Phytoplankton communities in rock pools on the Åland Islands, SW Finland – environmental variables, functional groups and strategies

Biodiversity ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Kerstin Häggqvist ◽  
Tore Lindholm
Keyword(s):  
Functional Groups ◽  
Environmental Variables ◽  
Rock Pools ◽  
Phytoplankton Communities

scholarly journals Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

2018 ◽  
Author(s):  
Mridul K. Thomas ◽  
Simone Fontana ◽  
Marta Reyes ◽  
Francesco Pomati
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Functional Groups ◽  
Clustering Algorithm ◽  
Unsupervised Clustering ◽  
Learning Tools ◽  
Time Resolved ◽  
Automated Method ◽  
Phytoplankton Communities ◽  
Cell Densities

AbstractScanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

scholarly journals Seasonal Succession of Phytoplankton Functional Groups and Driving Factors of Cyanobacterial Blooms in a Subtropical Reservoir in South China

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1167 ◽  
Author(s):  
Lingai Yao ◽  
Xuemin Zhao ◽  
Guang-Jie Zhou ◽  
Rongchang Liang ◽  
Ting Gou ◽  
...  
Keyword(s):  
Functional Groups ◽  
South China ◽  
Algal Cell ◽  
Seasonal Succession ◽  
Geographic Location ◽  
Water Source ◽  
Cyanobacterial Blooms ◽  
Nitrogen And Phosphorus ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups

Freshwater phytoplankton communities can be classified into a variety of functional groups that are based on physiological, morphological, and ecological characteristics. This classification method was used to study the temporal and spatial changes in the phytoplankton communities of Gaozhou Reservoir, which is a large municipal water source in South China. Between January 2015 and December 2017, a total of 155 taxa of phytoplankton that belong to seven phyla were identified. The phytoplankton communities were classified into 28 functional groups, nine of which were considered to be representative functional groups (relative biomass > 10%). Phytoplankton species richness was greater in the summer and autumn than in the winter and spring; cyanobacterial blooms occurred in the spring. The seasonal succession of phytoplankton functional groups was characterized by the occurrence of functional groups P (Staurastrum sp. and Closterium acerosum) and Y (Cryptomonas ovata and Cryptomonas erosa) in the winter and spring, and functional groups NA (Cosmarium sp. and Staurodesmus sp.) and P (Staurastrum sp. and Closterium acerosum) in the summer and autumn. The temperature, nitrogen, and phosphorus levels were the main factors driving seasonal changes in the phytoplankton communities of Gaozhou Reservoir. The functional group M (Microcystis aeruginosa) dominated the community during the cyanobacterial blooms in spring 2016, with the maximum algal cell density of 3.12 × 108 cells L−1. Relatively low temperature (20.8 °C), high concentrations of phosphorus (0.080–0.110 mg L−1), suitable hydrological and hydrodynamic conditions (e.g., relatively long retention time), and relatively closed geographic location in the reservoir were the key factors that stimulated the cyanobacterial blooms during the early stages.

scholarly journals Global ecotypes in the ubiquitous marine clade SAR86

2019 ◽  
Author(s):  
Adrienne Hoarfrost ◽  
Stephen Nayfach ◽  
Joshua Ladau ◽  
Shibu Yooseph ◽  
Carol Arnosti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Diversity ◽  
High Resolution ◽  
Functional Groups ◽  
Environmental Variables ◽  
Gene Clusters ◽  
Environmental Data ◽  
Marine Environments ◽  
Surface Ocean ◽  
Globally Distributed

