Philippa Kaur

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

Rufener, C. et al. Keel bone fractures are associated with individual mobility of laying hens in an aviary system. Appl. Anim. Behav. Sci. 217, 48–56 (2019).
Google Scholar 
Rentsch, A. K., Rufener, C. B., Spadavecchia, C., Stratmann, A. & Toscano, M. J. Laying hen’s mobility is impaired by keel bone fractures and does not improve with paracetamol treatment. Appl. Anim. Behav. Sci. 216, 19–25 (2019).
Google Scholar 
Rodriguez-Aurrekoetxea, A. & Estevez, I. Use of space and its impact on the welfare of laying hens in a commercial free-range system. Poult. Sci. 95, 2503–2513 (2016).CAS 

Google Scholar 
Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).
Google Scholar 
Campbell, D. L. M., Talk, A. C., Loh, Z. A., Dyall, T. R. & Lee, C. Spatial cognition and range use in free-range laying hens. Animals 8, 26 (2018).
Google Scholar 
de Jager, M., Weissing, F. J., Herman, P. M. J., Nolet, B. A. & van de Koppel, J. Lévy walks evolve through interaction between movement and environmental complexity. Science 1979(332), 1551–1553 (2011).
Google Scholar 
Krause, J., James, R. & Croft, D. P. Personality in the context of social networks. Philos. Trans. R. Soc. B Biol. Sci. 365, 4099–4106 (2010).CAS 

Google Scholar 
Ihwagi, F. W. et al. Poaching lowers elephant path tortuosity: Implications for conservation. J. Wildl. Manag. 83, 1022–1031 (2019).
Google Scholar 
Shaw, A. K. Causes and consequences of individual variation in animal movement. Mov. Ecol. 8, 1–12 (2020).
Google Scholar 
Matthews, S. G., Miller, A. L., Plötz, T. & Kyriazakis, I. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep. 7, 1–12 (2017).CAS 

Google Scholar 
Berger-Tal, O. & Saltz, D. Using the movement patterns of reintroduced animals to improve reintroduction success. Curr. Zool. 60, 515–526 (2014).
Google Scholar 
Stuber, E. F., Carlson, B. S. & Jesmer, B. R. Spatial personalities: A meta-analysis of consistent individual differences in spatial behavior. Behav. Ecol. https://doi.org/10.1093/BEHECO/ARAB147 (2022).Article 

Google Scholar 
Sirovnik, J., Würbel, H. & Toscano, M. J. Feeder space affects access to the feeder, aggression, and feed conversion in laying hens in an aviary system. Appl. Anim. Behav. Sci. 198, 75–82 (2018).
Google Scholar 
Sirovnik, J., Voelkl, B., Keeling, L. J., Würbel, H. & Toscano, M. J. Breakdown of the ideal free distribution under conditions of severe and low competition. Behav. Ecol. Sociobiol. 75, 1–11 (2021).
Google Scholar 
Becot, L., Bedere, N., Burlot, T., Coton, J. & le Roy, P. Nest acceptance, clutch, and oviposition traits are promising selection criteria to improve egg production in cage-free system. PLoS ONE 16, e0251037 (2021).CAS 

Google Scholar 
Thompson, M. J., Evans, J. C., Parsons, S. & Morand-Ferron, J. Urbanization and individual differences in exploration and plasticity. Behav. Ecol. 29, 1415–1425 (2018).
Google Scholar 
Stamps, J. & Groothuis, T. G. G. The development of animal personality: Relevance, concepts and perspectives. Biol. Rev. 85, 301–325 (2010).
Google Scholar 
Salinas-Melgoza, A., Salinas-Melgoza, V. & Wright, T. F. Behavioral plasticity of a threatened parrot in human-modified landscapes. Biol. Conserv. 159, 303–312 (2013).
Google Scholar 
Stamps, J. A., Briffa, M. & Biro, P. A. Unpredictable animals: Individual differences in intraindividual variability (IIV). Anim. Behav. 83, 1325–1334 (2012).
Google Scholar 
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Google Scholar 
Biro, P. A. & Adriaenssens, B. Predictability as a personality trait: Consistent differences in intraindividual behavioral variation. Am. Nat. 182, 621–629 (2013).
Google Scholar 
Henriksen, R. et al. Intra-individual behavioural variability: A trait under genetic control. Int. J. Mol. Sci. 21, 8069 (2020).CAS 

Google Scholar 
Rufener, C. et al. Finding hens in a haystack: Consistency of movement patterns within and across individual laying hens maintained in large groups. Sci. Rep. 8, (2018).Campbell, D. L. M., Karcher, D. M. & Siegford, J. M. Location tracking of individual laying hens housed in aviaries with different litter substrates. Appl. Anim. Behav. 184, 74–79 (2016).
Google Scholar 
Weeks, C. A. & Nicol, C. J. Behavioural needs, priorities and preferences of laying hens. Worlds Poult. Sci. J. 62, 296–307 (2006).
Google Scholar 
Hartcher, K. M. & Jones, B. The welfare of layer hens in cage and cage-free housing systems. Worlds Poult. Sci. J. 73, 767–782 (2017).
Google Scholar 
Zeltner, E. & Hirt, H. Effect of artificial structuring on the use of laying hen runs in a free-range system. Br. Poult. Sci. 44, 533–537 (2010).
Google Scholar 
Stratmann, A. et al. Modification of aviary design reduces incidence of falls, collisions and keel bone damage in laying hens. Appl. Anim. Behav. Sci. 165, 112–123 (2015).
Google Scholar 
Vandekerchove, D., Herdt, P., Laevens, H. & Pasmans, F. Colibacillosis in caged layer hens: Characteristics of the disease and the aetiological agent. Avian Pathol. 33, 117–125 (2004).CAS 

