Carbon Stocks in Two Aquatic Marshes on the Caribbean and Pacific Coast of Panama

Abstract

Wetlands are critical ecosystems globally, boasting significant ecological and economic value. They play a crucial role in the hydrological cycle by storing water and carbon, thereby helping to mitigate climate variability. But in Panama, little is known about the carbon stored in freshwater wetlands. This research presents the estimation of the carbon stocks of two freshwater wetlands in Panama, located on both sides of the Caribbean (Portobelo) and Pacific (Tonosi) coasts. The methodology consisted of transects of 125 m and 40 m wide, with six circular plots every 25 m; in each transect, the diameter of the tree trunk was measured at breast height (1.3 m) and the species was recorded, and in the same plots, soil samples were collected in triplicate by depth intervals. The average total ecosystem carbon storage (TECS) for the aquatic wetlands of Tonosí was 106.26 ± 18.3 Mg C ha⁻¹, and for Portobelo, it was 355.09 ± 70.02 Mg C ha⁻¹. These recorded values can contribute to the conservation of wetlands, supporting Panama’s nationally determined NDC contributions. However, despite the acceptance that wetlands are important nature-based solutions, national data on soil carbon stocks in freshwater wetlands are still scarce and their protection should be increased.

1. Introduction

Worldwide, wetlands are ecosystems with high ecological value due to the biodiversity they house and the productivity they generate. These ecosystems offer multiple benefits, including economic and cultural, for the local populations that depend on them. This contributes to wetlands having important ecological functions [1,2]. The Ramsar Convention defines wetlands as “expanses of stagnant or flowing water, fresh or salty, including areas of marine water whose depth at low tide does not exceed six meters” [3].

Most wetlands are located on organic soils and are common along coasts, especially in tropical coastal plains [1]. Natural wetlands occupy between 4 and 6% of the earth’s surface and play a fundamental role in the hydrological cycle due to their capacity to retain water and carbon, and their efficiency in storing the latter, which contributes to mitigating climate variability. Wetland soils contain a high percentage of carbon, a result of the high productivity of plants and the low decomposition of organic matter that occurs in flooded soils [1,4,5].

On the other hand, peatlands are a type of wetland composed of partially decomposed organic matter stored for a long period of time under conditions of water saturation. Like many wetlands, peatlands provide important ecosystem services, such as the regulation and provision of water and nutrients, and protection against erosion [5,6]. Recent studies have highlighted their importance [7,8,9].

In Panama, research on freshwater wetlands, and especially peatlands, is scarce, so the conditions and environmental characteristics of these ecosystems are unknown. The expansion of the agricultural, livestock and urban frontier is modifying the ecological dynamics of wetland areas and their surroundings, causing progressive deterioration and loss of the biodiversity that lives in them. On the other hand, the presence of peat bogs is only known for the Caribbean slope in the San San Pond Sak wetland [10], and the presence of peat was recently identified in the Matusagarati lagoon on the Pacific coast of Panama [11,12]. Furthermore, mangroves and peatlands in Panama play a vital role in preserving biodiversity and providing critical ecosystem services in Central America [13].

In Panama, peatland ecosystems have not been well studied or mapped, resulting in widespread disregard by the general population and increasing the probability that these ecosystems suffer land use change from agricultural activities or other ecologically detrimental uses. Peatlands have been shown to be important carbon sinks, representing 3% of total land cover, but they capture and store up to one-third of global soil carbon, more than twice as much as any other terrestrial ecosystem, underlining their importance in the face of climate change [14,15]. However, to ensure that this carbon is stored in the soil, peatlands need to be protected to achieve global climate targets. According to [15], about 12% of current peatlands have been drained and degraded, contributing to 4% of annual global emissions caused by anthropological activities.

Therefore, this research aims to establish a baseline regarding the existence of previously undescribed sites with peat ecosystems in Panama, based on the publication by [16] in which they present possible sites with peat in coastal zones for Central America and the Caribbean. The present investigation evaluated the presence of peat and carbon sinks where two sites were evaluated: one on the Caribbean slope and another on the Pacific slope in Panama.

