Philippa Kaur

In this study, we generated a new time-series dataset to investigate the response of dark-synchronized MT+ cultures to SIP− filtrate (6 time points, 0–9 h, Fig. 2a) and to compare expression patterns with two existing datasets on the response of MT− to SIP+ [16, 18] (4 time points, 0–10 h, Fig. 2b). Here, we first report the results of a conventional DE analysis for each dataset separately. Next, we use these results to discover genes or biological processes related to sexual reproduction, either based on their predicted functional annotation or the current literature. Finally, we integrate the results of both the novel and publicly available datasets in an integrative analysis that aims at identifying key genes involved in either a single or both mating types.
Fig. 2: Transcriptional responses induced by SIP treatment of Seminavis robusta.

a Multidimensional scaling (MDS) plot for MT+ expression data (0 h–9 h), and (b) MDS plots of two MT− expression datasets (0 h–3 h and 10 h, respectively). Distances between samples in the MDS plot approximate the log2 fold changes of the top 500 genes. c Number of significant DE genes between control and SIP treated cultures for each time point in both mating types. Each dataset was analyzed on a 5% overall FDR (OFDR) level, i.e., the fraction of false positive genes over all rejected genes. The color represents mating type (MT+: red, MT−: blue) and the shade denotes direction of change (dark: downregulated after SIP treatment, light: upregulated after SIP treatment). d Number of significantly enriched GO terms on a 5% FDR level for each time point. The color represents mating type (MT+: red, MT−: blue). A high number of GO terms are discovered in the early time points for MT+, in comparison to the number of DE genes.

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General transcriptional response and identification of key SIP responsive genes
Multidimensional scaling plots of the RNA-seq data (Fig. 2a, b) showed that in both mating types the dark-to-light transition and time since illumination were the major drivers of gene expression change throughout the experiments. However, as time progresses, the effect of SIP becomes more pronounced. This is supported by DE analysis showing that the number of significant genes increased markedly at later time points (Fig. 2c). Overall, most genes are significantly DE in only a single time point on a 5% overall FDR (OFDR) level (Supplementary Fig. 1B). Combined, on a 5% OFDR level, 4037 genes are DE in response to SIP treatment for MT+, while 5486 genes are found to be DE in MT− in the first 3 h and 6079 genes after 10 h. The stronger response during the first 3 time points in MT− versus MT+ may be the result of the use of a chromatographic fraction of SIP+ for MT− while MT+ cultures were treated with a SIP− containing filtrate. Gene sets for many biological processes were significantly enriched in the conventional lists of DE genes: a total of 1081 and 740 enriched biological process terms were discovered for the response to sex pheromones in MT+ and MT−, respectively (Fig. 2d, Supplemenatry Fig. 2).
We discovered key genes by developing a statistical integrative analysis workflow that is capable of testing for equivalent, i.e., non-DE, expression between conditions. Coupling equivalence testing in one mating type with DE calls in the other allowed for the discovery of key genes exhibiting mating type specific responses to SIP, while DE calls in both datasets found key genes responsive in both mating types. This workflow revealed 52 key genes responding to SIP in both mating types (SRBs), 12 genes uniquely responding in MT+ (SRPs) and 70 genes uniquely responding in MT− (SRMs) (Fig. 3a, Supplementary Figs. 3, 4, 5). Similar to the conventional DE analysis, the response of MT− was more pronounced compared to MT+, likely due to technical differences such as different protocols for pheromone administration. Remarkably, while in MT− we discovered a comparable number of down- and upregulated SRMs, we only found upregulated SRPs and SRBs, possibly indicating that sexual processes induced by SIPs are mainly driven by the induction of key genes rather than the downregulation of inhibitory genes (Fig. 3a).
Fig. 3: Visualization and main results of the integrative workflow.

a Schematic representation of the integrative workflow indicating how SIP responsive genes with a shared response (SRBs) or mating type specific response (SRPs, SRMs) were discovered. Non-responsive genes consist of genes that are equivalently expressed after SIP treatment versus control, or that are very lowly/not expressed (filtered). A log fold change (LFC) cutoff of ±log(3) was used to define responsive (differentially expressed) genes and equivalent genes. At the right side of the panel, log2 fold changes of SRMs, SRPs and SRBs in both mating types are plotted. Each gene is plotted for the time point at which they are differentially expressed. Genes which were not expressed (“filtered”) in the non-responsive mating type are plotted as diamonds. The red horizontal lines represent the fold change cutoff used to determine equivalence and differential expression. The number of discovered genes is indicated in the top left corner of each plot. b Expression of a selection of SIP responsive genes (SRMs, SRPs, SRBs). For each gene, counts per million (CPM) are plotted as a function of time for both mating types. The data points correspond to gene expression of the replicates in each time point and the solid line represents the mean. Data points and lines are colored according to condition, i.e., black for control condition and orange for SIP treatment. c Expression of the five genes belonging to the gene family of SRP12 (Sro2882_g339270). Data are presented in the plots in the same manner as in (b).

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In what follows we will discuss in more detail the genes and pathways that are responding to SIP in both mating types or uniquely in one mating type and link these changes to physiological events in the mating process. In each section, we first discuss key genes highlighted by the integrative analysis (Fig. 3), after which we discuss results from the conventional DE analysis, focussing on selected biological processes (Fig. 4).
Fig. 4: Gene expression of Seminavis robusta genes involved in mating-related processes.

a Heatmap of genes related to mitotic and meiotic cell cycle progression, which are differentially expressed (DE) in both mating types in the conventional DE analysis. Each gene is plotted for control and SIP treated conditions in both S. robusta mating types. Genes are specified as row names and are scaled relative to the mean expression, amounting to counts per million (CPM) standardized to zero mean and unit variance for each gene in each mating type separately. Blue indicates low expression, while red indicates high expression. b Expression of genes related to diproline synthesis and reactive oxygen species (ROS) production, which are significantly DE in only one mating type in the conventional DE analysis. CPM are plotted as a function of time for both mating types. The data points correspond to gene expression of the replicates in each time point while the solid line represents the mean. Data points and lines are colored according to condition, i.e., black for control condition and orange for SIP treatment. P5CS = Δ1-pyrroline-5-carboxylate synthetase; P5CR = Δ1-pyrroline-5-carboxylate reductase.

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Responses to SIP conserved in both mating types
Integrative analysis reveals key genes responsive in both mating types
A large fraction (22/52) of SRBs, i.e., key genes with a strong response to SIP in both mating types, lack any functional annotation and homology to sequenced genomes of other diatoms (Supplementary Table 2), suggesting that the molecular mechanisms underlying early mating are highly species-specific. The remaining 30 SRBs can be linked to energy metabolism, ROS signaling and meiosis, amongst others. Pyruvate kinase (Sro373_g129070) and isocitrate dehydrogenase (Sro492_g153950), respectively involved in glycolysis and the citric acid cycle, are strongly upregulated in both mating types (Fig. 3b), suggesting an increased energy demand. Interestingly, pyruvate kinase is also upregulated during gametogenesis in the brown alga Saccharina latissima [26] and the parasite Plasmodium berghei [27]. In addition, two enzymes from the pentose phosphate pathway (PPP) are among the SRBs: transketolase (Sro524_g159900) and transaldolase (Sro196_g083630) (Supplementary Fig. 3). The PPP generates NADPH, a reductive compound needed in various metabolic reactions and involved in detoxification of ROS by regenerating glutathione [28, 29]. Furthermore, one SRB encoding a heme peroxidase (Sro1252_g256250) exhibited strong upregulation upon SIP treatment (Fig. 3b). Upregulation of heme peroxidases was also reported during sexual reproduction in other eukaryotes, e.g., mosquitoes (Anopheles gambiae) [30] and fungi [31, 32]. Heme peroxidases promote substrate oxidation in various metabolic pathways and are essential for the detoxification of ROS [33], suggesting that ROS signaling plays a role in the response to SIP, as seen in the green algae Volvox carteri, where high ROS levels trigger sex [34]. Finally, a highly expressed SRB encodes a transmembrane protein containing an Epidermal Growth Factor-like (EGF-like) domain (Sro65_g036830, Fig. 3b), with potential orthologs encoded in pennate and centric diatoms including P. tricornutum and T. pseudonana (BLASTp, E  More

1.
Darwin, C. On the Origin of Species (John Murray, London, 1859).
Google Scholar 
2.
Gause, G. F. The Struggle for Existence (Williams & Wilkins, Philadelphia, 1934).
Google Scholar 

3.
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Article  Google Scholar 

4.
Vamosi, S. M., Heard, S. B., Vamosi, J. C. & Webb, C. O. Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18, 572–592 (2009).
CAS  Article  Google Scholar 

5.
Biere, A. & Bennett, A. E. Three-way interactions between plants, microbes and insects. Funct. Ecol. 27, 567–573 (2013).
Article  Google Scholar 

6.
Biesmeijer, J. C. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).
ADS  CAS  Article  Google Scholar 

7.
Harvey, J. A. et al. International scientists formulate a roadmap for insect conservation and recovery. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-019-1079-8 (2020).
Article  Google Scholar 

8.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed Central  Article  CAS  PubMed  Google Scholar 

9.
Leather, S. R. “Ecological Armageddon”—more evidence for the drastic decline in insect numbers: Insect declines. Ann. Appl. Biol. 172, 1–3 (2018).
Article  Google Scholar 

10.
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Article  Google Scholar 

11.
Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).
Article  Google Scholar 

12.
Ford, H. A., Barrett, G. W., Saunders, D. A. & Recher, H. F. Why have birds in the woodlands of Southern Australia declined?. Biol. Conserv. 97, 71–88 (2001).
Article  Google Scholar 

13.
Córdoba-Aguilar, A. & Rocha-Ortega, M. Damselfly (Odonata: Calopterygidae) population decline in an urbanizing watershed. J. Insect Sci. 19, 30 (2019).
PubMed Central  Article  PubMed  Google Scholar 

14.
Kalkman, V. J. et al. Diversity and conservation of European dragonflies and damselflies (Odonata). Hydrobiologia 811, 269–282 (2018).
Article  Google Scholar 

15.
Rosenberg, K. V. et al. Decline of the North American avifauna. Science https://doi.org/10.1126/science.aaw1313 (2019).
Article  PubMed  PubMed Central  Google Scholar 

16.
Rodhouse, T. J. et al. Evidence of region-wide bat population decline from long-term monitoring and Bayesian occupancy models with empirically informed priors. Ecol. Evol. https://doi.org/10.1002/ece3.5612 (2019).
Article  PubMed Central  PubMed  Google Scholar 

17.
Kaunisto, K. M. et al. Threats from the air: Damselfly predation on diverse prey taxa. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13184 (2020).
Article  PubMed  PubMed Central  Google Scholar 

18.
Simmons, B. I. et al. Worldwide insect declines: An important message, but interpret with caution. Ecol. Evol. 9, 3678–3680 (2019).
PubMed Central  Article  PubMed  Google Scholar 

19.
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on earth and in the ocean?. PLoS Biol. 9, e1001127 (2011).
CAS  PubMed Central  Article  PubMed  Google Scholar 

20.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on earth. Proc. Natl. Acad. Sci. 115, 6506–6511 (2018).
CAS  Article  Google Scholar 

21.
Nyffeler, M., Şekercioğlu, ÇH. & Whelan, C. J. Insectivorous birds consume an estimated 400–500 million tons of prey annually. Sci. Nat. 105, 47 (2018).
Article  CAS  Google Scholar 

22.
Vesterinen, E. J. et al. What you need is what you eat? Prey selection by the bat Myotis daubentonii. Mol. Ecol. 25, 1581–1594 (2016).
CAS  Article  Google Scholar 

23.
Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. & Lilley, T. M. Table for five, please: Dietary partitioning in boreal bats. Ecol. Evol. 8, 10914–10937 (2018).
PubMed Central  Article  PubMed  Google Scholar 

24.
Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lilley, T. M. Data from: Table for five, please: Dietary partitioning in boreal bats. Dryad Dataset https://doi.org/10.5061/dryad.6880rf1 (2019).
Article  Google Scholar 

25.
Kaunisto, K. M., Roslin, T., Sääksjärvi, I. E. & Vesterinen, E. J. Pellets of proof: First glimpse of the dietary composition of adult odonates as revealed by metabarcoding of feces. Ecol. Evol. 7, 8588–8598 (2017).
PubMed Central  Article  PubMed  Google Scholar 

26.
Kaunisto, K. M., Roslin, T. L., Sääksjärvi, I. E. & Vesterinen, E. J. Data from: Pellets of proof: first glimpse of the dietary composition of adult odonates as revealed by metabarcoding of feces. Dryad Dataset https://doi.org/10.5061/dryad.5n92p (2018).
Article  Google Scholar 

27.
Vesterinen, E. J. et al.Threats from the air: damselfly predation on diverse prey taxa. 1438406240 bytes (2019) https://doi.org/10.5061/DRYAD.ZS7H44J4Z.

28.
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).
Article  Google Scholar 

29.
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).
Article  Google Scholar 

30.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).

