lists the codes for the indicators.

Additionally, in the SMAF spreadsheet, the “crop factor” reflects the scores of soil pH and available soil P associated with the current crop at the time of sampling. In this study, the “Mangrove 117” crop was created (Table 1). In the “Mangrove 117” crop factor, we set up the optimum pH value and available P content to adjust the nonlinear scoring curves of these two chemical factors. We adopted pH = 7 as the ideal pH value because healthy mangrove soils usually present a high capacity for buffering acidity [41,48], and for available P we considered the contents registered in the MM plot (i.e., 30.47 mg kg⁻¹; see Table 2).

Individual scores of the indicators were calculated and grouped into chemical (pH and available P), physical (BD), and biological (SOC) components. The SQI was calculated using the weighted additive approach (Equation (1)). Regardless of the number of indicators, the groups (i.e., chemical, physical, and biological) were integrated and had an equal weight (33.33%) in the final index, i.e., the SQI [14,26]:

(1)$SQI={\displaystyle \sum }_{i=1}^n{S}_i{W}_i$

where S_i is the indicator score and W_i is the weighted value of the indicators.

2.5. Statistical Analysis

The differences between the means of the soil parameters (i.e., SOC, pH, available P, and BD), SQ indicators, and SQI in the study plots (i.e., DM, 3Y, 7Y, and MM) were tested using analysis of variance (ANOVA). When significant, the means were compared using Tukey’s test (p < 0.05).

3. Results

The SOC content varied significantly between the study plots, indicating a gradual increase with vegetation development (Table 2). Higher SOC contents were observed in the MM plot (1.85 ± 0.07%), whereas lower contents occurred in the DM plot (0.44 ± 0.06%; Table 2). No significant differences were observed in the SOC content between the 3Y (0.91 ± 0.10%) and 7Y plots (0.92 ± 0.34%). Gradual increases in the SOC content were also observed in the SMAF score, which was attributed to the biological indicator of SQ (SOC; Table 2). The SMAF scores for the SOC were significantly higher in the MM (0.99 ± 0.01), followed by a significant decrease in 7Y (0.59 ± 0.14), 3Y (0.40 ± 0.08), and DM (0.13 ± 0.02) (Table 2).

For the chemical indicators, the soil pH values varied significantly between the study plots, ranging from slightly alkaline (7.4–7.8) in the DM plot (7.68 ± 0.22) to acidic (6.1–6.5) in the MM plot (6.33 ± 0.05; Table 2). In the 3Y (6.98 ± 0.15) and 7Y (6.95 ± 0.10) plots, the soil pH values were close to neutral (6.6–7.3), yielding no significant differences (Table 2). For the SQ scores, the higher significant values were attributed to the 3Y (0.99 ± 0.01) and 7Y (1.00 ± 0.01) plots, whereas lower values occurred in the DM (0.79 ± 0.12) and MM plots (0.80 ± 0.03; Table 2). The available P content did not show a gradual increase with vegetation development (Table 2). The MM plot had a significantly higher available P content (30.47 ± 1.04 mg kg⁻¹), whereas the 3Y plot (4.34 ± 0.81 mg kg⁻¹) presented lower content (Table 2). The available P content was also significantly higher in the DM plot (19.67 ± 2.74 mg kg⁻¹) compared to the 7Y plot (8.44 ± 0.71 mg kg⁻¹; Table 2). The SQ score for the available P in the DM plot (0.95 ± 0.02) did not differ significantly from the MM plot (i.e., optimum SQ score: 1.00 ± 0.00; Table 2). In contrast, the SQ score for available P in the 7Y plot (0.60 ± 0.06) was significantly higher than the SQ score in the 3Y plot (0.17 ± 0.08; Table 2).

The physical indicator (i.e., BD) in the replanted plots (3Y: 1.33 ± 0.03 g cm⁻³; 7Y: 1.35 ± 0.11 g cm⁻³) differed significantly from that of the degraded mangrove (DM: 1.51 ± 0.08 g cm⁻³). Higher BD values were observed in the MM plot (1.08 ± 0.02 g cm⁻³; Table 2). Accordingly, the highest significant SQ score for the BD occurred in the MM (0.99 ± 0.01) and 3Y (0.99 ± 0.01) plots, whereas the lowest was in the DM plot (0.81 ± 0.16; Table 2). The SQ score for the BD in the 7Y (0.97 ± 0.03) plot did not differ from the 3Y and DM plots (Table 2).

The SQI score obtained from the integrated SMAF scores for the biological (i.e., SOC), chemical (i.e., pH and available P), and physical (i.e., BD) components gradually and significantly increased with vegetation development. According to the observed SQI (DM: 0.60 ± 0.04; 3Y: 0.66 ± 0.04; 7Y: 0.74 ± 0.09; and MM: 96 ± 0.01), the mangrove SQ gradually increased following vegetation development (Figure 2).

4. Discussions

Several available methodologies focus on integrating physical, chemical, and biological indicators to assess the quality of mangrove soils [21,45]. Thus, in this study, we tested the SMAF, a widely used international tool [49,50], to assess SQ in two replanted mangrove plots, which were compared with degraded and mature mangroves. In this study, we observed a gradual increase in the SQI scores with mangrove vegetation development. The SQI scores were between 0.74 ± 0.09 and 0.66 ± 0.04 for the three- and seven-year-old plantation plots, respectively (Figure 2).

