Trait response of three Baltic Sea spring dinoflagellates to temperature, salinity, and light gradients

Climate change is driving Baltic Sea shifts, with predictions for decrease in salinity and increase in temperature and light limitation. Understanding the responses of the spring phytoplankton community to these shifts is essential to assess potential changes in the Baltic Sea biogeochemical cycles and functioning. In this study we use a high-throughput well-plate setup to experimentally define growth and the light acquisition traits over gradients of salinity, temperature and irradiance for three dinoflagellates commonly occurring during spring in the Baltic Sea, Apocalathium malmogiense, Gymnodinium corollarium and Heterocapsa arctica subsp. frigida. By analysing the response of cell volume, growth, and light-acquisition traits to temperature and salinity gradients, we showed that each of the three dinoflagellates have their own niches and preferences and are affected differently by small changes in salinity and temperature. A. malmogiense has a more generalist strategy, its growth being less affected by temperature, salinity, and light gradients in comparison to the other tested dinoflagellates, with G. corollarium growth being more sensitive to higher light intensities. On the other hand, G. corollarium light acquisition traits seem to be less sensitive to changes in temperature and salinity than those of A. malmogiense and H. arctica subsp. frigida. We contextualized our experimental findings using data collected on ships-of-opportunity between 1993-2011 over natural temperature and salinity gradients in the Baltic Sea. The Apocalathium complex and H. arctica subsp. frigida were mostly found in temperatures<10°C and salinities 4-10 ‰, matching the temperature and salinity gradients used in our experiments. Our results illustrate that trait information can complement phytoplankton monitoring observations, providing powerful tools to answer questions related to species’ capacity to adapt and compete under a changing environment

Dataset from a mesocosm experiment on brownification in the Baltic Sea

Refers to Brownification affects phytoplankton community composition but not primary productivity in eutrophic coastal waters: A mesocosm experiment in the Baltic Sea Science of The Total Environment, Volume 841, 1 October 2022, Pages 156510 Kristian Spilling, Eero Asmala, Noora Haavisto, Lumi Haraguchi, Kaisa Kraft, Anne-Mari Lehto, Aleksandra M. Lewandowska, Joanna Norkko, Jonna Piiparinen, Jukka Seppälä, Mari Vanharanta, Anu Vehmaa, Pasi Ylöstalo, Timo TamminenClimate change is projected to cause brownification of some coastal seas due to increased runoff of terrestrially derived organic matter. We carried out a mesocosm experiment over 15 days to test the effect of this on the planktonic ecosystem. The experiment was set up in 2.2 m3 plastic bags moored outside the Tvärminne Zoological Station at the SW coast of Finland. We used four treatments, each with three replicates: control (Contr) without any manipulation; addition of a commercially available organic carbon additive called HuminFeed (Hum; 2 mg L−1); addition of inorganic nutrients (Nutr; 5.7 µM NH4 and 0.65µM PO4); and a final treatment of combined Nutr and Hum (Nutr+Hum) additions. Water samples were taken daily, and measured variables included water transparency, organic and inorganic nutrient pools, chlorophyll a (Chla), primary and bacterial production and particle counts by flow cytometry.Peer reviewe

First application of IFCB high-frequency imaging-in-flow cytometry to investigate bloom-forming filamentous cyanobacteria in the Baltic Sea

Cyanobacteria are an important part of phytoplankton communities, however, they are also known for forming massive blooms with potentially deleterious effects on recreational use, human and animal health, and ecosystem functioning. Emerging high-frequency imaging flow cytometry applications, such as Imaging FlowCytobot (IFCB), are crucial in furthering our understanding of the factors driving bloom dynamics, since these applications provide community composition information at frequencies impossible to attain using conventional monitoring methods. However, the proof of applicability of automated imaging applications for studying dynamics of filamentous cyanobacteria is still scarce. In this study we present the first results of IFCB applied to a Baltic Sea cyanobacterial bloom community using a continuous flow-through setup. Our main aim was to demonstrate the pros and cons of the IFCB in identifying filamentous cyanobacterial taxa and in estimating their biomass. Selected environmental parameters (water temperature, wind speed and salinity) were included, in order to demonstrate the dynamics of the system the cyanobacteria occur in and the possibilities for analyzing high-frequency phytoplankton observations against changes in the environment. In order to compare the IFCB results with conventional monitoring methods, filamentous cyanobacteria were enumerated from water samples using light microscopical analysis. Two common bloom forming filamentous cyanobacteria in the Baltic Sea, Aphanizomenon flosaquae and Dolichospermum spp. dominated the bloom, followed by an increase in Oscillatoriales abundance. The IFCB results compared well with the results of the light microscopical analysis, especially in the case of Dolichospermum. Aphanizomenon biomass varied slightly between the methods and the Oscillatoriales results deviated the most. Bloom formation was initiated as water temperature increased to over 15°C and terminated as the wind speed increased, dispersing the bloom. Community shifts were closely related to movements of the water mass. We demonstrate how using a high-frequency imaging flow cytometry application can help understand the development of cyanobacteria summer blooms

Loadings of dissolved organic matter and nutrients from the Neva River into the Gulf of Finland - Biogeochemical composition and spatial distribution within the salinity gradient