31.
Oksanen, J. et al. vegan: Community Ecology Package. (2013).

32.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS  CAS  Article  Google Scholar 

33.
Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).
ADS  CAS  Article  Google Scholar 

34.
Fuszara, E. et al. Population changes in Natterer’s bat (Myotis nattereri) and Daubenton’s bat (M. daubentonii) in winter roosts of central Poland. Pol. J. Ecol. 58, 769–781 (2010).
Google Scholar 

35.
Kim, K. C. & Byrne, L. B. Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecol. Res. 21, 794 (2006).
Article  Google Scholar 

36.
Sekercioglu, C. H. et al. Disappearance of insectivorous birds from tropical forest fragments. Proc. Natl. Acad. Sci. USA. 99, 263–267 (2002).
ADS  CAS  Article  Google Scholar 

37.
Spiller, K. J. & Dettmers, R. Evidence for multiple drivers of aerial insectivore declines in North America. Condor 121, 10 (2019).
Article  Google Scholar 

38.
Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl. Acad. Sci. 115, E10397–E10406 (2018).
CAS  Article  Google Scholar 

39.
Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Ochieng, H., de Ruyter van Steveninck, E. D. & Wanda, F. M. Mouthpart deformities in Chironomidae (Diptera) as indicators of heavy metal pollution in northern Lake Victoria, Uganda. Afr. J. Aquat. Sci. 33, 135–142 (2008).
CAS  Article  Google Scholar 

41.
Luoto, T. P. Hydrological change in lakes inferred from midge assemblages through use of an intralake calibration set. Ecol. Monogr. 80, 303–329 (2010).
Article  Google Scholar 

42.
Aquatic insects of North Europe—A Taxonomic Handbook. vol. 2 (Apollo Books, 1997).

43.
Wirta, H. K. et al. Exposing the structure of an Arctic food web. Ecol. Evol. 5, 3842–3856 (2015).
PubMed Central  Article  PubMed  Google Scholar 

44.
Vesterinen, E. J., Lilley, T., Laine, V. N. & Wahlberg, N. Next generation sequencing of fecal DNA reveals the dietary diversity of the widespread insectivorous predator Daubenton’s bat (Myotis daubentonii) in southwestern Finland. PLoS ONE 8, e82168 (2013).
ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

45.
Clare, E. L., Fraser, E. E., Braid, H. E., Fenton, M. B. & Hebert, P. D. N. Species on the menu of a generalist predator, the eastern red bat (Lasiurus borealis): Using a molecular approach to detect arthropod prey. Mol. Ecol. 18, 2532–2542 (2009).
Article  Google Scholar 

46.
Clare, E. L. et al. The diet of Myotis lucifugus across Canada: Assessing foraging quality and diet variability. Mol. Ecol. 23, 3618–3632 (2014).
Article  Google Scholar 

47.
Rytkönen, S. et al. From feces to data: A metabarcoding method for analyzing consumed and available prey in a bird-insect food web. Ecol. Evol. 9, 631–639 (2019).
Article  Google Scholar 

48.
Eitzinger, B. et al. Assessing changes in arthropod predator–prey interactions through DNA-based gut content analysis—variable environment, stable diet. Mol. Ecol. 28, 266–280 (2019).
CAS  Article  Google Scholar 

49.
Schmidt, N. M., Mosbacher, J. B., Eitzinger, B., Vesterinen, E. J. & Roslin, T. High resistance towards herbivore-induced habitat change in a high Arctic arthropod community. Biol. Lett. 14, 20180054 (2018).
PubMed Central  Article  CAS  PubMed  Google Scholar 

50.
Schmidt, N. M., Mosbacher, J. B., Vesterinen, E. J., Roslin, T. & Michelsen, A. Limited dietary overlap amongst resident Arctic herbivores in winter: Complementary insights from complementary methods. Oecologia 187, 689–699 (2018).
ADS  Article  Google Scholar 

51.
Gripenberg, S. et al. A highly resolved food web for insect seed predators in a species-rich tropical forest: Host use by insect seed predators. Ecol. Lett. https://doi.org/10.1111/ele.13359 (2019).
Article  PubMed Central  PubMed  Google Scholar 

52.
Basset, Y. et al. A cross-continental comparison of assemblages of seed- and fruit-feeding insects in tropical rain forests: Faunal composition and rates of attack. J. Biogeogr. 45, 1395–1407 (2018).
Article  Google Scholar 

53.
Raitif, J., Plantegenest, M., Agator, O., Piscart, C. & Roussel, J.-M. Seasonal and spatial variations of stream insect emergence in an intensive agricultural landscape. Sci. Total Environ. 644, 594–601 (2018).
ADS  CAS  Article  Google Scholar 

54.
Rogers, L. E., Buschbom, R. L. & Watson, C. R. Length-weight relationships of shrub-steppe invertebrates1. Ann. Entomol. Soc. Am. 70, 51–53 (1977).
Article  Google Scholar 

55.
De Felici, L., Piersma, T. & Howison, R. A. Abundance of arthropods as food for meadow bird chicks in response to short- and long-term soil wetting in Dutch dairy grasslands. PeerJ 7, e7401 (2019).
PubMed Central  Article  PubMed  Google Scholar 

56.
Aziz, M. A. et al. Using non-invasively collected genetic data to estimate density and population size of tigers in the Bangladesh Sundarbans. Glob. Ecol. Conserv. 12, 272–282 (2017).
Article  Google Scholar 

57.
Greenop, A., Woodcock, B. A., Wilby, A., Cook, S. M. & Pywell, R. F. Functional diversity positively affects prey suppression by invertebrate predators: A meta-analysis. Ecology 99, 1771–1782 (2018).
PubMed Central  Article  PubMed  Google Scholar 

58.
Kissick, A. L., Dunning, J. B., Fernandez-Juricic, E. & Holland, J. D. Different responses of predator and prey functional diversity to fragmentation. Ecol. Appl. 28, 1853–1866 (2018).
Article  PubMed  PubMed Central  Google Scholar 

59.
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).
Article  PubMed  PubMed Central  Google Scholar  More

To enable successful spawning of Atlantic eels in remote offshore areas of the ocean, three conditions need to be met. This requires, first, appropriate navigation abilities and cues leading to the remote spawning area; second, a meeting point; and third, an adequate timing.
Orientation and navigation cues towards the spawning area
Eels are thought to imprint a magnetic map on their first transoceanic migration from the spawning areas to the coasts21. Moreover, silver eels are known to be sensitive to magnetic cues22 that are likely involved in navigation towards the Sargasso sea with a very high spatial accuracy23. Under this hypothesis, silver eels are expected to choose the fastest or shortest route to join the Sargasso Sea. Indeed, recent studies showed that European silver eel swam south-westward24, 25 while American silver eels swam south-eastward26. Surprisingly, none of the tagged eels reached the spawning areas within the Sargasso Sea26. One single American eel reached the north west boundaries of the North Atlantic Convergence zone at  > 2,000 km from the center of the Sargasso Sea26, while a few European eels where detected at the North East of the Azores at c.a. 3,000 km from the Sargasso Sea24, 25. This was interpreted as a consequence of the tagging rather than a biological fact.
Interestingly, all European eels, whatever their release points (Baltic Sea, Ireland, the Bay of Biscay, Mediterranean) converged towards the Azores, which is not the shortest way back to the Sargasso Sea24. So what could be the advantage for silver eels for choosing a longer route? The most parsimonious hypothesis is that the Azores serve as a meeting point located along the Mid-Atlantic Ridge. Once they reach this point they turn southwest, following the Mid-Atlantic Ridge. This could be made possible by the striking vertical diel migration behavior that takes eels from epipelagic layers (150–300 m) during the night to mesopelagic and bathypelagic depths during the daytime (down to 1,200 m)24,25,26. This behavior could enable silver eels to detect and follow the Mid Atlantic Ridge and associated seamounts that culminate at 2,000 m to 3,500 m above the seafloor that lies at  > 4,000 m depths. Moreover, it is likely that eels detect chemical variations of the seawater using their high olfactory abilities enabling them to detect specific odors or plumes from subducted or convected deep layer waters27, 28. Indeed, the volcanic activity and deep currents disturbed by the sea level rise around the ridge likely modify the chemical composition and related odor of the water thus providing signposts28.
Following this north south Y axis, silver eels may finally reach favorable thermic conditions of 22 to 24 °C to spawn, which are located between two parallel east–west thermal fronts that occur in the Sargasso Sea at about 24°N and 28°N (X-axis)8, 29. Worth mentioning, small leptocephali of both Atlantic eel species have been collected over a wide longitudinal range (75–50°W) between these two fronts8. Although the collected area of American and European eel larvae partly overlapped in Sargasso Sea, the southern-most collection of European eel larvae was about 100–200 km north compared to American eel larvae8, apart from thermal fronts that were suggested earlier as an X-axis, European eels may follow a different hint. One of the major water masses in the Sargasso Sea is the North Atlantic Subtropical Mode Water, which has unique vertical temperature distribution, in which the temperature is nearly uniform in the Mode Water layer, especially in winter and early spring30, 31. Its southern boundary is around 22–26°N; therefore, the mode water’s boundary could also potentially serve as a destination hint (X-axis) for European eels.
Meeting point to mitigate lack of migration timing
Once eels have reached favorable habitat conditions to spawn, they have to find their mates to breed. Random mating in the huge Sargasso Sea (c.a. 3 million km2) is highly unlikely. Indeed, male and female silver eels do not have a synchronized migration. Males start their migration from August to September, whereas females migrate between November and December24. Telemetry data demonstrated that migrating silver eels disperse after they are released24. Migration speed is highly variable according to size24, 32, because males that are approximately 45 cm long on average have much lower swimming speeds than female eels, which have bodies up to twice the size as males. This suggests that, unlike tuna or mackerel, eels do not form schools, and even if they start their spawning migration in a school from continental rivers, they eventually scatter and arrive in the Sargasso Sea one by one. These arguments strongly suggest that synchronized migration and schooling do not likely occur, meaning that successful mating and spawning depends on the existence of clear physical, chemical, geological, or biological signals that eels can use to locate a meeting point in the ocean. However, such east–west and north–south hints (X and Y axis) or any kind of gradient do not exist in the large Sargasso Sea.
Egg distributions of Japanese eel within the spawning area indicated that spawning occurred just south of the crossing point where north–south seamount chain and east–west salinity front between two water masses with different salinities—caused by evaporation in the north and tropical rainfall in the south13, 16. It has been speculated that eels can locate the spawning site using a combination of the seamount chain (Y-axis) and salinity front (X-axis) as a signpost for forming spawning aggregations in the ocean.
To ensure successful external fertilization of eggs, eels must meet their mates in the ocean, meaning that time and space must precisely coincide for successful mating. If the same strategy can be adapted to Atlantic eels, waters near the Mid-Atlantic Ridge could be chosen as a spawning site because of unusual topographical features, geomagnetic anomalies33 or variation of chemical compositions that could serve as an olfactory cue for eels. Indeed, active hydrothermal vents have been observed along the Mid-Atlantic Ridge across the entire Atlantic34, and the release of chemical elements from hydrothermal vents may serve as a cue for locating a spawning site. This kind of signpost remains very large, and therefore it is likely that pheromones might be released by silver eels to favor the final meeting of the partners.
Simulating departure from the Mid Atlantic Ridge and from the Sargasso Sea
Using the same principle as Japanese eel, volcanically active parts of the Mid-Atlantic Ridge could be one of the spawning sites for Atlantic eels due to unusual topographical features, geomagnetic anomalies, or differing water chemical composition21. The 22 °C and 24 °C thermal fronts between which Atlantic eel larvae have been frequently observed8 are used to extend farther east, interacting with the Mid-Atlantic Ridge at around 27 and 20°N, respectively. To the south of these thermal fronts exists a discernible salinity front around the northern limit of the North Equatorial Current (NEC) in the Atlantic at 15–18°N. Thus, we modeled the transport of virtual leptocephali larvae from the area chosen to be 15–29°N and 43–48°W which included intersections of the Mid-Atlantic Ridge by one salinity front and two thermal fronts (Fig. 1 top).
We then released v-larvae near the Mid-Atlantic Ridge from 15 to 29°N. We classified v-larvae by their initial positions as north of the 22 °C isotherm (yellow), between the 22 and 23 °C isotherm (blue), between the 23 and 24 °C isotherm (green), south of the 24 °C isotherm (red), and the north of NEC with a salinity front at around 18–19°N (cyan) (Figs. 1, 2). Passive swimming v-larvae were widely dispersed to the west and east of the release area after 720 days of migration (Fig. 2). V-larvae departing from north of 24°N (yellow and blue dots) finally arrived at the Azores front and North Atlantic drift, with easternmost positions near 15°W, showing similar distribution to observed European eel larvae. In contrast, v-larvae departing from south of 24°N (green, red, and cyan dots) could make it to the Caribbean Sea and the Gulf of Mexico, and some v-larvae entrained in the Loop Current and Gulf Stream, arriving at the east coast of North America, that is similar to the observed American eel larvae distribution. The percentage of v-larvae reaching 25°W after 720 days decreased from north to south: 0.71% in the northernmost area (yellow in Fig. 2), 0.13% (blue), and 0% (green, red, and cyan). Arrival at the Caribbean Sea and Gulf of Mexico increased from north to south: 0.13% (yellow in Fig. 2), 0.77% (blue), 4.64% (green), 19.3% (red), and 38.9% in cyan.
Figure 2

Distribution of passive swimming v-larvae departing from near the Mid-Atlantic Ridge. Colors correspond to release areas (north of the 22 °C isotherm (yellow), between the 22 and 23 °C isotherm (blue), between the 23 and 24 °C isotherm (green), south of the 24 °C isotherm (red), and the north of NEC with a salinity front at around 18–19°N (cyan)) as indicated in the top panel. The simulation period was 1993–2000 and included both positive (1993–1994, 1999–2000) and negative (1995–1996, 1997–1998) North Atlantic Oscillation events, and the results are based on an eight-year composite.

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By comparison, v-larvae released in the Sargasso Sea were widely distributed throughout the northwestern Atlantic Ocean, including the Caribbean Sea and Gulf of Mexico (Fig. 3). A total of 0.14% of the v-larvae released from the suggested European eel spawning area in the Sargasso Sea reached 25°W after 720 days (Fig. 3 right), whereas 0.27% of those released in the American eel spawning area reached 25°W (Fig. 3 left). Arrival at the Caribbean Sea and Gulf of Mexico was 6.56% and 11.9% of those released from European and American eel spawning areas, respectively.
Figure 3

Distribution of v-larvae released in the Sargasso Sea for American (left) and European eels (right).

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Although the distribution patterns were similar to v-larvae departing from the newly proposed spawning area (Figs. 3, 5), differences were detected. A significant fraction of v-larvae representing European eels released in the Sargasso Sea dispersed to the Caribbean Sea and Gulf of Mexico (Fig. 3, right). As the v-larvae departing from the northern proposed area did not enter these areas (Fig. 2), and only American eel larvae but not European eel larvae have been collected in the Caribbean Sea and Gulf of Mexico. In addition, some of v-larvae representing American eel departing from Sargasso Sea were transported far northeast by Gulf Stream and North Atlantic Drift to east of 40°W, where American eel larvae were not observed8. In contrast, v-larvae departing from southern proposed area showed closer distribution to observations of American eel leptocephali, while v-larvae departing from central to northern sub areas of the Mid-Atlantic Ridge presented similar distributions to observations of European eel leptocephali. Therefore, it could be suggested that both European and American eels may indeed spawn in the newly proposed area near the Mid-Atlantic Ridge.
Interestingly, distributions of v-larvae departing from the American eel spawning area or from the European eel are very similar (Fig. 3) suggesting that swimming and orientations are likely. If v-larvae could swim at 1 body length per second (BL/s) northeastward, arrival rate at 25°W would increase substantially, especially for those departing from the northern area (Fig. 4, left). On the other hand, v-larvae swimming at the same speed of 1BL/s but heading northwestward would not reach 25°W (Fig. 4, right), instead, distribution of v-larvae would be concentrated at northwestern Atlantic Ocean. The simulations with swimming ability indeed also revealed similar distribution as observations. V-larvae departure from northern area would swim towards eastern north Atlantic (yellow and blue, Fig. 4 left), whereas those departing from southern area (cyan and red, Fig. 4 right) would move towards western north Atlantic and some of them may bypass Caribbean Sea and Gulf of Mexico.
Figure 4

Same as Fig. 2, but for northwestward swimming (left), and northeastward swimming (right) at swimming speed of 1 BL/s.

