Variability in the chlorophyll (chl) a-specific absorption coefficients of living phytoplankton a•h(A) was analyzed using a data set including 815 spectra determined with the wet filter technique in different regions of the world ocean (covering the chlorophyll concentration range 0.02-25 mg m-3). The a* ph values were observed to decrease rather regularly from oligotrophic to eutrophic waters, spanning over more than 1 order of magnitude (0.18 to 0.01 m 2 mg -1) at the blue absorption maximum. The observed covariation between a•h(A) and the field chl a concentration (chl} can be explained considering (1) the level of pigment packaging and (2) the contribution of accessory pigments to absorption. Empirical relationships between a•h(A ) andwere derived by least squares fitting to power functions. These relationships can be used to produce a ph spectra as a function of (chl}. Such a simple parameterization, if confirmed with further data, can be used, e.g., for refining estimates of the carbon fixation rate at global or regional scales, such as those obtained by combining satellite pigment concentration maps with primary production models based on physiological parameters among which a* , ph is an important one. R(A) = Fbb(A)/a(A ) where a and bb are, respectively, the absorption and backscattering coefficients of the water body, and the dimensionless number F depends, in particular, on the volume scattering function within water [Prieur and Morel, 1975] and on the geometrical structure of the incident light field [Kirk, 1984; Gordon, 1989; Morel and Gentili, 1991]. Analytical models of the phytoplankton growth and primary production also basically rely on the in vivo absorption capacity of living algal cells [Kiefer and Mitchell, 1983; Platt and Sathyendranath, 1988; Morel, 1991; Anderson, 1993], namely, on the absorption coefficients of phytoplankton per unit of chlorophyll (chl) a concentration ("chl a-specific" coefficients or, equivalently, absorption cross sections of algae per mass unit of chl a hereafter denoted a* , ph ('•) and expressed as m 2 mg chl a-•). Such models, combined with satellite data, have been recently used to convert maps of surface pigment concentration into maps of the carbon fixation rate at global or regional scales [e.g., Morel and Andre, 1991]. Up until now, in the application of these models (with the exception of that of Platt and Sathyen-Paper number 95JC00463. 0148-0227/95/95JC-00463 $05.00 dranath [1988]) a* (A) coefficients have been assumed as , ph constant, whatever the water type, and values considered as "typical" have been introduced. These coefficients, however, are now widely recognized as varying, not only for individual species grown in culture, but also for natural phytoplanktonic assemblages [e.g., Mitchell and Kiefer, 1988b; Bricaud and Stramski, 1990]. These variations result from the combined influences of the pigment composition [Bidigare et al., 1990; Hoepffner and Sathyendranath, 1992] and the so-called "package effect" [Kirk, 1975]. The latter, as predicted by...

[1] We measured the absorption properties of phytoplankton, nonalgal particles (NAP), and colored dissolved organic matter (CDOM) at about 350 stations in various coastal waters around Europe including the English Channel, Adriatic Sea, Baltic Sea, Mediterranean Sea, and North Sea. For comparison, we also collected data in the open ocean waters of North Atlantic. The exponential slope of the CDOM absorption spectrum varied within a narrow range around 0.0176 nm À1 (SD = 0.0020 nm À1 ). When data from all the regions were considered altogether, the relationship between phytoplankton absorption and chlorophyll concentration was generally similar to the one previously established for open oceanic waters. Our coastal data, however, show that significant departures from the general trend may occur due to peculiar pigment composition and cell size. In some coastal areas, high phaeopigment concentrations gave rise to especially high blue-to-red ratio of phytoplankton absorption. The NAP absorption covaried with the particle dry weight. Most absorption spectra of these particles were well described by an exponential function with a slope averaging 0.0123 nm À1 (SD = 0.0013 nm À1 ). In some highly turbid waters, the spectra exhibited a signature possibly associated with iron oxides. In the Baltic Sea, NAP absorption systematically showed lower values at wavelengths shorter than 440 nm than predicted from the fitted exponential function. Overall, the variability in the absorption properties of European coastal waters showed some consistent patterns despite the high diversity of the examined waters. Distinct features were identified in the phytoplankton and NAP components. An absorption budget is presented and parameterizations are proposed.

Microbes drive most ecosystems and are modulated by viruses that impact their lifespan, gene flow and metabolic outputs. However, ecosystem-level impacts of viral community diversity remains difficult to assess due to classification issues and few reference genomes. Here we establish a ~12-fold expanded global ocean DNA virome dataset of 195,728 60 viral populations, now including the Arctic Ocean, and validate that these populations form discrete genotypic clusters. Meta-community analyses revealed five ecological zones throughout the global ocean, including two distinct Arctic regions. Across the zones, local and global patterns and drivers in viral community diversity were established for both macrodiversity (interpopulation diversity) and microdiversity (intra-population genetic variation). These patterns 65 sometimes, but not always, paralleled those from macro-organisms and revealed temperate and tropical surface waters and the Arctic as biodiversity hotspots and mechanistic hypotheses to explain them. Such further understanding of ocean viruses is critical for broader inclusion in ecosystem models. Introduction: 70Biodiversity is essential for maintaining ecosystem functions and services (reviewed by Tilman et al., 2014). In the oceans, the vast majority of biodiversity is contained within the microbial fraction containing prokaryotes and eukaryotic microbes, which represents ~60% of its biomass (Bar-On et al., 2018). Meta-analyses looking at changes in marine biodiversity show that biodiversity loss increasingly impairs the ocean's capacity to produce food, maintain water 75 quality, and recover from perturbations (Worm et al., 2006). To date, marine conservation efforts have focused on specific organismal communities, such as fisheries or coral reefs, rather than conserving whole ecosystem biodiversity. However, emerging studies across diverse sampled, global-scale, viruses-to-fish-larvae datasets (de Vargas et al., 2015; Sunagawa et al., 125 2015;Brum et al., 2015;Lima-Mendez et al., 2015;Pesant et al. 2015;Roux et al., 2016), and help establish foundational ecological hypotheses for the field and a roadmap for the broader life sciences community to better study viruses in complex communities. Results & Discussion:The dataset. The Global Ocean Viromes 2.0 (GOV 2.0) dataset is derived from 3.95 Tb 130 of sequencing across 145 samples distributed throughout the world's oceans ( Fig. 1A and Table S3; see Methods). These data build on the prior GOV dataset (Roux et al., 2016) by increased sequencing for mesopelagic samples (defined in our dataset as waters between 150m to 1,000m) and upgrading assemblies, both of which drastically improved sampling of the ocean viruses in these samples (results below). Additionally, we added 41 new samples derived from the Tara 135Oceans Polar Circle (TOPC) expedition, which traveled 25,000 km around the Arctic Ocean in 2013. These 41 Arctic Ocean viromes were generated to represent the most significantly climateimpacted region of the ocean, and an extreme environment. N...

and data sets are compared. It is stressed that for a given (chl), the ap(it) coefficients show large residual variability around the regression lines (for instance, by a factor of 3 at 440 nm). The consequences of such a variability, when predicting or interpreting the diffuse reflectance of the ocean, are examined, according to whether or not these variations in ap are associated with concomitant variations in particle scattering. In most situations the deviations in ap actually are not compensated by those in particle scattering, so that the amplitude of reflectance is affected by these variations.