AbstractSAR86 is an abundant and ubiquitous heterotroph in the surface ocean that plays a central role in the function of marine ecosystems. We hypothesized that despite its ubiquity, different SAR86 subgroups may be endemic to specific ocean regions and functionally specialized for unique marine environments. However, the global biogeographical distributions of SAR86 genes, and the manner in which these distributions correlate with marine environments, have not been investigated. We quantified SAR86 gene content across globally-distributed metagenomic samples and modeled these gene distributions as a function of 51 environmental variables. We identified five distinct clusters of genes within the SAR86 pangenome, each with a unique geographic distribution associated with specific environmental characteristics. Gene clusters are characterized by strong taxonomic enrichment of distinct SAR86 genomes and partial assemblies, as well as differential enrichment of certain functional groups, suggesting differing functional and ecological roles of SAR86 ecotypes. We then leveraged our models and high-resolution, remote sensing-derived environmental data to predict the distributions of SAR86 gene clusters across the world’s oceans, creating global maps of SAR86 ecotype distributions. Our results reveal that SAR86 exhibits previously unknown, complex biogeography, and provide a framework for exploring geographic distributions of genetic diversity from other microbial clades.

scholarly journals Seasonal dynamics of Zooplankton functional groups in relation to environmental variables in Xiquanyan Reservoir, Northeast China

2018 ◽  
Vol 54 ◽  
pp. 33
Author(s):  
Patteson Chula Mwagona ◽  
Ma Chengxue ◽  
Yu Hongxian
Keyword(s):  
Functional Groups ◽  
Total Nitrogen ◽  
Total Phosphorus ◽  
Environmental Variables ◽  
Water Transparency ◽  
Functional Feeding Groups ◽  
Total Biomass ◽  
Top Down ◽  
Community Group ◽  
Filter Feeders

In this study, the concept of functional feeding groups was used to classify and model the seasonal variation of zooplankton functional groups in relation to environmental variables. A total of 48 zooplankton species were observed in the reservoir and grouped into 8 functional groups. Both environmental variable and the biomass of zooplankton functional groups vary spatially and seasonally. Water temperature, water transparency, total nitrogen and nitrates were significantly higher in summer, while chlorophyll-a and ammonium were higher in autumn and spring, respectively. Biomass of zooplankton was significantly higher in summer (245.81 μg/L), followed by autumn (196.54 μg/L) and spring (54.25 μg/L). Group RF (rotifer filter feeders) dominated in spring, accounting for 80% of the total biomass. In summer and autumn, group RC (rotifer carnivore) and SCF (small copepods and cladocerans filter feeders) were the dominant, respectively. Total nitrogen, total phosphorus, ammonium, chlorophyll-a and water transparency were the major factor influencing zooplankton community. Group RF was positively influenced by ammonium and total phosphorus, while RC, SCF and MCF (middle copepods and cladocerans filter feeders) were positively correlated with chlorophyll-a. Top-down control of phytoplankton by groups RC, SCF and MCF in Xiquanyan reservoir is not strong enough to produce negative effect. Increase in predator size biomass did not strengthen top-down control on prey. It is quite clear that the zooplankton function groups of Xiquanyan reservoir followed a predictable seasonal pattern. This therefore highlights the significance of environmental variables in structuring plankton composition in the reservoir.

scholarly journals Assessment of water quality in the lower Nyong estuary (Cameroon, Atlantic Coast) from environmental variables and phytoplankton communities composition

2018 ◽  
Vol 12 (6) ◽  
pp. 198-208
Author(s):  
Crepin Mama Anselme ◽  
Flodore Youbouni Ghepdeu Gisele ◽  
Rémi Ngoupayou Ndam Jules ◽  
Desire Bonga Manfred ◽  
Mammert Onana Fils ◽  
...  
Keyword(s):  
Water Quality ◽  
Environmental Variables ◽  
Atlantic Coast ◽  
Phytoplankton Communities

scholarly journals Seasonal and spatial patterns of eukaryotic phytoplankton communities in an urban river based on marker gene

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Yang ◽  
Junping Lv ◽  
Qi Liu ◽  
Fangru Nan ◽  
Bo Li ◽  
...  
Keyword(s):  
Functional Groups ◽  
Environmental Gradients ◽  
Pearson Correlation ◽  
Marker Gene ◽  
Variance Partitioning ◽  
Linear Discriminant ◽  
Eukaryotic Phytoplankton ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups ◽  
Determining Factors