2. Materials and Methods

2.1. Study Sites

The ecosystem of interest in this research presents brackish waters that register a salinity between 0 and 3 PSU with a large presence of plants with aquatic habitats, mainly herbaceous of the families Cyperaceae and Poaceae, with the presence of some species of dispersed trees and bushes such as Bactris guineensis (L.) H. E. Moore, Guazuma ulmifolia Lam., Acrocomia aculeata (Jacq.) Lodd. ex Mart., Acrostichum aureum L., Annona glabra L., Astronium graveolens Jacq., Hamelia patens Jacq., Elais oleifera (Kunth) Cortés, Erythrina fusca Lour., Pterocarpus officinalis (Jacq.) and Elaeis oleifera (Kunth) Cortés. (Figures S1 and S2 in the Supplementary Material show the landscape of the sampling area in Tonosí and Portobelo). In Tonosí, the main economic activities are agriculture, livestock, fishing, and tourism, and the climate is characteristic of a tropical dry forest according to the Holdridge classification, usually with a rainy season that runs from May or early June to late November, and a dry season that includes the remaining months. Precipitation fluctuates between 1100 and 1650 mm on average, and the average minimum and maximum temperatures are 24 °C and 32 °C, respectively [17,18]. In Portobelo, the main economic activities are livestock and tourism, and the climate is tropical rainforest according to the Holdridge classification, with an annual rainfall of 2732 mm, and the average minimum and maximum temperatures are 23 °C and 30 °C, respectively. The dry season in the region extends from January to April, while the rainy season runs from May to December [14,15].

In Tonosí, a total of 3.8 km² of sampling area was established, divided into separate areas. The main area measures 3.6 km² and covers the marshes that form inland before the mangroves at the mouth of the Tonosí River. The secondary globe measures 0.2 km² and covers the wetlands that form before the mangroves northeast of the mouth of the Cañas River, which is a tributary of the Tonosí River. Geographic reference points for Tonosi (WGS84 geographic coordinates): southwest corner: 7.34939° N, 80.40943° W; northeast corner: 7.43651° N, 80.22949° W.

In Portobelo, a total of 2.92 km² of sampling area was established, divided into two areas. The main area measures 2.85 km² and covers the wetlands that form inland before the mangroves southeast of the mouth of the Cascajal River. The second area measures 0.07 km² and is north of the mouth of the same river. Portobelo geographic reference points (WGS84 geographic coordinates) Southwest corner: 9.55087° N, 79.65443° W; Northeast corner: 9.57865° N, 79.62273° W.

2.2. Collection of Samples in the Field

This study aimed to measure both aerial and soil carbon; therefore, a nationally established methodology design based on [19] for measuring carbon in mangrove ecosystems in Panama [20] was used to take and process samples. The methodology consists of drawing transects of 125 m from the body of water to the mainland. In our case, the starting point was close to the mangrove ecosystem inland boundary. Along 125 m and 40 m wide, up to 6 circular plots were marked with a radius of 7 m every 25 m, as shown in Figure 1. Within the transects, the diameter of the tree trunk was measured at breast height (1.3 m) and the species was recorded. Soil samples were collected at depth intervals of 0–15 cm, 15–30 cm, 30–50 cm, and 50–100 cm with 3 replicates per subplot. Each depth sample was labeled and taken to the Laboratory of the Agronomic Research Center of the University of Costa Rica. The samples were analyzed using the Modified KCl-Olsen method and a C/N autoanalyzer by dry combustion, where the percentage of soil carbon at each depth was obtained. Most transects were less than 125 m long as the plot ended when a change in the type of ecosystem was reached.

The biomass of the underground roots of the trees within the plot was quantified using allometric root equations, as established by [21,22] used in [19,23]. This estimation was based on the measurement of tree diameters and wood density for each individual tree within the subplot.

