Kaingen is one of 23 barangays in the municipality of Kawit, in the province of Cavite, belonging to Region IV-A (CALABARZON), with an estimated elevation above the sea level of 7.5 meters (24.6 feet). According to the 2020 census, the population in the area was 1,723, which represents 1.60% of the municipality's total population. The researchers examined three sites within the mangrove community, measuring an area of 2,762 m2 for each site, using a 100-m field tape measure – Site 1 (14°26'55" N 120°54'26" E) is located near the aquaculture site of barangay Kaingen and residential area, Site 2 (14°27'16" N 120°54'32" E) is located near the aquaculture sites along Kaingen River, and Site 3 (14°27'21" N 120°54'33" E) is located next to Site 2 and end of Kaingen River.

Mangroves are considered taxonomically diverse and salt-tolerant species. They grow in transition or intertidal zones, a part of the ocean where it meets the land, and on the fringing tropical and subtropical coastlines. Mangroves are known for their relationships within terrestrial and marine habitats due to their physical, biochemical, and biological interactions in the environment. They serve as a breeding ground, shelter, and feeding habitat for various aquatic animals (Primavera et al., 2018). Mangroves are distinguishable from other plant species due to their ability to withstand changes in salinity and allow oxygen exchange in sediments (Feller, 2018). Furthermore, mangroves have two categories: true and associate mangroves (Pototan et al., 2020).

In the Philippines, there are 42 mangrove species representing 18 families with a rich number of true mangrove species (Abino et al., 2013). In the study by Thomas et al. (2017), Southeast Asia accounted for the most mangrove deforestation per year between 2000 and 2012. The mangrove diversity's pronounced loss was due to the conversion of aquaculture (30%) and rice agriculture (22%) (Primavera et al., 2018).

Garcia et al. (2014) found that, quantitatively and qualitatively, the Philippines possess a high level of biodiversity. Due to its remote location, varied ecosystems, and high endemism rates, it is considered one of the seventeen countries with "mega biodiversity." Out of roughly 65 species of mangroves in the world, at least half can be found in the Philippines. However, the country is gradually losing its rich biodiversity resources, including mangroves, because of anthropogenic and natural disturbances.

Carbon sequestration is a process done by aquatic and terrestrial trees in capturing and storing CO2 in the atmosphere. Among different forests, mangroves comprised most of the carbon-rich ecosystems due to their ability to store five times more carbon per unit area, as Ahmed et al. (2022) studied. Quantifying the potential storage sequestered by the mangrove ecosystem is crucial, especially in reducing the effects of climate change, as supported by Nesperos et al. (2021).

Mangrove forests rapidly lost the carbon stored in their tree biomass due to cutting, human consumption, decomposing, and carbon export to neighboring ecosystems. Alongi (2014) stated that most carbon sequestered for future use is underground. The quadrat technique is a non-destructive method used to determine a mangrove tree's carbon sequestration at a specific sampling location. Species were determined, and baseline measurements were taken, including height and the circumference of a tree's base. Komiyama's allometric equations were used to determine a tree's aboveground and belowground biomass. Biomass above ground consists of stems, branches, and leaves. All individual biomass values were added together to arrive at the total biomass. The calculated mean total biomass was then multiplied by the carbon conversion factor for mangroves (0.50 for woody and dry biomass matter) from the Intergovernmental Panel on Climate Change. Nesperos et al. (2021), reported that the amount of carbon sequestered was calculated by multiplying the amount of carbon stored by 3.667 to obtain the number of carbon dioxide (CO2) emissions.

Mangroves are good indicators of water quality. It is known to provide good water quality by filtrating harmful pollutants in water through its aboveground roots and providing a habitat for aquatic species. The following physicochemical parameters were quantified to determine the water quality in Kaingen Riverine.

Soil is known as the largest terrestrial carbon sink; it can supply significant amounts of nutrients for the growth and development of plants, as well as mangroves. Mangroves are known to store more carbon than any terrestrial forests. Due to the carbon sequestration ability of mangroves, it increases soil productivity, hence, improves the soil quality by increasing the volume of soil and adding its organic matter such as leaves, woody material, and roots. The following soil quality was quantified to calculate the potential of mangroves in the Kaingen Riverine to store carbon.

The following are studies reviewed to serve as the basis of the study.

Anthropogenic activities in Kaingen River, Kawit, Cavite, Philippines, were determined through interviews and survey forms handed to the residents of Barangay Kaingen, Kawit, Cavite. Written consent was provided to the residents to ensure that the data obtained would remain confidential and intended only for academic purposes following Republic Act No. 10173 or the Data Privacy Act of 2012. The total number of respondents was determined using Slovin’s formula.

Slovin’s Formula where:

The samples of water were collected using grab and composite sampling methods following the standard procedure of Department of Environment and Natural Resources Administrative Order (DAO) No. 2016-08, entitled “Water Quality Guidelines and General Effluent Standards of 2016” and DAO No. 2021-19, entitled “Updated Water Quality Guidelines (WQG) and General Effluent Standards (GES) for Selected Parameters”. Grab

samples of some physicochemical parameters were measured in-situ such as temperature, turbidity, total dissolved solids (TDS), salinity, pH, and dissolved oxygen. At the same time, composite samples of phosphates and nitrates were sent to the laboratory for analysis. The water body classification of the three sites in the sampling location was categorized as Class C, which is intended for beneficial use for fish growth and propagation, including other aquatic resources as well as Recreational Water Class II.

