Vegetation Structure and Species Distribution of Mangroves along a Soil Salinity Gradient in a Micro Tidal Estuary on the North-western Coast of Sri Lanka

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Table 1. Basic information about the study sites at Kala Oya estuary

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2.2. Vegetation Structure

Data on mangrove vegetation structure i.e. species diversity, tree diameter at breast height (dbh) and tree height were collected using standard methods ^{[1, 11]} from each sub-plot (100 m²) in the belt transects. Plants with less than 2.5 cm in girth at breast height (gbh) were excluded.

Complexity of index ^{[12, 13]} which has taken into account the number of species, stand density, stand basal area, and stand height, was modified and leaf area index too was added to the equation.

Relative contribution of each constituent species to the mangrove vegetation structure was assesses by calculating the importance value for each species.

^[11]

2.3. Potential Gross Primary Productivity (GPP)

Leaf area index was determined by measuring the difference between photon flux density beneath the mangrove canopy and top of the canopy, by using LI-COR Terrestrial radiation sensor (LI-191SA Line Quantum sensor) and following the method described by ^{[11, 12]}.

I = photon flux density beneath the canopy

I_o = photon flux density incident on the top of the canopy or fully exposed position outside the canopy

L^I = Leaf area index correction; L = Leaf area index; θ = zenith angle

k= canopy light extinction coefficient.

Clough (1997) and English et al (1997) reported that k commonly lies between 0.40 and 0.65 in a variety of mangrove canopies and hence the average value, i.e. 0.5 was used for calculations.

Approximately 50 readings were taken beneath the canopy (I) from each sub plot and accordingly 200-250 readings were obtained from each transect/study site during 08.00 and 14.00. Zenith angle (angle of the sun from the vertical) at the time of measurement was obtained from the Metrological Dept. of Sri Lanka.

Potential gross primary productivity (GPP) of the canopy per unit area was calculated according to the method proposed as below ^[10].

P_N = Potential gross primary productivity of the canopy per unit area

d = day length in hours; L = leaf area index

A= Average rate of photosynthesis, the value for dry season was taken as 0.216 g C m^-2hr^-1 and for the wet season as 0.432 g C m^-2hr^-1

Since the rate of photosynthesis varies from dry to wet season ^{[14, 15, 16]}, and therefore the present study, the average rate for dry season was taken as 0.216 g C m^-2 hr^-1 while that for the wet period was considered as 0.648 g C m^-2 hr^-1 ^[17].

2.4. Soil Salinity

Soil was collected from the first 5 cm from the surface and water was obtained by pressing soil with a plastic syringes to obtain interstitial water and its salinity was measured using a potable refractometer (iuchi IS-Mill-E). Mean salinity was calculated with salinity measurements made at 4 randomly selected localities in each sub-plot.

2.5. Mangrove Biomass of Mangrove Plants

Above ground and below ground biomass of mangrove species in the sub-plots was determined by allometric equations derived for individual species ^{[18, 19, 20]}. Table 2 includes the allometric equations used for the purpose.

Table 2. Summary of the used allometric equations for determine above and belowground biomass in mangroves

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3. Results

3.1. Relative Contribution of Constituent Mangrove Species to Vegetation Structure

Rhizophora mucronata was the most abundant (528 trees/ha) mangrove species in Kala Oya estuary. Lumnitzera racemosa retains the highest biomass (1.27 t/ha) while Aegiceras corniculatum population retains less than 0.5 ton of biomass per ha. The mean tree size (as indicated by mean Excoecaria agallocha, and Lumnitzera racemosa (as indicated by mean dbh, basal area and height) are the most dominant and it is corroborated by the importance value index (IVI). Structural data on constituent species of mangrove areas at Kala oya estuary are presented in Table 3.

Table 3. Structural data of constituent species of Kala Oya estuarine mangroves

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3.2. Mangrove Vegetation (Plant Community) Structure

Highest stand density (3375 trees/ha) was revealed from the sampling site that is situated 2.1 km from the estuarine mouth where the soil salinity is 13.25 mg l^-1 (Table 4). The highest basal area (48.25 m² per ha) and leaf area index (6.52) were recorded from the study site located 3.5 km from the estuary mouth where the soil salinity is 8 mg l^-1. Highest species richness occurs at the study site located 2.4 km from the estuary mouth where the soil salinity is 13 mg/ l.

