Ecology

Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing arctic. Science 370, 712–715 (2020).ADS 
CAS 

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Chang. 8, 224–228 (2018).ADS 

Google Scholar 
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
CAS 

Google Scholar 
Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Sci. B. 278, 3437–3443 (2011).
Google Scholar 
González, A. M., Bayly, N. J. & Hobson, K. A. Earlier and slower or later and faster: spring migration pace linked to departure time in a Neotropical migrant songbird. J. Anim. Ecol. 89, 2840–2851 (2020).
Google Scholar 
Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569 (2011).
Google Scholar 
Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).ADS 
CAS 

Google Scholar 
Mettler, R., Segelbacher, G. & Schaefer, M. H. Interactions between a candidate gene for migration (ADCYAP1), morphology and sex predict spring arrival in blackcap populations. PLoS ONE 10, e0144587 (2015).
Google Scholar 
Bazzi, G. et al. Clock gene polymorphism and scheduling of migration: a geolocator study of the barn swallow Hirundo rustica. Sci. Rep. 5, 12443 (2015).ADS 

Google Scholar 
Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migratoin in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 

Google Scholar 
Bossu, C. M. et al. Clock-linked genes underlie seasonal migratory timing in a diurnal raptor. Proc. R. Soc. B. 289, 20212507 (2022).CAS 

Google Scholar 
O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B. 277, 3703–3714 (2010).
Google Scholar 
Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2 (2013).McKinnon, E. A. & Ten Love, O. P. years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135, 834–856 (2018).
Google Scholar 
Fraser, K. C. et al. Continent-wide tracking to determine migratory connectivity and tropical habitat associations of a declining aerial insectivore. Proc. R. Soc. B. 279, 4901–4906 (2012).
Google Scholar 
Neufeld, L. R. et al. Breeding latitude is associated with the timing of nesting and migration around the annual calendar among purple martin Progne subis populations. J. Ornithol. 162, 1009–1024 (2021).
Google Scholar 
Peona, V. et al. Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise. Mol. Ecol. 21(1), 263–286 (2020).
Google Scholar 
Coelho, L. A., Musher, L. J. & Cracraft, J. A multireference-based whole genome assembly for the obligate ant-following antbird, Rhegmatorhina melanosticta (Thamnophilidae). Diversity 11(19), 144 (2019).CAS 

Google Scholar 
Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).CAS 

Google Scholar 
Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Towards a genomic predictor of bleaching. Science 369(6501) (2019).Jones, S., Pfister-Genskow, M., Benca, R. M. & Cirelli, C. Molecular correlates of sleep and wakefulness in the brain of the white-crowned sparrow. J. Neurochem. 105, 46–62 (2008).CAS 

Google Scholar 
Ma, C. et al. Sleep regulation by neurotensinergic neurons in a thalamo-amygdala circuit. Neuron 103 (2019).Wong, J. M. & Eirin-Lopez, J. M. Evolution of methyltransferase-like (METTL) proteins in metazoan: a complex gene family involved in epitranscriptomic regulation and other epigenetic processes. Mol. Biol. Evol. 38, 5309–5327 (2021).CAS 

Google Scholar 
Jia, Z. et al. ACSS3 in brown fast drives propionate catabolism and its deficiency leads to autophagy and systemic metabolic dysfunction. Clin. Transl. Med. 12, e665 (2022).CAS 

Google Scholar 
Muller, F. et al. Towards a conceptual framework for explaining variation in nocturnal departure time of songbird migrants. Mov. Ecol. 4, 24 (2016).
Google Scholar 
Fraser, K. C. et al. Individual variability in migration timing can explain long-term population-level advances in a songbird. Front. Ecol. Evol. 7, 324 (2019).ADS 

Google Scholar 
Barret, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23(1), 38–44 (2008).
Google Scholar 
Colodro-Conde, L. et al. A direct test of the diathesis-stress model for depression. Mol. Psychiatry 23, 1590–1596 (2017).
Google Scholar 
Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLOS Genetics 9(4) (2013).Lavallée, C. D. et al. The use of nocturnal flights for barrier crossing in a diurnally migrating songbird. Mov. Ecol. 9, 21 (2021).
Google Scholar 
Saino, N. et al. Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Sci. Rep. 7, 45412 (2017).ADS 
CAS 

Google Scholar 
Henry, R. A. et al. Changing the selectivity of p300 by acetyl-CoA modulation of histone acetylation. ACS Chem. Biol 10, 146–156 (2015).CAS 

Google Scholar 
Sun, H., Skorgerbø, G., Wang, Z., Liu, W. & Li, Y. Structural relationships between highly conserved elements and genes in vertebrate genomes. PLoS ONE 3, e3727 (2008).ADS 

Google Scholar 
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 

Google Scholar 
Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).ADS 

Google Scholar 
Coombe, L. et al. ARKS: Chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinform. 19, 1–10 (2018).
Google Scholar 
Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER‐P. Curr. Protocols Bioinform. 48, 4.11.1–4.11.39 (2014).Malmberg, M. M. et al. Evaluation and recommendations for routine genotyping using skim whole genome re-sequencing in canola. Front. Plant. Sci. 9 (2018).Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 

Google Scholar 
Golicz, A. A., Bayer, P. E. & Edwards, D. Skim-based genotyping by sequencing. Methods Mol. Biol. 1245, 257–270 (2015).CAS 

Google Scholar 
Hill, R. D. Theory of geolocation by light levels. In B. J. L. Boeuf, & R. M. Laws (Ed.), Elephant seals: Population ecology, behaviour and physiology, pp. 227–236. Berkeley, CA: University of California Press (1994).Wotherspoon, S., Summer, M. & Lisovski, S. BAStag: basic data processing for light based geolocation archival tags. Version 0.1.3. (2016).Lisovski, S. & Hahn, S. GeoLight-processing and anslysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
Gompert, Z., Lucas, L. K., Nice, C. C. & Buerkle, C. A. Genome divergence and the genetic architecture of barriers to gene flow between Lycaeides idas and L. melissa. Evolution 67, 2498–2514 (2013).
Google Scholar 
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 

Google Scholar 
Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

Google Scholar 
Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analysis. Nat Protoc 15, 2759–2772 (2020).CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 

Google Scholar 
Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).
Google Scholar 
Vijay, N. et al. Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nat. Commun. 7, 13195 (2016).ADS 
CAS 

Google Scholar 
Delmore, K. et al. The evolutionary history and genomics of European blackcap migration. eLife 9, e54462 (2020). More

Literature search and screeningOur analysis included a systematic literature search and was conducted by following the PRISMA protocol55 (Supplementary Fig. 7). We searched through Web of Science and China National Knowledge Infrastructure (CNKI) platforms by using keywords listed in Supplementary Table 3. A total of 3299 potentially relevant articles were found (Mandarin and English). The availability of peer-reviewed datasets associated with these published articles11,15,56,57,58,59 and online databases (The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) database, https://www2.cifor.org/swamp) were also considered. We then removed a significant number of articles through title screening, leaving 551 articles for further inspection.For these remaining articles, we used a four-step critique process to screen their title, abstract, and full text. We determined that firstly, they must provide carbon density data for at least one of the four mangrove carbon pools (i.e., aboveground biomass, belowground biomass, sediment organic carbon, or total ecosystem carbon). Secondly, articles needed to state the forest age or the starting date of the restoration action. For those studies providing only age intervals (e.g., 10–25 years, >66 years), we excluded them from the analysis. Thirdly, a description of prior land use was required. From these, mangrove restoration could be divided into two categories—reforestation and afforestation—on whether mangroves previously existed in that location. For reforestation, the initial conditions for inclusion were: (1) abandoned agricultural/aquacultural sites built previously by excavating mangrove forests, (2) clear-felled mangrove lands after wars, timber harvest, and silvicultural management, and (3) mangrove forests with mortality due to spraying of defoliants and hydrological alteration caused by the construction of embankments. We compared the carbon densities of reforested mangroves among sites with different causes of degradation/deforestation, and no significant difference is found (Supplementary Fig. 9). For those reforested mangroves, we assumed they would be protected and conserved by local governments and non-government organizations, so that there will not be human-driven degradation or deforestation in the near future. However, we acknowledge that a fraction of mangrove reforestation is managed for wood production, which means logging would happen at a certain interval after reforestation at these sites. For these logging sites, we used their reported measurements after clear-cut, such as 0-, 5-, 10-, 15-, and 25-year post-harvest sites in Sundarbans, Bangladesh60. On the other hand, the future occurrence of natural-driven deforestation (e.g., cyclones) is difficult to predict, and thus not considered in our study. For afforestation, the initial condition for inclusion was the presence of non-mangrove habitat immediately before afforestation began, such as mudflats, seagrass, saltmarsh, coral reef, or denuded areas. In most cases, reforestation and afforestation were undertaken through active planting without much re-engineering4, but for reforestation, natural regeneration could have, and in many places likely did, augment recruitment61. Moreover, we only considered mangrove succession that started from near-barren land with an insignificant amount of biomass, and introductions of exotic species to degraded areas with sparse trees were not incorporated. Lastly, if the forest age or prior land use type was not given, the articles needed to specify the location of sampling plots (latitude, longitude). With the coordinates matching, prior land use type and establishment dates were sometimes identifiable through remote sensing (Supplementary Fig. 10). For those articles sharing the same restoration sites but showing different aspects of the data collection, we combined the results and considered the collective work as one source. Based on the space-for-time method, data in the control sites before mangrove restoration actions were also collected as a paired site of restoration (e.g., abandoned ponds before mangrove reforestation; mudflats before mangrove afforestation). In total, we obtained data from 379 mangrove restoration sites described by 106 articles.Data extractionWe extracted aboveground living biomass carbon (AGC), belowground living biomass carbon (BGC), sediment carbon (SCS), and total ecosystem carbon (TECS) density from the 106 original data sources. In most cases, numeric values were provided. For those data not provided numerically but graphed, we determined values from figures with the application of GetData Graph Digitizer (http://getdata-graph-digitizer.com/).Among the articles, aboveground and belowground biomass (Mg ha−1) data were obtained using either a harvesting method (empirical) or an allometric method (calculation). Aboveground biomass represented the sum of stem, leaf, and branch dry weight, and we included prop root biomass when Rhizophora spp. were present. For soil coring methods that determined belowground biomass or sediment carbon density, belowground biomass was considered the dry weight of living coarse and fine roots multiplied by the ratio of core area to land surface area62. For allometric methods, trunk diameter at breast height (DBH, ~1.3 m) and tree height were used to calculate aboveground and belowground biomass by species-specific or common allometric equations63. These equations were also used to calculate the belowground biomass when articles provided plot information (DBH, height) but not belowground biomass (Supplementary Table 4). Total biomass was calculated as the sum of aboveground and belowground biomass. Deadwood and pneumatophore biomass were not included in our analysis; these data are rarely provided and/or methods of determination are inconsistent among global studies64. Some articles provided total biomass and shoot/root biomass ratio (S/R), and in such cases, above- and belowground biomass data were obtained through calculation as follows:$${{{{{rm{Aboveground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{frac{S}{R}}{frac{S}{R}+1}$$
(1)
$${{{{{rm{Belowground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{1}{frac{S}{R}+1}$$
(2)
For those articles measuring carbon content, study-specific carbon conversion factors were used to transform biomass to biomass carbon density (Mg C ha−1). If carbon content data were not provided, we converted aboveground and belowground biomass to carbon density by applying a conversion of 0.47 and 0.39, respectively65. The aboveground biomass carbon density was divided by its corresponding age to get the average aboveground biomass carbon accumulation rate (Mg C ha−1 yr−1).For sediment carbon density (SCS, Mg C ha−1), we selected the top 1 m because this depth equated to the most commonly reported depth and could reflect the impact of root mass input in the deeper depth66, which is also consistent with recent blue carbon standing stock assessment guidance64,67. Sediment carbon stock was calculated by multiplying sediment organic carbon content (SOC, %) by bulk density (BD, g cm−3), integrated over depth (cm). For studies that reported sediment carbon stock to More

Wilson, E. O. The Insect Societies (Oxford University Press, 1971).
Google Scholar 
Beshers, S. N. & Fewell, J. H. Models of division of labor in social insects. Annu. Rev. Entomol. 46, 413–440 (2001).CAS 

Google Scholar 
Seeley, T. D. Adaptive significance of the age polyethism schedule in honeybee colonies. Behav. Ecol. Sociobiol. 4, 287–293 (1982).
Google Scholar 
Tallamy, D. W. Insect parental care. Bioscience 34, 20–24. https://doi.org/10.2307/1309421 (1984).Article 