The Water Quality Index (WQI) was calculated with the use of the Weighted Arithmetic Method (Banaag & Velasco, 2021) using Microsoft Excel. The unit weight (Wn) factor of each of the water parameter was :

where:

On the totality of all chosen parameters’ unit weight factor, Wn = 1 (unity). After calculating the proportionality constant (K), the Sub-Index (Qn) value was computed using the formula as follows:

where:

Generally, most of the parameters have actual values equivalent to zero (V_O = 0), except for certain parameters such as pH (V_O = 7) and DO (V_O = 14.6). Therefore:

After obtaining the sub-index (Qn) for each parameter, the unit weight per parameter (Wn) was multiplied by the Qn, where the total WnQn of all parameters was calculated before dividing it by the summation of unit weight (Wn) of all parameters

Following the Bureau of Soils and Water Management (2020) in soil sampling, a soil probe, a meter stick, a plastic, and a pail were prepared for the soil sample collection in the area. The soil samples' temperatures were analyzed in-situ. The ex-situ analysis used a test kit to analyze the sample's pH, nitrogen, phosphorus, and potassium. In addition, water holding capacity and soil texture were analyzed by the laboratory. The area was divided based on the sampling site, and three soil samples were taken on each site without any trials being conducted. These samples were taken at 10 points in a zigzag pattern and placed in a separate container. Afterward, organic matter and trash from the soil sample collected were removed. The soil sample was collected directly using a soil probe at a depth of 25 to 30 cm below the rim of the crown of the mangrove, wherein the sample was a composite soil sample. The soil sample was air-dried away from any contaminants. The sample used for the analysis was crushed and sorted away from the remaining organic matter in the soil sample and divided into four sides where two opposite sides were used, placed in the plastic bag upon obtaining one kilo of the sample and labeled before being sent to the Bureau of Soils and Water Management Laboratory for analysis. Testing methods for each soil quality parameter varied according to the current standard methods of BSWM. Soil pH and NPK were tested using a Soil Test Kit from the Bureau of Soils and Water Management.

In contrast, soil texture was examined using a Bouyoucos Hydrometer. The tapping method was used to determine the values of the samples' soil water holding capacity, while the organic matter used Walkey and Black Method. On the other hand, the test for soil organic carbon was obtained by multiplying the analysis result for soil organic matter by 1.72.

The Allometric equations used were established by Komiyama et al. (2005), as referenced by Abino et al. (2013), in the estimation of the aboveground and belowground biomass per mangrove tree. With the use of measuring tape, the girth of individual mangrove trees was obtained by measuring the circumference of mangrove trees not less than 10 cm, at the standard breast height of 1.37 m above the ground in centimeters.

The girth was divided by π at the value of 3.1416 to obtain the diameter (cm). The diameter of each tree was used in allometric equations along with the wood density for each mangrove species as follows:

Aboveground biomass

Belowground biomass where:

After the biomass for the aboveground and belowground of an individual tree was obtained, the two are summed to obtain the total biomass.

The mean total biomass was obtained by taking the sum of the total biomass of each mangrove tree per site. Then it was divided by the site's total area before converting it to tons per hectare (t/ha).

where:

Whereas the area was determined by multiplying the measured width of 27.62 m by the established length of 100 m using field measuring tape.

In determining the amount of carbon stored, the computed mean total biomass was multiplied by the carbon conversion factor for mangroves at 0.50 value based on the Intergovernmental Panel on Climate Change's default carbon fraction value for woody and dry biomass matter. In contrast, the amount of carbon sequestered was obtained by converting the amount of carbon stored to carbon dioxide (CO2) emissions by multiplying it by 3.667, according to Nesperos et al. (2021) and US EPA.

Amount of Carbon Stored (tC/ha)	Amount of Carbon dioxide Sequestered (tCO2/ha)
(Mean total carbon biomass) (0.50)	(tC/ha) (3.667)

The Continuous Line Transect Method was used to measure the population of mangrove species in three sites within the area measuring 100 meters in length and 27.62 meters in width. The book Field Guide to Philippine Mangroves by J.H. Primavera (2009 & 2022) could serve as the standard reference for mangrove species identification. A sample of each mangrove species was collected for the herbarium, measuring a length of 12 inches which includes the bud, flower, leaves, and fruits. The sample was sent to the

University of the Philippines - Diliman Quezon City Institute of Biology and Dr. Jurgenne

H. Primavera, a marine scientist known for verification and authentication. The collected sample species of mangroves were photographed as part of the photo documentation method.

The evenness, richness, and diversity of species were used to compare a variety of biological populations.

The Species Importance Value, often known as the SIV, is a metric utilized to acquire the general significance of individual species within the community. The total of the percentage values of relative frequency, relative density, and relative dominance is what is referred to as the SIV.

Statistical tests used in the study are Kruskal-Wallis, Friedman, and Pearson’s r Correlation Test. These were done using the IBM SPSS version 26 Software.

Anthropogenic activities can directly affect the condition of thriving bodies of water; hence, assessing the respondent’s daily activities near the Kaingen River is important in determining how it can affect the water quality of the Kaingen River. There were 336 total survey questionnaires distributed to the residents of Barangay Kaingen, Kawit, Cavite.

Based on Table 1, the anthropogenic activities near Kaingen River are fishing and bathing which occurred rarely. These activities were observed during the five months of sampling, wherein residents of Brgy. Kaingen, mostly kids were bathing or swimming in the river. The throwing of garbage in the river was also observed. Most soil samples per site have evidence of thrown plastic residues and pieces of clothing. However, the result of the survey showed this activity has never occurred in the river, this meant that the collected garbage residues were resident wastes thriving in the river for a long time. Fishing activities can be observed since most residents residing nearby the Kaingen River were local fishermen. Kaingen River has situated nearby Bacoor Bay and Manila Bay. It serves as an estuary, rich in diverse aquatic species such as fishes and mollusks. On the other hand, anthropogenic activities such as washing clothes, disposing of chemical wastes, excreting, and bathing animals were never done by the residents due to precautions implemented by the management of Brgy. Kaingen Kawit, Cavite.

Protection and conservation measures are necessary for the controlling and monitoring of the river’s water quality to mitigate the deteriorating effects of harmful anthropogenic activities on the thriving aquatic species in Kaingen River. The perspicacity of Brgy. Kaingen’s residents in protection and conservation measures for the Kaingen River were asked and assessed through survey questionnaires. Table 2 showed the administered response of the residents for the protection and conservation of the Kaingen River.