The increase in the SQI score with replanting reflects shifts in the chemical, biological, and physical indicators. However, the chemical SQ scores showed different tendencies among the degraded, replanted, and mature mangroves (Table 2). For example, the soil pH values observed in the study plots ranged from 6.3 to 7.7 (Table 2). These soil pH values are common for mangrove soils [22,33,51]. Within mangrove soils, the constant influence of seawater via tidal activity, root exudates, bioturbation, biogenic carbonates, and redox oscillations results in soil pH values that can vary between ~6.5 and 7.0 [22,33,51,52]. Thus, despite the significant pH variation, this did not reflect significant variations in the SQ score associated with the pH; the observed values are plausible for mangrove ecosystems. However, the SQ score for the pH ranged from 0.77 to 1.00, yielding significant differences (Table 2). Additionally, as the chemical indicator was composed of only two components (pH and available P), the SQ score associated with the soil pH values strongly influenced the chemical component. A potential contribution from this study for the future use of the SMAF for mangrove ecosystems is that adjustments to the pH scoring curves should be made to better represent these specific soil environments. Furthermore, the scoring curves for interpreting the electrical conductivity and sodium adsorption rate available in the SMAF spreadsheet should be tested in future studies such that they can represent other important processes that occur in mangrove soils, such as salinization and microbial redox processes.

The available P, which is a chemical indicator for nutrient availability, did not follow the conservation gradient for mangrove plots. Although P is a key nutrient for vegetation in mangrove soils, it is usually a limiting nutrient [53,54]. In plantation plots (i.e., 3Y and 7Y), P is a limiting nutrient; plants require large quantities of P, which may explain the lower available P content [46]. In contrast, in the MM plot, a significantly higher available P content was likely associated with nutrient cycling [54]. For example, the atypical values observed in DM may be related to sewage disposal or other anthropogenic effluents [55]. Therefore, in mangrove forests exposed to P-rich waste, we recommend the use of additional chemical indicators in the minimum dataset. Nevertheless, P should not be substituted with other indicators because it is an important nutrient for plant species [54,56]. Furthermore, as mangroves are one of the ecosystems most affected by anthropogenic activities (e.g., effluent discharge and urban waste disposal) [9,57,58], P as a chemical indicator must be carefully analyzed to avoid positive scores at eutrophication or organic pollution sites.

In contrast, the biological indicator (SOC) increased with plantation development (Table 2). This result indicates that mangrove reforestation initiatives have successfully restored the soil carbon stocks in degraded mangrove forests and improved the SQ. Vegetation development mainly enhances organic matter inputs into mangrove soils through dead roots, microbial biomass, litterfall, wood debris, and fauna activity [59,60]. Additionally, vegetation development and, ultimately, root system development of Rhizophora mangle L. decreased the turbulence kinetics and favored a higher water residence time, enhanced fine particle trapping, decreased oxygen diffusion, and stimulated anaerobic metabolism [61,62]. These changes may favor carbon accumulation [63,64,65,66,67,68]. A recent study in this region showed that an increase in fine particles enhanced organomineral interactions, increasing the SOC with vegetation development [69], which may be enhanced by anaerobic metabolism with low organic matter degradation rates [63,64,65,66,67,68]. Owing to the reforestation initiative [5,70], the increase in the SQ associated with the SOC may indicate an improvement in one of the most important ESs provided by mangrove soils (i.e., carbon sequestration).

Moreover, SOC is a soil variable that directly or indirectly has a strong influence on the overall SQI owing to its relationship with other variables (e.g., BD and available P). Although the biological SQ corresponded to 33.3% of the SQI, it gradually increased with plant development. The increased SOC content also directly affected the BD results and its SQ scores [71,72]. Additionally, soil organic matter is an important source of P in mangrove soils [55,73,74], which can indirectly affect the chemical SQ scores.

However, soil health and the development of soil functions are not the sole responses to the presence of SOC; thus, asserting that the variations in the integrated SQ depend on a particular aspect of the soil may be inaccurate [22,72,75]. For example, SOC in mangrove soils depends on several factors (e.g., climate, soil texture, tidal regime, plant species, and redox potential), which leads to significant variations in the SOC content in mangrove soils (>1000%) [22,76,77,78]. Therefore, adjustments regarding the optimum SOC content in mangrove soils in the SMAF could avoid overestimations of the SQ at the expense of the SOC content.

Additionally, we used a small dataset to generate a SMAF score that is accessible and replicable. However, for mangrove soils, our findings indicate that more indicators could ensure soil function development and increase the relevance of the SQ scores. Therefore, the use of SOC as a biological indicator may limit inferences on soil biological health. One of the most important functions of mangrove soils is their potential to immobilize contaminants [79,80]. This soil function is closely associated with microbial activity, iron and sulfate reduction processes, and the formation of metallic sulfides, pyrite, and acidic volatile sulfides [16,81,82,83]. Given the importance of anaerobic metabolism in the diverse soil functions of mangrove soils [84,85,86], future studies should consider other biological indicators in the minimum dataset, such as microbial biomass carbon and the enzymatic activity of β-glucosidase, both of which already have scoring curves in the algorithms within the SMAF spreadsheet. Additionally, other chemical indicators (e.g., the Fe and S content) may play a key role in predicting soil functions because the quantity of these elements directly affects important soil processes within mangrove soils (e.g., pyritization) [51,87]. These indicators are not available in the current version of the SMAF spreadsheet; therefore, developing reliable scoring curves for new indicators such as these is a challenging task for future soil quality research in mangrove soils.

5. Conclusions

This study used the SMAF tool to monitor the effect of a mangrove reforestation initiative on SQ. Using the SMAF scores, we observed an increase in mangrove SQ with vegetation development, which was mainly driven by physical and biological indicators (e.g., SOC and BD). Our findings provide novel information on the use of the SMAF as an effective tool for monitoring SQ in mangrove forests under protection and recovery initiatives. Despite the encouraging results obtained using SMAF in mangrove soils, we suggest that future studies include additional biological and chemical indicators in the minimum dataset to better represent specific processes and functions (e.g., salinization, microbial redox reactions, and contaminant immobilization), which can alter the quality of mangrove soils.