Google Scholar 
Queller, D. C. Extended parental care and the origin of eusociality. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 256, 105–111. https://doi.org/10.1098/rspb.1994.0056 (1994).Article 
ADS 

Google Scholar 
Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta 1,2. Ann. Entomol. Soc. Am. 68, 301–304 (1975).CAS 

Google Scholar 
Endler, A. et al. Surface hydrocarbons of queen eggs regulate worker reproduction in a social insect. Proc. Natl. Acad. Sci. USA 101, 2945–2950. https://doi.org/10.1073/pnas.0308447101 (2004).Article 
ADS 
CAS 

Google Scholar 
Maisonnasse, A., Lenoir, J. C., Beslay, D., Crauser, D. & Le Conte, Y. E-beta-ocimene, a volatile brood pheromone involved in social regulation in the honey bee colony (Apis mellifera). PLoS ONE 5, e13531. https://doi.org/10.1371/journal.pone.0013531 (2010).Article 
ADS 
CAS 

Google Scholar 
Schultner, E., Oettler, J. & Helantera, H. The role of brood in eusocial hymenoptera. Q. Rev. Biol. 92, 39–78. https://doi.org/10.1086/690840 (2017).Article 

Google Scholar 
Amdam, G. V., Hartfelder, K., Norberg, K., Hagen, A. & Omholt, S. W. Altered physiology in worker honey bees (Hymenoptera: Apidae) infested with the mite Varroa destructor (Acari: Varroidae): A factor in colony loss during overwintering? J. Econ. Entomol. 97, 741–747 (2004).
Google Scholar 
Calabi, P. & Traniello, J. F. Behavioral flexibility in age castes of the ant Pheidole dentata. J. Insect Behav. 2, 663–677 (1989).
Google Scholar 
Gordon, D. W. Dynamics of task switching in harvester ants. Anim. Behav. 38, 194–204 (1989).
Google Scholar 
Robinson, G. E. Regulation of division of labor in insect societies. Annu. Rev. Entomol. 37, 637–665. https://doi.org/10.1146/annurev.en.37.010192.003225 (1992).Article 
CAS 

Google Scholar 
Robinson, E. J., Feinerman, O. & Franks, N. R. Flexible task allocation and the organization of work in ants. Proc. R. Soc. B: Biol. Sci. 276, 4373–4380 (2009).
Google Scholar 
Nijhout, H. F. & Wheeler, D. E. Juvenile-hormone and the physiological-basis of Insect polymorphisms. Q. Rev. Biol. 57, 109–133. https://doi.org/10.1086/412671 (1982).Article 
CAS 

Google Scholar 
Herb, B. R. et al. Reversible switching between epigenetic states in honeybee behavioral subcastes. Nat. Neurosci. 15, 1371–1373. https://doi.org/10.1038/nn.3218 (2012).Article 
CAS 

Google Scholar 
Kensuke, N. Age polyethism, idiosyncrasy and behavioural flexibility in the queenless ponerine ant, Diacamma sp. J. Ethol. 13, 113–123 (1995).
Google Scholar 
Kensuke, N. Does behavioral flexibility compensate or constrain colony productivity? Relationship among age structure, labor allocation, and production of workers in ant colonies. J. Insect Behav. 9, 557–569 (1996).
Google Scholar 
Shimoji, H., Kasutani, N., Ogawa, S. & Hojo, M. K. Worker propensity affects flexible task reversion in an ant. Behav. Ecol. 74, 1–8 (2020).
Google Scholar 
Bernadou, A., Busch, J. & Heinze, J. Diversity in identity: Behavioral flexibility, dominance, and age polyethism in a clonal ant. Behav. Ecol. Sociobiol. 69, 1365–1375 (2015).
Google Scholar 
Kohlmeier, P., Feldmeyer, B. & Foitzik, S. Vitellogenin-like A—Associated shifts in social cue responsiveness regulate behavioral task specialization in an ant. PLoS Biol. 16, e2005747 (2018).
Google Scholar 
Tripet, F. & Nonacs, P. Foraging for work and age-based polyethism: The roles of age and previous experience on task choice in ants. Ethology 110, 863–877 (2004).
Google Scholar 
Kohlmeier, P., Alleman, A. R., Libbrecht, R., Foitzik, S. & Feldmeyer, B. Gene expression is more strongly associated with behavioural specialisation than with age or fertility in ant workers. Mol. Ecol. https://doi.org/10.1111/mec.14971 (2018).Article 

Google Scholar 
Levenbook, L. & Bauer, A. C. The fate of the larval storage protein calliphorin during adult development of Calliphora vicina. Insect Biochem. 14, 77–86 (1984).CAS 

Google Scholar 
Zhou, X., Oi, F. M. & Scharf, M. E. Social exploitation of hexamerin: RNAi reveals a major caste-regulatory factor in termites. Proc. Natl. Acad. Sci. 103, 4499–4504 (2006).ADS 
CAS 

Google Scholar 
Zhou, X., Tarver, M. R., Bennett, G., Oi, F. & Scharf, M. Two hexamerin genes from the termite Reticulitermes flavipes: Sequence, expression, and proposed functions in caste regulation. Gene 376, 47–58 (2006).CAS 

Google Scholar 
Hawkings, C., Calkins, T. L., Pietrantonio, P. V. & Tamborindeguy, C. Caste-based differential transcriptional expression of hexamerins in response to a juvenile hormone analog in the red imported fire ant (Solenopsis invicta). PLoS ONE 14, e0216800 (2019).CAS 

Google Scholar 
Hoffman, E. A. & Goodisman, M. A. Gene expression and the evolution of phenotypic diversity in social wasps. BMC Biol. 5, 1–9 (2007).
Google Scholar 
Hunt, J. H., Buck, N. A. & Wheeler, D. E. Storage proteins in vespid wasps: Characterization, developmental pattern, and occurrence in adults. J. Insect Physiol. 49, 785–794 (2003).CAS 

Google Scholar 
Colgan, T. J. et al. Polyphenism in social insects: Insights from a transcriptome-wide analysis of gene expression in the life stages of the key pollinator, Bombus terrestris. BMC Genom. 12, 1–20 (2011).
Google Scholar 
Cremer, S., Armitage, S. A. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, R693–R702 (2007).CAS 

Google Scholar 
Cremer, S., Pull, C. D. & Fuerst, M. A. Social immunity: Emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).CAS 

Google Scholar 
Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: A key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
Google Scholar 
Koch, S. I. et al. Caste-specific expression patterns of immune response and chemosensory related genes in the leaf-cutting ant, Atta vollenweideri. PLoS ONE 8, e81518 (2013).ADS 

Google Scholar 
Chardonnet, F. et al. Food searching behaviour of a Lepidoptera pest species is modulated by the foraging gene polymorphism. J. Exp. Biol. 217, 3465–3473 (2014).
Google Scholar 
Scheiner, R., Page, R. E. Jr. & Erber, J. Responsiveness to sucrose affects tactile and olfactory learning in preforaging honey bees of two genetic strains. Behav. Brain Res. 120, 67–73 (2001).CAS 

Google Scholar 
Wang, Z. et al. Visual pattern memory requires foraging function in the central complex of Drosophila. Learn. Mem. 15, 133–142 (2008).
Google Scholar 
Zhou, Y., Lei, Y., Lu, L. & He, Y. Temperature-and food-dependent foraging gene expression in foragers of the red imported fire ant Solenopsis invicta Buren (Hymenoptera: Formicidae). Physiol. Entomol. 45, 1–6 (2020).
Google Scholar 
Ingram, K. K. et al. Context-dependent expression of the foraging gene in field colonies of ants: The interacting roles of age, environment and task. Proc. R. Soc. B: Biol. Sci. 283, 20160841 (2016).
Google Scholar 
Ingram, K. K., Oefner, P. & Gordon, D. M. Task-specific expression of the foraging gene in harvester ants. Mol. Ecol. 14, 813–818 (2005).CAS 

Google Scholar 
Lucas, C. & Sokolowski, M. B. Molecular basis for changes in behavioral state in ant social behaviors. Proc. Natl. Acad. Sci. 106, 6351–6356 (2009).ADS 
CAS 

Google Scholar 
Ben-Shahar, Y. The foraging gene, behavioral plasticity, and honeybee division of labor. J. Comp. Physiol. A. 191, 987–994 (2005).CAS 

Google Scholar 
Daugherty, T., Toth, A. & Robinson, G. Nutrition and division of labor: Effects on foraging and brain gene expression in the paper wasp Polistes metricus. Mol. Ecol. 20, 5337–5347 (2011).CAS 

Google Scholar 
Morrison, L. W., Porter, S. D., Daniels, E. & Korzukhin, M. D. Potential global range expansion of the invasive fire ant, Solenopsis invicta. Biol. Invasions 6, 183–191 (2004).
Google Scholar 
Valles, S. M., Wetterer, J. K. & Porter, S. D. The red imported fire ant (Hymenoptera: Formicidae) in the West Indies: Distribution of natural enemies and a possible test bed for release of self-sustaining biocontrol agents. Fls. Entomol. 98, 1101–1105 (2015).
Google Scholar 
Greenberg, L., Vinson, S. & Ellison, S. Nine-year study of a field containing both monogyne and polygyne red imported fire ants (Hymenoptera: Formicidae). Ann. Entomol. Soc. Am. 85, 686–695 (1992).
Google Scholar 
Keller, L. & Ross, K. G. Selfish genes: A green beard in the red fire ant. Nature 394, 573–575 (1998).ADS 
CAS 

Google Scholar 
Vinson, S. B. Impact of the invasion of the imported fire ant. Insect Sci. 20, 439–455 (2013).
Google Scholar 
Tschinkel, W. R. The Fire Ants (Harvard University Press, 2006).
Google Scholar 
Cassill, D. L. & Tschinkel, W. R. Task selection by workers of the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol. 45, 301–310 (1999).
Google Scholar 
Mirenda, J. T. & Vinson, S. B. Division of labour and specification of castes in the red imported fire ant Solenopsis invicta Buren. Anim. Behav. 29, 410–420 (1981).
Google Scholar 
Wilson, E. O. Division of labor in fire ants based on physical castes (Hymenoptera: Formicidae: Solenopsis). J. Kansas Entomol. Soc. 51, 615–636 (1978).
Google Scholar 
Sorensen, A., Busch, T. M. & Vinson, S. B. Behavioral flexibility of temporal subcastes in the fire ant, Solenopsis invicta in response to food. Psyche 91, 319–331 (1984).
Google Scholar 
Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 2, 301–304 (1975).
Google Scholar 
Bajracharya, P., Lu, H. L. & Pietrantonio, P. V. The red imported fire ant (Solenopsis invicta Buren) kept Y not F: Predicted sNPY endogenous ligands deorphanize the short NPF (sNPF) receptor. PLoS ONE 9(10), e109590 (2014).ADS 

Google Scholar 
Castillo, P. Short neuropeptide F receptor in the worker brain of the red imported fire ant (Solenopsis invicta Buren) and methodology for RNA interference M.S. thesis, Texas A&M University (2015).Castillo, P. & Pietrantonio, P. V. Differences in sNPF receptor-expressing neurons in brains of fire ant (Solenopsis invicta Buren) worker subcastes: Indicators for division of labor and nutritional status? PLoS ONE 8, e83966 (2013).ADS 

Google Scholar 
Cassill, D. L. & Tschinkel, W. R. Allocation of liquid food to larvae via trophallaxis in colonies of the fire ant, Solenopsis invicta. Anim. Behav. 3, 801–813 (1995).
Google Scholar 
Cassill, D. L., Stuy, A. & Buck, R. G. Emergent properties of food distribution among fire ant larvae. J. Theor. Biol. 3, 371–381 (1998).ADS 

Google Scholar 
Dussutour, A. & Simpson, S. J. Communal nutrition in ants. Curr. Biol. 19, 740–744. https://doi.org/10.1016/j.cub.2009.03.015 (2009).Article 
CAS 