Google Scholar 
Montalcini, C. M., Voelkl, B., Gómez, Y., Gantner, M. & Toscano, M. J. Evaluation of an active LF tracking system and data processing methods for livestock precision farming in the poultry sector. Sensors 22, 659 (2022).ADS 

Google Scholar 
Revelle, W. Procedures for psychological, psychometric, and personality research. (2021).Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).
Google Scholar 
Rufener, C., Baur, S., Stratmann, A. & Toscano, M. J. A reliable method to assess keel bone fractures in laying hens from radiographs using a tagged visual analogue scale. Front. Vet. Sci. 5, 124 (2018).
Google Scholar 
Tauson, R., Kjaer, J., Maria, G. A., Cepero, R. & Holm, K.-E. The creation of a common scoring system for the integument and health of laying hens: Applied scoring of integument and health in laying hens. Final report Health from the Laywell project. https://www.laywel.eu/web/pdf/deliverables%2031-33%20health.pdf (2005).Hertel, A. G. et al. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, (2020).Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. 85, 935–956 (2010).
Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J Stat Softw 67, (2015).Cleasby, I. R., Nakagawa, S. & Schielzeth, H. Quantifying the predictability of behaviour: Statistical approaches for the study of between-individual variation in the within-individual variance. Methods Ecol. Evol. 6, 27–37 (2015).
Google Scholar 
Bürkner, P.-C. brms: An R package for bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
MATH 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).
Google Scholar 
Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. Don’t poke the bear: Using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).
Google Scholar 
Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 

Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).
Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, (2022).Moinard, C. et al. Accuracy of laying hens in jumping upwards and downwards between perches in different light environments. Appl. Anim. Behav. Sci. 85, 77–92 (2004).
Google Scholar 
Baur, S., Rufener, C., Toscano, M. J. & Geissbühler, U. Radiographic evaluation of keel bone damage in laying hens—Morphologic and temporal observations in a longitudinal study. Front. Vet. Sci. 1, 129 (2020).
Google Scholar 
Cordiner, L. S. & Savory, C. J. Use of perches and nestboxes by laying hens in relation to social status, based on examination of consistency of ranking orders and frequency of interaction. Appl. Anim. Behav. Sci. 71, 305–317 (2001).
Google Scholar 
Rufener, C. & Makagon, M. M. Keel bone fractures in laying hens: A systematic review of prevalence across age, housing systems, and strains. J. Anim. Sci. 98, S36–S51 (2020).
Google Scholar 
Nasr, M. A. F., Nicol, C. J., Wilkins, L. & Murrell, J. C. The effects of two non-steroidal anti-inflammatory drugs on the mobility of laying hens with keel bone fractures. Vet. Anaesth. Analg. 42, 197–204 (2015).CAS 

Google Scholar 
Nasr, M., Murrell, J., Wilkins, L. J. & Nicol, C. J. The effect of keel fractures on egg-production parameters, mobility and behaviour in individual laying hens. Anim. Welf. 21, 127–135 (2012).CAS 

Google Scholar 
Koolhaas, J. M. & van Reenen, C. G. Animal behavior and well-being symposium: Interaction between coping style/personality, stress, and welfare: Relevance for domestic farm animals. J. Anim. Sci. 94, 2284–2296 (2016).CAS 

Google Scholar 
Coppens, C. M., de Boer, S. F. & Koolhaas, J. M. Coping styles and behavioural flexibility: Towards underlying mechanisms. Philos. Trans. R. Soc. B Biol. Sci. 365, 4021 (2010).
Google Scholar 
Koolhaas, J. M., de Boer, S. F., Coppens, C. M. & Buwalda, B. Neuroendocrinology of coping styles: Towards understanding the biology of individual variation. Front. Neuroendocrinol. 31, 307–321 (2010).CAS 

Google Scholar 
Finkemeier, M.-A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front. Vet. Sci. 5, 131 (2018).
Google Scholar 
Martin, J. G. A., Pirotta, E., Petelle, M. B. & Blumstein, D. T. Genetic basis of between-individual and within-individual variance of docility. J. Evol. Biol. 30, 796–805 (2017).CAS 

Google Scholar 
Prentice, P. M., Houslay, T. M., Martin, J. G. A. & Wilson, A. J. Genetic variance for behavioural ‘predictability’ of stress response. J. Evol. Biol. 33, 642–652 (2020).
Google Scholar  More

Differences in mucus physicochemical factors and nutrient elements among electrical stimuliPhysical and chemical factorsMucus contains electrolytes, such as potassium and multivalent calcium and magnesium ions, which participate in the osmoregulation of the earthworm body to maintain the metabolic balance of the organism7,23. When earthworms are subjected to different stimuli, the mucus composition changes10. As shown in Fig. 1a, earthworms produced mucus with significant (P  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

Costanza, R. et al. Twenty years of ecosystem services: How far have we come and how far do we still need to go?. Ecosyst. Serv. 28, 1–16. https://doi.org/10.1016/j.ecoser.2017.09.008 (2017).Article 

Google Scholar 
Harwell, M. A. et al. Conceptual framework for assessing ecosystem health. Integr. Environ. Assess. Manag. 15, 544–564. https://doi.org/10.1002/ieam.4152 (2019).Article 

Google Scholar 
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952. https://doi.org/10.1126/science.1149345 (2008).Article 
ADS 
CAS 

Google Scholar 
Roca, G. et al. Response of seagrass indicators to shifts in environmental stressors: A global review and management synthesis. Ecol. Ind. 63, 310–323. https://doi.org/10.1016/j.ecolind.2015.12.007 (2016).Article 

