Structural and functional microbial diversity along a eutrophication gradient of interconnected lakes undergoing anthropopressure

Abstract

We present the results of an analysis of the 16S rRNA-based taxonomical structure of bacteria together with an analysis of carbon source utilization ability using EcoPlate (Biolog, USA) metabolic fingerprinting assessment against the backdrop of physicochemical parameters in fifteen interconnected lakes. The lakes exhibit a wide spectrum of trophic gradients and undergo different intensities of anthropopressure. Sequences of V3–V4 16S rRNA genes binned by taxonomic assignment to family indicated that bacterial communities in the highly eutrophicated lakes were distinctly different from the bacterial communities in the meso-eutrophic lakes (ANOSIM r = 0.99, p = 0.0002) and were characterized by higher richness and more diverse taxonomical structure. Representatives of the Actinobacteria , Proteobacteria , Cyanobacteria , Planctomycetes , Verrucomicrobia , Bacteroides phyla predominated. In most cases their relative abundance was significantly correlated with lake trophic state. We found no similar clear relationship of community-level physiological profiling with lake trophic state. However, we found some significant links between the taxonomic and metabolic structure of the microbes in the studied lakes (Mantel’s correlation r = 0.22, p = 0.006). The carbon source utilization ability of the studied microorganisms was affected not only by the taxonomic groups present in the lakes but also by various characteristics like a high PO 4 3− concentration inhibiting the utilization of phosphorylated carbon.

Bacterioplankton diversity and pollution levels in the estuarine regions of Chaliyar and Anjarakkandi rivers, Kerala, India

“…The present study observed significantly higher number of proteobacteria, Actinobacteria, and Cyanobacteria in the estuarine waters of Anjarakkandi river, Dharmadam. Previous studies have indicated that the bacterioplankton belonging to the proteobacteria, Actinobacteria, and Cyanobacteria predominates in the eutrophic waters compared to other bacterial groups (Kiersztyn et al 2019). The increased diversity of these bacteria correlates with the increased level of nitrogenous compounds present in the Anjarakkandi river and possibly an indicative of the eutrophication status of the estuary.…”

Mortality and coexistence time both cause changes in predator-prey co-evolutionary dynamics

“…Following this, environmental 46 changes can affect community structure and composition and disrupt vital functions pivotal for 47 ecosystem functioning. Such triggering changes in the abiotic environment may include the 48 use of antibiotics [10], or eutrophication of lake ecosystems [11]. 49…”

Quantifying the information content of lake microbiomes using a machine learning-based framework

“…Our main contribution is a machine learning-based framework for the quantification of the information shared between the microbiome and a total of 25 physico-chemical and positional (i.e., GPS coordinates and altitude) parameters of an ecosystem. It builds upon a wealth of studies that elucidate the role of the microbiome in ecology using machine learning [19,[31][32][33][34][35][36][37][38][39]. In our framework, a model learns a projection of the microbial prevalence space to a single dimension for each of the parameters, which makes it able to handle the extremely high dimensionality of amplicon-based microbiome datasets.…”