Google Scholar 
Petralia, R. S. & Vinson, S. B. Feeding in the larvae of the imported fire ant, Solenopsis invicta: Behavior and morphological adaptations. Ann. Entomol. Soc. Am. 71, 643–648 (1978).
Google Scholar 
Petralia, R. S. & Vinson, S. B. Developmental morphology of larvae and eggs of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 72, 472–484 (1979).
Google Scholar 
Chen, J. Advancement on techniques for the separation and maintenance of the red imported fire ant colonies. Insect Sci. 14, 1–4 (2007).
Google Scholar 
Banks, W. A. et al. (Agricultural Research (Southern Region), Science and Education…, 1981).Valles, S. M. & Porter, S. D. Identification of polygyne and monogyne fire ant colonies (Solenopsis invicta) by multiplex PCR of Gp-9 alleles. Insectes Soc. 2, 199–200 (2003).
Google Scholar 
Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101 (2008).CAS 

Google Scholar 
Cheng, D., Zhang, Z., He, X. & Liang, G. Validation of reference genes in Solenopsis invicta in different developmental stages, castes and tissues. PLoS ONE 8, e57718. https://doi.org/10.1371/journal.pone.0057718 (2013).Article 
ADS 
CAS 

Google Scholar 
Qiu, H.-L., Zhao, C.-Y. & He, Y.-R. On the molecular basis of division of labor in Solenopsis invicta (Hymenoptera: Formicidae) workers: RNA-seq analysis. J. Insect Sci. 17, 48 (2017).
Google Scholar 
Chen, J. et al. Role of the foraging gene in worker behavioral transition in the red imported fire ant, Solenopsis invicta (Hymenoptera: Formicidae). Pest Manag. Sci. https://doi.org/10.1002/ps.6921 (2022).Article 

Google Scholar 
Shorter, J. R. & Tibbetts, E. A. The effect of juvenile hormone on temporal polyethism in the paper wasp Polistes dominulus. Insectes Soc. 56, 7–13 (2009).
Google Scholar 
Pankiw, T., Page, R. E. Jr. & Kim Fondrk, M. Brood pheromone stimulates pollen foraging in honey bees (Apis mellifera). Behav. Ecol. Sociobiol. 44, 193–198. https://doi.org/10.1007/s002650050531 (1998).Article 

Google Scholar 
Smedal, B., Brynem, M., Kreibich, C. D. & Amdam, G. V. Brood pheromone suppresses physiology of extreme longevity in honeybees (Apis mellifera). J. Exp. Biol. 212, 3795–3801. https://doi.org/10.1242/jeb.035063 (2009).Article 
CAS 

Google Scholar 
Solis, C. R. & Strassmann, J. E. Presence of brood affects caste differentiation in the social wasp, Polistes exclamans Viereck (Hymenoptera, Vespidae). Funct. Ecol. 4, 531–541. https://doi.org/10.2307/2389321 (1990).Article 

Google Scholar 
Traynor, K. S. Decoding Brood Pheromone: The Releaser and Primer Effects of Young and Old Larvae on Honey Bee (Apis mellifera) Workers (Arizona State University, 2014).
Google Scholar 
Wagoner, K. M., Spivak, M. & Rueppell, O. Brood affects hygienic behavior in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 111, 2520–2530. https://doi.org/10.1093/jee/toy266 (2018).Article 
CAS 

Google Scholar 
Nijhout, H. F. & Wheeler, D. E. Juvenile hormone and the physiological basis of insect polymorphisms. Q. Rev. Biol. 57, 109–133 (1982).CAS 

Google Scholar  More

The flightless dodo went extinct in the seventeenth century. Biotech company Colossal Biosciences plans to resurrect it.Credit: Hart, F/Bridgeman Images

A biotech company announced an audacious effort to ‘de-extinct’ the dodo last week. The flightless birds vanished from the island of Mauritius — in the Indian Ocean — in the late seventeenth century, and became emblematic of humanity’s negative impacts on the natural world. Could the plan actually work?Colossal Biosciences, based in Dallas, Texas, has landed US$225 million in investment (including funds from the celebrity Paris Hilton) — having previously announced plans to de-extinct thylacines, an Australian marsupial, and create elephants with woolly mammoth traits. But Colossal’s plans depend on huge advances in genome editing, stem-cell biology and animal husbandry, making success far from certain.“It’s incredibly exciting that there’s that kind of money available,” says Thomas Jensen, a cell and molecular reproductive physiologist at Wells College in Aurora, New York. “I’m not sure that the end goal they’re going for is something that’s super feasible in the near future.”Iridescent pigeonsColossal’s plan starts with the dodo’s closest living relative, the iridescent-feathered Nicobar pigeon (Caloenas nicobarica). The company plans to isolate and culture specialized primordial germ cells (PGCs) — which make sperm and egg-producing cells — from developing Nicobars. Colossal’s scientists would edit DNA sequences in the PGCs to match those of dodos using tools such as CRISPR. These gene-edited PGCs would then be inserted into embryos from a surrogate bird species to generate chimeric — those with DNA from both species — animals that make dodo-like egg and sperm. These could potentially produce something resembling a dodo (Raphus cucullatus).To gene-edit Nicobar pigeon PGCs, scientists first need to identify the conditions that allow these cells to flourish in the laboratory, says Jae Yong Han, an avian-reproduction scientist at Seoul National University. Researchers have done this with chickens, but it will take time to identify the appropriate culture conditions that suit other birds’ PGCs.A greater challenge will be determining the genetic changes that could transform Nicobar pigeons into Dodos. A team including Beth Shapiro, a palaeogeneticist at the University of California, Santa Cruz, who is advising Colossal on the dodo project, has sequenced the dodo genome but has not yet published the results. Dodos and Nicobar pigeons shared a common ancestor that lived around 30 million to 50 million years ago, Shapiro’s team reported in 20161. By comparing the nuclear genomes of the two birds, the researchers hope to identify most of the DNA changes that distinguish between them.Insights from ratsTom Gilbert, an evolutionary biologist at the University of Copenhagen, who also advises Colossal, expects the dodo genome to be of high quality — it comes from a museum sample he provided to Shapiro. But he says that finding all the DNA differences between the two birds is not possible. Ancient genomes are cobbled together from short sequences of degraded DNA, and so are filled with unavoidable gaps and errors. And research he published last year comparing the genome of the extinct Christmas Island rat (Rattus macleari) with that of the Norwegian brown rat (Rattus norvegicus)2 suggests that gaps in the dodo genome could lie in the very DNA regions that have changed the most since its lineage split from that of Nicobar pigeons.Even if researchers could identify every genetic difference, introducing the thousands of changes to PGCs would not be simple. “I’m not sure it’s feasible in the near future,” says Jensen, whose team is encountering difficulties making a single genetic change to the genomes of quail.Focusing on only a subset of DNA changes, such as those that alter protein sequences, could slash the number of edits needed. But it’s still not clear that this would yield anything resembling a wild dodo, says Gilbert. “My worry is that Paris Hilton thinks she’s going to get a dodo that looks like a dodo,” he says.A further problem will be the need to find a large bird, such as an emu (Dromaius novaehollandiae), that can act as the surrogate, says Jensen. “Dodo eggs are much, much larger than Nicobar pigeon eggs, you couldn’t grow a dodo inside of a Nicobar egg.”Chicken embryos are fairly receptive to PGCs from other birds, and Jensen’s team has created chimeric chickens that can produce quail sperm — efforts to generate eggs have failed so far. But he thinks it will be far more challenging to transfer PGCs — particularly heavily gene-edited ones — from one wild bird into another.Conservation boon?Colossal chief executive Ben Lamm acknowledges these hurdles, but argues they aren’t dealbreakers. Work towards dodo de-extinction will help with conservation efforts for other birds, he adds. “It will bring a lot of new technologies to the field of bird conservation,” agrees Jensen.Vikash Tatayah, conservation director at the Mauritian Wildlife Foundation in Vacoas-Phoenix, is also enthusiastic about the attention dodo de-extinction could bring to conservation. “It’s something we would like to embrace,” he says.But he points out that the predators that threatened the dodo in the seventeeth century haven’t gone away, whereas most of its habitat has. “You do have to ask,” he says, “if we could have such money, wouldn’t it be better spent on restoring habitat on Mauritius and preventing species from going extinct?” More

The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

Van Dover, C. L. et al. Environmental management of deep-sea chemosynthetic ecosystems: justification of and considerations for a spatially based approach. ISA Technical Study: No.9. (International Seabed Authority, 2011).Ikehata, K., Suzuki, R., Shimada, K., Ishibashi, J., & Urabe, T. Mineralogical and Geochemical Characteristics of Hydrothermal Minerals Collected from Hydrothermal Vent Fields in the Southern Mariana Spreading Center. In Subseafloor biosphere linked to hydrothermal systems: TAIGA Concept. 275–288 (Springer Tokyo, 2015).Rona, P. A. & Scott, S. D. A special issue on sea-floor hydrothermal mineralization; new perspectives; preface. Econ. Geol. 88, 1935–1976 (1993).
Google Scholar 
Glasby, G. P., Iizasa, K., Yuasa, M. & Usui, A. Submarine hydrothermal mineralization on the Izu-Bonin arc, south of Japan: an overview. Mar. Georesources Geotech. 18, 141–176 (2000).
Google Scholar 
Van Dover, C. L. Inactive sulfide ecosystems in the deep sea: a review. Front. Mar. Sci. 6, 461. https://doi.org/10.3389/fmars.2019.00461 (2019).Article 

Google Scholar 
Boschen, R. E., Rowde, A. A., Clark, M. R. & Gardner, J. P. Mining of deep-sea seafloor massive sulfides: a review of the deposits, their benthic communities, impacts from mining, regulatory frameworks and management strategies. Ocean Coast. Manag. 84, 54–67 (2013).
Google Scholar 
Washburn, T. W. et al. Ecological risk assessment for deep-sea mining. Ocean Coast. Manag. 176, 24–39 (2019).
Google Scholar 
Matsui, T., Sugishima, H., Okamoto, N., Igarashi, Y. Evaluation of turbidity and resedimentation through seafloor disturbance experiments for assessment of environmental impacts associated with exploitation of seafloor massive sulfides mining. Proceedings of the Twenty-eighth. International Ocean and Polar Engineering Conference. 144–151 (2018).International Seabed Authority. Recommendations for the guidance of contractors for the assessment of the possible environmental impacts arising from exploration for marine minerals in the Area. https://www.isa.org.jm/documents/isba19ltc8 (2013).Suzuki, K., Yoshida, K., Watanabe, H. & Yamamoto, H. Mapping the resilience of chemosynthetic communities in hydrothermal vent fields. Sci. Rep. 8, 9364. https://doi.org/10.1038/s41598-018-27596-7 (2018).Article 
ADS 
CAS 

Google Scholar 
Yahagi, T., Watanabe, H., Ishibashi, J. I. & Kojima, S. Genetic population structure of four hydrothermal vent shrimp species (Alvinocarididae) in the Okinawa Trough, Northwest Pacific. Mar. Ecol. Prog. Ser. 529, 159–169 (2015).ADS 

Google Scholar 
Mullineaux, L. S. Deep-sea hydrothermal vent communities. In Marine community ecology and conservation (eds Bertness, M. D. et al.) 383–400 (Sinauer, 2013).
Google Scholar 
Van Dover, C. L., German, C. R., Speer, K. G., Parson, L. M. & Vrijenhoek, R. C. Evolution and biogeography of deep-sea vent and seep invertebrates. Science 295, 1253–1257 (2002).ADS 

Google Scholar 
Yahagi, T., Kayama-Watanabe, H., Kojima, S. & Kano, Y. Do larvae from deep-sea hydrothermal vents disperse in surface waters?. Ecology 98, 1524–1534 (2017).
Google Scholar 
Hebert, P. D. & Gregory, T. R. The promise of DNA barcoding for taxonomy. Syst. Biol. 54, 852–859 (2005).
Google Scholar 
Iguchi, A. et al. Comparative analysis on the genetic population structures of the deep-sea whelks Buccinum tsubai and Neptunea constricta in the Sea of Japan. Mar. Biol. 151, 31–39 (2007).
Google Scholar 
Goode, G. B. & Bean, T. H. A catalogue of the fishes of Essex County, Massachusetts, including the fauna of Massachusetts Bay and the contiguous deep waters. Bull. Essex Inst. 11, 1–38 (1879).
Google Scholar 
Johnson, J. Y. Descriptions of some new genera and species of fishes obtained at Madeira. Proc. Zool. Soc. Lond. 1862, 167–180 (1862).
Google Scholar 
Bate, C. S. Report on the Crustacea Macrura collected by the Challenger during the years 1873–76. Report on the scientific results of the Voyage of H.M.S. Challenger during the years 1873–76. Zoology 24, 1–942 (1888).
Google Scholar 
Folmer, O., Black, M., Hoeh, W. R., Lutz, R. & Vrijenhoek, R. C. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol Biotech. 3, 294–299 (1994).CAS 