Google Scholar 
Westgate, M. J., Likens, G. E. & Lindenmayer, D. B. Adaptive management of biological systems: A review. Biol. Cons. 158, 128–139. https://doi.org/10.1016/j.biocon.2012.08.016 (2013).Article 

Google Scholar 
Logan, M. et al. Ecosystem health report cards: An overview of frameworks and analytical methodologies. Ecol. Indic. 113, 105834. https://doi.org/10.1016/j.ecolind.2019.105834 (2020).Article 

Google Scholar 
Dennison, W. C., Lookingbill, T. R., Carruthers, T. J., Hawkey, J. M. & Carter, S. L. An eye-opening approach to developing and communicating integrated environmental assessments. Front. Ecol. Environ. 5, 307–314. https://doi.org/10.1890/1540-9295(2007)5[307:AEATDA]2.0.CO;2 (2007).Article 

Google Scholar 
Harwell, M. A. et al. A framework for an ecosystem integrity report card: examples from south Florida show how an ecosystem report card links societal values and scientific information. Bioscience 49, 543–556. https://doi.org/10.2307/1313475 (1999).Article 

Google Scholar 
Collier, C. J. et al. An evidence-based approach for setting desired state in a complex Great Barrier Reef seagrass ecosystem: A case study from Cleveland Bay. Environ. Sustain. Indic. 7, 100042. https://doi.org/10.1016/j.indic.2020.100042 (2020).Article 

Google Scholar 
Coles, R. G. et al. Seagrass: Ecology, Uses and Threats (Nova Science Publishers, Inc., 2011).
Google Scholar 
Grech, A. et al. A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions. Environ. Res. Lett. 7, 024006. https://doi.org/10.1088/1748-9326/7/2/024006 (2012).Article 
ADS 

Google Scholar 
Lambert, V. M. et al. Connecting targets for catchment sediment loads to ecological outcomes for seagrass using multiple lines of evidence. Mar. Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2021.112494 (2021).Article 

Google Scholar 
Adams, M. P. et al. Predicting seagrass decline due to cumulative stressors. Environ. Model. Softw. 130, 104717. https://doi.org/10.1016/j.envsoft.2020.104717 (2020).Article 

Google Scholar 
Chartrand, K. M., Szabó, M., Sinutok, S., Rasheed, M. A. & Ralph, P. J. Living at the margins: The response of deep-water seagrasses to light and temperature renders them susceptible to acute impacts. Mar. Environ. Res. 136, 126–138. https://doi.org/10.1016/j.marenvres.2018.02.006 (2018).Article 
CAS 

Google Scholar 
Chartrand, K., Bryant, C., Carter, A., Ralph, P. & Rasheed, M. Light thresholds to prevent dredging impacts on the Great Barrier Reef seagrass, Zostera muelleri spp. capricorni. Front. Mar. Sci. 3, 17. https://doi.org/10.3389/fmars.2016.00106 (2016).Article 

Google Scholar 
Abal, E. & Dennison, W. Seagrass depth range and water quality in southern Moreton Bay, Queensland, Australia. Mar. Freshwater Res. 47, 763–771. https://doi.org/10.1071/MF9960763 (1996).Article 
CAS 

Google Scholar 
Dennison, W. et al. Assessing water quality with submersed aquatic vegetation: Habitat requirements as barometers of Chesapeake Bay health. Bioscience 43, 86–94. https://doi.org/10.2307/1311969 (1993).Article 

Google Scholar 
Carter, A. B., Collier, C., Coles, R., Lawrence, E. & Rasheed, M. A. Community-specific, “desired” states for seagrasses through cycles of loss and recovery. J. Environ. Manag. 314, 115059. https://doi.org/10.1016/j.jenvman.2022.115059 (2022).Article 

Google Scholar 
Kaldy, J. E., Brown, C. A. & Pacella, S. R. Carbon limitation in response to nutrient loading in an eelgrass mesocosm: Influence of water residence time. Mar. Ecol. Prog. Ser. 689, 1–17. https://doi.org/10.3354/meps14061 (2022).Article 
CAS 

Google Scholar 
Carter, A. B. et al. A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area. Sci. Rep. https://doi.org/10.1038/s41598-021-01471-4 (2021).Article 

Google Scholar 
Kenworthy, W. J., Wyllie-Echeverria, S., Coles, R. G., Pergent, G. & Pergent-Martini, C. Seagrasses: Biology, Ecology and Conservation 595–623 (Springer, 2006).
Google Scholar 
Hayes, M. A. et al. The differential importance of deep and shallow seagrass to nekton assemblages of the great barrier reef. Diversity 12, 292. https://doi.org/10.3390/d12080292 (2020).Article 

Google Scholar 
Marsh, H., O’Shea, T. J. & Reynolds, J. E. III. Ecology and Conservation of the Sirenia: Dugongs and Manatees Vol. 18 (Cambridge University Press, 2011).Book 

Google Scholar 
Scott, A. L. et al. The role of herbivory in structuring tropical seagrass ecosystem service delivery. Front. Plant Sci. 9, 1–10. https://doi.org/10.3389/fpls.2018.00127 (2018).Article 

Google Scholar 
York, P. H., Macreadie, P. I. & Rasheed, M. A. Blue carbon stocks of Great Barrier Reef deep-water seagrasses. Biol. Lett. 14, 20180529. https://doi.org/10.1098/rsbl.2018.0529 (2018).Article 
CAS 

