More stories

  • in

    Incorporating dead material in ecosystem assessments and projections

    Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge Univ. Press, 2012).Turetsky, M. R. et al. Nat. Geosci. 8, 11–14 (2014).Article 

    Google Scholar 
    Wenger, S. J., Subalusky, A. L. & Freeman, M. C. Food Webs 18, e00106 (2019).Article 

    Google Scholar 
    Tomatsuri, M. & Kon, K. Hydrobiologia 790, 225–232 (2017).Article 

    Google Scholar 
    Henry, L. A. & Roberts, J. M. in Marine Animal Forests (eds Rossi, S. et al.) 235–256 (Springer, 2017).Walton, M. E. M. et al. Sci. Total Environ. 820, 153191 (2022).Article 
    CAS 

    Google Scholar 
    Wolfe, K., Kenyon, T. M. & Mumby, P. J. Coral Reefs 40, 1769–1806 (2021).Article 

    Google Scholar 
    Kim, H. et al. Glob. Change Biol. 28, 6180–6193 (2022).Jackson, R. B. et al. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

    Google Scholar 
    Pan, Y. et al. Science 333, 988–993 (2011).Article 
    CAS 

    Google Scholar 
    Hedges, J. I., Keil, R. G. & Benner, R. Org. Geochem. 27, 195–212 (1997).Article 
    CAS 

    Google Scholar 
    Lønborg, C. et al. Front. Mar. Sci. 7, 466 (2020).Article 

    Google Scholar 
    Harden, J. W. et al. Glob. Change Biol. 6, 174–184 (2000).Davidson, E. A. & Janssens, I. A. Nature 440, 165–173 (2006).Article 
    CAS 

    Google Scholar 
    Hugelius, G. et al. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).Article 
    CAS 

    Google Scholar 
    Hennige, S. J. et al. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00668 (2020).Article 

    Google Scholar 
    Wolfram, U. et al. Sci. Rep. 12, 8052 (2022).Article 
    CAS 

    Google Scholar 
    Roberts, J. M., Wheeler, A. J. & Freiwald, A. Science 312, 543–547 (2006).Article 
    CAS 

    Google Scholar 
    Mortensen, P. B. & Fosså, J. H. Species diversity and spatial distribution of invertebrates on deep-water Lophelia reefs in Norway. In Proc. 10th Int. Coral Reef Symp. 1849–1868 (ICRS, 2006).Maier, S. R. et al. Deep Sea Res. I 175, 103574 (2021).. More

  • in

    Epibiotic fauna of the Antarctic minke whale as a reliable indicator of seasonal movements

    Rice, D. W. Marine mammals of the world: systematics and distribution. In The Society for Marine Mammalogy (ed. Rice, D. W.) 231 (Allen Press, 1998).
    Google Scholar 
    Best, P. B. External characters of southern minke whales and the existence of a diminutive form. Sci. Rep. Whales Res. Inst. 36, 1–33 (1985).
    Google Scholar 
    Acevedo, J. et al. Occurrence of dwarf minke whales (Balaenoptera acutorostrata subsp.) around the Antarctic Peninsula. Polar Biol. 34, 313–318 (2011).Article 

    Google Scholar 
    Risch, D., Norris, T., Curnock, M. & Friedlaender, A. Common and Antarctic minke whales: Conservation status and future research directions. Front. Mar. Sci. 6, 247. https://doi.org/10.3389/fmars.2019.00247 (2019).Article 

    Google Scholar 
    International Whaling Commission (IWC). Report of the scientific committee. J. Cetacean Res. Manag. 14, 102 (2013).
    Google Scholar 
    Matsuoka, K. et al. Overview of minke whale sightings surveys conducted on IWC/IDCR and SOWER Antarctic cruises from 1978/79 to 2000/01. J. Cetacean Res. Manag. 5, 173–201 (2003).
    Google Scholar 
    Glover, K. A. et al. Migration of Antarctic minke whales to the Arctic. PLoS One 5, e15197. https://doi.org/10.1371/journal.pone.0015197 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Williams, R., Brierley, A., Friedlaender, A. & Scheidat, M. Densitiy of Antarctic minke whales in Weddell Sea from helicopter survey data. Ecology 63, IA14 (2011).
    Google Scholar 
    Williams, R. et al. Counting whales in a challenging, changing environment. Sci. Rep. 4, 4170. https://doi.org/10.1038/srep04170 (2014).Article 
    CAS 

    Google Scholar 
    Shabangu, F. W., Findlay, K. & Stafford, K. M. Seasonal acoustic occurrence, diel vocalizing patterns and bioduck call-type composition of Antarctic minke whales off the west coast of South Africa and the Maud Rise Antarctica. Mar. Mamm. Sci. 36, 658–675 (2019).Article 

    Google Scholar 
    Kasamatsu, F., Nishiwaki, S. & Ishikawa, H. Breeding areas and southbound migrations of southern minke whales Balaenoptera acutorostrata. Mar. Ecol. Prog. Ser. 119, 1–10 (1995).Article 
    ADS 

    Google Scholar 
    Tamura, T. & Konishi, K. Food habit and prey consumption of Antarctic minke whale Balaenoptera bonaerensis in the JARPA research area. J. Northwest Atl. Fish. Sci. 42, 13–25 (2009).Article 

    Google Scholar 
    Perrin, W. F., Mallette, S. D. & Brownell, R. L. Minke whales. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 608–613 (Academic Press, 2018).Chapter 

    Google Scholar 
    Taylor, R. J. F. An unusual record of three species of whale being restricted to pools in Antarctic sea-ice. Proc. R. Soc. Lond. 129, 325–331 (1957).
    Google Scholar 
    Ensor, P. H. Minke whales in the pack ice zone, East Antarctica, during the period of maximum annual ice extent. Rep. Int. Whal. Commn 39, 219–225 (1989).
    Google Scholar 
    Scheidat, M. et al. Cetacean surveys in the Southern Ocean using icebreaker-supported helicopters. Polar Biol. 34, 1513–1522 (2011).Article 

    Google Scholar 
    Meirelles, A. C. O. & Furtado-Neto, M. A. A. Stranding of an Antarctic minke whale, Balaenoptera bonaerensis Burmeister, 1867, on the northern coast of South America. Lat. Am. J. Aquat. Mamm. 3, 81–82 (2004).Article 

    Google Scholar 
    Juri, E., Valdivia, M., Simoes-Lopes, P. C. & Le Bas, A. A note on minke whales (Cetacea: Balaenopteridae) in Uruguay: Strandings review. JCRM 21, 135–140 (2020).Article 

    Google Scholar 
    Williamson, G. R. Minke whales off Brazil. Sci. Rep. Whales Res. Inst. 27, 37–59 (1975).
    Google Scholar 
    Pastene, L. A. & Goto, M. Genetic characterization and population genetic structure of the Antarctic minke whale Balaenoptera bonaerensis in the Indo-Pacific region of the Southern Ocean. Fish Sci. 82, 873–886 (2016).Article 
    CAS 

    Google Scholar 
    Balbuena, J. A., Aznar, F. J., Fernández, M. & Raga, J. A. Parasites as indicators of social structure and stock identity of marine mammals. Dev. Mar. Biol. 4, 133–139 (1995).
    Google Scholar 
    Kuramochi, T., Araki, J., Uchida, Moriyama, N., Takeda, Y., Hayashi, N., Wakao, H., Machida, M. & Nagasawa, K. Summary of parasite and epizoit investigations during JARPN surveys 1994–1999, with reference to stock structure analysis for the western North Pacific minke whales. IWC Scientific Committee Workshop to Review the Japanese Whaling Programme under Special Permit for North Pacific Minke Whales (JARPN) SC/F2K/J19 (2000).Kaliszewska, Z. A. et al. Population histories of right whales (Cetacea: Eubalaena) inferred from mitochondrial sequence diversities and divergences of their whale lice (Amphipoda: Cyamus). Mol. Ecol. 14, 3439–3456 (2005).Article 
    CAS 

    Google Scholar 
    Ólafsdóttir, D. & Shinn, A. P. Epibiotic macrofauna on common minke whales, Balaenoptera acutorostrata Lacépède, 1804 Icelandic waters. Parasit. Vectors 6, 1–10 (2013).Article 

    Google Scholar 
    Matthews, C. J., Ghazal, M., Lefort, K. J. & Inuarak, E. Epizoic barnacles on Arctic killer whales indicate residency in warm waters. Mar. Mamm. Sci. 36, 1010–1014 (2020).Article 

    Google Scholar 
    Flach, L., Van Bressem, M. F., Pitombo, F. & Aznar, F. J. Emergence of the epibiotic barnacle Xenobalanus globicipitis in Guiana dolphins after a morbillivirus outbreak in Sepetiba Bay Brazil. Estuar. Coast. Shelf Sci. 263, 107632. https://doi.org/10.1016/j.ecss.2021.107632 (2021).Article 

    Google Scholar 
    Ten, S., Raga, J. A. & Aznar, F. J. Epibiotic fauna on cetaceans worldwide: A systematic review of records and indicator potential. Front. Mar. Sci. 9, 846558. https://doi.org/10.3389/fmars.2022.846558 (2022).Article 

    Google Scholar 
    Liouville, J. Cétacés de l’Antarctique. Paris: Deuxième Expédition Antarctique Française (1908–1910) (1913).Ohsumi, S., Masaki, Y. & Kawamura, A. Stock of the Antarctic minke whale. Sci. Rep. Whales Res. Inst. 22, 75–125 (1970).
    Google Scholar 
    Ohsumi, S. Find of marlin spear from the Antarctic minke whales. Sci. Rep. Whales Res. Inst. 25, 237–239 (1973).
    Google Scholar 
    Ivashin, M. V. External Parasites on Lesser Rorquals in the Antarctic 125–127 (Naukova Dumka, 1975).
    Google Scholar 
    Berzin, A. A. & Vlasova, L. P. Fauna of the Cetacea Cyamidae (Amphipoda) of the world ocean. Investig. Cet. 13, 149–164 (1982).
    Google Scholar 
    Best, P. B. Seasonal abundance, feeding, reproduction, age and growth in minke whales off Durban (with incidental observations from the Antarctic). Rep. Int. Whal. Commn 32, 759–786 (1982).
    Google Scholar 
    Avdeev, V. V. Parasitic amphipods of the family Cyamidae and the problem of Cetacea origin. Biol. Morja 4, 27–33 (1989).
    Google Scholar 
    Bushuev, S. G. A study of the population structure of the southern minke whale (Balaenoptera acutorostrata Lacepede) based on morphological and ecological variability. Rep. Int. Whal. Commn 40, 317–324 (1990).
    Google Scholar 
    Sedlak-Weinstein, E. Preliminary report of parasitic infestation of the minke whale Balaenoptera acutorostrata taken during the 1988/89 Antarctic expedition. Unpublished paper (1990).Dailey, M. D. & Vogelbein, W. Parasite fauna of 3 species of Antarctic whales with reference to their use as potential stock indicators. Fish. Bull. 89, 355–365 (1991).
    Google Scholar 
    Nemoto, T., Best, P. B., Ishimaru, K. & Takano, H. Diatom films on whales in South African waters. Sci. Rep. Whales Res. Inst. 32, 97–103 (1980).
    Google Scholar 
    Donovan, G. A review of IWC stock boundaries. Rep. Int. Whal. Commn 13, 39–68 (1991).
    Google Scholar 
    Lester, R. J. G. & MacKenzie, K. The use and abuse of parasites as stock markers for fish. Fish. Res. 97, 1–2 (2009).Article 

    Google Scholar 
    Ten, S. et al. Epibiotic barnacles of sea turtles as indicators of habitat use and fishery interactions: an analysis of juvenile loggerhead sea turtles, Caretta caretta, in the western Mediterranean. Ecol. Indic. 107, 105672. https://doi.org/10.1016/j.ecolind.2019.105672 (2019).Article 

    Google Scholar 
    Calman, W. T. A whale-barnacle of the genus Xenobalanus from Antarctic Seas. Ann. Mag. Nat. Hist. 6, 165–166 (1920).Article 

    Google Scholar 
    Kato, H., Hiroyama, H., Fujise, Y. & Ono, K. Preliminary report of the 1987/88 Japanese feasibility study of the special permit proposal for Southern Hemisphere Minke Whales. Rep. int. Whal. Commn 39, 235–248 (1989).
    Google Scholar 
    International Whaling Commission (IWC). Report of the Intersessional Workshop to review data and results from special permit research on minke whales in the Antarctic, Tokyo, 7–8 December 2006. J. Cetacean Res. Manag. 10, 411–445 (2008).
    Google Scholar 
    Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583 (1997).Article 
    CAS 

    Google Scholar 
    Kim, H., Chan, B., Kang, C., Kim, H. & Kim, W. How do whale barnacles live on their hosts? Functional morphology and mating-group sizes of Coronula diadema (Linnaeus, 1767) and Conchoderma auritum (Linnaeus, 1767) (Cirripedia: Thoracicalcarea). J. Crustac. Biol. 40, 808–824 (2020).Article 

    Google Scholar 
    Reiczigel, J. Confidence intervals for the binomial parameter: Some new considerations. Stat. Med. 22, 611–621 (2003).Article 

    Google Scholar 
    Kato, H. Migration strategy of southern minke whales to maintain high reproductive rate. Dev. Mar. Biol. 4, 465–480 (1995).
    Google Scholar 
    Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. In Statistics for Biology and Health (ed. Gail, M.) (Springer, 2009).MATH 

    Google Scholar 
    Fransen, C. H. J. M. & Smeenk, C. Whale-lice (Amphipoda: Cyamidae) recorded from The Netherlands. Zool. Meded. 65, 393–405 (1991).
    Google Scholar 
    Barton, N. A., Farewell, T. S. & Hallett, S. H. Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks. NPJ Clean Water 3, 31. https://doi.org/10.1038/s41545-020-0077-3 (2020).Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).Article 
    CAS 

    Google Scholar 
    Kane, E. A., Olson, P. A., Gerrodette, T. & Fiedler, P. Prevalence of the commensal barnacle Xenobalanus globicipitis on cetacean species in the eastern tropical Pacific Ocean, and a review of global occurrence. Fish. Bull. 106, 395–404 (2008).
    Google Scholar 
    Aznar, F. J., Balbuena, J. A. & Raga, J. A. Are epizoites biological indicators of a western Mediterranean striped dolphin die-off?. Dis. Aquat. Organ. 18, 159–163 (1994).Article 

    Google Scholar 
    Carrillo, J. M., Overstreet, R. M., Raga, J. A. & Aznar, F. J. Living on the edge: Settlement patterns by the symbiotic barnacle Xenobalanus globicipitis on small cetaceans. PLoS One 10, e0127367. https://doi.org/10.1371/journal.pone.0127367 (2015).Article 
    CAS 

    Google Scholar 
    Moreno-Colom, P., Ten, S., Raga, J. A. & Aznar, F. J. Spatial distribution and aggregation of Xenobalanus globicipitis on the flukes of striped dolphins, Stenella coeruleoalba: An indicator of host hydrodynamics?. Mar. Mamm. Sci. 36, 897–914 (2020).Article 

    Google Scholar 
    Aznar, F. J. et al. Changes in epizoic crustacean infestations during cetacean die-offs: The mass mortality of Mediterranean striped dolphins Stenella coeruleoalba revisited. Dis. Aquat. Org. 67, 239–247 (2005).Article 
    CAS 

    Google Scholar 
    Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Modell. 157, 157–177 (2002).Article 

    Google Scholar 
    Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).Book 
    MATH 

    Google Scholar 
    Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–58 (2003).Article 

    Google Scholar 
    Beasley, I. et al. Stomach contents of long-finned pilot whales, Globicephala melas mass-stranded in Tasmania. PLoS One 14, e0206747. https://doi.org/10.1371/journal.pone.0206747 (2019).Article 
    CAS 