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Estimating spawning location of small leptocephali caught in historical surveys
Historical surveys have spent great effort to search the eggs and spawning adult eels in the past century. However, the larval surveys to date have not explicitly considered the possibility of alternative spawning areas or an extension eastward. This introduced an evident gap, both geographically and temporally, in larval surveys. Indeed, our numerical simulation showed that a different departure point (spawning area), located above the Mid-Atlantic Ridge, resulted in a distribution of leptocephali larvae similar to historical observations in the Atlantic Ocean. Hence, these results strongly suggest that oceanographic surveys should be organized outside the Sargasso Sea, in the vicinity of the Mid Atlantic Ridge.
We applied passive backward particle tracking to trace the origin of those observed Atlantic eel larvae. We released v-larvae in the Sargasso Sea where ≤ 10.9 mm Atlantic eel larvae have been collected8. The distribution of potential v-larvae origins 30 days prior to on-site collections was not far from where eel larvae have been collected (Fig. 5), as ocean currents were rather weak and lacked a unified direction. The results suggest a few possibilities, such as eggs may occur nearby the area where eel larvae were collected although they have not been collected; eel larvae indeed were observed in rather a wide region, suggesting eel larvae (or eggs) may also occur in areas located outside the hot-spot survey zone of the Sargasso Sea. Learning from the experience of Japanese eel surveys would allow exploring the hypothesis of alternative spawning locations.
Figure 5

Distributions of passive backward tracking v-larvae 30 days prior to collection for (a) American eels, and (b) European eels. Black crosses showed the released locations that followed the positions where eel larvae were collected8.

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Disagreement from microchemistry aspect
Ocean currents in the Sargasso Sea are generally weak, with average speeds less than 5 cm/s in the top 200 m (Fig. 1 bottom). Eddy activity is inactive in the Sargasso Sea and eddy nonlinearity is relatively low compared to those formed near the Gulf Stream or Azores Current35, indicating less trapping and transporting by westward propagating eddies for marine organisms. Additionally, Japanese eels are spawned in the faster (10–20 cm/s) NEC in the Pacific (Fig. 6), thus, it can be said that the Sargasso Sea, which is the presumed Atlantic eel spawning area, is relatively quiet and has less transporting ability because of a subtropical gyre convergence zone. This convergence zone is unfavorable for the transport of eel larvae to continental rivers.
Figure 6

Bathymetry (shading) and mean ocean circulation (vectors) in the western Pacific. Fast and slow currents with criteria of 0.15 m/s are indicated by magenta and white vectors, respectively. The yellow circle marks the spawning area of Japanese eels. See the analogy of ocean current systems in both the Atlantic (Fig. 1) and Pacific (Fig. 6), i.e., the relationship between possible eel spawning locations and currents in the western subtropical gyre such as the North Equatorial Current and western boundary currents (Gulf Stream or Kuroshio).

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Interestingly, concentrations of Mn, a trace element signature, in the central part of otoliths of glass eels caught in western European estuaries were significantly greater than those of leptocephali collected in the Sargasso Sea20. Mn is a geochemical fingerprint of volcanic activity mainly found along the Mid-Atlantic Ridge34.The numerical experiment had shown that the buoyant hydrothermal plume could transport dissolved elements vertically by 1000–1500 m, and could also spread thousands of kilometers by horizontal advection. This suggests that glass eels caught in European estuaries spent their early life in the plume of a volcanic activity zone whereas leptocephali born in the Sargasso Sea may not successfully migrate to Europe due to being trapped in the convergence zone. This supports the existence of multiple spawning areas or batches suggested by Baltazar-Soares36, without affecting the well-established panmixia36,37,38,39 given that larvae seeded from any location are dispersed along with large recovering areas (Figs. 2, 4).
General discussion
The present study explores the existence of another spawning area near the Mid-Atlantic Ridge at the east of the Sargasso Sea, that has been assumed to be the sole spawning area for almost 100 years since Schmidt’s research. This scenario relies on the combination of ecological and environmental inferences, comparative biology, the need of biotracers to pilot the catadromous fish and modelling.
The distribution of v-larvae departing from the newly proposed spawning area near the Mid-Atlantic Ridge showed possibilities of successful migration of both species to their respective geographic continental distributions. The v-larvae released in the northern region of the newly proposed spawning area showed distributions similar to those of collected European eel larvae, whereas those that departed from the southern region, within the salinity front, had distributions closer to the those of American eel larvae8. These fits between our model and observations of larval distribution are even stronger when assigning orientation and swimming skills to v-larvae. We therefore assume that swimming and orientation behaviors likely occur supporting previous findings and hypothesis21, 40.
Salinity fronts have been suggested to be related to Japanese eel spawning41 in the Pacific Ocean. Indeed, approximately 600 eggs have been collected over five research cruises at the intersection of the salinity front and the West Mariana Ridge. Similarly, a salinity front has also been observed in the Atlantic Ocean between the Sargasso Sea and NEC at around 15–18°N. In the new spawning area tested in this study, a strong salinity front with a rapid increased from 36.3 PSU at 15°N to 36.9 PSU at 19°N down to depths around 200 m was observed, and the front extended below 200 m south of 18°N. The salinity front could potentially provide a landmark for silver eels during breeding migration. In this study, v-larvae released near the salinity front showed quick dispersion westward, entering the Caribbean Sea and the Gulf of Mexico with some going to the Gulf Stream. This pattern is similar to that of Japanese eels in the Pacific Ocean that established their migration loop in the southwest corner of the subtropical gyre using the NEC and the Kuroshio42 (Fig. 6).
For the migration of adult eels, routes proposed by Righton et al.24 from a pop-up tag study showed that silver European eels seemed to converge toward the Azores regardless of origin (Baltic, North Sea, Celtic Sea, Bay of Biscay, Mediterranean). This does not fit with the Sargasso Sea hypothesis as the most direct routes from northern Europe and the Mediterranean to the Sargasso Sea do not encompass the Azores. Our hypothesis is that the Azores acts as a landmark for silver eels swimming southwest.
On their spawning migration, silver eels need to find the most efficient way to reach the spawning area using the safest and less energy costly route. It could be suggested that silver eels simply backtrack the migration route they used as leptocephali. This would imply that eels imprint their larval route, and that silver eels would have to swim against the strongest currents of the North Atlantic Ocean as the Gulf Stream, the Azores Currents and the North Atlantic drift (ie Miller and Tsukamoto43). This strategy would probably cost too much energy. Alternatively, by converging towards the Azores, as suggested by Righton et al.24, Silver eels avoid the strongest marine currents thus saving energy expenditures, which is a more likely evolutionary scenario. However, this would involve the existence of a genetically imprinted geomagnetic map that would enable eels to navigate towards the Azores whatever their departure point. Although possible, this assumption remains speculative as to date, science has not addressed how DNA encodes for such a behavior.
Because of their diel vertical migration ranging from ~ 800 m during the day to 300 m at night24, 25, 44, these eels could detect the topography and specific odors of the ridge they follow until they reach to favorable thermal fronts. Strong magnetic abnormalities occur along the Mid-Atlantic Ridge from the Azores to the junction with the Kane fracture zone (23.5 N; 46.4 W) and then make a bend westward along the Krane fracture33. For the American eel, an individual released from the Gulf of St. Lawrence near the northernmost distributional range of American eel leptocephali showed a long-distance migration to the northern Sargasso Sea26. We need to further observe the route in the southern Sargasso Sea. Additionally, the release of silver eels with pop-up tags from the Caribbean Sea near the southernmost distributional range and nearest areas to both the Sargasso Sea and Mid-Atlantic Ridge is the next step to confirm the success of adults migrating to their spawning area.
The collection of tiny larvae, known as preleptocephali, has been reported for both species in the Sargasso Sea. Preleptocephali are newly hatched larvae less than 6 mm long, and are genetically identified to be American eel, European eel, or other marine eel species. Molecular techniques are indispensable because morphological species identification does not work for undeveloped eggs and preleptocephali. Preleptocephali collected in the Sargasso Sea appear to be approximately one week old after hatching, which seems a too short duration for transportation of eggs and preleptocephali by currents from the newly proposed spawning area to the collection area in the Sargasso Sea. Therefore, it is indeed a fact that eel spawning occurs in the Sargasso Sea. Although eggs and spawning-condition adults have not been collected there, this lack of collection does not mean absence. There has also been no collection of eggs and adults or even preleptocephali outside Sargasso Sea. These apparent “false negatives” may result from insufficient sampling efforts in the Sargasso Sea and Mid-Atlantic Ridge areas as shown by Westerberg et al. 201845. It is also noteworthy that sampling efforts were not necessarily conducted with appropriate timing, place, and sampling methods, for example, with attention to peak spawning season, lunar phase, sampling grid mesh size of sampling grid, etc.
Based on molecular phylogenetic analyses of all anguillid eels, Atlantic eel ancestors were speculated to have invaded the North Atlantic from the Indo-Pacific through the ancient Tethys Sea before the Isthmus of Suez closed 30 million years ago46. They established their small migration loop around the coasts of the North Atlantic. They had a spawning area near the Mid-Atlantic Ridge in the narrow ancient North Atlantic that had not yet well expanded, and larvae were transported to Europe and North America randomly. Based on the expansion of the Atlantic Ocean floor, it is likely that the Atlantic eel split into two distinct species, American and European eels, due to the separation of their spawning areas, migration routes, and recruitment places42. The segregation of the two spawning areas probably is still the current situation considering the limited hybridization between both species and the introgression from American eels to European eels47. Moreover, the introgression force declines from northern to southern Europe, suggesting that spawning may have taken place in the central part of the newly proposed hatching zone near the Mid-Atlantic Ridge. For effective conservation of these endangered species, we must understand Atlantic eel reproductive ecology, including their respective present-day spawning areas and the evolutionary processes of both eel species. The first step in this process is to organize research cruises to enlarge the domain of survey and to validate a newly proposed Mid-Atlantic ridge hypothesis. More

Our overall approach is as follows: (1) develop a mathematical model (an SEIR-type compartmental model)18,19 that incorporates social-distancing data, case identification via testing, isolation of detected cases and contact tracing; (2) assess the model’s predictive performance by training (calibrating) it to reported cases and mortality data from 19 March to 30 April 2020 and validating its predictions against data from 1 May to 20 June 2020; and (3) use the model, trained on data to 22 July 2020, to predict future incidence and mortality. The final stage of our approach predicts future events under a set of scenarios that include increased case detection through expansion of testing rate, contact tracing and relaxation or increase of measures to promote social distancing. All model fitting is performed in a Bayesian framework to incorporate available prior information and address multivariate uncertainty in model parameters.
Model formulation
We modified the standard SEIR model to address testing and contact tracing, as well as asymptomatic individuals. A fraction fA of those exposed (E) to enter the asymptomatic A class (divided into AU for untested and AC for contact traced) instead of the infected I class, which in our model formulation also includes infectious presymptomatic individuals. With respect to testing, separate compartments were added for untested, ‘freely roaming’ infected individuals (IU), tested/isolated cases (IT) and fatalities (FT). Following recovery, untested infected individuals (IU) and all asymptomatic individuals move to the untested recovered compartment, IU, and tested infected individuals move to the tested recovered compartment, IT. In balancing considerations of model fidelity and parameter identifiability, we made the reasonably conservative assumptions that all tested cases are effectively isolated (through self-quarantine or hospitalization) and thus unavailable for transmission, and that all COVID-related deaths are identified/tested.
With respect to contact tracing, the additional compartment SC represents unexposed contacts who undergo a period of isolation during which they are not susceptible before returning to S, while EC, AC and IC represent contacts who were exposed. Again, the reasonably conservative assumption was made that all exposed contacts undergo testing, with an accelerated testing rate compared to the general population. We assume a closed population of constant size, N, for each state.
The ordinary differential equations governing our model are as follows:

$$begin{array}{l}frac{{mathrm{d}S}}{{mathrm{d}t}} = – S times c times left[ {beta + (1 – beta ) times f_{mathrm{C}}} right] times (I_{mathrm{U}} + A_{mathrm{U}})/N + S_{mathrm{C}} times gamma \ frac{{mathrm{d}S_{mathrm{C}}}}{{mathrm{d}t}} = – S_{mathrm{C}} times gamma + S times c times (1 – beta ) times f_{mathrm{C}} times (I_{mathrm{U}} + A_{mathrm{U}})/N\ frac{{mathrm{d}E}}{{mathrm{d}t}} = – E times kappa + S times c times beta times (1 – f_{mathrm{C}}) times (I_{mathrm{U}} + A_{mathrm{U}})/N\ frac{{mathrm{d}E_{mathrm{C}}}}{{mathrm{d}t}} = – E_{mathrm{C}} times kappa + S times c times beta times f_{mathrm{C}} times (I_{mathrm{U}} + A_{mathrm{U}})/N\ frac{{mathrm{d}I_{mathrm{U}}}}{{mathrm{d}t}} = – I_{mathrm{U}} times (lambda + rho ) + E times kappa times (1 – f_{mathrm{A}})\ frac{{mathrm{d}A_{mathrm{U}}}}{{mathrm{d}t}} = – A_{mathrm{U}} times rho + E times kappa times f_{mathrm{A}}\ frac{{mathrm{d}I_{mathrm{C}}}}{{mathrm{d}t}} = – I_{mathrm{C}} times (lambda _{mathrm{C}} + rho _{mathrm{C}}) + E_{mathrm{C}} times kappa times (1 – f_{mathrm{A}})\ frac{{mathrm{d}A_{mathrm{C}}}}{{mathrm{d}t}} = – A_{mathrm{C}} times rho _{mathrm{C}} + E_{mathrm{C}} times kappa times f_{mathrm{A}}\ frac{{mathrm{d}R_{mathrm{U}}}}{{mathrm{d}t}} = (I_{mathrm{U}} + A_{mathrm{U}} + A_{mathrm{C}}) times rho + I_{mathrm{C}} times rho _{mathrm{C}}\ frac{{mathrm{d}I_{mathrm{T}}}}{{mathrm{d}t}} = – I_{mathrm{T}} times (rho + delta ) + I_{mathrm{U}} times lambda + I_{mathrm{C}} times lambda _{mathrm{C}}\ frac{{mathrm{d}R_{mathrm{T}}}}{{mathrm{d}t}} = I_{mathrm{T}} times rho \ frac{{mathrm{d}F_{mathrm{T}}}}{{mathrm{d}t}} = I_{mathrm{T}} times delta end{array}$$

where c is the contact rate between individuals, β is the transmission probability per infected contact, fC is the fraction of contacts identified through contact tracing, 1/γ is the duration of self-isolation after contact tracing, 1/κ is the latent period, fA is the fraction of exposed who are asymptomatic, λ is the testing rate, δ is the fatality rate, ρ is the recovery rate and λC and ρC are the testing and recovery rates, respectively, of contact-traced individuals. The testing rates λ and λC, the fatality rate δ and the recovery rate of traced contacts ρC are each composites of several underlying parameters. The testing rate defined as

$$lambda (t) = F_{{mathrm{test}},0} times left[ {1 – frac{1}{{1 + mathrm{e}^{(t – T50_T)/tau _T}}}} right] times {mathrm{Sens}_{rm{test}}} times k_{{mathrm{test}}},$$

where Ftest,0 is the current testing coverage (fraction of infected individuals tested), Senstest is the test sensitivity (true positive rate) and ktest is the rate of testing for those tested, with a typical time-to-test equal to 1/ktest. The time-dependence term models the ramping up of testing using a logistic function with a growth rate of 1/τT d−1, where T50T is the time where 50% of the current testing rate is achieved. Similarly, for testing of traced contacts, the same definition is used with the assumption that all identified contacts are tested, Ftest,0 = 1 and at a faster assumed testing rate, kC,test:

$$lambda _{mathrm{C}}(t) = left[ {1 – frac{1}{{1 + mathrm{e}^{(t – T50_T)/tau _T}}}} right] times {mathrm{Sens}_{rm{test}}} times k_{{mathrm{C,test}}},$$

