Marine viruses are critical drivers of ocean biogeochemistry and their abundances vary spatiotemporally in the global oceans, with upper estimates exceeding 10 8 per ml. Over many years, a consensus has emerged that virus abundances are typically 10-fold higher than prokaryote abundances. The use of a fixed-ratio suggests that the relationship between virus and prokaryote abundances is both predictable and linear. However, the true explanatory power of a linear relationship and its robustness across diverse ocean environments is unclear. Here, we compile 5671 prokaryote and virus abundance estimates from 25 distinct marine surveys to characterize the relationship between virus and prokaryote abundances. We find that the median virus-to-prokaryote ratio (VPR) is 10:1 and 16:1 in the near-and sub-surface oceans, respectively. Nonetheless, we observe substantial variation in the VPR and find either no or limited explanatory power using fixed-ratio models. Instead, virus abundances are better described as nonlinear, power-law functions of prokaryote abundances -particularly when considering relationships within distinct marine surveys. Estimated power-laws have scaling exponents that are typically less than 1, signifying that the VPR decreases with prokaryote density, rather than remaining fixed. The emergence of power-law scaling presents a challenge for mechanistic models seeking to understand the ecological causes and consequences of marine virusmicrobe interactions. Such power-law scaling also implies that efforts to average viral effects on microbial mortality and biogeochemical cycles using "representative" abundances or abundanceratios need to be refined if they are to be utilized to make quantitative predictions at regional or global ocean scales.

In Table 3 of this Data Descriptor the units of Mean_N 2 O and Mean_CH 4 are incorrectly stated as "Nanomolar (μM)". This should instead read "Nanomolar (nM)".

Harmful algal blooms threaten the water quality of many eutrophic and hypertrophic lakes and cause severe ecological and economic damage worldwide. Dense blooms often deplete the dissolved CO2 concentration and raise pH. Yet, quantitative prediction of the feedbacks between phytoplankton growth, CO2 drawdown and the inorganic carbon chemistry of aquatic ecosystems has received surprisingly little attention. Here, we develop a mathematical model to predict dynamic changes in dissolved inorganic carbon (DIC), pH and alkalinity during phytoplankton bloom development. We tested the model in chemostat experiments with the freshwater cyanobacterium Microcystis aeruginosa at different CO2 levels. The experiments showed that dense blooms sequestered large amounts of atmospheric CO2, not only by their own biomass production but also by inducing a high pH and alkalinity that enhanced the capacity for DIC storage in the system. We used the model to explore how phytoplankton blooms of eutrophic waters will respond to rising CO2 levels. The model predicts that (1) dense phytoplankton blooms in low- and moderately alkaline waters can deplete the dissolved CO2 concentration to limiting levels and raise the pH over a relatively wide range of atmospheric CO2 conditions, (2) rising atmospheric CO2 levels will enhance phytoplankton blooms in low- and moderately alkaline waters with high nutrient loads, and (3) above some threshold, rising atmospheric CO2 will alleviate phytoplankton blooms from carbon limitation, resulting in less intense CO2 depletion and a lesser increase in pH. Sensitivity analysis indicated that the model predictions were qualitatively robust. Quantitatively, the predictions were sensitive to variation in lake depth, DIC input and CO2 gas transfer across the air-water interface, but relatively robust to variation in the carbon uptake mechanisms of phytoplankton. In total, these findings warn that rising CO2 levels may result in a marked intensification of phytoplankton blooms in eutrophic and hypertrophic waters.

Climate change scenarios predict a doubling of the atmospheric CO2 concentration by the end of this century. Yet, how rising CO2 will affect the species composition of aquatic microbial communities is still largely an open question. In this study, we develop a resource competition model to investigate competition for dissolved inorganic carbon in dense algal blooms. The model predicts how dynamic changes in carbon chemistry, pH and light conditions during bloom development feed back on competing phytoplankton species. We test the model predictions in chemostat experiments with monocultures and mixtures of a toxic and non-toxic strain of the freshwater cyanobacterium Microcystis aeruginosa. The toxic strain was able to reduce dissolved CO2 to lower concentrations than the non-toxic strain, and became dominant in competition at low CO2 levels. Conversely, the non-toxic strain could grow at lower light levels, and became dominant in competition at high CO2 levels but low light availability. The model captured the observed reversal in competitive dominance, and was quantitatively in good agreement with the results of the competition experiments. To assess whether microcystins might have a role in this reversal of competitive dominance, we performed further competition experiments with the wild-type strain M. aeruginosa PCC 7806 and its mcyB mutant impaired in microcystin production. The microcystin-producing wild type had a strong selective advantage at low CO2 levels but not at high CO2 levels. Our results thus demonstrate both in theory and experiment that rising CO2 levels can alter the community composition and toxicity of harmful algal blooms.