    Google Scholar 
    Ohno, M. & Fujino, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets, season 1950/51. Sci. Rep. Whales Res. Inst. 7, 125–188 (1952).
    Google Scholar 
    Clarke, R. The stalked barnacle Conchoderma, ectoparasitic on whales. Norsk Hvalfangst-Tidende 55, 153–168 (1966).
    Google Scholar 
    Christensen, I. First record of gooseneck barnacles (Conchoderma auritum) on a minke whale (Balaenoptera acutorostrata). ICES C. M. 1985/N:9 (1985).Bertulli, C. G., Cecchetti, A., Van Bressem, M. F. & Van Waerebeek, K. Skin disorders in common minke whales and white-beaked dolphins off Iceland, a photographic assessment. J. Mar. Anim. Ecol. 5, 29–40 (2012).
    Google Scholar 
    Knowlton, N. Sibling species in the sea. Annu. Rev. Ecol. Evol. Syst. 24, 189–216 (1993).Article 

    Google Scholar 
    Trontelj, P. & Fišer, C. Perspectives: Cryptic species diversity should not be trivialised. Syst. Biodivers. 7, 1–3 (2009).Article 

    Google Scholar 
    Norris, R. & Hull, P. The temporal dimension of marine speciation. Evol. Ecol. 26, 393–415 (2011).Article 

    Google Scholar 
    Rawson, P., Macnamee, R., Frick, M. & Williams, K. Phylogeography of the coronulid barnacle, Chelonibia testudinaria, from loggerhead sea turtles Caretta caretta. Mol. Ecol. 12, 2697–2706 (2003).Article 
    CAS 

    Google Scholar 
    Cabezas, M. P., Cabezas, P., Machordom, A. & Guerra-García, J. M. Hidden diversity and cryptic speciation refute cosmopolitan distribution in Caprella penantis (Crustacea: Amphipoda: Caprellidae). J. Zool. Syst. Evol. 51, 85–99 (2013).Article 

    Google Scholar 
    Boyd, L. L., Zardus, J. D., Knauer, C. M. & Wood, L. D. Evidence for host selectivity and specialization by epizoic Chelonibia barnacles between hawksbill and green sea turtles. Front. Ecol. Evol. 9, 807237. https://doi.org/10.3389/fevo.2021.807237 (2021).Article 

    Google Scholar 
    Schell, D., Rowntree, V. & Pfeiffer, C. Stable-isotope and electron-microscopic evidence that cyamids (Crustacea: Amphipoda) feed on whale skin. Can. J. Zool. 78, 721–727 (2000).Article 

    Google Scholar 
    Iwasa-Arai, T. & Serejo, C. S. Phylogenetic analysis of the family Cyamidae (Crustacea: Amphipoda): A review based on morphological characters. Zool. J. Linn. Soc. 184, 66–94 (2018).Article 

    Google Scholar 
    Fraija-Fernández, N. et al. Living in a harsh habitat: Epidemiology of the whale louse, Syncyamus aequus (Cyamidae), infecting striped dolphins in the Western Mediterranean. J. Zool. 303, 199–206 (2017).Article 

    Google Scholar 
    Angot, M. Rapport scientifique sur les expeditions baleinieres autour de Madagascar (saisons 1949 et 1950). Mem. Inst. Sci. Madag. Ser. A 6, 439–486 (1951).
    Google Scholar 
    Newman, W. A. & Abbott, D. P. Cirripedia: The barnacles. In Intertidal Invertebrates of California (eds Morris, R. H. et al.) 504–535 (Stanford University Press, 1980).
    Google Scholar 
    Nogata, Y. & Matsumura, K. Larval development and settlement of a whale barnacle. Biol Lett. 2, 92–93 (2006).Article 

    Google Scholar 
    Hiro, F. The fauna of Akkeshi Bay. II. Cirripedia. J. Fac. Sci. Hokkaido Univ. 4, 213–229 (1935).
    Google Scholar 
    Rice, D. W. Progress report on biological studies of the larger Cetacea in the waters off California. Norsk Hvalfangst-Tid 52, 181–187 (1963).
    Google Scholar 
    Klinkhart, E. G. The beluga whale in Alaska. State Alsk. Dep. Fish 7, 11 (1966).
    Google Scholar 
    Nilsson-Cantell, C. A. Cirripedia Thoracica and Acrothoracica. MIOS 5, 1–133 (1978).
    Google Scholar 
    Scarff, J. E. Occurrence of the barnacles Coronula diadema, C. reginae and Cetopirus complanatus (Cirripedia) on right whales. Sci. Rep. Whales Res. Inst. 37, 129–153 (1986).
    Google Scholar 
    Kakuwa, Z., Kawakami, T. & Iguchi, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets in the 1951–52 season. Sci. Rep. Whales Res. Inst. 8, 147–213 (1953).
    Google Scholar 
    Nishiwaki, M. Humpback whales in Ryukyuan waters. Sci. Rep. Whales Res. Inst. 14, 49–87 (1959).
    Google Scholar 
    Best, P. B. The presence of coronuline barnacles on a southern right whale Eubalaena australis. S. Afr. J. Mar. Sci. 11, 585–587 (1991).Article 

    Google Scholar 
    Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Disc. Rep. 1, 257–540 (1929).
    Google Scholar 
    Nilsson-Cantell, C. A. Thoracic cirripedes collected in 1925–1927. Disc. Rep. 2, 223–260 (1930).
    Google Scholar 
    Nishiwaki, M. & Hayashi, K. Biological survey of fin and blue whales taken in the Antarctic season 1947–48 by the Japanese fleet. Sci. Rep. Whales Res. Inst. 3, 132–190 (1950).
    Google Scholar 
    Mizue, K. & Murata, T. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets season 1949–50. Sci. Rep. Whales Res. Inst. 6, 73–131 (1951).
    Google Scholar 
    Nishiwaki, M. & Oye, T. Biological investigation on blue whales (Balaenoptera musculus) and Fin Whales (Balaenoptera physalus) caught by the Japanese Antarctic Whaling Fleets. Sci. Rep. Whales Res. Inst. 5, 91–167 (1951).
    Google Scholar 
    Tomilin, A. G. Cetacea. In Mammals of the U.S.S.R. and Adjacent Countries Vol. 9 (ed. Tomilin, A. G.) 717 (Akademii Nauk SSSR, 1957).
    Google Scholar 
    Cockrill, W. R. Pathology of the cetacea. A veterinary study on whales. Br. Vet. J. 116, 1–28 (1960).
    Google Scholar 
    Kawamura, A. Some consideration on the stock unit of sei whales by the aspect of ectoparasitic organisms on the body. Bull. Jpn. Soc. Fish. Oceanogr. 14, 38–43 (1969).
    Google Scholar 
    Fraija-Fernández, N., Hernández-Hortelano, A., Ahuir-Baraja, A. E., Raga, J. A. & Aznar, F. J. Taxonomic status and epidemiology of the mesoparasitic copepod Pennella balaenoptera in cetaceans from the western Mediterranean. Dis. Aquat. Org. 128, 249–258 (2018).Article 

    Google Scholar 
    Foster, B. A. & Willan, R. C. Foreign barnacles transported to New Zealand on an oil platform. N. Z. J. Mar. Freshw. Res. 13, 143–149 (1979).Article 

    Google Scholar 
    González, J. et al. Cirripedia of the Canary islands: Distribution and ecological notes. J. Mar. Biol. Assoc. U.K. 92, 129–141 (2012).Article 

    Google Scholar 
    Zettler, M. L. An example for transatlantic hitchhiking by macrozoobenthic organisms with a research vessel. Helgol. Mar. Res. 75, 4. https://doi.org/10.1186/s10152-021-00549-w (2021).Article 

    Google Scholar 
    Matthews, L. H. The humpback whale Megaptera novaeangliae. Disc. Rep. 17, 7–92 (1937).
    Google Scholar 
    Scheffer, V. B. Organisms collected from whales in the Aleutian Islands. Murrelet 20, 67–69 (1939).Article 

    Google Scholar 
    Symons, H. W. & Weston, R. D. Studies on the humpback whale (Megaptera nodosa) in the Bellinghausen Sea. Norsk Hvalfangsttid 47, 53–81 (1958).
    Google Scholar 
    Van Waerebeek, K., Reyes, J. C. & Alfaro, J. Helminth parasites and phoronts of dusky dolphins Lagenorhynchus obscurus (Gray, 1828) from Peru. Aquat. Mamm. 19, 159–169 (1993).
    Google Scholar 
    Fertl, D. Barnacles. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 75–78 (Academic Press, 2002).
    Google Scholar 
    Cornwall, I. E. The barnacles of british Columbia. Br. Col. Prov. Mus. Dept. 7, 5–69 (1955).
    Google Scholar 
    Abaunza, P., Arroyo, N. L. & Preciado, I. A contribution to the knowledge on the morphometry and the anatomical characters of Pennella balaenopterae (Copepoda, Ciphonostomatoida, Pennellidae), with special reference to the buccal complex. Crustaceana 74, 193–210 (2001).Article 

    Google Scholar 
    Marcer, F. et al. Parasitological and pathological findings in fin whales Balaenoptera physalus stranded along Italian coastlines. Dis. Aquat. Org. 133, 25–37 (2019).Article 
    CAS 

    Google Scholar 
    Turner, W. On Pennella balænopteræ: A crustacean, parasitic on a finner whale, Balaenoptera musculus. Earth. Environ. Sci. Trans. R. Soc. Edinb. 41, 409–434 (1905).Article 

    Google Scholar 
    Walker, W. A. & Hanson, M. B. Biological observations on Stejneger’s beaked whale, Mesoplodon stejnegeri, from strandings on Adak Alaska. Mar. Mamm. Sci. 15, 1314–1329 (1999).Article 

    Google Scholar 
    Delaney, M. A., Ford, J. K. B., Tang, K. & Gaydos, J. K. Mesoparasitic copepod (Pennella balaenopterae) infestation of a stranded offshore orca (Orcinus orca) in Southeast Alaska: Review of significance as a health indicator in cetaceans. In IAAAM 21–26 (2016).Suyama, S., Kakehi, S., Yanagimoto, T. & Chow, S. Infection of the pacific saury Cololabis saira (Brevoort, 1856) (Teleostei: Beloniformes: Scomberesocidae) by Pennella sp. (Copepoda: Siphonostomatoida: Pennellidae) south of the Subarctic Front. J. Crust. Biol. 40, 384–389 (2020).Article 

    Google Scholar 
    Rowntree, V. J. Feeding, distribution and reproductive behavior of cyamids (Crustacea: Amphipoda) living on humpback and right whales. Can. J. Zool. 74, 103–109 (1996).Article 

    Google Scholar 
    Leung, Y. M. Life cycle of Cyamus scammoni (Amphipoda: Cyamidae), ectoparasite of gray whale, with a remark on the associated species. Sci. Rep. Whales Res. Inst. 28, 153–160 (1976).
    Google Scholar 
    MacIntyre, R. J. Rapid growth in stalked barnacles. Nature 212, 637–638 (1966).Article 
    ADS 

    Google Scholar 
    Rasmussen, T. Notes on the biology of the shipfouling gooseneck barnacle Conchoderma auritum Linnaeus, 1776 (Cirripedia; Lepadomorpha). Biol. Mar. 2, 37–44 (1980).
    Google Scholar 
    Dalley, R. & Crisp, D. J. Conchoderma: A fouling hazard to ships underway. Mar. Biol. Lett. 2, 141–152 (1981).
    Google Scholar 
    Dalley, R. The larval stages of the oceanic, pedunculate barnacle Conchoderma auritum (L) (Cirripedia, Thoracica). Crustaceana 46, 39–54 (1984).Article 

    Google Scholar 
    Foskolos, I., Provata, M. T. & Frantzis, A. First record of Conchoderma auritum (Cirripedia: Lepadidae) on Ziphius cavirostris (Cetacea: Ziphiidae) in Greece. Ann. Ser. Hist. 27, 29–34 (2017).
    Google Scholar 
    Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelem. 5, 23. https://doi.org/10.1186/s40317-017-0138-7 (2017).Article 

    Google Scholar 
    Darwin, C. A Monograph on the Subclass Cirripedia Vol. 1 (The Ray Society, 1851).
    Google Scholar 
    Tsikhon-Lukanina, V. A., Soldatova, I. N., Kuznetsova, I. A. & Il’in, I. I. Macrofouling community in the Strait of Tunisia (Sicily). Oceanology 16, 519–522 (1977).
    Google Scholar 
    Nilsson-Cantell, C. A. Cirripedien von der Stewart Insel und von Südgeorgien. Senckenbergiana 12, 210–213 (1930).
    Google Scholar 
    Slijper, E. J. Whales (Hutchinson, 1962).
    Google Scholar 
    Kaufman, G. D. & Forestell, P. H. Hawaii’s humpback whales, a complete whalewatching guide (Pacific Whale Foundation Press, 1986).
    Google Scholar 
    Dawbin, W. H. Baleen whales. In Whales, Dolphins and Porpoises (eds Harrison, R. & Bryden, M.) 44–65 (Facts on File, 1988).
    Google Scholar 
    Félix, F., Bearson, B. & Falconí, J. Epizoic barnacles removed from the skin of a humpback whale after a period of intense surface activity. Mar. Mamm. Sci. 22, 979–984 (2006).Article 

    Google Scholar 
    Towers, J. R. et al. Seasonal movements and ecological markers as evidence for migration of common minke whales photo-identified in the eastern North Pacific. J. Cetacean Res. Manag. 13, 221–229 (2013).
    Google Scholar 
    Iwasa-Arai, T. et al. The host-specific whale louse (Cyamus boopis) as a potential tool for interpreting humpback whale (Megaptera novaeangliae) migratory routes. J. Exp. Mar. Biol. Ecol. 505, 45–51 (2018).Article 

    Google Scholar 
    Lehnert, K. et al. Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts – Morphological and molecular characterization and health implications. Int. J. Parasitol. 15, 22–30 (2021).
    Google Scholar 
    Dreyer, N. et al. How whale and dolphin barnacles attach to their hosts and the paradox of remarkably versatile attachment structures in cypris larvae. Org. Divers. Evol. 20, 233–249 (2020).Article 

    Google Scholar 
    Visser, I. N., Cooper, T. E. & Grimm, H. Duration of pseudo-stalked barnacles (Xenobalanus globicipitis) on a New Zealand Pelagic ecotype orca (Orcinus orca), with comments on cookie cutter shark bite marks (Isistius sp.); can they be used as biological tags?. Biol. Divers. 11, 1067–1086 (2020).
    Google Scholar 
    Van Waerebeek, K. & Reyes, J. C. A note on incidental fishery mortality of southern minke whales off western South America. Rep. Int. Whal. Commn 15, 521–523 (1994).
    Google Scholar 
    Félix, F. & Haase, B. A note on the northernmost record of the Antarctic minke whale (Balaenoptera bonaerensis) in the Eastern Pacific. J. Cetacean Res. Manag. 13, 191–194 (2013).
    Google Scholar 
    Esposito, C., Bichet, O. & Petit, M. First sightings of Antarctic minke whale (Balaenoptera bonaerensis) mother–calf pairs in French Polynesia. Aquat. Mamm. 47, 175–180 (2021).Article 

    Google Scholar 
    Karaa, S., Insacco, G., Bradai, M. N. & Scaravelli, D. Records of Xenobalanus globicipitis on Balaenoptera physalus and Stenella coeruleoalba in Tunisian and Sicilian waters. Nat. Rerum 1, 55–59 (2011).
    Google Scholar 
    Oliveira, J. B., Morales, J. A., González-Barrientos, R. C., Hernández-Gamboa, J. & Hernández-Mora, G. Parasites of cetaceans stranded on the Pacific Coast of Costa Rica. Vet. Parasitol. 182, 319–328. https://doi.org/10.1016/j.vetpar.2011.05.014 (2011).Article 
    CAS 

