More stories

  • in

    Biogeographic problem-solving reveals the Late Pleistocene translocation of a short-faced bear to the California Channel Islands

    1.
    Whittaker, R. J., Fernández-Palacios, J. M., Matthews, T. J., Borregaard, M. K. & Triantis, K. A. Island biogeography: taking the long view of nature’s laboratories. Science. 357, eaam8326 (2017).
    PubMed  Google Scholar 
    2.
    Lyons, S. K. et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol. Lett. 12, 20160342 (2016).
    PubMed  PubMed Central  Google Scholar 

    3.
    Mychajliw, A. M. & Harrison, R. G. Genetics reveal the origin and timing of a cryptic insular introduction of muskrats in North America. PLoS ONE 9, e111856 (2014).
    ADS  PubMed  PubMed Central  Google Scholar 

    4.
    Hofman, C. A. & Rick, T. C. Ancient biological invasions and island ecosystems: tracking translocations of wild plants and animals. J. Archaeol. Res. 21, 217–306 (2017).
    Google Scholar 

    5.
    Muhs, D. R. et al. Late Quaternary sea-level history and the antiquity of mammoths (Mammuthus exilis and Mammuthus columbi), Channel Islands National Park, California USA. Q. Res. 83, 502–521 (2015).
    ADS  Google Scholar 

    6.
    Reeder-Myers, L., Erlandson, J., Muhs, D. R. & Rick, T. Sea level, paleogeography, and archaeology on California’s Northern Channel Islands. Quat. Res. 83, 263–272 (2015).
    Google Scholar 

    7.
    Rick, T. C. et al. Ecological change on California’s Channel Islands from the Pleistocene to the Anthropocene. Bioscience 64, 680–692 (2014).
    Google Scholar 

    8.
    Erlandson, J. et al. An archaeological and paleontological chronology for Daisy Cave (CA-SMI-261), San Miguel Island California. Radiocarbon 38, 355–373 (1996).
    CAS  Google Scholar 

    9.
    McLaren, D. et al. Late Pleistocene archaeological discovery models along the Pacific Coast of North America. PaleoAmerica. 6, 43–63 (2020).
    Google Scholar 

    10.
    Collins, P. W., Guthrie, D. A., Whistler, E. L., Vellanoweth, R. L. & Erlandson, J. M. Terminal Pleistocene-Holocene avifauna of San Miguel and Santa Rosa Islands: identifications of previously unidentified avian remains recovered from fossil sites and prehistoric cave deposits. West. North Am. Nat. 78, 370–403 (2018).
    Google Scholar 

    11.
    Rick, T. C., Culleton, B. J., Smith, C. B., Johnson, J. R. & Kennett, D. J. Stable isotope analysis of dog, fox, and human diets at a Late Holocene Chumash village (CA-SRI-2) on Santa Rosa Island California. J. Archaeol. Sci. 38, 1385–1393 (2011).
    Google Scholar 

    12.
    Hofman, C. A. et al. Tracking the origins and diet of an endemic island canid (Urocyon littoralis) across 7300 years of human cultural and environmental change. Quat. Sci. Rev. 146, 147–160 (2016).
    ADS  Google Scholar 

    13.
    Shirazi, S., Rick, T. C., Erlandson, J. M. & Hofman, C. A. A tale of two mice: a trans-Holocene record of Peromyscus nesodytes and Peromyscus maniculatus at Daisy Cave, San Miguel Island California. The Holocene 28, 827–833 (2017).
    ADS  Google Scholar 

    14.
    Kurten, B. Pleistocene bears of North America, Part 2. Genus Arctodus, short-faced bears. Acta Zool. Fennica. 117, 1–60 (1967).
    Google Scholar 

    15.
    Buckley, M. Zooarchaeology by mass spectrometry (ZooMS) collagen fingerprinting for the species identification of archaeological bone fragments. In Zooarchaeology in practice (eds Giovas, C. & LeFebvre, M.) 22–247 (Springer, Cham, 2018).
    Google Scholar 

    16.
    Figueirido, B., Perez-Claros, J. A., Torregrosa, V., Martin-Serra, A. & Palmqvist, P. Demythologizing Arctodus simus, the ‘short-faced’ long-legged and predaceous bear that never was. J. Vertebr. Paleontol. 30, 262–275 (2010).
    Google Scholar 

    17.
    Schubert, B. W. Late Quaternary chronology and extinction of North American giant short-faced bears (Arctodus). Quat. Int. 217, 188–194 (2010).
    Google Scholar 

    18.
    Matheus, P. E. Diet and co-ecology of Pleistocene short-faced bears and brown bears in Eastern Beringia. Quat. Res. 44, 447–453 (1995).
    CAS  Google Scholar 

    19.
    Emslie, S. D. & Czaplewski, N. J. A new record of the giant short-faced bear, Arctodus simus, from western North America with a reevaluation of its paleobiology. Nat Hist. Mus. Los Angel. Cty. Contrib. Sci. 371, 1–12 (1985).
    Google Scholar 

    20.
    Fox-Dobbs, K., Leonard, J. A. & Koch, P. L. Pleistocene megafauna from eastern Beringia: paleoecological and paleoenvironmental interpretations of stable carbon and nitrogen isotope and radiocarbon records. Palaeogeogr. Palaeoclimatol. Palaeoecol. 261, 30–46 (2008).
    Google Scholar 

    21.
    Bocherens, H., Emslie, S. D., Billiou, D. & Mariotti, A. Stable isotopes (13C, 15N) and paleodiet of the giant short-faced bear (Arctodus simus). Comptes Rendus de l’Académie des Sciences, Série II, Paris. 320, 779–784 (1995).
    CAS  Google Scholar 