Google Scholar 
Unsworth, R. K., Collier, C. J., Waycott, M., Mckenzie, L. J. & Cullen-Unsworth, L. C. A framework for the resilience of seagrass ecosystems. Mar. Pollut. Bull. 100, 34–46. https://doi.org/10.1016/j.marpolbul.2015.08.016 (2015).Article 
CAS 

Google Scholar 
Madden, C. J., Rudnick, D. T., McDonald, A. A., Cunniff, K. M. & Fourqurean, J. W. Ecological indicators for assessing and communicating seagrass status and trends in Florida Bay. Ecol. Ind. 9, S68–S82. https://doi.org/10.1016/j.ecolind.2009.02.004 (2009).Article 
CAS 

Google Scholar 
York, P. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: Annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167. https://doi.org/10.1038/srep13167 (2015).Article 
ADS 
CAS 

Google Scholar 
Rasheed, M. A., McKenna, S. A., Carter, A. B. & Coles, R. G. Contrasting recovery of shallow and deep water seagrass communities following climate associated losses in tropical north Queensland, Australia. Mar. Pollut. Bull. 83, 491–499. https://doi.org/10.1016/j.marpolbul.2014.02.013 (2014).Article 
CAS 

Google Scholar 
Smith, T., Chartrand, K., Wells, J., Carter, A. & Rasheed, M. Seagrasses in Port Curtis and Rodds Bay 2019 Annual long-term monitoring and whole port survey. 71, https://www.tropwater.com/wp-content/uploads/2022/10/20-64-Annual-Seagrass-monitoring-in-Port-Curtis-and-Rodds-Bay-2019.pdf (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/64, James Cook University, Cairns, 2020).Ruaro, R., Gubiani, E. A., Hughes, R. M. & Mormul, R. P. Global trends and challenges in multimetric indices of biological condition. Ecol. Indic. 110, 105862. https://doi.org/10.1016/j.ecolind.2019.105862 (2020).Article 

Google Scholar 
Kilminster, K. et al. Unravelling complexity in seagrass systems for management: Australia as a microcosm. Sci. Total Environ. 534, 97–109. https://doi.org/10.1016/j.scitotenv.2015.04.061 (2015).Article 
ADS 
CAS 

Google Scholar 
Collier, C. J., Chartrand, K., Honchin, C., Fletcher, A. & Rasheed, M. Light thresholds for seagrasses of the GBR: a synthesis and guiding document. Including knowledge gaps and future priorities. 41, http://nesptropical.edu.au/wp-content/uploads/2016/05/NESP-TWQ-3.3-FINAL-REPORTa.pdf (Report to the National Environmental Science Programme, Cairns, 2016).Bryant, C., Jarvis, J. C., York, P. & Rasheed, M. Gladstone Healthy Harbour Partnership Pilot Report Card; ISP011: Seagrass., 74, https://researchonline.jcu.edu.au/44549/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 14/53, James Cook University, Cairns, 2014).McIntosh, E. J. et al. Designing report cards for aquatic health with a whole-of-system approach: Gladstone Harbour in the Great Barrier Reef. Ecol. Ind. 102, 623–632. https://doi.org/10.1016/j.ecolind.2019.03.012 (2019).Article 

Google Scholar 
Birch, W. & Birch, M. Succession and pattern of tropical intertidal seagrasses in Cockle Bay, Queensland, Australia: A decade of observations. Aquat. Bot. 19, 343–367. https://doi.org/10.1016/0304-3770(84)90048-2 (1984).Article 

Google Scholar 
Rasheed, M. A. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: The role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310, 13–45. https://doi.org/10.1016/j.jembe.2004.03.022 (2004).Article 

Google Scholar 
Christiaen, B., Lehrter, J., Goff, J. & Cebrian, J. Functional implications of changes in seagrass species composition in two shallow coastal lagoons. Mar. Ecol. Prog. Ser. 557, 11. https://doi.org/10.3354/meps11847 (2016).Article 

Google Scholar 
Hyndes, G. A., Kendrick, A. J., MacArthur, L. D. & Stewart, E. Differences in the species- and size-composition of fish assemblages in three distinct seagrass habitats with differing plant and meadow structure. Mar. Biol. 142, 1195–1206. https://doi.org/10.1007/s00227-003-1010-2 (2003).Article 

Google Scholar 
Ray, B. R., Johnson, M. W., Cammarata, K. & Smee, D. L. Changes in seagrass species composition in Northwestern Gulf of Mexico Estuaries: Effects on associated seagrass Fauna. PLoS ONE 9, e107751. https://doi.org/10.1371/journal.pone.0107751 (2014).Article 
ADS 
CAS 

Google Scholar 
Ondiviela, B. et al. The role of seagrasses in coastal protection in a changing climate. Coast. Eng. 87, 11. https://doi.org/10.1016/j.coastaleng.2013.11.005 (2014).Article 

Google Scholar 
Lavery, P. S., Mateo, M. -Á., Serrano, O. & Rozaimi, M. Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service. PLoS ONE 8, e73748. https://doi.org/10.1371/journal.pone.0073748 (2013).Article 
ADS 
CAS 

Google Scholar 
Coles, R. G. et al. The Great Barrier Reef World Heritage Area seagrasses: Managing this iconic Australian ecosystem resource for the future. Estuar. Coast. Shelf Sci. 153, A1–A12. https://doi.org/10.1016/j.ecss.2014.07.020 (2015).Article 
ADS 

