Techniques for quantifying phytoplankton biodiversity

Composition and temporal dynamics of the phytoplankton community in Laizhou Bay revealed by microscopic observation and rbcL gene sequencing

Laizhou Bay, a major breeding ground for economic marine organisms in the northern waters of China, is facing rapid environmental degradation. In this study, field surveys in this area were conducted in the spring, summer, and autumn of 2020. Microscopic observation and RuBisCO large subunit (rbcL) gene analysis were employed to understand the community structure and temporal dynamics of phytoplankton. The phytoplankton community structures detected by the two methods showed significant differences. Microscopic observation revealed the dominance of dinoflagellates in spring that shifted to the dominance of diatoms in summer and autumn. However, rbcL gene sequencing consistently identified diatoms as dominant throughout all three seasons, with their relative abundance showing an increasing trend. Conversely, the relative abundance of the second- and third-most abundant taxa, namely, haptophytes and ochrophytes, decreased as the seasons transitioned. rbcL gene sequencing annotated more species than microscopy. It could detect haptophytes and cryptophytes, which were overlooked by microscopy. In addition, rbcL gene sequencing detected a remarkable amount of Thalassiosira profunda, which was previously unidentified in this sea area. However, it appeared to underestimate the contribution of dinoflagellates considerably, with most taxa being only identified through microscopic identification. The two methods jointly identified 28 harmful algal bloom taxa with similar detection quantities but substantial differences in species composition. Phytoplankton communities were influenced by temperature, salinity, and nutrients. The results of this work suggest that a combination of multiple techniques is necessary for a comprehensive understanding of phytoplankton.

Underwater dual-magnification imaging for automated lake plankton monitoring

The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 μm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.

Phytoplankton composition in a eutrophic estuary: Comparison of multiple taxonomic approaches and influence of environmental factors

To assess the comparability between taxonomic identification methods for phytoplankton, multiple approaches were used to characterize phytoplankton community composition within the Neuse River Estuary (NRE), North Carolina. Small subunit 18S rRNA gene sequencing and accessory pigment analysis displayed similar trends, indicating chlorophytes were the dominant microalgal group during most of the year, whereas results from microscopic cell counts, biovolume analysis and metatranscriptomics suggested diatom and dinoflagellate-dominated communities. Spatial environmental gradients drove variation in taxonomic composition due to preferences for specific environmental conditions among different microalgal groups. Cryptophytes were a greater proportion of the phytoplankton community within high nutrient, fresher environments whereas diatoms and dinoflagellates dominated higher salinity sections of the estuary. This study provides a detailed examination of phytoplankton communities associated with environmental gradients present in the NRE. The high level of taxonomic resolution offered by DNA sequencing (i.e., species to sub-species level) provides a better understanding of population dynamics at the base of estuarine food webs.

Combining Molecular Observations and Microbial Ecosystem Modeling: A Practical Guide

Advances in technologies for molecular observation are leading to novel types of data, including gene, transcript, protein, and metabolite levels, which are fundamentally different from the types traditionally compared with microbial ecosystem models, such as biomass (e.g., chlorophyll a) and nutrient concentrations. A grand challenge is to use these data to improve predictive models and use models to explain observed patterns. This article presents a framework that aligns observations and models along the dimension of abstraction or biological organization-from raw sequences to ecosystem patterns for observations, and from sequence simulators to ecological theory for models. It then reviews 16 studies that compared model results with molecular observations. Molecular data can and are being combined with microbial ecosystem models, but to keep up with and take advantage of the full scope of observations, models need to become more mechanistically detailed and complex, which is a technical and cultural challenge for the ecological modeling community.

Very Low Phytoplankton Diversity in a Tropical Saline-Alkaline Lake, with Co-dominance of Arthrospira fusiformis (Cyanobacteria) and Picocystis salinarum (Chlorophyta)