    22.
    Figueirido, B. et al. Dental caries in the fossil record: a window to the evolution of dietary plasticity in an extinct bear. Sci. Rep. 7, 17813 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    23.
    Donohue, S. L., DeSantis, L. R. G., Schubert, B. W. & Ungar, P. S. Was the giant short-faced bear a hyper-scavenger? A new approach to the dietary study of ursids using dental microwear textures. PLoS ONE 8, e77531 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    24.
    Marks, S. A. & Erickson, A. W. Age determination in the black bear. J. Wildl. Manag. 30, 389–410 (1966).
    Google Scholar 

    25.
    Kurten, B. A. Skull of the grizzly bear (Ursus arctos L.) from Pit 10, Rancho La Brea. Nat Hist. Mus. Los Angel. Cty. Sci. Ser. 39, 1–7 (1960).
    Google Scholar 

    26.
    Stock, C. & Harris, J. M. Rancho la Brea: a record of Pleistocene life in California. Nat Hist. Mus. Los Angel. Cty. Sci. Ser 37, 1–113 (1992).
    Google Scholar 

    27.
    Fuller, B. T., Harris, J. M., Farrell, A. B., Takeuchi, G. & Southon, J. R. Sample preparation for radiocarbon dating and isotopic analysis of bone from Rancho La Brea. Nat Hist. Mus. Los Angel. Cty. Sci. Ser. 42, 151–167 (2015).
    Google Scholar 

    28.
    Walker, P. L. Archaeological evidence for the recent extinction of three terrestrial mammals on San Miguel Island. In: The California Islands: proceedings of a multidisciplinary symposium (ed. D. M. Powers). Santa Barbara Museum of Natural History, Santa Barbara, CA, 703–717 (1980).

    29.
    Buckley, M., Collins, M., Thomas-Oates, J. & Wilson, J. C. Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Rapid Commun. Mass Spectr. 23, 3843–3854 (2009).
    ADS  CAS  Google Scholar 

    30.
    Delisle, I. & Strobeck, C. Conserved primers for rapid sequencing of the complete mitochondrial genome from carnivores, applied to three species of bears. Mol. Biol. Evol. 19, 357–361 (2002).
    CAS  PubMed  Google Scholar 

    31.
    Yu, L., Li, Y. W., Ryder, O. A. & Zhang, Y. P. Analysis of complete mitochondrial genome sequences increases phylogenetic resolution of bears (Ursidae), a mammalian family that experienced rapid speciation. BMC Evol. Biol. 7, 198 (2007).
    PubMed  PubMed Central  Google Scholar 

    32.
    Krause, J. et al. Mitochondrial genomes reveal an explosive radiation of extinct and extant bears near the Miocene-Pliocene boundary. BMC Evol. Biol. 8, 220 (2008).
    PubMed  PubMed Central  Google Scholar 

    33.
    Mitchell, K. J. Ancient mitochondrial DNA reveals convergent evolution of giant short-faced bears (Tremarctinae) in North and South America. Biol. Lett. 12, 20160062 (2016).
    PubMed  PubMed Central  Google Scholar 

    34.
    Steffen, M. L. & Harington, C. R. Giant short-faced bear (Arctodus simus) from late Wisconsinan deposits at Cowichan Head, Vancouver Island British Columbia. Can. J. Earth Sci. 47, 1029–1036 (2010).
    ADS  Google Scholar 

    35.
    Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672 (2010).
    ADS  PubMed  PubMed Central  Google Scholar 

    36.
    Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
    MathSciNet  Google Scholar 

    37.
    Tahmasebi, F., Longstaffe, F. J. & Zazula, G. Nitrogen isotopes suggest a change in nitrogen dynamics between the Late Pleistocene and modern time in Yukon Canada. PLoS ONE 13, e0192713 (2018).
    PubMed  PubMed Central  Google Scholar 

    38.
    Long, E. S., Sweitzer, R. A., Diefenbach, D. R. & Ben-David, M. Controlling for anthropogenically induced atmospheric variation in stable carbon isotopes studies. Oecologia 146, 148–156 (2005).
    ADS  PubMed  Google Scholar 

    39.
    Coltrain, J. B. et al. Rancho La Brea stable isotope biogeochemistry and its implications for the paleoecology of late Pleistocene, coastal southern California. Palaeogeogr. Palaeoclimatol. Palaeoecol. 205, 199–219 (2004).
    Google Scholar 

    40.
    Chamberlain, C. P. et al. Pleistocene to recent dietary shifts in California condors. Proc. Natl. Acad. Sci. 102, 16707–16711 (2005).
    ADS  CAS  PubMed  Google Scholar 

    41.
    Newsome, S. D. et al. The shifting baseline of northern fur seal ecology in the northeast Pacific Ocean. Proc. Natl. Acad. Sci. 104, 9709–9714 (2007).
    ADS  CAS  PubMed  Google Scholar 

    42.
    Semprebon, G. M. et al. Dietary reconstruction of pygmy mammoths from Santa Rosa Island of California. Quat. Int. 406, 123–136 (2016).
    Google Scholar 

    43.
    Anderson, R. S., Starratt, S., Jass, R. M. B. & Pinter, N. Fire and vegetation history on Santa Rosa Island, Channel Islands, and long-term environmental change in southern California. J. Quat. Sci. 25, 782–797 (2010).
    Google Scholar 

    44.
    Erlandson, J. et al. Paleoindian seafaring, maritime technologies, and coastal foraging on California’s Channel Islands. Science 331, 1181–1185 (2011).
    ADS  CAS  PubMed  Google Scholar 

    45.
    Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).
    Google Scholar 

    46.
    Saltré, F. et al. Uncertainties in dating constrain model choice for inferring extinction time from fossil records. Quat. Sci. Rev. 112, 128–137 (2015).
    ADS  Google Scholar 

    47.
    Naito, Y. I. et al. Evidence for herbivorous cave bears (Ursus spelaeus) in Goyet Cave, Belgium: implications for paleodietary reconstruction of fossil bears using amino acid δ15N approaches. J. Quat. Sci. 31, 598–606 (2016).
    Google Scholar 

    48.
    Naito, Y. et al. Heavy reliance on plants for Romanian cave bears evidenced by amino acid nitrogen isotope analysis. Sci. Rep. 10, 6612 (2020).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    49.
    Mowat, G. & Heard, D. C. Major components of grizzly bear diet across North America. Can. J. Zool. 84, 473–489 (2011).
    Google Scholar 

    50.
    Trayler, R. B., Dundas, R. G., Fox-Dobbs, K. & Van De Water, P. K. Inland California during the Pleistocene- megafaunal stable isotope records reveal new paleoecological and paleoenvironmental insights. Palaeogeogr. Palaeoclimatol. Palaeoecol. 437, 132–140 (2015).
    Google Scholar 

    51.
    Goldberg, C. F. The application of stable carbon and nitrogen isotope analysis to human dietary reconstruction in prehistoric Southern California. PhD Thesis, University of California, Los Angeles, Los Angeles, California (1993).

