DOI: 10.5061/dryad.bnzs7h4hd
title: Warming stimulates mangrove carbon sequestration in rising sea-level at their northern limit: an in situ simulation

description: Global warming and sea-level rise are directly influencing the growth,
 distribution, and greenhouse gas emissions of mangrove forests. However,
 mangrove forests growing at their latitudinal limits are relatively
 susceptible to warming; nevertheless, few studies have focused on GHG
 emissions of latitudinal limits mangrove forests as related to global
 climate change. To address this knowledge gap, a multi-year in situ
 control experiment was established in a restored plantation at the
 northern distribution limit of Kandelia obovata, the most cold-tolerant
 mangrove species in China, to simulate both warming and sea-level rise. We
 investigated the growth patterns and sediment greenhouse gas emissions of
 a K. obovata population and identified the primary factors contributing to
 these changes. The results showed that warming and moderate sea-level rise
 enhanced biomass by more than 18%, indicating that warming stimulated
 plant growth while excessive sea-level rise inhibited it. The sediment
 greenhouse gas emissions ranged from 45.5 to 484.6 mgCO2 m-2 h-1, 5.6 to
 590.3 μgCH4 m-2 h-1, and 11.4 to 385 μgN2Om-2 h-1, which increased with
 warming while decreased with sea-level rise, acting as the net source of
 greenhouse gas emission. Our study predicted that sea-level rise, while
 directly changing sediment properties, had combined effects with warming
 on these studied mangrove forests that were predicted to emit more
 greenhouse gases from sediments in the future. These findings indicated
 that K. obovata plantations within their latitudinal limits tend to
 accumulate more CO2 for biomass carbon storage under warming conditions,
 while stimulating sediment greenhouse gas emissions, which will offset
 their climate mitigating effect in future climatic scenarios.
description: <strong>Greenhouse gases sampling, flux measurements, and
 global warming potential analysis</strong> In
 this experiment, sediment greenhouse gases were collected using a Closed
 Static Chamber. The chamber covered 0.013 m<sup>2</sup> of
 sediment surface with a headspace volume of 4.5 liters. The chamber was
 placed on the sediment surface with the edge inserted 2 cm into the
 sediment; the round hole channel at the top was plugged with a soft rubber
 plug, and a syringe needle was inserted into the rubber plug to maintain
 the air pressure balance inside and outside the chamber. The pressure was
 controlled in the chambers with a small vent on the chamber top. Chambers
 were sampled between 09:00 and 15:00 h after 2 h of an ebb tide. Air
 temperature (T<sub>a</sub>) and sediment surface temperature
 at a depth of 0–5 cm (T<sub>s</sub>) inside chambers at the
 beginning and end of the gas sampling process were measured using a HOBO
 MX2202 air temperature sensor and a sediment thermometer. For each gas
 sample, a 0.5# needle was connected to a 60 mL syringe through the air
 sampling port to collect each 20 mL of gas sample. The greenhouse gases
 were extracted from the chamber at 0-, 30-, and 60-min intervals, and then
 the gas samples in the syringes were transferred to a special gas
 collection bag immediately (Hede Technologies Ltd.). Each gas sample was
 injected into 50 mL air bags (Hede Technologies Ltd.) and analyzed within
 24 h using a 7890B Greenhouse Gas Chromatograph (Agilent Technologies,
 Inc.). The flame ionization detector and electron capture detector were
 set at 25 °C and 30 °C, respectively, with N<sub>2</sub> as
 the carrier gas at a flow rate of 2 mL·min<sup>−1</sup>, and
 mixed air and H<sub>2</sub> into the flame ionization detector
 and electron capture detector at flow rates of 450 and
 50 mL·min<sup>−1</sup>, respectively. A set
 of standard gases (Linde China, http://www.linde-gas.com.cn/) was used to
 test the equipment after measuring every 10 samples. Standard curves were
 established between the concentrations of CO<sub>2</sub>,
 CH<sub>4</sub>, and N<sub>2</sub>O according to
 the peak area of each set of standard gases. Gas fluxes
 were calculated using Eq. 1: <strong>Equation
 1:</strong> <pre>F = (M × P × V) / (R × (273 + T) ×
 A) × (dC/dt)</pre> Where: 
 <em>F</em> is the gas flux
 (mg·m<sup>−2</sup>·h<sup>−1</sup> or
 μg·m<sup>−2</sup>·h<sup>−1</sup>)
 <em>M</em> is the molar mass of each greenhouse gas
 (g·mol<sup>−1</sup>) 
 <em>P</em> is the atmospheric pressure (P = 1.013 ×
 10<sup>5</sup> Pa) 
 <em>R</em> is the universal gas constant
