Time after time: Detecting annual patterns in stream bacterial biofilm communities

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Exploring freshwater stream bacterial communities as indicators of land use intensity

BACKGROUND

Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February-March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site's catchment land use and environmental conditions based on the composition of the stream bacterial communities.

RESULTS

Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69).

CONCLUSIONS

Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.

Impact of Season on Intestinal Bacterial Communities and Pathogenic Diversity in Two Captive Duck Species

Vertebrates and their gut bacteria interact in complex and mutually beneficial ways. The intestinal microbial composition is influenced by several external influences. In addition to food, the abiotic elements of the environment, such as temperature, humidity, and seasonal fluctuation are also important determinants. Fecal samples were collected from two captive duck species, Baikal teal (Sibirionetta formosa) and common teal (Anas crecca) across four seasons (summer, autumn, winter, and spring). These ducks were consistently fed the same diet throughout the entire experiment. High throughput sequencing (Illumina Mi-seq) was employed to analyze the V4-V5 region of the 16sRNA gene. The dominant phyla in all seasons were Proteobacteria and Firmicutes. Interestingly, the alpha diversity was higher in winter for both species. The NMDS, PCoA, and ANOSIM analysis showed the distinct clustering of bacterial composition between different seasons, while no significant differences were discovered between duck species within the same season. In addition, LefSe analysis demonstrated specific biomarkers in different seasons, with the highest number revealed in winter. The co-occurrence network analysis also showed that during winter, the network illustrated a more intricate structure with the greatest number of nodes and edges. However, this study identified ten potentially pathogenic bacterial species, which showed significantly enhanced diversity and abundance throughout the summer. Overall, our results revealed that season mainly regulated the intestinal bacterial community composition and pathogenic bacteria of captive ducks under the instant diet. This study provides an important new understanding of the seasonal variations in captive wild ducks' intestinal bacterial community structure. The information available here may be essential data for preventing and controlling infections caused by pathogenic bacteria in captive waterbirds.

Spatial scale impacts microbial community composition and distribution within and across stream ecosystems in North and Central America

A mechanistic understanding of factors that structure spatiotemporal community composition is a major challenge in microbial ecology. Our study of microbial communities in the headwaters of three freshwater stream networks showed significant community changes at the small spatial scale of benthic habitats when compared to changes at mid- and large-spatial scales associated with stream order and catchment. Catchment (which included temperate and tropical catchments) had the strongest influence on community composition followed by habitat type (epipsammon or epilithon) and stream orders. Alpha diversity of benthic microbiomes resulted from interactions between catchment, habitat, and canopy. Epilithon contained relatively more Cyanobacteria and algae while Acidobacteria and Actinobacteria proportions were higher in epipsammic habitats. Turnover from replacement created ~60%-95% of beta diversity differences among habitats, stream orders, and catchments. Turnover within a habitat type generally decreased downstream indicating longitudinal linkages in stream networks while between habitat turnover also shaped benthic microbial community assembly. Our study suggests that factors influencing microbial community composition shift in dominance across spatial scales, with habitat dominating locally and catchment dominating globally.

Temporal disturbance of a model stream ecosystem by high microbial diversity from treated wastewater

Microbial communities in freshwater streams play an essential role in ecosystem functioning via biogeochemical cycling. Yet, the impacts of treated wastewater influx into stream ecosystems on microbial strain diversity remain mostly unexplored. Here, we coupled full‐length 16S ribosomal RNA gene Nanopore sequencing and strain‐resolved metagenomics to investigate the impact of treated wastewater on a mesocosm system (AquaFlow) run with restored river water. Over 10 days, community Bray–Curtis dissimilarities between treated and control mesocosm decreased (0.57 ± 0.058 to 0.26 ± 0.046) based on ribosomal protein S3 gene clustering, finally converging to nearly identical communities. Similarly, strain‐resolved metagenomics revealed a high diversity of bacteria and viruses after the introduction of treated wastewater; these microbes also decreased over time resulting in the same strain clusters in control and treatment at the end of the experiment. Specifically, 39.2% of viral strains detected in all samples were present after the introduction of treated wastewater only. Although bacteria present at low abundance in the treated wastewater introduced additional antibiotic resistance genes, signals of naturally occurring ARG‐encoding organisms resembled the resistome at the endpoint. Our results suggest that the previously stressed freshwater stream and its microbial community are resilient to a substantial introduction of treated wastewater.