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...

Recent receding of the ice pack allows more sunlight to penetrate into the Arctic Ocean, enhancing productivity of a single annual phytoplankton bloom. Increasing river runoff may, however, enhance the yet pronounced upper ocean stratification and prevent any significant wind-driven vertical mixing and upward supply of nutrients, counteracting the additional light available to phytoplankton. Vertical mixing of the upper ocean is the key process that will determine the fate of marine Arctic ecosystems. Here we reveal an unexpected consequence of the Arctic ice loss: regions are now developing a second bloom in the fall, which coincides with delayed freezeup and increased exposure of the sea surface to wind stress. This implies that wind-driven vertical mixing during fall is indeed significant, at least enough to promote further primary production. The Arctic Ocean seems to be experiencing a fundamental shift from a polar to a temperate mode, which is likely to alter the marine ecosystem.

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The Southern Ocean (SO), an area highly sensitive to climate change, is currently experiencing rapid warming and freshening. Such drastic physical changes might significantly alter the SO's biological pump. For more accurate predictions of the possible evolution of this pump, a better understanding of the environmental factors controlling SO phytoplankton dynamics is needed. Here we present a satellite‐based study deciphering the complex environmental control of phytoplankton biomass (PB) and phenology (PH; timing and magnitude of phytoplankton blooms) in the SO. We reveal that PH and PB are mostly organized in the SO at two scales: a large latitudinal scale and a regional scale. Latitudinally, a clear gradient in the timing of bloom occurrence appears tightly linked to the seasonal cycle in irradiance, with some exceptions in specific light‐limited regimes (i.e., well‐mixed areas). Superimposed on this latitudinal scale, zonal asymmetries, up to 3 orders of magnitude, in regional‐scale PB are mainly driven by local advective and iron supply processes. These findings provide a global understanding of PB and PH in the SO, which is of fundamental interest for identifying and explaining ongoing changes as well as predicting future changes in the SO biological pump.

Abstract. Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for in the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e., 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (Chl a) according to (1) the shelf–offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom, and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface Chl a concentration (Chl asurf; 0.7–30 mg m−3) throughout the open water period, the Chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when Chl asurf is low (0–0.5 mg m−3). By applying our empirical model to annual Chl asurf time series, instead of the conventional method assuming vertically homogenous Chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in Chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e., pre-bloom, post-bloom > 0.7 mg m−3, and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.e., post-bloom < 0.5 mg m−3). SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.

Phytoplankton blooms in the Barents Sea are highly sensitive to seasonal and interannual changes in sea ice extent, water mass distribution, and oceanic fronts. With the ongoing increase of Atlantic Water inflows, we expect an impact on these blooms. Here, we use a state‐of‐the‐art collection of in situ hydrogeochemical data for the period 1998–2014, which includes ocean color satellite‐derived proxies for the biomass of calcifying and noncalcifying phytoplankton. Over the last 17 years, sea ice extent anomalies were evidenced having direct consequences for the spatial extent of spring blooms in the Barents Sea. In years of minimal sea ice extent, two spatially distinct blooms were clearly observed: one along the ice edge and another in ice‐free water. These blooms are thought to be triggered by different stratification mechanisms: heating of the surface layers in ice‐free waters and melting of the sea ice along the ice edge. In years of maximal sea ice extent, no such spatial delimitation was observed. The spring bloom generally ended in June when nutrients in the surface layer were depleted. This was followed by a stratified and oligotrophic summer period. A coccolithophore bloom generally developed in August, but was confined only to Atlantic Waters. In these same waters, a late summer bloom of noncalcifying algae was observed in September, triggered by enhanced mixing, which replenishes surface waters with nutrients. Altogether, the 17 year time‐series revealed a northward and eastward shift of the spring and summer phytoplankton blooms.