Because all contacts are assumed to be tested, the rate ρC at which they enter the ‘recovered’ compartment, RU is simply the rate of false negative test results:

$$rho _{mathrm{C}}(t) = left[ {1 – frac{1}{{1 + mathrm{e}^{(t – T50_T)/tau _T}}}} right] times (1 – {mathrm{Sens}_{rm{test}}}) times k_{{mathrm{test}}}$$

The fatality rate is adjusted to maintain consistency with the assumption that all COVID-19 deaths are identified, assuming constant IFR. Specifically, we first calculated the fraction of infected that is tested and positive:

$$f_{{mathrm{pos}}}(t) = f_{mathrm{C}}frac{{lambda _{mathrm{C}}(t)}}{{lambda _{mathrm{C}}(t) + rho _{mathrm{C}}(t)}} + (1 – f_{mathrm{C}})frac{{lambda (t)}}{{lambda (t) + rho }}.$$

Then the case fatality rate CFR(t) = IFR/fpos(t). Because CFR = δ/(δ + ρ), this implies

$$delta (t) = rho frac{{{mathrm{CFR}}(t)}}{{1 – {mathrm{CFR}}(t)}} = rho frac{{{mathrm{IFR}}}}{{f_{{mathrm{pos}}}(t) – {mathrm{IFR}}}}.$$

The model is ‘seeded’ Ninitial cases on 29 February 2020. Because in the early stages of the outbreak there may be multiple ‘imported’ cases, we fit to data only from 19 March 2020 onwards, 1 week after the US travel ban was put in place31.
Our model is fit to daily case yc and death yd data (cumulative data are not used for fitting because of autocorrelation). To adequately fit the case and mortality data, we accounted for two lag times. First, a lag is assumed between leaving the IU compartment and public reporting of a positive test result, accounting for the time it takes to seek a test, obtain testing and have the result reported. No lag is assumed for tests from contact tracing. Second, a lag time is assumed between entering the fatally ill compartment FT and publicly reported deaths. Additionally, we use a negative binomial likelihood to account for the substantial day-to-day over-dispersion in reporting results. The corresponding equations are as follows:

$$begin{array}{l}y_{{mathrm{obs}},[c,d]}(t) approx {mathrm{NegBin}}[alpha _{[c,d]},p_{[c,d]}(t)]\ p_{[c,d]}(t) = frac{{y_{{mathrm{pred}},[c,d]}(t)}}{{alpha _{[c,d]} + y_{{mathrm{pred}},[c,d]}(t)}}\ y_{{mathrm{pred}},c}(t) = I_{mathrm{U}}(t – tau _{{mathrm{case}}}) times lambda (t) + I_{mathrm{C}}(t) times lambda _{mathrm{C}}(t)\ y_{{mathrm{pred}},d}(t) = I_{mathrm{T}}(t – tau _{{mathrm{death}}}) times delta (t)end{array}$$

In this parameterization, because the dispersion parameter α → ∞, the likelihood becomes a Poisson distribution with expected value ypred,[c,d], whereas for small values of α there is substantial interindividual variability. Case and death data were sourced from The COVID Tracking Project32.
Finally, we derived the time-dependent reproduction number, R(t) and the effective reproduction number, Reff(t) of this model, given by

$$R(t) = c times beta times (1 – f_{mathrm{C}})left( {frac{{1 – f_{mathrm{A}}}}{{lambda + rho }} + frac{{f_{mathrm{A}}}}{rho }} right)$$

and

$$R_{{mathrm{eff}}}(t) = R(t) times frac{{{{S}}(t)}}{N}$$

Reff(t) is the average number of secondary infection cases generated by a single infectious individual during their infectious period in partially susceptible population at time t. It is equal to the product of the transmission risk per contact of an infectious individual with their untraced contacts, c × β × (1 − fC), times their average duration of infection, (left( {frac{{1 – f_{mathrm{A}}}}{{lambda + rho }} + frac{{f_{mathrm{A}}}}{rho }} right)), and the portion of contacts that are susceptible, (frac{{{{S}}(t)}}{N}). This accounts for the relative contribution of asymptomatic, (c times beta times left( {1 – f_{mathrm{C}}} right)left( {frac{{f_{mathrm{A}}}}{rho }} right) times frac{{{{S}}(t)}}{N}) and symptomatic infection, (c times beta times (1 – f_{mathrm{C}})left( {frac{{1 – f_{mathrm{A}}}}{{lambda + rho }}} right) times frac{{{{S}}(t)}}{N}). Using posterior samples for all 50 states and the District of Columbia, we conducted an analysis of variance using a linear model to characterize the contributions to the combined interstate and intrastate variation in Reff. Specifically, we used a linear model for Reff with the model parameters R0, η, θmin, rmax, fC, fA, λ and ρ as predictors, and evaluated the percentage of variance in Reff contributed by each parameter.
Incorporating social distancing, enhanced hygiene practices and reopening
The impact of social distancing, hygiene practices and reopening was modelled through a time dependence in the contact rate, c and the transmission probability per infected contact, β:

$$begin{array}{l}c(t) = c_0 times left[ {theta (t) + (1 – theta _{mathrm{min}}) times r(t)} right]\ beta (t) = beta _0 times theta (t)^eta end{array}$$

The θ(t) function parameterized social distancing during the progression to shelter-in-place, and is modelled as a Weibull function:

$$theta (t) = theta _{{mathrm{min}}} + (1 – theta _{{mathrm{min}}}){mathrm{e}}^{ – (t/tau _theta )^{n_theta }},$$

which starts as unity and decreases to θmin, with τθ being the Weibull scale parameter and nθ the Weibull shape parameter (Fig. 1).
The r(t) function parameterized relative increase in contacts due to reopening after shelter-in-place, with r = 1 corresponding to a return to baseline c = c0.

$$begin{array}{l}r(t) = r_{{mathrm{max}}}frac{{t – tau _theta – tau _s}}{{tau _r}}left[ {u(t – t_r) – u(t – t_{r{mathrm{max}}})} right] + u(t – t_{r{mathrm{max}}})\ u(t) = {mathrm{Heaviside}}(t) approx 1 – frac{1}{{1 + {mathrm{e}}^{4t}}}\ t_r = tau _theta + tau _s\ t_{r{mathrm{max}}} = tau _theta + tau _s + tau _rend{array}$$

The term r(t) is 0 before tr, linear between tr and trmax and constant at a value of rmax after that, and made continuous by approximating the Heaviside function by a logistic function. The reopening time is defined as τs days after τθ, and the maximum relative increase in contacts rmax happens τr days after that.
We selected the functional form above for c(t) because it was found to be able to represent a wide variety of social-distancing data, including mobile phone mobility data from Unacast33 and Google34 as well as restaurant booking data from OpenTable35. We used these different mobility sources to derive state-specific prior distributions because different social-distancing datasets had different values for θmin, τθ, nθ, τs, rmax and τr (Supplementary Fig. 1).
With respect to the reduction in transmission probability β, we assumed that during the shelter-in-place phase, hygiene-based mitigation paralleled this decline with an effectiveness power η, and that this mitigation continued through reopening.
Finally, we define an overall reopening parameter Δ that measures the rebound in disease transmission, c × β relative to its minimum, defined to be 0 during shelter-in-place (that is, R(t) is at a minimum) and 1 when all restrictions are removed (when R(t) = R0), which can be derived as:

$${Delta}(t) = frac{{c times beta /(c_0 times beta _0) – theta _{{mathrm{min}}}^{1 + eta }}}{{1 – theta _{{mathrm{min}}}^{1 + eta }}}.$$

Our model is illustrated in Fig. 1, with parameters and prior distributions listed in Table 1.
Scenario evaluation
We used the model to make several inferences about the current and future course of the pandemic in each state. First, we consider the effective reproduction number. Two time points of particular interest are the time of minimum Reff, reflecting the degree to which shelter-in-place and other interventions were effective in reducing transmission, and the final time of the simulation, 22 July 2020, reflecting the extent to which reopening has increased Reff. Additional parameters of interest are the current levels of reopening Δ(t), testing λ and contact tracing fC.
We then conducted scenario-based prospective predictions using our model’s parameters as estimated to 22 July 2020. We then asked the following questions:
(1)
Assuming current levels of reopening, what increases in general testing λ and/or contact tracing fC would be necessary to bring Reff  More

1.
Pacific Salmon Life Histories. (eds. Groot, C & Margolis, L.) (University of British Columbia Press, 1991).
2.
Beamish, R. J. & Mahnken, C. A critical size and period hypothesis to explain natural regulation of salmon abundance and the linkage to climate and climate change. Prog. Oceanogr. 49, 423–437 (2001).
ADS  Article  Google Scholar 

3.
Bradford, M. J. Comparative review of Pacific salmon survival rates. Can. J. Fish. Aquat. Sci. 52, 1327–1338 (1995).
Article  Google Scholar 

4.
Mueter, F. J., Peterman, R. M. & Pyper, B. J. Corrigendum: Opposite effects of ocean temperature on survival rates of 120 stocks of Pacific salmon (Oncorhynchus spp.) in northern and southern areas. Can. J. Fish. Aquat. Sci. 60, 757–757 (2003).
Article  Google Scholar 

5.
Zimmerman, M. S. et al. Spatial and temporal patterns in smolt survival of wild and hatchery coho salmon in the Salish Sea. Mar. Coast. Fish. 7, 116–134 (2015).
Article  Google Scholar 

6.
Dale, K. E., Daly, E. A. & Brodeur, R. D. Interannual variability in the feeding and condition of subyearling Chinook salmon off Oregon and Washington in relation to fluctuating ocean conditions. Fish. Oceanogr. 26, 1–16 (2017).
Article  Google Scholar 

7.
Davis, N. D. et al. Review of BASIS salmon food habits studies. North Pacific Anadromous Fish Comm. Bull. 5, 197–208 (2009).
Google Scholar 

8.
Qin, Y. & Kaeriyama, M. Feeding habits and trophic levels of Pacific salmon (Oncorhynchus spp.) in the North Pacific Ocean. North Pacific Anadromous Fish Comm. Bull. 6, 469–481 (2016).
Article  Google Scholar 

9.
Chapman, W. M. The Pilchard Fishery of the State of Washington in 1936 with Notes on the Food of the Silver and Chinook Salmon off the Washington Coast. Biological Report No. 36C (State of Washington, Division of Scientific Research, Department of Fisheries, 1936).

10.
Silliman, R. P. Fluctuations in the diet of the Chinook and silver salmons (Oncorhynchus tschawytscha and O. kisutch) off Washington, as related to the troll catch of salmon. Copeia 1941, 80–87 (1941).
Article  Google Scholar 

11.
Brodeur, R. D., Daly, E. A., Schabetsberger, R. A. & Mier, K. L. Interannual and interdecadal variability in juvenile coho salmon (Oncorhynchus kisutch) diets in relation to environmental changes in the northern California Current. Fish. Oceanogr. 16, 395–408 (2007).
Article  Google Scholar 

12.
Brodeur, R. D. A Synthesis of the Food Habits and Feeding Ecology of Salmonids in Marine Waters of the North Pacific. INPFC Doc; FRI-UW-9016. (Fisheries Research Institute, University of Washington, 1990).

13.
Karpenko, V. I., Volkov, F. & Koval, M. V. Diets of Pacific salmon in the Sea of Okhotsk. Bering Sea, and Northwest Pacific Ocean. North Pacific Anadromous Fish Comm. Bull. 4, 105–116 (2007).
Google Scholar 

14.
Starovoytov, A. N. Trends in abundance and feeding of chum salmon in the Western Bering Sea. North Pacific Anadromous Fish Comm. Bull. 4, 45–51 (2007).
Google Scholar 

15.
Kaeriyama, M. et al. Change in feeding ecology and trophic dynamics of Pacific salmon (Oncorhynchus spp.) in the central Gulf of Alaska in relation to climate events. Fish. Oceanogr. 13, 197–207 (2004).
Article  Google Scholar 

16.
Jamieson, G., Livingston, P. & Zhang, C.-I. Report of Working Group 19 on Ecosystem-based Management Science and its Application to the North Pacific. PICES Scientific Report 37 (North Pacific Marine Science Organization, 2010).

17.
Schoen, E. R. et al. Future of Pacific salmon in the face of environmental change: Lessons from one of the world’s remaining productive salmon regions. Fisheries 42, 538–553 (2017).
Article  Google Scholar 

18.
Healey, M. The cumulative impacts of climate change on Fraser River sockeye salmon (Oncorhynchus nerka) and implications for management. Can. J. Fish. Aquat. Sci. 68, 718–737 (2011).
Article  Google Scholar 

19.
Carmack, E., Winsor, P. & Williams, W. The contiguous panarctic Riverine Coastal Domain: A unifying concept. Prog. Oceanogr. 139, 13–23 (2015).
ADS  Article  Google Scholar 

20.
MySQL version 8.0.18. MySQL, https://www.mysql.com/ (2019).

21.
Graham, C, Pakhomov, E. A., & Hunt, B. P. V. North Pacific Marine Salmon Diet Database. GitHub, https://github.com/mcarolinegraham/North_Pacific_Marine_Salmon_Diet_Database (2020).