Google Scholar 
Pilgrim, E. M., Blum, M. J., Reusser, D. A., Lee, H. & Darling, J. A. Geographic range and structure of cryptic genetic diversity among Pacific North American populations of the non-native amphipod Grandidierella japonica. Biol. Invasions 15, 2415–2428 (2013).
Google Scholar 
Suyama, Y. & Matsuki, Y. MIG-seq: an effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci. Rep. 5, 16963. https://doi.org/10.1038/srep16963 (2015).Article 
ADS 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 

Google Scholar 
Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962. https://doi.org/10.1371/journal.pone.0163962 (2016).Article 
CAS 

Google Scholar 
Paradis, E. pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).CAS 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 

Google Scholar 
Darriba, D. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol. Biol. Evol. 37, 291–294 (2020).CAS 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RaxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 

Google Scholar 
Ronquist, F. R. & Huelsenbeck, J. P. MRBAYES 3: Bayesian inference of phylogeny. Bioinformatics 19, 1572–1574 (2003).CAS 

Google Scholar 
Puillandre, N., Brouillet, S. & Achaz, G. ASAP: assemble species by automatic partitioning. Mol. Ecol. Resour. 21, 609–620 (2021).
Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, http://journal.embnet.org/index.php/embnetjournal/article/view/200/479 (2011).Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

Google Scholar 
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2013).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–6. https://CRAN.R-project.org/package=vegan (2019).Dana, J. D. Synopsis of the genera of Gammaracea. Am. J. Sci. Arts 8, 135–140 (1849).
Google Scholar 
Hansen, H. J. Malacostraca marina Groenlandiæ occidentalis Oversigt over det vestlige Grønlands Fauna af malakostrake Havkrebsdyr. Vidensk. Meddel. Natuirist. Foren Kjobenhavn, Aaret 9, 5–226 (1888).
Google Scholar 
Van Dover, C. L. The ecology of deep-sea hydrothermal vents (Princeton University Press, 2000).
Google Scholar 
Tunnicliffe, V. The biology of hydrothermal vents: ecology and evolution. Oceanogr. Mar. Biol. Annu. Rev. 29, 319–407 (1991).
Google Scholar 
Priede, I. G., Bagley, P. M., Smith, A., Creasey, S. & Merrett, N. R. Scavenging deep demersal fishes of the Porcupine Seabight, north-east Atlantic: observations by baited camera, trap and trawl. J. Mar. Biol. Assoc. U. K. 74, 481–498 (1994).
Google Scholar 
Causse, R., Biscoito, M. & Briand, P. First record of the deep-sea eel Ilyophis saldanhai (Synaphobranchidae, Anguilliformes) from the Pacific Ocean. Cybium 29, 413–416 (2005).
Google Scholar 
King, N. J., Bagley, P. M. & Priede, I. G. Depth zonation and latitudinal distribution of deep-sea scavenging demersal fishes of the Mid-Atlantic Ridge, 42 to 53°N. Mar. Ecol. Prog. Ser. 319, 263–274 (2006).ADS 

Google Scholar 
Leitner, A. B., Durden, J. M., Smith, C. R., Klingberg, E. D. & Drazen, J. C. Synaphobranchid eel swarms on abyssal seamounts: largest aggregation of fishes ever observed at abyssal depths. Deep Sea Res. Oceanogr. Res. Part I Pap. 167, 103423. https://doi.org/10.1016/j.dsr.2020.103423 (2021).Article 

Google Scholar 
Fishelson, L. Comparative internal morphology of deep-sea eels, with particular emphasis on gonads and gut structure. J. Fish. Biol. 44, 75–101 (1994).
Google Scholar 
Bailey, D. M. et al. High swimming and metabolic activity in the deep-sea eel Synaphobranchus kaupii revealed by integrated in situ and in vitro measurements. Physiol. Biochem. Zool. 78, 335–346 (2005).
Google Scholar 
Trenkel, V. M. & Lorance, P. Estimating Synaphobranchus kaupii densities: contribution of fish behaviour to differences between bait experiments and visual strip transects. Deep Sea Res. Oceanogr. Res. Part I Pap. 58, 63–71 (2011).ADS 

Google Scholar 
Raupach, M. J. et al. Genetic homogeneity and circum-Antarctic distribution of two benthic shrimp species of the Southern Ocean, Chorismus antarcticus and Nematocarcinus lanceopes. Mar. Biol. 157, 1783–1797 (2010).CAS 

Google Scholar 
Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344 (2016).ADS 
CAS 

Google Scholar 
Dambach, J., Raupach, M. J., Mayer, C., Schwarzer, J. & Leese, F. Isolation and characterization of nine polymorphic microsatellite markers for the deep-sea shrimp Nematocarcinus lanceopes (Crustacea: Decapoda: Caridea). BMC Res. Notes 6, 75. https://doi.org/10.1186/1756-0500-6-75 (2013).Article 

Google Scholar 
Ritchie, H., Jamieson, A. J. & Piertney, S. B. Phylogenetic relationships among hadal amphipods of the Superfamily Lysianassoidea: Implications for taxonomy and biogeography. Deep Sea Res. Part I 105, 119–131 (2015).CAS 

Google Scholar 
Bowen, B. W. et al. Phylogeography unplugged: comparative surveys in the genomic era. Bull. Mar. Sci. 90, 13–46 (2014).
Google Scholar 
Ritchie, H., Jamieson, A. J. & Piertney, S. B. Population genetic structure of two congeneric deep-sea amphipod species from geographically isolated hadal trenches in the Pacific Ocean. Deep Sea Res. Part I. 119, 50–57 (2017).
Google Scholar 
Iguchi, A. et al. Deep-sea amphipods around cobalt-rich ferromanganese crusts: taxonomic diversity and selection of candidate species for connectivity analysis. PLoS ONE 15, e0228483. https://doi.org/10.1371/journal.pone.0228483 (2020).Article 
CAS 

Google Scholar 
Baco, A. R. et al. A synthesis of genetic connectivity in deep-sea fauna and implications for marine reserve design. Mol. Ecol. 25, 3276–3298 (2016).
Google Scholar 
Taylor, M. L. & Roterman, C. N. Invertebrate population genetics across Earth’s largest habitat: the deep-sea floor. Mol. Ecol. 26, 4872–4896 (2017).CAS 

Google Scholar  More

WHO. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021; https://apps.who.int/iris/bitstream/handle/10665/341666/9789240027336-eng.pdfVan Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, et al. Reducing antimicrobial use in food animals. Science. 2017;357:1350–2. https://doi.org/10.1126/science.aao1495Article 
CAS 

Google Scholar 
ONeill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. Rev Antimicrob Resistance; 2015:1–28.Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, et al. Global trends in antimicrobial use in food animals. Proc Natl Acad Sci USA. 2015;112:5649–54. https://doi.org/10.1073/pnas.1503141112Article 
CAS 

Google Scholar 
Managaki S, Murata A, Takada H, Tuyen BC, Chiem NH. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ Sci Technol. 2007;41:8004–10. https://doi.org/10.1021/es0709021Article 
CAS 

Google Scholar 
Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083 https://doi.org/10.1098/rstb.2014.0083Article 
CAS 

Google Scholar 
Noyes NR, Yang X, Linke LM, Magnuson RJ, Cook SR, Zaheer R, et al. Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci Rep. 2016;6:24645. https://doi.org/10.1038/srep24645Article 
CAS 

Google Scholar 
Agga GE, Cook KL, Netthisinghe AMP, Gilfillen RA, Woosley PB, Sistani KR. Persistence of antibiotic resistance genes in beef cattle backgrounding environment over two years after cessation of operation. PLoS One. 2019;14:e0212510. https://doi.org/10.1371/journal.pone.0212510Article 
CAS 

Google Scholar 
Hudson JA, Frewer LJ, Jones G, Brereton PA, Whittingham MJ, Stewart G. The agri-food chain and antimicrobial resistance: a review. Trends Food Sci Technol. 2017;69:131–47. https://doi.org/10.1016/j.tifs.2017.09.007Article 
CAS 

Google Scholar 
Gillings MR. Lateral gene transfer, bacterial genome evolution, and the Anthropocene. Ann NY Acad Sci. 2017;1389:20–36. https://doi.org/10.1111/nyas.13213Article 

Google Scholar 
Rodríguez-Beltrán J, DelaFuente J, León-Sampedro R, MacLean RC, San Millán Á. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat Rev Microbiol. 2021;6:347–59. https://doi.org/10.1038/s41579-020-00497-1Article 
CAS 

Google Scholar 
Zhang T, Zhang XX, Ye L. Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One. 2011;6:e26041. https://doi.org/10.1371/journal.pone.0026041Article 
CAS 

Google Scholar 
Li AD, Li LG, Zhang T. Exploring antibiotic resistance genes and metal resistance genes in plasmid metagenomes from wastewater treatment plants. Front Microbiol. 2015;6:1025. https://doi.org/10.3389/fmicb.2015.01025Article 

Google Scholar 
Bukowski M, Piwowarczyk R, Madry A, Zagorski-Przybylo R, Hydzik M, Wladyka B. Prevalence of antibiotic and heavy metal resistance determinants and virulence-related genetic elements in plasmids of Staphylococcus aureus. Front Microbiol. 2019;10:805. https://doi.org/10.3389/fmicb.2019.00805Article 

Google Scholar 
Ramírez-Díaz MI, Díaz-Magaña A, Meza-Carmen V, Johnstone L, Cervantes C, Rensing C. Nucleotide sequence of Pseudomonas aeruginosa conjugative plasmid pUM505 containing virulence and heavy-metal resistance genes. Plasmid. 2011;66:7–18. https://doi.org/10.1016/j.plasmid.2011.03.002Article 
CAS 

Google Scholar 
Haenni M, Poirel L, Kieffer N, Châtre P, Saras E, Métayer V, et al. Co-occurrence of extended spectrum β lactamase and MCR-1 encoding genes on plasmids. Lancet Infect Dis. 2016;16:281–2. https://doi.org/10.1016/S1473-3099(16)00007-4Article 
CAS 

Google Scholar 
Peter S, Bosio M, Gross C, Bezdan D, Gutierrez J, Oberhettinger P, et al. Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by nanopore sequencing. mSphere. 2020;5. https://doi.org/10.1128/mSphere.00525-20Halary S, Leigh JW, Cheaib B, Lopez P, Bapteste E. Network analyses structure genetic diversity in independent genetic worlds. Proc Natl Acad Sci USA. 2010;107:127–32. https://doi.org/10.1073/pnas.0908978107Article 

Google Scholar 
Bosi E, Fani R, Fondi M. The mosaicism of plasmids revealed by atypical genes detection and analysis. BMC Genom. 2011;12:403. https://doi.org/10.1186/1471-2164-12-403Article 
CAS 

Google Scholar 
Pesesky MW, Tilley R, Beck DAC. Mosaic plasmids are abundant and unevenly distributed across prokaryotic taxa. Plasmid. 2019;102:10–18. https://doi.org/10.1016/j.plasmid.2019.02.003Article 
CAS 

Google Scholar 
Casjens SR, Gilcrease EB, Vujadinovic M, Mongodin EF, Luft BJ, Schutzer SE, et al. Plasmid diversity and phylogenetic consistency in the Lyme disease agent Borrelia burgdorferi. BMC Genom. 2017;18:165. https://doi.org/10.1186/s12864-017-3553-5Article 
CAS 

Google Scholar 
Madec JY, Haenni M. Antimicrobial resistance plasmid reservoir in food and food-producing animals. Plasmid. 2018;99:72–81. https://doi.org/10.1016/j.plasmid.2018.09.001Article 
CAS 

Google Scholar 
Ceccarelli D, Kant A, van Essen-Zandbergen A, Dierikx C, Hordijk J, Wit B, et al. Diversity of plasmids and genes encoding resistance to extended spectrum cephalosporins in commensal escherichia coli from dutch livestock in 2007–2017. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00076Auffret MD, Dewhurst RJ, Duthie CA, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome. 2017;5:159. https://doi.org/10.1186/s40168-017-0378-zArticle 

Google Scholar 
Sabino YNV, Santana MF, Oyama LB, Santos FG, Moreira AJS, Huws SA, et al. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nat Commun. 2019;10:5252. https://doi.org/10.1038/s41467-019-13118-0Article 
CAS 