As presented in Table 2, the respondents strongly agreed with all of the conditions for the protection and conservation efforts of the Kaingen River such as preventing people from committing unlawful behavior in the river, taking part in environmental groups, creating groups and organizations, river conservation guidelines, promoting sustainable methods, preventing the depositing of wastes, river management, and supporting environmental programs. The result revealed that residents recognized the value of the river; its living thing components, beauty, and purpose as commercial rivers that most fishermen were relying on for their living. Residents are willing to exert extra effort to protect the river from the harmful effects of pollution by preventing unlawful behavior and depositing waste. Respondents were open to new knowledge and willing to join with environmental groups or create groups that will manage the river. A willingness to have river conservation guidelines, policies, and ordinances was observed in the interviews conducted during the distribution of survey questionnaires. The respondents wanted to promote sustainability and support environmental programs that seek to protect the Kaingen River and improve the Barangay's current approach to the protection and conservation of the river. Furthermore, respondents also agreed to have proper river management, which seeks to control the collection and extraction of fish and other aquatic resources in the river, as well as creating groups or organizations whose sole purpose is to oversee, manage, and monitor the condition of Kaingen River.

Water quality parameters were measured in-situ using different multi-tester for each designated parameter and conducting three trials per site. The data and results obtained from the tests were tabulated as presented in Table 3.

Table 4 summarizes the water quality index of Kaingen River per site during the five-month sampling period using the Weighted Arithmetic Method.

Based on the results depicted in Table 4, the water quality index in all sites during the sampling period from starting from November 2022 to March 2023 ranged from 108.45 to 159.44 which implied that the water quality in Kaingen River is unsuitable and deteriorated, which means that it is unfit or unsuitable for human consumption.

The water quality index of the three sites throughout the sampling period all exceeded the 100 range which indicated that all sites were under the unsuitable category. The highest water quality index in November was recorded in Site 3 having a WQI value of 125.64 which was significantly affected due to the high values of turbidity, biological oxygen demand, and phosphates. The highest WQI for December, January, and February was recorded in Site 2, which was mainly caused by the high WnQn of the parameter’s turbidity and phosphates. Lastly, the month of March showed that the highest water quality index was recorded in Site 3 showing a significant difference from the WQI recorded in sites 1 and 2. The high WQI in March was attributed to its high total coliform and turbidity and showed that the phosphate levels in March have significantly dropped in value compared to the preceding months. In a comparison of all the months included in the sampling period, the highest average water quality index was recorded in December 2022, which is equivalent to 154.82, followed by January 2023 with 146.48, February 2023 with an average WQI of 139.96, then November 2022 with 119.96 and March 2023 with 114.58 which was slightly lower than November and all fall under the unsuitable category which indicated that the water in Kaingen River throughout the sampling period was unfit for human consumption. In addition, the average WQI per site including all five months revealed that Site 2 has the highest WQI equivalent to 138.91, followed by Site 3 equivalent to 137.00, while Site 3 garnered the least average WQI equivalent to 129.57. It can be observed that the water quality index was mainly affected by the WnQn values of the parameters including turbidity and phosphates which contributed a lot to the dark and murky appearance of the water in the Kaingen River. However, the most notable water parameter that contributed to the increase in the WQI was the phosphates which have exceedingly high values compared to the other parameters especially starting from December 2022 to February 2023. The increased phosphate levels resulted from anthropogenic activities present in the area (Indicators: Phosphorus | US EPA, 2022). Anthropogenic activities present in the Kaingen River such as fishing have caused the water quality index to appear high. In addition, precipitation runoff and other natural phenomena like tides, weathering, and erosion, as well as the decomposition of debris and leaves may have contributed to its increase. Specifically, phosphate levels tend to increase due to the presence of septic systems in which, according to the survey conducted, at least 65.88% of the total population in Barangay Kaingen have proper sewage systems in their households. In addition to this, frequent occurrence of precipitation runoff from urban areas may have been directed into the river which was also responsible for the river's increase in phosphate levels.

The determination of soil texture was done at the Bureau of Soils and Water Management. The texture of the representative soil samples in total sand, silt, and clay, were analyzed using the Bouyoucos Hydrometer whereas the water holding capacity of the soil samples was measured using the Tapping method of testing. The data and results obtained from the tests are tabulated in Table 5.

The physicochemical properties of the soil along the Kaingen River were determined through a series of in-situ and ex-situ soil testing from November 2022 to March 2023. Table 6 shows the parameters for soil analysis include temperature, which was tested in-situ using a laboratory thermometer while soil pH, nitrogen, phosphorus, and potassium were tested using the colorimetric method from the Bureau of Soils and Water Management (BSWM) Soil Test Kit. Soil Organic Matter was tested in the laboratory using the Walkley and Black Method while Soil Organic Carbon was obtained by dividing the organic matter to 1.72. The data and results obtained from the tests are tabulated as presented in Table 6 and Table 7.

Pearson’s r Correlation is a test statistic used to identify the relationship between the water quality and abundance of mangroves in Kaingen River. Table 8 showed the summarized results of the abundance of mangrove trees for each water parameter.

Based on Pearson’s r Correlation Test, the temperature (r = -0.032; p = 0.911), turbidity (r = -0.298; p = 0.28), total dissolved solids (r = 0.16; p = 0.568), salinity (r = 0.16; p = 0.568), conductivity (r = 0.16; p = 0.568), pH (r = 0.288; p = 0.299) and dissolved oxygen (r = 0.054; p = 0.849) were observed to have negligible correlation and has no significant relationship with the abundance of mangroves as the p-values obtained were above the significance level of 0.05 (two-tailed).

Temperature is an indicator of the absence and presence of mangroves, a sudden fluctuation can negatively affect the ability of mangroves to photosynthesize, hence, distorting their ability to flourish. Mangroves only survived at temperatures above 19° C and not exceeding 10 ° C (Noor et al., 2015). Although Table 3 showed that the temperature of water in Kaingen River is accepted for mangrove habitat, the result of Pearson’s r correlation resulted in a negligible correlation and was not significant in mangrove abundance (r = -0.032; p = 0.911).

Turbidity measures the clarity and affects the amount of light passing through the water. When water is turbid, it appeared to be cloudy caused by inorganic and organic matter, silt, clay, algae, plankton, and other microbes (Water Science School, 2019). Based on the correlation between the mangrove abundance and turbidity, there is no significant relationship between the two variables (r = 0.16; p = 0.568).

Dissolved oxygen is an indicator of healthy water bodies. According to the study of Chunkao et al., (2012), mangrove communities increase the amount of DO concentration in water. However, the correlation obtained depicted that the DO has a negligible correlation with the mangrove abundance (r = 0.054; p = 0.849).

