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73 results for “phytoplankton community”
Functional redundancy in natural pico-phytoplankton communities depends on temperature and biogeography
<p><span><span><span><span><span><span><span><span><span><span><span>Biodiversity affects ecosystem function, and how this relationship will change in a warming world is a major and well-examined question in ecology. Yet, it remains understudied for pico-phytoplankton communities, which contribute to carbon cycles and aquatic food webs year-round. Observational studies show a link between phytoplankton community diversity and ecosystem stability, but there is only scarce causal or empirical evidence. Here, we sampled phytoplankton communities from two geographically related regions with distinct thermal and biological properties in the Southern Baltic Sea, and carried out a series of dilution/regrowth experiments across three assay temperatures. This allowed us to investigate the effects of loss of rare taxa and establish causal links in natural communities between species richness and several ecologically relevant traits (e.g. size, biomass production, and oxygen production), depending on sampling location and assay temperature. We found that the samples' bio-geographical origin determined whether and how functional redundancy changed as a function of temperature for all traits under investigation. Samples obtained from the slightly warmer and more thermally variable regions showed overall high functional redundancy. Samples from the slightly cooler, less variable, stations showed little functional redundancy, i.e. function decreased when species were lost from the community. The differences between regions were more pronounced at elevated assay temperatures. Our results imply that the importance of rare species and the amount of species required to maintain ecosystem function even under short-term warming may differ drastically even within geographically closely related regions of the same ecosystem. </span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Phytoplankton community interactions and environmental sensitivity in coastal and offshore habitats
Assessing the relative importance of environmental conditions and community interactions is necessary for evaluating the sensitivity of biological communities to anthropogenic change. Phytoplankton communities have a central role in aquatic food webs and biogeochemical cycles, therefore, consequences of differing community sensitivities may have broad ecosystem effects. Using two long-term time series (28 and 20 years) from the Baltic Sea, we evaluated coastal and offshore major phytoplankton taxonomic group biovolume patterns over annual and monthly time-scales and assessed their response to environmental drivers and biotic interactions. Overall, coastal phytoplankton responded more strongly to environmental anomalies than offshore phytoplankton, although the specific environmental driver changed with time scale. A trend indicating a state shift in annual biovolume anomalies occurred at both sites and the shift's timing at the coastal site closely tracked other long-term Baltic Sea ecosystem shifts. Cyanobacteria and the autotrophic ciliate Mesodinium rubrum were more strongly related than other groups to this trend with opposing relationships that were consistent across sites. On a monthly scale, biotic interactions within communities were rare and did not overlap between the coastal and offshore sites. Annual scales may be better able to assess general patterns across habitat types in the Baltic Sea, but monthly community dynamics may differ at relatively small spatial scales and consequently respond differently to future change.
Data from: CO2 alters community composition and response to nutrient enrichment of freshwater phytoplankton
Nutrients can limit the productivity of ecosystems and control the composition of the communities of organisms that inhabit them. Humans are causing atmospheric CO2 concentrations to reach levels higher than those of the past millions of years while at the same time propagating eutrophication through the addition of nutrients to lakes and rivers. We studied the effect of elevated CO2 concentrations, nutrient addition and their interaction in a series of freshwater mesocosm experiments using a factorial design. Our results highlight the important role of CO2 in shaping phytoplankton communities and their response to nutrient addition. We found that CO2 greatly magnified the increase in phytoplankton growth caused by the increased availability of nutrients. Elevated CO2 also caused changes in phytoplankton community composition. As predicted from physiology and laboratory experiments, the taxonomic group that was most limited by current day CO2 concentrations, chlorophytes, increased in relative frequency at elevated CO2. This predictable change in community composition with changes in CO2 is not altered by changes in the availability of other nutrients.
Data from: The predictability of a lake phytoplankton community, over time-scales of hours to years
Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. We used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.