Within a circular plot with a 7 m radius, trees with a diameter (D) greater than or equal to 5 cm were recorded (Figure 2). In a 12.6 m² subplot (with a radius of 2 m), trees with a diameter of less than 5 cm were counted. In addition, in four perpendicular transects of 12 m length, fallen wood was measured. In the center of the 2 m radius main plot, soil samples were collected (indicated by blue dots; if it was not possible to collect in the center, samples were taken in other parts of the main plot without disturbing the soil). In 1 m² subplots, herbaceous plants were sampled. Both soil and herbaceous sampling were carried out at random locations within the main plot, as established by [20].

For the fallen wood, the intersection plane technique described in [20] was used, which consists of measuring the diameter of the pieces of fallen wood that cross an imaginary intersection plane 2 m high and 12 m long (Figure 3). The plane is made visible in the field through a transect that is placed on the ground using a tape or graduated rope. Fallen wood means fallen dead or live material from trees and shrubs, including aerial roots that are at an angle < 45° with respect to the horizontal. Along the transect, fallen wood was measured in two categories: 2.5–5 cm and ≥5 cm in diameter. To speed up the measurement, pieces 2.5–5 cm in diameter were measured from 2 m from the beginning of the transect to 7 m, while pieces ≥ 5 cm in diameter were measured along the entire transect. Following the methodology described in [20], the degree of decomposition was recorded based on the classification of [24] used in the national forest carbon inventory for Panama (

Table 1. Categories to classify the decomposition state of fallen wood in carbon inventories used in [20].

Category	Description
No rot	Solid wood, recently fallen, with intact bark.
Intermediate	Medium solid wood, with the bark beginning to detach.
Decomposed	Soft or rotten wood, which breaks easily.

). Pieces of fallen wood still attached to a standing tree were not considered for sampling. If a piece of wood crossed the transect several times, it was measured again at each intersection point.

Based on [25], the total number of sampling units was adjusted to the budget and execution time of the project so that the sampling effort was maximized. The plots were located randomly and proportionally for both sites, with seven plots in Tonosí in an area of 380 hectares as shown in Figure 4, and in Portobelo, six plots in an area of 292 hectares, as shown in Figure 5.

2.3. Laboratory Analysis Methodology

Once the soil samples were collected, the empty aluminum container for sample disposal was weighed, and then the sample was weighed with the container and the weight of the container was subtracted to establish the weight of the samples before placing them in the oven until a constant weight was reached (105 °C, approximately 72 h). Subsequently, the dried samples were ground and passed through a number 30 (0.54 mm) sieve. A total of 2 g of ground soil from each depth was weighed on an analytical balance and placed in a muffle at 550 °C for 4 h. The percentage of organic matter was calculated by the difference between weights before and after incineration.

In Equation (1), the percentage of organic matter is presented:

% organic matter = [(Dry sample weight − Calcined weight) × 100]

(1)

Each sampling unit had three replicates.

Then, 100 mg of each sample was placed in a bottle to be processed in the soil and foliar laboratory of the Agronomic Research Center of the University of Costa Rica to pass through the Elemental Analyzer to obtain the Carbon–Nitrogen relationship. All other laboratory analyses were performed in the Soils and Related Laboratory at the Faculty of Agronomy of the National University of Panama.

Other components were also considered, such as aerial carbon, fallen wood, dead wood, and herbaceous matter, which were considered in the calculation of total carbon.

2.4. Soil Carbon Calculation Methodology

Soil apparent density was calculated using Equation (2) to measure soil carbon:

$$\mathrm{Soil} \mathrm{apparent} \mathrm{density} (\mathrm{g}/{\mathrm{m}}^3)={\displaystyle \frac{\mathrm{Oven}-\mathrm{dry} \mathrm{sample} \mathrm{mass} \left(\mathrm{g}\right)}{\mathrm{Sample} \mathrm{volume} \left(\mathrm{m}³\right)}}$$

(2)

Soil carbon was measured by considering the sum of the mass for each soil depth (Sd) sample that was divided into intervals (0 to 15 cm, 15 to 30 cm, 30 to 50 cm, 50 to 100 cm), together with the apparent density (Ad) and carbon concentration in each layer.