AbstractThe seasonal and spatial eukaryotic phytoplankton composition in the Fenhe River was investigated based on the 18S rDNA V4 region. The relationship between phytoplankton functional groups and environmental factors was explored to effectively capture the responses of these taxa to environmental gradients and their effects on ecosystem function. Our results indicated that the Chlorophyta and Bacillariophyta had higher relative abundance than other taxa, and their diversity and richness indices in spring were higher than those in other seasons. The linear discriminant analysis effect size (LEfSe) analyses detected that the potential seasonal biomarkers included Desmodesmus, Cyclotella, Pseudoschroederia, Discostella, Scenedesmus, Monoraphidium, and Nannochloropsis; the spatial biomarkers included Amphora, Neochloris, Hindakia, Pseudomuriella, Coccomyxa, Chloroidium, Scherffelia, Chromochloris, and Scotinosphaera. The systemic evolution and distribution characteristics of the first 50 representative sequences showed that the dominant genus included Desmodesmus in spring, Pseudopediastrum in summer, Mychonastes in autumn, and Monoraphidium in winter. Main seasonal variation of phytoplankton functional groups was as follows: spring (J + F + C + X1) → summer (J + F + X1 + X2) → autumn (J + F + X1 + C) → winter (X1 + J + B + X2). Pearson correlation, redundancy analysis, and variance partitioning analysis showed temperature and phosphate were the determining factors causing the changes of phytoplankton functional groups and community composition in the Fenhe River.

Environmental variables influencing phytoplankton communities in hydrologically connected aquatic habitats in the Lake Xingkai basin

2018 ◽  
Vol 91 ◽  
pp. 1-12 ◽  
Author(s):  
Yuxiang Yuan ◽  
Ming Jiang ◽  
Xingtu Liu ◽  
Hongxian Yu ◽  
Marinus L. Otte ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Aquatic Habitats ◽  
Phytoplankton Communities

scholarly journals Spatial-Related Community Structure and Dynamics in Phytoplankton of the Ross Sea, Antarctica

2020 ◽  
Vol 7 ◽  
Author(s):  
Francesco Bolinesi ◽  
Maria Saggiomo ◽  
Francisco Ardini ◽  
Pasquale Castagno ◽  
Angelina Cordone ◽  
...  
Keyword(s):  
Community Structure ◽  
Functional Groups ◽  
Ross Sea ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Relative Proportion ◽  
Chl A ◽  
South Central ◽  
Physical Chemical ◽  
Phytoplankton Communities

The Ross Sea exhibits the largest continental shelf and it is considered to be the most productive region in Antarctica, with phytoplankton communities that have so far been considered to be driven by the seasonal dynamics of the polynya, producing the picture of what is considered as the classical Antarctic food web. Nevertheless, the Ross Sea is made up of a complex mosaic of sub-systems, with physical, chemical, and biological features that change on different temporal and spatial scales. Thus, we investigated the phytoplankton community structure of the Ross Sea with a spatial scale, considering the different ecological sub-systems of the region. The total phytoplankton biomass, maximum quantum efficiency (Fv/Fm), size classes, and main functional groups were analyzed in relation to physical–chemical properties of the water column during the austral summer of 2017. Data from our study showed productivity differences between polynyas and other areas, with high values of biomass in Terra Nova Bay (up to 272 mg chl a m–2) and the south-central Ross Sea (up to 177 mg chl a m–2) that contrast with the HNLC nature of the off-shore waters during summer. Diatoms were the dominant group in all the studied subsystems (relative proportion ≥ 50%) except the southern one, where they coexisted with haptophytes with a similar percentage. Additionally, the upper mixed layer depth seemed to influence the level of biomass rather than the dominance of different functional groups. However, relatively high percentages of dinoflagellates (∼30%) were observed in the area near Cape Adare. The temporal variability observed at the repeatedly sampled stations differed among the sub-systems, suggesting the importance of Long-Term Ecological Research (L-TER) sites in monitoring and studying the dynamics of such an important system for the global carbon cycle as the Ross Sea. Our results provide new insights into the spatial distribution and structure of phytoplankton communities, with different sub-systems following alternative pathways for primary production, identifiable by the use of appropriate sampling scales.

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