Author Contributions

Conceptualization, L.C.Z.J.; methodology, L.C.Z.J.; writing—original draft preparation, L.C.Z.J., H.M.Q., and M.R.C.; writing—review and editing, H.M.Q., M.R.C., and T.O.F.; supervision, T.O.F.; funding acquisition, T.O.F. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Finance Code 001), the National Council for Scientific and Technological Development (CNPQ; grant numbers 305996/2018-5 and 430010/2018-4 to TOF), and the São Paulo Research Foundation (FAPESP; grant number 2021/00221-3).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author, T.O.F., upon reasonable request.

Acknowledgments

The authors acknowledge the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), the National Council for Scientific and Technological Development (CNPq), and the São Paulo Research Foundation (FAPESP) for funding and scholarships.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Location of the studied mangrove in the Cocó River estuary, and the degraded (red area) and mature (green area) mangrove and plots with 3-year-old (yellow area) and 7-year-old plantations (blue area). The satellite image was obtained from Google EarthTM. In the satellite image, the XY axes represent latitude and longitude. In the detail (bottom photo) are the plots with 3- and 7-year-old plantations compared with mature mangroves, showing the vegetation development differences. Photo credits: Claudia Albuquerque, Igor de Melo, and Michele Boroh.

Figure 2. Soil quality index (SQI) score for each stage of mangrove development in Ceará state, northeastern Brazil: degraded mangrove (DM); 3- and 7-year-old plantations (3Y and 7Y, respectively) and mature mangrove (MM). Means followed by the same lowercase letters did not differ among studied plots according to Tukey’s test (p < 0.05).

Table 1

Factor codes selected in the soil management assessment framework (SMAF) spreadsheet to interpret the soil quality (SQ) indicators according to the conditions of this study.

Parameter	Factor Codes	Indicator Scoring Curve Affected by Class Factor
Soil type	3 (medium–low SOC)	SOC
Texture	1 (low clay content)	SOC, BD, available P
Soil mineralogy	3 (other)	BD
Weathering class	3 (other)	Available P
Slope of field	1 (flat)	Available P
Climate	2 (high temperature and low rainfall)	SOC, available P
Crop	Mangrove 117 *	pH, available P
P method	1 (Mehlich-1)	Available P

SOC: soil organic carbon; BD: bulk density. * Mangrove 117 was a created crop factor whose optimal values for the pH and available P were set as follows: pH = 7; available P = 30.47 mg kg^–1.

Table 2

Mean contents of the SOC and available P, mean values of the pH and BD, and their corresponding soil quality (SQ) score in the degraded and mature mangrove plots and replanted areas (3 and 7 years).

Plot	SOC (%)	pH	Available P (mg kg−1)	BD (g cm−3)
Means
DM	0.44 ± 0.06 c	7.68 ± 0.22 a	19.67 ± 2.74 b	1.51 ± 0.08 a
3Y	0.91 ± 0.10 b	6.98 ± 0.15 b	4.34 ± 0.81 d	1.33 ± 0.03 b
7Y	0.92 ± 0.34 b	6.95 ± 0.10 b	8.44 ± 0.71 c	1.35 ± 0.11 b
MM	1.85 ± 0.07 a	6.33 ± 0.05 c	30.47 ± 1.04 a	1.08 ± 0.02 c
SMAF Scores (0 to 1.00)
DM	0.13 ± 0.02 d	0.79 ± 0.12 b	0.95 ± 0.02 a	0.81 ± 0.16 b
3Y	0.40 ± 0.08 c	0.99 ± 0.01 a	0.17 ± 0.08 c	0.99 ± 0.01 a
7Y	0.59 ± 0.14 b	1.00 ± 0.01 a	0.60 ± 0.06 b	0.97 ± 0.03 ab
MM	0.99 ± 0.01 a	0.80 ± 0.03 b	1.00 ± 0.00 a	0.99 ± 0.01 a

DM: degraded mangrove; 3Y: 3 years after replanting; 7Y: 7 years after replanting; MM: mature mangrove; SOC: soil organic carbon; BD: bulk density. Means followed by the same lowercase letters did not differ among the study plots according to Tukey’s test (p < 0.05).

References

1. Grammatikopoulou, I.; Vačkářová, D. The value of forest ecosystem services: A meta-analysis at the European scale and application to national ecosystem accounting. Ecosyst. Serv.; 2021; 48, 101262. [DOI: https://dx.doi.org/10.1016/j.ecoser.2021.101262]

2. Ahmed, M.T. Millennium ecosystem assessment. Environ. Sci. Pollut. Res.; 2002; 9, pp. 219-220. [DOI: https://dx.doi.org/10.1007/BF02987493]

3. Jakovac, C.C.; Latawiec, A.E.; Lacerda, E.; Leite Lucas, I.; Korys, K.A.; Iribarrem, A.; Malaguti, G.A.; Turner, R.K.; Luisetti, T.; Baeta Neves Strassburg, B. Costs and Carbon Benefits of Mangrove Conservation and Restoration: A Global Analysis. Ecol. Econ.; 2020; 176, 106758. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2020.106758]

4. Costanza, R.; D’Arge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J. et al. The value of the world’s ecosystem services and natural capital. Nature; 1997; 387, pp. 253-260. [DOI: https://dx.doi.org/10.1038/387253a0]

5. Costanza, R.; de Groot, R.; Sutton, P.; van der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang.; 2014; 26, pp. 152-158. [DOI: https://dx.doi.org/10.1016/j.gloenvcha.2014.04.002]

6. Macreadie, P.I.; Costa, M.D.P.; Atwood, T.B.; Friess, D.A.; Kelleway, J.J.; Kennedy, H.; Lovelock, C.E.; Serrano, O.; Duarte, C.M. Blue carbon as a natural climate solution. Nat. Rev. Earth Environ.; 2021; 2, pp. 826-839. [DOI: https://dx.doi.org/10.1038/s43017-021-00224-1]