Google Scholar 
Brown Kav A, Benhar I, Mizrahi I. Rumen plasmids. In: Gophna U, editor. Lateral gene transfer in evolution. New York, NY: Springer New York; 2013. p. 105–20.Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19:553–66. https://doi.org/10.1038/s41579-021-00543-6Article 
CAS 

Google Scholar 
Dionisio F, Zilhão R, Gama JA. Interactions between plasmids and other mobile genetic elements affect their transmission and persistence. Plasmid. 2019;102:29–36. https://doi.org/10.1016/j.plasmid.2019.01.003Article 
CAS 

Google Scholar 
Brown Kav A, Sasson G, Jami E, Doron-Faigenboim A, Benhar I, Mizrahi I. Insights into the bovine rumen plasmidome. Proc Natl Acad Sci USA. 2012;109:5452–7. https://doi.org/10.1073/pnas.1116410109Article 

Google Scholar 
Kav AB, Rozov R, Bogumil D, Sørensen SJ, Hansen LH, Benhar I, et al. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ Microbiol. 2020;22:32–44. https://doi.org/10.1111/1462-2920.14813Article 

Google Scholar 
Jørgensen TS, Xu Z, Hansen MA, Sørensen SJ, Hansen LH. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS One. 2014;9:e87924. https://doi.org/10.1371/journal.pone.0087924Article 
CAS 

Google Scholar 
He Q, Pilosof S, Tiedje KE, Ruybal-Pesántez S, Artzy-Randrup Y, Baskerville EB, et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun. 2018;9:1817. https://doi.org/10.1038/s41467-018-04219-3Article 
CAS 

Google Scholar 
Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun. 2020;11:2452. https://doi.org/10.1038/s41467-020-16282-wArticle 
CAS 

Google Scholar 
Redondo-Salvo S, Fernández-López R, Ruiz R, Vielva L, de Toro M, Rocha EPC, et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun. 2020;11:3602. https://doi.org/10.1038/s41467-020-17278-2Article 
CAS 

Google Scholar 
Savary P, Foltête JC, Moal H, Vuidel G, Garnier S. Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Mol Ecol Resour. 2021;21:1167–85. https://doi.org/10.1111/1755-0998.13333Article 

Google Scholar 
Pilosof S, He Q, Tiedje KE, Ruybal-Pesántez S, Day KP, Pascual M. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum. PLoS Biol. 2019;17:e3000336. https://doi.org/10.1371/journal.pbio.3000336Article 
CAS 

Google Scholar 
Brilli M, Mengoni A, Fondi M, Bazzicalupo M, Liò P, Fani R. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinform. 2008;9:551. https://doi.org/10.1186/1471-2105-9-551Article 
CAS 

Google Scholar 
Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, et al. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol. 2016;8:1388–1400. https://doi.org/10.1093/gbe/evw077Article 

Google Scholar 
Tamminen M, Virta M, Fani R, Fondi M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol Biol Evol. 2012;29:1225–40. https://doi.org/10.1093/molbev/msr292Article 
CAS 

Google Scholar 
Yamashita A, Sekizuka T, Kuroda M. Characterization of antimicrobial resistance dissemination across plasmid communities classified by network analysis. Pathogens. 2014;3:356–76. https://doi.org/10.3390/pathogens3020356Article 
CAS 

Google Scholar 
Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Rev Mod Phys. 2015;87:925–79. https://doi.org/10.1103/RevModPhys.87.925Article 

Google Scholar 
Pilosof S, Morand S, Krasnov BR, Nunn CL. Potential parasite transmission in multi-host networks based on parasite sharing. PLoS One. 2015;10:e0117909 https://doi.org/10.1371/journal.pone.0117909Article 
CAS 

Google Scholar 
VanderWaal KL, Atwill ER, Isbell LA, McCowan B.Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).J Anim Ecol.2014;83:406–14. https://doi.org/10.1111/1365-2656.12137Article 

Google Scholar 
Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, et al. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface. 2022;19:20210690. https://doi.org/10.1098/rsif.2021.0690Article 

Google Scholar 
Dallas TA, Han BA, Nunn CL, Park AW, Stephens PR, Drake JM. Host traits associated with species roles in parasite sharing networks. Oikos. 2019;128:23–32. https://doi.org/10.1111/oik.05602Article 

Google Scholar 
Matlock W, Chau KK, AbuOun M, Stubberfield E, Barker L, Kavanagh J, et al. Genomic network analysis of environmental and livestock F-type plasmid populations. ISME J. 2021;15:2322–35. https://doi.org/10.1038/s41396-021-00926-wArticle 
CAS 

Google Scholar 
Pilosof S, Porter MA, Pascual M, Kéfi S. The multilayer nature of ecological networks. Nat Ecol Evol. 2017;1:0101. https://doi.org/10.1038/s41559-017-0101Article 

Google Scholar 
Paull SH, Song S, McClure KM, Sackett LC, Kilpatrick AM, Johnson PTJ. From superspreaders to disease hotspots: linking transmission across hosts and space. Front Ecol Environ. 2012;10:75–82. https://doi.org/10.1890/110111Article 

Google Scholar 
Hutchinson MC, Bramon Mora B, Pilosof S, Barner AK, Kéfi S, Thébault E, et al. Seeing the forest for the trees: putting multilayer networks to work for community ecology. Funct Ecol. 2019;33:206–17. https://doi.org/10.1111/1365-2435.13237Article 

Google Scholar 
Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2:203–71. https://doi.org/10.1093/comnet/cnu016Article 

Google Scholar 
Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438:355–9. https://doi.org/10.1038/nature04153Article 
CAS 

Google Scholar 
Fortuna MA, Popa-Lisseanu AG, Ibáñez C, Bascompte J. The roosting spatial network of a bird-predator bat. Ecology. 2009;90:934–44. https://doi.org/10.1890/08-0174.1Article 

Google Scholar 
Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;69:026113. https://doi.org/10.1103/PhysRevE.69.026113Article 
CAS 

Google Scholar 
Rosvall M, Bergstrom CT. Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA. 2008;105:1118–23. https://doi.org/10.1073/pnas.0706851105Article 

Google Scholar 
De Domenico M, Lancichinetti A, Arenas A, Rosvall M. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev X. 2015;5:011027. https://doi.org/10.1103/PhysRevX.5.011027Article 
CAS 

Google Scholar 
Farage C, Edler D, Eklöf A, Rosvall M, Pilosof S. Identifying flow modules in ecological networks using Infomap. Methods Ecol Evol. 2021;12:778–86. https://doi.org/10.1111/2041-210x.13569Article 

Google Scholar 
Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome Res. 2011;21:599–609. https://doi.org/10.1101/gr.115592.110Article 
CAS 

Google Scholar 
Smillie C, Garcillán-Barcia MP, Francia MV, Rocha EPC, de la Cruz F. Mobility of plasmids. Microbiol Mol Biol Rev. 2010;74:434–52. https://doi.org/10.1128/MMBR.00020-10Article 
CAS 

Google Scholar 
Garcillán-Barcia MP, Francia MV, de la Cruz F. The diversity of conjugative relaxases and its application in plasmid classification. FEMS Microbiol Rev. 2009;33:657–87. https://doi.org/10.1111/j.1574-6976.2009.00168.xArticle 
CAS 

Google Scholar 
Coluzzi C, Guédon G, Devignes MD, Ambroset C, Loux V, Lacroix T, et al. A glimpse into the world of integrative and mobilizable elements in streptococci reveals an unexpected diversity and novel families of mobilization proteins. Front Microbiol. 2017;8:443. https://doi.org/10.3389/fmicb.2017.00443Article 

Google Scholar 
Moraïs S, Mizrahi I. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. FEMS Microbiol Rev. 2019;43:362–79. https://doi.org/10.1093/femsre/fuz007Article 
CAS 

Google Scholar 
León-Sampedro R, DelaFuente J, Díaz-Agero C, Crellen T, Musicha P, Rodríguez-Beltrán J, et al. Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients. Nat Microbiol. 2021;6:606–16. https://doi.org/10.1038/s41564-021-00879-yArticle 
CAS 

Google Scholar 
Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep. 2020;10:9336. https://doi.org/10.1038/s41598-020-66270-9Article 
CAS 

Google Scholar 
Lerner A, Adler A, Abu-Hanna J, Cohen Percia S, Kazma Matalon M, Carmeli Y. Spread of KPC-producing carbapenem-resistant Enterobacteriaceae: the importance of super-spreaders and rectal KPC concentration. Clin Microbiol Infect. 2015;21:470.e1–7. https://doi.org/10.1016/j.cmi.2014.12.015Article 
CAS 

Google Scholar 
Stein RA, Katz DE. Escherichia coli, cattle and the propagation of disease. FEMS Microbiol Lett. 2017;364. https://doi.org/10.1093/femsle/fnx050.de Freslon I, Martínez-López B, Belkhiria J, Strappini A, Monti G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl Anim Behav Sci. 2019;213:47–54. https://doi.org/10.1016/j.applanim.2019.01.006Article 

Google Scholar 
Rushmore J, Caillaud D, Hall RJ, Stumpf RM, Meyers LA, Altizer S. Network-based vaccination improves prospects for disease control in wild chimpanzees. J R Soc Interface. 2014;11:20140349. https://doi.org/10.1098/rsif.2014.0349Article 

Google Scholar 
Xue H, Cordero OX, Camas FM, Trimble W, Meyer F, Guglielmini J, et al. Eco-evolutionary dynamics of episomes among ecologically cohesive bacterial populations. MBio. 2015;6:e00552–15. https://doi.org/10.1128/mBio.00552-15Article 
CAS 

Google Scholar 
Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. Elife. 2020;9. https://doi.org/10.7554/eLife.53886Abe R, Oyama F, Akeda Y, Nozaki M, Hatachi T, Okamoto Y, et al. Hospital-wide outbreaks of carbapenem-resistant Enterobacteriaceae horizontally spread through a clonal plasmid harbouring bla IMP-1 in children’s hospitals in Japan. J Antimicrob Chemother. 2021;76:3314–7.Article 
CAS 

Google Scholar 
Bingen EH, Desjardins P, Arlet G, Bourgeois F, Mariani-Kurkdjian P, Lambert-Zechovsky NY, et al. Molecular epidemiology of plasmid spread among extended broad-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates in a pediatric hospital. J Clin Microbiol. 1993;31:179–84. https://doi.org/10.1128/jcm.31.2.179-184.1993.Bai H, He LY, Wu DL, Gao FZ, Zhang M, Zou HY, et al. Spread of airborne antibiotic resistance from animal farms to the environment: dispersal pattern and exposure risk. Environ Int. 2022;158:106927 https://doi.org/10.1016/j.envint.2021.106927Article 
CAS 

Google Scholar 
Boyland NK, Mlynski DT, James R, Brent LJN, Croft DP. The social network structure of a dynamic group of dairy cows: from individual to group level patterns. Appl Anim Behav Sci. 2016;174:1–10. https://doi.org/10.1016/j.applanim.2015.11.016Article 

Google Scholar 
Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:e22970. https://doi.org/10.1002/ajp.22970Article 

Google Scholar 
Dib JR, Wagenknecht M, Farías ME, Meinhardt F. Strategies and approaches in plasmidome studies—uncovering plasmid diversity disregarding of linear elements? Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00463Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. https://doi.org/10.1089/cmb.2012.0021Article 
CAS 

Google Scholar 
Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E, Mizrahi I, et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics. 2017;33:475–82. https://doi.org/10.1093/bioinformatics/btw651Article 
CAS 

Google Scholar 
Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ, Peto T, et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol. 2017;8:182. https://doi.org/10.3389/fmicb.2017.00182Article 

Google Scholar 
Komsta L, Novomestky F. Moments, cumulants, skewness, kurtosis and related tests. R package version. 2015;14.Rosvall M, Axelsson D, Bergstrom CT. The map equation. Eur Phys J Spec Top. 2010;178:13–23. https://doi.org/10.1140/epjst/e2010-01179-1Article 

Google Scholar 
Bascompte J, Jordano P, Melián CJ, Olesen JM. The nested assembly of plant–animal mutualistic networks. Proc Natl Acad Sci USA. 2003;100:9383–7. https://doi.org/10.1073/pnas.1633576100Article 
CAS 

Google Scholar 
Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI. Species abundance and the distribution of specialization in host–parasite interaction networks. J Anim Ecol. 2005;74:946–55.Article 

