Sampling Design
Three intertidal continuums in northern Singapore were selected: Sungei Buloh (1°26′47”N, 103°43′25″E), Seletar Island (1°26′36”N, 103°51′43″E) and Chek Jawa (1°24′39”N, 103°59′26″E). Those sites included two mangrove forests-tidal flat (Sungei Buloh and Seletar Island) and one mangrove forests-seagrass bed ecotones (Chek Jawa). In each location, mangrove forests were present alongside other intertidal ecosystems such as tidal flats or seagrass beds (Fig. 1). Sampling was conducted during the late Northeast Monsoon (Dry Phase) from February to March 2017. Daily averages of temperature were between 23 and 32 °C and total daily rainfall between 0 and 31 mm (Meteorological service Singapore, http://www.weather.gov.sg).
Transects were set across the intertidal zone, starting at the tidal flat or seagrass bed and ending at the mangrove forest-terrestrial ecotone, extending landward. Within each location we established three transects separated by a distance of 50 to 500 m, depending on alongshore mangrove forest extent. The distance between points along each transect was determined by the total cross-shore length along the intertidal gradient of the mangrove forest, but sampling points were generally separated by 20 to 120 m, to gain a representation of the entire forest width.
Sungei Buloh and Chek Jawa had between three to five points on each transect, whilst Seletar Island had two points on each transect; terrestrial sampling points were not possible here due to impenetrable forest cover. Terrestrial points at Sungei Buloh did not follow a line but were taken at the closest point possible to the last mangrove-terrestrial forest ecotone. Terrestrial sampling was not possible at Seletar due to impenetrable forest cover. Transect points inside the mangrove that were closest to the ocean are defined as mangrove forest 1 (MF1), and the three points furthest from the ocean as mangrove forest 3 (MF3). For seagrass beds, the three transect points furthest from the mangrove forest are defined as seagrass bed 1 (SB1), and the three closest to the mangrove forest as seagrass bed 3 (SB3). The tidal flat transect point was classified with (TF), and the terrestrial area with (TE).
Biomass Carbon Estimation
At each transect point inside the mangrove forest, a plot of 100 m2 was established, all mangrove species were identified, and the total number of trees of each species present was recorded. The diameter at breast height (DBH) at 1.3 m from the forest floor, was measured for each tree, except for species of the genus Rhizophora sp. where the diameter at 30 cm above the highest root of the main stem was taken (Kauffman and Donato 2012). From this, we estimated aboveground and belowground biomass (AGB and BGB respectively), using species-and region-specific allometric equations (Table 1). If no allometric equation had been developed for the species, a genus-level or general equation was used, as commonly conducted in other studies (e.g., Rahman et al. 2015). Species-specific wood densities were used, which is recommended if stand-specific measurements are not available (Chave et al. 2005). Carbon pools were derived from the living biomass measurements using a standard and conservative biomass-to-carbon ratio of 0.464 (Kauffman and Donato 2012).
Table 1 Allometric equations used for estimation of biomass of mangrove trees using measured diameter at breast height (DBH) and wood density (ρ) of trees. Wood density values ρ were obtained at (worldagroforestry.org) At each transect point inside the seagrass bed a core of 15 cm diameter, total area of 176 cm2 and 20 cm depth was used to harvest seagrass ABG and BGB. Seagrass parts were cleaned and rinsed in the field with seawater, and again in the laboratory with distilled water. The amount of organic carbon in seagrass structures was calculated by multiplying % carbon content of each species by the biomass present at each point (Howard et al. 2014). Carbon content in the plant biomass and carbon stocks in the ecosystems were calculated as explained below. Carbon stocks were reported in Mg (megagrams) of carbon per hectare, however other units were reported in mg (miligrams) due to the quantities differing by at least ten orders of magnitude.
Sediment Sampling
At each point along the transects, two types of suspended sediment samples were evaluated, one from sediment traps on the sediment surface, and a second one from the water column (suspended particulate matter (SPM)).