7. Lee, S.Y.; Hamilton, S.; Barbier, E.B.; Primavera, J.; Lewis, R.R. Better restoration policies are needed to conserve mangrove ecosystems. Nat. Ecol. Evol.; 2019; 3, pp. 870-872. [DOI: https://dx.doi.org/10.1038/s41559-019-0861-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31036899]

8. Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr.; 2011; 20, pp. 154-159. [DOI: https://dx.doi.org/10.1111/j.1466-8238.2010.00584.x]

9. Donato, D.C.; Kauffman, J.B.; Murdiyarso, D.; Kurnianto, S.; Stidham, M.; Kanninen, M. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci.; 2011; 4, pp. 293-297. [DOI: https://dx.doi.org/10.1038/ngeo1123]

10. Bastakoti, U.; Robertson, J.; Marchand, C.; Alfaro, A.C. Mangrove removal: Effects on trace metal concentrations in temperate estuarine sediments. Mar. Chem.; 2019; 216, 103688. [DOI: https://dx.doi.org/10.1016/j.marchem.2019.103688]

11. Twilley, R.R.; Chen, R.H.; Hargis, T. Carbon sinks in mangroves and their implications to carbon budget of tropical coastal ecosystems. Water Air Soil Pollut.; 1992; 64, pp. 265-288. [DOI: https://dx.doi.org/10.1007/BF00477106]

12. Ha, T.H.; Marchand, C.; Aimé, J.; Dang, H.N.; Phan, N.H.; Nguyen, X.T.; Nguyen, T.K.C. Belowground carbon sequestration in a mature planted mangroves (Northern Viet Nam). For. Ecol. Manag.; 2018; 407, pp. 191-199. [DOI: https://dx.doi.org/10.1016/j.foreco.2017.06.057]

13. Karlen, D.L.; Mausbach, M.J.; Doran, J.W.; Cline, R.G.; Harris, R.F.; Schuman, G.E. Soil Quality: A Concept, Definition, and Framework for Evaluation (A Guest Editorial). Soil Sci. Soc. Am. J.; 1997; 61, pp. 4-10. [DOI: https://dx.doi.org/10.2136/sssaj1997.03615995006100010001x]

14. Cherubin, M.R.; Karlen, D.L.; Cerri, C.E.P.; Franco, A.L.C.; Tormena, C.A.; Davies, C.A.; Cerri, C.C. Soil Quality Indexing Strategies for Evaluating Sugarcane Expansion in Brazil. PLoS ONE; 2016; 11, e0150860. [DOI: https://dx.doi.org/10.1371/journal.pone.0150860] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26938642]

15. Ferreira, T.O.; Otero, X.L.; de Souza Junior, V.S.; Vidal-Torrado, P.; Macías, F.; Firme, L.P. Spatial patterns of soil attributes and components in a mangrove system in Southeast Brazil (São Paulo). J. Soils Sediments; 2010; 10, pp. 995-1006. [DOI: https://dx.doi.org/10.1007/s11368-010-0224-4]

16. Otero, X.L.; Ferreira, T.O.; Vidal-Torrado, P.; Macías, F. Spatial variation in pore water geochemistry in a mangrove system (Pai Matos island, Cananeia-Brazil). Appl. Geochem.; 2006; 21, pp. 2171-2186. [DOI: https://dx.doi.org/10.1016/j.apgeochem.2006.07.012]

17. Queiroz, H.M.; Nóbrega, G.N.; Otero, X.L.; Ferreira, T.O. Are acid volatile sulfides (AVS) important trace metals sinks in semi-arid mangroves?. Mar. Pollut. Bull.; 2018; 126, pp. 318-322. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2017.11.020]

18. Zornoza, R.; Acosta, J.A.; Bastida, F.; Domínguez, S.G.; Toledo, D.M.; Faz, A. Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health. Soil; 2015; 1, pp. 173-185. [DOI: https://dx.doi.org/10.5194/soil-1-173-2015]

19. Andrews, S.S.; Karlen, D.L.; Cambardella, C.A. The Soil Management Assessment Framework. Soil Sci. Soc. Am. J.; 2004; 68, pp. 1945-1962. [DOI: https://dx.doi.org/10.2136/sssaj2004.1945]

20. Mukherjee, A.; Lal, R. Comparison of Soil Quality Index Using Three Methods. PLoS ONE; 2014; 9, e105981. [DOI: https://dx.doi.org/10.1371/journal.pone.0105981]

21. Tripathi, R.; Shukla, A.; Shahid, M.; Nayak, D.; Puree, C.; Mohanty, S.; Raja, R.; Lal, B.; Gautam, P.; Bhattacharyya, P. et al. Soil quality in mangrove ecosystem deteriorates due to rice cultivation. Ecol. Eng.; 2016; 90, pp. 163-169. [DOI: https://dx.doi.org/10.1016/j.ecoleng.2016.01.062]

22. Grellier, S.; Janeau, J.L.; Dang Hoai, N.; Kim, C.N.T.; Phuong, Q.L.T.; Thu, T.P.T.; Tran-Thi, N.T.; Marchand, C. Changes in soil characteristics and C dynamics after mangrove clearing (Vietnam). Sci. Total Environ.; 2017; 593–594, pp. 654-663. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2017.03.204]

23. Cherubin, M.R.; Tormena, C.A.; Karlen, D.L. Soil quality evaluation using the soil management assessment framework (SMAF) in Brazilian oxisols with contrasting texture. Rev. Bras. Cienc. Solo; 2017; 41, pp. 1-18. [DOI: https://dx.doi.org/10.1590/18069657rbcs20160148]

24. Karlen, D.L.; Cambardella, C.A.; Kovar, J.L.; Colvin, T.S. Soil quality response to long-term tillage and crop rotation practices. Soil Tillage Res.; 2013; 133, pp. 54-64. [DOI: https://dx.doi.org/10.1016/j.still.2013.05.013]

