Stand Structures and Carbon Storage Potential of Mangroves in Chaungkaphee Protected Public Forest, Tanintharyi Coastal Region, Myanmar

Abstract

Coastal ecosystems, particularly mangroves, are essential for ecological stability and human livelihoods, yet they face significant degradation from natural and anthropogenic pressures. This study focuses on the Chaungkaphee Protected Public Forest (PPF) in the Tanintharyi region of Myanmar, which hosts diverse mangrove species critical for carbon storage. Between 2010 and 2020, mangrove forest cover in Myanmar decreased from 540,000 ha to 431,228 ha, resulting in a loss of 108,772 ha. This decline is primarily attributed to illegal logging and agricultural expansion. Our research aims to assess the structural characteristics, biomass, and carbon storage potential of mangrove ecosystems within the Chaungkaphee PPF. Field data collected in early 2024 applied non-destructive sampling methods to gather information on tree structure, species composition, and soil carbon stocks. We identified six dominant mangrove species, with Rhizophora apiculata Blume showing the highest biomass and carbon storage potential. The total biomass was measured at 493.91 Mg ha⁻¹, yielding a carbon stock of 218.76 Mg C ha⁻¹. Soil carbon assessments revealed an average organic carbon stock of 921.09 Mg C ha⁻¹, underscoring the vital role of soil in carbon sequestration. Our findings highlight the significant contribution of mangrove ecosystems to climate change mitigation, emphasizing the urgent need for effective conservation strategies and community involvement in restoration efforts. This study enhances the understanding of mangrove resilience and sustainability, advocating for the protection of these crucial ecosystems amidst ongoing environmental challenges. By recognizing the ecological functions and services provided by mangroves, we can better address the threats they face and promote their restoration for future generations.

1. Introduction

Coastal areas are shaped by intricate interactions between geomorphological and biological processes, particularly evident in the adaptive strategies of mangrove species responding to these dynamics [1]. Mangroves, which thrive in tropical and subtropical intertidal zones, are uniquely adapted with morphological traits that enable their survival in such challenging environments. These ecosystems provide crucial functions, including coastal protection through sediment regulation facilitated by their specialized root systems [2,3,4]. Beyond their structural benefits, mangrove ecosystems provide vital services categorized into provisioning, regulating, supporting, and cultural roles, significantly benefiting both biodiversity and coastal communities [5,6]. Despite their importance, mangrove habitats are facing severe degradation due to both natural factors, such as storms and climate change, and anthropogenic pressures, including illegal logging and land conversion for agriculture. These threats compromise their ecological integrity and the livelihoods of human populations reliant on these resources.

In Myanmar, mangroves occupy approximately 431,227.96 hectares, accounting for 0.64% of the country’s total area and representing the eighth-largest mangrove region globally [7,8,9]. The nation is home to 37 true and 148 associated species of mangroves [7] and features a coastline of 2800 km divided into six coastal regions. The Ayeyarwady Delta hosts the majority of primary mangrove forests of Myanmar [10], which are vital for local livelihoods, providing resources such as charcoal, firewood, and timber while simultaneously providing protection against storms and salinity intrusion [11].

According to the Forest Resource Assessment (FRA) reports from 2010 and 2020, mangrove forest cover in Myanmar decreased from 540,000 ha in 2010 to 431,228 ha in 2020, resulting in a loss of 108,772 ha [8,12]. The main drivers of this decline include illegal logging, agricultural expansion, and expansion of aquaculture, especially shrimp farming [13]. The Ayeyarwady region is particularly impacted, facing the highest rates of mangrove loss in Southeast Asia due to population pressures and agricultural expansion [14]. While Tanintharyi has seen comparatively less degradation, issues such as rice cultivation pose significant threats to remaining mangrove ecosystems, with rice paddies responsible for 88% of total mangrove loss in Myanmar [14]. This suggests a potential shift towards agricultural systems aimed at national exports, particularly given the region’s low productivity and high export costs attributable to infrastructure deficits [15].

In response to these challenges, Myanmar has implemented various management strategies, including the establishment of Marine Protected Areas and the Constitution of Permanent Forest Estates (Reserved Forests and Protected Public Forests), along with efforts to restore mangrove ecosystems through community forestry initiatives and private sector involvement. As of June 2024, Myanmar has designated six protected areas totaling 142,661.33 acres, alongside 47 forest reserves and protected public forests covering 728,511.54 acres. Initiatives like the Myanmar Reforestation and Rehabilitation Program (MRRP) aim to establish approximately 21,830 acres of new mangrove plantations by planting around 26.4 million saplings between 2017 and 2024. The implementation of innovative strategies, such as the construction of “T-fencing” to combat erosion, further supports these restoration efforts, fostering ecological resilience and the sustainable growth of mangrove habitats [16,17].

In the Thanintharyi region, effective ecological recovery efforts are underway, particularly through community-led mangrove restoration initiatives supported by organizations like the World Wildlife Fund (WWF). These projects not only restore vital ecosystems but also improve local livelihoods [18]. Collaborations with organizations such as the Worldview International Foundation (WIF) and local universities have further enhanced these efforts, emphasizing the importance of partnerships in environmental conservation.

