Effects of salt marsh vegetation zonation on carbon and nitrogen cycling in Connecticut

Coastal marshes fringing the Long Island Sound (Connecticut, USA) are dynamic ecosystems positioned at the interface between land and sea, and provide an array of essential ecosystem services to society associated with improved water quality, carbon sequestration, and disturbance regulation. However, these wetlands are increasingly altered by rising seas and invasive species, and have been affected by historical management such as tidal manipulation. We conducted a survey of 20 Connecticut salt marshes (10 tidally restored, 10 unrestricted references) in 2017 to quantify carbon mineralization, denitrification potential, microbial community composition, above and belowground biomass and a suite of sediment characteristics. Carbon density was our only paramenter that differed between unrestricted and tidally restored marshes, but we observed strong differences across vegetation zones, with vegetation being a top predictor of microbial respiration and potential denitrification rates. Based on sea-level rise model projections, the replacement of S. patens by short-form S. alterniflora is expected to be widespread across the Connecticut coastline, decreasing statewide potential denitrification from the low-to-high marsh transitional zone. Our results suggest that changes in vegetation zones can serve as landscape-scale predictors of the rapid changes occurring in salt marshes.

Methods
We sampled 20 polyhaline salt marshes (10 reference marshes with no history of restriction or restoration (i.e., "reference sites"), 10 tidally restored) along the north shore of the Long Island Sound in Connecticut (CT), USA. Sites were selected based on their restoration history and presence of target vegetation: Spartina alterniflora (short-form, < 30 cm tall), S. patens, and Phagmites australis. Within each vegetation zone at each site, we sampled three 1-m2 plots in mid-August 2017. We visually estimated percent cover of all plants in each plot. From each plot we collected aboveground biomass (from a randomly selected 25 x 25 cm subplot) and three surface soil cores (5-cm diameter to 10-cm depth; 196-cm3 volume); samples were composited by zone and used to estimate above and belowground biomass (C and N content), bulk density, microbial process rates (denitrification potential, carbon mineralization, substrate induced respiration) and a suite of soil chemistry parameters using standarized methods (soil pH and specific electrical conductivity, soil moisture, total C and N, soil organic matter, carbon density, and soil SO42-, Cl-, NH4+  concentrations). 

Please see associated manuscripts (Barry et al. 2021, Ooi et al in revision) for details on data collection and analysis:

Barry, A., Ooi, S.K., Helton, A.M. et al. Vegetation Zonation Predicts Soil Carbon Mineralization and Microbial Communities in Southern New England Salt Marshes. Estuaries and Coasts (2021). https://doi.org/10.1007/s12237-021-00943-0

环绕美国康涅狄格州长岛海湾（Long Island Sound）的滨海盐沼（coastal marshes）是坐落于海陆交界地带的动态生态系统，可为人类社会提供一系列关键生态系统服务（ecosystem services），涵盖水质改善、碳封存（carbon sequestration）与干扰调控等方面。然而，这类湿地正日益受到海平面上升与外来入侵物种的干扰，同时还受到历史上潮汐调控等人类管理活动的影响。
2017年，我们针对康涅狄格州的20处盐沼开展了调查，其中包括10处经潮汐修复（tidally restored）的样地与10处未受限制的对照样地，旨在量化碳矿化作用（carbon mineralization）、反硝化潜力（denitrification potential）、微生物群落组成（microbial community composition）、地上与地下生物量（above and belowground biomass），以及一系列沉积物特征参数。本研究发现，碳密度是唯一在未受限制盐沼与潮汐修复盐沼间存在显著差异的参数，但我们观察到不同植被带间存在显著分化，其中植被是预测微生物呼吸作用与潜在反硝化速率的核心影响因子之一。基于海平面上升模型的预测，短株型互花米草（S. alterniflora）替代盐地鼠尾粟（S. patens）的现象预计将在康涅狄格州沿海广泛发生，这将导致全州高低潮沼过渡带的潜在反硝化能力出现下降。本研究结果表明，植被带的变化可作为盐沼快速演变的景观尺度预测因子。

## 方法
我们在美国康涅狄格州长岛海湾北岸选取了20处多盐型盐沼（polyhaline salt marshes），其中包括10处无任何阻隔或修复历史的参照样地（"reference sites"），以及10处经潮汐修复的样地。样地的选取基于其修复历史与目标植被的存在情况：短株型互花米草（Spartina alterniflora，株高<30cm）、盐地鼠尾粟（S. patens）与芦苇（Phragmites australis）。2017年8月中旬，我们在每个样地的各植被带内设置3个1㎡样方。对每个样方内所有植物的盖度进行目视估测。从每个样方中采集地上生物量（取自随机选取的25×25cm小样方）与3份表层土壤岩心（直径5cm，深度10cm，体积196cm³）；样品按植被带混合后，采用标准化方法测定地上与地下生物量（碳、氮含量）、容重、微生物过程速率（反硝化潜力、碳矿化作用、底物诱导呼吸作用），以及一系列土壤化学参数，包括土壤pH值、比电导率、土壤含水率、总碳与总氮、土壤有机质、碳密度，以及土壤SO₄²⁻、Cl⁻、NH₄⁺浓度。

详细的数据采集与分析方法可参见相关研究论文（Barry等，2021；Ooi等，待刊）：
Barry, A., Ooi, S.K., Helton, A.M. 等. 植被分带可预测新英格兰南部盐沼的土壤碳矿化作用与微生物群落. 《河口与海岸》(Estuaries and Coasts), 2021. https://doi.org/10.1007/s12237-021-00943-0