Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.Peer reviewe

Cyanobacterial blooms

Cyanobacteria can form dense and sometimes toxic blooms in freshwater and marine environments, which threaten ecosystem functioning and degrade water quality for recreation, drinking water, fisheries and human health. Here, we review evidence indicating that cyanobacterial blooms are increasing in frequency, magnitude and duration globally. We highlight species traits and environmental conditions that enable cyanobacteria to thrive and explain why eutrophication and climate change catalyse the global expansion of cyanobacterial blooms. Finally, we discuss management strategies, including nutrient load reductions, changes in hydrodynamics and chemical and biological controls, that can help to prevent or mitigate the proliferation of cyanobacterial blooms

Appendix A. Detailed methods of additional experiments to explain the lack of biological control at high nitrogen loads.

Detailed methods of additional experiments to explain the lack of biological control at high nitrogen loads

Rapid adaptation of harmful cyanobacteria to rising CO2

Rising atmospheric CO2 concentrations are likely to affect many ecosystems worldwide. However, to what extent elevated CO2 will induce evolutionary changes in photosynthetic organisms is still a major open question. Here, we show rapid microevolutionary adaptation of a harmful cyanobacterium to changes in inorganic carbon (Ci) availability. We studied the cyanobacterium Microcystis, a notorious genus that can develop toxic cyanobacterial blooms in many eutrophic lakes and reservoirs worldwide. Microcystis displays genetic variation in the Ci uptake systems BicA and SbtA, where BicA has a low affinity for bicarbonate but high flux rate, and SbtA has a high affinity but low flux rate. Our laboratory competition experiments show that bicA + sbtA genotypes were favored by natural selection at low CO2 levels, but were partially replaced by the bicA genotype at elevated CO2. Similarly, in a eutrophic lake, bicA + sbtA strains were dominant when Ci concentrations were depleted during a dense cyanobacterial bloom, but were replaced by strains with only the high-flux bicA gene when Ci concentrations increased later in the season. Hence, our results provide both laboratory and field evidence that increasing carbon concentrations induce rapid adaptive changes in the genotype composition of harmful cyanobacterial blooms

Appendix A. An assessment of the nutrient status of Lake Volkerak.

An assessment of the nutrient status of Lake Volkerak

Normalized sensitivity coefficients of selected model parameters at atmospheric CO₂ levels of 400 ppm (<i>SC</i>₄₀₀) and 750 ppm (<i>SC</i>₇₅₀).

<p>The normalized sensitivity coefficient expresses the relative change in model output with respect to a relative change in input parameter. We used several species traits and lake properties as input parameters, and the level of carbon limitation and phytoplankton population density as model output.</p

Steady-state patterns of phytoplankton population density and inorganic carbon chemistry in chemostat experiments.

<p>Steady-state results are shown for 6 chemostats with <i>Microcystis</i> HUB5-2-4 exposed to different pCO₂ levels in the gas flow and two different bicarbonate concentrations in the mineral medium (0.5 or 2.0 mmol L⁻¹). (A) Phytoplankton population density (expressed as biovolume), (B) light intensity penetrating through the chemostat (<i>I_OUT</i>), (C) dissolved CO₂ concentration, (D) bicarbonate concentration, (E) pH, (F) alkalinity, (G) DIC concentration, and (H) carbon sequestration rate. Symbols show the mean (± s.d.) of 5 measurements in each steady-state chemostat, lines show the model fits. For comparison, dashed lines show steady-state patterns predicted for chemostats without phytoplankton. Shading indicates the level of carbon limitation (<i>L_C</i>) predicted by the model. The model and its parameter values are detailed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104325#pone.0104325.s002" target="_blank">Text S2</a>.</p

Sensitivity of the model predictions to variation in phytoplankton traits.

<p>Contour plots of the level of carbon limitation (left panels) and steady-state phytoplankton population density (right panels, expressed as biovolume, in mm³L⁻¹) predicted for different atmospheric pCO₂ levels and phytoplankton traits. The phytoplankton traits are (A, B) the half-saturation constant for CO₂ uptake (<i>H_CO2</i>), (C, D) the half-saturation constant for bicarbonate uptake (<i>H_HCO3</i>), (E, F) the maximum CO₂ uptake rate (<i>u_{MAX, CO2}</i>), and (G, H) the cellular N:C ratio (<i>c_N</i>). The model considers a low-alkaline lake (<i>ALK_IN</i> = 0.5 mEq L⁻¹). Vertical lines represent atmospheric CO₂ levels of 400 ppm (present-day) and 750 ppm (predicted for the year 2150 by the RCP6 scenario of the IPCC). Horizontal dotted lines represent our default parameter values. The contour plots are based on steady-state solutions across a grid of 40×50 = 2,000 simulations.</p

Seasonal dynamics of phytoplankton blooms in Lake Volkerak.

<p>(A) Changes in phytoplankton population density (strongly dominated by the cyanobacterium <i>Microcystis</i>) and measured dissolved CO₂ concentration ([CO₂]) during two consecutive years. The dashed line is the expected dissolved CO₂ concentration ([CO₂*]) when assuming equilibrium with atmospheric pCO₂. Dark shading indicates that the lake is supersaturated with CO₂, while light shading indicates undersaturation. (B) Changes in pH, bicarbonate and total DIC concentration. Sampling details are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104325#pone.0104325.s001" target="_blank">Text S1</a>.</p

Steady-state patterns predicted for phytoplankton blooms in low-alkaline lakes.

<p>Steady-state predictions of the model evaluated across a wide range of atmospheric pCO₂ levels. (A) Phytoplankton population density (expressed as biovolume), (B) light intensity reaching the lake sediment (<i>I_OUT</i>), (C) dissolved CO₂ concentration, (D) bicarbonate concentration, (E) pH, (F) alkalinity, (G) DIC concentration, and (H) carbon sequestration rate. Shading indicates the level of carbon limitation (<i>L_C</i>). For comparison, dashed lines show steady-state patterns predicted for low-alkaline waters without phytoplankton. The model parameters are representative for eutrophic low-alkaline lakes (ALK_IN = 0.5 mEq L⁻¹) dominated by the cyanobacterium <i>Microcystis</i> HUB5-2-4. The model and its parameter values are detailed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104325#pone.0104325.s002" target="_blank">Text S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104325#pone.0104325.s003" target="_blank">Text S3</a>.</p