    Google Scholar 
    Dı́az-Gamboa, R. E. Varamiento de orcas pigmeas (Feresa attenuata Gray 1874) en Yucatán: Reporte de caso. Bioagrociencias 8, 36–43 (2015).
    Google Scholar 
    IJsseldijk, L. L. et al. Beached bachelors: An extensive study on the largest recorded sperm whale Physeter macrocephalus mortality event in the north sea. PloS One 13, e0201221. https://doi.org/10.1371/journal.pone.0201221 (2018).Article 
    CAS 

    Google Scholar 
    Guerrero-Ruiz, M. & Urbán, J. R. First report of remoras on two killer whales (Orcinus orca) in the Gulf of California Mexico. Aquat. Mamm. 26, 148–150 (2000).
    Google Scholar 
    Kautek, G., Van Bressem, M. F. & Ritter, F. External body conditions in cetaceans from La Gomera, Canary Islands Spain. J. Marine Anim. Ecol. 11, 4–17 (2008).
    Google Scholar 
    Bearzi, M. & Patonai, K. Occurrence of the barnacle (Xenobalanus globicipitis) on coastal and offshore common bottlenose dolphins (Tursiops truncatus) in Santa Monica Bay and adjacent areas California. Bull. S. Calif. Acad. Sci. 109, 37–44. https://doi.org/10.3160/0038-3872-109.2.37 (2010).Article 

    Google Scholar 
    Foote, A. D. et al. Genetic differentiation among North Atlantic killer whale populations. Mol. Ecol. 20, 629–641. https://doi.org/10.1111/j.1365-294X.2010.04957.x (2011).Article 

    Google Scholar 
    Toth, J. L., Hohn, A. A., Able, K. W. & Gorgone, A. M. Defining bottlenose dolphin (Tursiops truncatus) stocks based on environmental, physical and behavioral characteristics. Mar. Mamm. Sci. 28, 461–478. https://doi.org/10.1111/j.1748-7692.2011.00497.x (2012).Article 

    Google Scholar 
    Urian, K. W., Kaufmann, R., Waples, D. M. & Read, A. J. The prevalence of ectoparasitic barnacles discriminates stocks of Atlantic common bottlenose dolphins (Tursiops truncatus) at risk of entanglement in coastal gill net fisheries. Mar. Mamm. Sci. 35, 290–299. https://doi.org/10.1111/mms.12522 (2019).Article 

    Google Scholar 
    Siciliano, S. et al. Epizoic barnacle (Xenobalanus globicipitis) infestations in several cetacean species in South-Eastern Brazil. Mar. Biol. Res. 16, 1–13. https://doi.org/10.1080/17451000.2020.1783450 (2020).Article 

    Google Scholar 
    Whitehead, T. O., Rollinson, D. P. & Reisinger, R. R. Pseudostalked barnacles Xenobalanus globicipitis attached to killer whales Orcinus orca in South African waters. Mar. Biodivers. Rec. 45, 873–876. https://doi.org/10.1007/s12526-014-0296-2 (2014).Article 

    Google Scholar 
    Methion, S. & Dı́az López, B. First record of atypical pigmentation pattern in fin whale Balaenoptera physalus in the Atlantic ocean. Dis. Aquat. Org. 135, 121–125. https://doi.org/10.3354/dao03385 (2019).Article 

    Google Scholar 
    Herr, H., Burkhardt-Holm, P., Heyer, K., Siebert, U. & Selling, J. Injuries, malformations and epidermal conditions in cetaceans of the strait of Gibraltar. Aquat. Mamm. 46, 215–235. https://doi.org/10.1578/AM.46.2.2020.215 (2020).Article 

    Google Scholar 
    Herr, H. et al. Return of large fin whale feeding aggregations to historical whaling grounds in the southern ocean. Sci. Rep. 12, 9458. https://doi.org/10.1038/s41598-022-13798-7 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Gruvel, J. A. Cirrhipèdes Provenant Des Campagnes Scientifiques De S.A.S. Le Prince De Monaco, (1885– 1913). In Résultas Des Campagnes Scientifiques Accomplies Sur Son Yacht Par Albert Ler (Monaco: Prince Souverain de Monaco) 1-88 (1920).Annandale, N. The rate of growth in Conchoderma and Lepas. Rec. Indian Mus. 3, 295 (1909).
    Google Scholar 
    Il’in, I. I., Kuznetsova, L. A. & Starostin, I. V. Oceanic fouling in the equatorial Atlantic. Oceanology 18, 597–599 (1978).
    Google Scholar 
    Eckert, K. L. & Eckert, S. A. Growth rate and reproductive condition of the barnacle Conchoderma virgatum on gravid leatherback sea turtles in Caribbean waters. J. Crust. Biol. 7, 682–690. https://doi.org/10.2307/1548651 (1987).Article 

    Google Scholar 
    Arroyo, N. L., Abaunza, P. & Preciado, I. The first naupliar stage of Pennella balaenopterae Koren and Danielssen 1877 (Copepoda: Siphonostomatoida, Pennellidae). Sarsia 87, 333–337. https://doi.org/10.1080/0036482021000155785 (2002).Article 