25. Lisboa, I.P.; Cherubin, M.R.; Satiro, L.S.; Siqueira-Neto, M.; Lima, R.P.; Gmach, M.R.; Wienhold, B.J.; Schmer, M.R.; Jin, V.L.; Cerri, C.C. et al. Applying Soil Management Assessment Framework (SMAF) on short-term sugarcane straw removal in Brazil. Ind. Crops Prod.; 2019; 129, pp. 175-184. [DOI: https://dx.doi.org/10.1016/j.indcrop.2018.12.004]

26. Cherubin, M.R.; Bordonal, R.O.; Castioni, G.A.; Guimarães, E.M.; Lisboa, I.P.; Moraes, L.A.A.; Menandro, L.M.S.; Tenelli, S.; Cerri, C.E.P.; Karlen, D.L. et al. Soil health response to sugarcane straw removal in Brazil. Ind. Crops Prod.; 2021; 163, 113315. [DOI: https://dx.doi.org/10.1016/j.indcrop.2021.113315]

27. Cherubin, M.R.; Karlen, D.L.; Franco, A.L.C.; Cerri, C.E.P.; Tormena, C.A.; Cerri, C.C. A Soil Management Assessment Framework (SMAF) Evaluation of Brazilian Sugarcane Expansion on Soil Quality. Soil Sci. Soc. Am. J.; 2016; 80, pp. 215-226. [DOI: https://dx.doi.org/10.2136/sssaj2015.09.0328]

28. Da Luz, F.B.; da Silva, V.R.; Mallmann, F.J.K.; Pires, C.A.B.; Debiasi, H.; Franchini, J.C.; Cherubin, M.R. Monitoring soil quality changes in diversified agricultural cropping systems by the Soil Management Assessment Framework (SMAF) in southern Brazil. Agric. Ecosyst. Environ.; 2019; 281, pp. 100-110. [DOI: https://dx.doi.org/10.1016/j.agee.2019.05.006]

29. Ruiz, F.; Cherubin, M.R.; Ferreira, T.O. Soil quality assessment of constructed Technosols: Towards the validation of a promising strategy for land reclamation, waste management and the recovery of soil functions. J. Environ. Manag.; 2020; 276, 111344. [DOI: https://dx.doi.org/10.1016/j.jenvman.2020.111344]

30. Herrera, D.; Cunniff, S.; DuPont, C.; Cohen, B.; Gangi, D.; Kar, D.; Peyronnin Snider, N.; Rojas, V.; Wyerman, J.; Norriss, J. et al. Designing an environmental impact bond for wetland restoration in Louisiana. Ecosyst. Serv.; 2019; 35, pp. 260-276. [DOI: https://dx.doi.org/10.1016/j.ecoser.2018.12.008]

31. Adame, M.F.; Hermoso, V.; Perhans, K.; Lovelock, C.E.; Herrera-Silveira, J.A. Selecting cost-effective areas for restoration of ecosystem services. Conserv. Biol.; 2015; 29, pp. 493-502. [DOI: https://dx.doi.org/10.1111/cobi.12391]

32. Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; De Moraes Gonçalves, J.L.; Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Z.; 2013; 22, pp. 711-728. [DOI: https://dx.doi.org/10.1127/0941-2948/2013/0507]

33. Nóbrega, G.N.; Ferreira, T.O.; Romero, R.E.; Marques, A.G.B.; Otero, X.L. Iron and sulfur geochemistry in semi-arid mangrove soils (Ceará, Brazil) in relation to seasonal changes and shrimp farming effluents. Environ. Monit. Assess.; 2013; 185, pp. 7393-7407. [DOI: https://dx.doi.org/10.1007/s10661-013-3108-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23355026]

34. Behling, H.; da Costa, M.L. Mineralogy, geochemistry, and palynology of modern and late Tertiary mangrove deposits in the Barreiras Formation of Mosqueiro Island, northeastern Pará state, eastern Amazonia. J. South. Am. Earth Sci.; 2004; 17, pp. 285-295. [DOI: https://dx.doi.org/10.1016/j.jsames.2004.08.002]

35. Bigarella, J.J. Estrutura e Origem Das Paisagens Tropicais e Subtropicais; 3rd ed. Editora da UFSC: Florianópolis, Brazil, 1994; ISBN 8532802664

36. Maia, L.P.; de Lacerda, L.D.; Monteiro, L.H.U.; e Souza, G.M. Estudo das Áreas de Manguezais do Nordeste do BRASIL-Avaliação das Áreas de Manguezais dos Estados do Piauí, Ceará, Rio Grande do Norte, Paraíba e Pernambuco; 1st ed. Universidade Federal do Ceará, Instituo de Ciências do Mar Sociedade Internacional Para Ecossistemas de Manguezal—ISME-BR: Fortaleza, Brazil, 2005.

37. Ferreira, T.O.; Nóbrega, G.N.; Albuquerque, A.G.B.M.B.M.; Sartor, L.R.; Gomes, I.S.; Artur, A.G.; Otero, X.L. Pyrite as a proxy for the identification of former coastal lagoons in semiarid NE Brazil. Geo-Mar. Lett.; 2015; 35, pp. 355-366. [DOI: https://dx.doi.org/10.1007/s00367-015-0412-8]

38. Barros, F.P.; Santos, D.M.; de Andrade, N.A.; de Lira Freitas, A.; Neto, A.C.; Bezerra, D.H.S.; de Holande Leite, M.J.; de Araújo Brilhante, J.C. The natural ecomuseum of mangrove: Educational and reforestation actions / O ecomuseu natural do mangue: Ações educativas e de reflorestamento. Braz. Appl. Sci. Rev.; 2021; 5, pp. 482-497. [DOI: https://dx.doi.org/10.34115/basrv5n1-031]