Google Scholar 
Smith, T. M., Reason, C., McKenna, S. & Rasheed, M. A. Seagrasses in Port Curtis and Rodds Bay 2020. Annual long-term monitoring. 54, https://www.dropbox.com/s/f5yb6bjjpbvc1f2/21%2016%20Smith%20et%20al%202021%20Annual%20Seagrass%20monitoring%20in%20Port%20Curtis%20and%20Rodds%20Bay%202020_Final%20version.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/16, James Cook University, Cairns, 2021).Windle, J., Rolfe, J. & Pascoe, S. Assessing recreational benefits as an economic indicator for an industrial harbour report card. Ecol. Ind. 80, 224–231. https://doi.org/10.1016/j.ecolind.2017.05.036 (2017).Article 

Google Scholar 
Scott, A. & Rasheed, M. A. Port of Karumba long-term annual seagrass monitoring 2020. 28, https://www.dropbox.com/s/fwtys67ljssbp9t/21%2005%20Scott%20%26%20Rasheed%202021%20FINAL%202020%20Karumba%20Long-term%20seagrass%20monitoring%20report%20low%20res.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/05, James Cook University, Cairns, 2021).
Google Scholar 
Smith, T., Reason, C., McKenna, S. & Rasheed, M. Port of Weipa long‐term seagrass monitoring program, 2000 ‐ 2020. 49, https://www.dropbox.com/s/ghqy3bmn9p8jbsi/20%2058%20Smith%20et%20al%202020%20Port%20of%20Weipa%20Annual%20Long%20Term%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/58, James Cook University, Cairns, 2020).Reason, C. L., Smith, T. M. & Rasheed, M. A. Seagrass habitat of Cairns Harbour and Trinity Inlet: Cairns Shipping Development Program and Annual Monitoring Report 2020. 54, https://www.dropbox.com/s/m7xtrytjjip3a42/21%2009%20Final_Cairns%20Harbour%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/09, James Cook University, Cairns, 2021).Reason, C. L., York, P. H. & Rasheed, M. A. Seagrass habitat of Mourilyan Harbour: Annual monitoring report – 2020. 36, https://www.dropbox.com/s/kg3toxmlifh62tg/21%2010%20Mourilyan%20Harbour%20seagrass%20monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/10, James Cook University, Cairns, 2021).McKenna, S., Wilkinson, J., Chartrand, K. & Van De Wetering, C. Port of Townsville Seagrass Monitoring Program: 2020. 62, https://www.dropbox.com/s/n8nsx8ts93fgr36/21%2014%20Final%20POTL%20Annual%20Seagrass%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/14, James Cook University, Cairns, 2021).McKenna, S. A., van de Wetering, C., Wilkinson, J. & Rasheed, M. A. Port of Abbot Point long-term seagrass monitoring program: 2020. 35, https://www.dropbox.com/s/l5a5l7pkikcjrfb/21%2025%20McKenna%20et%20al%20Port%20of%20Abbot%20Point%20Long-term%20seagrass%20Monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/25, James Cook University, Cairns, 2021).York, P. H. & Rasheed, M. A. Annual Seagrass Monitoring in the Mackay-Hay Point Region – 2020. 42, https://www.dropbox.com/s/u45yezm3984lw1a/21%2020%20Hay%20Point%20and%20Mackay%20Seagrass%20Final%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/20, James Cook University, Cairns, 2021).van de Wetering, C., Carter, A. B. & Rasheed, M. A. Mackay-Whitsunday-Isaac Seagrass Monitoring 2017–2020: Marine Inshore South Zone. 30, https://researchonline.jcu.edu.au/70923/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/06, James Cook University, Cairns, 2021).Carter, A. B. et al. Torres Strait Seagrass 2021 Report Card. 76, https://researchonline.jcu.edu.au/70797/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/13, James Cook University, Cairns, 2021).Gladstone Ports Corporation. Port of Gladstone. https://www.gpcl.com.au/port-of-gladstone (2022).Sawynok, B., Venables, B. & Pinto, U. Incorporating a fish recruitment indicator into a health report card: A case study from Gladstone Harbour, Australia. Ecol. Indic. 115, 106329. https://doi.org/10.1016/j.ecolind.2020.106329 (2020).Article 

Google Scholar 
Pascoe, S. et al. Developing a social, cultural and economic report card for a regional industrial harbour. PLoS ONE 11, e0148271. https://doi.org/10.1371/journal.pone.0148271 (2016).Article 
CAS 

Google Scholar 
Chartrand, K. M., Bryant, C. V., Sozou, A., Ralph, P. J. & Rasheed, M. A. Final Report: Deep‐water seagrass dynamics ‐ Light requirements, seasonal change and mechanisms of recruitment. 67, https://www.dropbox.com/sh/mo8dcq1322qv5c3/AAAgu3lEnJsLgxdawXaOltu-a/2017?dl=0&preview=17+16+Final+Report+Deep-water+seagrass+dynamics.pdf&subfolder_nav_tracking=1 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 17/16, James Cook University, Cairns, 2017).Kirkman, H. Decline of seagrass in northern areas of Moreton Bay, Queensland. Aquat. Bot. 5, 63–76. https://doi.org/10.1016/0304-3770(78)90047-5 (1978).Article 

Google Scholar 
Mellors, J. E. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquat. Bot. 42, 67–73. https://doi.org/10.1016/0304-3770(91)90106-F (1991).Article 

Google Scholar 
Emmer, I. et al. Methodology for tidal wetland and seagrass restoration VM0033, version 2.0. https://verra.org/wp-content/uploads/2018/03/VM0033-Methodology-for-Tidal-Wetland-and-Seagrass-Restoration-v2.0-30Sep21-1.pdf (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

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

Guichard, F., Levin, S. A., Hastings, A. & Siegel, D. Toward a dynamic metacommunity approach to marine reserve theory. BioScience 54(11), 1003. https://doi.org/10.1641/0006-3568(2004)054[1003:tadmat]2.0.co;2 (2004).Article 