    Google Scholar  More

  • in

    Economic and biophysical limits to seaweed farming for climate change mitigation

    Monte Carlo analysisSeaweed production costs and net costs of climate benefits were estimated on the basis of outputs of the biophysical and technoeconomic models described below. The associated uncertainties and sensitivities were quantified by repeatedly sampling from uniform distributions of plausible values for each cost and economic parameter (n = 5,000 for each nutrient scenario from the biophysical model, for a total of n = 10,000 simulations; see Supplementary Figs. 14 and 15)47,48,49,50,51,52. Parameter importance across Monte Carlo simulations (Fig. 3 and Supplementary Fig. 9) was determined using decision trees in LightGBM, a gradient-boosting machine learning framework.Biophysical modelG-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass30,53, that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km2 per year)33,34. In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth54. Seaweed biomass is then lost via mortality, which includes breakage from variable ocean wave intensity. The conversion from stored nitrogen to biomass is based on the minimum internal nitrogen requirements of macroalgae, and the conversion from biomass to units of carbon is based on an average carbon content of macroalgal dry weight (~30%)55. The model accounts for farming intensity (sub-grid-scale crowding) and employs a conditional harvest scheme, where harvest is optimized on the basis of growth rate and standing biomass33.The G-MACMODS model is parameterized for four types of macroalgae: temperate brown, temperate red, tropical brown and tropical red. These types employed biophysical parameters from genera that represent over 99.5% of present-day farmed macroalgae (Eucheuma, Gracilaria, Kappahycus, Sargassum, Porphyra, Saccharina, Laminaria, Macrocystis)39. Environmental inputs were derived from satellite-based and climatological model output mapped to 1/12-degree global resolution, which resolves continental shelf regions. Nutrient distributions were derived from a 1/10-degree resolution biogeochemical simulation led by the National Center for Atmospheric Research (NCAR) and run in the Community Earth System Model (CESM) framework35.Two nutrient scenarios were simulated with G-MACMODS and evaluated using the technoeconomic model analyses described below: the ‘ambient nutrient’ scenario where seaweed growth was computed using surface nutrient concentrations without depletion or competition, and ‘limited nutrient’ simulations where seaweed growth was limited by an estimation of the nutrient supply to surface waters (computed as the flux of deep-water nitrate through a 100 m depth horizon). For each Monte Carlo simulation in the economic analysis, the technoeconomic model randomly selects either the 5th, 25th, 50th, 75th or 95th percentile G-MACMODS seaweed yield map from a normal distribution to use as the yield map for that simulation. Figures and numbers reported in the main text are based on the ambient-nutrient scenario; results based on the limited-nutrient scenario are shown in Supplementary Figures.Technoeconomic modelAn interactive web tool of the technoeconomic model is available at https://carbonplan.org/research/seaweed-farming.We estimated the net cost of seaweed-related climate benefits by first estimating all costs and emissions related to seaweed farming, up to and including the point of harvest at the farm location, then estimating costs and emissions related to the transportation and processing of harvested seaweed, and finally estimating the market value of seaweed products and either carbon sequestered or GHG emissions avoided.Production costs and emissionsSpatially explicit costs of seaweed production ($ tDW−1) and production-related emissions (tCO2 tDW−1) were calculated on the basis of ranges of capital costs ($ km−2), operating costs (including labour, $ km−2), harvest costs ($ km−2) and transport emissions per distance travelled (tCO2 km−1) in the literature (Table 1, Supplementary Tables 1 and 2); annual seaweed biomass (tDW km−2, for the preferred seaweed type in each grid cell), line spacing and number of harvests (species-dependent) from the biophysical model; as well as datasets of distances to the nearest port (km), ocean depth (m) and significant wave height (m).Capital costs were calculated as:$$c_{cap} = c_{capbase} + left( {c_{capbase} times left( {k_d + k_w} right)} right) + c_{sl}$$
    (1)
    where ccap is the total annualized capital costs per km2, ccapbase is the annualized capital cost per km2 (for example, cost of buoys, anchors, boats, structural rope) before applying depth and wave impacts, kd and kw are the impacts of depth and waviness on capital cost, respectively, each expressed as a multiplier between 0 and 1 modelled using our Monte Carlo method and applied only to grid cells with depth >500 m and/or significant wave height >3 m, respectively, and csl is the total annual cost of seeded line calculated as:$$c_{sl} = c_{slbase} times p_{sline}$$
    (2)
    where cslbase is the cost per metre of seeded line, and psline is the total length of line per km2, based on the optimal seaweed type grown in each grid cell.Operating and maintenance costs were calculated as:$$c_{op} = c_{ins} + c_{lic} + c_{lab} + c_{opbase}$$
    (3)
    where cop is the total annualized operating and maintenance costs per km2, cins is the annual insurance cost per km2, clic is the annual cost of a seaweed aquaculture license per km2, clab is the annual cost of labour excluding harvest labour, and copbase is all other operating and maintenance costs.Harvest costs were calculated as:$$c_{harv} = c_{harvbase} times n_{harv}$$
    (4)
    where charv is the total annual costs associated with harvesting seaweed per km2, charvbase is the cost per harvest per km2 (including harvest labour but excluding harvest transport), and nharv is the total number of harvests per year.Costs associated with transporting equipment to the farming location were calculated as:$$c_{eqtrans} = c_{transbase} times m_{eq} times d_{port}$$
    (5)
    where ceqtrans is total annualized cost of transporting equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.The total production cost of growing and harvesting seaweed was therefore calculated as:$$c_{prod} = frac{{left( {c_{cap}} right) + left( {c_{op}} right) + left( {c_{harv}} right) + (c_{eqtrans})}}{{s_{dw}}}$$
    (6)
    where cprod is total annual cost of seaweed production (growth + harvesting), ccap is as calculated in equation (1), cop is as calculated in equation (3), charv is as calculated in equation (4), ceqtrans is as calculated in equation (5) and sdw is the DW of seaweed harvested annually per km2.Emissions associated with transporting equipment to the farming location were calculated as:$$e_{eqtrans} = e_{transbase} times m_{eq} times d_{port}$$
    (7)
    where eeqtrans is the total annualized CO2 emissions in tons from transporting equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.Emissions from maintenance trips to/from the seaweed farm were calculated as:$$e_{mnt} = left( {left( {2 times d_{port}} right) times e_{mntbase} times left( {frac{{n_{mnt}}}{{a_{mnt}}}} right)} right) + (e_{mntbase} times d_{mnt})$$
    (8)
    where emnt is total annual CO2 emissions from farm maintenance, dport is the ocean distance to the nearest port in km, nmnt is the number of maintenance trips per km2 per year, amnt is the area tended to per trip, dmnt is the distance travelled around each km2 for maintenance and emntbase is the CO2 emissions from travelling 1 km on a typical fishing maintenance vessel (for example, a 14 m Marinnor vessel with 2 × 310 hp engines) at an average speed of 9 knots (16.67 km h−1), resulting in maintenance vessel fuel consumption of 0.88 l km−1 (refs. 28,56).Total emissions from growing and harvesting seaweed were therefore calculated as:$$e_{prod} = frac{{(e_{eqtrans}) + (e_{mnt})}}{{s_{dw}}}$$
    (9)
    where eprod is total annual emissions from seaweed production (growth + harvesting), eeqtrans is as calculated in equation (7), emnt is as calculated in equation (8) and sdw is the DW of seaweed harvested annually per km2.Market value and climate benefits of seaweedFurther transportation and processing costs, economic value and net emissions of either sinking seaweed in the deep ocean for carbon sequestration or converting seaweed into usable products (biofuel, animal feed, pulses, vegetables, fruits, oil crops and cereals) were calculated on the basis of ranges of transport costs ($ tDW−1 km−1), transport emissions (tCO2-eq t−1 km−1), conversion cost ($ tDW−1), conversion emissions (tCO2-eq tDW−1), market value of product ($ tDW−1) and the emissions avoided by product (tCO2-eq tDW−1) in the literature (Table 1). Market value was treated as globally homogeneous and does not vary by region. Emissions avoided by products were determined by comparing estimated emissions related to seaweed production to emissions from non-seaweed products that could potentially be replaced by seaweed (including non-CO2 greenhouse gas emissions from land use)24. Other parameters used are distance to nearest port (km), water depth (m), spatially explicit sequestration fraction (%)57 and distance to optimal sinking location (km; cost-optimized for maximum emissions benefit considering transport emissions combined with spatially explicit sequestration fraction; see ‘Distance to sinking point calculation’ below). Each Monte Carlo simulation calculated the cost of both CDR via sinking seaweed and GHG emissions mitigation via seaweed products.For seaweed CDR, after the seaweed is harvested, it can either be sunk in the same location that it was grown, or be transported to a more economically favourable sinking location where more of the seaweed carbon would remain sequestered for 100 yr (see ‘Distance to optimal sinking point’ below). Immediately post-harvest, the seaweed still contains a large amount of water, requiring a conversion from dry mass to wet mass for subsequent calculations33:$$s_{ww} = frac{{s_{dw}}}{{0.1}}$$
    (10)
    where sww is the annual wet weight of seaweed harvested per km2 and sdw is the annual DW of seaweed harvested per km2.The cost to transport harvested seaweed to the optimal sinking location was calculated as:$$c_{swtsink} = c_{transbase} times d_{sink} times s_{ww}$$
    (11)
    where cswtsink is the total annual cost to transport harvested seaweed to the optimal sinking location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10).The costs associated with transporting replacement equipment (for example, lines, buoys,anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type) in the sinking CDR pathway were calculated as:$$c_{eqtsink} = left( {c_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (c_{transbase} times d_{port} times m_{eq})$$
    (12)
    where ceqtsink is the total annualized cost to transport both used and replacement equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port (see Supplementary Fig. 16). These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).The total value of seaweed that is sunk for CDR was therefore calculated as:$$v_{sink} = frac{{left( {v_{cprice} – left( {c_{swtsink} + c_{eqtsink}} right)} right)}}{{s_{dw}}}$$
    (13)
    where vsink is the total value (cost, if negative) of seaweed farmed for CDR in $ tDW−1, vcprice is a theoretical carbon price, cswtsink is as calculated in equation (11), ceqtsink is as calculated in equation (12) and sdw is the annually harvested seaweed DW in t km−2. We did not assume any carbon price in our Monte Carlo simulations (vcprice is equal to zero), making vsink negative and thus representing a net cost.To calculate net carbon impacts, our model included uncertainty in the efficiency of using the growth and subsequent deep-sea deposition of seaweed as a CDR method. The uncertainty is expected to include the effects of reduced phytoplankton growth from nutrient competition, the relationship between air–sea gas exchange and overturning circulation (hereafter collectively referred to as the ‘atmospheric removal fraction’) and the fraction of deposited seaweed carbon that remains sequestered for at least 100 yr. The total amount of atmospheric CO2 removed by sinking seaweed was calculated as:$$e_{seqsink} = k_{atm} times k_{fseq} times frac{{tC}}{{tDW}} times frac{{tCO_2}}{{tC}}$$
    (14)
    where eseqsink is net atmospheric CO2 sequestered annually in t km−2, katm is the atmospheric removal fraction and kfseq is the spatially explicit fraction of sunk seaweed carbon that remains sequestered for at least 100 yr (see ref. 57).The emissions from transporting harvested seaweed to the optimal sinking location were calculated as:$$e_{swtsink} = e_{transbase} times d_{sink} times s_{ww}$$
    (15)
    where eswtsink is the total annual CO2 emissions from transporting harvested seaweed to the optimal sinking location in tCO2 km−2, etransbase is the CO2 emissions (tons) from transporting 1 ton of material 1 km on a barge (tCO2 per t-km), dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10). Since the unit for etransbase is tCO2 per t-km, the emissions from transporting seaweed to the optimal sinking location are equal to (e_{mathrm{transbase}} times d_{mathrm{sink}} times s_{mathrm{ww}}), and the emissions from transporting seaweed from the optimal sinking location back to the farm are equal to 0 (since the seaweed has already been deposited, the seaweed mass to transport is now 0). Note that this does not yet include transport emissions from transport of equipment post-seaweed-deposition (see equation 16 below and Supplementary Fig. 16).The emissions associated with transporting replacement equipment (for example, lines, buoys, anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type)28,41 in the sinking CDR pathway were calculated as:$$e_{eqtsink} = left( {e_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (e_{transbase} times d_{port} times m_{eq})$$
    (16)
    where eeqtsink is the total annualized CO2 emissions in tons from transporting both used and replacement equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port. These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).Net CO2 emissions removed from the atmosphere by sinking seaweed were thus calculated as:$$e_{remsink} = frac{{left( {e_{seqsink} – left( {e_{swtsink} + e_{eqtsink}} right)} right)}}{{s_{dw}}}$$
    (17)
    where eremsink is the net atmospheric CO2 removed per ton of seaweed DW, eseqsink is as calculated in equation (14), eswtsink is as calculated in equation (15), eeqtsink is as calculated in equation (16) and sdw is the annually harvested seaweed DW in t km−2.Net cost of climate benefitsSinkingTo calculate the total net cost and emissions from the production, harvesting and transport of seaweed for CDR, we combined the cost and emissions from the sinking-pathway cost and value modules. The total net cost of seaweed CDR per DW ton of seaweed was calculated as:$$c_{sinknet} = c_{prod} – v_{sink}$$
    (18)
    where csinknet is the total net cost of seaweed for CDR per DW ton harvested, cprod is the net production cost per DW ton as calculated in equation (6) and vsink is the net value (or cost, if negative) per ton seaweed DW as calculated in equation (13).The total net CO2 emissions removed per DW ton of seaweed were calculated as:$$e_{sinknet} = e_{remsink} – e_{prod}$$
    (19)
    where esinknet is the total net atmospheric CO2 removed per DW ton of seaweed harvested annually (tCO2 tDW−1 yr−1), eremsink is the net atmospheric CO2 removed via seaweed sinking annually as calculated in equation (17) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where esinknet is negative (that is, net emissions rather than net removal) were not included in subsequent calculations since they would not be contributing to CDR in that location under the given scenario. Note that these net emissions cases only occur in areas far from port in specific high-emissions scenarios. Even in such cases, most areas still contribute to CO2 removal (negative emissions), hence costs from locations with net removal were included.Total net cost was then divided by total net emissions to get a final value for cost per ton of atmospheric CO2 removed:$$c_{pertonsink} = frac{{c_{sinknet}}}{{e_{sinknet}}}$$
    (20)
    where cpertonsink is the total net cost per ton of atmospheric CO2 removed via seaweed sinking ($ per tCO2 removed), csinknet is total net cost per ton seaweed DW harvested as calculated in equation (18) ($ tDW−1) and esinknet is the total net atmospheric CO2 removed per ton seaweed DW harvested as calculated in equation (19) (tCO2 tDW−1).GHG emissions mitigationInstead of sinking seaweed for CDR, seaweed can be used to make products (including but not limited to food, animal feed and biofuels). Replacing convention products with seaweed-based products can result in ‘avoided emissions’ if the emissions from growing, harvesting, transporting and converting seaweed into products are less than the total greenhouse gas emissions (including non-CO2 GHGs) embodied in conventional products that seaweed-based products replace.When seaweed is used to make products, we assumed it is transported back to the nearest port immediately after being harvested. The annualized cost to transport the harvested seaweed and replacement equipment (for example, lines, buoys, anchors) was calculated as:$$c_{transprod} = frac{{left( {c_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
    (21)
    where ctransprod is the annualized cost per ton seaweed DW to transport seaweed and equipment back to port from the farm location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.The total value of seaweed that is used for seaweed-based products was calculated as:$$v_{product} = v_{mkt} – left( {c_{transprod} + c_{conv}} right)$$
    (22)
    where vproduct is the total value (cost, if negative) of seaweed used for products ($ tDW−1), vmkt is how much each ton of seaweed would sell for, given the current market price of conventional products that seaweed-based products replace ($ tDW−1), ctransprod is as calculated in equation (21) and cconv is the cost to convert each ton of seaweed to a usable product ($ tDW−1).The annualized CO2 emissions from transporting harvested seaweed and equipment back to port were calculated as:$$e_{transprod} = frac{{left( {e_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
    (23)
    where etransprod is the annualized CO2 emissions per ton seaweed DW to transport seaweed and equipment back to port from the farm location, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.Total emissions avoided by each ton of harvested seaweed DW were calculated as:$$e_{avprod} = e_{subprod} – left( {e_{transprod} + e_{conv}} right)$$
    (24)
    where eavprod is total CO2-eq emissions avoided per ton of seaweed DW per year (including non-CO2 GHGs using a GWP time period of 100 yr), esubprod is the annual CO2-eq emissions avoided per ton seaweed DW by replacing a conventional product with a seaweed-based product, etransprod is as calculated in equation (23) and econv is the annual CO2 emissions per ton seaweed DW from converting seaweed into usable products. esubprod was calculated by converting seaweed DW to caloric content58 for food/feed and comparing emissions intensity per kcal to agricultural products24, or by converting seaweed DW into equivalent biofuel content with a yield of 0.25 tons biofuel per ton DW59 and dividing the CO2 emissions per ton fossil fuel by the seaweed biofuel yield.To calculate the total net cost and emissions from the production, harvesting, transport and conversion of seaweed for products, we combined the cost and emissions from the product-pathway cost and value modules. The total net cost of seaweed for products per ton DW was calculated as:$$c_{prodnet} = c_{prod} – v_{product}$$
    (25)
    where cprodnet is the total net cost per ton DW of seaweed harvested for use in products, cprod is the net production cost per ton DW as calculated in equation (6) and vproduct is the net value (or cost, if negative) per ton DW as calculated in equation (22).The total net CO2-eq emissions avoided per ton DW of seaweed used in products were calculated as:$$e_{prodnet} = e_{avprod} – e_{prod}$$
    (26)
    where eprodnet is the total net CO2-eq emissions avoided per ton DW of seaweed harvested annually (tCO2 tDW−1 yr−1), eavprod is the net CO2-eq emissions avoided by seaweed products annually as calculated in equation (24) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where eprodnet is negative (that is, net emissions rather than net emissions avoided) were not included in subsequent calculations since they would not be avoiding any emissions in that scenario.Total net cost was then divided by total net emissions avoided to get a final value for cost per ton of CO2-eq emissions avoided:$$c_{pertonprod} = frac{{c_{prodnet}}}{{e_{prodnet}}}$$
    (27)
    where cpertonprod is the total net cost per ton of CO2-eq emissions avoided by seaweed products ($ per tCO2-eq avoided), cprodnet is total net cost per ton seaweed DW harvested for products as calculated in equation (25) ($ tDW−1) and eprodnet is total net CO2-eq emissions avoided per ton seaweed DW harvested for products as calculated in equation (26) (tCO2 tDW−1).Parameter ranges for Monte Carlo simulationsFor technoeconomic parameters with two or more literature values (see Supplementary Table 1), we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions. For parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. Values at each end of parameter ranges were then rounded before Monte Carlo simulations as follows: capital costs, operating costs and harvest costs to the nearest $10,000 km−2, labour costs and insurance costs to the nearest $1,000 km−2, line costs to the nearest $0.05 m−1, transport costs to the nearest $0.05 t−1 km−1, transport emissions to the nearest 0.000005 tCO2 t−1 km−1, maintenance transport emissions to the nearest 0.0005 tCO2 km−1, product-avoided emissions to the nearest 0.1 tCO2-eq tDW−1, conversion cost down to the nearest $10 tDW−1 on the low end of the range and up to the nearest $10 tDW−1 on the high end of the range, and conversion emissions to the nearest 0.01 tCO2 tDW−1.We extended the minimum range values of capital costs to $10,000 km−2 and transport emissions to 0 to reflect potential future innovations, such as autonomous floating farm setups that would lower capital costs and net-zero emissions boats that would result in 0 transport emissions. To calculate the minimum value of $10,000 km−2 for a potential autonomous floating farm, we assumed that the bulk of capital costs for such a system would be from structural lines and flotation devices, and we therefore used the annualized structural line (system rope) and buoy costs from ref. 41 rounded down to the nearest $5,000 km−2. The full ranges used for our Monte Carlo simulations and associated literature values are shown in Supplementary Table 1.Distance to optimal sinking pointDistance to the optimal sinking point was calculated using a weighted distance transform (path-finding algorithm, modified from code in ref. 60) that finds the shortest ocean distance from each seaweed growth pixel to the location at which the net CO2 removed is maximized (including impacts of both increased sequestration fraction and transport emissions for different potential sinking locations) and the net cost is minimized. This is not necessarily the location in which the seaweed was grown, since the fraction of sunk carbon that remains sequestered for 100 yr is spatially heterogeneous (see ref. 57). For each ocean grid cell, we determined the cost-optimal sinking point by iteratively calculating equations (11–20) and assigning dsink as the distance calculated by weighted distance transform to each potential sequestration fraction (0.01–1.00) in increments of 0.01. Except for transport emissions, the economic parameter values used for these calculations were the averages of unrounded literature value ranges; we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions, or for parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. For transport and maintenance transport emissions, we extended the minimum values of the literature ranges to zero to reflect potential net-zero emissions transport options and used the mean values of the resulting ranges. The dsink that resulted in minimum net cost per ton CO2 for each ocean grid cell was saved as the final dsink map, and the associated sequestration fraction value that the seaweed is transported to via dsink was assigned to the original cell where the seaweed was farmed and harvested (Supplementary Fig. 19). If the cost-optimal location to sink using this method was the same cell where the seaweed was harvested, then dsink was 0 km and the sequestration fraction was not modified from its original value (Supplementary Fig. 18).Comparison of gigaton-scale sequestration area to previous estimatesPrevious related work estimating the ocean area suitable for macroalgae cultivation13 and/or the area that might be required to reach gigaton-scale carbon removal via macroalgae cultivation13,19,36 has yielded a wide range of results, primarily due to differences in modelling methods. For example, Gao et al. (2022)36 estimate that 1.15 million km2 would be required to sequester 1 GtCO2 annually when considering carbon lost from seaweed biomass/sequestered as particulate organic carbon (POC) and refractory dissolved organic carbon (rDOC), and assume that the harvested seaweed is sold as food such that the carbon in the harvested seaweed is not sequestered. The area (0.31 million km2) required to sequester 1 GtCO2 in our study assumes that all harvested seaweed is sunk to the deep ocean to sequester carbon.Additionally, Wu et al.19 estimates that roughly 12 GtCO2 could be sequestered annually via macroalgae cultivation in approximately 20% of the world ocean area (that is, 1.67% ocean area per GtCO2), which is a much larger area per GtCO2 than our estimate of 0.085% ocean area. This notable difference arises for several reasons (including differences in yields, which in Wu et al. are around 500 tDW yr−1 in the highest-yield areas, whereas yields in our cheapest sequestration areas from G-MACMODS average 3,400 tDW km−2 yr−1) that arise from differences in model methodology. First, Wu et al. model temperate brown seaweeds, while our study considers different seaweed types, many of which have higher growth rates, and uses the most productive seaweed type for each ocean grid cell. The G-MACMODS seaweed growth model we use also has a highly optimized harvest schedule, includes luxury nutrient uptake (a key feature of macroalgal nutrient physiology) and does not directly model competition with phytoplankton during seaweed growth. Finally, tropical red seaweeds (the seaweed type in our cheapest sequestration areas) grow year-round, while others, such as the temperate brown seaweeds modelled by Wu et al., only grow seasonally. These differences all contribute to higher productivity in our model, leading to a smaller area required for gigaton-scale CO2 sequestration compared with Wu et al.Conversely, the ocean areas we model for seaweed-based CO2 sequestration or GHG emissions avoided are much larger than the 48 million km2 that Froehlich et al.13 estimate to be suitable for macroalgae farming globally. Although our maps show productivity and costs everywhere, the purpose of our modelling was to evaluate where different types of seaweed grow best and how production costs and product values vary over space, to highlight the lowest-cost areas (which are often the highest-producing areas) under various technoeconomic assumptions.Comparison of seaweed production costs to previous estimatesAlthough there are not many estimates of seaweed production costs in the scientific literature, our estimates for the lowest-cost 1% area of the ocean ($190–$2,790 tDW−1) are broadly consistent with previously published results: seaweed production costs reported in the literature range from $120 to $1,710 tDW−1 (refs. 40,41,61,62), but are highly dependent on assumed seaweed yields. For example, Camus et al.41 calculate a cost of $870 tDW−1 assuming a minimum yield of 12.4 kgDW m−1 of cultivation line (equivalent to 8.3 kgDW m−2 using 1.5 m spacing between lines). Using the economic values from Camus et al. but with our estimates of average yield for the cheapest 1% production cost areas (2.6 kgDW m−2) gives a much higher average cost of $2,730 tDW−1. Contrarily, van den Burg et al.40 calculate a cost of $1,710 tDW−1 using a yield of 20 tDW ha−1 (that is, 2.0 kg m−2). Instead assuming the average yield to be that from our lowest-cost areas (that is, 2.6 kgDW m−2 or 26 tDW ha−1) would decrease the cost estimated by van den Burg et al. (2016) to $1,290 tDW−1. Most recently, Capron et al.62 calculate an optimistic scenario cost of $120 tDW−1 on the basis of an estimated yield of 120 tDW ha−1 (12 kg m−2; over 4.5 times higher than the average yield in our lowest-cost areas). Again, instead assuming the average yield to be that in our lowest-cost areas would raise Capron et al.’s production cost to $540 tDW−1 (between the $190–$880 tDW−1 minimum to median production costs in the cheapest 1% areas from our model; Fig. 1a,b).Data sourcesSeaweed biomass harvestedWe used spatially explicit data for seaweed harvested globally under both ambient and limited-nutrient scenarios from the G-MACMODS seaweed growth model33.Fraction of deposited carbon sequestered for 100 yrWe used data from ref. 57 interpolated to our 1/12-degree grid resolution.Distance to the nearest portWe used the Distance from Port V1 dataset from Global Fishing Watch (https://globalfishingwatch.org/data-download/datasets/public-distance-from-port-v1) interpolated to our 1/12-degree grid resolution.Significant wave heightWe used data for annually averaged significant wave height from the European Center for Medium-range Weather Forecasts (ECMWF) interpolated to our 1/12-degree grid resolution.Ocean depthWe used data from the General Bathymetric Chart of the Oceans (GEBCO).Shipping lanesWe used data of Automatic Identification System (AIS) signal count per ocean grid cell, interpolated to our 1/12-degree grid resolution. We defined a major shipping lane grid cell as any cell with >2.25 × 108 AIS signals, a threshold that encompasses most major trans-Pacific and trans-Atlantic shipping lanes as well as major shipping lanes in the Indian Ocean, the North Sea, and coastal routes worldwide.Marine protected areas (MPAs)We used data from the World Database on Protected Areas (WDPA) and defined an MPA as any ocean MPA >20 km2.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

  • in

    A metagenomic insight into the microbiomes of geothermal springs in the Subantarctic Kerguelen Islands