39. Howard, J.; Hoyt, S.; Isensee, K.; Telszewski, M.; Pidgeon, E.; Telszewski, M. Coastal Blue Carbon: Methods for Assessing Carbon Stocks and Emissions Factors in Mangroves, Tidal Salt Marshes, and Seagrasses; Conservation International, Intergovernmental Oceanographic Commission of UNESCO: Paris, France, International Union for Conservation of Nature: Gland, Switzerland, 2014; Volume 1, ISBN 9782831717623

40. Mehlich, A. Uniformity of soil test results as influenced by volume weight. Commun. Soil Sci. Plant. Anal.; 1973; 4, pp. 475-486. [DOI: https://dx.doi.org/10.1080/00103627309366469]

41. Ponnamperuma, F.N. Effects of Flooding on Soils. Flooding and Plant Growth; Academic Press: Madison, WI, USA, 1984; pp. 9-45. [DOI: https://dx.doi.org/10.1016/B978-0-12-424120-6.50007-9]

42. Fitzpatrick, R.W.; Shand, P.; Mosley, L.M. Acid sulfate soil evolution models and pedogenic pathways during drought and reflooding cycles in irrigated areas and adjacent natural wetlands. Geoderma; 2017; 308, pp. 270-290. [DOI: https://dx.doi.org/10.1016/j.geoderma.2017.08.016]

43. Farias, C.O.; Hamacher, C.; de Luca Wagener, A.R.; de Campos, R.C.; Godoy, J.M. Trace metal contamination in mangrove sediments, Guanabara Bay, Rio de Janeiro, Brazil. J. Braz. Chem. Soc.; 2007; 18, pp. 1194-1206. [DOI: https://dx.doi.org/10.1590/S0103-50532007000600014]

44. Ferreira, T.O.; Otero, X.L.; Vidal-Torrado, P.; Macías, F. Effects of bioturbation by root and crab activity on iron and sulfur biogeochemistry in mangrove substrate. Geoderma; 2007; 142, pp. 36-46. [DOI: https://dx.doi.org/10.1016/j.geoderma.2007.07.010]

45. Faridah-Hanum, I.; Yusoff, F.M.; Fitrianto, A.; Ainuddin, N.A.; Gandaseca, S.; Zaiton, S.; Norizah, K.; Nurhidayu, S.; Roslan, M.K.; Hakeem, K.R. et al. Development of a comprehensive mangrove quality index (MQI) in Matang Mangrove: Assessing mangrove ecosystem health. Ecol. Indic.; 2019; 102, pp. 103-117. [DOI: https://dx.doi.org/10.1016/j.ecolind.2019.02.030]

46. Reef, R.; Feller, I.C.; Lovelock, C.E. Nutrition of mangroves. Tree Physiol.; 2010; 30, pp. 1148-1160. [DOI: https://dx.doi.org/10.1093/treephys/tpq048] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20566581]

47. Arias-Ortiz, A.; Masqué, P.; Glass, L.; Benson, L.; Kennedy, H.; Duarte, C.M.; Garcia-Orellana, J.; Benitez-Nelson, C.R.; Humphries, M.S.; Ratefinjanahary, I. et al. Losses of Soil Organic Carbon with Deforestation in Mangroves of Madagascar. Ecosystems; 2021; 24, pp. 1-19. [DOI: https://dx.doi.org/10.1007/s10021-020-00500-z]

48. Smith, T.J. Forest Structure. Tropical Mangrove Ecosystems; Robertson, A.I.; Alongi, D. American Geophysical Union: Washington, DC, USA, 1992; ISBN 9781118665084

49. Bünemann, E.K.; Bongiorno, G.; Bai, Z.; Creamer, R.E.; De Deyn, G.; de Goede, R.; Fleskens, L.; Geissen, V.; Kuyper, T.W.; Mäder, P. et al. Soil quality—A critical review. Soil Biol. Biochem.; 2018; 120, pp. 105-125. [DOI: https://dx.doi.org/10.1016/j.soilbio.2018.01.030]

50. Karlen, D.L.; Veum, K.S.; Sudduth, K.A.; Obrycki, J.F.; Nunes, M.R. Soil & Tillage Research Soil health assessment: Past accomplishments, current activities, and future opportunities. Soil Tillage Res.; 2019; 195, 104365. [DOI: https://dx.doi.org/10.1016/j.still.2019.104365]

51. Ferreira, T.O.; Queiroz, H.M.; Nóbrega, G.N.; de Souza Júnior, V.S.; Barcellos, D.; Ferreira, A.D.; Otero, X.L. Litho-climatic characteristics and its control over mangrove soil geochemistry: A macro-scale approach. Sci. Total Environ.; 2021; 811, 152152. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.152152] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34919924]

52. Kauffman, J.B.; Bernardino, A.F.; Ferreira, T.O.; Bolton, N.W.; Gomes, L.E.d.O.; Nobrega, G.N. Shrimp ponds lead to massive loss of soil carbon and greenhouse gas emissions in northeastern Brazilian mangroves. Ecol. Evol.; 2018; 8, pp. 5530-5540. [DOI: https://dx.doi.org/10.1002/ece3.4079] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29938071]

53. Koch, M.S. Rhizophora mangle L. Seedling Development into the Sapling Stage across Resource and Stress Gradients in Subtropical Florida1. Biotropica; 1997; 29, pp. 427-439. [DOI: https://dx.doi.org/10.1111/j.1744-7429.1997.tb00037.x]

54. Feller, I.C.; McKee, K.L.; Whigham, D.F.; O’Neill, J.P. Nitrogen vs. phosphorus limitation across an ecotonal gradient in a mangrove forest. Biogeochemistry; 2003; 62, pp. 145-175. [DOI: https://dx.doi.org/10.1023/A:1021166010892]