Sediment traps were installed during low tide at a height of 0.05 m above the sediment. The traps were plastic cylinders of 24 cm length and 6 cm diameter, with twenty-four 0.5 cm-diameter holes evenly distributed in the upper 10 cm, with a lid at the bottom that collected the suspended particulate matter that entered the trap. Sediment traps were attached to steel rods that were anchored in the sediment. The traps were emptied after 24 h. To avoid salt contamination, samples were exposed in the oven at 60 °C for 24 h, supernatant water was carefully removed and an additional 150 mL of distilled water was added to dissolve remaining salt. This process was repeated until salt particles were not detected visually.
For SPM, water samples of 1 L from the upper 40 cm of the water column were taken at each point during ebb tide. Admittedly, we acknowledge that SPM being sampled at ebb tide may be biased towards mangroves and terrestrial sources, whereas sediment traps captured POM over two ebb and two flood tides. Samples were kept in freezer bags with freezer blocks and transported within 4 h to the laboratory, where they were filtered onto pre-combusted (450 °C, 24 h) glass fiber filters (GF/C, 696 grade, 1.2 μm pore diameter). Suspended particulate material, dry filters and sediment trap samples were analyzed for isotopic composition and used later in the mixing model to determine the origin of the POM.
At all transect points, a sediment core of 15 cm depth and 7 cm diameter was taken during low tide. The core was divided into three subsamples of 0–5, 5–10 and 10–15 cm sediment layers. The amount of organic carbon in each layer was calculated by measuring the % OC in the sample and multiplying by sediment density (Howard et al. 2014).
Water and Gas Flux Sampling
Fluxes of CO2 were measured using a portable sampling CO2 data logger (K33-BLG CO2Meter), with an internal CO2 sensor using NDIR (non-dispersive infrared), with an accuracy of ±0.2% volume CO2. The loggers were calibrated using outdoor air as a reference, having 400 ppm as reference value, and the software DAS gas lab®. The sensor was configured with a 15 cm diameter light and dark (covered with aluminum foil) 9 L survey chamber. Light and dark chambers were used to evaluate the influence that photosynthetic microorganism could have on CO2 fluxes. CO2 flux density (mg CO2 -C m−2 h−1) (FCO2) was calculated following the methods and equation described by Chojnicki et al. (2009) using light and dark chambers.
$$ {\mathrm{FCO}}_2=\mathrm{k}{\mathrm{CO}}_2\ \left(273\cdotp \mathrm{Tair}-1\right)\cdotp \left(\mathrm{V}\cdotp \mathrm{A}-1\ \right)\cdotp \left(\mathrm{dc}\cdotp \mathrm{dt}-1\ \right) $$
(1)
Where kCO2 is the gas-constant at 273.15 K = 0.536 (μg C μl−1), Tair represents the air temperature inside the chamber (K), V is the chamber volume (L), A is the collar area (m2) and dc·dt-1 is the rate of CO2 concentration change in chamber (ml l −1 h−1).
At each plot, 20 mL of water was taken from the water column during the ebb tide and filtered (45 μm pore size) into pre-combusted glass vials. Samples were kept in a cooler bag with external freezer blocks and transported to the laboratory. For DOC samples, approximately 200 μL of HCl was added to decrease pH below 2, and preserved samples were transported to ZMT. Dissolved organic carbon was analyzed using an infra-red gas analyzer with a Skalar SAN System.
Samples for Chlorophyll a (Chl-a) were obtained by filtering 500 mL through GF/F filters (1.2 μm). After filtering, samples were stored frozen until measurements were done. All samples were transported in cooler bags with freezer blocks, before being analyzed at ZMT chemical analytical laboratories. For Chl-a, each filter was cut in pieces and left in constant agitation overnight in 8 mL of 96% ethanol in the dark at 20 °C. Later, samples were centrifuged for 20 min at 5000 rpm at 4 °C. Absorbance was measured at 665 nm and 470 nm using a photometer (Shimadzu UV-1700). Calculations of Chl-a were done following the procedure described by Ritchie (2008).