    MAG binning and general featuresFrom the four hot springs, we assembled four associated metagenomes and then binned a total of 42 MAGs. We recovered 12 MAGs from RB10 hot spring, 13 from RB13, 14 from RB32 and 3 from RB108. Out of these 42 MAGs, 7 were of high-quality, 25 of nearly-high quality, 9 of medium quality and 1 of low quality (Table 1) based on metagenomic standards26. The GC% was quite variable, ranging from 25.76 to 70.35% among all MAGs and between 32.15 and 69.21% only among the high- and near high-quality MAGs. With the exception of RB108 from which we only recovered bacterial MAGs, we retrieved both bacterial and archaeal MAGs in the other hot springs. Two thirds of the MAGs (26/42) were assigned to the domain Bacteria and the rest to the domain Archaea (16/42) (Table 2).Table 1 General characteristics of the 42 MAGs obtained from RB10, RB13, RB32 and RB108 samples.Full size tableTable 2 Classification of the MAGs based on the taxonomic classification of GTDB-Tk software (v2.1.0) and the Genome Taxonomy Database (07-RS207 release).Full size tableTaxonomic and phylogenomic analyses of MAGsThe taxonomic affiliation of the MAGs was investigated in detail through the workflow classify of GTDB-Tk (v 2.1.0; GTDB reference tree 07-RS207) (Table 2) and through de novo phylogenomic analyses (Fig. S1a–i). We also tried to classify MAGs on the basis of overall genome relatedness indices (OGRI), which is detailed in supplementary material (Text S1, Table S2, Fig. S2).De novo phylogenomic analyses globally confirmed the positioning of MAGs provided by GTDB-Tk, with high branching support. For Bacteria, GTDB-Tk analyses allowed us to place the MAGs in the following clades: six in the phylum Aquificota from the four different springs, comprising four MAGs belonging to the genus Hydrogenivirga (family Aquificaceae) (RB10-MAG07, RB13-MAG10, RB32-MAG07, RB108-MAG02), and two belonging to the family ‘Hydrogenobaculaceae’ (RB10-MAG12, RB32-MAG11) (Table 2, Fig. S1a). Their closest cultured relatives originated either from hot springs or from deep-sea hydrothermal vents27. Three MAGs from three geothermal springs belonged to the phylum Armatimonadota (RB10-MAG03, RB13-MAG04, RB32-MAG03) and had no close cultured relatives. Seven MAGs have been classified into the phylum Chloroflexota: three MAGs belonging to the genus Thermoflexus from three different springs (RB10-MAG04, RB13-MAG05, RB32-MAG02), one affiliating with the genus Thermomicrobium (RB32-MAG08), one falling into the family Ktedonobacteraceae (RB108-MAG03), one belonging to the class Dehalococcoidia (RB32-MAG04) and another one whose phylogenetic position is more difficult to assert because it is a MAG of medium quality (RB32-MAG14). Six MAGs from four various hot springs belonged to the phylum Deinococcota, and to the genera Thermus (RB10-MAG08, RB10-MAG11, RB13-MAG09, RB32-MAG10, RB108-MAG01) and Meiothermus (RB13-MAG13). One MAG belonged to the family ‘Sulfurifustaceae’ (RB13-MAG01), in the phylum Proteobacteria (Gamma-class). The MAG referenced as RB32-MAG13 was classified into the phylum ‘Patescibacteria’, in the class ‘Paceibacteria’, and was distantly related to MAGs originating from groundwater and from hot springs. Finally, two MAGs from two different springs belonged to the phylum WOR-3, in the Candidatus genus ‘Caldipriscus’ (RB32-MAG12, RB10-MAG09).For Archaea, almost all the MAGs reconstructed in this study, e.g. 15 of the 16 archaeal MAGs, belonged to the phylum Thermoproteota. Among them, four belonged to the genus Ignisphaera (RB10-MAG05, RB13-MAG08, RB13-MAG11, RB32-MAG05), three to the genus Infirmifilum (RB10-MAG06, RB13-MAG03, RB32-MAG09), two to the genus Zestosphaera (RB10-MAG02, RB13-MAG06), three to the family Acidilobaceae (RB10-MAG01, RB13-MAG02, RB32-MAG01) and two to the order Geoarchaeales (RB10-MAG10, RB32-MAG06). Additionally, one belonged to the family Thermocladiaceae (RB13-MAG07). Lastly, the MAG belonging to another phylum (RB13-MAG12) was affiliated with the ‘Aenigmatarchaeota’, class ‘Aenigmatarchaeia’, and was distantly related to MAGs from hot springs and from deep-sea hydrothermal vent sediments28,29.Out of these 42 MAGs, at least 19 MAGs corresponded to different taxa at the taxonomic rank of species or higher according to GTDB (Table 2). Eighteen of them belonged to lineages with several cultivated representatives including the species Thermus thermophilus. 13 new genomic species within the GTDB genera Hydrogenivirga, HRBIN17, Thermoflexus, SpSt-223, CADDYT01, Zestosphaera, Ignisphaera, Infirmifilum, Thermus, Thermus_A, Meiothermus_B, JAHLMO01 and Caldipriscus, and 6 putative new genomic genera belonging to the GTDB families Hydrogenobaculaceae, Acidilobaceae, WAQG01, Thermocladiaceae, Sulfurifustaceae and HR35 could be identified (Table 2). In addition, 9 MAGs belonged to lineages that are predominantly or exclusively known through environmental DNA sequences. Thus, these 42 MAGs comprised a broad phylogenetic range of Bacteria and Archaea at different levels of taxonomic organization, of which a large majority were not reported before.The approaches implemented here were not intended to describe the microbial diversity present in these sources in an exhaustive way or to compare them in a fine way, and cannot allow it because of a 2-year storage at 4 °C. This long storage has probably led to changes in the microbial communities and to the selective loss or enrichment of some taxa. As a result, no analysis of abundance or absence of taxa can be conducted from these metagenomes and the results are discussed taking this bias into account. However, they do provide an overview of the microbial diversity effectively present. If we compare the phylogenetic diversity of the MAGs found in the four hot springs, we can observe that 3 shared phyla (Deinococcota, Aquificota and Chloroflexota: phyla names according to GTDB), 2 shared families (Thermaceae and Aquificaceae), and one shared genus (Hydrogenivirga) were found among the four sources (Fig. 2). In addition, hot springs RB10, RB13 and RB32, that are geographically close ( More

  • in

    Multiscale responses and recovery of soils to wildfire in a sagebrush steppe ecosystem

    Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).Article 
    ADS 
    CAS 

    Google Scholar 
    Gorham, E., Vitousek, P. M. & Reiners, W. A. The regulation of element budgets over the course of terrestrial ecosystem succession. Annu. Rev. Ecol. Syst. 10, 53–84 (1979).Article 
    CAS 

    Google Scholar 
    Corman, J. R. et al. Foundations and frontiers of ecosystem science: Legacy of a classic paper (Odum 1969). Ecosystems 22, 1160–1172. https://doi.org/10.1007/s10021-018-0316-3 (2019).Article 

    Google Scholar 
    Santín, C. et al. Towards a global assessment of pyrogenic carbon from vegetation fires. Glob. Change Biol. 22, 76–91. https://doi.org/10.1111/gcb.12985 (2016).Article 
    ADS 

    Google Scholar 
    Kominoski, J. S., Gaiser, E. E. & Baer, S. G. Advancing theories of ecosystem development through long-term ecological research. Bioscience 68, 554–562. https://doi.org/10.1093/biosci/biy070 (2018).Article 

    Google Scholar 
    Balch, J. K., Bradley, B. A., D’Antonio, C. M. & Gómez-Dans, J. Introduced annual grass increases regional fire activity across the arid western USA (1980–2009). Glob. Change Biol. 19, 173–183. https://doi.org/10.1111/gcb.12046 (2013).Article 
    ADS 

    Google Scholar 
    Abatzoglou, J. T. & Kolden, C. A. Climate change in Western US deserts: Potential for increased wildfire and invasive annual grasses. Rangeland Ecol. Manag. 64(5), 471–478 (2011).Article 

    Google Scholar 
    Shi, H. et al. Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management. Ecosystems 21, 913–929. https://doi.org/10.1007/s10021-017-0191-3 (2018).Article 

    Google Scholar 
    Seyfried, M. S. & Wilcox, B. P. Scale and the nature of spatial variability: Field examples having implications for hydrologic modeling. Water Resour. Res. 31, 173–184. https://doi.org/10.1029/94WR02025 (1995).Article 
    ADS 

    Google Scholar 
    Gasch, C. K., Huzurbazar, S. V. & Stahl, P. D. Description of vegetation and soil properties in sagebrush steppe following pipeline burial, reclamation, and recovery time. Geoderma 265, 19–26. https://doi.org/10.1016/j.geoderma.2015.11.013 (2016).Article 
    ADS 

    Google Scholar 
    Huber, D. P. et al. Vegetation and precipitation shifts interact to alter organic and inorganic carbon storage in desert soils. Ecosphere 10, e02655. https://doi.org/10.1002/ecs2.2655 (2019).Article 

    Google Scholar 
    Knight, D. H., Jones, G. P., Reiners, W. A. & Romme, W. H. Mountains and Plains: The Ecology of Wyoming Landscapes (Yale University Press, 2014).
    Google Scholar 
    Patton, N. R., Lohse, K. A., Seyfried, M. S., Godsey, S. E. & Parsons, S. Topographic controls on soil organic carbon on soil mantled landscapes. Sci. Rep. 9, 6390. https://doi.org/10.1038/s41598-019-42556-5 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Schwabedissen, S. G., Lohse, K. A., Reed, S. C., Aho, K. A. & Magnuson, T. S. Nitrogenase activity by biological soil crusts in cold sagebrush steppe ecosystems. Biogeochemistry 134, 57–76. https://doi.org/10.1007/s10533-017-0342-9 (2017).Article 
    CAS 

    Google Scholar 
    You, Y. et al. Biological soil crust bacterial communities vary along climatic and shrub cover gradients within a sagebrush steppe ecosystem. Front. Microbiol. 12, 2365. https://doi.org/10.3389/fmicb.2021.569791 (2021).Article 

    Google Scholar 
    Burke, I. C., Reiners, W. A. & Olson, R. K. Topographic control of vegetation in a mountain big sagebrush steppe. Vegetation 84, 77–86 (1989).Article 

    Google Scholar 
    Poulos, M. J., Pierce, J. L., Flores, A. N. & Benner, S. G. Hillslope asymmetry maps reveal widespread, multi-scale organization. Geophys. Res. Lett. 39, 6. https://doi.org/10.1029/2012GL051283 (2012).Article 

    Google Scholar 
    Smith, T. & Bookhagen, B. Climatic and biotic controls on topographic asymmetry at the global scale. J. Geophys. Res.: Earth Surf. 126, e2020JF005692. https://doi.org/10.1029/2020JF005692Received22 (2021).Article 
    ADS 

    Google Scholar 
    Seyfried, M., Link, T., Marks, D. & Murdock, M. Soil temperature variability in complex terrain measured using fiber-optic distributed temperature sensing. Vadose Zone J. 15, 6. https://doi.org/10.2136/vzj2015.09.0128 (2016).Article 

    Google Scholar 
    Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: Implications for state and transition models and management treatments. Rangel. Ecol. Manage. 67, 440–454. https://doi.org/10.2111/REM-D-13-00074.1 (2014).Article 

    Google Scholar 
    Chambers, J. C. et al. Operationalizing resilience and resistance concepts to address invasive grass-fire cycles. Front. Ecol. Evol. 7, 2369. https://doi.org/10.3389/fevo.2019.00185 (2019).Article 

    Google Scholar 
    Boehm, A. R. et al. Slope and aspect effects on seedbed microclimate and germination timing of fall-planted seeds. Rangel. Ecol. Manage. 75, 58–67. https://doi.org/10.1016/j.rama.2020.12.003 (2021).Article 

    Google Scholar 
    Sankey, J. B., Germino, M. J., Sankey, T. T. & Hoover, A. N. Fire effects on the spatial patterning of soil properties in sagebrush steppe, USA: A meta-analysis. Int. J. Wildl. Fire 21, 545–556. https://doi.org/10.1071/WF11092 (2012).Article 

    Google Scholar 
    Fellows, A., Flerchinger, G., Seyfried, M. S. & Lohse, K. A. Rapid recovery of mesic mountain big sagebrush gross production and respiration following prescribed fire. Ecosystems 21, 1283–1294. https://doi.org/10.1007/s10021-017-0218-9 (2018).Article 

    Google Scholar 
    Vega, S. P. et al. Interaction of wind and cold-season hydrologic processes on erosion from complex topography following wildfire in sagebrush steppe. Earth Surf. Process. Landforms https://doi.org/10.1002/esp.4778 (2019).Article 

    Google Scholar 
    Xie, J., Li, Y., Zhai, C., Li, C. & Lan, Z. CO2 absorption by alkaline soils and its implication to the global carbon cycle. Environ. Geol. 56, 953–961 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Stanbery, C., Pierce, J. L., Benner, S. G. & Lohse, K. On the rocks: Quantifying storage of inorganic soil carbon on gravels and determining pedon-scale variability. CATENA 157, 436–442. https://doi.org/10.1016/j.catena.2017.06.011 (2017).Article 
    CAS 

    Google Scholar 
    Stanbery, C. et al. Controls on the presence and concentration of soil inorganic carbon in a semi-arid watershed. CATENA https://doi.org/10.2139/ssrn.4267018 (2023).Article 

    Google Scholar 
    Cerling, T. E. & Quade, J. Stable carbon and oxygen isotopes in soil carbonates. Geophys. Monogr. 78, 217–231 (1993).ADS 

    Google Scholar 
    Tappa, D. J., Kohn, M. J., McNamara, J. P., Benner, S. G. & Flores, A. N. Isotopic composition of precipitation in a topographically steep, seasonally snow-dominated watershed and implications of variations from the global meteoric water line. Hydrol. Process. 30, 4582–4592. https://doi.org/10.1002/hyp.10940 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Salomons, W., Goudie, A. & Mook, W. G. Isotopic composition of calcrete deposits from Europe, Africa and India. Earth Surf. Process. 3, 43–57. https://doi.org/10.1002/esp.3290030105 (1978).Article 
    CAS 

    Google Scholar 
    Salomons, W. & Mook, W. G. In Handbook of Environmental Isotope Geochemistry (eds P. Fritz & J. Fontes) Ch. 6, 241–269 (Elsevier, 1986).Bodí, M. B. et al. Wildland fire ash: Production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127. https://doi.org/10.1016/j.earscirev.2013.12.007 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Kéraval, B. et al. Soil carbon dioxide emissions controlled by an extracellular oxidative metabolism identifiable by its isotope signature. Biogeosciences 13, 6353–6362. https://doi.org/10.5194/bg-13-6353-2016 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Goforth, B. R., Graham, R. C., Hubbert, K. R., Zanner, C. W. & Minnich, R. A. Spatial distribution and properties of ash and thermally altered soils after high-severity forest fire, southern California. Int. J. Wildland Fire 14, 343–354 (2005).Article 

    Google Scholar 
    Glossner, K. L. et al. Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory. Hydrol. Process. 36, e14484. https://doi.org/10.1002/hyp.14484 (2022).Article 
    CAS 

    Google Scholar 
    Seyfried, M. S. et al. Reynolds creek experimental watershed and critical zone observatory. Vadoze Zone J. 17, 180129. https://doi.org/10.2136/vzj2018.07.0129 (2018).Article 
    CAS 

    Google Scholar 
    McIntyre, D. H. Cenozoic geology of the Reynolds Creek Experimental Watershed, Owyhee County, Idaho (Idaho Bureau of Mines and Geology, 1972).Earth Resources Observation and Science (EROS) Center, U. Image of the week: Burned Area Analysis for the Soda Fire, Idaho, https://eros.usgs.gov/media-gallery/image-of-the-week/burned-area-analysis-the-soda-fire-idaho (2015).Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).Book 