55. Barcellos, D.; Queiroz, H.M.; Nóbrega, G.N.; de Oliveira Filho, R.L.; Santaella, S.T.; Otero, X.L.; Ferreira, T.O. Phosphorus enriched effluents increase eutrophication risks for mangrove systems in northeastern Brazil. Mar. Pollut. Bull.; 2019; 142, pp. 58-63. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2019.03.031] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31232342]

56. Feller, I.C. Effects of nutrient enrichment on growth and herbivory of dwarf red mangrove (Rhizophora mangle). Ecol. Monogr.; 1995; 65, pp. 477-505. [DOI: https://dx.doi.org/10.2307/2963499]

57. Keuskamp, J.A.; Feller, I.C.; Laanbroek, H.J.; Verhoeven, J.T.A.; Hefting, M.M. Short- and long-term effects of nutrient enrichment on microbial exoenzyme activity in mangrove peat. Soil Biol. Biochem.; 2015; 81, pp. 38-47. [DOI: https://dx.doi.org/10.1016/j.soilbio.2014.11.003]

58. Koch, M.S.; Snedaker, S.C. Factors influencing Rhizophora mangle L. seedling development in Everglades carbonate soils. Aquat. Bot.; 1997; 59, pp. 87-98. [DOI: https://dx.doi.org/10.1016/S0304-3770(97)00027-2]

59. Kusumaningtyas, M.A.; Hutahaean, A.A.; Fischer, H.W.; Pérez-Mayo, M.; Ransby, D.; Jennerjahn, T.C. Variability in the organic carbon stocks, sources, and accumulation rates of Indonesian mangrove ecosystems. Estuar. Coast. Shelf Sci.; 2019; 218, pp. 310-323. [DOI: https://dx.doi.org/10.1016/j.ecss.2018.12.007]

60. Marchand, C. Soil carbon stocks and burial rates along a mangrove forest chronosequence (French Guiana). For. Ecol. Manag.; 2017; 384, pp. 92-99. [DOI: https://dx.doi.org/10.1016/j.foreco.2016.10.030]

61. Mudd, S.M.; D’Alpaos, A.; Morris, J.T. How does vegetation affect sedimentation on tidal marshes? Investigating particle capture and hydrodynamic controls on biologically mediated sedimentation. J. Geophys. Res. Earth Surf.; 2010; 115, pp. 1-14. [DOI: https://dx.doi.org/10.1029/2009JF001566]

62. Jay, D.A.; Orton, P.M.; Chisholm, T.; Wilson, D.J.; Fain, A.M.V. Particle trapping in stratified estuaries: Consequences of mass conservation. Estuaries Coasts; 2007; 30, pp. 1095-1105. [DOI: https://dx.doi.org/10.1007/BF02841399]

63. Dicen, G.P.; Navarrete, I.A.; Rallos, R.V.; Salmo, S.G.; Garcia, M.C.A. The role of reactive iron in long-term carbon sequestration in mangrove sediments. J. Soils Sediments; 2019; 19, pp. 501-510. [DOI: https://dx.doi.org/10.1007/s11368-018-2051-y]

64. Xiong, Y.; Liao, B.; Proffitt, E.; Guan, W.; Sun, Y.; Wang, F.; Liu, X. Soil carbon storage in mangroves is primarily controlled by soil properties: A study at Dongzhai Bay, China. Sci. Total Environ.; 2018; 619–620, pp. 1226-1235. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2017.11.187] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29734601]

65. Eusterhues, K.; Rumpel, C.; Kleber, M.; Kögel-Knabner, I. Stabilisation of soil organic matter by interactions with minerals as revealed by mineral dissolution and oxidative degradation. Org. Geochem.; 2003; 34, pp. 1591-1600. [DOI: https://dx.doi.org/10.1016/j.orggeochem.2003.08.007]

66. Chen, Y.; Li, Y.; Thompson, C.; Wang, X.; Cai, T.; Chang, Y. Differential sediment trapping abilities of mangrove and saltmarsh vegetation in a subtropical estuary. Geomorphology; 2018; 318, pp. 270-282. [DOI: https://dx.doi.org/10.1016/j.geomorph.2018.06.018]

67. Chmura, G.L.; Anisfeld, S.C.; Cahoon, D.R.; Lynch, J.C. Global carbon sequestration in tidal, saline wetland soils. Glob. Biogeochem. Cycles; 2003; 17, 1111. [DOI: https://dx.doi.org/10.1029/2002GB001917]

68. Kauffman, J.B.; Giovanonni, L.; Kelly, J.; Dunstan, N.; Borde, A.; Diefenderfer, H.; Cornu, C.; Janousek, C.; Apple, J.; Brophy, L. Total ecosystem carbon stocks at the marine-terrestrial interface: Blue carbon of the Pacific Northwest Coast, United States. Glob. Chang. Biol.; 2020; 26, pp. 5679-5692. [DOI: https://dx.doi.org/10.1111/gcb.15248] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32779311]

69. Jimenez, L.C.Z.; Queiroz, H.M.; Otero, X.L.; Nóbrega, G.N.; Ferreira, T.O. Soil Organic Matter Responses to Mangrove Restoration: A Replanting Experience in Northeast Brazil. Int. J. Environ. Res. Public Health; 2021; 18, 8981. [DOI: https://dx.doi.org/10.3390/ijerph18178981]

70. Zarate-Barrera, T.G.; Maldonado, J.H. Valuing Blue Carbon: Carbon Sequestration Benefits Provided by the Marine Protected Areas in Colombia. PLoS ONE; 2015; 10, e0126627. [DOI: https://dx.doi.org/10.1371/journal.pone.0126627] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26018814]

71. Feng, J.; Cui, X.; Zhou, J.; Wang, L.; Zhu, X.; Lin, G. Effects of exotic and native mangrove forests plantation on soil organic carbon, nitrogen, and phosphorus contents and pools in Leizhou, China. Catena; 2019; 180, pp. 1-7. [DOI: https://dx.doi.org/10.1016/j.catena.2019.04.018]