Data from: Rehabilitating the cyanobacteria – niche partitioning, resource use efficiency, and phytoplankton community structure during diazotrophic cyanobacterial blooms
1. Blooms of nitrogen-fixing cyanobacteria are recurrent phenomena in marine and freshwater habitats, and their supplying role in aquatic biogeochemical cycles is generally considered vital. The objective of this study is to analyze if an increasing proportion of nitrogen-fixing cyanobacteria affects (i) the composition of the non-diazotrophic component of ambient phytoplankton communities, and (ii) resource use efficiency (RUE; ratio of chl a to total nutrients) – an important ecosystem function. We hypothesize that diazotrophs increase community P use, and decrease N use efficiencies, as new N is brought into the system, relaxing N, and concomitantly aggravating P limitation. We test this by analyzing an extensive dataset from the Baltic Sea (> 3700 quantitative phytoplankton samples), known to harbor conspicuous and recurrent blooms of Nodularia spumigena and Aphanizomenon sp. 2. System-level phosphorus use efficiency (RUEP) was positively related with high proportion of diazotrophic cyanobacteria, suggesting aggravation of phosphorus limitation. However, concomitant decrease of nitrogen use efficiency (RUEN) was not observed. Nodularia spumigena, a dominant diazotroph and a notorious toxin producer, had a significantly stronger relationship with RUEP, compared to the competing non-toxic Aphanizomenon sp., confirming niche differentiation in P acquisition strategies between the major bloom-forming cyanobacterial species in the Baltic Sea. Nodularia occurrences were associated with stronger temperature stratification in more offshore environments, indicating higher reliance on in situ P regeneration. 3. By using constrained and unconstrained ordination, permutational multivariate analysis of variance, and local similarity analysis, we show that diazotrophic cyanobacteria explained no more than a few percent of the ambient phytoplankton community variation. The analyses furthermore yielded rather evenly distributed negative and positive effects on individual co-occurring phytoplankton taxa, with no obvious phylogenetic or functional trait-based patterns. 4. Synthesis. Our study reveals that despite the widely acknowledged noxious impacts of cyanobacterial blooms, the overall effect on phytoplankton community structure is minor. There are no predominantly positive or negative associations with ambient phytoplankton species. Species-specific niche differences in cyanobacterial resource acquisition affect important ecosystem functions, like biomass production per unit limiting resource.
Data from: The effect of elevated CO2 on growth and competition in experimental phytoplankton communities
We report an experiment designed to identify the effect of elevated CO2 on species of phytoplankton in a simple laboratory system. Major taxa of phytoplankton differ in their ability to take up CO2, which might lead to predictable changes in the growth rate of species and thereby shifts in the composition of phytoplankton communities in response to rising CO2. Six species of phytoplankton belonging to three major taxa (cyanobacteria, diatoms and chlorophytes) were cultured in atmospheres whose CO2 concentration was gradually increased from ambient levels to 1000 parts per million over about 100 generations and then maintained for a further 200 generations at elevated CO2. The experimental design allowed us to trace a predictive sequence, from physiological features to the growth response of species to elevated CO2 in pure culture, from the growth response in pure culture to competitive ability in pairwise mixtures and from pairwise competitive ability to shifts in the relative abundance of species in the full community of all six species. CO2 altered the dynamics of growth in a fashion consistent with known differences among major taxa in their ability to take up and use CO2. This pure-culture response was partly successful in predicting the outcome of competition in pairwise mixtures, especially the enhanced competitive ability of chlorophytes relative to cyanobacteria, although generally statistical support was weak. The competitive response in pairwise mixtures was a good predictor of changes in competitive ability in the full community. Hence, there is a potential for forging a logical chain of inferences for predicting how phytoplankton communities will respond to elevated CO2. Clearly further extensive experiments will be required to validate this approach in the greater complexity found in diverse communities and environments of natural systems.