Salinity is a key environmental factor that controls the growth of mangroves. The study of Basyuni et al., (2019) showed that Avicennia alba can resist high salinity, while Rhizophora and Xylocarpus only grow within a specific range, due to their characteristic as non-salt secretors (ultrafiltration). Hence, the growth response of mangroves differs according to the response of mangrove species to a change in salinity concentration. On the other hand, total dissolved solids and conductivity are driving factors to salinity level (Rusydi, 2018), wherein table 3 accounted for a direct relationship within the three parameters. Although the result of Pearson's r correlation showed that salinity, TDS, and conductivity yielded the same r and p-value (r =0.16; p = 0.568), all three parameters have negligible correlation, and therefore, not significant in the abundance of mangroves in Kaingen River.

The pH of water quantifies the limit of distribution of species in aquatic habitats, it indicates whether an aquatic species can thrive at a certain pH level. Mangroves in Kaingen River have been found to thrive within the pH level of 7.540 to 8.150. The result of Pearson’s r correlation for pH resulted in a negligible correlation and was not significant in mangrove abundance (r = -0.054; p = 0.849).

Overall, the correlation results showed that the physicochemical parameters of the water measured were not related to the abundance of mangroves in the Kaingen River.

Pearson’s r Correlation is a test statistic used to identify the relationship between the soil quality and abundance of mangroves in Kaingen River. Table 9 showed the summarized results of the abundance of mangrove trees for each soil parameter.

According to Pearson’s r Correlation Test, there is a low negative correlation and has no significant relationship between organic matter (r = -0.452; p = 0.090), organic carbon (r = -0.452; p = 0.090), available nitrogen (r = -0.414; p = 0.127), and available potassium (r = 0.226; p = 0.417) with the abundance of mangroves in all sites from November to March. Whereas a negligible correlation and no significant relationship occur between water holding capacity (r = -0.132; p = 0.639), soil temperature (r = -0.187; p = 0.504), and available phosphorous (r = 0.226; p = 0.417), with the mangrove abundance found in all sites during the 5-month sampling period. On the other hand, the soil pH showed that the correlation (r) coefficient cannot be computed because one or more variables of soil pH were constant and therefore, cannot describe its relationship with the abundance of mangrove trees.

Organic matter indirectly affects the growth of mangroves by influencing the soil structure in which it binds and forms stable soil aggregates, thus, improving the soil structure to increase the water-holding capacity of soil which is beneficial for the growth and distribution of mangroves. A decrease in soil organic matter corresponds to a decrease in the soil’s porosity and turn decreases the soil’s capacity to hold water (Jiao et al., 2020) which is an essential factor that supports mangal abundance in an area. However, Pearson’s r Correlation indicated that there is a low negative correlation (r = -0.452) which infers that as organic matter increases, the abundance of mangroves slightly decreases. It also showed that the relationship between the two is not significant (p = 0.090); therefore, there is no significant relationship that exists between organic matter and the abundance of mangroves in Kaingen River, Kawit, Cavite.

Soil organic carbon affects the physical property of soil in terms of the soil’s pore structure. The study conducted by Fukumasu et al. (2020), showed that there is a positive correlation between soil organic carbon and pore size distribution in soil which then contributes to the capacity of soil to hold and retain water that is essential for the growth of mangroves. Pearson’s correlation revealed a low negative correlation (r = -0.452) which presents that as organic carbon increases, a slight abundance of mangroves occurs. However, the statistical treatment also presented that the relationship between the two parameters is not significant (p = 0.090). Hence, this indicates that there is no relationship between organic carbon and the abundance of mangroves.

Nitrogen availability in soil is considered one of the most important nutrients that affect plant growth. According to Pradipta et al. (2021), nutrients such as nitrogen affect the number of photosynthetic processes occurring in mangroves wherein the concentration of nitrogen found in leaves increases the electron transport in the photosynthesis process. However, most mangrove forests thrive even in nutrient-limiting soils, and that nitrogen availability only enhances the mangrove's growth but not its abundance in an area. Thus, the difference in the abundance of mangroves at each site despite the values of nitrogen availability shows that there is a low negative correlation (p = -0.414) which means that as nitrogen availability increase, the abundance of mangroves sightly decrease in Kaingen River, Kawit, Cavite but also shows that it is not significant (r = 0.127). Therefore, the result indicates the absence of a significant relationship between available nitrogen in soil with the abundance of mangroves.

Potassium availability in the soil is an internal factor that influences the abundance of mangroves in an area. Potassium is utilized in fertilizers, boosting soil fertility, and serving as an indicator of healthy plant growth. A lack of potassium in the soil may result in unhealthy plant development (Sofawi et al., 2017). Several variables, including precipitation, moisture content, textural class, and temperature, may have contributed to the highest Potassium availability at each site. The statistical test revealed that there is also a low negative correlation (r = -0.384) which means that as potassium increases, a slight decrease in the abundance of mangroves occurs. However, it also showed that it is not significant (p = 0.158) which indicates that there is no significant relationship between soil potassium availability and mangrove abundance.

The water-holding capacity of soil is the ability of soil to retain moisture. It pertains to the moisture content of the soil that persists in the soil after the water is exuded. A higher water holding capacity estimates the highest volume of water that is stored by the soil that will be utilized by plants for growth and survival (Zhang et. al, 2021). However, results showed a negligible correlation (r = -0.132) between the two variables which also showed that it is not significant (p = 0.639). In conclusion, there is no existing significant relationship between water-holding capacity and mangrove abundance.

Soil temperature is one of the main factors that influences the spread of mangroves in the ecosystem. Higher density of mangrove populations tends to grow in sheltered tropical and subtropical temperatures which have higher temperatures which are commonly abundant along the Southern and Northern Hemispheres (Ward et al., 2016). The soil temperature varies according to the fluctuations in atmospheric and solar temperature, hence, an increase in solar and atmospheric temperature also increases the temperature of the soil. The increase in the temperature increases the rate of evaporation in soil, thus increasing the soil salinity. The increase in salinity affects mangroves by slowing their growth rates. Comparing the three sites, Site 2 with the slightly lower range of soil temperatures garnered the highest number of total mangroves found in the site whereas Site 1 having the highest range of temperatures from November to December shows the least number of total mangroves found within the site. However, the results indicate a negligible correlation (r = -0.187) between soil temperature and the abundance of mangroves which is also not significant. Therefore, there is no significant relationship between the temperature of the soil and the mangrove abundance in the Kaingen River.