    Google Scholar 
    Kormos, P. R. et al. 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory. Earth Syst. Sci. Data 10, 1197–1205. https://doi.org/10.5194/essd-10-1197-2018 (2018).Article 
    ADS 

    Google Scholar 
    Thomas, G. W. In Methods in Soil Analysis. Part 3. Chemical Methods (ed Sparks, D. L. ) (Soil Science Society of America and American Society of Agronomy, 1996).Brodie, C. R. et al. Evidence for bias in C and N concentrations and δ13C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods. Chem. Geol. 282, 67–83. https://doi.org/10.1016/j.chemgeo.2011.01.007 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Patton, N. P., Lohse, K. A., Seyfried, M. S., Will, R. & Benner, S. G. Lithology and coarse fraction adjusted bulk density estimates for determining total organic carbon stocks in dryland soils. Geoderma 337, 844–852. https://doi.org/10.1016/j.geoderma.2018.10.036 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    McGuire, L. A., Rasmussen, C., Youberg, A. M., Sanderman, J. & Fenerty, B. Controls on the Spatial distribution of near-surface pyrogenic carbon on hillslopes 1 year following wildfire. J. Geophys. Res.: Earth Surf. 126, e2020JF005996. https://doi.org/10.1029/2020JF005996 (2021).Article 
    ADS 

    Google Scholar 
    Jiménez-González, M. A. et al. Spatial distribution of pyrogenic carbon in Iberian topsoils estimated by chemometric analysis of infrared spectra. Sci. Total Env. 790, 148170. https://doi.org/10.1016/j.scitotenv.2021.148170 (2021).Article 
    CAS 

    Google Scholar 
    Baldock, J. A. et al. Quantifying the allocation of soil organic carbon to biologically significant fractions. Soil Res. 51, 561–576. https://doi.org/10.1071/SR12374 (2013).Article 
    CAS 

    Google Scholar 
    Sanderman, J. et al. Soil organic carbon fractions in the Great Plains of the United States: An application of mid-infrared spectroscopy. Biogeochemistry 156, 97–114. https://doi.org/10.1007/s10533-021-00755-1 (2021).Article 
    CAS 

    Google Scholar 
    Sherrod, L. A., Dunn, G., Peterson, G. A. & Kolberg, R. L. Inorganic carbon analysis by modified pressure-calcimeter method. Soil Sci. Soc. Am. J. 66, 299–305 (2002).Article 
    ADS 
    CAS 

    Google Scholar 
    Mikutta, R., Kleber, M., Kaiser, K. & Jahn, R. Review. Soil Sci. Soc. Am. J. 69, 120–135. https://doi.org/10.2136/sssaj2005.0120 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Risk, D., Nickerson, N., Creelman, C., McArthur, G. & Owens, J. Forced Diffusion soil flux: A new technique for continuous monitoring of soil gas efflux. Agric. For. Meteorol. 151, 1622–1631. https://doi.org/10.1016/j.agrformet.2011.06.020 (2011).Article 
    ADS 

    Google Scholar 
    Fierer, N. & Schimel, J. P. Effects of drying–rewetting frequency on soil carbon and nitrogen transformations. Soil Biol. Biochem. 34, 777–787. https://doi.org/10.1016/S0038-0717(02)00007-X (2002).Article 
    CAS 

    Google Scholar 
    Dane, J. H., Topp, G. C. & Campbell, G. S. In Methods of Soil Analysis: Physical Methods. Vol. 4 (ed SSSA) 721–738 (2002). More

  • in

    Dung beetles prefer used land over natural greenspace in urban landscape

    Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    McDonald, R. I., Marcotullio, P. J. & Güneralp, B. Urbanization and global trends in biodiversity and ecosystem services. in Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 31–52 (Springer, 2013).McDonald, R. I., Kareiva, P. & Forman, R. T. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

    Google Scholar 
    Müller, N., Ignatieva, M., Nilon, C. H., Werner, P. & Zipperer, W. C. Patterns and trends in urban biodiversity and landscape design. In Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 123–174 (Springer, 2013).Lahr, E. C., Dunn, R. R. & Frank, S. D. Getting ahead of the curve: Cities as surrogates for global change. Proc. R. Soc. B. 285, 20180643 (2018).Article 

    Google Scholar 
    Cadotte, M. W., Yasui, S. L. E., Livingstone, S. & MacIvor, J. S. Are urban systems beneficial, detrimental, or indifferent for biological invasion?. Biol. Invasions. 19, 3489–3503 (2017).Article 

    Google Scholar 
    Thompson, K. A., Rieseberg, L. H. & Schluter, D. Speciation and the city. Trends Ecol. Evol. 33, 815–826 (2018).Article 

    Google Scholar 
    Borden, J. B. & Flory, S. L. Urban evolution of invasive species. Front. Ecol. Environ. 19, 184–191 (2021).Article 

    Google Scholar 
    Melliger, R. L., Braschler, B., Rusterholz, H. P. & Baur, B. Diverse effects of degree of urbanisation and forest size on species richness and functional diversity of plants, and ground surface-active ants and spiders. PLoS ONE 13, e0199245 (2018).Article 

    Google Scholar 
    McKinney, M. L. Urbanization, biodiversity, and conservation: The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bioscience 52, 883–890 (2002).Article 

    Google Scholar 
    Roshnath, R. & Sinu, P. A. Nesting tree characteristics of heronry birds of urban ecosystems in peninsular India: Implications for habitat management. Curr. Zool. 63, 599–605 (2017).Article 

    Google Scholar 
    Roshnath, R., Athira, K. & Sinu, P. A. Does predation pressure drive heronry birds to nest in the urban landscape?. J. Asia Pac. Biodivers. 12, 311–315 (2019).Article 

    Google Scholar 
    Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: A meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429 (2020).Article 

    Google Scholar 
    Saari, S. et al. Urbanization is not associated with increased abundance or decreased richness of terrestrial animals-dissecting the literature through meta-analysis. Urban Ecosyst. 19, 1251–1264 (2016).Article 

    Google Scholar 
    Lessard, J. P. & Buddle, C. M. The effects of urbanization on ant assemblages (Hymenoptera: Formicidae) associated with the Molson Nature Reserve. Quebec. Can. Entomol. 137, 215–225 (2005).Article 

    Google Scholar 
    Uno, S., Cotton, J. & Philpott, S. M. Diversity, abundance, and species composition of ants in urban green spaces. Urban Ecosyst. 13, 425–441 (2010).Article 

    Google Scholar 
    Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).Article 
    ADS 

    Google Scholar 
    Baldock, K. C. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. R. Soc. B. 282, 20142849 (2015).Article 

    Google Scholar 
    Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).Article 

    Google Scholar 
    Rocha, E. A. & Fellowes, M. D. Urbanisation alters ecological interactions: Ant mutualists increase and specialist insect predators decrease on an urban gradient. Sci. Rep. 10, 1–8 (2020).Article 
    ADS 

    Google Scholar 
    Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).Article 

    Google Scholar 
    Carvalho, R. L. et al. Understanding what bioindicators are actually indicating: Linking disturbance responses to ecological traits of dung beetles and ants. Ecol. Indic. 108, 105764 (2020).Article 

    Google Scholar 
    Asha, G., Manoj, K., Megha, P. P. & Sinu, P. A. Spatiotemporal effects on dung beetle activities in island forests-home garden matrix in a tropical village landscape. Sci. Rep. 11, 1–13 (2021).Article 

    Google Scholar 
    Correa, C. M. A., da Silva, P. G., Ferreira, K. R. & Puker, A. Residential sites increase species loss and cause high temporal changes in functional diversity of dung beetles in an urbanized Brazilian Cerrado landscape. J. Insect Conserv. 25, 417–428 (2021).Article 

    Google Scholar 
    Correa, C. M. A., Ferreira, K. R., Puker, A., Audino, L. D. & Korasaki, V. Greenspace sites conserve taxonomic and functional diversity of dung beetles in an urbanized landscape in the Brazilian Cerrado. Urban Ecosyst. 24, 1023–1034 (2021).Article 

    Google Scholar 
    Beiroz, W. et al. Spatial and temporal shifts in functional and taxonomic diversity of dung beetle in a human-modified tropical forest landscape. Ecol. Indic. 95, 418–526 (2018).Article 

    Google Scholar 
    Fuzessy, L. F. et al. Identifying the anthropogenic drivers of declines in tropical dung beetle communities and functions. Biol. Conserv. 256, 109063 (2021).Article 

    Google Scholar 
    Barragan, F., Moreno, C. E., Escobar, F., Halffter, G. & Navarrete, D. Negative impacts of human land use on dung beetle functional diversity. PLoS ONE 6, e17976 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Salomão, R. P. et al. Urbanization effects on dung beetle assemblages in a tropical city. Ecol. Indic. 103, 665–675 (2019).Article 

    Google Scholar 
    Filgueiras, B. K. C., Liberal, C. N., Aguiar, C. D. M., Hernández, M. I. M. & Iannuzzi, L. Attractivity of omnivore, carnivore and herbivore mammalian dung to Scarabaeinae (Coleoptera: Scarabaeidae) in a tropical Atlantic rainforest remnant. Rev. Bras. Entomol. 53, 422–427 (2009).Article 

    Google Scholar 
    Ramírez-Restrepo, L. & Halffter, G. Copro-necrophagous beetles (Coleoptera: Scarabaeinae) in urban areas: A global review. Urban Ecosyst. 19, 1179–1195 (2016).Article 

    Google Scholar 
    Krell, F. T. et al. Human influence on the dung fauna in Afrotropical grasslands (Insecta: Coleoptera). In African Biodiversity: Molecules Organisms Ecosystems (eds Huber, B. A. et al.) 133–139 (Springer, 2005).Chapter 

    Google Scholar 
    Jiménez-Ferbans, L., Mendieta-Otálora, W., García, H. & Amat-García, G. Notes on dung beetles (Coleoptera: Scarabaeinae) in dry environments of the Santa Marta region, Colombia. Acta Biol. Colomb. 13, 203–208 (2008).
    Google Scholar 
    Costa, F. C. et al. What is the importance of open habitat in a predominantly closed forest area to the dung beetle (Coleoptera, Scarabaeinae) assemblage?. Rev. Bras. Entomol. 57, 329–334 (2013).Article 

    Google Scholar 
    Korasaki, V., Lopes, J., Gardner, B. G. & Louzada, J. Using dung beetles to evaluate the effects of urbanization on Atlantic Forest biodiversity. Insect Sci. 20, 393–406 (2013).Article 

    Google Scholar 
    Audino, L., Louzada, J. & Comita, L. Dung beetles as indicators of tropical forest restoration success: Is it possible to recover species and functional diversity?. Biol. Conserv. 169, 248–257 (2014).Article 

    Google Scholar 
    Gómez-Cifuentez, A., Munevar, A., Gimenez, V. C., Gatti, M. G. & Zurita, G. A. Influence of land use on the taxonomic and functional diversity of dung beetles (Coleoptera: Scarabaeinae) in the southern Atlantic Forest of Argentina. J. Insect. Conserv. 21, 147–156 (2017).Article 

    Google Scholar 
    Gómez-Cifuentes, A., Gómez, V. C. G., Moreno, C. E. & Zurita, G. A. Tree retention in cattle ranching systems partially preserves dung beetle diversity and functional groups in the semideciduous Atlantic forest: The role of microclimate and soil conditions. Basic Appl. Ecol. 34, 64–74 (2019).Article 

    Google Scholar 
    Magnano, L. F. S. et al. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J. Ecol. 102, 475–485 (2014).Article 

    Google Scholar 
    GiménezGómez, V. C., Verdú, J. R., Casanoves, F. & Zurita, G. A. Functional responses to anthropogenic disturbance and the importance of selected traits: a study case using dung beetles. Ecol. Entomol. 1, 1–12 (2022).
    Google Scholar 
    Lobo, J. M. Decline of roller dung beetle (Scarabaeinae) populations in the Iberian Peninsula during the 20th century. Biol. Conserv. 97, 43–50 (2001).Article 

    Google Scholar 
    Ballullaya, U. P. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmentalperceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104213 (2019).Article 

    Google Scholar 
    Lowman, M. D. & Sinu, P. A. Can the spiritual values of forests inspire effective conservation?. Bioscience 67, 688–690 (2017).Article 

    Google Scholar 
    Bhagwat, S. A., Kushalappa, C. G., Williams, P. H. & Brown, N. D. The role of informal protected areas in maintaining biodiversity in the Western Ghats of India. Ecol. Soc 10, 108 (2005).Article 

    Google Scholar 
    Rajesh, T. P., Prashanth Ballullaya, U., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

    Google Scholar 
    Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitats are different. J. Trop. Ecol. 37, 209–213 (2021).Article 

    Google Scholar 
    Arrow, G. J. The Fauna Of British India Including Ceylon And Burma, Coleoptera: Lamellicornia (Coprinae) (Taylor and Francis, 1931).
    Google Scholar 
    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.20. http://chao.stat.nthu.edu.tw/wordpress/software-download/ (2020).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package (2017).Warnes, G. R. et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.1.1. https://CRAN.R-project.org/package=gplots (2020).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Venugopal, K. S., Thomas, S. K. & Flemming, A. T. Diversity and community structure of dung beetles (Coleoptera: Scarabaeinae) associated with semi-urban fragmented agricultural land in the Malabar coast in southern India. J. Threat. Taxa. 4, 2685–2692 (2012).Article 

    Google Scholar 
    Sabu, T. K. & Nithya, S. Comparison of the arboreal dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) of the wet and dry forests of the western Ghats. India. Coleopt. Bull. 70, 144–148 (2016).Article 

    Google Scholar 
    Sabu, T. K., Vinod, K. V. & Vineesh, P. J. Guild structure, diversity and succession of dung beetles associated with Indian elephant dung in South Western Ghats forests. J. Insect Sci. 6, 6–17 (2006).Article 

    Google Scholar 
    Rodrigues, M. M., Uchôa, M. A. & Ide, S. Dung beetles (Coleoptera: Scarabaeoidea) in three landscapes in Mato Grosso do Sul, Brazil. Braz. J. Biol. 73, 211–220 (2013).Article 
    CAS 

    Google Scholar 
    Rios-Diaz, C. L. et al. Sheep herding in small grasslands promotes dung beetle diversity in a mountain forest landscape. J. Insect. Conserv. 25, 13–26 (2020).Article 

    Google Scholar 
    Carrión-Paladines, V. et al. Effects of land-use change on the community structure of the dung beetle (Scarabaeinae) in an altered ecosystem in Southern Ecuador. Insects. 12, 306 (2021).Article 

    Google Scholar 
    Gómez, V. C. G., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 1–14 (2020).ADS 

    Google Scholar 
    Slade, E. M., Mann, D. J., Villanueva, J. F. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1094–1104 (2007).Article 

    Google Scholar 
    Vinod, K. V. & Sabu, T. K. Species composition and community structure of dung beetles attracted to dung of gaur and elephant in the moist forests of South Western Ghats. J. Insect. Sci. 7, 1–14 (2007).Article 
    CAS 

    Google Scholar 
    Milotić, T. et al. Functionally richer communities improve ecosystem functioning: Dung removal and secondary seed dispersal by dung beetles in the Western Palaearctic. J. Biogeogr. 46, 70–82 (2019).Article 

    Google Scholar 
    Braga, R. F., Korasaki, V., Andresen, E. & Louzada, J. Dung beetle community and functions along a habitat-disturbance gradient in the amazon: A rapid assessment of ecological functions associated to biodiversity. PLoS ONE 8, e57786 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Nichols, E. et al. Trait-dependent response of dung beetle populations to tropical forest conversion at local and regional scales. Ecology 94, 180–189 (2013).Article 

    Google Scholar 
    Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).Article 

    Google Scholar  More

  • in

    Effects of phytoplankton, viral communities, and warming on free-living and particle-associated marine prokaryotic community structure