22.
Graham, C, Pakhomov, E. A., & Hunt, B. P. V. A salmon diet database for the North Pacific Ocean. figshare https://doi.org/10.6084/m9.figshare.c.4974128 (2020)

23.
R Core Development Team. R: A language and environment for statistical computing, version 3.6.1. The R Project for Statistical Computing https://www.r-project.org/ (2019).

24.
Andrievskaya, L. D. Food relationships of the Pacific salmon in the sea. Vopr. Ikhtiologii 6, 84–90 (1966).
Google Scholar 

25.
Carlson, H. R. Foods of juvenile sockeye salmon, Oncorhynchus nerka, in the inshore coastal waters of Bristol Bay, Alaska, 1966–67. Fish. Bull. 74, 458–462 (1976).
Google Scholar 

26.
Chuchukalo, V. L., Volkov, A. F., Efimkin, A. Y. & Kuznetsova, N. A. Feeding and Daily Rations of Sockeye Salmon (Oncorhynchus nerka) During the Summer Period. NPAFC Doc. 125 (Pacific Research Institute of Fisheries Oceanography (TINRO), 1995).

27.
Davis, N. D., Takahashi, M. & Ishida, Y. The 1996 Japan-U.S. Cooperative High-seas Salmon Research Cruise of the Wakatake maru and a Summary of 1991-1996 Results. NPAFC Doc. 194; FRI-UW-9617 (Fisheries Research Institute, University of Washington; National Research Institute of Far Seas Fisheries, 1996).

28.
Davis, N. D., Aydin, K. Y. & Ishida, Y. Diel Feeding Habits and Estimates of Prey Consumption of Sockeye, Chum, and Pink Salmon in the Bering Sea in 1997. NPAFC Doc. 363; FRI-UW-9816 (Fisheries Research Institute, University of Washington; National Research Institute of Far Seas Fisheries, 1998).

29.
Davis, N. D., Aydin, K. Y. & Ishida, Y. Diel catches and food habits of sockeye, pink, and chum salmon in the Central Bering Sea in summer. North Pacific Anadromous Fish Comm. Bull. 2, 99–109 (2000).
Google Scholar 

30.
Dulepova, E. P. & Dulepov, V. I. Interannual and Interregional Analysis of Chum Salmon Feeding Features in the Bering Sea and Adjacent Pacific Waters of Eastern Kamchatka. NPAFC Doc. 728 (Pacific Research Fisheries Centre, TINRO-Centre, 2003).

31.
Fukataki, H. Stomach contents of the pink salmon, Oncorhynchus gorbuscha (Walbaum), in the Japan Sea during the spring season of 1965. Bull. Jap. Sea Reg. Fish. Res. Lab. 17, 49–66 (1967).
Google Scholar 

32.
Glebov, I. I. Chinook and Coho Salmon Feeding Habits in the Far Eastern Seas in the Course of Yearly Migration Cycle. NPAFC Doc. 378 (Pacific Research Fisheries Centre TINRO-Centre, 1998).

33.
Ito, J. Food and feeding habits of Pacific salmon (genus Oncorhynchus) in their oceanic life. Bull. Hokkaido Reg. Fish. Res. Lab. 29, 85–97 (1964).
Google Scholar 

34.
Kaeriyama, M. et al. Feeding ecology of sockeye and pink salmon in the Gulf of Alaska. North Pacific Anadromous Fish Comm. Bull. 2, 55–63 (2000).
Google Scholar 

35.
Kanno, Y. & Hamai, I. Food of salmonid fish in the Bering Sea in summer of 1966. Bull. Fac. Fish. Hokkaido Univ. 22, 107–128 (1971).
Google Scholar 

36.
Manzer, J. I. Food of Pacific salmon and steelhead trout in the Northeast Pacific Ocean. J. Fish. Res. Board Canada 25, 1085–1089 (1968).
Article  Google Scholar 

37.
Perry, R. I., Hargreaves, N. B., Waddell, B. J. & Mackas, D. L. Spatial variations in feeding and condition of juvenile pink and chum salmon off Vancouver Island, British Columbia. Fish. Oceanogr. 5, 73–88 (1996).
Article  Google Scholar 

38.
Tadokoro, K., Ishida, Y., Davis, N. D., Ueyanagi, S. & Sugimoto, T. Change in chum salmon (Oncorhynchus keta) stomach contents associated with fluctuation of pink salmon (O. gorbuscha) abundance in the central subarctic Pacific and Bering Sea. Fish. Oceanogr. 5, 89–99 (1996).
Article  Google Scholar 

39.
Takeuchi, I. Food animals collected from the stomachs of three salmonid fishes (Oncorhynchus) and their distribution in the natural environments in the northern North Pacific. Bull. Hokkaido Reg. Fish. Res. Lab. 38, 1–119 (1972).
MathSciNet  Google Scholar 

40.
Ueno, M., Kosaka, S. & Ushiyama, H. Food and feeding behavior of Pacific salmon—II. Sequential change of stomach contents. Bull. Japanese Soc. Sci. Fish. 35, 1060–1066 (1969).
Article  Google Scholar 

41.
Volkov, A. F., Chuchukalo, V. I., Efimkin, A. Y. Feeding of Chinook and Coho Salmon in the Northwestern Pacific Ocean. NPAFC Doc. 124 (Pacific Research Institute of Fisheries Oceanography, 1995).

42.
Auburn, M. E. & Ignell, S. E. Food habits of juvenile salmon in the Gulf of Alaska July–August 1996. North Pacific Anadromous Fish Comm. Bull. 2, 89–97 (2000).
Google Scholar 

43.
Aydin, K. Y. Abiotic and Biotic Factors Influencing Food Habits of Pacific Salmon in the Gulf of Alaska. In Technical report: Workshop of Climate Change and Salmon Production (ed. Myers, K. W.) 39–40 (North Pacific Anadromous Fish Commission, 1998).

44.
Daly, E. A. & Brodeur, R. D. Warming ocean conditions relate to increased trophic requirements of threatened and endangered salmon. PLoS One 10, e0144066 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Davis, N. D., Armstrong, J. L. & Myers, K. W. Bering Sea Salmon Food Habits: Diet Overlap in Fall and Potential for Interactions Among Salmon. SAFS-UW-0311 (Fisheries Research Institute, School of Aquatic and Fisheries Sciences, University of Washington, 2003).

46.
Kawamura, H., Miyamoto, M., Nagata, M. & Hirano, K. Interaction between chum salmon and fat greenling juveniles in the coastal Sea of Japan off northern Hokkaido. North Pacific Anadromous Fish Comm. Bull. 1, 412–418 (1998).
Google Scholar 

47.
Ueno, Y. Deepwater migrations of chum salmon (Oncorhynchus keta) along the Pacific coast of northern Japan. Can. J. Fish. Aquat. Sci. 49, 2307–2312 (1992).
Article  Google Scholar 

48.
Ueno, Y., Seki, J., Shimizu, I. P. & Shershnev, A. Large juvenile chum salmon Oncorhynchus keta collected in coastal waters of Iturup Island. Nippon Suisan Gakkaishi 58, 1393–1397 (1992).
Article  Google Scholar 

49.
Waddell, B. J., Morris, J. F. T. & Healey, M. C. The abundance, distribution, and biological characteristics of Chinook and coho salmon on the fishing banks off southwest Vancouver Island, May 18-30, 1989 and April 23-May 5, 1990. Can. Tech. Rep. Fish. Aquat. Sci. 1891, 1–113 (1992).
Google Scholar 

50.
Andrievskaya, L. D. The feeding of Pacific salmon fry in the sea. Proceedings of the Pacific Research Institute of Fisheries and Oceanography 64, 73–80 (1970).
Google Scholar 

51.
Atcheson, M. E., Myers, K. W., Beauchamp, D. A. & Mantua, N. J. Bioenergetic response by steelhead to variation in diet, thermal habitat, and climate in the North Pacific Ocean. Trans. Am. Fish. Soc. 141, 1081–1096 (2012).
Article  Google Scholar 

52.
Carlson, H. R. et al. Cruise Report of the F/V Great Pacific Survey of Young Salmon in the North Pacific–Dixon Entrance to Western Aleutians—July–August 1996. NPAFC Doc. 222 (Auke Bay Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 1996).

53.
Davis, N. D., Fukuwaka, M., Armstrong, J. L. & Myers, K. W. Salmon food habits studies in the Bering Sea. 1960 to present. North Pacific Anadromous Fish Comm. Tech. Rep. 6, 24–28 (2005).
Google Scholar 

54.
Myers, K. W. & Aydin, K. Y. The 1996 International Cooperative Salmon Research Cruise of the Oshoro maru and a Summary of 1994-1996 Results. NPAFC Doc. 195; FRI-UW-9613 (University of Washington, Fisheries Research Institute, 1996).

55.
Myers, K. W. et al. Migrations, Abundance, and Origins of Salmonids in Offshore Waters of the North Pacific – 1995. NPAFC Doc. 152; FRI-UW-9613 (University of Washington, Fisheries Research Institute, 1995).

56.
Sturdevant, M. V, Ignell, S. E. & Morris, J. Diet of Juvenile Salmon off Southeastern Alaska, October-November 1995. NPAFC Doc. 275 (Auke Bay Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 1997).

57.
Walker, R. V. Summary of Cooperative U.S.-Japan High Seas Salmonid Research Aboard the Japanese Research Vessel Oshoro Maru, 1993. NPAFC Doc. 21 (Fisheries Research Institute, University of Washington, 1993).

58.
Suzuki, T. et al. Feeding selectivity of juvenile chum salmon in the Japan Sea Coast of Northern Honshu. Sci. Reports Hokkaido Salmon Hatch. 48, 11–16 (1994).
Google Scholar 

59.
Shimazaki, K. & Mishima, S. On the diurnal change of the feeding activity of salmon in the Okhotsk Sea. Bull. Fac. Fish. Hokkaido University 20, 82–93 (1969).
Google Scholar 

60.
Weitkamp, L. A. Ocean Conditions, Marine Survival, and Performance of Juvenile Chinook (Oncorhynchus tshawytscha) and Coho (O. kisutch) Salmon in Southeast Alaska. PhD thesis, University of Washington (2004).

61.
Starovoytov, A. N. Chum salmon (Oncorhynchus keta (Walbaum)) in the Far East Seas – biological description of the species 2. Diet composition and trophic linkages of chum salmon in the Far East Seas and adjacent waters of the Northwest Pacific Ocean. Izv. TINRO 133, 3–34 (2003).
Google Scholar 

62.
LeBrasseur, R. J. & Doidge, D. A. Stomach Contents of Salmonids Caught in the Northeastern Pacific Ocean – 1959 & 1960. In Circular, Statistical Series. vol. 3 (Fisheries Research Board of Canada, 1966).

63.
Lebrasseur, R. J. & Doidge, D. A. Stomach Contents of Salmonids Caught in the Northeastern Pacific Ocean – 1962. In Circular, Statistical Series. vol. 4 (Fisheries Research Board of Canada, 1966).

64.
Lebrasseur, R. J. & Doidge, D. A. Stomach Contents of Salmonids Caught in the Northeastern Pacific Ocean – 1963 & 1964. In Circular, Statistical Series. vol. 5 (Fisheries Research Board of Canada, 1966).

65.
Ishida, Y. & Davis, N. D. Chum salmon feeding habits in relation to growth reduction. Salmon Rep. Ser. 47, 104–110 (1999).
Google Scholar 

66.
Tamura, R., Shimazaki, K. & Ueno, Y. Trophic relations of juvenile salmon (genus Oncorhynchus) in the Okhotsk Sea and Pacific waters off the Kuril Islands. Salmon Rep. Ser. 47, 138–168 (1999).
Google Scholar 

67.
Seki, J. & Shimizu, I. Diel migration of zooplankton and feeding behavior of juvenile chum salmon in the central Pacific coast of Hokkaido. Bull. Nat. Salmon Resour. Cent. 1, 13–27 (1998).
Google Scholar 

68.
Suzuki, T., Fukuwaka, M., Kawana, M., Ohkuma, K. & Seki, J. Investigation on survival mechanism of juvenile chum salmon during the early sea life in 1994. Salmon Database 3, 59–68 (1995).
Google Scholar 

69.
Andrievskaya, L. D. The feeding of pink salmon in the wintering areas in the Sea of Japan. Izv. TINRO 90, 97–110 (1974).
Google Scholar 

70.
Andrievskaya, L. D. Feeding of Pacific salmon juveniles in the Sea of Okhotsk. Izv. TINRO 78, 105–115 (1970).
Google Scholar 

71.
Chuchukalo, V. I., Volkov, A. F., Efimkin, Ay. & Blagoderov, A. I. Distribution and feeding of the Chinook salmon (Oncorhynchus tschawytscha) in the northwest Pacific. Izv. TINRO, 137–141 (1994).

72.
Gorbatenko, K. M. Food and feeding habits of juvenile pink and chum salmons in the epipelagic zone of the Okhotsk Sea in winter. Izv. TINRO 199, 234–243 (1996).
Google Scholar 

73.
Kayev, A. M., Chupakhin, V. M. & Fedotova, N. A. Feeding peculiarities and interrelationships between juvenile salmons in coastal waters of the Etorofu Island. Vopr. Ikhtiologii 33, 215–224 (1993).
Google Scholar 

74.
Klovatch, N. V. Ecological Consequences of Large-scale Breeding Operations of Chum Salmon (Oncorhynchus keta). PhD extended summary (VNIRO, 2002).

75.
Shershnev, A. P., Chupakhin, V. M. & Rudnev, V. A. Some features of the ecology of young Sakhalin and Iturup pink salmon Oncorhynchus gorbuscha (Walbaum) (Salmonidae) during marine period of life. Vopr. Ikhtiologii 22, 441–448 (1982).
Google Scholar 

76.
Tutubalin, B. G. & Chuchukalo, V. I. The Feeding of Genus Oncorhynchus Pacific Salmons in the North Pacific During the Winter-Spring Period. In Living Resources of the Pacific Ocean: Collected Papers (eds. Gristenko, O. F., Churikov, A. A. & Klovach, N. V.) 77–85 (VNIRO, 1992).