Mangroves in this region are critical carbon sinks capable of storing significant amounts of biomass. Kauffman et al. (2020) described that globally, mangroves are recognized as vital carbon sinks, with the capacity to store about 11.7 Pg C [19]. This includes an aboveground carbon stock of 1.6 Pg C and a belowground carbon stock of 10.2 Pg C. However, accurately assessing their biomass remains challenging due to complex ecological factors [20]. Moreover, the role of tidal inundation in reducing oxidation and methane emissions boosts their effectiveness in mitigating greenhouse gas impacts [21]. Despite substantial carbon sequestration, more than half of the carbon fixed by these forests remains understudied, highlighting the need for comprehensive research on the variability of carbon storage across different mangrove species and habitats [22,23,24].

This paper aims to assess the structural characteristics, biomass, and carbon storage potential of mangrove ecosystems within Myanmar’s protected public forests, contributing to a deeper understanding of their ecological functions and the mechanisms that underpin their resilience and sustainability.

In this scientific study, we conduct a comprehensive analysis of the biomass and carbon storage potential of the mangrove forests within the Chaungkaphee PPF. As one of the most biodiverse regions in Myanmar, understanding these unique mangrove ecosystems is crucial for addressing climate change implications and ensuring their ecological conservation. Through a detailed examination of the Chaungkaphee PPF, we aim to elucidate the complex relationships among mangrove ecosystems, climate change mitigation, and biodiversity conservation. This study underscores the urgent need to recognize and protect the vital role that mangroves play in combating the global climate crisis and maintaining the ecological balance of coastal environments.

2. Methodology

2.1. Study Site

This research focuses on the Chaungkaphee Protected Public Forest (PPF) in the Tanintharyi coastal region of Myanmar, located at latitude 11.053911° and longitude 98.726167°. Covering an area of 35,641 acres (14,423 hectares), this predominantly mangrove forest is situated approximately 1 m above sea level along the Andaman Sea coast. The region is sparsely populated and surrounded by small villages. Figure 1 describes the location of the study area.

Since 2013, the Forest Department has actively worked to establish mangrove plantations in the Chaungkaphee PPF to enhance environmental stability, primarily focusing on two species: Rhizophora mucronata Lam. and Bruguiera gymnorhiza (L.) Lam. Despite these efforts, numerous natural mangrove stands in the vicinity continue to thrive, serving as valuable reference forests for study and conservation. This forest is home to vital mangrove ecosystems, which serve as crucial carbon sinks and provide essential ecosystem services to local communities.

The study area is characterized by a tropical climate, with average temperatures between 25 °C and 28 °C (77 °F to 82 °F) and significant rainfall of 2500 mm to 3500 mm (98 to 138 inches) annually. This region experiences two main seasons: a dry season from November to April and a wet season from May to October, which is marked by heavy rainfall and tidal inundations. The fertile alluvial soils support a rich biodiversity, including mangrove forests and diverse wildlife. Indigenous communities in this area practice sustainable fishing and farming, maintaining a delicate balance with their environment.

2.2. Sampling Design and Data Collection

The field data collection was performed in February and March 2024, at the end of the rainy season. A non-destructive approach was used, integrating line transect and quadrat sampling techniques across various research sites. Each sampling station featured a 100 m line transect with a 10 × 10 m quadrat placed every 50 m along the line (Figure 1). Transects were oriented perpendicular to the coastline, extending from the landward to the seaward edge of the mangrove forest, with a total of 19 sampling plots established within the natural mangrove stand of Chaungkaphee PPF.

Within each transect, we implemented a tiered approach consisting of three nested plots of varying sizes to capture the diversity of mangrove vegetation. The first plot (10 × 10 m) focused on trees with a diameter at breast height (DBH) of 5 cm or greater (DBH ≥ 5 cm). The second plot (5 × 5 m) targeted saplings, defined as individuals with a DBH of less than 5 cm (DBH < 5 cm) and a height of greater than 1.5 m. Lastly, the smallest plot (2 × 2 m) was designated for seedlings, documenting those with a height between 0 and 1.5 m. The plots were spaced 40 m apart to minimize environmental variability. Data collected for each plot included tree counts, DBH, and height, with DBH measured using a diameter tape and total tree height assessed with a Suunto PM-5/360 PC Clinometer (Suunto Oy Co. Ltd., Vantaa, Finland). Global positioning system (GPS) coordinates were recorded for each plot.

Measuring DBH in mangroves is essential for estimating biomass, which is crucial for understanding carbon storage and mitigating climate change [25]. Additionally, these measurements also provide insights into forest structure, aiding assessments of forest health and biodiversity [26,27]. For Rhizophora species, DBH was measured at 30 cm above the highest prop root, while for other mangroves, it was measured approximately 130 cm above the ground [28,29]. Rhizophora species develop prop roots, also called stilt roots, that extend from the trunk, branches, or other prop roots [30]. By measuring above the highest root, researchers ensure a consistent and accurate representation of the trunk diameter.