The amount of phosphorus present in the area is one factor that influences the growth and abundance of mangroves. Throughout the entirety of the sampling period, the value of the phosphorus that was measured was consistently between 16 and more than 20 milligrams per liter (mg/L). Insufficient levels of phosphorus and potassium in soil are classified as inadequate nutrients that cannot sustain plant growth. It has been suggested by Alhassan et al. (2021) that because of the decreased levels of nitrogen and phosphorus in the soil, it may cause dwarfism in mangrove trees. Additionally, the concentration of phosphorus in the soil may have been influenced by various factors, including the soil’s pH level and the presence of avian fauna in the area. This shows a negligible correlation (p = 0.226) between the abundance of mangroves and the availability of phosphorus in the soil, as evidenced by the consistent levels of phosphorus across various sites but also showed that it is not significant which concludes that the relationship occurred between available phosphorus and mangroves abundance is not significant.

Moreover, soil pH is another external factor that affects the growth and abundance of mangroves in an area. According to Alsumaiti and Shahid (2018), it was claimed that mangroves cannot withstand soils with extreme pH that are not within the 5.160 to 7.720 pH range. Based on the results of the tests for soil pH, all three sites from November 2022 to March 2023 have a consistent soil pH of 5.800 which falls under moderately acidic pH for soil, thus, showing that the soils in all three sites are within the tolerable range of soil pH for the survival of mangroves. However, consistency in soil pH values indicates constant variables and therefore cannot identify both the relationship between soil pH and mangrove abundance, as well as their significance in their relationship.

Overall, the correlation results showed that the physicochemical parameters of the soil measured are not related to the abundance of mangrove in Kaingen River, except for the soil pH where its relationship to mangrove abundance was not determined since the value of the variable was constant.

The species of mangrove trees present along the river of Kaingen were initially identified using Dr. J.H. Primavera's manual Field Guide to Philippine Mangroves (2009) and (2022) and were then verified by Dr. Primavera as well as Jose Vera Santos Memorial Herbarium, Institute of Biology in the University of the Philippines – Diliman. The percentage occurrences quantify the population of mangrove trees found in each sampling site and determine the abundant mangrove species within the Kaingen Riverine ecosystem. The result for the percentage occurrences of mangrove trees was presented in Table 10.

Based on the tabulated data presented in Table 10, Avicennia alba was observed to be dominant in Site 1, while Rhizophora mucronata of family Meliaceae became dominant in sites 2 and 3. Among all three identified mangrove species present in three sites, Rhizophora mucronata was the most abundant having 218 total trees and an occurrence of 85.16%, followed by Avicennia alba with an abundance of 24 and a total occurrence of 9.380%. On the other hand, the least abundant mangrove species was Xylocarpus granatum with only 14 total trees and a total occurrence of 5.470%.

The biodiversity and conservation status of mangrove trees is necessary to monitor their biological status and whether the trees were categorized in the International Union for Conservation of Nature (IUCN) Red List or known as the list of threatened, endangered, and critically endangered flora and fauna across the world. Table 11 showed the biological and IUCN status of Mangrove trees found in the Kaingen River.

Based on tabulated data presented in the table, the biodiversity and conservation status of mangrove species found in three sampling sites of Kaingen River showed that only Avicennia alba commonly known as Bungalon was introduced in the Philippines. All three species of mangroves were categorized as least concerned according to the International Union for Conservation of Nature (IUCN). Moreover, an accessory tree was found in Site 2 alongside mangrove trees of Kaingen River called Thespesia populneoides of the family Malvaceae or commonly known as Banago. It is a flowering plant native to terrestrial (coastal forest) and shorelines where mangroves are thriving. This accessory tree has introduced biological stats and is usually found in tropical regions of the world, including the Philippines.

The Species Importance Value of the mangrove species was determined by calculating the relative frequency (Rf), relative abundance (RA), and relative dominance (RD), and were ranked from highest to lowest species importance value as summarized in Table 12.

The table illustrated that Rhizophora mucronata exhibited the highest species importance value, as indicated by its 87.50 IV. Rhizophora mucronata, which held the highest Importance Value (IV) ranking, exhibited abundance within Site 2 of the mangrove forest. The study of Rastegar and Gozari (2017) has reported that Rhizophora mucronata, commonly referred to as Asiatic mangrove, is distributed in tropical and sub-tropical regions along coastal areas. Rhizophora mucronata is a crucial constituent of the mangrove ecosystem, as this species plays an essential part in safeguarding the adjacent terrestrial regions from the deleterious impacts of the marine environment. Specifically, Rhizophora mucronata contributes to shoreline stabilization and serves as a protective barrier against high-velocity winds and storms.

Ranked as the species with the second highest importance was Avicennia alba showing an importance value of 11.72. This species was found in all three sites of the riverine system and the most abundant species of mangrove in Site 1. Avicennia alba is a woody type of mangrove species that is known to have a high salt tolerance and can adapt to extreme saline conditions. In addition, Avicennia alba is an important species of mangrove because of its salt exclusion abilities. The species of Avicennia ranked as first among the salt-secretor species of mangroves. Salt secretors allow the species to exclude excess amounts of salts through their leaves and root systems, thus, not only tolerating salt but also maintaining salt concentration in its system (Basyuni et al., 2019). Furthermore, Avicennia species are also known to have thinner leaves containing fewer chemicals such as lignin – species with a higher decomposing rate than other mangrove species, such as Rhizophora mucronata, which contributes more nutrients to the environment (Muliawan & Bengen, 2020).

The mangrove species that ranked third and has the least species importance was the Xylocarpus granatum with an importance value of 7.813. Unlike the other two species, Xylocarpus granatum was found only in Site 2 which accounted for a total of 14 trees. The root system of Xylocarpus granatum is composed of stanniferous cells which it contains the chemical 'tannin', a derivative of phenolics that prevents infestation of bacteria and fungi as well as reduces the damages caused by excess ions, hydrogen sulfides, and salts (Chorchuhirun, 2020). According to Siddique et al. (2017), although Xylocarpus granatum was able to thrive better in non-saline conditions, the species can still adapt in areas with up to 25 PSU (Practical Salinity Unit). This characteristic of mangroves allowed them to perform carbon sequestration and provide other environmental benefits even in moderately saline conditions.