Lake Dziani Dzaha (Mayotte Island, Indian Ocean) is a tropical thalassohaline lake which geochemical and biological conditions make it a unique aquatic ecosystem considered as a modern analogue of Precambrian environments. In the present study, we focused on the diversity of phytoplanktonic communities, which produce very high and stable biomass (mean_2014-2015 = 652 ± 179 μg chlorophyll a L^-1). As predicted by classical community ecology paradigms, and as observed in similar environments, a single species is expected to dominate the phytoplanktonic communities. To test this hypothesis, we sampled water column in the deepest part of the lake (18 m) during rainy and dry seasons for two consecutive years. Phytoplanktonic communities were characterized using a combination of metagenomic, microscopy-based and flow cytometry approaches, and we used statistical modeling to identify the environmental factors determining the abundance of dominant organisms. As hypothesized, the overall diversity of the phytoplanktonic communities was very low (15 OTUs), but we observed a co-dominance of two, and not only one, OTUs, viz., Arthrospira fusiformis (Cyanobacteria) and Picocystis salinarum (Chlorophyta). We observed a decrease in the abundance of these co-dominant taxa along the depth profile and identified the adverse environmental factors driving this decline. The functional traits measured on isolated strains of these two taxa (i.e., size, pigment composition, and concentration) are then compared and discussed to explain their capacity to cope with the extreme environmental conditions encountered in the aphotic, anoxic, and sulfidic layers of the water column of Lake Dziani Dzaha.

Metagenomic Sequencing Identifies Highly Diverse Assemblages of Dinoflagellate Cysts in Sediments from Ships' Ballast Tanks

Ships' ballast tanks have long been known as vectors for the introduction of organisms. We applied next-generation sequencing to detect dinoflagellates (mainly as cysts) in 32 ballast tank sediments collected during 2001-2003 from ships entering the Great Lakes or Chesapeake Bay and subsequently archived. Seventy-three dinoflagellates were fully identified to species level by this metagenomic approach and single-cell polymerase chain reaction (PCR)-based sequencing, including 19 toxic species, 36 harmful algal bloom (HAB) forming species, 22 previously unreported as producing cysts, and 55 reported from ballast tank sediments for the first time (including 13 freshwater species), plus 545 operational taxonomic units (OTUs) not fully identified due to a lack of reference sequences, indicating tank sediments are repositories of many previously undocumented taxa. Analyses indicated great heterogeneity of species composition among samples from different sources. Light and scanning electron microscopy and single-cell PCR sequencing supported and confirmed results of the metagenomic approach. This study increases the number of fully identified dinoflagellate species from ballast tank sediments to 142 (> 50% increase). From the perspective of ballast water management, the high diversity and spatiotemporal heterogeneity of dinoflagellates in ballast tanks argues for continuing research and stringent adherence to procedures intended to prevent unintended introduction of non-indigenous toxic and HAB-forming species.

Quantitative macromolecular patterns in phytoplankton communities resolved at the taxonomical level by single-cell Synchrotron FTIR-spectroscopy

BACKGROUND

Technical limitations regarding bulk analysis of phytoplankton biomass limit our comprehension of carbon fluxes in natural populations and, therefore, of carbon, nutrients and energy cycling in aquatic ecosystems. In this study, we took advantage of Synchrotron FTIR micro-spectroscopy and the partial least square regression (PLSr) algorithm to simultaneously quantify the protein, lipid and carbohydrate content at the single-cell level in a mock phytoplankton community (composed by a cyanobacterium, a green-alga and a diatom) grown at two temperatures (15 °C and 25 °C).

RESULTS

The PLSr models generated to quantify cell macromolecules presented high quality fit (R² ≥ 0.90) and low error of prediction (RMSEP 2-6% of dry weight). The regression coefficients revealed that the prediction of each macromolecule was not exclusively dependent on spectral features corresponding to that compound, but rather on all major macromolecular pools, reflecting adjustments in the overall cell carbon balance. The single-cell analysis, studied by means of Kernel density estimators, showed that the modes of density distribution of macromolecules were different at 15 °C and 25 °C. However, a substantial proportion of cells was biochemically identical at the two temperatures because of population heterogeneity.

CONCLUSIONS

The spectroscopic approach presented in this study allows the quantification of macromolecules in single phytoplankton cells. This method showed that population heterogeneity most likely ensures a backup of non-acclimated cells that may rapidly exploit new favourable niches. This finding may have important consequences for the ecology of phytoplankton populations and shows that the "average cell" concept might substantially limit our comprehension of population dynamics and biogeochemical cycles in aquatic ecosystems.

Disentangling effects of temperature on microbial community and copper extraction in column bioleaching of low grade copper sulfide

The lack of knowledge about responses of microbial community to temperature in copper sulfide bioleaching, and subsequent effects on copper extraction hampered understanding of how to improve bioleaching efficiency. This study presents first detailed quantitative data on microbial diversity and dynamics during bioleaching of low grade copper sulfide at different temperatures. The results demonstrate that temperature had significant effects on microbial community and copper extraction. The microbial structures on the ore surfaces were independent of communities in the leachates. Different species dominated the communities at different temperatures and portions of laboratory scale heap column. Moderate thermophiles rather than extreme thermophiles dominated the communities at 65 °C. The height of ore bed was sufficient to affect microbial communities at 30 °C and 65 °C. Sulfur-oxidizers were very important to improve copper extraction. High microbial diversity also were beneficial to enhance copper extraction within a certain temperature range in the final stage.