2.3. Soil Carbon Pool Estimation

To evaluate soil carbon stock variations, we extracted soil cores from the centers of 12 plots—4 for each forest type. Using an open-face PVC sediment auger (10 cm diameter, 120 cm length), we collected samples down to 75 cm depth, dividing the soil into depth intervals of 0–25 cm, 25–50 cm, and 50–75 cm for accurate representation with minimal disturbance. Samples were stored in clean polythene bags.

Soil samples were stored at −4 °C until analysis. For the loss on ignition (LOI) procedure, each sample was placed in a 4.15 cm × 2.55 cm container. Subsamples were halved, debris was removed, and wet weights were recorded. Samples were dried at 80 °C in a muffle furnace for eight to ten hours until constant mass. After noting the dry weight, samples were ignited at 500 °C for three to five hours to determine organic matter loss. Each drying step took about one hour and was repeated until stable.

Bulk density was estimated from the core samples by dividing the dry weight (oven-dried at 80 °C to a constant weight) of the soil sample by the volume of the core. In addition, bulk soil samples were collected from each plot (0–25 cm, 25–50 cm, and 50–75 cm) for organic carbon estimation [31,32,33,34]. These samples were air dried, powdered, and sieved (2 mm sieve) for further analysis.

To determine organic carbon content and total carbon stock per hectare, we adapted a formula from Donato et al. (2011) [35]. This calculation was repeated across all sampled stations, yielding an average total carbon stock for the forest types.

Soil carbon (Mg ha⁻¹) = bulk density (g/cm³) × soil depth interval (cm) × %C

(1)

Total soil carbon stock of the area (Mg) = Total soil carbon (Mg ha⁻¹) × Area (ha) of the whole forest

(2)

2.4. Forest Structure and Biodiversity Index

We quantified the species composition, tree density (stem ha⁻¹), and basal area (m² ha⁻¹) for each sample plot. We calculated the importance value index (IVI) using the following formulas according to Cintrón and Schaeffer Novelli (1984) [36,37]:

Density (trees/ha) = (Number of individuals of one species)/(sample area)

(3)

$$\mathrm{RD}={\displaystyle \frac{(\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s})}{(\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s})}} \times 100\%$$

(4)

Frequency = (Number of plots where one species is found)/(total number of plots)

(5)

RF = (frequency of the species)/(sum of all frequencies) × 100%

(6)

$$\mathrm{Basal} \mathrm{area} ({\mathrm{m}}^2)={\displaystyle \frac{\pi \times {DBH}^2\times 0.0001}{4}}$$

(7)

where π = a constant (3.146), DBH = diameter at breast height (cm), and 0.0001 is a constant used to convert the measurement in centimeters squared into meters squared.

$$\mathrm{Total} \mathrm{Stand} \mathrm{Basal} \mathrm{Area} ({\mathrm{m}}^2/\mathrm{ha})={\displaystyle \frac{Sum of basal area for each tree}{0.01}}$$

(8)

where 0.01 is the plot size in hectares.

$$\mathrm{Total} \mathrm{Stand} \mathrm{Basal} \mathrm{Area} ({\mathrm{m}}^2/\mathrm{ha})={\displaystyle \frac{Sum of basal area for each tree}{0.01}}$$

(9)

RBA = (combined BA of the species)/(total BA of all species) × 100%

(10)

IVI = RD + RBA + RF

(11)

where RD is relative density, RF is relative frequency, and RBA is relative basal area.

The species diversity index was determined using the Shannon–Wiener index [38], which measures species composition and diversity. The formula is:

H′ = −∑ (P_i ln (P_i))

(12)

Evenness Index, E = H′/ln(S)

(13)

where H′ = the value of the Shannon–Wiener diversity index, P_i = the proportion of individuals of a particular species to total species individuals. ln = the natural logarithm of P_i. S = Number of species in the study area

2.5. Estimation of Carbon Stock

Tree identification at the species or genus level enabled biomass calculations based on species-specific wood density. We estimated both aboveground biomass (AGB) and belowground biomass (BGB) grounded in biological and structural theories, applying the common allometric equations developed by Komiyama et al. (2005) [39].

AGB estimates were derived from DBH using species-specific density values from the Global Wood Density Database [40]. The specific density values (ꝭ) used were as follows: Bruguiera cylindrica (0.72), Bruguiera gymnorhiza (0.76375), Bruguiera parviflora (0.772), Ceriops tagal (0.8374), Rhizophora apiculata (0.8425), and Rhizophora mucronata (0.814). For biomass determination, we applied the following allometric equations:

AGB = 0.251× ꝭ× DBH^2.46

(14)

BGB = 0.199 × ꝭ^0.899 × DBH^2.22

(15)

where AGB = aboveground biomass (kg/tree);

BGB = belowground biomass (kg/tree);

DBH = diameter at breast height (cm);

ꝭ = wood density (g cm⁻³).