We studied the loadings of dissolved organic matter (DOM) and nutrients from the Neva River into the Eastern Gulf of Finland, as well as their distribution within the salinity gradient. Concentrations of dissolved organic carbon (DOC) ranged from 390 to 840 mu M, and were related to absorption of colored DOM (CDOM) at 350 nm, a(CDOM)(350), ranging from 2.70 to 17.8 m(-1). With increasing salinity both DOC and a(CDOM) decreased, whereas the slope of a(CDOM) spectra, S-CDOM(300-700), ranging from 14.3 to 21.2 mu m(-1), increased with salinity. Deviations of these properties from conservative mixing models were occasionally observed within the salinity range of approximately 1-4, corresponding to the region between 27 and 29 degrees E. These patterns are suggested to mostly reflect seasonal changes in properties of river end-member and hydrodynamics of the estuary, rather than non-conservative processes. On the other hand, observed nonlinear relationships observed between a(CDOM)*(350) and S-CDOM(275-295) emphasized the importance of photochemistry among various transformation processes of DOM. Dissolved inorganic nitrogen was effectively transformed in the estuary into particulate organic nitrogen (PON) and dissolved organic nitrogen (DON), of which DON was mostly exported from the estuary, enhancing productivity in nitrogen limited parts of the Gulf of Finland. DON concentrations ranged from 12.4 to 23.5 mu M and its estuarine dynamics were clearly uncoupled from DOC. In contrast to DOC, estuarine DON dynamics suggest that its production exceeds losses in the estuary. Total nitrogen (TN) and phosphorus (TP) loadings from the Neva River and St. Petersburg were estimated as 73.5 Gg N yr(-1) and 4.2 Gg P yr(-1), respectively. Approximately 59% of TN and 53% of TP loads were in organic forms. DOC and DON loadings were estimated as 741.4 Gg C yr(-1) and 19.0 Gg N yr(-1), respectively. Our estimate for DOC loading was evaluated against a previously published carbon budget of the Baltic Sea. According to the updated model, the Baltic Sea could be identified as a weak source of carbon into the atmosphere. (C) 2016 The Authors. Published by Elsevier B.V.Peer reviewe

Interaction Effects of Light, Temperature and Nutrient Limitations (N, P and Si) on Growth, Stoichiometry and Photosynthetic Parameters of the Cold-Water Diatom Chaetoceros wighamii

Light (20-450 mu mol photons m(-2) s(-1)), temperature (3-11 degrees C) and inorganic nutrient composition (nutrient replete and N, P and Si limitation) were manipulated to study their combined influence on growth, stoichiometry (C:N:P:Chl a) and primary production of the cold water diatom Chaetoceros wighamii. During exponential growth, the maximum growth rate (similar to 0.8 d(-1)) was observed at high temperture and light; at 3 degrees C the growth rate was similar to 30% lower under similar light conditions. The interaction effect of light and temperature were clearly visible from growth and cellular stoichiometry. The average C:N:P molar ratio was 80:13:1 during exponential growth, but the range, due to different light acclimation, was widest at the lowest temperature, reaching very low C:P (similar to 50) and N:P ratios (similar to 8) at low light and temperature. The C:Chl a ratio had also a wider range at the lowest temperature during exponential growth, ranging 16-48 (weight ratio) at 3 degrees C compared with 17-33 at 11 degrees C. During exponential growth, there was no clear trend in the Chl a normalized, initial slope (alpha*) of the photosynthesis-irradiance (PE) curve, but the maximum photosynthetic production (P-m) was highest for cultures acclimated to the highest light and temperature. During the stationary growth phase, the stoichiometric relationship depended on the limiting nutrient, but with generally increasing C:N:P ratio. The average photosynthetic quotient (PQ) during exponential growth was 1.26 but decreased toPeer reviewe

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea.

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0-3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data

The coefficient of multiple determination (R²) for a fitted plane and the modeled response surface with a statistical test of differences between these two ways of representing the data.

<p>The R² is a measure of the goodness of fit and was calculated from the total and residual sum of squares (SS) according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126308#pone.0126308.e003" target="_blank">Eq 3</a>. The fitted plane represents a plane tilted to best fit the data (by polynomial regression), whereas the modeled response surface is presented in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126308#pone.0126308.g001" target="_blank">1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126308#pone.0126308.g002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126308#pone.0126308.g005" target="_blank">5</a>. The p-values are from Fishers-F test of variance comparing the residuals from the fitted plane with the modeled response surface.</p><p>The coefficient of multiple determination (R²) for a fitted plane and the modeled response surface with a statistical test of differences between these two ways of representing the data.</p

The photosynthetic quotient (PQ; mol O₂ produced per mol C fixed) at exponential and stationary growth phases (both N and P limited), and at the initial slope (α*) and photosynthetic maximum (Pm*) of the PE curve.

<p>The horizontal line is the median, the box represents the 25–75% confidence interval, and the error bars the 10–90% confidence interval (n = 12 for exponential growth; n = 5 for N and P limitation). The data is presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126308#pone.0126308.t001" target="_blank">Table 1</a>.</p