77.
Volkov, A. F. Food and feeding habits of young Pacific salmon in the Okhotsk Sea during the autumn-winter period. Okeanologiya 36, 80–85 (1996).
Google Scholar 

78.
Volkov, A. F. Food and feeding habits of pink, chum and sockeye salmon during their anadromous migrations. Izv. TINRO 116, 128–137 (1994).
Google Scholar 

79.
Fisheries Agency of Japan. Report on research by Japan for the International North Pacific Fisheries Commission during the year 1965. International North Pacific Fisheries Commission Ann. Rep., 42–55 (1965).

80.
Davis, N. D. U.S.-Japan Cooperative High Seas Salmonid Research in 1990: Summary of Research Aboard the Japanese Research Vessel Hokuho Maru, 4 June to 19 July. INPFC Doc.; FRI-UW-9010. (Fisheries Research Institute, University of Washington, 1990). More

1.
Koonin, E. V. The wonder world of microbial viruses. Expert Rev. Anti Infect. Ther. 8, 1097–1099 (2010).
PubMed  PubMed Central  Article  Google Scholar 
2.
Yong, E. I Contain Multitudes: The Microbes Within Us and A Grander View of Life (Ecco, 2016).

3.
Breitbart, M. & Rohwer, F. Here a virus, there a virus, everywhere the same virus? Trends Microbiol. 13, 278–284 (2005).
CAS  PubMed  Article  Google Scholar 

4.
Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016). This is a massive metagenomic study on global viral diversity and distribution and host specificity of viruses. A total of 125,000 partial DNA virus genomes are discovered.
CAS  PubMed  Article  Google Scholar 

5.
Edwards, R. A. & Rohwer, F. Viral metagenomics. Nat. Rev. Microbiol. 3, 504–510 (2005).
CAS  PubMed  Article  Google Scholar 

6.
Suttle, C. A. Marine viruses-major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007). This is a fundamental must-read review of the general role of viruses in marine ecosystems.
CAS  PubMed  Article  Google Scholar 

7.
Abedon, S. T. Bacteriophage Ecology: Population Growth, Evolution, and Impact of Bacterial Viruses (Cambridge Univ. Press, 2008).

8.
Sullivan, M. B., Waterbury, J. B. & Chisholm, S. W. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature 424, 1047–1051 (2003).
CAS  PubMed  Article  Google Scholar 

9.
Sullivan, M. B. et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ. Microbiol. 12, 3035–3056 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Kauffman, K. M. et al. A major lineage of non-tailed dsDNA viruses as unrecognized killers of marine bacteria. Nature 554, 118–122 (2018).
CAS  PubMed  Article  Google Scholar 

11.
Atanasova, N. S., Roine, E., Oren, A., Bamford, D. H. & Oksanen, H. M. Global network of specific virus-host interactions in hypersaline environments. Environ. Microbiol. 14, 426–440 (2012).
CAS  PubMed  Article  Google Scholar 

12.
Marston, M. F. et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc. Natl Acad. Sci. USA 109, 4544–4549 (2012).
CAS  PubMed  Article  Google Scholar 

13.
Enav, H., Kirzner, S., Lindell, D., Mandel-Gutfreund, Y. & Béjà, O. Adaptation to sub-optimal hosts is a driver of viral diversification in the ocean. Nat. Commun. 9, 1–11 (2018).
CAS  Article  Google Scholar 

14.
Rappé, M. S. & Giovannoni, S. J. The uncultured microbial majority. Annu. Rev. Microbiol. 57, 369–394 (2003). This is a comprehensive review addressing a fundamental question in microbial ecology on the difficulty of culturing most microorganisms in the laboratory and how this bias impacts microbial discovery.
PubMed  Article  CAS  Google Scholar 

15.
Pedrós-Alió, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 449–466 (2012).
PubMed  Article  Google Scholar 

16.
Brum, J. R., Schenck, R. O. & Sullivan, M. B. Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses. ISME J. 7, 1738–1751 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, 1261498 (2015). This is a pioneering, comprehensive metagenomic study on global marine viral diversity from hundreds of samples collected during the Tara expedition.
PubMed  Article  CAS  Google Scholar 

18.
Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3, e00076-18 (2018).
PubMed  PubMed Central  Article  Google Scholar 

19.
Paez-Espino, D. et al. IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 47, D678–D686 (2019). This article describes the most comprehensive genome database of uncultured viruses recovered by metagenomics from different ecosystems, including the human body, with more than 700,000 viral genome fragments.
CAS  PubMed  Article  Google Scholar 

20.
Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).
PubMed  Article  Google Scholar 

21.
Carroll, D. et al. The Global Virome Project. Science 359, 872–874 (2018).
CAS  PubMed  Article  Google Scholar 

22.
Cesar Ignacio-Espinoza, J., Solonenko, S. A. & Sullivan, M. B. The global virome: not as big as we thought? Curr. Opin. Virol. 3, 566–571 (2013). The authors address a hot topic in viral ecology (that is, how big the viral diversity in nature is) and estimate the total number of different viral proteins, which is a proxy for quantifying the number of different existing viruses.
PubMed  Article  Google Scholar 

23.
Rohwer, F. Global phage diversity. Cell 113, 141 (2003).
CAS  PubMed  Article  Google Scholar 

24.
Suttle, C. A. Environmental microbiology: viral diversity on the global stage. Nat. Microbiol. 1, 1–2 (2016).
Article  CAS  Google Scholar 

25.
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).
CAS  PubMed  Article  Google Scholar 

26.
Schulz, F. et al. Hidden diversity of soil giant viruses. Nat. Commun. 9, 1–9 (2018). The article reports the discovery of several relevant giant viruses, including one with a genome of 2.4 Mb, using metagenomics and a method that is similar to those used in SVG, but in this case targeting multiple sets of 100 viruses, instead of single-virus particles.
Article  CAS  Google Scholar 

27.
Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208–211 (2015).
CAS  PubMed  Article  Google Scholar 

28.
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 1–11 (2016).
Article  CAS  Google Scholar 

29.
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
CAS  PubMed  Article  Google Scholar 

30.
Schulz, F. et al. Giant virus diversity and host interactions through global metagenomics. Nature 578, 432–436 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Al-Shayeb, B. et al. Clades of huge phages from across Earth’s ecosystems. Nature 578, 425–431 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Breitbart, M. et al. Genomic analysis of uncultured marine viral communities. Proc. Natl Acad. Sci. USA 99, 14250–14255 (2002).
CAS  PubMed  Article  Google Scholar 

33.
Dávila-Ramos, S. et al. A review on viral metagenomics in extreme environments. Front. Microbiol. 10, 2403 (2019).
PubMed  PubMed Central  Article  Google Scholar 

34.
Chatterjee, A., Sicheritz-Pontén, T., Yadav, R. & Kondabagil, K. Genomic and metagenomic signatures of giant viruses are ubiquitous in water samples from sewage, inland lake, waste water treatment plant, and municipal water supply in Mumbai, India. Sci. Rep. 9, 1–9 (2019).
Article  CAS  Google Scholar 

35.
Simmonds, P. et al. Consensus statement: virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).
CAS  PubMed  Article  Google Scholar 

36.
Martinez-Hernandez, F. et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat. Commun. 8, 1–13 (2017). This is a pioneering reference high-throughput SVG study that unveils extremely abundant and ubiquitous uncultured marine viruses overlooked for years by current state-of-the-art, standard metagenomic-based studies.
Article  CAS  Google Scholar 

37.
Roux, S., Emerson, J. B., Eloe-Fadrosh, E. A. & Sullivan, M. B. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ 5, e3817 (2017). This in silico study performs a through bioinformatic comparison of different tools used commonly in viral metagenomics and aims to provide useful recommendations and standards for the scientific community.
PubMed  PubMed Central  Article  Google Scholar 

38.
Aguirre de Cárcer, D., Angly, F. E. & Alcamí, A. Evaluation of viral genome assembly and diversity estimation in deep metagenomes. BMC Genomics 15, 1–12 (2014).
Article  CAS  Google Scholar 

39.
López-Pérez, M., Haro-Moreno, J. M., Gonzalez-Serrano, R., Parras-Moltó, M. & Rodriguez-Valera, F. Genome diversity of marine phages recovered from Mediterranean metagenomes: size matters. PLoS Genet. 13, e1007018 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Labonté, J. M. et al. Single-cell genomics-based analysis of virus–host interactions in marine surface bacterioplankton. ISME J. 9, 2386–2399 (2015). The screening of sequencing data from hundreds of single cells obtained from seawater unveils virus–host interactions in different ecologically important bacterial and archaeal groups.
PubMed  PubMed Central  Article  CAS  Google Scholar 

41.
Roux, S. et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. eLife 2014, e03125 (2014).
Article  CAS  Google Scholar 

42.
Yoon, H. S. et al. Single-cell genomics reveals organismal interactions in uncultivated marine protists. Science 332, 714–717 (2011). This is the first report of SCG in uncultivated widespread microbial eukaryotes, showing complex viral interactions and metabolic insights into phycobiliphyte groups.
CAS  PubMed  Article  Google Scholar 

43.
Castillo, Y. M. et al. Assessing the viral content of uncultured picoeukaryotes in the global‐ocean by single cell genomics. Mol. Ecol. 28, 4272–4289 (2019).
CAS  PubMed  Article  Google Scholar 

44.
Benites, L. F. et al. Single cell ecogenomics reveals mating types of individual cells and ssDNA viral infections in the smallest photosynthetic eukaryotes. Phil. Trans. R. Soc. B 374, 20190089 (2019).
CAS  PubMed  Article  Google Scholar 

45.
Martinez-Hernandez, F. et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 13, 232–236 (2019).
CAS  PubMed  Article  Google Scholar 

46.
Brussaard, C. P. D., Noordeloos, A. A. M., Sandaa, R. A., Heldal, M. & Bratbak, G. Discovery of a dsRNA virus infecting the marine photosynthetic protist Micromonas pusilla. Virology 319, 280–291 (2004).
CAS  PubMed  Article  Google Scholar 

47.
Dean, F. B. et al. Comprehensive human genome amplification using multiple displacement amplification. Proc. Natl Acad. Sci. USA 99, 5261–5266 (2002).
CAS  PubMed  Article  Google Scholar 

48.
Raghunathan, A. et al. Genomic DNA amplification from a single bacterium. Appl. Environ. Microbiol. 71, 3342–3347 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Stepanauskas, R. & Sieracki, M. E. Matching phylogeny and metabolism in the uncultured marine bacteria, one cell at a time. Proc. Natl Acad. Sci. USA 104, 9052–9057 (2007).
CAS  PubMed  Article  Google Scholar 

50.
Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).
CAS  PubMed  Article  Google Scholar 

51.
Martinez-Garcia, M., Martinez-Hernandez, F. & Martínez Martínez, J. Single-virus genomics: studying uncultured viruses, one at a time. Ref. Module Life Sci. https://doi.org/10.1016/b978-0-12-809633-8.21497-0 (2020). The authors provide methodological details and protocols for implementing SVG to complement other existing methods in viral ecology.
Article  Google Scholar 

52.
Lindell, D. et al. Transfer of photosynthesis genes to and from Prochlorococcus viruses. Proc. Natl Acad. Sci. USA 101, 11013–11018 (2004).
CAS  PubMed  Article  Google Scholar 

53.
Breitbart, M., Thompson, L., Suttle, C. & Sullivan, M. Exploring the vast diversity of marine viruses. Oceanography 20, 135–139 (2007).
Article  Google Scholar 

54.
Brum, J. R. & Sullivan, M. B. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat. Rev. Microbiol. 13, 147–159 (2015). This review is recommended for readers who would like an introduction to recent technological advances in marine virology.
CAS  PubMed  Article  Google Scholar 

55.
De Corte, D. et al. Viral communities in the global deep ocean conveyor belt assessed by targeted viromics. Front. Microbiol. 10, 1801 (2019).
PubMed  PubMed Central  Article  Google Scholar 

56.
Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc. Natl Acad. Sci. USA 114, 11446–11451 (2017).
CAS  PubMed  Article  Google Scholar 

57.
Luo, E., Aylward, F. O., Mende, D. R. & Delong, E. F. Bacteriophage distributions and temporal variability in the ocean’s interior. mBio 8, e01903-17 (2017).
PubMed  PubMed Central  Article  Google Scholar 

58.
Angly, F. E. et al. The marine viromes of four oceanic regions. PLoS Biol. 4, 2121–2131 (2006).
CAS  Article  Google Scholar 

59.
Coutinho, F. H., Rosselli, R. & Rodríguez-Valera, F. Trends of microdiversity reveal depth-dependent evolutionary strategies of viruses in the Mediterranean. mSystems 4, 1–17 (2019).
Article  Google Scholar 

60.
Roux, S., Krupovic, M., Debroas, D., Forterre, P. & Enault, F. Assessment of viral community functional potential from viral metagenomes may be hampered by contamination with cellular sequences. Open Biol. 3, 130160 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Zolfo, M. et al. Detecting contamination in viromes using ViromeQC. Nat. Biotechnol. 37, 1408–1412 (2019).
CAS  PubMed  Article  Google Scholar 

62.
Amgarten, D., Braga, L. P. P., da Silva, A. M. & Setubal, J. C. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front. Genet. 9, 304 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

63.
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

64.
Ponsero, A. J. & Hurwitz, B. L. The promises and pitfalls of machine learning for detecting viruses in aquatic metagenomes. Front. Microbiol. 10, 806 (2019).
PubMed  PubMed Central  Article  Google Scholar 

65.
Crummett, L. T., Puxty, R. J., Weihe, C., Marston, M. F. & Martiny, J. B. H. The genomic content and context of auxiliary metabolic genes in marine cyanomyoviruses. Virology 499, 219–229 (2016).
CAS  PubMed  Article  Google Scholar 

66.
Pagarete, A., Allen, M. J., Wilson, W. H., Kimmance, S. A. & de Vargas, C. Host-virus shift of the sphingolipid pathway along an Emiliania huxleyi bloom: survival of the fattest. Environ. Microbiol. 11, 2840–2848 (2009).
CAS  PubMed  Article  Google Scholar 

67.
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Kavagutti, V. S., Andrei, A. Ş., Mehrshad, M., Salcher, M. M. & Ghai, R. Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics. Microbiome 7, 1–15 (2019).
Article  Google Scholar 

69.
Sutton, T. D. S., Clooney, A. G., Ryan, F. J., Ross, R. P. & Hill, C. Choice of assembly software has a critical impact on virome characterisation. Microbiome 7, 12 (2019).
PubMed  PubMed Central  Article  Google Scholar 

70.
Madoui, M.-A. et al. Genome assembly using Nanopore-guided long and error-free DNA reads. BMC Genomics 16, 327 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