Carbon Source Analysis
Five sources of POM were considered in this study: oceanic (plankton and SPM), mangrove tree leaves, seagrass leaves, terrestrial plants leaves and macroalgal tissues. Stable isotope signatures, δ13C and δ15N, of the five different POM sources, analyzed through the Bayesian mixing model MixSIAR (Stock et al. 2018), were used for estimating the contribution of the different sources to the POM sampled in each plot. Isotopic signal values for oceanic sources used in this study were taken from studies done in Johor strait (Zhang et al. 2017), in this instance oceanic samples were mostly comprised of plankton and suspended sediment matter. The most abundant species at each mangrove location (Rhizophora apiculata, Avicennia alba, Bruguiera cylindrica, Avicennia rumphiana, Nypa fruticans (dominant only in Chek Jawa)), seagrass (Cymodocea rotundata, Halodule uninervis, Halophila ovalis), terrestrial plants (Caryota mitis, Canavalia cathartica, Barringtonia sp., Thespesia populnea, Pinus sp. (dominant only in Seletar Island)) and macroalgae (Ulva sp. and Dictyota sp.) were sampled. Fresh plant leaves (4–5) and algal thalli were taken, placed in separate sample bags and transported to the laboratory. The leaves and thalli were rinsed with distilled water and dried at 60 °C for 48–72 h to constant weight.
All samples were analyzed for POC, and δ13C and δ15N. Sediment traps, SPM, plant, and macroalgal samples were homogenized, then acidified to remove carbonates and analyzed for OC by combustion in an elemental analyzer (EuroVector EA 3000) with a precision of 0.06% for OC and 0.01% for total nitrogen for sediments (organic soil standard), and a precision of 0.36% for OC and 0.05% for total nitrogen for plant materials (Apple leaves standard SRM1515 reference material). Carbon and nitrogen stable isotope ratios were determined using a gas isotope ratio mass spectrometer (Thermo Finnigan Delta Plus) after high temperature combustion in an elemental analyzer (Flash 1112 EA). Isotope ratios were expressed in the delta notation (δ13C, δ15N) relative to Vienna PDB and atmospheric nitrogen. Analytical precision was ±0.10 ‰ for nitrogen and 0.13 ‰ for carbon, as estimated from an international standard (Peptone) analyzed together with the samples.
Data Analysis
We compared the water parameters (DOC; Chl-a, and POC), SPM concentration, CO2 fluxes and sediment OC stocks across the three locations and the different ecosystems present in the intertidal zone (i.e. mangrove forest, seagrass bed or tidal flat). A Generalized Linear Mixed-Effects Model was constructed for each variable using location and ecosystem as a fixed effect, and the transect point within the ecosystem (MF1, MF2, SB1 etc.) as a nested random effect within each location. For carbon in AGB and BGB of mangrove trees, only location was used as the fixed effect, and the transect point within the ecosystem (MF1, MF2, etc.) was a random effect, nested within the location.
To test significant differences across the locations, transects points and the different ecosystems in each location, an Analysis of Variance (ANOVA), followed by Tukey’s HSD post hoc comparisons were used. T-tests were used to test for differences between light and dark CO2 incubations. A type 1-linear regression was used to evaluate the relationship between Chl-a and SPM, POC, and DOC, to evaluate the influence of phytoplankton on DOC, POC and PN.
Diagnostic plots and visual assessments of normality and homogeneity of variation were used to confirm the data conformed to major statistical assumptions (residual homogeneity, independence and normality). Statistical significance was assessed using α = 0.05. Statistical analyses were completed using R version 3.0.2 (R Core Team, 2013), using the packages ´lm4´ for GLMM (Bates et al. 2015), `CAR` for ANOVA (Fox and Weisberg 2011) and `eemeans` for Tukey’s HSD post hoc comparisons (Lenth et al. 2018).
For the MixSIAR, both SPM and sediment traps isotopic samples were in the range of the 5 potential sources (Fig. 6). Markov Chain Monte Carlo MCM runs with a Chain Length of 1,000,000 were selected, in order to obtain Gelman diagnostics <1.05. Discrimination coefficient was set at 0 (Stock et al. 2018). The relative contribution dimensionless index was calculated by dividing the contribution of each source given by the mixing model (% Contribution) by the percent of surface area (% Area) occupied by the respective ecosystem (terrestrial, mangrove forest, seagrass bed).
$$ Relative\ Contribution=\frac{\% Contribution}{\% Area} $$
(2)
Percent of surface (% Area) was calculated by dividing the surface area of the ecosystem (i.e. mangrove forest) by the area of the adjacent catchment terrestrial system plus the intertidal systems (mangrove forest, plus tidal flats or seagrass beds).