Sample Preservation, DNA or RNA Extraction and Data Analysis for High-Throughput Phytoplankton Community Sequencing

Phytoplankton is the basis for aquatic food webs and mirrors the water quality. Conventionally, phytoplankton analysis has been done using time consuming and partly subjective microscopic observations, but next generation sequencing (NGS) technologies provide promising potential for rapid automated examination of environmental samples. Because many phytoplankton species have tough cell walls, methods for cell lysis and DNA or RNA isolation need to be efficient to allow unbiased nucleic acid retrieval. Here, we analyzed how two phytoplankton preservation methods, three commercial DNA extraction kits and their improvements, three RNA extraction methods, and two data analysis procedures affected the results of the NGS analysis. A mock community was pooled from phytoplankton species with variation in nucleus size and cell wall hardness. Although the study showed potential for studying Lugol-preserved sample collections, it demonstrated critical challenges in the DNA-based phytoplankton analysis in overall. The 18S rRNA gene sequencing output was highly affected by the variation in the rRNA gene copy numbers per cell, while sample preservation and nucleic acid extraction methods formed another source of variation. At the top, sequence-specific variation in the data quality introduced unexpected bioinformatics bias when the sliding-window method was used for the quality trimming of the Ion Torrent data. While DNA-based analyses did not correlate with biomasses or cell numbers of the mock community, rRNA-based analyses were less affected by different RNA extraction procedures and had better match with the biomasses, dry weight and carbon contents, and are therefore recommended for quantitative phytoplankton analyses.

Genome complexity of harmful microalgae

During the past decade, next generation sequencing (NGS) technologies have provided new insights into the diversity, dynamics, and metabolic pathways of natural microbial communities. But, these new techniques face challenges related to the genome size and level of genome complexity of the species under investigation. Moreover, the coverage depth and the short-read length achieved by NGS based approaches also represent a major challenge for assembly. These factors could limit the use of these high-throughput sequencing methods for species lacking a reference genome and characterized by a high level of complexity. In the present work, the evolutionary history, mainly consisting of gene transfer events from bacteria and unicellular eukaryotes to microalgae, including harmful species, is discussed and reviewed as it relates to NGS application in microbial communities, with a particular focus on harmful algal bloom species and dinoflagellates. In the context of genetic population studies, genotyping-by-sequencing (GBS), an NGS based approach, could be used for the discovery and analysis of single nucleotide polymorphisms (SNPs). The NGS technologies are still relatively new and require further improvement. Specifically, there is a need to develop and standardize tools and approaches to handle large data sets, which have to be used for the majority of HAB species characterized by evolutionary highly dynamic genomes.

Niche partitioning and biogeography of high light adapted Prochlorococcus across taxonomic ranks in the North Pacific

The distribution of major clades of Prochlorococcus tracks light, temperature and other environmental variables; yet, the drivers of genomic diversity within these ecotypes and the net effect on biodiversity of the larger community are poorly understood. We examined high light (HL) adapted Prochlorococcus communities across spatial and temporal environmental gradients in the Pacific Ocean to determine the ecological drivers of population structure and diversity across taxonomic ranks. We show that the Prochlorococcus community has the highest diversity at low latitudes, but seasonality driven by temperature, day length and nutrients adds complexity. At finer taxonomic resolution, some 'sub-ecotype' clades have unique, cohesive responses to environmental variables and distinct biogeographies, suggesting that presently defined ecotypes can be further partitioned into ecologically meaningful units. Intriguingly, biogeographies of the HL-I sub-ecotypes are driven by unique combinations of environmental traits, rather than through trait hierarchy, while the HL-II sub-ecotypes appear ecologically similar, thus demonstrating differences among these dominant HL ecotypes. Examining biodiversity across taxonomic ranks reveals high-resolution dynamics of Prochlorococcus evolution and ecology that are masked at phylogenetically coarse resolution. Spatial and seasonal trends of Prochlorococcus communities suggest that the future ocean may be comprised of different populations, with implications for ecosystem structure and function.