Influence of Salinity Gradient Changes on Phytoplankton Growth Caused by Sluice Construction in Yongjiang River Estuary Area

Abstract

Though the number of sluices and dams in coastal areas has increased rapidly in recent years, the influence of their construction on phytoplankton in estuary areas is hardly known. This paper aims to provide a reference for quantitative research on the ecological influence of sluice construction and give ecological justifications for the setting of environmental standards in the estuary areas. The survey data gained at the lower reach of the Yongjiang River and its estuarine areas in June 2015 were used in MIKE21 software (Danish Hydraulic Institute (DHI), Denmark)) for establishing a two-dimensional numerical model to simulate the salinity field distribution after sluice construction. Based on the simulation results, the salinity gradient changes caused by the construction were analyzed. The one-dimensional Gaussian model was applied to calculated the phytoplankton’s ecological threshold interval over the salinity changes, which helped predict the influence of salinity changes on phytoplankton cell density. The study shows that salinity in the Yongjiang estuary increases obviously, beyond the phytoplankton ecological threshold, after sluice construction without water discharge. Salinity will become a restriction factor to phytoplankton growth after sluice construction in the study area, which may cause a sharp decrease of certain phytoplankton species.

Ecological responses of spawning habitat suitability to changes in flow and thermal regimes influenced by hydropower operation

“…mikepoweredbydhi.com) (DHI, 2017) was adopted to construct a 2D hydrodynamic and water temperature model for the flow velocity, water depth and water temperature simulations under varying dam discharge scenarios. MIKE21 is a widely used state-of-the-art software, and it has been verified by different researchers (Al Mamoon et al, 2020;Benjankar et al, 2018;Li et al, 2017;Yang et al, 2019;Yuan et al, 2020;Zeng et al, 2019) for hydrodynamic and water temperature predictions. The MIKE21 FM is a modelling system using a flexible mesh approach, which adopts the hydrodynamic (HD) module and temperature-salinity A high-resolution triangle mesh was developed for the Yichang-Zhicheng Reach based on measured riverbed topography data, as shown in Figure 1 (c).…”

Nitrogen enrichment decreases seagrass contributions to refractory organic matter pools

“…Where the unitary quadratic curves provided a much better fit to the data than the general linear models, the Gaussian model was used to calculate the ecological threshold of the plant's physicochemical index with better predicting environmental nutrient loading as an early warning measurement, as shown in Eq. (1) (Yuan et al 2020). where, y is the biological characteristic indexes of plants (i.e., OM content), c is the maximum value of corresponding biological indicator (i.e., OM content), t means the tolerance of the biological characteristic indexes, and u is the optimum value of plants to certain environmental factors (i.e., site nutrient concentration).…”

“…Where the unitary quadratic curves provided a much better fit to the data than the general linear models, the Gaussian model was used to calculate the ecological threshold of the plant's physicochemical index with better predicting environmental nutrient loading as an early warning measurement, as shown in Eq. (1) (Yuan et al 2020).…”

“…In contrast, a study along natural nutrient gradients found that the non‐structural carbohydrates of rhizome increased with the increase of nutrients, while roots showed the opposite trend (Perez et al 2008). Further, previous studies have explored the correlational relationship between biochemical traits of plant and environmental factors using linear and nonlinear correlations at different environmental conditions (Liu et al 2016 a ; Yuan et al 2020). The nonlinear correlation may indicate that there is a threshold effect in response to environmental pressure.…”

“…Ecological thresholds need to be identified in order to improve the detection of relationships between nitrogen loading and seagrass biochemical traits and nutrient management practices. The piecewise regression and Gaussian model have been always applied to reveal the threshold of plant responses to environmental press in terrestrial and estuarine ecosystems (Sasaki et al 2008; Yuan et al 2020). However, there is still a lack of threshold development and implementation for seagrass ecosystems and management.…”