The amount of ice present in clouds can affect cloud lifetime, precipitation and radiative properties 1,2 . The formation of ice in clouds is facilitated by the presence of airborne ice nucleating particles 1,2 . Sea spray is one of the major global sources of atmospheric particles, but it is unclear to what extent these particles are capable of nucleating ice 3-11 . Sea spray aerosol contains large amounts of organic material that is ejected into the atmosphere during bubble bursting at the organically enriched sea-air interface or sea surface microlayer [12][13][14][15][16][17][18][19] . Here we show that organic material in the sea surface microlayer nucleates ice under conditions relevant for mixed-phase cloud and high-altitude ice cloud formation. The ice nucleating material is likely biogenic and less than ~0.2 μm in size. We find that exudates separated from cells of the marine diatom T. Pseudonana nucleate ice and propose that organic material associated with phytoplankton cell exudates is a likely candidate for the observed ice nucleating ability of the microlayer samples. Global model simulations of marine organic aerosol in combination with our measurements suggest that marine organic material may be an important source of ice nucleating particles in remote marine environments such as the Southern Ocean, North Pacific and North Atlantic.Atmospheric ice nucleating particles (INPs) allow ice to nucleate heterogeneously at higher temperatures or lower relative humidity than is typical for homogeneous ice nucleation. Heterogeneous ice nucleation proceeds via different pathways depending on temperature and humidity. In low altitude mixed-phase clouds, INPs are commonly immersed in supercooled liquid droplets and freezing can occur on them at temperatures between -36 and 0°C 2 . At higher altitudes and lower temperatures (<-36°C) where cirrus clouds form, nucleation occurs below water saturation, proceeding by homogeneous, deposition or immersion-in-solution nucleation 1 . Understanding the sources of atmospheric INPs is important because they affect cloud lifetime, cloud albedo and precipitation 1,2 . Recent modelling work has shown that the ocean is potentially an important source of biogenic atmospheric INPs particularly in remote, high latitude regions 9,10 . However, it has never been directly shown that there is a source of atmospheric INPs associated with organic material found in marine waters or sea-spray aerosol.Organic material makes up a substantial fraction of sub-micron sea-spray aerosol and it is estimated that 10±5 Tg yr -1 of primary organic sub-micron aerosol is emitted from marine sources globally 12 . Rising bubbles scavenge surface active organic material from the water column at their interfaces and this process facilitates the formation of the organic enriched sea-air interface known as the sea surface microlayer (SML). This organic material is ejected into the atmosphere during bubble bursting resulting in sea spray aerosol containing similar organic material to that of the microlaye...

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We evaluate the substantial amount of information accumulated on bacterial diversity in a variety of environments and address several fundamental questions, focusing on aquatic systems but including other environments to provide a broader context. Bacterial diversity data were extracted from 225 16S rDNA libraries described in published reports, representing a variety of aquatic and non-aquatic environments. Libraries were predominantly composed of rare phylotypes that appeared only once or twice in the library, and the number of phylotypes observed was correlated with library size (implying that few libraries are exhaustive samples of diversity in the source community). Coverage, the estimated proportion of phylotypes in the environment represented in the library, ranged widely but on average was remarkably high and not correlated with library size. Phylotype richness was calculated by methods based on the frequency of occurrence of different phylotypes in 194 libraries that provided appropriate data. For 90% of aquatic-system libraries, and for 79% of non-aquatic libraries, the estimated phylotype richness was 6 200 phylotypes. Nearly all of the larger estimates were in aquatic sediments, digestive systems and soils. However, the approaches used to estimate phylotype richness may yield underestimates when libraries are too small. A procedure is described to provide an objective means of determining when a library is large enough to provide a stable and unbiased estimate of phylotype richness. A total of 56 libraries, including 44 from aquatic systems, were considered 'large enough' to yield stable estimates suitable for comparing richness among environments. Few significant differences in phylotype richness were observed among aquatic environments. For one of two richness estimators, the average phylotype richness was significantly lower in hyperthermal environments than in sediment and bacterioplankton, but no other significant differences among aquatic environments were observed. In general, and with demonstrated exceptions, published studies have captured a large fraction of bacterial diversity in aquatic systems. In most cases, the estimated bacterial diversity is lower than we would have expected, although many estimates should be considered minimum values. We suggest that on local scales, aquatic bacterial diversity is much less than any predictions of their global diversity, and remains a tractable subject for study. The global-scale diversity of aquatic Bacteria, on the other hand, may be beyond present capabilities for effective study.

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As a necessary step in the study of prokaryotic diversity using 16S rDNA libraries, authors should evaluate how well their libraries represent diversity in the source environment. Phylotype-richness estimates can be used to judge whether a library represents diversity sufficiently for its intended purpose. We have argued that richness estimates are most useful if libraries are first shown to be large enough to yield stable estimates. In this article, we (1) evaluate two potentially suitable, non-parametric richness estimators (S ACE and S Chao1 ), tested against model libraries and libraries drawn from natural prokaryotic communities; (2) evaluate whether stable richness estimates are also unbiased; and (3) examine characteristics of prokaryotic libraries that influence the usefulness of richness estimators. Richness estimates consistently reached a stable asymptotic value for libraries that sampled diversity exhaustively. Stable estimates appear to be unbiased or minimally biased estimates of phylotype richness. The S ACE estimator was often undefined, sometimes overestimated phylotype richness at intermediate sampling efforts, and sometimes stabilized at a larger library size than the S Chao1 estimator. The S Chao1 estimator appears well suited for estimating phylotype richness from prokaryotic 16S rDNA libraries. Libraries judged too small to yield a stable richness estimate typically had a highly uneven frequency distribution of phylotypes, with a preponderance of phylotypes that occurred only once in the library. Libraries considered large enough typically had a more even frequency distribution of phylotypes. A software tool is provided to aid others in assessing whether their libraries are large enough to yield stable phylotype-richness estimates. LIMNOLOGY and OCEANOGRAPHY: METHODScedure gave us greater confidence that the richness estimates and the comparisons based on them were valid.The assessment procedure we applied was rudimentary, and critically important questions remain unanswered. Stable phylotype-richness estimates are not necessarily unbiased estimates, and as Hughes et al. (2001) commented "To test for bias, one needs to know the true richness to compare against the sample estimates. As yet, this comparison is impossible for microbes, because no communities have been exhaustively sampled." Furthermore, the relationship of richness estimators to library size has been examined in several recent studies and by subsampling 16S rDNA libraries, and results have been mixed. Hughes et al. (2001) observed that in several large (128 to 284 clones) prokaryotic libraries, richness estimates based on S Chao1 first increased and then usually stabilized with increasing subsample size and were independent of sample size thereafter. However, S Chao1 estimates did not stabilize for bacteria in a high-productivity aquatic mesocosm, and S ACE did not yield stable estimates for any library. Hill et al. (2003) observed that S Chao1 estimates stabilized with one but not with a second large bacterial libra...

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