Google Scholar 
Fortuna MA, Stouffer DB, Olesen JM, Jordano P, Mouillot D, Krasnov BR, et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J Anim Ecol. 2010;79:811–7. https://doi.org/10.1111/j.1365-2656.2010.01688.xArticle 

Google Scholar 
Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81:2340–61. https://doi.org/10.1021/j100540a008Article 
CAS 

Google Scholar 
R Core Team. R: a language and environment for statistical computing; 2021. More

Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annual Review of Microbiology 71, 711–730 (2017).CAS 

Google Scholar 
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Research 44, D733 (2016).
Google Scholar 
Bobay, L. M. & Ochman, H. Biological species are universal across life’s domains. Genome Biology and Evolution 9, 491–501 (2017).
Google Scholar 
Magnabosco, C., Moore, K., Wolfe, J. & Fournier, G. Dating phototrophic microbial lineages with reticulate gene histories. Geobiology 16, 179–189 (2018).CAS 

Google Scholar 
Louca, S. et al. Function and functional redundancy in microbial systems. Nature Ecology & Evolution 2, 936–943 (2018).ADS 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nature Communications 9, 5114 (2018).ADS 

Google Scholar 
Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains bacteria and archaea. Nature Communications 10, 5477 (2019).ADS 
CAS 

Google Scholar 
Royalty, T.M. & Steen, A.D. Quantitatively partitioning microbial genomic traits among taxonomic ranks across the microbial tree of life. mSphere 4 (2019).Murray, C. S., Gao, Y. & Wu, M. Re-evaluating the evidence for a universal genetic boundary among microbial species. Nature Communications 12, 4059 (2021).ADS 
CAS 

Google Scholar 
Powell, S. et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Research 42, D231–D239 (2014).CAS 

Google Scholar 
Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Research 43, D593–D598 (2014).
Google Scholar 
Douglas, G. M. et al. Picrust2 for prediction of metagenome functions. Nature Biotechnology 38, 685–688 (2020).CAS 

Google Scholar 
Wemheuer, F. et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome 15, 1–12 (2020).
Google Scholar 
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).ADS 
CAS 

Google Scholar 
Wu, D. et al. A phylogeny-driven genomic encyclopaedia of bacteria and archaea. Nature 462, 1056–1060 (2009).ADS 
CAS 

Google Scholar 
Louca, S. & Pennell, M. W. A general and efficient algorithm for the likelihood of diversification and discrete-trait evolutionary models. Systematic Biology 69, 545–556 (2020).
Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).ADS 
CAS 

Google Scholar 
Sharon, I. & Banfield, J. F. Genomes from metagenomics. Science 342, 1057–1058 (2013).ADS 
CAS 

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

Google Scholar 
Chen, L. X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Research 30, 315–333 (2020).CAS 

Google Scholar 
Konstantinidis, K. T. & Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes. Proceedings of the National Academy of Sciences 102, 2567–2572 (2005).ADS 
CAS 

Google Scholar 
Kim, M., Oh, H. S., Park, S. C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Journal of Systematic and Evolutionary Microbiology 64, 346–351 (2014).CAS 

Google Scholar 
Shapiro, B.J. What microbial population genomics has taught us about speciation. In Polz, M.F. & Rajora, O.P. (eds.) Population Genomics: Microorganisms, 31–47 (Springer International Publishing, Cham, Switzerland, 2019).Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 5, e00731–19 (2020).CAS 

Google Scholar 
Lagkouvardos, I., Overmann, J. & Clavel, T. Cultured microbes represent a substantial fraction of the human and mouse gut microbiota. Gut Microbes 8, 493–503 (2017).
Google Scholar 
Zhang, Z., Wang, J., Wang, J., Wang, J. & Li, Y. Estimate of the sequenced proportion of the global prokaryotic genome. Microbiome 8, 1–9 (2020).
Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).
Google Scholar 
Mira, A., Ochman, H. & Moran, N. A. Deletional bias and the evolution of bacterial genomes. Trends in Genetics 17, 589–596 (2001).CAS 

Google Scholar 
Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. MBio 3, e00036–12 (2012).
Google Scholar 
Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME Journal 8, 1553–1565 (2014).
Google Scholar 
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).ADS 
CAS 

Google Scholar 
Gary, P.R. Adjusting for nonresponse in surveys. In Smart, J.C. (ed.) Higher Education: Handbook of Theory and Research, chap. 8, 411–449 (Springer, Dordrecht, Netherlands, 2007).Maguire, F. et al. Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands. Microbial Genomics 6, mgen000436 (2020).
Google Scholar 
Huerta-Cepas, J. et al. eggnog 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research 47, D309–D314 (2019).CAS 

Google Scholar 
Abdel-Hamid, A.M., Solbiati, J.O., Cann, I.K.O., Sariaslani, S. & Gadd, G.M. Insights into lignin degradation and its potential industrial applications, vol. 82, chap. 1, 1–28 (Academic Press, 2013).El-Bondkly, A.M. Sequence analysis of industrially important genes from trichoderma. In Biotechnology and biology of Trichoderma, chap. 28, 377–392 (Elsevier, 2014).Dawood, A. & Ma, K. Applications of microbial β-mannanases. Frontiers in Bioengineering and Biotechnology 8 (2020).Khelaifia, S., Raoult, D. & Drancourt, M. A versatile medium for cultivating methanogenic archaea. PLOS ONE 8, e61563 (2013).ADS 
CAS 

Google Scholar 
Khelaifia, S. et al. Aerobic culture of methanogenic archaea without an external source of hydrogen. European Journal of Clinical Microbiology & Infectious Diseases 35, 985–991 (2016).CAS 

Google Scholar 
Michał, B. et al. Phymet2: a database and toolkit for phylogenetic and metabolic analyses of methanogens. Environmental Microbiology Reports 10, 378–382 (2018).
Google Scholar 
Albright, S. & Louca, S. Trait biases in microbial reference genomes, figshare., https://doi.org/10.6084/m9.figshare.c.6055004.v1 (2022).Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 

Google Scholar 
Murray, A. E. et al. Roadmap for naming uncultivated archaea and bacteria. Nature Microbiology 5, 987–994 (2020).CAS 

Google Scholar 
Palleroni, N. J. Prokaryotic diversity and the importance of culturing. Antonie van Leeuwenhoek 72, 3–19 (1997).CAS 

Google Scholar 
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology 31, 814–821 (2013).CAS 

Google Scholar 
Tran, P. Q. et al. Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika. The ISME Journal 15, 1971–1986 (2021).CAS 

Google Scholar 
Kroeger, M. E. et al. New biological insights into how deforestation in amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Frontiers in Microbiology 9, 1635 (2018).
Google Scholar 
Nathani, N. M. et al. 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Scientific Data 8, 194 (2021).
Google Scholar 
Irazoqui, J. M., Eberhardt, M. F., Adjad, M. M., Amadio, A. F. & Collado, M. C. Identification of key microorganisms in facultative stabilization ponds from dairy industries, using metagenomics. PeerJ 10, e12772 (2022).
Google Scholar 
Hwang, Y. et al. Leave no stone unturned: individually adapted xerotolerant Thaumarchaeota sheltered below the boulders of the Atacama Desert hyperarid core. Microbiome 9, 234 (2021).CAS 

Google Scholar 
Tully, B., Wheat, C. G., Glazer, B. T. & Huber, J. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME Journal 12, 1–16 (2018).CAS 

Google Scholar 
Vanwonterghem, I., Jensen, P. D., Rabaey, K. & Tyson, G. W. Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion. Environmental Microbiology 18, 3144–3158 (2016).CAS 

Google Scholar 
Glasl, B. et al. Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes. The ISME Journal 14, 1435–1450 (2020).
Google Scholar 
Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nature Microbiology 4, 2090–2100 (2019).
Google Scholar 
Engelberts, J. P. et al. Characterization of a sponge microbiome using an integrative genome-centric approach. The ISME Journal 14, 1100–1110 (2020).CAS 

Google Scholar 
Bowerman, K. L. et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease. Nature Communications 11, 5886 (2020).ADS 
CAS 

Google Scholar 
Chen, Y. J. et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. The ISME Journal 16, 750–763 (2022).CAS 

Google Scholar 
Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biology 16, 279 (2015).
Google Scholar 
Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Communications Biology 3, 119 (2020).
Google Scholar 
Di Cesare, A. et al. Genomic comparison and spatial distribution of different Synechococcus phylotypes in the Black Sea. Frontiers in Microbiology 11, 1979 (2020).
Google Scholar 
van Vliet, D. M. et al. The bacterial sulfur cycle in expanding dysoxic and euxinic marine waters. Environmental Microbiology 23, 2834–2857 (2021).
Google Scholar 
Dalcin Martins, P. et al. Enrichment of novel Verrucomicrobia, Bacteroidetes, and Krumholzibacteria in an oxygen-limited methane- and iron-fed bioreactor inoculated with Bothnian Sea sediments. MicrobiologyOpen 10, e1175 (2021).CAS 

Google Scholar 
Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology 37, 953–961 (2019).CAS 

Google Scholar 
Segura-Wang, M., Grabner, N., Koestelbauer, A., Klose, V. & Ghanbari, M. Genome-resolved metagenomics of the chicken gut microbiome. Frontiers in Microbiology 12, 726923 (2021).
Google Scholar 
Ruuskanen, M. O. et al. Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnology and Oceanography 65, S233–S247 (2020).CAS 

Google Scholar 
Haas, S., Desai, D. K., LaRoche, J., Pawlowicz, R. & Wallace, D. W. R. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environmental Microbiology 21, 3927–3952 (2019).CAS 

Google Scholar 
Spasov, E. et al. High functional diversity among Nitrospira populations that dominate rotating biological contactor microbial communities in a municipal wastewater treatment plant. The ISME Journal 14, 1857–1872 (2020).CAS 

Google Scholar 
Vigneron, A. et al. Genomic evidence for sulfur intermediates as new biogeochemical hubs in a model aquatic microbial ecosystem. Microbiome 9, 46 (2021).CAS 

Google Scholar 
Galambos, D., Anderson, R. E., Reveillaud, J. & Huber, J. A. Genome-resolved metagenomics and metatranscriptomics reveal niche differentiation in functionally redundant microbial communities at deep-sea hydrothermal vents. Environmental Microbiology 21, 4395–4410 (2019).CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nature Communications 9, 870 (2018).ADS 

Google Scholar 
Xing, P. et al. Stratification of microbiomes during the holomictic period of Lake Fuxian, an alpine monomictic lake. Limnology and Oceanography 65, S134–S148 (2020).
Google Scholar 
Zhang, S., Hu, Z. & Wang, H. Metagenomic analysis exhibited the co-metabolism of polycyclic aromatic hydrocarbons by bacterial community from estuarine sediment. Environment International 129, 308–319 (2019).CAS 

Google Scholar 
Lin, Y., Wang, L., Xu, K., Li, K. & Ren, H. Revealing taxon-specific heavy metal-resistance mechanisms in denitrifying phosphorus removal sludge using genome-centric metaproteomics. Microbiome 9, 67 (2021).CAS 

Google Scholar 
Liu, L. et al. High-quality bacterial genomes of a partial-nitritation/anammox system by an iterative hybrid assembly method. Microbiome 8, 155 (2020).CAS 

Google Scholar 
Kantor, R. S. et al. Bioreactor microbial ecosystems for thiocyanate and cyanide degradation unravelled with genome-resolved metagenomics. Environmental Microbiology 17, 4929–4941 (2015).CAS 

Google Scholar 
Zhou, Z. et al. Gammaproteobacteria mediating utilization of methyl-, sulfur- and petroleum organic compounds in deep ocean hydrothermal plumes. The ISME Journal 14, 3136–3148 (2020).CAS 

Google Scholar 
Reysenbach, A. L. et al. Complex subsurface hydrothermal fluid mixing at a submarine arc volcano supports distinct and highly diverse microbial communities. Proceedings of the National Academy of Sciences 117, 32627–32638 (2020).ADS 
CAS 

Google Scholar 
Hou, J. et al. Microbial succession during the transition from active to inactive stages of deep-sea hydrothermal vent sulfide chimneys. Microbiome 8, 102 (2020).CAS 

Google Scholar 
Campanaro, S. et al. Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy. Biotechnology for Biofuels 9, 26 (2016).
Google Scholar 
Singleton, C. M. et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nature Communications 12, 2009 (2021).CAS 