To determine the diversity of mangrove trees found in three sampling sites in Kaingen River, Kawit, Cavite, two diversity indices were used to quantify the mangrove trees such as Shannon-Wiener and Simpson’s Diversity Index. Table 13 showed the summarized results of Shannon-Weiner and Simpson’s Diversity Index of mangrove trees found in Sites 1, 2, and 3 of Kaingen River.

The depicted data from above showed the result of computed value for Shannon- Weiner Diversity Indices of mangroves in the three sampling sites of Kaingen River. The Shannon-Weiner Diversity (H) results were 0.5983 (Site 1), 0.4311 (Site 2), and 0.1105 (Site 3).

In Shannon-Weiner Diversity Index, H with a value of 1.99 and below represents very low diversity. Based on the result, all sampling sites were categorized as having very low diversity. However, among the three sampling sites, Site 1 has the higher computed value for Shannon-Weiner diversity (H = 0.5983) and evenness (E = 0.1994). The result for the Shannon-Weiner diversity index can be inferred from the outcome of Simpson's Diversity Index (Ds) such as 0.5714 (Site 1), 0.7899 (Site 2), and 0.9318 (Site 3). According to the principle of Simpson's index, the diversity value ranges from 0 to 1, the higher the value corresponds to the lower diversity. On the other hand, as the dominance (1-Ds) approaches 1, a high diversity of species is found in sampling sites. Hence, among the three sampling sites, mangrove species were diverse (Ds = 0.5714) and dominant (1-Ds = 0.4286) in Site 1. In addition, it also had high diversity in Simpson's index reciprocal (1/Ds = 1.750). The result suggested that Site 1 possessed a more favorable habitat for mangroves than Site 2 and Site 3.

The difference in quantified mangrove species per sampling site was caused by the uneven distribution of mangrove species driven by the following factors such as human inhabitants near the sampling sites, anthropogenic activities, and soil quality. Site 1 is located near the Kaingen River's entrance and is more likely inhabited by households, and on its other side, a river's flash flood barrier was constructed. Site 2 was usually affected by human activities such as fishing and pathway for boats. Since there were few households on this site, residents are likely to throw waste within the open bodies of water due to insufficient land areas. Furthermore, only one side of Site 3 is inhabited by mangrove trees,

while the other side is an open body of water separating Bacoor Bay from Manila Bay. Based on Table 10, only two mangrove species were found in Site 1 and Site 3, while three species were found in Site 2. The data showed that even though Site 1 was low in species richness, the site possessed much-distributed mangrove trees than other sampling sites. Both sites 2 and 3 have uneven mangrove distribution, hence, affecting the result of Shannon-Weiner and Simpon’s indices. As supported by Cañizares et al., (2016), the result of their study showed, although Brgy. Imelda of Dinagat Island has a relatively high species richness of mangroves, the uneven distribution of its species resulted in low diversity.

Another factor that affected mangrove species distribution is the soil quality. During the five-month sampling period, different concentrations of nitrogen, phosphorus, and potassium (NPK) were observed. Nitrogen for Site 1 ranged from 0-2 to >4.5 mg/L which is likely to possess less range of nitrogen concentration than the other two sites with both ranging from 3.6 to >4.5 mg/L. On the other hand, phosphorus concentrations have been consistent for Site 1 which ranged from 16-20 to >20 mg/L, while sites 2 and 3 have been consistent at >20 mg/L. It showed that Site 1 can acquire less phosphorus concentration. Both nitrogen and phosphorus are limiting nutrients for the growth of mangroves, high concentrations can inflect thriving mangrove ecosystems, whereas moderate concentrations are best for mangrove trees. Subsequently, the concentration of potassium among the three sampling sites is as follows: Site 1 (low to sufficient ++), Site 2 (low to sufficient), and Site 3 (low to sufficient +). It can be inferred that during the five- month sampling period, Site 1 can potentially yield a substantial amount if potassium (sufficient++) suitable for mangrove trees. In the mangrove ecosystem, potassium is an essential nutrient that helps mangroves to regulate protein synthesis, osmotic pressure, and photosynthesis.

To determine the similarity of the three sampling sites in terms of mangrove species composition, Sorensen’s Index of Similarity was used. Table 14 showed the summarized result of an index of similarity between Site 1 and Site 2, Site 2 and Site 3, and Site 1 and Site 3.

The highest similarity index was found between sites 1 and 3, at 100.0%, while the lowest was found between sites 1 and 2 and between sites 2 and 3 at 80.00%. Possible explanations include the changes in water salinity leading to the introduction of new species on each site. In addition, a few numbers of Xylocarpus granatum species in the area may be due to its competition with Avicennia alba since they are both exclusively pollinated by animals, while Rhizophora mucronata is wind or self-pollinated.

Moreover, the location of Site 1 and Site 3 could also be a factor that contributed to their similarity index. Both areas were affected by anthropogenic activities, creating large anthropogenic disturbances present in the area. Natural factors may have also contributed to the seed dispersal of the mangrove species since the mangrove forest is a sanctuary for waterbirds.

The mangrove species were identified using Dr. Jurgenne H. Primavera’s Manual Field Guide and verified by the Jose Vera Santos Memorial Herbarium Institute. Table 15 presented the total biomass of each mangrove species and their corresponding amount of carbon stored and sequestered in tons per hectare.

The calculated aboveground biomass (AGB) and belowground biomass (BGB) were summed to obtain the total biomass in tons per hectare (t/ha) and multiplied by the conversion factor of 0.50 to obtain the amount of carbon stored as referenced from the Intergovernmental Panel on Climate Change (Harishma et al., 2020). Among the three identified species present in the Kaingen River, Rhizophora mucronata has the highest total biomass calculated which yielded the highest carbon stored equivalent to 35.16 tC/ha, while the amount of carbon dioxide sequestered of the species, obtained by multiplying the carbon stored by 3.667, resulted to 128.92 tCO2/ha. This is followed by Avicennia alba which stored 34.92 tC/ha of carbon and sequestered 128.06 tCO2/ha carbon dioxide. Lastly, the least carbon stored, and carbon dioxide sequestered was yielded by Xylocarpus granatum which is equivalent to 1.683 tC/ha and 6.173 tCO2/ha.