    52.
    Newsome, S. D. et al. Pleistocene to historic shifts in bald eagle diets on the Channel Islands California. Proc. Natl. Acad. Sci. 107, 9246–9251 (2010).
    ADS  CAS  PubMed  Google Scholar 

    53.
    Schubert, B. W. & Kaufmann, J. E. A partial short-faced bear skeleton from an Ozark cave with comments on the paleobiology of the species. J. Cave Karst Stud. 65, 101–110 (2003).
    Google Scholar 

    54.
    Steffen, M. L. & Fulton, T. L. On the association of giant short-faced bear (Arctodus simus) and brown bear (Ursus arctos) in late Pleistocene North America. Geobios 51, 61–74 (2018).
    Google Scholar 

    55.
    Pigati, J. S., Muhs, D. R. & McGeehin, J. P. On the importance of stratigraphic control for vertebrate fossil sites in Channel Islands National Park, California, USA: Examples from new Mammuthus finds on San Miguel Island. Quat. Int. 443, 129–139 (2017).
    Google Scholar 

    56.
    Erlandson, J. M. & Moss, M. L. Shellfish eaters, carrion feeders, and the archaeology of aquatic adaptations. Am. Antiquity 66, 413–432 (2001).
    Google Scholar 

    57.
    Richards, R. I., Churcher, C. S. & Turnbull, W. D. Distribution and size variation in North American short-faced bears, Arctodus simus. In Palaeoecology and Palaeoenvironments of Late Cenozoic Mammals (eds Stewart, K. M. & Semour, K. L.) 191–246 (University of Toronto Press, Toronto, 1996).
    Google Scholar 

    58.
    Orr, P. C. Appendix: Additional Bone Artifacts. In California Shell Artifacts by Anthropological Records (ed. Gifford, E. W.) (University of California Press, California, 1947).
    Google Scholar 

    59.
    Fox-Dobbs, K., Dundas, R. G., Trayler, R. B. & Holroyd, P. A. Paleoecological implications of new megafaunal 14C ages from the McKittrick tar seeps California. J. Vertebr. Paleontol. 34, 220–223 (2014).
    Google Scholar 

    60.
    Guthrie, D. A. Analysis of Avifaunal and Bat Remains from Midden Sites on San Miguel Island. In: D. M. Powers (eds) The California Islands: proceedings of a multidisciplinary symposium. Santa Barbara Museum of Natural History, Santa Barbara, 689–702 (1980).

    61.
    Guthrie, D. A. Fossil vertebrates from Pleistocene terrestrial deposits on the northern Channel Islands, southern California. Contributions to the Geology of the Northern Channel Islands, So. California, 187–192 (1998).

    62.
    Hofman, C. A. et al. Collagen fingerprinting and the earliest marine mammal hunting in North America. Sci. Rep. 8, 10014 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    63.
    Bocherens, H. Isotopic tracking of large carnivore palaeoecology in the mammoth steppe. Quat. Sci. Rev. 117, 42–71 (2015).
    ADS  Google Scholar 

    64.
    van der Sluis, L. G. et al. Combining histology, stable isotope analysis and ZooMS collagen fingerprinting to investigate the taphonomic history and dietary behavior of the extinct giant tortoises from the Mare aux Songes deposit on Mauritius. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 80–91 (2014).
    Google Scholar 

    65.
    Buckley, M. & Collins, M. J. Collagen survival and its use for species identification in Holocene-lower Pleistocene bone fragments from British archaeological and paleontological sites. Antiqua 1, e1 (2011).
    Google Scholar 

    66.
    Dabney, J. et al. mtDNA genome from a Middle Pleistocene cave bear. Proc. Natl. Acad. Sci. 110, 15758–15763 (2013).
    ADS  CAS  PubMed  Google Scholar 

    67.
    Rohland, N., Harney, E., Mallick, S., Nordenfelt, S. & Reich, D. Partial uracil DNA glycosylase treatment for screening of ancient DNA. Philos. Trans. R. Soc. Lond Biol. Sci. 370, 624 (2015).
    Google Scholar 

    68.
    Caroe, C. et al. Single-tube library preparation for degraded DNA. Methods Ecol. Evolut. 9, 410–419 (2018).
    Google Scholar 

    69.
    Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage20: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    70.
    Katoh, K. & Standley, D. MAFFT Multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    71.
    Hasegawa, M., Kishino, H. & Yano, T. A. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evolut. 22, 160–174 (1985).
    CAS  Google Scholar 

    72.
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evolut. 35, 1547–1549 (2018).
    CAS  Google Scholar 

    73.
    Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evolut. 37, 1237–1239 (2020).
    Google Scholar 

    74.
    Felsenstein, J. Confidence limits on phylogenies with a molecular clock. Syst. Zool. 34, 152–161 (1985).
    Google Scholar 

    75.
    Feranec, R. S., Hadly, E. A., Blois, J. L., Barnosky, A. D. & Paytan, A. Radiocarbon dates from the Pleistocene fossil deposits of Samwel Cave, Shasta County, California USA. Radiocarbon 49, 117–121 (2007).
    CAS  Google Scholar 

    76.
    Feranec, R. S. Implications of radiocarbon dates from Potter Creek Cave, Shasta County, California USA. Radiocarbon 51, 931–936 (2009).
    CAS  Google Scholar 

    77.
    Jefferson, G. T. A catalogue of Late Quaternary vertebrates from California part two: mammals. Nat. Hist. Mus. LosAngel. Cty. Tech. Rep. 7, 1–129 (1991).
    Google Scholar 

    78.
    Jefferson, G. T. Stratigraphy and paleontology of the middle to late Pleistocene Manix Formation, and paleoenvironments of the central Mojave River, southern California. In: Paleoenvironments and Paleohydrology of the Mojave and Southern Great Basin Deserts (Y. Enzel, S.G. Wells (Eds.)), Geol. Soc. Amer. Spec. Paper 368. 43–60 (2003).

