The correlation analyses of bacterial community composition and spatial factors between freshwater and sediment in Poyang Lake wetland by using artificial neural network (ANN) modeling

Abstract

As one of the most important components of the lake ecosystem, microorganisms from the freshwater and sediment play an important role in many ecological processes. However, the difference and correlation of bacterial community between these two niches were not clear. This study investigated the diversity of microbial community of freshwater and sediment samples from fifteen locations in Poyang Lake wetland. The correlation between the bacterial community and physicochemical property of Poyang Lake wetland was analyzed by artificial neural network (ANN). Our results demonstrated that the freshwater and sediment bacterial community were dominated by groups of the Bacteroidetes (23.33%) and β-Proteobacteria (22.54%) separately, whereas, Canalipalpata, Bacillariophyta, Gemmatimonadetes, and Verrucomicrobia were detected in freshwater niches only. Phylogenetic analysis further indicated that bacterial composition in freshwater significantly differed with the sediment niches. There are 34 unique species accounted for 85% in fresh water samples and 28 unique species accounted for 82% in sediment samples. Cluster analysis further proved that all the samples from freshwater niches clustered closely together, far from the rest sediment samples. ANN analysis revealed that the freshwater with high N and P nutrients will greatly increase the diversity of the bacterial communities. In general, both environmental physicochemical properties, not each factor independently, contributed to the shift in the bacterial community structure. The five tributaries (Gan, Fu, Xin, Rao, Xiu Rivers) play a vital role in shaping the bacterial communities of Poyang Lake. This study provides new insights for understanding of microbial community compositions and structures of Poyang Lake wetland.

Vertical Distribution of Bacterial Community in Water Columns of Reservoirs With Different Trophic Conditions During Thermal Stratification

“…In the vertical distribution of the water column, the OTUs, richness, and diversity of bacteria in eutrophic reservoirs were also significantly higher than those in oligotrophic reservoirs and were significantly correlated with variables related to the nutritional status. Recent studies have shown that the nutritional status might be an important driving force for increased bacterial community diversity (Li et al, 2017;Sun et al, 2020). In our study, bacterial OTUs, richness, and diversity of the hypolimnion were found to be significantly greater than those of the epilimnion and thermocline in the same reservoir.…”

Sedimentary DNA records long‐term changes in a lake bacterial community in response to varying nutrient availability

“…Other bacterial phyla declined over the period of accelerated eutrophication, such as Nitrospirae which dropped in relative abundance between 1970 and 1990 when nutrient concentrations were highest. Previous studies have also found that the relative abundance of Nitrospirae declined with increasing phosphorus (Liu et al, 2020; Xiong et al, 2012) and NH 4 ‐N concentrations (Sun et al, 2020). Chloroflexi were relatively abundant from the 1900s but consistently declined over time.…”

A Neural Network Model for Color Element Data Analysis for Urban Spatial Environment

“…Urban color, in the general sense, can be regarded as the collection of color attributes of all visual objects in the urban spatial environment. It includes the color attributes of buildings and structures in the city, the colors of natural mountains and geography as the background of the urban spatial domain, and even the people and vehicles moving in the urban spatial entity [21].…”

“…Urban color, in the general sense, can be regarded as the collection of color attributes of all visual objects in the urban spatial environment. It includes the color attributes of buildings and structures in the city, the colors of natural mountains and geography as the background of the urban spatial domain, and even the people and vehicles moving in the urban spatial entity [ 21 ]. From the perspective of color creation and planning design of the urban spatial environment, we often divide the color information covered in the urban spatial environment into fixed color information attributes, semifixed color information attributes, and mobile color information attributes.…”