For the calculation of soil carbon, Equation (3) was used, reported by [19]:

Soil carbon (Mg ha⁻¹) = Ad (g cm⁻³) × Sd interval (cm) × %C

(3)

%C is the carbon concentration expressed as a whole number.

2.4.1. Carbon Calculation in Living Trees

Allometric equations were used to calculate biomass (kg). Estimating the carbon stored in living trees involved a two-step process. We used the general equation of the Americas [25] suggested by [20] due to the lack of local data for Panama. The biomass of the trees was calculated based on the diameter of their trunk at breast height (1.3 m) including all components in the soil (leaves, branches, trunk and aerial roots). First, we had to calculate the biomass using Equation (4):

B = 0.168 × p × (D)^2.471

(4)

where B is the biomass in Kg, p is the wood density (g cm⁻³), and D is the diameter of the trunk at breast height (cm). Wood density values were obtained based on the Forest Reference Level of Panama [26] and the atlas of suitability of important agroforestry species under future climates in Central America [27].

Then, we converted biomass (kg) to carbon (kgC) using Equation (5):

C = B × 0.4752

(5)

Next, we converted kgC to MgC/Ha using the following equation:

C = (kg C/Transect area(m²) × (10,000 m²/1Ha) × (1 MgC/1000 kgC)

2.4.2. Carbon Calculation in Fallen Wood

Twenty pieces of fallen wood were randomly collected for the two size classes and the three decomposition categories (Table 1) defined above (D: 2.5–5 cm and ≥5 cm). To determine the volume (“fresh”), these pieces were immersed in a container with water placed on an analytical balance, with its marker at zero after recording the weight. The change in mass indicated by the scale was noted, which referred to the displaced volume of the wood, since the specific gravity of water is 1 g/cm³. To obtain the specific gravity, the mass of each piece was divided by volume. Then, each piece was placed in an oven at 105 °C, until a constant weight was obtained, and the data were recorded as the dry mass of the wooden piece.

The dry mass of each piece was divided by the fresh volume of the wood, and the average was calculated for each size category and degree of decomposition of the wood; with this, the average wood density (g/m³) was obtained using Equation (6) used in [20]:

$$\mathrm{Average} \mathrm{wood} \mathrm{density} (\mathrm{g}/{\mathrm{cm}}^3)={\displaystyle \frac{\mathrm{dry} \mathrm{weight} \left(\mathrm{g}\right)}{\mathrm{fresh} \mathrm{wood} \mathrm{volume} \left(\mathrm{cm}³\right)}}$$

(6)

The calculation was carried out by previously determined categories of size and degree of decomposition. The individual volume per size was calculated using Equation (7), described and used by [19]:

$$\mathrm{Volume} ({\mathrm{m}}^3 {\mathrm{ha}}^{-1})={\mathrm{\pi }}^2 \times ( {\displaystyle \frac{{\mathrm{N}}_{¡}\ast {\mathrm{QMD}}_{¡}^{²}}{8\ast \mathrm{L}}})$$

(7)

where N¡ is the number of pieces of the class ¡, QMD¡ is the quadratic mean diameter of size class ¡ (cm), and L is the transect length (m).

Then, the amount of carbon in the fallen wood (C_fw) was calculated with Equation (8):

C_fw = V × Sg × %C

(8)

V: wood volume;

Sg: average wood density (g/cm³);

% C: carbon concentration in the sample.

2.4.3. Belowground Biomass

The belowground biomass of the trees present in the plot was estimated through root allometric equations [21,22]. For this, the diameters and density of the wood of each of the trees within the subplot were used.

Below, we present Equation (9), which was reviewed and reported by [19,20,21,22]:

B_TB = 0.199 × 𝜌^0.899 × (D)^2.22

(9)

B_TB = tree belowground biomass (kg);

𝜌 = wood density (g/cm³);

D = tree diameter at breast height (cm).

Following what was described by [19], root carbon mass was calculated as the product of root biomass and root carbon concentration. Then, the results were scaled to hectares to obtain the carbon estimate.