    79.
    Springer, K., Scott, E., Sagebiel, J. C. & Murray, L. K. Late Pleistocene large mammal faunal dynamics from inland southern California: the Diamond Valley Lake local fauna. Quat. Int. 217, 256–265 (2010).
    Google Scholar 

    80.
    Buckley, M. Species identification of bovine, ovine and porcine type 1 collagen; comparing peptide mass fingerprinting and LC-based proteomics methods. Int. J. Mol. Sci. 17, 445 (2016).
    PubMed  PubMed Central  Google Scholar 

    81.
    Johnson, J. R., Stafford Jr, T. W., Ajie, H. O., & Morris, D. P. Arlington springs revisited. In Proceedings of the fifth California Islands symposium (Vol. 5, pp. 541–545). Santa Barbara, CA: Santa Barbara Museum of Natural History (2002).

    82.
    Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0-50,000 years cal bp. Radiocarbon 55, 1869–1887 (2013).   More

  • in

    Metabolic trait diversity shapes marine biogeography

    1.
    Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).
    ADS  PubMed  Google Scholar 
    2.
    Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford Univ. Press, 2009).

    3.
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).
    PubMed  Google Scholar 

    4.
    Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Ecophysiology. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).
    ADS  PubMed  Google Scholar 

    5.
    Mandic, M., Todgham, A. E. & Richards, J. G. Mechanisms and evolution of hypoxia tolerance in fish. Proc. R. Soc. B 276, 735–744 (2009).
    PubMed  Google Scholar 

    6.
    Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Phil. Trans. R. Soc. Lond. B 362, 2061–2078 (2007).
    Google Scholar 

    7.
    Brey, T. An empirical model for estimating aquatic invertebrate respiration. Methods Ecol. Evol. 1, 92–101 (2010).
    Google Scholar 

    8.
    Peterson, C. C., Nagy, K. A. & Diamond, J. Sustained metabolic scope. Proc. Natl Acad. Sci. USA 87, 2324–2328 (1990).
    ADS  PubMed  Google Scholar 

    9.
    Hammond, K. A. & Diamond, J. Maximal sustained energy budgets in humans and animals. Nature 386, 457–462 (1997).
    ADS  PubMed  Google Scholar 

    10.
    Fry, F. E. J. Effect of the environment on animal activity. Univ. Tor. Stud. Biol. Ser. 55, 1–62 (1947).
    Google Scholar 

    11.
    Brett, J. R. Energetic responses of salmon to temperature. A study of some thermal relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). Am. Zool. 11, 99–113 (1971).
    Google Scholar 

    12.
    Pörtner, H.-O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).
    PubMed  Google Scholar 

    13.
    Piiper, J., Dejours, P., Haab, P. & Rahn, H. Concepts and basic quantities in gas exchange physiology. Respir. Physiol. 13, 292–304 (1971).
    PubMed  Google Scholar 

    14.
    Chan, F. et al. Emergence of anoxia in the California current large marine ecosystem. Science 319, 920 (2008).
    ADS  PubMed  Google Scholar 

    15.
    Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).
    ADS  PubMed  Google Scholar 

    16.
    Wishner, K. F. et al. Ocean deoxygenation and zooplankton: very small oxygen differences matter. Sci. Adv. 4, eaau5180 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    17.
    Howard, E. M. et al. Climate-driven aerobic habitat loss in the California current system. Sci. Adv. 6, eaay3188 (2020).
    ADS  PubMed  PubMed Central  Google Scholar 

    18.
    Nilsson, G. E. & Östlund-Nilsson, S. Does size matter for hypoxia tolerance in fish? Biol. Rev. Camb. Philos. Soc. 83, 173–189 (2008).
    PubMed  Google Scholar 

    19.
    DeLong, J. P., Okie, J. G., Moses, M. E., Sibly, R. M. & Brown, J. H. Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life. Proc. Natl Acad. Sci. USA 107, 12941–12945 (2010).
    ADS  PubMed  Google Scholar 

    20.
    Deutsch, C., Brix, H., Ito, T., Frenzel, H. & Thompson, L. Climate-forced variability of ocean hypoxia. Science 333, 336–339 (2011).
    ADS  PubMed  Google Scholar 

    21.
    Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).
    ADS  PubMed  Google Scholar 

    22.
    Verberk, W. C. E. P., Bilton, D. T., Calosi, P. & Spicer, J. I. Oxygen supply in aquatic ectotherms: partial pressure and solubility together explain biodiversity and size patterns. Ecology 92, 1565–1572 (2011).
    PubMed  Google Scholar 

    23.
    Emerson, S. & Hedges, J. Chemical Oceanography and the Marine Carbon Cycle (Cambridge Univ. Press, 2008).

    24.
    Kristensen, E. Ventilation and oxygen uptake by three species of Nereis (Annelida: Polychaeta). II. Effects of temperature and salinity changes.  Mar. Ecol. Prog. Ser. 12, 229–306 (1983).