    Azam, F. et al. The ecological role of water-column microbes in the sea. Marine Ecol. Prog. Ser. 10, 257–263 (1983).Fuhrman, J. A. & Caron D. A. in Manual of Environmental Microbiology (eds Yates, M. V. et al.) 4.2.2–4.2.2.-34 (ASM Press, 2016).Gasol, J. M. & Kirchman, D. L. Microbial Ecology of the Oceans (John Wiley & Sons, 2018).Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. 105, 7774–7778 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Gilbert, J. A. et al. The seasonal structure of microbial communities in the Western English Channel. Environ. Microbiol. 11, 3132–3139 (2009).Article 
    CAS 

    Google Scholar 
    Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).Article 
    CAS 

    Google Scholar 
    Hatosy, S. M. et al. Beta diversity of marine bacteria depends on temporal scale. Ecology 94, 1898–1904 (2013).Article 

    Google Scholar 
    Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).Article 

    Google Scholar 
    Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. 103, 13104–13109 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Gonzalez, J. M., Sherr, E. B. & Sherr, B. F. Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates. Appl. Environ. Microbiol. 56, 583–589 (1990).Article 
    ADS 
    CAS 

    Google Scholar 
    Guixa-Boixereu, N., Vaque, D., Gasol, J. M. & Pedros-Alio, C. Distribution of viruses and their potential effect on bacterioplankton in an oligotrophic marine system. Aquat. Microb. Ecol. 19, 205–213 (1999).Article 

    Google Scholar 
    Šimek, K. et al. Shifts in bacterial community composition associated with different microzooplankton size fractions in a eutrophic reservoir. Limnol. Oceanogr. 44, 1634–1644 (1999).Article 
    ADS 

    Google Scholar 
    Hewson, I., Vargo, G. & Fuhrman, J. Bacterial diversity in shallow oligotrophic marine benthos and overlying waters: effects of virus infection, containment, and nutrient enrichment. Microb. Ecol. 46, 322–336 (2003).Article 
    CAS 

    Google Scholar 
    Schwalbach, M. S., Hewson, I. & Fuhrman, J. A. Viral effects on bacterial community composition in marine plankton microcosms. Aquat. Microb. Ecol. 34, 117–127 (2004).Article 

    Google Scholar 
    Winter, C., Smit, A., Herndl, G. J. & Weinbauer, M. G. Linking bacterial richness with viral abundance and prokaryotic activity. Limnol. Oceanogr. 50, 968–977 (2005).Article 
    ADS 

    Google Scholar 
    Chow, C.-E. T., Kim, D. Y., Sachdeva, R., Caron, D. A. & Fuhrman, J. A. Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J. 8, 816–829 (2014).Article 
    CAS 

    Google Scholar 
    Suzuki, S. et al. Comparison of community structures between particle-associated and free-living prokaryotes in tropical and subtropical Pacific Ocean surface waters. J. Oceanogr. 73, 383–395 (2017).Article 
    CAS 

    Google Scholar 
    Milici, M. et al. Diversity and community composition of particle‐associated and free‐living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: evidence of dispersal limitation in the Bransfield Strait. Limnol. Oceanogr. 62, 1080–1095 (2017).Article 
    ADS 

    Google Scholar 
    D’ambrosio, L., Ziervogel, K., MacGregor, B., Teske, A. & Arnosti, C. Composition and enzymatic function of particle-associated and free-living bacteria: a coastal/offshore comparison. ISME J. 8, 2167–2179 (2014).Article 

    Google Scholar 
    Rieck, A., Herlemann, D. P., Jürgens, K. & Grossart, H.-P. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6, 1297 (2015).Article 

    Google Scholar 
    Yung, C.-M., Ward, C. S., Davis, K. M., Johnson, Z. I. & Hunt, D. E. Insensitivity of diverse and temporally variable particle-associated microbial communities to bulk seawater environmental parameters. Appl. Environ. Microbiol. 82, 3431–3437 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).Article 
    CAS 

    Google Scholar 
    Duret, M. T., Lampitt, R. S. & Lam, P. Prokaryotic niche partitioning between suspended and sinking marine particles. Environ. Microbiol. Rep. 11, 386–400 (2019).Article 
    CAS 

    Google Scholar 
    Crespo, B. G., Pommier, T., Fernández‐Gómez, B. & Pedrós‐Alió, C. Taxonomic composition of the particle‐attached and free‐living bacterial assemblages in the Northwest Mediterranean Sea analyzed by pyrosequencing of the 16S rRNA. Microbiologyopen 2, 541–552 (2013).Article 
    CAS 

    Google Scholar 
    Mestre, M., Borrull, E., Sala, M. & Gasol, J. M. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 11, 999–1010 (2017).Yeh, Y. C. et al. Comprehensive single‐PCR 16S and 18S rRNA community analysis validated with mock communities, and estimation of sequencing bias against 18S. Environ. Microbiol. 23, 3240–3250 (2021).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
    CAS 

    Google Scholar 
    Needham, D. M. et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 12, 2417 (2018).Article 
    CAS 

    Google Scholar 
    McNichol, J., Berube, P. M., Biller, S. J. & Fuhrman, J. A. Evaluating and improving small subunit rRNA PCR primer coverage for bacteria, archaea, and eukaryotes using metagenomes from global ocean surveys. Msystems 6, e00565–00521 (2021).Article 
    CAS 

    Google Scholar 
    Chow, C. E. T. & Fuhrman, J. A. Seasonality and monthly dynamics of marine myovirus communities. Environ. Microbiol. 14, 2171–2183 (2012).Article 

    Google Scholar 
    Filée, J., Tétart, F., Suttle, C. A. & Krisch, H. Marine T4-type bacteriophages, a ubiquitous component of the dark matter of the biosphere. Proc. Natl Acad. Sci. 102, 12471–12476 (2005).Article 
    ADS 

    Google Scholar 
    Pagarete, A. et al. Strong seasonality and interannual recurrence in marine myovirus communities. Appl. Environ. Microbiol. 79, 6253–6259 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Comeau, A. M. & Krisch, H. M. The capsid of the T4 phage superfamily: the evolution, diversity, and structure of some of the most prevalent proteins in the biosphere. Mol. Biol. Evolution 25, 1321–1332 (2008).Article 
    CAS 

    Google Scholar 
    Needham, D. M. et al. Short-term observations of marine bacterial and viral communities: patterns, connections and resilience. ISME J. 7, 1274–1285 (2013).Article 
    CAS 

    Google Scholar 
    Needham, D. M., Sachdeva, R. & Fuhrman, J. A. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 11, 1614–1629 (2017).Article 

    Google Scholar 
    Ahlgren, N. A., Perelman, J. N., Yeh, Y. C. & Fuhrman, J. A. Multi‐year dynamics of fine‐scale marine cyanobacterial populations are more strongly explained by phage interactions than abiotic, bottom‐up factors. Environ. Microbiol. 21, 2948–2963 (2019).Article 
    CAS 

    Google Scholar 
    Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 1–20 (2017).Article 

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

    Google Scholar 
    Ignacio-Espinoza, J. C., Ahlgren, N. A. & Fuhrman, J. A. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat. Microbiol. 5, 265–271 (2020).Article 
    CAS 

    Google Scholar 
    Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, (2015).Brown, M. V. et al. Global biogeography of SAR11 marine bacteria. Mol. Syst. Biol. 8, 595 (2012).Article 
    ADS 

    Google Scholar 
    Johnson, Z. I. et al. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Zwirglmaier, K. et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ. Microbiol. 10, 147–161 (2008).
    Google Scholar 
    Martiny, A. C., Tai, A. P., Veneziano, D., Primeau, F. & Chisholm, S. W. Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus. Environ. Microbiol. 11, 823–832 (2009).Article 

    Google Scholar 
    Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).Article 
    ADS 

    Google Scholar 
    Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).Article 
    ADS 

    Google Scholar 
    Traving, S. J. et al. Prokaryotic responses to a warm temperature anomaly in northeast subarctic Pacific waters. Commun. Biol. 4, 1–12 (2021).Article 

    Google Scholar 
    Peña, M. A., Nemcek, N. & Robert, M. Phytoplankton responses to the 2014–2016 warming anomaly in the northeast subarctic Pacific Ocean. Limnol. Oceanogr. 64, 515–525 (2019).Article 
    ADS 

    Google Scholar 
    Yang, B., Emerson, S. R. & Peña, M. A. The effect of the 2013–2016 high temperature anomaly in the subarctic Northeast Pacific (the “Blob”) on net community production. Biogeosciences 15, 6747–6759 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Cavole, L. M. et al. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 

    Google Scholar 
    Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).Article 
    CAS 

    Google Scholar 
    Grossart, H. P., Levold, F., Allgaier, M., Simon, M. & Brinkhoff, T. Marine diatom species harbour distinct bacterial communities. Environ. Microbiol. 7, 860–873 (2005).Article 
    CAS 

    Google Scholar 
    Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).Article 

    Google Scholar 
    Teeling, H. et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. elife 5, e11888 (2016).Article 

    Google Scholar 
    Unfried, F. et al. Adaptive mechanisms that provide competitive advantages to marine bacteroidetes during microalgal blooms. ISME J. 12, 2894–2906 (2018).Article 
    CAS 

    Google Scholar 
    Francis, T. B. et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 15, 2336–2350 (2021).Thingstad, T. F. & Lignell, R. Theoretical models for the control of bacterial growth rate, abundance, diversity and carbon demand. Aquat. Microb. Ecol. 13, 19–27 (1997).Article 

    Google Scholar 
    Thingstad, T. F., Våge, S., Storesund, J. E., Sandaa, R.-A. & Giske, J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. Proc. Natl Acad. Sci. 111, 7813–7818 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Thingstad, T. F., Pree, B., Giske, J. & Våge, S. What difference does it make if viruses are strain-, rather than species-specific? Front. Microbiol. 6, 320 (2015).Article 

    Google Scholar 
    Prokopowich, C. D., Gregory, T. R. & Crease, T. J. The correlation between rDNA copy number and genome size in eukaryotes. Genome 46, 48–50 (2003).Article 
    CAS 

    Google Scholar 
    Zhu, F., Massana, R., Not, F., Marie, D. & Vaulot, D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52, 79–92 (2005).Article 
    CAS 

    Google Scholar 
    Sintes, E. & Del Giorgio, P. A. Feedbacks between protistan single-cell activity and bacterial physiological structure reinforce the predator/prey link in microbial foodwebs. Front. Microbiol. 5, 453 (2014).Article 

    Google Scholar 
    Del Giorgio, P. A. et al. Bacterioplankton community structure: protists control net production and the proportion of active bacteria in a coastal marine community. Limnol. Oceanogr. 41, 1169–1179 (1996).Article 
    ADS 

    Google Scholar 
    Andersson, A., Larsson, U. & Hagström, Å. Size-selective grazing by a microflagellate on pelagic bacteria. Marine Ecol. Prog. Ser. 33, 51–57 (1986).Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).Article 
    CAS 

    Google Scholar 
    Baltar, F. et al. Marine bacterial community structure resilience to changes in protist predation under phytoplankton bloom conditions. ISME J. 10, 568–581 (2016).Article 

    Google Scholar 
    Suzuki, M. T. Effect of protistan bacterivory on coastal bacterioplankton diversity. Aquat. Microb. Ecol. 20, 261–272 (1999).Article 

    Google Scholar 
    Yokokawa, T. & Nagata, T. Growth and grazing mortality rates of phylogenetic groups of bacterioplankton in coastal marine environments. Appl. Environ. Microbiol. 71, 6799–6807 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evolution 4, 1111–1119 (2013).Article 

    Google Scholar 
    Coleman, M. L. & Chisholm, S. W. Code and context: Prochlorococcus as a model for cross-scale biology. Trends Microbiol. 15, 398–407 (2007).Article 
    CAS 

    Google Scholar 
    Scanlan, D. J. et al. Ecological genomics of marine picocyanobacteria. Microbiol. Mol. Biol. Rev. 73, 249–299 (2009).Article 
    CAS 

    Google Scholar 
    Moore, L. R., Rocap, G. & Chisholm, S. W. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 393, 464–467 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Rusch, D. B., Martiny, A. C., Dupont, C. L., Halpern, A. L. & Venter, J. C. Characterization of Prochlorococcus clades from iron-depleted oceanic regions. Proc. Natl Acad. Sci. 107, 16184–16189 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Larkin, A. A. et al. Persistent El Niño driven shifts in marine cyanobacteria populations. PloS ONE 15, e0238405 (2020).Article 
    CAS 

    Google Scholar 
    Arandia‐Gorostidi, N. et al. Warming the phycosphere: differential effect of temperature on the use of diatom‐derived carbon by two copiotrophic bacterial taxa. Environ. Microbiol. 22, 1381–1396 (2020).Article 

    Google Scholar 
    Arandia‐Gorostidi, N., Huete‐Stauffer, T. M., Alonso‐Sáez L, G. & Morán, X. A. Testing the metabolic theory of ecology with marine bacteria: different temperature sensitivity of major phylogenetic groups during the spring phytoplankton bloom. Environ. Microbiol. 19, 4493–4505 (2017).Article 

    Google Scholar 
    Fagan, A. J., Moreno, A. R. & Martiny, A. C. Role of ENSO conditions on particulate organic matter concentrations and elemental ratios in the Southern California Bight. Front. Mar. Sci. 6, 386 (2019).Article 

    Google Scholar 
    Chang, C. W. et al. Reconstructing large interaction networks from empirical time series data. Ecol. Lett. 24, 2763–2774 (2021).Article 

    Google Scholar 
    Lie, A. A., Kim, D. Y., Schnetzer, A. & Caron, D. A. Small-scale temporal and spatial variations in protistan community composition at the San Pedro Ocean Time-series station off the coast of southern California. Aquat. Microb. Ecol. 70, 93–110 (2013).Article 

    Google Scholar 
    Yeh, Y.-C., Needham, D. M., Sieradzki, E. T. & Fuhrman, J. A. Taxon disappearance from microbiome analysis reinforces the value of mock communities as a standard in every sequencing run. MSystems 3, e00023–00018 (2018).Article 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
    CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
    CAS 

    Google Scholar 
    Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D579–D604 (2013).
    Google Scholar 
    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2, (2017).Decelle, J. et al. Phyto REF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15, 1435–1445 (2015).Article 
    CAS 

    Google Scholar 
    Amin, S. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).Article 
    ADS 

    Google Scholar 
    Hill, M. O. & Gauch, H. G. J. Detrended correspondence analysis: an improved ordination technique. Vegetatio 42, 47–58 (1980).Ter Braak, C. J. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

    Google Scholar 
    Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).Article 

    Google Scholar  More

  • in

    A regulatory hydrogenase gene cluster observed in the thioautotrophic symbiont of Bathymodiolus mussel in the East Pacific Rise

    Sogin, E. M., Leisch, N. & Dubilier, N. Chemosynthetic symbioses. Curr. Biol. 30, R1137–R1142 (2020).Article 
    CAS 

    Google Scholar 
    Dubilier, N., Bergin, C. & Lott, C. Symbiotic diversity in marine animals: The art of harnessing chemosynthesis. Nat. Rev. Microbiol. 6, 725–740 (2008).Article 
    CAS 

    Google Scholar 
    Barry, J. P. et al. Methane-based symbiosis in a mussel, Bathymodiolus platifrons, from cold seeps in Sagami Bay Japan. Invertebr. Biol. 121, 47–54 (2002).Article 

    Google Scholar 
    Le Pennec, M., Donval, A. & Herry, A. Nutritional strategies of the hydrothermal ecosystem bivalves. Prog. Oceanogr. 24, 71–80 (1990).Article 
    ADS 

    Google Scholar 
    Rau, G. H. & Hedges, J. I. Carbon-13 depletion in a hydrothermal vent mussel: Suggestion of a chemosynthetic food source. Science 203, 648–649 (1979).Article 
    ADS 
    CAS 