71.
Warwick-Dugdale, J. et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ 7, e6800 (2019). This pioneering study successfully combines long-read and short-read sequencing data to improve viral metagenomic assemblies and shows the potential of Nanopore sequencing data to advance virus discovery.
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Beaulaurier, J. et al. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities. Genome Res. 30, 437–446 (2020).
PubMed  PubMed Central  Article  Google Scholar 

73.
Mizuno, C. M., Rodriguez-Valera, F., Kimes, N. E. & Ghai, R. Expanding the marine virosphere using metagenomics. PLoS Genet. 9, e1003987 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

74.
Garcia-Heredia, I. et al. Reconstructing viral genomes from the environment using fosmid clones: the case of haloviruses. PLoS ONE 7, e33802 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Chow, C. E. T., Winget, D. M., White, R. A., Hallam, S. J. & Suttle, C. A. Combining genomic sequencing methods to explore viral diversity and reveal potential virus-host interactions. Front. Microbiol. 6, 265 (2015).
PubMed  PubMed Central  Google Scholar 

76.
Mizuno, C. M., Ghai, R., Saghaï, A., López-García, P. & Rodriguez-Valera, F. Genomes of abundant and widespread viruses from the deep ocean. mBio 7, e00805–e00816 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Martinez-Garcia, M. et al. High-throughput single-cell sequencing identifies photoheterotrophs and chemoautotrophs in freshwater bacterioplankton. ISME J. 6, 113–123 (2012).
CAS  PubMed  Article  Google Scholar 

78.
Stepanauskas, R. Single cell genomics: an individual look at microbes. Curr. Opin. Microbiol. 15, 613–620 (2012).
CAS  PubMed  Article  Google Scholar 

79.
Sieracki, M. E. et al. Single cell genomics yields a wide diversity of small planktonic protists across major ocean ecosystems. Sci. Rep. 9, 1–11 (2019).
CAS  Article  Google Scholar 

80.
Lasken, R. S. Genomic sequencing of uncultured microorganisms from single cells. Nat. Rev. Microbiol. 10, 631–640 (2012).
CAS  PubMed  Article  Google Scholar 

81.
López-Escardó, D. et al. Evaluation of single-cell genomics to address evolutionary questions using three SAGs of the choanoflagellate Monosiga brevicollis. Sci. Rep. 7, 1–14 (2017).
Article  CAS  Google Scholar 

82.
Mangot, J. F. et al. Accessing the genomic information of unculturable oceanic picoeukaryotes by combining multiple single cells. Sci. Rep. 7, 1–12 (2017).
Article  CAS  Google Scholar 

83.
Seeleuthner, Y. et al. Single-cell genomics of multiple uncultured stramenopiles reveals underestimated functional diversity across oceans. Nat. Commun. 9, 1–10 (2018).
CAS  Article  Google Scholar 

84.
Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013). This article is an excellent example of the power of single-cell technologies to provide biological insights into uncultured microorganisms.
CAS  PubMed  Article  Google Scholar 

85.
Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA 110, 11463–11468 (2013).
CAS  PubMed  Article  Google Scholar 

86.
Garcia, S. L. et al. Metabolic potential of a single cell belonging to one of the most abundant lineages in freshwater bacterioplankton. ISME J. 7, 137–147 (2013).
CAS  PubMed  Article  Google Scholar 

87.
Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of verrucomicrobia. PLoS ONE 7, e35314 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Stepanauskas, R. et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 1–10 (2017). The authors use flow cytometry to sort uncultured single viruses and they amplify their genomes with a new variant of an efficient Φ29 enzyme, which is commonly used in SCG and SVG. This study is another SVG example targeting uncultured viruses.
Article  CAS  Google Scholar 

89.
Ghylin, T. W. et al. Comparative single-cell genomics reveals potential ecological niches for the freshwater acI Actinobacteria lineage. ISME J. 8, 2503–2516 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

90.
Wilson, W. H. et al. Genomic exploration of individual giant ocean viruses. ISME J. 11, 1736–1745 (2017). This reference SVG study targets for the first time uncultured giant viruses in nature, which are commonly ignored with standard metagenomic techniques.
PubMed  PubMed Central  Article  Google Scholar 

91.
de la Cruz Peña, M. et al. Deciphering the human virome with single-virus genomics and metagenomics. Viruses 10, 113 (2018). This is the first study on SVG applied to the human virome. The authors implement this novel technology, combined with metagenomics, in salivary human samples and discover important, abundant phages.
PubMed Central  Article  CAS  PubMed  Google Scholar 

92.
Allen, L. Z. et al. Single virus genomics: a new tool for virus discovery. PLoS ONE 6, e17722 (2011). This is the first report showing the feasibility of SVG as a new tool for virus discovery. The authors successfully use this technology to sequence several single sorted virus particles of viral isolates T4 and λ of E. coli.
CAS  PubMed  PubMed Central  Article  Google Scholar 

93.
Holmfeldt, K., Odić, D., Sullivan, M. B., Middelboe, M. & Riemann, L. Cultivated single-stranded DNA phages that infect marine bacteroidetes prove difficult to detect with DNA-binding stains. Appl. Environ. Microbiol. 78, 892–894 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
Pospichalova, V. et al. Simplified protocol for flow cytometry analysis of fluorescently labeled exosomes and microvesicles using dedicated flow cytometer. J. Extracell. Vesicles 4, 25530 (2015).
PubMed  Article  CAS  Google Scholar 

95.
Giesecke, C. et al. Determination of background, signal-to-noise, and dynamic range of a flow cytometer: a novel practical method for instrument characterization and standardization. Cytometry A 91, 1104–1114 (2017).
CAS  PubMed  Article  Google Scholar 

96.
Schmidt, H. & Hawkins, A. R. Single-virus analysis through chip-based optical detection. Bioanalysis 8, 867–870 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Brussaard, C., Payet, J. P., Winter, C. & Weinbauer, M. G. Quantification of aquatic viruses by flow cytometry. Man. Aquat. Viral Ecol. 11, 102–109 (2010).
Article  Google Scholar 

98.
Mojica, K. D. A. & Brussaard, C. P. D. Factors affecting virus dynamics and microbial host-virus interactions in marine environments. FEMS Microbiol. Ecol. 89, 495–515 (2014).
CAS  PubMed  Article  Google Scholar 

99.
Blainey, P. C. & Quake, S. R. Digital MDA for enumeration of total nucleic acid contamination. Nucleic Acids Res. 39, e19 (2011).
PubMed  Article  CAS  Google Scholar 

100.
Woyke, T. et al. Decontamination of MDA reagents for single cell whole genome amplification. PLoS ONE 6, e26161 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Povilaitis, T., Alzbutas, G., Sukackaite, R., Siurkus, J. & Skirgaila, R. In vitro evolution of phi29 DNA polymerase using isothermal compartmentalized self replication technique. Protein Eng. Des. Sel. 29, 617–628 (2016).
CAS  PubMed  Article  Google Scholar 

102.
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016). This is one of the most comprehensive technical and scientific reviews of SCG technologies of unicellular and multicellular organisms, and discusses how these technologies have enabled new discoveries in multiple fields from microbiology to cancer or immunology.
CAS  PubMed  Article  Google Scholar 

103.
Martínez Martínez, J., Swan, B. K. & Wilson, W. H. Marine viruses, a genetic reservoir revealed by targeted viromics. ISME J. 8, 1079–1088 (2014). This study uses technologies similar to those used in SVG to discover giant viruses and other relevant uncultured viruses from a sorted pool of marine uncultured viruses.
PubMed  Article  CAS  Google Scholar 

104.
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

105.
Woyke, T. et al. One bacterial cell, one complete genome. PLoS ONE 5, e10314 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

106.
Roux, S. et al. Minimum information about an uncultivated virus genome (MIUVIG). Nat. Biotechnol. 37, 29–37 (2019).
CAS  PubMed  Article  Google Scholar 

107.
Hercher, M., Mueller, W. & Shapiro, H. M. Detection and discrimination of individual viruses by flow cytometry. J. Histochem. Cytochem. 27, 350–352 (1979).
CAS  PubMed  Article  Google Scholar 

108.
Lippé, R. Flow virometry: a powerful tool to functionally characterize viruses. J. Virol. 92, e01765-17 (2017).
Article  Google Scholar 

109.
Koonin, E. V. & Yutin, N. Evolution of the large nucleocytoplasmic DNA viruses of eukaryotes and convergent origins of viral gigantism. Adv. Virus Res. 103, 167–202 (2019).
CAS  PubMed  Article  Google Scholar 

110.
Brum, J. R. et al. Illuminating structural proteins in viral ‘dark matter’ with metaproteomics. Proc. Natl Acad. Sci. USA 113, 2436–2441 (2016).
CAS  PubMed  Article  Google Scholar 

111.
Alonso-Sáez, L., Morán, X. A. G. & Clokie, M. R. Low activity of lytic pelagiphages in coastal marine waters. ISME J. 12, 2100–2102 (2018).
PubMed  PubMed Central  Article  Google Scholar 

112.
Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).
CAS  PubMed  Article  Google Scholar 

113.
McMullen, A., Martinez‐Hernandez, F. & Martinez‐Garcia, M. Absolute quantification of infecting viral particles by chip‐based digital polymerase chain reaction. Environ. Microbiol. Rep. 11, 855–860 (2019).
CAS  PubMed  Google Scholar 

114.
Fukuda, R., Ogawa, H., Nagata, T. & Koike, I. Direct determination of carbon and nitrogen contents of natural bacterial assemblages in marine environments. Appl. Environ. Microbiol. 64, 3352–3358 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

115.
Needham, D. M. et al. Targeted metagenomic recovery of four divergent viruses reveals shared and distinctive characteristics of giant viruses of marine eukaryotes. Phil. Trans. R. Soc. B 374, 20190086 (2019).
CAS  PubMed  Article  Google Scholar 

116.
Needham, D. M. et al. A distinct lineage of giant viruses brings a rhodopsin photosystem to unicellular marine predators. Proc. Natl Acad. Sci. USA 116, 20574–20583 (2019).
CAS  PubMed  Article  Google Scholar 

117.
Dieterich, D. C., Link, A. J., Graumann, J., Tirrell, D. A. & Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl Acad. Sci. USA 103, 9482–9487 (2006).
CAS  PubMed  Article  Google Scholar 

118.
Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ. Microbiol. 16, 2568–2590 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

119.
Pasulka, A. L. et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ. Microbiol. 20, 671–692 (2018).
CAS  PubMed  Article  Google Scholar 

120.
Dominguez-Medina, S. et al. Neutral mass spectrometry of virus capsids above 100 megadaltons with nanomechanical resonators. Science 362, 918–922 (2018).
CAS  PubMed  Article  Google Scholar 

121.
Hermelink, A. et al. Towards a correlative approach for characterising single virus particles by transmission electron microscopy and nanoscale Raman spectroscopy. Analyst 142, 1342–1349 (2017).
CAS  PubMed  Article  Google Scholar 

122.
Ruokola, P. et al. Raman spectroscopic signatures of echovirus 1 uncoating. J. Virol. 88, 8504–8513 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

123.
Schatz, D. et al. Communication via extracellular vesicles enhances viral infection of a cosmopolitan alga. Nat. Microbiol. 2, 1485–1492 (2017).
CAS  PubMed  Article  Google Scholar 

124.
Berleman, J. & Auer, M. The role of bacterial outer membrane vesicles for intra- and interspecies delivery. Environ. Microbiol. 15, 347–354 (2013).
CAS  PubMed  Article  Google Scholar 

125.
Van Niel, G., D’Angelo, G. & Raposo, G. Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19, 213–228 (2018).
PubMed  Article  CAS  Google Scholar 

126.
Machtinger, R., Laurent, L. C. & Baccarelli, A. A. Extracellular vesicles: roles in gamete maturation, fertilization and embryo implantation. Hum. Reprod. Update 22, 182–193 (2016).
CAS  PubMed  Google Scholar 

127.
Biller, S. J. et al. Membrane vesicles in sea water: heterogeneous DNA content and implications for viral abundance estimates. ISME J. 11, 394–404 (2017).
CAS  PubMed  Article  Google Scholar 

128.
Kulp, A. & Kuehn, M. J. Biological functions and biogenesis of secreted bacterial outer membrane vesicles. Annu. Rev. Microbiol. 64, 163–184 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

129.
Jacob, F. & Wollman, E. L. Viruses and genes. Sci. Am. 204, 93–107 (1961).
CAS  PubMed  Article  Google Scholar 

130.
Forterre, P. The virocell concept and environmental microbiology. ISME J. 7, 233–236 (2013).
CAS  PubMed  Article  Google Scholar 

131.
Forterre, P. Manipulation of cellular syntheses and the nature of viruses: the virocell concept. C. R. Chim. 14, 392–399 (2011).
CAS  Article  Google Scholar 

132.
Weitz, J. S., Li, G., Gulbudak, H., Cortez, M. H. & Whitaker, R. J. Viral invasion fitness across a continuum from lysis to latency. Virus Evol. 5, vez006 (2019).
PubMed  PubMed Central  Article  Google Scholar 

133.
Martinez-Garcia, M. et al. Unveiling in situ interactions between marine protists and bacteria through single cell sequencing. ISME J. 6, 703–707 (2012).
CAS  PubMed  Article  Google Scholar 

134.
Martínez-García, M., Santos, F., Moreno-Paz, M., Parro, V. & Antón, J. Unveiling viral–host interactions within the ‘microbial dark matter’. Nat. Commun. 5, 1–8 (2014).
Article  CAS  Google Scholar 

135.
Džunková, M. et al. Defining the human gut host–phage network through single-cell viral tagging. Nat. Microbiol. 4, 2192–2203 (2019). This is probably one of the most comprehensive SCG studies within the context of the human gut microbiota, and unveils a total of 363 unique host–phage pairings, expanding the known host–phage network of the gut microbiota.
PubMed  Article  CAS  Google Scholar 

136.
Munson-Mcgee, J. H. et al. A virus or more in (nearly) every cell: Ubiquitous networks of virus-host interactions in extreme environments. ISME J. 12, 1706–1714 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

137.
Jarett, J. K. et al. Insights into the dynamics between viruses and their hosts in a hot spring microbial mat. ISME J. 14, 2527–2541 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

138.
Deng, L. et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature 513, 242–245 (2014).
CAS  PubMed  Article  Google Scholar 

139.
Allers, E. et al. Single-cell and population level viral infection dynamics revealed by phageFISH, a method to visualize intracellular and free viruses. Environ. Microbiol. 15, 2306–2318 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

140.
Zanini, F. et al. Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue. Proc. Natl Acad. Sci. USA 115, E12363–E12369 (2018).
CAS  PubMed  Article  Google Scholar 

141.
Steuerman, Y. et al. Dissection of influenza infection in vivo by single-cell RNA sequencing. Cell Syst. 6, 679–691 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

