Abstract
AbstractThe ratio between total nitrogen (TN) and total phosphorus (TP) is an important limnological measure, potentially regulating the phytoplankton dynamics in lakes. However, information on the impact of the TN/TP ratio on phytoplankton biomass in artificial reservoirs with unstable physical boundary has been little studied. Here, we performed a 2-year biweekly monitoring program in the Pengxi River Backwater Area (PBA), a tributary of the Three Gorges Reservoir (the Yangtze River), to document the relationship of TN/TP ratio and phytoplankton. Based on Spearman Correlation Analysis, we found that significant seasonal variation of TN, TP, and TN/TP ratio was unrelated to variation in phytoplankton biomass. Three subsets of the 2-year data were divided according to reservoir operation mode and seasonal growth of phytoplankton to gain a deeper insight in their relationship. In the non-growth season, when water residence time in the PBA is longer due to impoundment in the Three Gorge Reservoir (TGR) and to decline of river discharge in the dry season, release of TN and TP from the newly submerged water fluctuation zone increased the input of nutrients and the ratio of TN/TP. This process co-occurred with a decline in the growth of phytoplankton, resulting in a positive correlation between TN/TP ratio and phytoplankton biomass. In the growth season, low water residence time (HRT < 50 day), intensive water exchange, and mass transport from river discharge at low water level caused unstable hydrodynamic conditions for the growth of phytoplankton. Light availability might be the controlling factor that regulated the biomass of phytoplankton. In the growth season with long water residence times (HRT ≥ 50 day), a relatively stable physical environment supported the occurrence of N-fixation in the PBA.KeywordsTotal NitrogenTotal PhosphorusRiver DischargePhytoplankton BiomassSoluble Reactive PhosphorusThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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