    25.
    Gehrke, P. C. Response surface analysis of teleost cardio-respiratory responses to temperature and dissolved oxygen. Comp. Biochem. Physiol. A 89, 587–592 (1988).
    Google Scholar 

    26.
    Spitzer, K. W., Marvin, D. E. Jr & Heath, A. G. The effect of temperature on the respiratory and cardiac response of the bluegill sunfish to hypoxia. Comp. Biochem. Physiol. 30, 83–90 (1969).
    PubMed  Google Scholar 

    27.
    Kielland, Ø. N., Bech, C. & Einum, S. Warm and out of breath: thermal phenotypic plasticity in oxygen supply. Funct. Ecol. 33, 2142–2149 (2019).
    Google Scholar 

    28.
    Chung, M.-T., Trueman, C. N., Godiksen, J. A., Holmstrup, M. E. & Grønkjær, P. Field metabolic rates of teleost fishes are recorded in otolith carbonate. Commun. Biol. 2, 24 (2019).
    PubMed  PubMed Central  Google Scholar 

    29.
    Pörtner, H.-O. Oxygen- and capacity-limitation of thermal tolerance: a matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).
    PubMed  Google Scholar 

    30.
    Verberk, W. C. E. P., Durance, I., Vaughan, I. P. & Ormerod, S. J. Field and laboratory studies reveal interacting effects of stream oxygenation and warming on aquatic ectotherms. Glob. Change Biol. 22, 1769–1778 (2016).
    ADS  Google Scholar 

    31.
    Verberk, W. C. E. P., Leuven, R. S. E. W., van der Velde, G. & Gabel, F. Thermal limits in native and alien freshwater peracarid Crustacea: the role of habitat use and oxygen limitation. Funct. Ecol. 32, 926–936 (2018).
    PubMed  PubMed Central  Google Scholar 

    32.
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
    ADS  Google Scholar 

    33.
    Verberk, W. C. E. P. et al. Does oxygen limit thermal tolerance in arthropods? A critical review of current evidence. Comp. Biochem. Physiol. A 192, 64–78 (2016).
    Google Scholar 

    34.
    Lefevre, S. Are global warming and ocean acidification conspiring against marine ectotherms? A meta-analysis of the respiratory effects of elevated temperature, high CO2 and their interaction. Conserv. Physiol. 4, cow009 (2016).
    PubMed  PubMed Central  Google Scholar 

    35.
    Jutfelt, F. et al. Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology. J. Exp. Biol. 221, jeb169615 (2018).
    PubMed  Google Scholar 

    36.
    Ern, R., Norin, T., Gamperl, A. K. & Esbaugh, A. J. Oxygen dependence of upper thermal limits in fishes. J. Exp. Biol. 219, 3376–3383 (2016).
    PubMed  Google Scholar 

    37.
    Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
    ADS  PubMed  Google Scholar 

    38.
    Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
    ADS  PubMed  Google Scholar 

    39.
    Rummer, J. L. et al. Life on the edge: thermal optima for aerobic scope of equatorial reef fishes are close to current day temperatures. Glob. Change Biol. 20, 1055–1066 (2014).
    ADS  Google Scholar 

    40.
    Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).
    ADS  PubMed  Google Scholar 

    41.
    Killen, S. S. et al. Ecological influences and morphological correlates of resting and maximal metabolic rates across teleost fish species. Am. Nat. 187, 592–606 (2016).
    PubMed  Google Scholar 

    42.
    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
    ADS  PubMed  Google Scholar 

    43.
    Malte, H. & Weber, R. E. A mathematical model for gas exchange in the fish gill based on non-linear blood gas equilibrium curves. Respir. Physiol. 62, 359–374 (1985).
    PubMed  Google Scholar 

    44.
    Rogers, N. J., Urbina, M. A., Reardon, E. E., McKenzie, D. J. & Wilson, R. W. A new analysis of hypoxia tolerance in fishes using a database of critical oxygen level (P crit). Conserv. Physiol. 4, cow012 (2016).
    PubMed  PubMed Central  Google Scholar 

    45.
    Locarnini, R. A. et al. World Ocean Atlas 2013. Volume 1: Temperature (National Centers for Environmental Information, National Oceanic and Atmospheric Administration, 2013).

    46.
    Garcia, H. E. et al. World Ocean Atlas 2013, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation (National Centers for Environmental Information, National Oceanic and Atmospheric Administration, 2013).

    47.
    Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 https://doi.org/10.7289/V5C8276M (2009)

    48.
    Shawe-Taylor, J. & Cristianini, N. Kernel Methods for Pattern Analysis (Cambridge Univ. Press, 2004).

    49.
    Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography 34, 232–243 (2011).
    Google Scholar 

    50.
    Schurmann, H. & Steffensen, J. F. Effects of temperature, hypoxia and activity on the metabolism of juvenile Atlantic cod. J. Fish Biol. 50, 1166–1180 (1997).
    Google Scholar 

    51.
    Heath, A. G. & Hughes, G. M. Cardiovascular and respiratory changes during heat stress in rainbow trout (Salmo gairdneri). J. Exp. Biol. 59, 323–338 (1973).
    PubMed  Google Scholar  More

  • in

    Long-term changes in habitat and trophic level of Southern Ocean squid in relation to environmental conditions

    1.
    Reid, K. & Croxall, J. P. Environmental response of upper trophic-level predators reveals a system change in an Antarctic marine ecosystem. Proc. R. Soc. B Biol. Sci. 268, 377–384 (2001).
    CAS  Google Scholar 
    2.
    Constable, A. J. et al. Climate change and Southern Ocean ecosystems I: How changes in physical habitats directly affect marine biota. Glob. Chang. Biol. 20, 3004–3025 (2014).
    ADS  PubMed  Google Scholar 

    3.
    Rintoul, S. R. et al. Choosing the future of Antarctica. Nature 558, 233–241 (2018).
    ADS  CAS  PubMed  Google Scholar 

    4.
    Gutt, J. et al. Cross-disciplinarity in the advance of Antarctic ecosystem research. Mar. Genom. 37, 1–17 (2018).
    CAS  Google Scholar 

    5.
    IPCC (2019) Meredith, M. et al. Special report on the ocean and cryosphere in a changing climate: Polar regions. In press.