Table 4. Summary of mangrove vegetation structural parameters and soil salinity recorded in each study sites at Kala Oya estuary. Standard error of the mean is given in the parantheses

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Figure 2.The relationship between vegetation structure in terms of complexity index (CI) with distance upstream from sea mouth of Kala Oya estuary

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Figure 3. Density of constituent plant species of mangrove stands occupying areas with varying soil salinities distributed within a distance of 3.5 km at Kal Oya estuary

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Complexity of index (CI) that represents the structural complexity of mangrove communities at each site shows a statistically significant positive correlation with distance from the estuary mouth and s oil salinity (Figure 2).

Along the 3.5 km length of Kala Oya estuary, Avicennia marina was observed to be most abundant in high salinity areas (13.25-23 mg/ l) near the estuary mouth and it gradually declines along the salinity gradient. L. racemosa occurs in low dominance in estuarine mouth (high salinity areas), and it increases at 2000- 2500 m upstream, nevertheless showing a decrease in the areas of low salinity (8 mg/l) about 3000-3500 m upstream from estuary mouth. E. agallocha and Bruguiera cylindrica recorded low occurrence in 850-1000 m area with high salinity (18-23 mg/l) and become highly dominant in low salinity (8 mg/l) around 3000-3500 m upstream from estuarine mouth (Figure 3).

A highly positive correlation was revealed between salinity and density of A. marina (0.730) (Table 5). This implies that density of A. marina increases with salinity. Nevertheless, a statistically significant linearly relationship was not found among salinity and density of A. corniculatum, B. gymnorrhiza and L. racemosa. Plant density of B. cylindrical (r = -0.843), E. agallocha (r = -0.747) and C. tagal (r = -0.647) showed a decrease in density with increasing salinity.

Table 5. Correlation between salinity and distribution of species density at Kala Oya estuary

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3.3. Mangrove Plant Biomass

A statistically significant positive relationship was revealed between total tree biomass and embedded total organic carbon (TOC) content with distance from sea mouth of the estuary where a salinity gradient exists (Figure 4).

Figure 4. Variation of total tree biomass and total organic carbon content with soil salinity along a distance of 3.5 km in Kala Oya estuary

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Figure 5. Relationship between potential gross primary productivity and soil salinity of Kala Oya estuary

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3.4. Potential Gross Primary Productivity (GPP)

A statistically negative correlation was revealed between GPP and soil salinity of the study sites at Kala Oya estuary. Pearson correlation (-0.849) revealed that the correlation is significant at the 0.01 level (2-tailed) (Figure 5).

3.5. Species Diversity

Species diversity (Shannon-Wienner index) calculated for each study plot at Kala Oya estuary showed that a highly negative correlation exists between salinity and species diversity( Pearson correlation = -0.797) and it was significant at the 0.01 level (2-tailed). The highest plant species diversity occurs in relatively low salinity range between 6 -12 mg/l.

Figure 6. Relationship between mangrove species diversity and soil salinity of Kala Oya estuary

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Table 6. Summary of the extraction of principal component analysis of 12 parameters at 21 study plots along the Kala Oya estuary

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Figure 7. Final results of the extraction of principal components plot in rotated space

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Data collected from 21 study plots were subjected for Principal component analysis (PCA). The PCA of the results suggested that three components together were able to explain 94.71% of the total variance in the data. The three PCs individually accounted for 57.18%, 29.11% and 8.41% of the variance respectively. The components loading indicated in Table 4, shows that structural parameters except for tree height, highly load along the first PC (> 94 %). The second component is loaded with environmental and physical parameters of the study area. The salinity of the sites indicated a strong negative contribution to the second PC. The tree height highly loaded in the third PC with value of 0.958. Overall extraction of principal components plot in rotated space is depicted in Figure 7.

The PCA scores derived for PC1 and PC2 were used to plot to observe the pattern reflected from the sites included in the study (Figure 8). The sampling plots of site 1 clustered into one group with certain amount of overlapping were with the plots of site 2. Similarly, the plots of site 3 and site 4 fall into a single cluster. The plots of the site 5, were well separated from the rest of the sites. The general pattern reflected from the study was that the salinity variation between sites seems to be contributed much to the species occurrence reflected in the Figure 8.

Figure 8. Distribution pattern of each sampling plot in study sites

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