2.4.4. Herbaceous

The methodology described in [20] was followed, with a 1 × 1 m quadrant being randomly placed within one of the 7 m radius plots, and all the vegetation present was collected. This sample was collected in already identified bags and its contents weighed “fresh” in the field (g). A subsample of 250 g was taken and dried in the oven at a temperature of 65 °C, maximum, until a constant weight was obtained. Then, the dry weight was adjusted to the wet field weight and the biomass was calculated for the sampled area for both sites.

The calculation of carbon in herbaceous plants was carried out using the carbon conversion factor of 0.45 [25] with Equation (10):

$$\mathrm{Herbaceous} \mathrm{biomass} (\mathrm{kg} \mathrm{C}/{\mathrm{m}}^2)={\displaystyle \frac{\mathrm{B} \times \mathrm{conversion} \mathrm{factor} 0.45 }{1 \mathrm{m}²}}$$

(10)

2.4.5. Total Carbon

Equation (11) reported in [20] was used to assess total carbon stocks at the ecosystem level (expressed in MgC/ha), summing the carbon stocks of all measured components: living trees, dead wood, herbaceous vegetation, fallen wood, soil, and belowground biomass:

CTot = C live + C dead + C herbaceous + C fallen wood + C soil + C roots

(11)

We used linear models to determine the influence of depth on carbon storage (percentage of carbon) for Tonosi and Portobelo. We included area as the control variable in the model to ensure that the effect of depth was estimated while controlling for differences between areas. We assessed the significance of model predictors using the Anova function of the R package “car”. We used the R package “emmeans” to perform pairwise comparisons to explore differences between depths. We used marginal means and 95% confidence intervals for the pairwise comparisons from “emmeans”. We used descriptive figures to explore the patterns of stored carbon (%) among plots from Tonosi and Portobelo.

3. Results

At the end of the field visits, the study area was readjusted based on the entire sampling work, obtaining a result of 3.04 km² in Tonosí for a total of seven plots of 125 m long by 40 m wide, and in Portobelo, 2.53 km² for a total of six plots of 125 m long by 40 m wide, characterized as flooded marsh ecosystems located in the transition between mangroves and terrestrial ecosystems. The study areas were not mangrove or peatland ecosystems (Figures S3 and S4 show the extension of the characterized ecosystem in the Supplementary Material).

In Tonosí, a total of seven plots with 25 subplots were created, resulting in 298 soil samples, and for Portobelo, a total of six plots were created with 33 subplots, resulting in 360 soil samples. The quantity of plots was proportional to the size of the study areas. The total carbon storage value of the ecosystem in Tonosí was 32,303.04 (tC), and for Portobelo, it was 89,837.77 (tC) (

Table 2. Distribution of sampling plots and total carbon for Tonosí and Portobelo.

Site/Unit	Hectares	% of Plots	Plots	Total, tC/ha	Total, tC
Tonosí	304	54	7	106.26 ± 18.3	32,303.04
Portobelo	253	46	6	355.09 ± 70.02	89,837.77

).

In the Tonosí site, a total carbon accumulation of 106.26 ± 18.3 tC/ha was found, and for the Portobelo site, a greater accumulation of total carbon was found, with a value of 355.09 ± 70.2 tC/ha.

In relation to the composition of total carbon, it was recorded that Tonosí had a greater contribution of soil carbon in relation to epigeous carbon, a different result than that obtained in Portobelo, where the greatest contribution was from the epigeum component in relation to soil carbon (Figure 6).

For carbon components, soil carbon results were obtained at both sites: carbon from living trees, carbon from roots, and only in Tonosí, carbon from fallen wood (

Table 3. Carbon results by component (tC) for Tonosí and Portobelo.

Component	Tonosí (tC ha)	Portobelo (tC ha)	Average (tC ha)
C soil	41.3	47.34	44.32 ± 31.34
C live trees	28.77	5.38	17.075 ± 12.07
C roots	27.31	302.37	164.84 ± 116.56
C fallen wood	8.88	0	8.88 ± 0
Total, at 1 m	106.26	355.09	230.675 ± 163.11

). The absence of dead trees or fallen wood may be associated with the type of ecosystem that presents alterations in the landscape for agricultural and livestock practices that take place in the surrounding areas. In herbaceous plants, average values lower than 0.0 ± 0.0 tC/ha were obtained. Due to this low carbon contribution, this component was not taken into account in the calculation of total carbon.