    6.
    Kwok, R. & Comiso, J. C. Spatial patterns of variability in Antarctic surface temperature: Connections to the southern hemisphere annular mode and the southern oscillation. Geophys. Res. Lett. 29, 1–4 (2002).
    Google Scholar 

    7.
    Thompson, D. W. J. & Solomon, S. Interpretation of recent Southern Hemisphere climate change. Science 296, 895–899 (2002).
    ADS  CAS  PubMed  Google Scholar 

    8.
    IPCC (2013) Stocker, T. F. et al. Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK, Cambridge Univ. Press.

    9.
    Meredith, M. P., Murphy, E. J., Hawker, E. J., King, J. C. & Wallace, M. I. On the interannual variability of ocean temperatures around South Georgia, Southern Ocean: Forcing by El Niño/Southern Oscillation and the Southern Annular Mode. Deep Res. Part II Top. Stud. Oceanogr. 55, 2007–2022 (2008).
    ADS  Google Scholar 

    10.
    Turner, J. The El Niño-Southern oscillation and Antarctica. Int. J. Climatol. 24, 1–31 (2004).
    Google Scholar 

    11.
    Pardo, D. et al. Additive effects of climate and fisheries drive ongoing declines in multiple albatross species. Proc. Natl. Acad. Sci. USA. 114, e10829–e10837 (2017).
    CAS  PubMed  Google Scholar 

    12.
    Trathan, P. N. & Murphy, E. J. Sea surface temperature anomalies near South Georgia: Relationships with the Pacific el niño regions. J. Geophys. Res. C. Ocean. 108, 1–10 (2002).
    Google Scholar 

    13.
    Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Glob. Chang. Biol. 15, 1618–1630 (2009).
    ADS  Google Scholar 

    14.
    Horswill, C. et al. Unravelling the relative roles of top-down and bottom-up forces driving population change in an oceanic predator. Ecology 97, 1919–1928 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    15.
    Gillett, N. P. & Thompson, D. W. J. Simulation of recent Southern Hemisphere climate change. Science 302, 273–275 (2003).
    ADS  CAS  PubMed  Google Scholar 

    16.
    Lovenduski, N. S. & Gruber, N. Impact of the Southern annular mode on Southern Ocean circulation and biology. Geophys. Res. Lett. 32, 1–4 (2005).
    Google Scholar 

    17.
    Inchausti, P. et al. Inter-annual variability in the breeding performance of seabirds in relation to oceanographic anomalies that affect the Crozet and the Kerguelen sectors of the Southern Ocean. J. Avian Biol. 2, 170–176 (2003).
    Google Scholar 

    18.
    Siniff, D. B., Garrott, R. A., Rotella, J. J., Fraser, W. R. & Ainley, D. G. Opinion: Projecting the effects of environmental change on Antarctic seals. Antarct. Sci. 20, 425–435 (2008).
    ADS  Google Scholar 

    19.
    Ito, M., Minami, H., Tanaka, Y. & Watanuki, Y. Seasonal and inter-annual oceanographic changes induce diet switching in a piscivorous seabird. Mar. Ecol. Prog. Ser. 393, 273–284 (2009).
    ADS  Google Scholar 

    20.
    Xavier, J. C. et al. Seasonal changes in the diet and feeding behaviour of a top predator indicate a flexible response to deteriorating oceanographic conditions. Mar. Biol. 160, 1597–1606 (2013).
    Google Scholar 

    21.
    Xavier, J. C. et al. A review on the biodiversity, distribution and trophic role of cephalopods in the Arctic and Antarctic marine ecosystems under a changing ocean. Mar. Biol. 165, 1–26 (2018).
    Google Scholar 

    22.
    Hill, S. L., Phillips, T. & Atkinson, A. Potential climate change effects on the habitat of Antarctic krill in the Weddell quadrant of the Southern Ocean. PLoS ONE 8, e72246 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    23.
    Freer, J. J., Tarling, G. A., Collins, M. A., Partridge, J. C. & Genner, M. J. Predicting future distributions of lanternfish, significant ecological resource within the Southern Ocean. Diver. Distr. 25, 1259–1272 (2019).
    Google Scholar 

    24.
    Xavier, J. C., Croxall, J. P., Trathan, P. & Wood, A. G. Feeding strategies and diets of breeding grey-headed and wandering albatrosses at South Georgia. Mar. Biol. 143, 221–232 (2003).
    Google Scholar 

    25.
    Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pup production of Antarctic fur seals. Ecology 86, 2408–2417 (2005).
    Google Scholar 

    26.
    Arthur, B. et al. Return Customers : Foraging site fidelity and the effect of environmental variability in wide-ranging Antarctic Fur Seals. PLoS ONE 10, 1–19 (2015).
    Google Scholar 

    27.
    Mills, W. F. et al. Long-term trends in albatross diets in relation to prey availability and breeding success. Mar. Biol. 167, 29 (2020).
    Google Scholar 

    28.
    Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).
    ADS  CAS  PubMed  Google Scholar 

    29.
    Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Chan. 9, 142–147 (2019).
    ADS  Google Scholar 

    30.
    Clarke, M. R. Cephalopod biomass—estimates from predation. Memoir. Natl. Museum Victoria. 44, 95–107 (1983).
    Google Scholar 

    31.
    Rodhouse, P. G. et al. Environmental effects on cephalopod population dynamics: Implications for management of fisheries. Adv. Mar. Biol. 67, 99–233 (2014).
    PubMed  Google Scholar 

    32.
    Xavier, J. C., Raymond, B., Jones, D. C. & Griffiths, H. Biogeography of cephalopods in the Southern Ocean using habitat suitability prediction models. Ecosystems 19, 220–247 (2016).
    CAS  Google Scholar 

    33.
    Saunders, R. A., Tarling, G. A., Hill, S. & Murphy, E. J. Myctophid fish (Family Myctophidae) are central consumers in the food web of the Scotia Sea (Southern Ocean). Front. Mar. Sci. 6, 530 (2019).
    Google Scholar 