Google Scholar 
Wieters, E. A., Gaines, S. D., Navarrete, S. A., Blanchette, C. A. & Menge, B. A. Scales of dispersal and the biogeography of marine predator-prey interactions. Am. Nat. 171(3), 405–417. https://doi.org/10.1086/527492 (2008).Article 

Google Scholar 
Martínez-Moreno, J. et al. Global changes in oceanic mesoscale currents over the satellite altimetry record. Nat. Clim. Changehttps://doi.org/10.1038/s41558-021-01006-9 (2021).Article 

Google Scholar 
van Gennip, S. J. et al. Going with the flow: The role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23(7), 2602–2617. https://doi.org/10.1111/gcb.13586 (2017).Article 
ADS 

Google Scholar 
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. U.S.A. 104(4), 1266–1271. https://doi.org/10.1073/pnas.0603422104 (2007).Article 
ADS 
CAS 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1(1), 443–466. https://doi.org/10.1146/annurev.marine.010908.163757 (2009).Article 

Google Scholar 
Ospina-Alvarez, A., Parada, C. & Palomera, I. Vertical migration effects on the dispersion and recruitment of European anchovy larvae: From spawning to nursery areas. Ecol. Model. 231, 65–79. https://doi.org/10.1016/j.ecolmodel.2012.02.001 (2012).Article 

Google Scholar 
Selkoe, K. A. & Toonen, R. J. Marine connectivity: A new look at pelagic larval duration and genetic metrics of dispersal. Mar. Ecol. Prog. Ser. 436, 291–305. https://doi.org/10.3354/meps09238 (2011).Article 
ADS 

Google Scholar 
Siegel, D. A. et al. The stochastic nature of larval connectivity among nearshore marine populations. Proc. Natl. Acad. Sci. U.S.A. 105(26), 8974–8979. https://doi.org/10.1073/pnas.0802544105 (2008).Article 
ADS 

Google Scholar 
De Lestang, S. et al. What caused seven consecutive years of low puerulus settlement in the western rock lobster fishery of Western Australia?. ICES J. Mar. Sci. 72, i49–i58. https://doi.org/10.1093/icesjms/fsu177 (2015).Article 

Google Scholar 
Linnane, A. et al. Evidence of large-scale spatial declines in recruitment patterns of southern rock lobster Jasus edwardsii, across south-eastern Australia. Fish. Res. 105(3), 163–171. https://doi.org/10.1016/j.fishres.2010.04.001 (2010).Article 

Google Scholar 
Briones-Fourzán, P., Candela, J. & Lozano-Álvarez, E. Postlarval settlement of the spiny lobster Panulirus argus along the Caribbean coast of Mexico: Patterns, influence of physical factors, and possible sources of origin. Limnol. Oceanogr. 53(3), 970–985. https://doi.org/10.4319/lo.2008.53.3.0970 (2008).Article 
ADS 

Google Scholar 
Haury, L. R., McGowan, J. A. & Wiebe, P. H. Patterns and processes in the time-space scales of plankton distributions. In Spatial Pattern in Plankton Communities (ed. Steele, J. H.) 277–327 (Springer US, 1978). https://doi.org/10.1007/978-1-4899-2195-6_12.Cowen, R. K., Paris, C. B. & Srinivasan, A. Scaling of connectivity in marine populations. Science 311(5760), 522–527. https://doi.org/10.1126/science.1122039 (2006).Article 
ADS 
CAS 

Google Scholar 
Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. 73(7), 1839–1850. https://doi.org/10.1093/icesjms/fsw086 (2016).Article 

Google Scholar 
Ospina-Alvarez, A., Weidberg, N., Aiken, C. M. & Navarrete, S. A. Larval transport in the upwelling ecosystem of central Chile: The effects of vertical migration, developmental time and coastal topography on recruitment. Prog. Oceanogr. 168, 82–99. https://doi.org/10.1016/j.pocean.2018.09.016 (2018) http://www.sciencedirect.com/science/article/pii/S0079661117300800.Article 
ADS 

Google Scholar 
Palumbi, S. Population genetics, demographic connectivity, and the design of marine reserves. Ecol. Appl. 13(1 Supplement), S146–S158 (2003).Article 

Google Scholar 
Barahona, M. et al. Environmental and demographic factors influence the spatial genetic structure of an intertidal barnacle in central-northern Chile. Mar. Ecol. Prog. Ser. 612, 151–165. https://doi.org/10.3354/meps12855 (2019) http://www.int-res.com/abstracts/meps/v612/p151-165/.Article 
ADS 

Google Scholar 
Spanier, E. et al. A concise review of lobster utilization by worldwide human populations from prehistory to the modern era. ICES J. Mar. Sci. 72(May), i7–i21. https://doi.org/10.1093/icesjms/fsv066 (2015).Article 

Google Scholar 
IUCN. Palinurus elephas: Goñi, R.: The IUCN Red List of Threatened Species 2014: e.T169975A1281221. Tech. Rep., International Union for Conservation of Nature (2013). http://www.iucnredlist.org/details/169975/0. Type: dataset.Canepa, A. et al. Pelagia noctiluca in the mediterranean sea (eds Pitt, K. A. & Lucas, C. H.) In Jellyfish Blooms, Vol. 9789400770 237–266 (Springer Netherlands, 2014). https://doi.org/10.1007/978-94-007-7015-7_11.Bosch-Belmar, M. et al. Jellyfish blooms perception in Mediterranean finfish aquaculture. Mar. Policy 76, 1–7. https://doi.org/10.1016/j.marpol.2016.11.005 (2017).Article 