Subsequently, the total aboveground and belowground biomass production in the plots was calculated by summing the biomass of all standing trees. The biomass of each sample plot was then converted to stand-level biomass (Mg ha⁻¹). Following this, we converted AGB and BGB into carbon stocks by multiplying 0.47 and 0.39 as a conversion factor [41] using the formulas provided below.

AGC = AGB × 0.47

(16)

BGC = BGB × 0.39

(17)

Total C = AGC + BGC + Soil Carbon

(18)

where AGC = aboveground carbon stock (Mg C ha⁻¹);

BGC = belowground carbon stock (Mg C ha⁻¹).

2.6. Statistical Analysis

The collected data on tree structure and carbon stock from the natural mangrove stand of Chaungkaphee Protected Public Forest (PPF) were utilized to test the hypotheses. Both the normality test and the homogeneity of variance assumptions were satisfied. Linear regression analysis was conducted to examine the relationships between biomass (Mg/ha) and the importance value index (IVI) of mangrove species, as well as the relationship between bulk density and carbon content. A significant level of 95% was applied to all tests. All statistical analyses and figures were generated using R-programming (Version 4.4.2). Data are reported as mean ± standard deviation throughout this study.

3. Results

3.1. Stand Structure and Species Composition

This study identified various species of mangroves at the tree, sapling, and seedling levels. At the tree level, we recorded a total of six species, Bruguiera cylindrica (L.) Blume, Bruguiera gymnorhiza (L.) Lam, Bruguiera parviflora (Roxb.) Wight & Arn. ex Griff., Ceriops tagal (Perr.) C.B.Rob., Rhizophora apiculata Blume, and Rhizophora mucronata Lam., representing one family and four genera in the Chaungkaphee Protected Public Forest (PPF). We enumerated 157 individuals with a diameter at breast height (DBH) of 5 cm or greater across 19 plots, each measuring 10 × 10 m. Among these, a significant 60.51% were comprised of the dominant species, Rhizophora apiculata. Other significant species included Rhizophora mucronata (17.83%), Bruguiera gymnorhiza (7.64%), Ceriops tagal (7.64%), and Bruguiera parviflora (5.73%), while Bruguiera cylindica accounted for the remaining 0.64% of the total species identified (

Table 1. Parameters of mangrove species in natural stands at Chaungkaphee PPF.

Species	DBH (cm)	H (m)	No. of Individual	Density (trees ha−1)	RD (%)	RF (%)	RBA (%)	IVI
Bruguiera cylindrica (L.) Blume	5.8	11.20	1	100	0.637	2.50	0.511	3.648
Bruguiera gymnorhiza (L.) Lam.	6.96 ± 1.57	4.44 ± 0.68	12	1200	7.643	10.00	9.245	26.888
Bruguiera parviflora (Roxb.) Wight & Arn. ex Griff.	6.04 ± 0.52	9.59 ± 0.94	9	900	5.732	10.00	5.033	20.765
Ceriops tagal (Perr.) C.B.Rob.	5.5 ± 0.23	4.47 ± 0.94	12	1200	7.643	17.50	5.528	30.672
Rhizophora apiculata Blume	6.14 ± 0.76	7.02 ± 2.05	95	9500	60.510	45.00	55.209	160.718
Rhizophora mucronata Lam.	7.48 ± 1.34	8.73 ± 1.69	28	2800	17.834	15.00	24.474	57.309
	6.39 ± 1.09	7.11 ± 2.28	157	15,700	100	100	100	300

).

Tree height and DBH showed significant variation, with heights ranging from 4.03 to 10.53 m (mean height: 7.11 ± 2.28 m) and DBH from 5.71 to 8.07 cm (mean DBH: 6.39 ± 1.09 cm). Among the six mangrove species studied, Rhizophora mucronata had the largest DBH, averaging 7.48 ± 1.34 cm, while Bruguiera parviflora recorded the greatest height at 9.59 ± 0.94 m. Conversely, Ceriops tagal had the smallest DBH at 5.50 ± 0.23 cm, and Bruguiera gymnorhiza had the lowest height at 4.44 ± 0.68 m.

Rhizophora apiculata also had the highest relative frequency (45.00%) and importance value index (IVI) of 160.718, indicating its dominance in the area. In contrast, Bruguiera cylindrica had the lowest relative frequency (2.50%) and IVI value (3.648). The ecological parameters for each species are presented in Table 1.

We used the Shannon–Wiener index to evaluate the diversity of species in the study area. The diversity indices of the study site are also presented in Table 1. At the tree stage, diversity was found to be low, with an H’ of 1.201 and a Shannon evenness index (SEI) of 0.67. This reflects the dominance of a few species, particularly R. apiculata and R. mucronata, over other species in terms of frequency.

