Ocean microbes drive global-scale biogeochemical cycling 1 , but do so under constraints imposed by viruses on host community composition, metabolism, and evolutionary trajectories [2][3][4][5] . Due to sampling and cultivation challenges, genome-level viral diversity remains poorly described and grossly understudied in nature such that <1% of observed surface ocean viruses, even those that are abundant and ubiquitous, are 'known' 5 . Here we analyze a global map of abundant, double stranded DNA (dsDNA) viruses and viral-encoded auxiliary metabolic genes (AMGs) with genomic and ecological contexts through the Global Ocean Viromes (GOV) dataset, which includes complete genomes and large genomic fragments from both surface and deep ocean viruses sampled during the Tara Oceans and Malaspina research expeditions 6,7 . A total of 15,222 epi-and mesopelagic viral populations were identified that comprised 867 viral clusters (VCs, approximately genus-level groups 8,9 ). This roughly triples known ocean viral populations 10 , doubles known candidate bacterial and archaeal virus genera 9 , and near-completely samples epipelagic communities at both the population and VC level. Thirty-eight of the 867 VCs were identified as the most impactful dsDNA viral groups in the oceans, as these were locally or globally abundant and accounted together for nearly half of the viral populations in any GOV sample. Most of these were predicted in silico to infect dominant, ecologically relevant microbes, while two thirds of them represent newly described viruses that lacked any cultivated representative. Beyond these taxon-specific ecological observations, we identified 243 viral-encoded AMGs in GOV, only 95 of which were known. Deeper analyses of 4 of these AMGs revealed that abundant viruses directly manipulate sulfur and nitrogen cycling, and do so throughout the epipelagic ocean. Together these data provide a critically-needed organismal catalog and functional context to begin meaningfully integrating viruses into ecosystem models as key players in nutrient cycling and trophic networks. Main textThe fundamental bottleneck preventing the incorporation of viruses of microbes into predictive ecosystem models is the lack of quantitative surveys of viral diversity in nature. This is because (i) most naturally-occurring microbes and viruses resist being cultured, and (ii) viruses lack a universally conserved marker gene, which precludes barcode surveys of uncultivated viral diversity 5 . While viral metagenomics was introduced to circumvent these issues, low-throughput sequencing technologies initially yielded highly fragmented datasets suitable only for strongly database-biased descriptions 11 , and gene-level analyses of environmental viral communities (reviewed in ref. 5).Subsequent improvements in experimental methods, sequencing technologies, and analytical approaches progressively enabled viral population ecology through the availability of genomic information 5,[12][13][14] . For example, 1,148 large viral genome fragmen...
Environmental predictors select individuals by their functional traits, shaping the anuran assembly patterns. Individuals respond to environmental filters that can be on a local or regional scale.In this study, we investigated the association between local (water and microhabitat) and landscape variables and the morphological traits of tadpoles of ponds and streams. The study was conducted in the southern region of the Brazilian Atlantic Forest. We sampled 28 waterbodies and recorded 22 anurans species. We performed RLQ and fourth-corner analyses to determine the patterns of trait-environment relationships and determine which environmental and landscape variables influence the morphological characteristics of tadpoles from streams and ponds. We found that the morphological traits of tadpoles are influenced mainly by physicochemical and microhabitat attributes, being distinct between ponds and streams. In ponds, water depth, pH, and the presence of vegetation influence the morphological traits of the tadpoles, while in the streams water pH, temperature, conductivity, total alkalinity, Alk HCO3, and microhabitat variables played a major role in defining the traits. Our results indicate that local components of habitat (water characteristics and microhabitat) influence functional traits of tadpoles in both ponds and streams, especially those supposedly related to locomotory, foraging and prey-detection abilities.
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