The amount of carbon stored and sequestered were both influenced by the population of each mangrove species. As indicated in Table 15, Rhizophora mucronata garnered the highest total biomass, carbon stored, and carbon dioxide sequestered due to its dominance in terms of abundance equivalent to 190 individual trees. Diana et.al (2021) stated that the carbon absorption rate is higher in a few species, such as Rhizophora mucronata, in which many of its trees are scattered throughout sites 2 and 3. However, Avicennia alba yielded results with only a marginal difference in the same categories despite only having 32 individual mangroves in all three sites. This may be attributed to the diameter size of each mangrove species, in which the same publication stated, that trees with greater diameter at breast height have greater carbon-storing and accumulation capabilities. This explained why Avicennia alba is not far behind Rhizophora mucronata in terms of storing and sequestering carbon due to its massive diameter size ranging from 7.640-54.97 cm despite its small abundance. Lastly, Xylocarpus granatum yielded the least amount of carbon stored and sequestered due to the combination of both its low abundance in the area and smaller diameter size only ranging from 4.270-14.96 cm.

Based on these findings, the presence of Avicennia alba should be protected and conserved in the Kaingen River. Hence, plant more of this species since it can sequester more CO2 compared to other mangrove species.

The Carbon Stored and Sequestered were calculated by determining first the Girth at Breast Height (GBH) and Diameter Breast Height (DBH) of individual species of mangroves present in 3 sampling sites of Kaingen River, Kawit, Cavite. After the verification of mangrove species, the Wood Density (WD) of each species was identified on the official website of the World Agroforestry Wood Density Database. The Aboveground Biomass (AGB) and Belowground Biomass (BGB) were calculated using the values from the Mean DBH per species of mangrove present per site. After that, the sum of AGB and BGB divided by the area of the sampling site was calculated to get the value of Mean Total Biomass (MTB) in Kilogram per square meter (kg/m2) which was then converted into Tons per hectare (tC/ha). Lastly, Carbon Stored was calculated by multiplying the value of MTB by 0.50, whereas Carbon Sequestered was calculated by multiplying the value of MTB by 3.667. Moreover, presented in Table 8 is the summary of the calculation of Carbon Stored and Sequestered.

Based on the results shown in Table 16, showed that Site 3 has the highest aboveground biomass (AGB) with a value of 12277.50 kg, followed by Site 1 with an AGB value of 12145.35 kg, and Site 2 with an AGB value of 4276.35 kg. For belowground biomass (BGB), Site 3 has the highest BGB value of 4716.35 kg, followed by Site 1 with a BGB value of 4205.67 kg, and Site 2 with a BGB value of 2090.53. For mean total biomass, Site 3 has the highest value with 61.53 tC/ha, followed by Site 1 with 59.20 tC/ha, and Site 2 with 23.05 tC/ha. For carbon stored, Site 3 has the highest value with 30.76 tC/ha, followed by Site 1 with 29.60 tC/ha, and Site 2 with 11.53 tC/ha. Lastly, for carbon sequestered, Site 3 has the highest CO2 stored with a value of 112.81 tCO2/ha, followed by Site 1 with 108.54 tCO2/ha, and Site 2 with 42.27 tCO2/ha.

The amount of carbon that can be stored by mangroves in their roots and leaves is proportional to the age of the mangroves. The high amount of carbon stored in sites 1 and 3 is due to the older age of mangrove trees in the two sites as referenced by greater diameter at breast height (DBH) measured, whereas the amount of carbon stored in Site 2 was relatively lower because of the smaller DBH. This is supported by Carnell et al. (2022), in which it was stated that the age of mangroves has a significant effect on the amount of carbon stocks, wherein mangroves with ages ranging from 17 to 35 years old double the carbon stored and sequestration rates. On the contrary, the amount of carbon sequestered by sites 1 and 3 is significantly lower than Site 2 despite having high carbon stocks because of the difference in mangrove abundance and spatial distribution. The mangrove trees in Site 2 are more condensed due to the smaller DBH which allows the mangroves to be relatively closer to each other and hence, give way to the growth of more mangrove trees which explains the site’s higher abundance and more carbon sequestered. Mangrove trees in sites 1 and 3, on the other hand, are older and have greater DBH which explained why mangroves in the two sites are scattered and less in number – contributing to the lower amount of carbon sequestered. Lastly, Sites 1 to 3 which represented the mangrove ecosystem of Kaingen River, Kawit, Cavite yielded a total carbon stored of 71.89 tC/ha and a total carbon sequestered of 263.62 tCO2/ha.

The mangrove forest in Kaingen River has the potential to sequester CO2 in the environment since Site 3 alone can sequester 112.81 tCO2/ha. Accumulatively, sites 1-3 sum up approximately 35% of the entire Kaingen River. Therefore, 35% of the entire Kaingen River accumulated 71.89 tC/ha and sequestered 263.62 tCO2/ha. Even though Site 3 has a greater number of matured mangroves than Site 1, mangroves in Site 1 were able to store and sequester higher concentrations of carbon and carbon dioxide, respectively. Moreover, Site 2 has the highest number of mangroves that are currently in their 1-2 years of age. As concluded in the study of Wiarta et al. (2019) Rhizophora apiculata sequestered more carbon in their 5th year compared to the amount of carbon sequestered in their 1st and 3rd year. This meant that in another 3-4 years, all mangroves that are currently present in Kaingen River, especially in Site 2, will reach their maturity stage and will allow them to maximize their potential to sequester CO2.