Google Scholar 
Diamond, S. et al. Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nature Microbiology 4, 1356–1367 (2019).CAS 

Google Scholar 
Rasigraf, O. et al. Microbial community composition and functional potential in Bothnian Sea sediments is linked to Fe and S dynamics and the quality of organic matter. Limnology and Oceanography 65, S113–S133 (2020).CAS 

Google Scholar 
Rissanen, A. J. et al. Vertical stratification patterns of methanotrophs and their genetic controllers in water columns of oxygen-stratified boreal lakes. FEMS Microbiology Ecology 97, fiaa252 (2021).CAS 

Google Scholar 
Campanaro, S. et al. New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters. Biotechnology for Biofuels 13, 25 (2020).CAS 

Google Scholar 
Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature Biotechnology 39, 105–114 (2021).CAS 

Google Scholar 
Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of hydrothermarchaeota in hydrothermal sediment. mSystems 5 (2020).Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).CAS 

Google Scholar 
Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).ADS 
CAS 

Google Scholar 
Greenlon, A. et al. Global-level population genomics reveals differential effects of geography and phylogeny on horizontal gene transfer in soil bacteria. Proceedings of the National Academy of Sciences 116, 15200–15209 (2019).ADS 
CAS 

Google Scholar 
Hervé, V. et al. Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites. PeerJ 8, e8614 (2020).
Google Scholar 
von Appen, W.J. The expedition PS114 of the research vessel POLARSTERN to the Fram Strait in 2018. Tech. Rep., Alfred Wegener Institute for Polar and Marine Research (2018).Dombrowski, N., Seitz, K. W., Teske, A. P. & Baker, B. J. Genomic insights into potential interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal sediments. Microbiome 5, 106 (2017).
Google Scholar 
Yu, J. et al. Dna-stable isotope probing shotgun metagenomics reveals the resilience of active microbial communities to biochar amendment in oxisol soil. Frontiers in Microbiology 11, 587972 (2020).
Google Scholar 
Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nature Biotechnology 37, 186–192 (2019).CAS 

Google Scholar 
Gharechahi, J. et al. Metagenomic analysis reveals a dynamic microbiome with diversified adaptive functions to utilize high lignocellulosic forages in the cattle rumen. The ISME Journal 15, 1108–1120 (2021).CAS 

Google Scholar 
Meier, D. V., Imminger, S., Gillor, O. & Woebken, D. Distribution of mixotrophy and desiccation survival mechanisms across microbial genomes in an arid biological soil crust community. mSystems 6, e00786–20 (2021).CAS 

Google Scholar 
Haro-Moreno, J. M. et al. Dysbiosis in marine aquaculture revealed through microbiome analysis: reverse ecology for environmental sustainability. FEMS Microbiology Ecology 96, fiaa218 (2020).CAS 

Google Scholar 
Haro-Moreno, J. M. et al. Fine metagenomic profile of the Mediterranean stratified and mixed water columns revealed by assembly and recruitment. Microbiome 6, 128 (2018).
Google Scholar 
Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nature Communications 10, 1816 (2019).ADS 

Google Scholar 
Poghosyan, L. et al. Metagenomic profiling of ammonia- and methane-oxidizing microorganisms in two sequential rapid sand filters. Water Research 185, 116288 (2020).CAS 

Google Scholar 
Paula, D. M., Jeroen, F., Hugh, M. & Meng, M. L. & J., W.M. Wetland sediments host diverse microbial taxa capable of cycling alcohols. Applied and Environmental Microbiology 85, 00189–19 (2019).
Google Scholar 
Aromokeye, D. A. et al. Crystalline iron oxides stimulate methanogenic benzoate degradation in marine sediment-derived enrichment cultures. The ISME Journal 15, 965–980 (2021).CAS 

Google Scholar 
Borchert, E. et al. Deciphering a marine bone-degrading microbiome reveals a complex community effort. mSystems 6, e01218–20 (2021).CAS 

Google Scholar 
Osvatic, J. T. et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proceedings of the National Academy of Sciences 118, e2104378118 (2021).CAS 

Google Scholar 
Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proceedings of the National Academy of Sciences 116, 11824–11832 (2019).ADS 
CAS 

Google Scholar 
Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).ADS 
CAS 

Google Scholar 
Alqahtani, M. F. et al. Enrichment of Marinobacter sp. and halophilic homoacetogens at the biocathode of microbial electrosynthesis system inoculated with Red Sea brine pool. Frontiers in Microbiology 10, 2563 (2019).
Google Scholar 
Haroon, M. F., Thompson, L. R., Parks, D. H., Hugenholtz, P. & Stingl, U. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Scientific Data 3, 160050 (2016).CAS 

Google Scholar 
Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6, 1–18 (2018).
Google Scholar 
Cabello-Yeves, P. J. et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnology and Oceanography 65, 1471–1488 (2020).ADS 
CAS 

Google Scholar 
Vavourakis, C. D. et al. Metagenomes and metatranscriptomes shed new light on the microbial-mediated sulfur cycle in a siberian soda lake. BMC Biology 17, 69 (2019).
Google Scholar 
Waterworth, S. C., Isemonger, E. W., Rees, E. R., Dorrington, R. A. & Kwan, J. C. Conserved bacterial genomes from two geographically isolated peritidal stromatolite formations shed light on potential functional guilds. Environmental Microbiology Reports 13, 126–137 (2021).CAS 

Google Scholar 
Huddy, R. J. et al. Thiocyanate and organic carbon inputs drive convergent selection for specific autotrophic Afipia and Thiobacillus strains within complex microbiomes. Frontiers in Microbiology 12, 643368 (2021).
Google Scholar 
Emerson, J. B. et al. Diverse sediment microbiota shape methane emission temperature sensitivity in Arctic lakes. Nature Communications 12, 5815 (2021).ADS 
CAS 

Google Scholar 
Chiri, E. et al. Termite gas emissions select for hydrogenotrophic microbial communities in termite mounds. Proceedings of the National Academy of Sciences 118, e2102625118 (2021).CAS 

Google Scholar 
Gong, G., Zhou, S., Luo, R., Gesang, Z. & Suolang, S. Metagenomic insights into the diversity of carbohydrate-degrading enzymes in the yak fecal microbial community. BMC Microbiology 20, 302 (2020).
Google Scholar 
Zhou, S. et al. Characterization of metagenome-assembled genomes and carbohydrate-degrading genes in the gut microbiota of Tibetan pig. Frontiers in Microbiology 11, 595066 (2020).
Google Scholar 
Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data 5, 170203 (2018).CAS 

Google Scholar 
Lavrinienko, A. et al. Two hundred and fifty-four metagenome-assembled bacterial genomes from the bank vole gut microbiota. Scientific Data 7, 312 (2020).CAS 

Google Scholar 
Peng, X. et al. Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes. Nature Microbiology 6, 499–511 (2021).CAS 

Google Scholar 
Dudek, N. K. et al. Novel microbial diversity and functional potential in the marine mammal oral microbiome. Current Biology 27, 3752–3762.e6 (2017).CAS 

Google Scholar 
Pinto, A. J. et al. Metagenomic evidence for the presence of comammox nitrospira-like bacteria in a drinking water system. mSphere 1, e00054–15 (2015).
Google Scholar 
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).ADS 
CAS 

Google Scholar 
Nobu, M. K. et al. Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses. Microbiome 8, 111 (2020).CAS 

Google Scholar 
Butterfield, C. N. et al. Proteogenomic analyses indicate bacterial methylotrophy and archaeal heterotrophy are prevalent below the grass root zone. PeerJ 4, e2687 (2016).
Google Scholar 
Castelle, C. J. et al. Protein family content uncovers lineage relationships and bacterial pathway maintenance mechanisms in DPANN Archaea. Frontiers in Microbiology 12, 660052 (2021).
Google Scholar 
Alteio, L. V. et al. Complementary metagenomic approaches improve reconstruction of microbial diversity in a forest soil. mSystems 5, e00768–19 (2020).
Google Scholar 
Shaiber, A. et al. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biology 21, 292 (2020).
Google Scholar 
Jungbluth, S. P., Amend, J. P. & Rappé, M. S. Metagenome sequencing and 98 microbial genomes from Juan de Fuca Ridge flank subsurface fluids. Scientific Data 4, 170037 (2017).CAS 

Google Scholar 
Sheik, C. S. et al. Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past, present and future of blooms. Journal of Great Lakes Research 48, 1191–1205 (2022).CAS 

Google Scholar 
Barnum, T. P. et al. Genome-resolved metagenomics identifies genetic mobility, metabolic interactions, and unexpected diversity in perchlorate-reducing communities. The ISME Journal 12, 1568–1581 (2018).CAS 

Google Scholar 
Julian, D. et al. Coastal ocean metagenomes and curated metagenome-assembled genomes from Marsh Landing, Sapelo Island (Georgia, USA). Microbiology Resource Announcements 8, e00934–19 (2019).
Google Scholar 
Breister, A. M. et al. Soil microbiomes mediate degradation of vinyl ester-based polymer composites. Communications Materials 1, 101 (2020).ADS 

Google Scholar 
Fu, H., Uchimiya, M., Gore, J. & Moran, M. A. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proceedings of the National Academy of Sciences 117, 3656–3662 (2020).ADS 
CAS 

Google Scholar 
Nobu, M. K. et al. Thermodynamically diverse syntrophic aromatic compound catabolism. Environmental Microbiology 19, 4576–4586 (2017).CAS 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nature Biotechnology 39, 499–509 (2021).CAS 

Google Scholar 
Li, Z. et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. The ISME Journal 15, 2366–2378 (2021).CAS 

Google Scholar 
Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nature Microbiology 6, 246–256 (2021).CAS 

Google Scholar 
Seyler, L. M., Trembath-Reichert, E., Tully, B. J. & Huber, J. A. Time-series transcriptomics from cold, oxic subseafloor crustal fluids reveals a motile, mixotrophic microbial community. The ISME Journal 15, 1192–1206 (2021).CAS 

Google Scholar 
Herold, M. et al. Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance. Nature Communications 11, 5281 (2020).ADS 
CAS 

Google Scholar 
Dong, X. et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nature Communications 11, 5825 (2020).ADS 
CAS 

Google Scholar 
Thompson, L. R. et al. Metagenomic covariation along densely sampled environmental gradients in the Red Sea. The ISME Journal 11, 138–151 (2017).CAS 

Google Scholar 
Dominik, S., Daniela, Z., Anja, P., Katharina, R. & Rolf, D. Metagenome-assembled genome sequences from different wastewater treatment stages in Germany. Microbiology Resource Announcements 10, e00504–21 (2021).
Google Scholar 
Langwig, M. V. et al. Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups. The ISME Journal 16, 307–320 (2022).CAS 

Google Scholar 
Rezaei Somee, M. et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Scientific Reports 11, 11316 (2021).ADS 
CAS 

Google Scholar 
Gilroy, R. et al. Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity. PeerJ 10, e13084 (2022).
Google Scholar 
Bhattarai, B., Bhattacharjee, A. S., Coutinho, F. H. & Goel, R. K. Viruses and their interactions with bacteria and archaea of hypersaline Great Salt Lake. Frontiers in Microbiology 12, 701414 (2021).
Google Scholar 
Liu, L. et al. Microbial diversity and adaptive strategies in the Mars-like Qaidam Basin, North Tibetan Plateau, China. Environmental Microbiology Reports (2022).Lin, H. et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. The ISME Journal 15, 1810–1825 (2021).CAS 

Google Scholar 
Martnez-Pérez, C. et al. Lifting the lid: nitrifying archaea sustain diverse microbial communities below the Ross Ice Shelf. SSRN (2020).Zhang, L. et al. Metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Science of The Total Environment 791, 148096 (2021).ADS 
CAS 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e00085–21 (2021).CAS 

Google Scholar 
Matthew, C. et al. Archaeal and bacterial metagenome-assembled genome sequences derived from pig feces. Microbiology Resource Announcements 11, 01142–21 (2022).
Google Scholar 
Wang, Y., Zhao, R., Liu, L., Li, B. & Zhang, T. Selective enrichment of comammox from activated sludge using antibiotics. Water Research 197, 117087 (2021).CAS 

Google Scholar 
Gilroy, R. et al. Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture. PeerJ 9, e10941 (2021).
Google Scholar 
Chen, Y. H. et al. Salvaging high-quality genomes of microbial species from a meromictic lake using a hybrid sequencing approach. Communications Biology 4, 996 (2021).CAS 

