An eco-parametric method to derive sedimentation rates for coastal saltmarshes

Abstract

The saltmarsh plant Spartina alterniflora was introduced to the Jiangsu coasts, China and serves as an ecological engineer to reduce near-bed shear stress, trap fine-grained sediments and protect the coast from wave-induced erosion. The saltmarshes thus could record the Spartina colonization-driven changes within the sedimentary layers. Based on these ecological and sedimentological changes in sediments, we present a new eco-parametric method to estimate the sedimentation rate for the newly-formed wetlands in the Yancheng Wetland Nature Reserve for Rare Birds, Jiangsu. Sediment cores and satellite imagery were used to identify the thickness of accumulated sediment layers and the time since the Spartina colonization. We defined the original ground on which Spartina alterniflora initially colonized using pigment concentrations, grain size and stable carbon isotopic compositions of organic matter (δ13C) in sediments. We also determined the time mark of the Spartina colonization by examining the Landsat images over 1982–2018 to discriminate the Spartina alterniflora from other native plants and geomorphological features. These two datasets yielded a sedimentation rate of 3.3 cm/yr for Core A and of 9.6 cm/yr for Core B, the latter evidenced by an increase of ~ 0.51 m in the bed level from 2008 to 2014. Combining the 210Pb dating method, we further estimated the sedimentation rate for the layers beneath the original ground, which was comparable to that of the bare flats in the Jiangsu coast. Even though this new method is only applicable to newly-formed saltmarshes, it helps identify the recent sedimentation events as well as reveal the environmental changes and the evolution of saltmarsh-bare flat systems due to the interplay between vegetation, hydrodynamics and sediment dynamics. It thus could be an efficient and cost-effective tool for an improved understanding of the response of coastal wetlands to a changing climate/environment.