Google Scholar 
Exceltur. Impactur baleares 2014. Tech. Rep., EXCELTUR – Govern de les Illes Balears, Madrid (2014).Vignudelli, S., Gasparini, G. P., Astraldi, M. & Schiano, M. E. A possible influence of the North Atlantic Oscillation on the circulation of the Western Mediterranean Sea. Geophys. Res. Lett. 26(5), 623–626. https://doi.org/10.1029/1999GL900038 (1999).Article 
ADS 

Google Scholar 
Somot, S. et al. Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim. Dyn. 51(3), 1179–1210. https://doi.org/10.1007/s00382-016-3295-0 (2018).Article 

Google Scholar 
Díaz, D., Marí, M., Abelló, P. & Demestre, M. Settlement and juvenile habitat of the European spiny lobster Palinurus elephas (Crustacea: Decapoda: Palinuridae) in the Western Mediterranean Sea. Sci. Mar. 65(4), 347–356. https://doi.org/10.3989/scimar.2001.65n4347 (2001).Article 

Google Scholar 
Muñoz, A. et al. Exploration of the inter-annual variability and multi-scale environmental drivers of European spiny lobster, Palinurus elephas (Decapoda: Palinuridae) settlement in the NW Mediterranean. Mar. Ecol.https://doi.org/10.1111/maec.12654 (2021).Article 

Google Scholar 
Malej, A. & Malej, M. Population dynamics of the jellyfish Pelagia noctiluca (Forsskal, 1775) In Marine Eutrophication and Population Dynamics (eds Colombo, G., Ferrari, I., V., C. & R., R.) 215–219 (Olsen and Olsen, 1992).Ottmann, D. et al. Abundance of Pelagia noctiluca early life stages in the western Mediterranean Sea scales with surface chlorophyll. Mar. Ecol. Prog. Ser. 658, 75–88. https://doi.org/10.3354/meps13423 (2021).Article 
ADS 
CAS 

Google Scholar 
Benedetti-Cecchi, L. et al. Deterministic factors overwhelm stochastic environmental fluctuations as drivers of jellyfish outbreaks. PLoS One 10(10), 1–16. https://doi.org/10.1371/journal.pone.0141060 (2015).Article 
CAS 

Google Scholar 
Licandro, P. et al. A blooming jellyfish in the northeast Atlantic and Mediterranean. Biol. Lett. 6(5), 688–691. https://doi.org/10.1098/rsbl.2010.0150 (2010).Article 
CAS 

Google Scholar 
Goy, J., Morand, P. & Etienne, M. Long-term fluctuations of Pelagia noctiluca (Cnidaria, Scyphomedusa) in the western Mediterranean Sea. Prediction by climatic variables. Deep Sea Res. Part A Oceanogr. Res. Pap. 36(2), 269–279 (1989). https://doi.org/10.1016/0198-0149(89)90138-6 .Yahia, M. N. D. et al. Are the outbreaks timing of Pelagia noctiluca (Forsskal, 1775) getting more frequent in the Mediterranean basin?. ICES Cooper. Res. Rep. 300, 8–14 (2010).
Google Scholar 
Ferraris, M. et al. Distribution of Pelagia noctiluca (Cnidaria, Scyphozoa) in the Ligurian Sea (NW Mediterranean Sea). J. Plankton Res. 34(10), 874–885. https://doi.org/10.1093/plankt/fbs049 (2012).Article 

Google Scholar 
Millot, C. Circulation in the Western Mediterranean Sea. J. Mar. Syst. 20(1–4), 423–442. https://doi.org/10.1016/S0924-7963(98)00078-5 (1999).Article 

Google Scholar 
Galarza, J. A. et al. The influence of oceanographic fronts and early-life-history traits on connectivity among littoral fish species. Proc. Natl. Acad. Sci. 106(5), 1473–1478. https://doi.org/10.1073/pnas.0806804106 (2009).Article 
ADS 

Google Scholar 
Fernández de Puelles, M. L. & Molinero, J. C. Decadal changes in hydrographic and ecological time-series in the Balearic Sea (western Mediterranean), identifying links between climate and zooplankton. ICES J. Mar. Sci. 65(3), 311–317. https://doi.org/10.1093/icesjms/fsn017 (2008).Article 

Google Scholar 
Arsouze, T. et al. CIESM (ed.) Sensibility analysis of the Western Mediterranean Transition inferred by four companion simulations. (ed. CIESM) EGU General Assembly Conference Abstracts, Vol. 1 of EGU General Assembly Conference Abstracts, 13073 (2013).Amores, A., Jordà, G., Arsouze, T. & Le Sommer, J. Up to what extent can we characterize ocean eddies using present-day gridded altimetric products?. J. Geophys. Res. Oceans 123(10), 7220–7236. https://doi.org/10.1029/2018JC014140 (2018).Article 
ADS 

Google Scholar 
Waldman, R. et al. Impact of the mesoscale dynamics on ocean deep convection: The 2012–2013 case study in the northwestern mediterranean sea. J. Geophys. Res. Oceans 122(11), 8813–8840. https://doi.org/10.1002/2016JC012587 (2017).Article 
ADS 

Google Scholar 
Lett, C. et al. A Lagrangian tool for modelling ichthyoplankton dynamics. Environ. Model. Softw. 23(9), 1210–1214. https://doi.org/10.1016/j.envsoft.2008.02.005 (2008).Article 

Google Scholar 
Brickman, D. & Smith, P. C. Lagrangian stochastic modeling in coastal oceanography. J. Atmos. Ocean. Technol. 19(1), 83–99. https://doi.org/10.1175/1520-0426(2002)0192.0.CO;2 (2002).Article 
ADS 