3.2. Species Abundance in Natural Regeneration

At the sapling level, we identified eight species, namely Avicennia officinalis L., Bruguiera cylindrica (L.) Blume, Bruguiera gymnorhiza (L.) Lam., Bruguiera parviflora (Roxb.) Wight & Arn. Ex Griff., Ceriops decandra (Griff.) Ding Hou, Ceriops tagal (Perr.) C.B.Rob., Rhizophora apiculata Blume., and Rhizophora mucronate Lam. In the seedling stage, we found five species: Bruguiera gymnorhiza, Bruguiera parviflora, Ceriops tagal, Rhizophora apiculata, and Rhizophora mucronata. Species abundance was assessed by totaling saplings and seedlings, measured in density (individuals per hectare) and dominance (basal area in m² ha⁻¹), as summarized in

Table 2. Density, frequency, and IVI of natural regeneration.

Species	Density (ind. ha−1)	RD (%)	RF (%)	IVI (RF + RD)
Avicennia officinalis L.	100	0.641	2.13	2.769
Bruguiera cylindrica (L.) Blume	1300	8.333	8.51	16.844
Bruguiera gymnorhiza (L.) Lam.	2300	14.744	17.02	31.765
Bruguiera parviflora (Roxb.) Wight & Arn. ex Griff.	3700	23.718	12.77	36.484
Ceriops decandra (Griff.) Ding Hou	1000	6.410	8.51	14.921
Ceriops tagal (Perr.) C.B.Rob.	2300	14.744	17.02	31.765
Rhizophora apiculata Blume	4300	27.564	25.53	53.096
Rhizophora mucronata Lam.	600	3.846	8.51	12.357
	15,600	100	100	200

. During natural regeneration, Rhizophora apiculata recorded the highest density at 4300 ind. ha⁻¹, followed by Bruguiera parviflora (3700 ind. ha⁻¹), both Bruguiera gymnorhiza and Ceriops tagal (2300 ind. ha⁻¹ each), Bruguiera cylindrica (1300 ind. ha⁻¹), and Ceriops decandra (1000 ind. ha⁻¹) (Table 2). Other species, such as Rhizophora mucronata and Avicennia officinalis, had densities below 1000 ind. ha⁻¹ (Table 2). At the tree stage, Rhizophora apiculata remained predominant, with a density of 9500 ind. ha⁻¹ and a basal area of 0.285 m² ha⁻¹ (Table 1).

In natural regeneration, the diversity index (H’) was found to be 1.802, while the Shannon evenness index (SEI) was 0.87.

3.3. Vegetation Biomass and Carbon Stocks

Based on the sample plots, we measured biomass and carbon stock for mangrove trees at the tree level (DBH ≥ 5 cm). In the mangrove forest, R. apiculata accounted for the highest tree biomass and carbon storage, with values of 272.52 Mg ha⁻¹ and 120.64 Mg C ha⁻¹, respectively. It was followed by R. mucronata (126.75 Mg ha⁻¹ and 56.22 Mg C ha⁻¹), Bruguiera gymnorhiza (44.28 Mg ha⁻¹ and 19.63 Mg C ha⁻¹), Ceriops tagal (25.74 Mg ha⁻¹ and 11.38 Mg C ha⁻¹), and Bruguiera parviflora (22.52 Mg ha⁻¹ and 9.96 Mg C ha⁻¹). Bruguiera cylindrica species had the lowest values, with a tree biomass of only 2.10 Mg/ha and carbon storage of 0.93 Mg C ha⁻¹. Overall, the mangrove vegetation in the study area contained a total biomass of 493.91 Mg ha⁻¹ and a carbon stock of 218.76 Mg C ha⁻¹ (

Table 3. Biomass and carbon stocks of the mangrove species in the natural mangrove stand of Chaungkaphee PPF.

Species	Biomass (Mg ha−1)	Carbon Stock (Mg C ha−1)
Bruguiera cylindrica (L.) Blume	2.10	0.93
Bruguiera gymnorhiza (L.) Lam.	44.28	19.63
Bruguiera parviflora (Roxb.) Wight & Arn. ex Griff.	22.52	9.96
Ceriops tagal (Perr.) C.B.Rob.	25.74	11.38
Rhizophora apiculata Blume	272.52	120.64
Rhizophora mucronata Lam.	126.75	56.22

).

To analyze the relation between the importance value index (IVI) and the biomass of mangrove species, linear regression analysis was conducted. The results revealed a strong correlation between mangrove biomass and IVI, with an R² value of 0.9697 and a significance level of p < 0.05 within the study area (Figure 2).

3.4. Vegetation Carbon Stocks per Plot

Figure 3 describes a wide range of carbon stock values across the plots, indicating significant variability in the data. The average carbon stock for each plot ranged from 1.03 Mg C ha⁻¹ to 2.04 Mg C ha⁻¹. The highest total carbon stock was found in plot 13, at 25.16 Mg C ha⁻¹, followed by plot 6, with 22.42 Mg C ha⁻¹, and plot 15, at 21.64 Mg C ha⁻¹ (

Table 4. Distribution of stand density, biomass, and carbon stocks in the natural mangrove stand.