Data from: Water-borne pharmaceuticals reduce phenotypic diversity and response capacity of natural phytoplankton communities
Chemical micropollutants occur worldwide in the environment at low concentrations and in complex mixtures, and how they affect the ecology of natural systems is still uncertain. Dynamics of natural communities are driven by the interaction between individual organisms and their growth environment, which is mediated by the organisms' expressed phenotypic traits. We tested whether exposure to a mixture of 12 pharmaceuticals and personal care products (PPCP) influences phenotypic trait diversity in lake phytoplankton communities and their ability to regulate biomass production to fit environmental changes (response capacity). We exposed natural phytoplankton assemblages to three mixture levels in permeable microcosms maintained at three depths in a eutrophic lake for one week, during which the environmental conditions were fluctuating. We studied individual-level traits, phenotypic diversity and community biomass. PPCP reduced individual-level trait variance and overall community phenotypic diversity, but maintained higher standing phytoplankton biomass compared to untreated controls. Estimated effect sizes of PPCP on traits and community properties were very large (partial Eta-squared > 0.15). The PPCP mixture antagonistically interacted with the natural environmental gradient in habitats offered by different depths and, at concentrations comparable to those in waste-water effluents, prevented communities from converging to the same phenotypic structure and total biomass of unexposed controls. We show that micropollutants can alter individual-level trait diversity of lake phytoplankton communities and therefore their capacity to respond to natural environmental gradients, potentially affecting aquatic ecosystem processes.
Phytoplankton community successions associated with monsoon periods with an emphasis on Trichodesmium in the southern South China Sea
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Phytoplankton Community Patterns in the Northeastern South China Sea: Implications of intensified Kuroshio intrusion during the 2015/16 El Niño
<p>Phytoplankton Community Patterns in the Northeastern South China Sea:<br> Implications of intensified Kuroshio intrusion during the 2015/16 El Niño</p>
Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
<p><strong>General description</strong></p> <p>The datasets presented in this repository have served in the development of a new ocean color algorithm to derive the relative cell abundance of seven phytoplankton groups (output of algorithm #1, called SOMRCA), as well as their contribution to total chlorophyll a (ChlaPG, output of algorithm #2, called SOMChlF) at the global scale using an omic-based marker: psbO. The outputs of the algorithm SOMChlF were compared to the HPLC-based definition of phytoplankton groups.</p> <p>All the details about this study are found in El Hourany, R., Pierella Karlusich, J., Zinger, L., Loisel, H., Levy, M., and Bowler, C.: Linking satellites to genes with machine learning to estimate phytoplankton community structure from space, Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, 2024.</p> <p>In "Tara_Oceans_psbO_dataset_Final.xlsx", it can be found the Tara Oceans' psbO metagenomic counts converted into relative cell abundance and Chlorophyll-a contribution for seven phytoplankton groups alongside satellite matchups. This dataset was used for algorithm development. In "Assets HPLC_SOMChlF.xlsx", the HPLC database was used as a comparison with Satellite-derived ChlaPG. Each data document presents a description sheet.</p> <p>In the following, the datasets used in this study are described.</p> <p><strong>Tara Oceans psbO metagenomic abundances</strong><br>The psbO gene is a single-copy gene in most eukaryotes and prokaryotes. We used psbO reads from the metagenomes generated by the Tara Oceans expedition as a proxy for phytoplankton relative cell abundance (see more details in Pierella Karlusich et al., 2023 Mol Ecol Res; https://doi.org/10.1111/1755-0998.13592).</p> <p>Among the 210 Tara Oceans stations, 145 stations sampled metagenomes in different ocean regimes from oligotrophic to eutrophic waters (Chl a from 0.01 to 10 mg m−3, median at 0.3 mg m−3) from 2009 to 2013. Seawater samples were filtered to differentiate five planktonic size fractions (0.22–3, 0.8–5, 5–20, 20–180, 180–2000 µm). <br>We retrieved the psbO read abundances from each Tara Oceans size-fractionated seawater sample from the Supplementary Material from Pierella Karlusich et al., 2023 Mol Ecol Res (https://www.ebi.ac.uk/biostudies/files/S-BSST761/psbO_mapping_against_Tara_Oceans_metagenomes.tsv).</p> <p>We used the psbO data to taxonomically differentiate seven phytoplankton groups: diatoms, dinoflagellates, green algae, haptophytes, pelagophytes, cryptophytes, and prokaryotes (cyanobacteria). The psbO read abundances of these seven groups are expressed as relative phytoplankton cell abundance (%) and their contribution to the Chlorophyll-a (Chla PG, in mg m-3). Phytoplankton that were not assigned to any of these seven groups (unclassified) represented less than 5 % of the total relative cell abundance among all size classes. To obtain a single value of relative cell abundance per station, we pooled the five size fractions into a single aggregated sample. For Chl a content estimation, we used a conversion via size-dependent weights (see formula 1 in El Hourany et al., 2024).