We found for the area of Tonosi that depth has a significant influence on carbon (F = 64.11, df = 3, p < 0.0001). Pairwise comparisons revealed that the carbon percentage is significantly higher at the 0–15 cm depth compared to the deeper depths (15–30 cm, 30–50 cm, and 50–100 cm) (Figure 7). Additionally, we found a significant difference between the carbon at the 15–30 cm depth and the 50–100 cm depth (p = 0.003), with the 15–30 cm depth having higher carbon (Figure 7). No significant differences were found between the carbon at the 15–30 cm depth and 30–50 cm depth (p = 0.32) or between the 30–50 cm and 50–100 cm depths (p = 0.08) (Figure 7). The carbon percentage (%) across individual plots of Tonosi is shown in Supplementary Material Figure S5.

Furthermore, we found for the area of Portobelo that depth has a significant carbon influence (F = 73.76, fd = 3, p < 0.0001). We found that carbon percentage is significantly higher at the 0–15 cm depth compared to the deeper depths (15–30 cm, 30–50 cm, and 50–100 cm; all pairwise comparisons p < 0.001) (Figure 8). We revealed significant differences between carbon at the 15–30 cm depth and carbon at the 30–50 cm depth (p = 0.002) and the 50–100 cm depth (p < 0.001), with the 15–30 cm depth having a higher carbon percentage (Figure 8). No significant differences were found between carbon at the 30–50 cm depth and the 50–100 cm depth (p = 0.99) (Figure 8). The carbon percentage (%) of the individual plots of Portobelo is shown in Supplementary Material Figure S6.

4. Discussion

Studies carried out with the same methodology in Panama applied in mangrove ecosystems recorded storage values per hectare up to one meter deep, obtaining values of 234.48 ± 19.45 tC/ha in Remedios, 445.10 ± 159.85 tC/ha in San Félix, 303.56 ± 91.90 tC/ha in San Lorenzo [28], and 393.26 ± 18.90 tC/ha in the mangroves of David [29]. Considering the Forest Reference Level of Panama [26], the carbon values obtained for Tonosí (106.26 ± 18.3 tC/ha) and Portobelo (355.09 ± 70.2 tC/ha) were high. The differences in the values obtained at each site may be related to land use, with Tonosí presenting greater agricultural and livestock pressure in relation to Portobelo. The differences in the values obtained in each site can be related to land use, with Tonosí presenting greater agricultural and livestock pressure in relation to Portobelo. Although it is estimated that rangelands are large deposits of soil carbon because they have high carbon density and occupy a very vast area [30], the change in land use and landscape from natural wetlands to production areas most likely generated the loss of carbon to the atmosphere. In fact, in the Gulf of Mexico [31], the carbon storage potential in herbaceous wetlands or marshes was determined, where the greatest carbon contribution was recorded in the soil or underground with an average of 322.4 ± 64.6 tC/ha.

Mangroves are estimated to store 12 billion metric tons of carbon worldwide [32], while tropical peatlands store a carbon content between approximately 152 and 288 Gt C, half of the carbon emitted by peatlands at a global level [33]. Moreover, if wetlands in transition zones between mangroves and terrestrial ecosystems were to be cosidered, global estimates of carbon storage by freshwater wetland ecosystems could increase.

The contribution to the accumulation of epigean carbon (tC) was greater in Tonosí, with 65% coming from the soil and 35% epigean, compared to Portobelo, where we had 85% of epigean origin and only 15% from the soil. These differences may be associated with land use and the dynamic nature of flooding, which is not regulated by tides in Portobelo. On the other hand, in mangroves where we have a tidal-regulated flooding periodicity, we can find a soil carbon percentage that ranges between 71 and 96% according to [34]. In the mangroves of eastern Chiriquí, a greater contribution of carbon (77%) to the ecosystem from the soil was recorded [28]; similarly, in the freshwater wetlands of Matusagarati in Panamá, a greater amount of soil carbon was recorded in orey forests and in mixed evergreen forests, but in cativo forests, that relationship was inverse [11].