Plot	Density per ha	Biomass (Mg ha−1)			Carbon Stock (Mg C ha−1)
		AGB	BGB	TB	AGC	BGC	TC
1	300	4.96	2.61	7.57	2.33	1.02	3.35
2	600	10.16	5.32	15.47	4.77	2.07	6.85
3	1300	21.32	11.22	32.54	10.02	4.38	14.40
4	900	16.45	8.56	25.01	7.73	3.34	11.07
5	900	17.79	9.20	26.99	8.36	3.59	11.95
6	1900	32.89	17.16	50.04	15.46	6.69	22.42
7	600	15.81	7.91	23.72	7.43	3.09	10.52
8	600	15.30	7.66	22.96	7.19	2.99	10.18
9	1100	24.74	12.56	37.30	11.63	4.90	16.52
10	800	12.13	6.44	18.58	5.70	2.51	8.21
11	700	11.72	6.17	17.89	5.51	2.41	7.92
12	200	4.83	2.45	7.28	2.27	0.96	3.23
13	1400	37.88	18.86	56.74	17.80	7.36	25.16
14	600	11.44	5.90	17.34	5.37	2.30	7.68
15	900	32.83	15.94	48.77	15.43	6.22	21.64
16	500	9.28	4.81	14.08	4.36	1.88	6.23
17	700	11.53	6.07	17.60	5.42	2.37	7.79
18	800	16.85	8.64	25.49	7.92	3.37	11.29
19	900	18.43	9.48	27.91	8.66	3.70	12.36
	826	326.32	166.97	493.29	153.37	65.12	218.49

). In plot 15, two species were identified: R. apiculata and R. mucronata. The stand density in this plot is 900 individuals per ha, with an average DBH of 8.07 cm. Although the stand density in plot 15 is lower than in plots 13 and 6, its carbon density is greater than that of both plots. In contrast, plots 1 and 12 indicated the lowest total carbon stocks, measuring 3.35 Mg C ha⁻¹ and 3.23 Mg C ha⁻¹, respectively. Overall, the total carbon stock vegetation across all plots was 218.49 Mg C ha⁻¹, with significant variations among plots.

3.5. Soil Carbon Stock

Table 5. Average soil carbon percentage and carbon stock of each depth in all stations.

Depth (cm)	Bulk Density (g/cm3)	% C	Mean C Stock (Mg ha−1)
0–25	0.69 ± 0.08	13.33 ± 3.06	902.98 ± 159.88
25–50	0.68 ± 0.11	13.28 ± 3.20	883.94 ± 187.93
50–75	0.76 ± 0.11	13.17 ± 3.13	976.36 ± 158.27

summarizes the average carbon stock and percentage of carbon (%C) at various soil depths. The mean values of %C and carbon stock varied by depth, indicating a trend of increased carbon accumulation with greater soil depth. Specifically, %C at the surface (0–25 cm) was 13.33 ± 3.06%, slightly decreased to 13.28 ± 3.20% at 25–50 cm, and further to 13.17 ± 3.13% at 50–75 cm depth. The mean organic carbon stock across the study area ranged from 902.98 to 976.36 Mg C ha⁻¹. At the surface (0–25 cm), the mean carbon stock was 902.98 ± 159.88 Mg C ha⁻¹; at 25–50 cm, it decreased to 883.94 ± 187.93 Mg C ha⁻¹; while at 50–75 cm, it increased to 976.36 ± 158.27 Mg C ha⁻¹. The average organic carbon stock for mangrove soil was determined to be 921.09-Mg C ha⁻¹.

Table 5 also illustrates that soil bulk density generally increases with depth. The average bulk density at the surface (0–25 cm) was 0.69 ± 0.08 g/cm³, slightly lower at 0.68 ± 0.11 g/cm³ for the 25–50 cm layer, and increased to 0.76 ± 0.11 g/cm³ at 50–75 cm.

Figure 4 presents a scatterplot depicting the relationship between bulk density and carbon content. A total of 19 data points is plotted, showing a general trend of decreasing carbon content with increasing bulk density. The coefficient of determination (R² = 0.3674, p < 0.05) indicates a moderate fit of the model to the data, suggesting that approximately 36.74% of the variation in carbon content can be explained by the variation in bulk density. Overall, the plot indicates an inverse relationship between carbon content and bulk density, highlighting that as bulk density increases, carbon content tends to decrease. Soil bulk density is indicative of moisture and ventilation conditions, which can influence the oxidation of sequestered carbon, leading to CO₂ loss. Generally, bulk density decreases as %C increases [42,43].

3.6. Ecosystem Carbon Stock

Table 6. Carbon storage capacity of natural mangrove stands in Chaungkaphee PPF.

Biomass (Mg ha−1)	Vegetation Carbon Stock (Mg C ha−1)	Soil Carbon Stock (Mg C ha−1)	Ecosystem Carbon Stock (Mg C ha−1)
25.96 ± 13.83	11.50 ± 6.13	921.09 ± 48.80	932.59 ± 54.93

highlights the carbon stock measurements within a specific ecosystem, quantified across various categories. Biomass is measured at 25.96 ± 13.83 Mg ha⁻¹, reflecting the total mass of living plant material per hectare, which plays a crucial role in carbon storage. The vegetation carbon stock is recorded at 11.50 ± 6.13 Mg C ha⁻¹, indicating the carbon contained within the vegetation itself and underscoring the importance of plant life in carbon sequestration. Additionally, the soil carbon stock is reported at 921.09 ± 48.80 Mg C ha⁻¹, emphasizing the vital role of soil in carbon storage, which is essential for maintaining ecosystem health and mitigating climate change. The total carbon stock is calculated at 932.59 ± 54.93 Mg C ha⁻¹, aggregating both vegetation and soil carbon stocks and providing a comprehensive understanding of the carbon storage capacity of the ecosystem.