142.
Wyler, E. et al. Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program. Nat. Commun. 10, 1–14 (2019).
Article  CAS  Google Scholar 

143.
Guo, Q., Duffy, S. P., Matthews, K., Islamzada, E. & Ma, H. Deformability based cell sorting using microfluidic ratchets enabling phenotypic separation of leukocytes directly from whole blood. Sci. Rep. 7, 1–11 (2017).
Article  CAS  Google Scholar 

144.
Liu, W. et al. More than efficacy revealed by single-cell analysis of antiviral therapeutics. Sci. Adv. 5, eaax4761 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

145.
Lasken, R. S. Single-cell genomic sequencing using multiple displacement amplification. Curr. Opin. Microbiol. 10, 510–516 (2007).
CAS  PubMed  Article  Google Scholar 

146.
Marcy, Y. et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl Acad. Sci. USA 104, 11889–11894 (2007).
CAS  PubMed  Article  Google Scholar 

147.
Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).
CAS  PubMed  Article  Google Scholar 

148.
Ahrendt, S. R. et al. Leveraging single-cell genomics to expand the fungal tree of life. Nat. Microbiol. 3, 1417–1428 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

149.
McConnell, M. J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

150.
Poulin, J. F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M. & Awatramani, R. Disentangling neural cell diversity using single-cell transcriptomics. Nat. Neurosci. 19, 1131–1141 (2016).
PubMed  Article  CAS  Google Scholar 

151.
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

152.
Sandberg, R. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22–24 (2014).
CAS  PubMed  Article  Google Scholar 

153.
Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

154.
Lindell, D. et al. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 449, 83–86 (2007).
CAS  PubMed  Article  Google Scholar 

155.
Roux, S., Tournayre, J., Mahul, A., Debroas, D. & Enault, F. Metavir 2: new tools for viral metagenome comparison and assembled virome analysis. BMC Bioinformatics 15, 1–12 (2014).
Article  CAS  Google Scholar 

156.
Watson, M., Schnettler, E. & Kohl, A. viRome: an R package for the visualization and analysis of viral small RNA sequence datasets. Bioinformatics 29, 1902–1903 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

157.
Jurtz, V. I., Villarroel, J., Lund, O., Voldby Larsen, M. & Nielsen, M. MetaPhinder—identifying bacteriophage sequences in metagenomic data sets. PLoS ONE 11, e0163111 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

158.
Zheng, T. et al. Mining, analyzing, and integrating viral signals from metagenomic data. Microbiome 7, 1–15 (2019).
CAS  Article  Google Scholar 

159.
Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 69 (2017).
PubMed  PubMed Central  Article  Google Scholar 

160.
Fang, Z. et al. PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. GigaScience 8, giz066 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

161.
Tampuu, A., Bzhalava, Z., Dillner, J. & Vicente, R. ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples. PLoS ONE 14, e0222271 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

162.
Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).
Article  CAS  Google Scholar 

163.
Schleyer, G. et al. In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids. Nat. Microbiol. 4, 527–538 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

164.
Van Etten, J. L., Burbank, D. E., Kuczmarski, D. & Meints, R. H. Virus infection of culturable Chlorella-like algae and development of a plaque assay. Science 219, 994–996 (1983).
Article  Google Scholar 

165.
Maxwell, K. L. & Frappier, L. Viral proteomics. Microbiol. Mol. Biol. Rev. 71, 398–411 (2007).
CAS  Article  Google Scholar 

166.
Lum, K. K. & Cristea, I. M. Proteomic approaches to uncovering virus-host protein interactions during the progression of viral infection. Expert Rev. Proteom. 13, 325–340 (2016).
CAS  Article  Google Scholar 

167.
Cheng, W. & Schimert, K. A method for tethering single viral particles for virus-cell interaction studies with optical tweezers. Proc. SPIE 10723, 107233B (2018).
Google Scholar 

168.
Ekeberg, T. et al. Three-dimensional reconstruction of the giant mimivirus particle with an X-ray free-electron laser. Phys. Rev. Lett. 114, 098102 (2015).
PubMed  Article  CAS  Google Scholar 

169.
Cheng, Y. Single-particle cryo-EM at crystallographic resolution. Cell 161, 450–457 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

170.
Lyumkis, D. Challenges and opportunities in cryo-EM single-particle analysis. J. Biol. Chem. 294, 5181–5197 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

171.
Subramaniam, S., Bartesaghi, A., Liu, J., Bennett, A. E. & Sougrat, R. Electron tomography of viruses. Curr. Opin. Struct. Biol. 17, 596–602 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

172.
Gamage, S. et al. Probing structural changes in single enveloped virus particles using nano-infrared spectroscopic imaging. PLoS ONE 13, e0199112 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

173.
Martínez Martínez, J., Schroeder, D. C., Larsen, A., Bratbak, G. & Wilson, W. H. Molecular dynamics of Emiliania huxleyi and cooccurring viruses during two separate mesocosm studies. Appl. Environ. Microbiol. 73, 554–562 (2007).
Article  CAS  Google Scholar 

174.
Martínez Martínez, J. et al. New lipid envelope-containing dsDNA virus isolates infecting Micromonas pusilla reveal a separate phylogenetic group. Aquat. Microb. Ecol. 74, 17–28 (2015).
Article  Google Scholar  More

1.
Aleklett K, Hart M, Shade A. The microbial ecology of flowers: an emerging frontier in phyllosphere research. Botany. 2014;92:253–66.
Article  Google Scholar 
2.
Shade A, McManus PS, Handelsman J. Unexpected diversity during community succession in the apple flower microbiome. MBio. 2013;4:e00602–12.
PubMed  PubMed Central  Article  Google Scholar 

3.
Ambika Manirajan B, Ratering S, Rusch V, Schwiertz A, Geissler‐Plaum R, Cardinale M, et al. Bacterial microbiota associated with flower pollen is influenced by pollination type, and shows a high degree of diversity and species‐specificity. Environ Microbiol. 2016;18:5161–74.
Article  Google Scholar 

4.
Tucker CM, Fukami T. Environmental variability counteracts priority effects to facilitate species coexistence: evidence from nectar microbes. Proc R Soc B: Biol Sci. 2014;281:20132637.
Article  Google Scholar 

5.
Pusey PL, Rudell DR, Curry EA, Mattheis JP. Characterization of stigma exudates in aqueous extracts from apple and pear flowers. HortScience. 2008;43:1471–8.
Article  Google Scholar 

6.
Stockwell V, McLaughlin R, Henkels M, Loper J, Sugar D, Roberts R. Epiphytic colonization of pear stigmas and hypanthia by bacteria during primary bloom. Phytopathology. 1999;89:1162–8.
CAS  Article  Google Scholar 

7.
Steven B, Huntley RB, Zeng Q. The influence of flower anatomy and apple cultivar on the apple flower phytobiome. Phytobiomes. 2018;2:171–9.
Article  Google Scholar 

8.
Norelli JL, Jones AL, Aldwinckle HS. Fire blight management in the twenty-first century: using new technologies that enhance host resistance in apple. Plant Dis. 2003;87:756–65.
Article  Google Scholar 

9.
Thomson S, Wagner A, Gouk S, editors. Rapid epiphytic colonization of apple flowers and the role of insects and rain. VIII International Workshop on Fire Blight. vol 489. ISHS Acta Horticulturae; Kusadasi, Turkey. 1998.

10.
Pusey PL, Stockwell VO, Mazzola M. Epiphytic bacteria and yeasts on apple blossoms and their potential as antagonists of Erwinia amylovora. Phytopathology. 2009;99:571–81.
Article  Google Scholar 

11.
Sinclair L, Osman OA, Bertilsson S, Eiler A. Microbial community composition and diversity via 16S rRNA gene amplicons: evaluating the illumina platform. PLoS ONE. 2015;10:e0116955.
PubMed  PubMed Central  Article  CAS  Google Scholar 

12.
Pirc M, Ravnikar M, Tomlinson J, Dreo T. Improved fireblight diagnostics using quantitative real‐time PCR detection of Erwinia amylovora chromosomal DNA. Plant Pathol. 2009;58:872–81.
CAS  Article  Google Scholar 

13.
Cui Z, Yuan X, Yang C-H, Huntley RB, Sun W, Wang J, et al. Development of a method to monitor gene expression in single bacterial cells during the interaction with plants and use to study the expression of the type III secretion system in single cells of Dickeya dadantii in potato. Front Microbiol. 2018;9:1429.
PubMed  PubMed Central  Article  Google Scholar 

14.
Schloss PD, W S, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Rognes T, F T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
PubMed  PubMed Central  Article  Google Scholar 

16.
Westcott SL, S P. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. MSphere. 2017;2:e00073–17.
PubMed  PubMed Central  Article  Google Scholar 

17.
Quast C, P E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Dixon P. VEGAN, a package of R functions for community ecology. J Vegetation Sci. 2003;14:927–30.
Article  Google Scholar 

20.
Wickham H. ggplot2: elegant graphics for data analysis. Springer; New York. 2016.

21.
Palacio-Bielsa A, R M, Llop P, López MM. Erwinia spp. from pome fruit trees: similarities and differences among pathogenic and non-pathogenic species. Trees. 2012;26:13–29.
Article  Google Scholar 

22.
Kube M, M A, Müller I, Kuhl H, Beck A, Reinhardt R, Geider K. The genome of Erwinia tasmaniensis strain Et1/99, a non‐pathogenic bacterium in the genus Erwinia. Environ Microbiol. 2008;10:2211–22.
CAS  Article  Google Scholar 

23.
Geider K, A G, Du Z, Jakovljevic V, Jock S, Völksch B. Erwinia tasmaniensis sp. nov., a non-phytopathogenic bacterium from apple and pear trees. Int J Syst Evolut Microbiol. 2006;56:2937–43.
CAS  Article  Google Scholar 

24.
Thomson S. The role of the stigma in fire blight infections. Phytopathology. 1986;76:476–82.
Article  Google Scholar 

25.
Johnson KB, S V. Management of fire blight: a case study in microbial ecology. Annu Rev Phytopathol. 1998;36:227–48.
CAS  Article  Google Scholar 

26.
Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Albrecht M, Padrón B, Bartomeus I, Traveset A. Consequences of plant invasions on compartmentalization and species’ roles in plant–pollinator networks. Proc R Soc B: Biol Sci. 2014;281:20140773.
Article  Google Scholar 

28.
Edlund AF, Swanson R, Preuss D. Pollen and stigma structure and function: the role of diversity in pollination. Plant Cell. 2004;16:S84–S97.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Fridman S, Izhaki I, Gerchman Y, Halpern M. Bacterial communities in floral nectar. Environ Microbiol Rep. 2012;4:97–104.
Article  Google Scholar 

30.
Yuan J, Chaparro JM, Manter DK, Zhang R, Vivanco JM, Shen Q. Roots from distinct plant developmental stages are capable of rapidly selecting their own microbiome without the influence of environmental and soil edaphic factors. Soil Biol Biochem. 2015;89:206–9.
CAS  Article  Google Scholar 

31.
Marschner P, Neumann G, Kania A, Weiskopf L, Lieberei R. Spatial and temporal dynamics of the microbial community structure in the rhizosphere of cluster roots of white lupin (Lupinus albus L.). Plant Soil. 2002;246:167–74.
CAS  Article  Google Scholar 

32.
Bardgett RD, Bowman WD, Kaufmann R, Schmidt SK. A temporal approach to linking aboveground and belowground ecology. Trends Ecol Evol. 2005;20:634–41.
Article  Google Scholar 

33.
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Pusey P, Stockwell V, Reardon C, Smits T, Duffy B. Antibiosis activity of Pantoea agglomerans biocontrol strain E325 against Erwinia amylovora on apple flower stigmas. Phytopathology. 2011;101:1234–41.
CAS  Article  Google Scholar 

35.
Herrera CM. Microclimate and individual variation in pollinators: flowering plants are more than their flowers. Ecology. 1995;76:1516–24.
Article  Google Scholar 

36.
Medzhitov R, Schneider DS, Soares MP. Disease tolerance as a defense strategy. Science. 2012;335:936–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Hamdan-Partida A, González-García S, de la Rosa García E, Bustos-Martínez J. Community-acquired methicillin-resistant Staphylococcus aureus can persist in the throat. Int J Med Microbiol. 2018;308:469–75.
Article  Google Scholar 

38.
Peacock SJ, de Silva I, Lowy FD. What determines nasal carriage of Staphylococcus aureus? Trends Microbiol. 2001;9:605–10.
CAS  Article  Google Scholar 

39.
Von Eiff C, Becker K, Machka K, Stammer H, Peters G. Nasal carriage as a source of Staphylococcus aureus bacteremia. N Engl J Med. 2001;344:11–6.
Article  Google Scholar 

40.
Paetzold B, Willis JR, de Lima JP, Knödlseder N, Brüggemann H, Quist SR, et al. Skin microbiome modulation induced by probiotic solutions. Microbiome. 2019;7:95.
PubMed  PubMed Central  Article  Google Scholar 

41.
Trosvik P, Stenseth NC, Rudi K. Convergent temporal dynamics of the human infant gut microbiota. ISME J. 2010;4:151.
CAS  Article  Google Scholar 

42.
Shenhav L, Furman O, Briscoe L, Thompson M, Silverman JD, Mizrahi I, et al. Modeling the temporal dynamics of the gut microbial community in adults and infants. PLoS Comput Biol. 2019;15:e1006960.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Giatsis C, Sipkema D, Smidt H, Verreth J, Verdegem M. The colonization dynamics of the gut microbiota in tilapia larvae. PLoS ONE. 2014;9:e103641.
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Booijink CC, El‐Aidy S, Rajilić‐Stojanović M, Heilig HG, Troost FJ, Smidt H, et al. High temporal and inter‐individual variation detected in the human ileal microbiota. Environ Microbiol. 2010;12:3213–27.
CAS  Article  Google Scholar 

45.
Bolnick DI, Snowberg LK, Hirsch PE, Lauber CL, Org E, Parks B, et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat Commun. 2014;5:4500.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Colman DR, Toolson EC, Takacs‐Vesbach C. Do diet and taxonomy influence insect gut bacterial communities? Mol Ecol. 2012;21:5124–37.
CAS  Article  Google Scholar 

47.
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027.
Article  Google Scholar 

48.
Mariat D, Firmesse O, Levenez F, Guimarăes V, Sokol H, Doré J, et al. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol. 2009;9:123.
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Råberg L, Sim D, Read AF. Disentangling genetic variation for resistance and tolerance to infectious diseases in animals. Science. 2007;318:812–4.
Article  CAS  Google Scholar  More

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