    34.
    Rodhouse, P. G. Role of squid in the Southern Ocean pelagic ecosystem and the possible consequences of climate change. Deep. Res. Part II Top. Stud. Oceanogr. 95, 129–138 (2013).
    ADS  CAS  Google Scholar 

    35.
    Doubleday, Z. A. et al. Global proliferation of cephalopods. Curr. Biol. 26, 406–407 (2016).
    Google Scholar 

    36.
    Boyle, P. & Rodhouse, P. G. Cephalopods ecology and fisheries (Blackell Science, Oxford, 2005).
    Google Scholar 

    37.
    Cherel, Y. & Hobson, K. A. Stable isotopes, beaks and predators: A new tool to study the trophic ecology of cephalopods, including giant and colossal squids. Proc. R. Soc. B. Biol. Sci. 272, 1601–1607 (2005).
    Google Scholar 

    38.
    Ruiz-Cooley, R. I., Villa, E. C. & Gould, W. R. Ontogenetic variation of δ13C and δ15N recorded in the gladius of the jumbo squid Dosidicus gigas: geographic differences. Mar. Ecol. Prog. Ser. 399, 187–198 (2010).
    ADS  CAS  Google Scholar 

    39.
    Xavier, J. C. Foraging ecology and interactions with fisheries of wandering albatrosses (Diomedea exulans) breeding at South Georgia. Fish. Oceanogr. 13, 324–344 (2004).
    Google Scholar 

    40.
    Cherel, Y., Xavier, J. C., Grissac, S., Trouvé, C. & Weimerskirch, H. Feeding ecology, isotopic niche, and ingestion of fishery-related items of the wandering albatross Diomedea exulans at Kerguelen and Crozet Islands. Mar. Ecol. Prog. Ser. 565, 197–215 (2017).
    ADS  CAS  Google Scholar 

    41.
    Xavier, J. C., Croxall, J. P. & Cresswell, K. A. Boluses: an effective method for assessing the proportions of cephalopods in the diet of albatrosses. Auk. 122, 403–413 (2005).
    Google Scholar 

    42.
    Cherel, Y., Fontaine, C., Jackson, G. D., Jackson, C. H. & Richard, P. Tissue, ontogenic and sex-related differences in δ13C and δ15N values of the oceanic squid Todarodes filippovae (Cephalopoda: Ommastrephidae). Mar. Biol. 156, 699–708 (2009).
    Google Scholar 

    43.
    Guerreiro, M. et al. Habitat and trophic ecology of Southern Ocean cephalopods from stable isotope analyses. Mar. Ecol. Prog. Ser. 530, 119–134 (2015).
    ADS  CAS  Google Scholar 

    44.
    Arkhipkin, A. I. et al. World squid fisheries. Rev. Fish. Sci. Aquac. 8249, 2 (2015).
    Google Scholar 

    45.
    Xavier, J. C. et al. Future challenges in Southern Ocean ecology research. Front. Mar. Sci. 3, 1–9 (2016).
    Google Scholar 

    46.
    Froy, H. et al. Age-related variation in foraging behaviour in the wandering albatross at South Georgia: No evidence for senescence. PLoS ONE 10, e0116415 (2015).
    PubMed  PubMed Central  Google Scholar 

    47.
    Pecl, G. T. & Jackson, G. D. The potential impacts of climate change on inshore squid: Biology, ecology and fisheries. Rev. Fish. Biol. Fish. 18, 373–385 (2008).
    Google Scholar 

    48.
    Rogers, A. D. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120 (2019).
    PubMed  Google Scholar 

    49.
    Griffiths, H. J. Antarctic marine biodiversity—what do we know about the distribution of life in the Southern Ocean?. PLoS ONE 5, e11683 (2010).
    ADS  PubMed  PubMed Central  Google Scholar 

    50.
    Turner, J. et al. Antarctic climate change and the environment : an update. Polar. Rec. 50, 237–259 (2014).
    Google Scholar 

    51.
    Rodhouse, P. G., Griffiths, H. J., & Xavier, J. C. Southern Ocean squid. In: The Biogeographic Atlas of the Southern Ocean. Cambridge, SCAR, 284–289 (2014a).

    52.
    Weimerskirch, H., Louzao, M., de Grissac, S. & Delord, K. Changes in wind pattern alter albatross distribution and life-history traits. Science 335, 211–214 (2012).
    ADS  CAS  PubMed  Google Scholar 

    53.
    Golikov, A. V., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Changes in distribution and range structure of Arctic cephalopods due to climatic changes of the last decades. Biodiversity 14, 28–35 (2013).
    Google Scholar 

    54.
    Golikov, A. V., Sabirov, R. M., Lubin, P. A., Jørgensen, L. L. & Beck, I. M. The northernmost record of Sepietta oweniana (Cephalopoda: Sepiolidae) and comments on boreo-subtropical cephalopod species occurrence in the Arctic. Mar. Biodivers. Rec. 7, e58 (2014).
    Google Scholar 

    55.
    Guerra, A., Gonzalez, A. F. & Rocha, F. Appearance of the common paper nautilus Argonauta argo related to the increase of the sea surface temperature in the north-eastern Atlantic. J. Mar. Biol. Assoc. UK 82, 855–858 (2002).
    Google Scholar 

    56.
    Stowasser, G. et al. Food web dynamics in the Scotia Sea in summer: a stable isotope study. Deep. Sea Res. Part II Top. Stud. Oceanogr. 59–60, 208–221 (2012).
    ADS  Google Scholar 

    57.
    Cherel, Y., Bustamante, P. & Richard, P. Amino acid δ13C and δ15N from sclerotized beaks: A new tool to investigate the foraging ecology of cephalopods, including giant and colossal squids. Mar. Ecol. Prog. Ser. 624, 89–102 (2019).
    ADS  CAS  Google Scholar 

