Quantifying Relative Contribution of Submerged Macrophytes to Sedimentary Organic Matter Using Concentrations and δ13C of n-Alkanes With the Bayesian Multi-Source Mixing Model: A Case Study From the Yangtze Floodplain

Abstract

Submerged macrophytes are important indicators of the state of shallow freshwater ecosystems. Reconstruction long-term changes in submerged macrophytes remains a challenge in paleoecology. Here, the relative biomass (mass weight) of different plants to sedimentary organic matter in a shallow lake in central China was estimated using a Bayesian multi-source mixing model with concentrations and δ¹³C of n-alkanes extracted from surface lake sediments. The spatial distribution of submerged macrophytes biomass estimated by the model correlates with water transparency, water depth, and total nitrogen. The correlation patterns are consistent with previously established patterns of submerged macrophyte growth and water conditions, which supports the utility of the Bayesian approach in shallow freshwater lakes. In comparison, P_aq, proportion of mid-chain length (C23, C25) to long-chain length (C29, C31) homologs, underestimated the contribution of submerged macrophytes, especially in samples with moderate P_aq values (0.3 < P_aq < 0.4). On the other hand, some discrepancies between the model output and the satellite imagery estimated macrophyte coverage are present, which suggests that ground-truthing is needed to further evaluate this approach. Our study demonstrates that the Bayesian mixing model combining the abundance and isotopes of n-alkanes makes a reasonable estimation of the relative biomass of submerged macrophytes in the sediments. This approach provides new insights into reconstructing long-term variations in submerged macrophytes for paleoecological studies, which is valuable for the restoration and conservation of shallow freshwater lakes when long-term limnological monitoring is lacking.