    Google Scholar 
    Wentrup, C., Wendeberg, A., Schimak, M., Borowski, C. & Dubilier, N. Forever competent: Deep-sea bivalves are colonized by their chemosynthetic symbionts throughout their lifetime. Environ. Microbiol. 16, 3699–3713 (2014).Article 

    Google Scholar 
    Dattagupta, S., Bergquist, D., Szalai, E., Macko, S. & Fisher, C. Tissue carbon, nitrogen, and sulfur stable isotope turnover in transplanted Bathymodiolus childressi mussels: Relation to growth and physiological condition. Limnol. Oceanogr. 49, 1144–1151 (2004).Article 
    ADS 
    CAS 

    Google Scholar 
    Ikuta, T. et al. Heterogeneous composition of key metabolic gene clusters in a vent mussel symbiont population. ISME J. 10, 990–1001 (2016).Article 

    Google Scholar 
    Takishita, K. et al. Genomic evidence that methanotrophic endosymbionts likely provide deep-sea Bathymodiolus mussels with a sterol intermediate in cholesterol biosynthesis. Genome Biol. Evol. 9, 1148–1160 (2017).Article 

    Google Scholar 
    Sayavedra, L. et al. Horizontal acquisition followed by expansion and diversification of toxin-related genes in deep-sea bivalve symbionts. BioRxiv 110, 330 (2019).
    Google Scholar 
    Ponnudurai, R. et al. Metabolic and physiological interdependencies in the Bathymodiolus azoricus symbiosis. ISME J. 11, 463–477 (2017).Article 
    CAS 

    Google Scholar 
    Ponnudurai, R. et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom. Sci. 12, 1–9 (2017).
    Google Scholar 
    Kiel, S. The Vent and Seep Biota: Aspects from Microbes to Ecosystems Vol. 33 (Springer Science & Business Media, 2010).
    Google Scholar 
    Lorion, J. et al. Adaptive radiation of chemosymbiotic deep-sea mussels. Proc. R. Soc. B 280, 20131243 (2013).Article 

    Google Scholar 
    Nussbaumer, A. D., Fisher, C. R. & Bright, M. Horizontal endosymbiont transmission in hydrothermal vent tubeworms. Nature 441, 345–348 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Gros, O., Liberge, M., Heddi, A., Khatchadourian, C. & Felbeck, H. Detection of the free-living forms of sulfide-oxidizing gill endosymbionts in the lucinid habitat (Thalassia testudinum environment). Appl. Environ. Microbiol. 69, 6264–6267 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Won, Y.-J. et al. Environmental acquisition of thiotrophic endosymbionts by deep-sea mussels of the genus Bathymodiolus. Appl. Environ. Microbiol. 69, 6785–6792 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Laming, S. R., Gaudron, S. M. & Duperron, S. Lifecycle ecology of deep-sea chemosymbiotic mussels: A review. Front. Mar. Sci. 5, 282 (2018).Article 

    Google Scholar 
    Laming, S. R., Duperron, S., Cunha, M. R. & Gaudron, S. M. Settled, symbiotic, then sexually mature: Adaptive developmental anatomy in the deep-sea, chemosymbiotic mussel Idas modiolaeformis. Mar. Biol. 161, 1319–1333 (2014).Article 

    Google Scholar 
    Salerno, J. L. et al. Characterization of symbiont populations in life-history stages of mussels from chemosynthetic environments. Biol. Bull. 208, 145–155 (2005).Article 

    Google Scholar 
    Wentrup, C., Wendeberg, A., Huang, J. Y., Borowski, C. & Dubilier, N. Shift from widespread symbiont infection of host tissues to specific colonization of gills in juvenile deep-sea mussels. ISME J. 7, 1244–1247 (2013).Article 
    CAS 

    Google Scholar 
    Pennec, M. L. & Beninger, P. G. Ultrastructural characteristics of spermatogenesis in three species of deep-sea hydrothermal vent mytilids. Can. J. Zool. 75, 308–316 (1997).Article 

    Google Scholar 
    Eckelbarger, K. & Young, C. Ultrastructure of gametogenesis in a chemosynthetic mytilid bivalve (Bathymodiolus childressi) from a bathyal, methane seep environment (northern Gulf of Mexico). Mar. Biol. 135, 635–646 (1999).Article 

    Google Scholar 
    Ansorge, R. et al. Diversity matters: Deep-sea mussels harbor multiple symbiont strains. bioRxiv 99, 1039 (2019).
    Google Scholar 
    Petersen, J. M., Wentrup, C., Verna, C., Knittel, K. & Dubilier, N. Origins and evolutionary flexibility of chemosynthetic symbionts from deep-sea animals. Biol. Bull. 223, 123–137 (2012).Article 
    CAS 

    Google Scholar 
    Sayavedra, L. et al. Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels. Elife 4, e07966 (2015).Article 

    Google Scholar 
    Ansorge, R. et al. Functional diversity enables multiple symbiont strains to coexist in deep-sea mussels. Nat. Microbiol. 4, 2487–2497 (2019).Article 

    Google Scholar 
    Petersen, J. M. et al. Hydrogen is an energy source for hydrothermal vent symbioses. Nature 476, 176–180 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Nakamura, K. & Takai, K. Theoretical constraints of physical and chemical properties of hydrothermal fluids on variations in chemolithotrophic microbial communities in seafloor hydrothermal systems. Prog. Earth Planet Sci. 1, 1–24 (2014).Article 
    ADS 

    Google Scholar 
    Perez, M. & Juniper, S. K. Insights into symbiont population structure among three vestimentiferan tubeworm host species at eastern Pacific spreading centers. Appl. Environ. Microbiol. 82, 5197–5205 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Wilbanks, E. G. et al. Metagenomic methylation patterns resolve bacterial genomes of unusual size and structural complexity. ISME J. https://doi.org/10.1038/s41396-022-01242-7 (2022).Article 

    Google Scholar 
    Rodriguez-Casariego, J. A., Cunning, R., Baker, A. C. & Eirin-Lopez, J. M. Symbiont shuffling induces differential DNA methylation responses to thermal stress in the coral Montastraea cavernosa. Mol. Ecol. 31, 588–602 (2022).Article 
    CAS 

    Google Scholar 
    Triant, D. A. & Whitehead, A. Simultaneous extraction of high-quality RNA and DNA from small tissue samples. J. Hered. 100, 246–250 (2009).Article 
    CAS 

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

    Google Scholar 
    Wick, R. R. et al. Trycycler: Consensus long-read assemblies for bacterial genomes. Genome Biol. 22, 1–17 (2021).Article 

    Google Scholar 
    Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).Article 
    CAS 

    Google Scholar 
    Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).Article 
    ADS 

    Google Scholar 
    Krawczyk, P. S., Lipinski, L. & Dziembowski, A. PlasFlow: Predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 46, e35–e35 (2018).Article 

    Google Scholar 
    Mikheenko, A., Prjibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).Article 
    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 Res. 25, 1043–1055 (2015).Article 
    CAS 

    Google Scholar 
    Couvin, D. et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 46, W246–W251 (2018).Article 
    CAS 

    Google Scholar 
    Perez, M., Angers, B., Young, C. R. & Juniper, S. K. Shining light on a deep-sea bacterial symbiont population structure with CRISPR. Microbial. Genom. https://doi.org/10.1099/mgen.0.000625 (2021).Article 

    Google Scholar 
    Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 1–11 (2010).Article 

    Google Scholar 
    Nielsen, H. Protein Function Prediction 59–73 (Springer, 2017).Book 

    Google Scholar 
    Krogh, A., Larsson, B., Von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).Article 
    CAS 

    Google Scholar 
    Lagesen, K. et al. RNAmmer: Consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100-31C08 (2007).Article 
    ADS 
    CAS 

    Google Scholar 
    Chan, P. P. & Lowe, T. M. Gene Prediction 1–14 (Springer, 2019).
    Google Scholar 
    Griffiths-Jones, S. et al. Rfam: Annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005).Article 
    CAS 

    Google Scholar 
    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).Article 
    CAS 

    Google Scholar 
    Siguier, P., Pérochon, J., Lestrade, L., Mahillon, J. & Chandler, M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 34, D32–D36 (2006).Article 
    CAS 

    Google Scholar 
    Bertelli, C. et al. IslandViewer 4: Expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 45, W30–W35 (2017).Article 
    CAS 

    Google Scholar 
    Arndt, D. et al. PHASTER: A better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).Article 
    CAS 

    Google Scholar 
    Roeselers, G. et al. Complete genome sequence of Candidatus Ruthia magnifica. Stand Genomic Sci. 3, 163–173 (2010).Article 

    Google Scholar 
    Emms, D. M. & Kelly, S. OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 1–14 (2019).Article 

    Google Scholar 
    Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 

    Google Scholar 
    Minh, B. Q. et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
    CAS 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547 (2018).Article 
    CAS 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive tree of life (iTOL) v4: Recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).Article 
    CAS 

    Google Scholar 
    Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
    CAS 

    Google Scholar 
    Darling, A. E., Mau, B. & Perna, N. T. progressiveMauve: Multiple genome alignment with gene gain, loss and rearrangement. PLoS ONE 5, e11147 (2010).Article 
    ADS 

    Google Scholar 
    Tesler, G. GRIMM: Genome rearrangements web server. Bioinformatics 18, 492–493 (2002).Article 
    CAS 

    Google Scholar 
    Cabanettes, F. & Klopp, C. D-GENIES: Dot plot large genomes in an interactive, efficient and simple way. PeerJ 6, e4958 (2018).Article 

    Google Scholar 
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 
    CAS 

    Google Scholar 
    Gilchrist, C. L. & Chooi, Y.-H. Clinker & clustermap. js: Automatic generation of gene cluster comparison figures. Bioinformatics 37, 2473–2475 (2021).Article 
    CAS 

    Google Scholar 
    Taboada, B., Estrada, K., Ciria, R. & Merino, E. Operon-mapper: A web server for precise operon identification in bacterial and archaeal genomes. Bioinformatics 34, 4118–4120 (2018).Article 
    CAS 

    Google Scholar 
    Søndergaard, D., Pedersen, C. N. & Greening, C. HydDB: A web tool for hydrogenase classification and analysis. Sci. Rep. 6, 1–8 (2016).Article 

    Google Scholar 
    NCBI Genome Browser. https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/. Accessed 12 March 2022.Mcmullin, E. R., Hourdez, S., Schaeffer, S. W. & Fisher, C. R. Review article phylogeny and biogeography of deep sea vestimentiferan tubeworms and their bacterial symbionts. Symbiosis. 34, 1–41 (2003).
    Google Scholar 
    Won, Y.-J., Jones, W. J. & Vrijenhoek, R. C. Absence of cospeciation between deep-sea mytilids and their thiotrophic endosymbionts. J. Shellfish Res. 27, 129–138 (2008).Article 

    Google Scholar 
    Miyazaki, J.-I., Martins, Ld. O., Fujita, Y., Matsumoto, H. & Fujiwara, Y. Evolutionary process of deep-sea Bathymodiolus mussels. PLoS ONE 5, e10363 (2010).Article 
    ADS 

    Google Scholar 
    Bright, M. & Bulgheresi, S. A complex journey: Transmission of microbial symbionts. Nat. Rev. Microbiol. 8, 218–230 (2010).Article 
    CAS 

    Google Scholar 
    Raggi, L., Schubotz, F., Hinrichs, K. U., Dubilier, N. & Petersen, J. M. Bacterial symbionts of Bathymodiolus mussels and Escarpia tubeworms from Chapopote, an asphalt seep in the southern Gulf of Mexico. Environ. Microbiol. 15, 1969–1987 (2013).Article 
    CAS 

    Google Scholar 
    Goris, J. et al. DNA–DNA hybridization values and their relationship to whole-genome sequence similarities. Int. J. Syst. Evol. Microbiol. 57, 81–91 (2007).Article 
    CAS 

    Google Scholar 
    Meier-Kolthoff, J. P., Auch, A. F., Klenk, H.-P. & Göker, M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 14, 1–14 (2013).Article 

    Google Scholar 
    Konstantinidis, K. T. & Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes. Proc. Natl. Acad. Sci. 102, 2567–2572 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Ho, P.-T. et al. Geographical structure of endosymbiotic bacteria hosted by Bathymodiolus mussels at eastern Pacific hydrothermal vents. BMC Evol. Biol. 17, 1–16 (2017).Article 

    Google Scholar 
    Romero Picazo, D. et al. Horizontally transmitted symbiont populations in deep-sea mussels are genetically isolated. ISME J. 13, 2954–2968 (2019).Article 

    Google Scholar 
    Perez, M. & Juniper, S. K. Is the trophosome of Ridgeia piscesae monoclonal?. Symbiosis 74, 55–65 (2018).Article 
    CAS 

    Google Scholar 
    Polzin, J., Arevalo, P., Nussbaumer, T., Polz, M. F. & Bright, M. Polyclonal symbiont populations in hydrothermal vent tubeworms and the environment. Proc. R. Soc. B 286, 20181281 (2019).Article 
    CAS 

    Google Scholar 
    Russell, S. L. & Cavanaugh, C. M. Intrahost genetic diversity of bacterial symbionts exhibits evidence of mixed infections and recombinant haplotypes. Mol. Biol. Evol. 34, 2747–2761 (2017).Article 
    CAS 

    Google Scholar 
    Breusing, C., Genetti, M., Russell, S. L., Corbett-Detig, R. B. & Beinart, R. A. Horizontal transmission enables flexible associations with locally adapted symbiont strains in deep-sea hydrothermal vent symbioses. Proc. Natl. Acad. Sci. 119, e2115608119 (2022).Article 
    CAS 

    Google Scholar 
    Lan, Y. et al. Endosymbiont population genomics sheds light on transmission mode, partner specificity, and stability of the scaly-foot snail holobiont. ISME J. https://doi.org/10.1038/s41396-022-01261-4 (2022).Article 

    Google Scholar 
    Anantharaman, K., Breier, J. A., Sheik, C. S. & Dick, G. J. Evidence for hydrogen oxidation and metabolic plasticity in widespread deep-sea sulfur-oxidizing bacteria. Proc. Natl. Acad. Sci. 110, 330–335 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Fritsch, J. et al. Rubredoxin-related maturation factor guarantees metal cofactor integrity during aerobic biosynthesis of membrane-bound [NiFe] hydrogenase. J. Biol. Chem. 289, 7982–7993 (2014).Article 
    CAS 

    Google Scholar 
    Petersen, J. M. et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat. Microbiol. 2, 1–11 (2016).Article 

    Google Scholar 
    Nakagawa, S. et al. Allying with armored snails: The complete genome of gammaproteobacterial endosymbiont. ISME J. 8, 40–51 (2014).Article 
    CAS 

    Google Scholar 
    Vignais, P. M., Billoud, B. & Meyer, J. Classification and phylogeny of hydrogenases. FEMS Microbiol. Rev. 25, 455–501 (2001).Article 
    CAS 

    Google Scholar 
    Perez, M. et al. Divergent paths in the evolutionary history of maternally transmitted clam symbionts. Proc. R. Soc. B 289, 20212137 (2022).Article 
    CAS 

    Google Scholar 
    Li, S. et al. N 4-cytosine DNA methylation is involved in the maintenance of genomic stability in Deinococcus radiodurans. Front. Microbiol. 10, 1905 (2019).Article 

    Google Scholar 
    Casadesús, J. & Low, D. Epigenetic gene regulation in the bacterial world. Microbiol. Mol. Biol. Rev. 70, 830–856 (2006).Article 

    Google Scholar 
    De Oliveira, A. L., Srivastava, A., Espada-Hinojosa, S. & Bright, M. The complete and closed genome of the facultative generalist Candidatus Endoriftia persephone from deep-sea hydrothermal vents. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13668 (2022).Article 

    Google Scholar 
    Ponnudurai, R. et al. Comparative proteomics of related symbiotic mussel species reveals high variability of host–symbiont interactions. ISME J. 14, 649–656 (2020).Article 
    CAS 

    Google Scholar 
    Yu, N. Y. et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).Article 
    CAS 

    Google Scholar  More