When comparing carbon pools, soil comprises the highest carbon stock, averaging about 921.09 Mg C ha⁻¹. This trend aligns with the principle that greater biomass production, as observed in this study, leads to increased carbon sequestration. Trees naturally accumulate biomass as they age, although the rate varies by species [44,45].

4. Discussion

4.1. Structural Characteristics

The importance value (IVI) serves as a key metric for assessing the structural significance of each species within the mangrove community, reflecting both dominance and overall contributions to productivity [46]. Our findings indicate that R. apiculata Blume stands out as the most important species among the six categorized, with the highest relative density and dominance values (Table 1). This dominance suggests that R. apiculata not only has the highest number of individuals per unit area but also plays a crucial role in overall mangrove biomass.

The mangrove forests in Chaungkaphee PPF display low species diversity, primarily dominated by the tall-stilt mangrove, R. apiculata. This species comprises 60.51% of the mangrove population and presents an IVI of 160.72%, making it the sole representative of the Rhizophoraceae family in this area. Its ability to thrive in the frequently submerged environments of estuaries highlights its essential adaptations for survival.

Rhizophora apiculatata thrives in a range of tidal conditions and demonstrates remarkable tolerance to varying salinity levels [47]. Its extensive root system plays a crucial role in stabilizing coastal soils, effectively reducing erosion and protecting shorelines from wave action. Additionally, this root structure promotes sediment accumulation and enhances soil fertility [48]. The species is also vital for carbon sequestration, as it absorbs CO₂ and stores it in both its biomass and the surrounding soil, which is critical for mitigating climate change [49]. Furthermore, the organic matter contributed by R. apiculata enriches the soil with nutrients, further boosting the productivity of mangrove ecosystems [50]. Economically, it serves as a valuable resource for local communities, providing wood and fuel, which necessitates careful management to ensure sustainability [51]. Thus, R. apiculata is integral to both the ecological health and economic viability of mangrove forests. This research aligns with the findings by Asadi et al. [52], which identified R. apiculata as a dominant species in the mangrove landscapes of mainland Java, corroborated by high IVI values. For example, in the Labuhan mangrove forest of East Java, R. apiculata had a relative dominance of 31.15% and an IVI of 80.46% [53]. Similarly, notable IVI values of 83.83% and 106.01% have been recorded in the North and South Andaman Islands of India [54]. The continuous distribution of R. apiculata from India to the western Pacific and northern Australia further signifies its preference for fine mud sediments that typify river estuaries [54,55,56]. The diversity index (H’) for the natural stand measured 1.201, accompanied by a Shannon evenness index (SEI) of 0.67. These relatively low values indicate the dominance of a few species within the Rhizophoraceae family, indicating that the environmental conditions are particularly favorable for this group. The limited species diversity may result from the introduction of specific mangrove species in plantation settings, which are often dominated by two or three species. This observation is consistent with studies indicating that mangrove forests typically show lower biodiversity relative to other tropical ecosystems [57,58].

Aye et al. (2022) and Patindol and Casas Eulito V (2019) classified Shannon diversity index (H’) values of 0.71 and 0.91 as low, indicating that the mangrove stands at their study sites were characterized by a limited number of mangrove species, dominated by just a few species [59,60]. The low diversity in this natural stand likely arises from the dominance of only R. apiculata species. To increase diversity, further restoration programs could focus on planting additional species.

Pototan et al. (2021) reported a high diversity index value (H’ of 3.145) in Banayabanay, Davao Oriental, with 33 plant species belonging to 14 families [61]. Similarly, Lillo et al. (2022) recorded a high diversity index value of H’ of 3.011 in Camotes Island, Cebu, where a total of 42 mangrove species were identified, including 31 true mangrove species and 11 mangrove associates [62]. Their study highlighted the presence of several threatened and near-threatened mangrove species, indicating a high level of biodiversity.

4.2. Carbon Stock Valuation Estimates

To estimate carbon stocks accurately, we used common allometric equations provided by Komiyama et al. (2005) [39], which consider tree trunk diameter and wood density—key factors influencing biomass and carbon storage potential [63]. Our study recorded an overall average biomass of 25.96 Mg ha⁻¹ and a carbon storage value of 11.50 Mg C ha⁻¹. Carbon stocks per individual plot ranged from 1.03 Mg C ha⁻¹ to 2.04 Mg C ha⁻¹, contributing to a total vegetation carbon stock across all plots of 218.49 Mg C ha⁻¹, showing significant variations among plots.