</p> <p>There are two levels of information derived from the molecular dataset: relative abundance of psbO reads as a proxy for relative cell abundance and the fraction of Chl a that each group represents. Both types of information have different implications. Chl a is often used as a proxy for biomass, which is a relevant parameter for energy and matter fluxes (e.g., food webs, biogeochemical cycles). At the same time, cell abundance corresponds to species abundance for unicellular organisms, which is an important measure for inferring community assembly processes.<br> <br><strong>Satellite Matchups</strong><br>We used ocean color products from the GlobColour project (R2019, full archive reprocessed, 2020) to retrieve satellite matchups for the psbO-derived abundances. These products were constructed by merging data from various satellite sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Colour Instrument (OLCI).</p> <p>We used 16 GlobColour products as inputs to retrieve the phytoplankton community structure: chlorophyll a concentration (Chl a, product name: CHL1-AVW), remote sensing reflectances (Rrs) at 11 wavelengths (412, 443, 469, 490, 510, 531, 547, 555, 620, 645, and 670 nm), light attenuation coefficient at 490 nm (Kd490), photosynthetically available radiation (PAR), normalized fluorescence light height (NFLH), and particulate backscattering at 443 nm (bbp). These products have daily and 4 km spatiotemporal resolution. In addition, we used the Climate Change Initiative (CCI) sea surface temperature (SST) product at 4 km resolution and daily frequency distributed by the Copernicus Marine Services (CMEMS) portal.</p> <p><strong>HPLC datasets</strong><br>To compare satellite-derived phytoplankton group Chla fractions' distribution (outputs of the algorithm named SOMChlF) with more conventional DPA-based products, we compiled a global HPLC dataset regrouping 12 000 HPLC observations from several HPLC datasets between 1997 and 2014. This HPLC dataset was collocated with the SOMChlF-based ChlaPG. This dataset depicts the abundance of the pigments most widely used to identify major phytoplankton groups: fucoxanthin (Fuco), peridinin (Perid), alloxanthin (Allo), zeaxanthin (Zea), chlorophyll b (Chl b), 19-hexanoyloxyfucoxanthin (19HF), and 19-butanoyloxyfucoxanthin (19BF).</p> <p>Diagnostic pigments were used to estimate the Chl a fraction for each phytoplankton group, namely diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelgophytes, and prokaryotes. The Chl a fraction per group is expressed by</p> <p>HPLC-based ChlaPG = Chla in-situ · DP · α / Sum (DP · α) where "α" is a coefficient associated with a diagnostic pigment (DP) for a specific PG.</p> <p>All the details are found in El Hourany, R., Pierella Karlusich, J., Zinger, L., Loisel, H., Levy, M., and Bowler, C.: Linking satellites to genes with machine learning to estimate phytoplankton community structure from space, Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, 2024.</p> <p> </p>
Dataset for 'Phytoplankton community response to episodic wet and dry aerosol deposition in the subtropical North Atlantic' by Yuan et al. 2023. Limnology and Oceanography.
<p>Dataset for 'Phytoplankton community response to episodic wet and dry aerosol deposition in the subtropical North Atlantic' by Yuan et al. 2023. Limnology and Oceanography.</p>
Functional redundancy in natural pico-phytoplankton communities depends on temperature and biogeography
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Data from: The effect of elevated CO2 on growth and competition in experimental phytoplankton communities
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Data from: CO2 alters community composition and response to nutrient enrichment of freshwater phytoplankton
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Data from: Water-borne pharmaceuticals reduce phenotypic diversity and response capacity of natural phytoplankton communities
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Data from: Phytoplankton community interactions and environmental sensitivity in coastal and offshore habitats
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Supplemental Tables for Heal et al: Marine community metabolomes carry fingerprints of phytoplankton community composition
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Data from: Rehabilitating the cyanobacteria – niche partitioning, resource use efficiency, and phytoplankton community structure during diazotrophic cyanobacterial blooms
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Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state
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Multiscale drivers of phytoplankton communities in north-temperate lakes
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