A greater percentage or amount of carbon due to the epigean contribution was also recorded in the Montijo mangroves in Panamá by [35], where the average of the total carbon of the ecosystem obtained was 178.36 ± 10.28 Mg/C ha and the highest composition came from the aerial component (live trees 139.53 ± 83.67 Mg/C ha and dead trees 1.47 ± 36 Mg/C ha of the total). According to [36], the evaluation of C reserves and sequestration rates in wetlands, considering spatial and temporal trends, ecological characteristics, and different levels of human pressure, is necessary to improve estimates of C reserves on a global scale.

The percentage of carbon in the soil of these freshwater wetlands was similar, caused by anthropogenic activity disturbances such as soil removal for agriculture and possible soil compaction by livestock. This is related to what was mentioned by [37] in studies on soil carbon content under different forest conditions, where they determined that the carbon storage capacity of soils depends on their properties and uses. On the other hand, ref. [38], who measured the carbon in the soil of coastal wetlands, reported that the amount of carbon accumulated varies according to climatic conditions, vegetation type, hydrology, and physicochemical conditions of the soil, which may be contributing factors in our study areas.

The vertical distribution of soil carbon at different depths in the Tonosí and Portobelo plots reveals that there is a high concentration of carbon in the surface layer and a decrease in the deeper layers, with no statistical differences between the plots of the two sites studied. Studies by [2,5,37] mention that conserved soils can store between 40 and 80% of soil carbon, mainly in the A horizon. This is related to waterlogging and soil moisture most of the year, which could indicate that there is a good relationship between soil microorganisms and organic matter mineralization processes. However, refs. [13,39] have demonstrated that agriculture and livestock farming have negative effects on freshwater wetland soils by irreversibly changing soil properties and decreasing soil carbon reserves, and the rate at which soil carbon is lost is poorly known in these ecosystems. This is related to the findings of this study, which found a higher percentage of carbon in the first soil layers in both sites, so these findings could be conclusive for the conservation of soils and carbon associated with these ecosystems, emphasizing the importance of protecting the surface layers of the soil to maintain its carbon storage capacity.

5. Conclusions

Our study highlights the critical role of transitional wetlands located between mangroves and drier continental areas in carbon accumulation and storage. No peatlands were found at either site, but we observed substantial ecosystem carbon values in Tonosí (106.26 ± 18.3 tC/ha) and Portobelo (355.09 ± 18.3 tC/ha), comparable to those found in mangroves. These findings underscore the necessity of reassessing current mangrove conservation strategies to prioritize adjacent wetlands. In Portobelo, it is necessary to increase the conservation measures of wetlands within the national park, and in Tonosí, wetland areas close to the mangroves must be included as conservation zones to preserve carbon reserves.

It is imperative for these ecosystems to be included in future Nationally Determined Contributions (NDCs) under the climate change convention, as well as in management plans and conservation strategies, including carbon offset initiatives.

It is possible to preserve these sites, even if they are altered by agriculture and livestock, through ecological restoration to productive wetlands that could capture carbon to maintain the ecological balance in the area.

Although the total ecosystem carbon storage (TECS) in these wetlands is low in relation to other wetlands such as mangroves and peatlands, we consider it important not to change the land use of these ecosystems in order to avoid emissions of carbon dioxide and other greenhouse gases into the atmosphere as a result of local anthropological activities that would contribute to climate change. However, ecological restoration could be a viable long-term strategy for wetland conservation, allowing the recovery of lost ecological services and increasing biodiversity in wetlands, helping to mitigate the effects of climate change, and increasing ecosystem services for human health and well-being.

Implementing a standardized carbon measurement methodology across Panama would mitigate biases, enabling more accurate comparisons and facilitating the identification of sites with the highest carbon stores. Incorporating these transitional wetlands into mangrove carbon estimates could significantly enhance the carbon sequestration potential of blue carbon initiatives, thereby enhancing the appeal of nature-based mitigation strategies.