 (8.314 Pa·m<sup>3</sup>·mol<sup>−1</sup>·K<sup>−1</sup>) <em>T</em> is the average air temperature (°C) <em>V</em> is the volume of the sampling chamber (m<sup>3</sup>) <em>A</em> is the chamber surface area (m<sup>2</sup>) <em>dC/dt</em> is the emission rate of gas concentration (ppm·h<sup>−1</sup>) The global warming potential of the three greenhouse gases was calculated to evaluate the contribution of each greenhouse gas to global warming. According to the Intergovernmental Panel on Climate Change (IPCC AR6 WGI, 2021), we used values of 100-year global warming potential (GWP<sub>100</sub>) measurements of 27.9 for CH<sub>4</sub> and 273 for N<sub>2</sub>O. These indicate that 1 kg CH<sub>4</sub> and 1 kg N<sub>2</sub>O are 27.9 and 273 times more potent than 1 kg of CO<sub>2</sub> in contributing to global warming over 100 years, respectively. <strong>Carbon dioxide removal analysis</strong> The carbon dioxide removal of mangrove forests in this study was calculated using Eq. 2. It represents the net carbon gain from biomass and greenhouse gas-equivalent CO<sub>2</sub>, which reflects CO<sub>2</sub> fixation by mangrove forests. In this study, the carbon dioxide removal potential did not include sediment carbon increment. <strong>Equation 2:</strong> <pre>CDR = ΔBiomass × C<sub>c</sub> × (44 / 12) − Σ<sub>i=1</sub><sup>3</sup>GWP<sub>i</sub></pre> Where: <em>ΔBiomass</em> is the biomass increment per square meter <em>C<sub>c</sub></em> is the carbon content of mangrove biomass (default = 0.47) <em>GWP<sub>i</sub></em> is the global warming potential for each greenhouse gas <strong>Statistical analyses</strong> The relationship between the R<sub>s</sub> rate and T<sub>s</sub> was fitted using an exponential model (Eq. 3; Lloyd and Taylor 1994): <strong>Equation 3:</strong> <pre>R = e<sup>bT<sub>s</sub></sup></pre> Where: <em>R</em> is the gas flux rate (mg·m<sup>−2</sup>·h<sup>−1</sup>) <em>T<sub>s</sub></em> is the sediment temperature (°C) <em>b</em> is the temperature response coefficient The responsiveness of the R<sub>s</sub> rate to temperature is expressed by the Q<sub>10</sub> value (Eq. 4): <strong>Equation 4:</strong> <pre>Q<sub>10</sub> = e<sup>10b</sup></pre> Where <em>b</em> is the same as in Equation 3. All data distributions were tested for normality with the Shapiro–Wilk normality test, and Levene’s test was performed to determine the homogeneity of variances. The Kruskal–Wallis test was used to test differences between groups. Two-way ANOVA was used to examine the main and interaction effects of warming and sea-level rise on plant biomass, sediment properties, greenhouse gas fluxes, and global warming potential. Nonlinear regression was performed to explore the relationship between greenhouse gas fluxes and T<sub>s</sub>. Correlation analysis was performed to detect the relationships between greenhouse gas emissions and driving factors such as warming, pH, and carbon content. The contribution of driving factors to R<sub>s</sub>, CH<sub>4</sub>, and N<sub>2</sub>O was determined using the hierarchical partitioning method through the R package <code>rdacca.hp</code> (Lai et al. 2022). A structural equation model was conducted to analyze the linkage between driving factors and sediment greenhouse gas emissions using the <code>piecewiseSEM</code> package in R.
description: # Warming stimulates mangrove carbon sequestration in rising sea-level at
 their northern limit: an in situ simulation in China
 [https://doi.org/10.5061/dryad.bnzs7h4hd](https://doi.org/10.5061/dryad.bnzs7h4hd) ## Description of the data and file structure Sheet 1 is the legend of the dataset, explaining all the abbreviations and units in this dataset. Data showed in different sheets matching with different figures. ## Code/Software Data analysis by using R verson 4. 1 .3, detail infromation could avaliable from article. **Fig. 1.** Location of study site (a, b), open-top chambers setup (c), field images at before and last sampling time (d, e), and air temperature differences between control and warming plots in 1st and 3rd years (f, g). **Fig. 2.** The response of height and basal diameter of Kandelia obovata to warming in low-tidal zone (a–f), medium-tidal zone (g–l), and high-tidal zone (m–r), respectively. **Fig. 3.** The response of sediment CO2 (a, b), CH4 (c, d), and N2O (e, f) emission rates and global warming potential (g–l) of Kandelia obovata to warming in different sea-levels, respectively. **Fig. 4.** The correlation analysis (a) and hierarchical partitioning analysis (b for Rs, c for CH4, and d for N2O) between driving factors and greenhouse gases emission rate. **Fig. 5.** Structural equation models of warming, sea-level rise and driving factors on the sediment CO2 (a), CH4 (b), and N2O (c) fluxes. **Fig. 6.** Prediction for growth and sediment greenhouse gas emissions of Kandelia obovata under climate warming and sea-level rise in future. **Figure S1** The response of soil respiration (Rs) rate of *Kandelia obovata* to warming in low tidal zone (LTZ, a and b), middle tidal zone (MTZ, c and d) and high tidal zone (HTZ, e and f), respectively. **Figure S2** The response of sediment methane (CH4) emission rate of *Kandelia obovata* to warming in low tidal zone (LTZ, a and b), middle tidal zone (MTZ, c and d) and high tidal zone (HTZ, e and f), respectively. **Figure S3** The response of sediment nitrous oxide (N2O) emission rate of *Kandelia obovata* to warming in low tidal zone (LTZ, a and b), middle tidal zone (MTZ, c and d) and high tidal zone (HTZ, e and f), respectively. **Figure S4** The response of global warming potential (GWP) of *Kandelia obovata* to warming in low tidal zone (LTZ, a and b), middle tidal zone (MTZ, c and d) and high tidal zone (HTZ, e and f), respectively. **Figure S5** The exponential regression for each greenhouse gases (GHGs) and average soil temperature (Ts). **Figure S6** The exponential regression for each greenhouse gases (GHGs) and average soil temperature (Ts) under different SLR.