Google Scholar 
Goñi, R. & Latrouite, D. Review of the biology, ecology and fisheries of Palinurus spp. species of European waters: Palinurus elephas (Fabricius, 1787) and Palinurus mauritanicus (Gruvel, 1911). Cahiers de Biol. Mar. 46(2), 127–142 (2005).
Google Scholar 
Bjornsson, H. & Venegas, S. A manual for EOF and SVD analyses of climatic data. Tech. Rep. CCGCR Report No. 97-1, McGill s Centre for Climate and Global Change Research (C2GCR) (1997).Herrmann, M., Somot, S., Sevault, F., Estournel, C. & Déqué, M. Modeling the deep convection in the northwestern mediterranean sea using an eddy-permitting and an eddy-resolving model: Case study of winter 1986–1987. J. Geophys. Res. Oceans 113(C4) (2008). https://doi.org/10.1029/2006JC003991.Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10, 252–266 (2019). https://doi.org/10.24381/cds.f17050d7 .Bernard, P., Berline, L. & Gorsky, G. Long term (1981–2008) monitoring of the jellyfish Pelagia noctiluca (Cnidaria, Scyphozoa) on Mediterranean Coasts (Principality of Monaco and French Riviera). J. Oceanogr. Res. Data 4(1), 1–10 (2011).
Google Scholar 
Kough, A. S., Paris, C. B. & Butler, M. J. IV. Larval connectivity and the international management of fisheries. PLoS One 8(6), 1–12. https://doi.org/10.1371/journal.pone.0064970 (2013).Article 
CAS 

Google Scholar 
Sandvik, H. et al. Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution. Nat. Commun. 7(May), 1–8. https://doi.org/10.1038/ncomms11599 (2016).Article 
CAS 

Google Scholar 
Notarbartolo-Di-Sciara, G., Agardy, T., Hyrenbach, D., Scovazzi, T. & Van Klaveren, P. The Pelagos Sanctuary for Mediterranean marine mammals. Aquat. Conserv. Mar. Freshw. Ecosyst. 18(4), 367–391. https://doi.org/10.1002/aqc.855 (2008).Article 

Google Scholar 
Astraldi, M., Gasparini, G. P., Vetrano, a. & Vignudelli, S. Hydrographic characteristics and interannual variability of water masses in the central Mediterranean: A sensitivity test for long-term changes in the Mediterranean Sea. Deep Sea Res. Part I Oceanogr. Res. Pap. 49(4), 661–680 (2002). https://doi.org/10.1016/S0967-0637(01)00059-0 .Muffett, K. & Miglietta, M. P. Planktonic associations between medusae (classes Scyphozoa and Hydrozoa) and epifaunal crustaceans. PeerJ 9, e11281. https://doi.org/10.7717/peerj.11281 (2021) https://peerj.com/articles/11281.Article 

Google Scholar 
Stopar, K., Ramšak, A., Trontelj, P. & Malej, A. Lack of genetic structure in the jellyfish Pelagia noctiluca (Cnidaria: Scyphozoa: Semaeostomeae) across European seas. Mol. Phylogenet. Evol. 57(1), 417–428. https://doi.org/10.1016/j.ympev.2010.07.004 (2010).Article 
CAS 

Google Scholar 
Berline, L., Zakardjian, B., Molcard, A., Ourmières, Y. & Guihou, K. Modeling jellyfish Pelagia noctiluca transport and stranding in the Ligurian Sea. Mar. Pollut. Bull. 70(1–2), 90–99. https://doi.org/10.1016/j.marpolbul.2013.02.016 (2013).Article 
CAS 

Google Scholar 
Prieto, L., Macías, D., Peliz, A. & Ruiz, J. Portuguese Man-of-War (Physalia physalis) in the Mediterranean: A permanent invasion or a casual appearance? Sci. Rep. 5 (2015). https://doi.org/10.1038/srep11545.Houghton, J. D. R. et al. Identification of genetically and oceanographically distinct blooms of jellyfish. J. R. Soc. Interface 10(80), 20120920–20120920. https://doi.org/10.1098/rsif.2012.0920 (2013).Article 

Google Scholar 
Segura-García, I. et al. Reconstruction of larval origins based on genetic relatedness and biophysical modeling. Sci. Rep. 9(1), 1–9. https://doi.org/10.1038/s41598-019-43435-9 (2019).Article 
ADS 
CAS 

Google Scholar 
Elphie, H., Raquel, G., David, D. & Serge, P. Detecting immigrants in a highly genetically homogeneous spiny lobster population (Palinurus elephas) in the northwest Mediterranean Sea. Ecol. Evol. 2(10), 2387–2396. https://doi.org/10.1002/ece3.349 (2012).Article 

Google Scholar 
Babbucci, M. et al. Population structure, demographic history, and selective processes: Contrasting evidences from mitochondrial and nuclear markers in the European spiny lobster Palinurus elephas (Fabricius, 1787). Mol. Phylogenet. Evol. 56(3), 1040–1050. https://doi.org/10.1016/j.ympev.2010.05.014 (2010).Article 
CAS 

Google Scholar 
Cau, A. et al. European spiny lobster recovery from overfishing enhanced through active restocking in Fully Protected Areas. Sci. Rep. 9(1) (2019). https://doi.org/10.1038/s41598-019-49553-8 .Macias, D., Garcia-Gorriz, E. & Stips, A. Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (Horizon 2030) scenarios. Sci. Rep. 8(1), 1–15. https://doi.org/10.1038/s41598-018-24965-0 (2018).Article 
CAS 

Google Scholar  More