72. Kristensen, E.; Bouillon, S.; Dittmar, T.; Marchand, C. Organic carbon dynamics in mangrove ecosystems: A review. Aquat. Bot.; 2008; 89, pp. 201-219. [DOI: https://dx.doi.org/10.1016/j.aquabot.2007.12.005]

73. Reddy, Y.; Ganguly, D.; Singh, G.; Prasad, M.H.; Arumughan, P.S.; Banerjee, K.; Kathirvel, A.; Ramachandran, P.; Ramachandran, R. Assessment of bioavailable nitrogen and phosphorus content in the sediments of Indian mangroves. Environ. Sci. Pollut. Res.; 2021; 28, pp. 42051-42069. [DOI: https://dx.doi.org/10.1007/s11356-021-13638-7]

74. Feng, J.; Zhou, J.; Wang, L.; Cui, X.; Ning, C.; Wu, H.; Zhu, X.; Lin, G. Effects of short-term invasion of Spartina alterniflora and the subsequent restoration of native mangroves on the soil organic carbon, nitrogen and phosphorus stock. Chemosphere; 2017; 184, pp. 774-783. [DOI: https://dx.doi.org/10.1016/j.chemosphere.2017.06.060]

75. Pham, V.H.; Luu, V.D.; Nguyen, T.T.; Koji, O. Will restored mangrove forests enhance sediment organic carbon and ecosystem carbon storage?. Reg. Stud. Mar. Sci.; 2017; 14, pp. 43-52. [DOI: https://dx.doi.org/10.1016/j.rsma.2017.05.003]

76. Passos, T.R.G.; Artur, A.G.; Nóbrega, G.N.; Otero, X.L.; Ferreira, T.O. Comparison of the quantitative determination of soil organic carbon in coastal wetlands containing reduced forms of Fe and S. Geo-Mar. Lett.; 2016; 36, pp. 223-233. [DOI: https://dx.doi.org/10.1007/s00367-016-0437-7]

77. Twilley, R.R.; Rovai, A.S.; Riul, P. Coastal morphology explains global blue carbon distributions. Front. Ecol. Environ.; 2018; 16, pp. 503-508. [DOI: https://dx.doi.org/10.1002/fee.1937]

78. Sanders, C.J.; Smoak, J.M.; Sanders, L.M.; Sathy Naidu, A.; Patchineelam, S.R. Organic carbon accumulation in Brazilian mangal sediments. J. S. Am. Earth Sci.; 2010; 30, pp. 189-192. [DOI: https://dx.doi.org/10.1016/j.jsames.2010.10.001]

79. Ye, S.; Laws, E.A.; Wu, Q.; Zhong, S.; Ding, X.; Zhao, G.; Gong, S. Pyritization of trace metals in estuarine sediments and the controlling factors: A case in Jiaojiang Estuary of Zhejiang Province, China. Environ. Earth Sci.; 2010; 61, pp. 973-982. [DOI: https://dx.doi.org/10.1007/s12665-009-0416-7]

80. Machado, W.; Borrelli, N.L.; Ferreira, T.O.; Marques, A.G.B.; Osterrieth, M.; Guizan, C. Trace metal pyritization variability in response to mangrove soil aerobic and anaerobic oxidation processes. Mar. Pollut. Bull.; 2014; 79, pp. 365-370. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2013.11.016]

81. Azzoni, R.; Giordani, G.; Viaroli, P. Iron–sulphur–phosphorus Interactions: Implications for Sediment Buffering Capacity in a Mediterranean Eutrophic Lagoon (Sacca di Goro, Italy). Hydrobiologia; 2005; 550, pp. 131-148. [DOI: https://dx.doi.org/10.1007/s10750-005-4369-x]

82. Otero, X.L.; Ferreira, T.O.; Huerta-Díaz, M.A.; Partiti, C.S.M.; Souza, V.; Vidal-Torrado, P.; Macías, F. Geochemistry of iron and manganese in soils and sediments of a mangrove system, Island of Pai Matos (Cananeia—SP, Brazil). Geoderma; 2009; 148, pp. 318-335. [DOI: https://dx.doi.org/10.1016/j.geoderma.2008.10.016]

83. Rickard, D.; Morse, J.W. Acid volatile sulfide (AVS). Mar. Chem.; 2005; 97, pp. 141-197. [DOI: https://dx.doi.org/10.1016/j.marchem.2005.08.004]

84. Liu, M.; Huang, H.; Bao, S.; Tong, Y. Microbial community structure of soils in Bamenwan mangrove wetland. Sci. Rep.; 2019; 9, 8406. [DOI: https://dx.doi.org/10.1038/s41598-019-44788-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31182804]

85. Spivak, A.C.; Sanderman, J.; Bowen, J.L.; Canuel, E.A.; Hopkinson, C.S. Global-change controls on soil-carbon accumulation and loss in coastal vegetated ecosystems. Nat. Geosci.; 2019; 12, pp. 685-692. [DOI: https://dx.doi.org/10.1038/s41561-019-0435-2]

86. Ray, R.; Shahraki, M. Multiple sources driving the organic matter dynamics in two contrasting tropical mangroves. Sci. Total Environ.; 2016; 571, pp. 218-227. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2016.07.157] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27474833]

87. Ferreira, T.O.; Nóbrega, G.N.; Queiroz, H.M.; de Souza Júnior, V.S.; Barcellos, D.; Ferreira, A.D.; Otero, X.L. Windsock behavior: Climatic control on iron biogeochemistry in tropical mangroves. Biogeochemistry; 2021; 156, pp. 437-452. [DOI: https://dx.doi.org/10.1007/s10533-021-00858-9]