The value obtained in this study for the natural stand is higher than those reported for natural mangrove forests in Samar, Philippines, which included 71.97 t C ha⁻¹ [64], 37.18 Mg ha⁻¹ (Panabo Mangrove Park) [65], and 61.34 t ha⁻¹ (Pagbilao, Quezon) [66]. Additionally, a value of 112.54 t C ha⁻¹ has been noted for mangrove forests on Belitung Island, Indonesia, which are predominantly composed of Rhizophora apiculata [67].

The carbon stock value obtained in this study is lower than those reported for planted mangrove forests in Samar, Philippines (391.44 t C ha⁻¹) [64] and in the mangrove-afforested areas of central coastal areas of Bangladesh, where the mean ecosystem carbon stock for young and mature mangroves was estimated at 350.53 ± 14.23 t C/ha and 483.64 ± 16.35 t C/ha [68]. Additionally, carbon stocks were reported at Bahile, Puerto Princesa City, Palawan (757.7 t ha⁻¹) [69], 401.07 t ha⁻¹ in Pinabacdao, Samar [28], and 1267.87 t ha⁻¹ in Sarangani Province [70]. Notably, Howard et al. (2014) reported that carbon stocks can range from 55 to 1376 Mg C ha⁻¹, with an average of 386 Mg C ha⁻¹, indicating that our findings are within an acceptable range [71].

When compared with other regions, the biomass and carbon stocks identified in the study area are significantly higher than those in the Labuhan mangrove forest in Lamongan, East Java (168.05 Mg ha⁻¹ biomass and 74.70 Mg ha⁻¹ carbon) [53]. Meanwhile, mangroves in Kerala, India, showed similar biomass levels to those in the Desa Daun Mangrove Forest (117 Mg ha⁻¹) [72]. Conversely, mature mangrove stands in Baluran National Park showed significantly higher biomass (533 Mg ha⁻¹), attributed to the presence of trees with a DBH greater than 50 cm [29].

The role of carbon content at varying soil depths is critical for delivering essential ecosystem services. Surface soil carbon plays a vital role in provisioning services (e.g., food, feed, etc.), regulatory services (e.g., atmospheric CO₂ exchange), and supporting services (e.g., soil structure, nutrient retention). Conversely, subsoil carbon primarily influences regulatory services, especially carbon sequestration, while contributing less to provisioning and supporting services [73]. The topsoil is crucial for provisioning services, providing habitat, nutrients, and raw materials; in contrast, subsoils contain a significant fraction of soil carbon, emphasizing their role in carbon sequestration within regulating ecosystem services. Our study identified significant soil organic carbon levels ranging from 902.98 to 976.36-Mg C ha⁻¹. This carbon pool is notable due to its long residence time among organic carbon sources in forest ecosystems. Donato et al. (2011) estimated that soil carbon storage constitutes approximately 98% of total carbon stored in these environments [35].

Given their significant carbon burial rates, mangroves are increasingly acknowledged as critical players in climate change mitigation. Our findings from repeated soil carbon measurements demonstrate the importance of soil carbon reservoirs in the context of mangrove reforestation, highlighting their potential to mitigate carbon emissions. We estimate the total mangrove soil carbon at 921.09 Mg C ha⁻¹. Compared to previous studies, our findings are comparable to mangrove carbon stocks reported in the Indo-Pacific Region of 1023 ton ha⁻¹ [35], with ranges of 853–1385 ton ha⁻¹ in Micronesia, 479–1068 ton ha⁻¹ in Palau [74], and 900–3400 Mg C ha⁻¹ in Baja California [75]. This underscores our study’s contribution to the broader understanding of mangrove carbon sequestration capacities globally.

5. Conclusions

In conclusion, our study highlights the structural and ecological significance of R. apiculata Blume within the mangrove community of Chaungkaphee PPF. Its dominance is evident through high relative density and importance values, with this species comprising over 60% of the mangrove population. This underscores the need to consider its role in habitat stability and biomass productivity. Despite low species diversity, characterized by a Shannon diversity index of 1.201, the mangrove ecosystem demonstrates unique adaptations that allow R. apiculata to thrive in estuarine environments. Furthermore, our findings regarding carbon storage reveal an average biomass of 25.96 Mg ha⁻¹, totaling 493.29 Mg ha⁻¹, with a vegetation carbon stock of 218.49 Mg C ha⁻¹. These figures illustrate the significant carbon sequestration potential of the area and align with existing literature, confirming that our estimates are competitive while emphasizing the critical role of mangrove ecosystems in climate change mitigation. The notable soil carbon levels, ranging from 902.98 to 976.36 Mg C ha⁻¹, emphasize the importance of maintaining and restoring mangrove habitats, which are essential not only for biodiversity but also for their profound contributions to carbon storage and ecosystem services. Future restoration efforts should focus on enhancing species diversity and maximizing the carbon storage capabilities of these vital ecosystems, recognizing their integral role in balancing environmental health and climate resilience. To implement effective restoration measures, gap-planting techniques and community participation in restoration efforts can be utilized. These approaches can strengthen ecosystem resilience while providing ecological and social benefits.