The study was conducted to determine the potential of mangrove trees present in Kaingen River, Kawit, Cavite, to store and sequester carbon from the environment, as well as identify both water and soil quality and its relationship with the abundance of mangroves throughout the five-month sampling period in three established sampling sites. The three sampling sites were located (1) near the aquaculture site and residential area of Barangay Kaingen, (2) between the aquaculture sites, and (3) the area located at the end of Kaingen River. Water quality was assessed by collecting and measuring physicochemical parameters such as water temperature, pH, turbidity, total dissolved solids, salinity, and dissolved oxygen through in-situ testing. Whereas phosphates and nitrates were sent to Mach Union Laboratories Inc. for testing. The soil quality on the other hand was tested by the researchers using the BSWM Soil Test Kit, while other parameters excluded from the test kit such as soil texture, water holding capacity, and organic matter were analyzed by the Bureau of Soils and Water Management (BSWM) laboratory. Mangrove species were identified using Dr. Jurgenne H. Primavera’s Manual Field Guide (2009) and (2022) and were verified by the Jose Vera Santos Memorial Herbarium, Institute of Biology in the University of the Philippines – Diliman. Diversity indices including the Shannon-Weiner Index, Simpson’s Diversity Index, as well as the Sorensen Index of Similarity, which was used to determine the abundance and diversity of mangroves found in Kaingen River. The amount of CO2 stored and sequestered by mangrove trees were obtained using a non-destructive method of collection by measuring the diameter at breast height (DBH) with the application of allometric equations introduced by Komiyama et al. (2006) for aboveground and belowground biomass. In addition, the use of 0.5 carbon conversion factor was used as referenced on Intergovernmental Panel on Climate Change (IPCC) which was indicated from the studies conducted by Harishma et al. (2020) and Indrayani et al. (2021).

The results for water quality of Kaingen River yielded results that were based on the DENR Standard for Class C Waters. This includes water temperature ranging from 26.53°C – 34.83°C, turbidity (8.000 NTU – 24.00 NTU), conductivity (1379.3 µS/cm – 9276.7 µS/cm), TDS (896.5 mg/L – 6029.9 mg/L), salinity (689.7 mg/L – 4638.4 mg/L), pH (pH 7.54 – 8.15), DO (3.0 mg/L – 9.5 mg/L), and phosphates 0.463 mg/L – 0.815, which exhibited negligible correlation but not significant. Nitrates (0.1 mg/L – 0.29 mg/L), however, exhibited a low negative correlation but also not significant. On the other hand, soil quality standard was based on the United States Department of Agriculture which yielded soil organic matter (0.66% - 12.28%), organic carbon (0.38% - 7.14%), nitrogen (Low – Very high) and potassium (Low to Sufficient++) which have low negative correlation and has no significant relationship. Phosphorus (High – Very High), water holding capacity (38.1% - 121.4%), and temperature (26.0°C - 31.0°C) yielded negligible correlation and no significant relationship whereas pH (pH 5.8) cannot be computed and therefore, cannot describe its relationship with the abundance of mangrove trees.

According to the Kruskal-Wallis Test, the water quality parameters that have a significant difference among the three sites with respective months are the following: temperature having a significant difference in November (p = 0.023) and January (p = 0.038); turbidity in November to March (p = 0.018); TDS, salinity, and conductivity on December (p = 0.027), January (p = 0.050), February (p = 0.027), and March (p = 0.027); pH on November to March (p = 0.018); and dissolved oxygen on November (p = 0.026) and February (p = 0.048). Whereas the Friedman test for water quality parameters shows no similarities among the three sites. Temperature on sites 1 (p = 0.031) and 3 (p = 0.017) while Site 2 showed a significant difference at (p = 0.189); TDS in sites 1 (p = 0.017), 2 (p = 0.022), and 3 (p = 0.017); salinity in sites 1 (p = 0.017), 2 (p = 0.022), and 3 (p = 0.017); conductivity in sites 1 (p = 0.017), 2 (p = 0.022), and 3 (p = 0.017); pH in sites 1 (p = 0.022), 2 (p = 0.017), and 3 (p = 0.017); and DO in sites 1 (p = 0.018), 2 (p = 0.017), and 3 (p = 0.022). However, the Friedman test shows having a significant difference in turbidity for sites 1-3 (p = 0.017), phosphates (p = 0.406), and nitrates (p = 0.406).

According to the Kruskal-Wallis Test, the following soil parameters were observed to have no significant differences in the measurement across the three sampling sites during the five-sampling period: temperature (p = 0.398), soil pH (p = 1.000), nitrogen (p = 0.135), phosphorus (p = 0.368), and potassium (p = 0.323). On the other hand, the water-holding capacity (p = 0.027), organic matter (p = 0.008), and organic carbon (p = 0.008) were observed to have significant differences in the measurements. There were significant differences in the measurement of water holding capacity across the three sampling sites between sites 1 and 3 (p = 0.020) and sites 2 and 3 (p = 0.020), while no significant difference was observed between sites 1 and 2 (p = 1.000). For organic matter, significant differences were observed between sites 1 and 3 (p = 0.003) and sites 2 and 3 (p = 0.020), while no significant difference was observed between sites 1 and 2 (p = 0.525). On the other hand, the differences in the measurement of organic carbon across the three sampling sites were observed between sites 1 and 3 (p = 0.020) and sites 2 and 3 (p = 0.003), while no significant difference between sites 1 and 2 (p = 0.525). The Friedman test shows that there is no significant differences in the measurement of the following parameters for Sites 1 to 3 across the five-month sampling period: water holding capacity (p = 0.189), temperature (p = 0.137), pH (p = 1.000), organic matter (p = 0.308), organic carbon (p = 0.308), nitrogen (p = 0.406), phosphorus (p = 0.406) and potassium (p = 0.322).

According to the inventory of mangroves, Rhizophora mucronata (Bakhaw babae) was the most abundant mangrove species among all identified mangrove species present in the three sites of the Kaingen River. This is followed by Avicennia alba (Bungalon), while the least abundant mangrove species is Xylocarpus granatum (Tabigi).

Among the three identified species of mangroves present in the three established sites of Kaingen River, Rhizophora mucronata has the highest total biomass calculated which also yielded the highest carbon stored and amount of carbon dioxide sequestered. This is followed by Avicennia alba then, the least carbon stored and carbon dioxide sequestered was yielded by Xylocarpus granatum. Accumulatively, all three established sites sum up approximately 35% of the entire Kaingen River. Therefore, 35% of the entire Kaingen River was able to accumulate 71.89 tC/ha of carbon stored and sequester 263.62 tCO2/ha of carbon dioxide. The results of the study urge the residents and authorities of Barangay Kaingen to practice conservation and protection measures for the mangrove ecosystem and its benefits.

Based on the result of the study, the following conclusions were drawn:

Based on the results and discussion obtained, the following recommendations were made: