The key environmental factors driving the succession of phytoplankton functional groups in Hongfeng Reservoir, southwest China

Reservoirs are an important water source in many densely populated areas in southwest China.Phytoplankton play an essential role in maintaining the structure and function of reservoir ecosystems.Understanding the succession in phytoplankton communities and the factors driving it are essential for eff ective water quality management in drinking water reservoirs.In this study,water samples were collected monthly at the surface layers from March 2016 to December 2019 in Hongfeng Reservoir,southwest China.The relationship between functional group succession was analyzed based on nonmetric multidimensional scaling analysis(NMDS),redundancy analysis(RDA),succession rate,and other analysis methods.The results showed distinct shifts in the community structure of phytoplankton functional groups within study period.The Cyclotella sp.was dominant in 2016 and 2017,and Pseudanabaena limnetica was the dominant group in 2018 and 2019.It appears that the phytoplankton composition and biomass are closely related to the water temperature and nutrient status in this reservoir.The results clearly showed that the permanganate index(COD_(Mn))was the key factor of dramatic phytoplankton functional group succession,and the change in succession rates was closely caused by total nitrogen concentration(TN).Therefore,the succession pattern and key factors of Hongfeng Reservoir revealed in this study were important guidance for the management of drinking water reservoirs in southwest China.A reasonable limit on exogenous nutrient input should be a priority,especially in high water temperature period.

AN INEXACT SMOOTHING NEWTON METHOD FOR EUCLIDEAN DISTANCE MATRIX OPTIMIZATION UNDER ORDINAL CONSTRAINTS

When the coordinates of a set of points are known, the pairwise Euclidean distances among the points can be easily computed. Conversely, if the Euclidean distance matrix is given, a set of coordinates for those points can be computed through the well known classical Multi-Dimensional Scaling （MDS）. In this paper, we consider the case where some of the distances are far from being accurate （containing large noises or even missing）. In such a situation, the order of the known distances （i.e., some distances are larger than others） is valuable information that often yields far more accurate construction of the points than just using the magnitude of the known distances. The methods making use of the order information is collectively known as nonmetric MDS. A challenging computational issue among all existing nonmetric MDS methods is that there are often a large number of ordinal constraints. In this paper, we cast this problem as a matrix optimization problem with ordinal constraints. We then adapt an existing smoothing Newton method to our matrix problem. Extensive numerical results demonstrate the efficiency of the algorithm, which can potentially handle a very large number of ordinal constraints.