Google Scholar 
Beach, N. K., Myers, K. S., Donohue, T. J. & Noguera, D. R. Metagenomes from 25 low-abundance microbes in a partial nitritation anammox microbiome. Microbiology Resource Announcements 11, 00212–22 (2022).CAS 

Google Scholar 
Solanki, V. et al. Glycoside hydrolase from the GH76 family indicates that marine Salegentibacter sp. Hel_I_6 consumes alpha-mannan from fungi. The ISME Journal 16, 1818–1830 (2022).CAS 

Google Scholar 
Hiraoka, S. et al. Diverse DNA modification in marine prokaryotic and viral communities. Nucleic Acids Research 50, 1531–1550 (2022).CAS 

Google Scholar 
Haryono, M.A.S. et al. Recovery of high quality metagenome-assembled genomes from full-scale activated sludge microbial communities in a tropical climate using longitudinal metagenome sampling. Frontiers in Microbiology 13 (2022).Rodrguez-Ramos, J.A. et al. Microbial genome-resolved metaproteomic analyses frame intertwined carbon and nitrogen cycles in river hyporheic sediments. Research Square (2021).Kim, M., Cho, H. & Lee, W. Y. Distinct gut microbiotas between southern elephant seals and Weddell seals of Antarctica. Journal of Microbiology 58, 1018–1026 (2020).CAS 

Google Scholar 
Voorhies, A. A. et al. Cyanobacterial life at low O2: community genomics and function reveal metabolic versatility and extremely low diversity in a Great Lakes sinkhole mat. Geobiology 10, 250–267 (2012).CAS 

Google Scholar 
McDaniel, E. A. et al. Tbasco: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome. ISME Communications 2, 111 (2022).
Google Scholar 
Wang, W. et al. Contrasting bacterial and archaeal distributions reflecting different geochemical processes in a sediment core from the Pearl River Estuary. AMB Express 10, 16 (2020).
Google Scholar 
Mandakovic, D. et al. Genome-scale metabolic models of Microbacterium species isolated from a high altitude desert environment. Scientific Reports 10, 5560 (2020).ADS 
CAS 

Google Scholar 
Wang, Y. et al. Seasonal prevalence of ammonia-oxidizing archaea in a full-scale municipal wastewater treatment plant treating saline wastewater revealed by a 6-year time-series analysis. Environmental Science & Technology 55, 2662–2673 (2021).ADS 
CAS 

Google Scholar 
Bulzu, P. A. et al. Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche. Nature Microbiology 4, 1129–1137 (2019).CAS 

Google Scholar 
Karen, J. et al. Hydrogen-oxidizing bacteria are abundant in desert soils and strongly stimulated by hydration. mSystems 5, e01131–20 (2020).
Google Scholar 
Rust, M. et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proceedings of the National Academy of Sciences 117, 9508–9518 (2020).ADS 
CAS 

Google Scholar 
Podowski, J. C., Paver, S. F., Newton, R. J. & Coleman, M. L. Genome streamlining, proteorhodopsin, and organic nitrogen metabolism in freshwater nitrifiers. mBio 13, e02379–21 (2022).
Google Scholar 
Coutinho, F. H. et al. New viral biogeochemical roles revealed through metagenomic analysis of Lake Baikal. Microbiome 8, 163 (2020).CAS 

Google Scholar 
Philippi, M. et al. Purple sulfur bacteria fix N2 via molybdenum-nitrogenase in a low molybdenum Proterozoic ocean analogue. Nature Communications 12, 4774 (2021).ADS 
CAS 

Google Scholar 
Katie, S. et al. Eight metagenome-assembled genomes provide evidence for microbial adaptation in 20,000- to 1,000,000-year-old Siberian permafrost. Applied and Environmental Microbiology 87, e00972–21 (2021).
Google Scholar 
Mert, K. et al. Unexpected abundance and diversity of phototrophs in mats from morphologically variable microbialites in Great Salt Lake, Utah. Applied and Environmental Microbiology 86, e00165–20 (2020).
Google Scholar 
Patin, N. V. et al. Gulf of Mexico blue hole harbors high levels of novel microbial lineages. The ISME Journal 15, 2206–2232 (2021).CAS 

Google Scholar 
Wang, J., Tang, X., Mo, Z. & Mao, Y. Metagenome-assembled genomes from Pyropia haitanensis microbiome provide insights into the potential metabolic functions to the seaweed. Frontiers in Microbiology 13, 857901 (2022).
Google Scholar 
Burgsdorf, I. et al. Lineage-specific energy and carbon metabolism of sponge symbionts and contributions to the host carbon pool. The ISME Journal 16, 1163–1175 (2022).CAS 

Google Scholar 
Suarez, C. et al. Disturbance-based management of ecosystem services and disservices in partial nitritation-anammox biofilms. npj Biofilms and Microbiomes 8, 47 (2022).CAS 

Google Scholar 
Kumar, D. et al. Textile industry wastewaters from Jetpur, Gujarat, India, are dominated by Shewanellaceae, Bacteroidaceae, and Pseudomonadaceae harboring genes encoding catalytic enzymes for textile dye degradation. Frontiers in Environmental Science 9, 720707 (2021).ADS 

Google Scholar 
Seitz, V. A. et al. Variation in root exudate composition influences soil microbiome membership and function. Applied and Environmental Microbiology 88, e00226–22 (2022).
Google Scholar 
Lindner, B. G. et al. Toward shotgun metagenomic approaches for microbial source tracking sewage spills based on laboratory mesocosms. Water Research 210, 117993 (2022).CAS 

Google Scholar 
Yancey, C. E. et al. Metagenomic and metatranscriptomic insights into population diversity of microcystis blooms: Spatial and temporal dynamics of mcy genotypes, including a partial operon that can be abundant and expressed. Applied and Environmental Microbiology 88, e02464–21 (2022).
Google Scholar 
Liu, L. et al. Charting the complexity of the activated sludge microbiome through a hybrid sequencing strategy. Microbiome 9, 205 (2021).CAS 

Google Scholar 
Speth, D. R. et al. Microbial communities of Auka hydrothermal sediments shed light on vent biogeography and the evolutionary history of thermophily. The ISME Journal 16, 1750–1764 (2022).CAS 

Google Scholar 
Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Maternal inheritance of the koala gut microbiome and its compositional and functional maturation during juvenile development. Environmental Microbiology 24, 475–493 (2022).CAS 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. The ISME Journal 14, 999–1014 (2020).CAS 

Google Scholar 
Jaffe, A. L. et al. Long-term incubation of lake water enables genomic sampling of consortia involving planctomycetes and candidate phyla radiation bacteria. mSystems 7, e00223–22 (2022).
Google Scholar 
Cabral, L. et al. Gut microbiome of the largest living rodent harbors unprecedented enzymatic systems to degrade plant polysaccharides. Nature Communications 13, 629 (2022).ADS 
CAS 

Google Scholar 
Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Characterization of the juvenile koala gut microbiome across wild populations. Environmental Microbiology 24, 4209–4219 (2022).CAS 

Google Scholar 
Xu, B. et al. A holistic genome dataset of bacteria, archaea and viruses of the Pearl River estuary. Scientific Data 9, 49 (2022).MathSciNet 
CAS 

Google Scholar 
Royo-Llonch, M. et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nature Microbiology 6, 1561–1574 (2021).CAS 

Google Scholar 
Sun, J., Prabhu, A., Aroney, S. T. N. & Rinke, C. Insights into plastic biodegradation: community composition and functional capabilities of the superworm (Zophobas morio) microbiome in styrofoam feeding trials. Microbial Genomics 8, 000842 (2022).CAS 

Google Scholar 
Kim, M. et al. Higher pathogen load in children from Mozambique vs. USA revealed by comparative fecal microbiome profiling. ISME Communications 2, 74 (2022).ADS 

Google Scholar 
Kelly, J. B., Carlson, D. E., Low, J. S. & Thacker, R. W. Novel trends of genome evolution in highly complex tropical sponge microbiomes. Microbiome 10, 164 (2022).CAS 

Google Scholar 
Bray, M. S. et al. Phylogenetic and structural diversity of aromatically dense pili from environmental metagenomes. Environmental Microbiology Reports 12, 49–57 (2020).CAS 

Google Scholar 
Cabello-Yeves, P. J. et al. α-cyanobacteria possessing form IA RuBisCO globally dominate aquatic habitats. The ISME Journal 16, 2421–2432 (2022).CAS 

Google Scholar 
Berben, T. et al. The Polar Fox Lagoon in Siberia harbours a community of Bathyarchaeota possessing the potential for peptide fermentation and acetogenesis. Antonie van Leeuwenhoek 115, 1229–1244 (2022).CAS 

Google Scholar 
Tamburini, F. B. et al. Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa. Nature Communications 13, 926 (2022).ADS 
CAS 

Google Scholar 
Kantor, R. S., Miller, S. E. & Nelson, K. L. The water microbiome through a pilot scale advanced treatment facility for direct potable reuse. Frontiers in Microbiology 10, 993 (2019).
Google Scholar 
Muratore, D. et al. Complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Nature Ecology & Evolution 6, 218–229 (2022).
Google Scholar 
Zhou, Y. L., Mara, P., Cui, G. J., Edgcomb, V. P. & Wang, Y. Microbiomes in the challenger deep slope and bottom-axis sediments. Nature Communications 13, 1515 (2022).ADS 
CAS 

Google Scholar 
Zhang, H. et al. Metagenome sequencing and 768 microbial genomes from cold seep in South China Sea. Scientific Data 9, 480 (2022).CAS 

Google Scholar 
Zhuang, J. L., Zhou, Y. Y., Liu, Y. D. & Li, W. Flocs are the main source of nitrous oxide in a high-rate anammox granular sludge reactor: insights from metagenomics and fed-batch experiments. Water Research 186, e116321 (2020).
Google Scholar 
Shiffman, M. E. et al. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for eucalyptus digestion. PeerJ 5, 4075 (2017).
Google Scholar 
Murphy, S. M. C., Bautista, M. A., Cramm, M. A. & Hubert, C. R. J. Diesel and crude oil biodegradation by cold-adapted microbial communities in the Labrador Sea. Applied and Environmental Microbiology 87, e00800–21 (2021).ADS 
CAS 

Google Scholar 
Suarez, C. et al. Metagenomic evidence of a novel family of anammox bacteria in a subsea environment. Environmental Microbiology 24, 2348–2360 (2022).CAS 

Google Scholar 
Dharamshi, J.E. et al. Genomic diversity and biosynthetic capabilities of sponge-associated chlamydiae. The ISME Journal (2022).Florian, P. O., Hugo, R. & Mathieu, A. Recovery of metagenome-assembled genomes from a human fecal sample with pacific biosciences high-fidelity sequencing. Microbiology Resource Announcements 11, e00250–22 (2022).
Google Scholar 
Bloom, S. M. et al. Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nature Microbiology 7, 434–450 (2022).CAS 

Google Scholar 
Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proceedings of the National Academy of Sciences 114, 11446–11451 (2017).ADS 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25, 1043–1055 (2015).CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology 35, 725 (2017).CAS 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).CAS 

Google Scholar 
Louca, S. The rates of global bacterial and archaeal dispersal. ISME Journal 16, 159–167 (2021).ADS 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using minhash. Genome Biology 17, 132 (2016).
Google Scholar 
Müllner, D. fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python. Journal of Statistical Software 53, 1–18 (2013).
Google Scholar 
Kinene, T., Wainaina, J., Maina, S., Boykin, L.M. & Kliman, R.M. Methods for rooting trees, vol. 3, 489–493 (Academic Press, Oxford, 2016).Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).CAS 

Google Scholar 
Rees, J. A. & Cranston, K. Automated assembly of a reference taxonomy for phylogenetic data synthesis. Biodiversity Data Journal 5, e12581 (2017).
Google Scholar 
Heck, K. et al. Evaluating methods for purifying cyanobacterial cultures by qPCR and high-throughput Illumina sequencing. Journal of Microbiological Methods 129, 55–60 (2016).CAS 

Google Scholar 
Cornet, L. et al. Consensus assessment of the contamination level of publicly available cyanobacterial genomes. PLOS ONE 13, e0200323 (2018).
Google Scholar 
Alneberg, J. et al. Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes. Microbiome 6, 173 (2018).
Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Computational Biology 7, e1002195 (2011).ADS 
MathSciNet 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nature Methods 12, 59–60 (2014).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar  More