    58.
    Steffan, S. A. et al. Trophic hierarchies illuminated via amino acid isotopic analysis. PLoS ONE 8, e76152 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    59.
    Collins, M. A. & Rodhouse, P. G. Southern ocean cephalopods. Adv. Mar. Biol. 50, 191–265 (2006).
    PubMed  Google Scholar 

    60.
    Clarke, M. R., Croxall, J. P. & Prince, P. A. Cephalopods remains in regurgitation of the wandering albatross Diomedea exulans L. at South Georgia. Br. Antarct. Surv. Bull. 54, 9–21 (1981).
    Google Scholar 

    61.
    Rodhouse, P. G., Clarke, M. R. & Murray, A. W. Cephalopod prey of the wandering albatross Diomedea exulans. Mar. Biol. 10, 1–10 (1987).
    Google Scholar 

    62.
    Xavier, J. C., Croxall, J. P., Trathan, P. N. & Rodhouse, P. G. Inter-annual variation in the cephalopod component of the diet of the wandering albatross, Diomedea exulans, breeding at Bird Island, South Georgia. Mar. Biol. 142, 611–622 (2003).
    Google Scholar 

    63.
    Kennicutt, M., Chown, S. & Cassano, J. Six priorities for Antarctic science. Nature 512, 23–25 (2014).
    ADS  CAS  PubMed  Google Scholar 

    64.
    Gutt, J. et al. The Southern Ocean ecosystem under multiple climate change stresses—an integrated circumpolar assessment. Glob. Chang. Biol. 21, 1434–1453 (2015).
    ADS  PubMed  Google Scholar 

    65.
    Polito, M. J. et al. Contrasting specialist and generalist patterns facilitate foraging niche partitioning in sympatric populations of Pygoscelis penguins. Mar. Ecol. Prog. Ser. 519, 221–237 (2015).
    ADS  CAS  Google Scholar 

    66.
    Xavier, J. C., & Cherel, Y. Cephalopod beak guide for Southern Ocean. (2009).

    67.
    Jaeger, A., Lecomte, V. J., Weimerskirch, H., Richard, P. & Cherel, Y. Seabird satellite tracking validates the use of latitudinal isoscapes to depict predators’ foraging areas in the Southern Ocean. Rapid. Commun. Mass. Spectrom. 24, 1457–1466 (2010).
    Google Scholar 

    68.
    Cherel, Y. & Hobson, K. A. Geographical variation in carbon stable isotope signatures of marine predators: A tool to investigate their foraging areas in the Southern Ocean. Mar. Ecol. Prog. Ser. 329, 281–287 (2007).
    ADS  CAS  Google Scholar 

    69.
    Hobson, K. A., Piatt, J. F. & Pitocchelli, J. Using stable isotopes to determine seabird trophic relationships. J. Anim. Ecol. 63, 786–798 (1994).
    Google Scholar 

    70.
    Brault, E. K. et al. Carbon and nitrogen zooplankton isoscapes in West Antarctica reflect oceanographic transitions. Mar. Ecol. Prog. Ser 593, 29–45 (2018).
    ADS  CAS  Google Scholar 

    71.
    Seco, J. et al. Distribution, habitat and trophic ecology of Antarctic squid Kondakovia longimana and Moroteuthis knipovitchi: inferences from predators and stable isotopes. Polar Biol. 39, 167–175 (2016).
    Google Scholar 

    72.
    Keeling, C. D. The Suess effect: 13Carbon-14Carbon interrelations. Environ. Int. 2, 229–300 (1979).
    CAS  Google Scholar 

    73.
    Hilton, G. M. et al. A stable isotopic investigation into the causes of decline in a sub-Antarctic predator, the rockhopper penguin. Glob. Chang. Biol. 12, 611–625 (2006).
    ADS  Google Scholar 

    74.
    Jaeger, A. & Cherel, Y. Isotopic investigation of contemporary and historic changes in penguin trophic niches and carrying capacity of the Southern Indian Ocean. PLoS ONE 6, e16484 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    75.
    Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
    PubMed  Google Scholar 

    76.
    R Development Core Team (2018) R: a language and environment for statistical computing Vienna More

  • in

    Mitochondrial genomics reveals the evolutionary history of the porpoises (Phocoenidae) across the speciation continuum

    1.
    Steeman, M. E. et al. Radiation of extant cetaceans driven by restructuring of the oceans. Syst. Biol. 58, 573–585 (2009).
    PubMed  PubMed Central  Google Scholar 
    2.
    Tolley, K. A. & Rosel, P. E. Population structure and historical demography of eastern North Atlantic harbour porpoises inferred through mtDNA sequences. Mar. Ecol. Prog. Ser. 327, 297–308 (2006).
    ADS  CAS  Google Scholar 

    3.
    Banguera-Hinestroza, E., Bjørge, A., Reid, R. J., Jepson, P. & Hoelzel, A. R. The influence of glacial epochs and habitat dependence on the diversity and phylogeography of a coastal dolphin species: Lagenorhynchus albirostris. Conserv. Genet. 11, 1823–1836 (2010).
    Google Scholar 

    4.
    Taguchi, M., Chivers, S. J., Rosel, P. E., Matsuishi, T. & Abe, S. Mitochondrial DNA phylogeography of the harbour porpoise Phocoena phocoena in the North Pacific. Mar. Biol. 157, 1489–1498 (2010).
    CAS  Google Scholar 

    5.
    Amaral, A. R. et al. Influences of past climatic changes on historical population structure and demography of a cosmopolitan marine predator, the common dolphin (genus Delphinus). Mol. Ecol. 21, 4854–4871 (2012).
    PubMed  Google Scholar 

    6.
    Moura, A. E. et al. Recent diversification of a Marine Genus (Tursiops spp.) tracks habitat preference and environmental change. Syst. Biol. 62, 865–877 (2013).
    PubMed  Google Scholar 

    7.
    Whitehead, H. Cultural selection and genetic diversity in matrilineal whales. Science 282, 1708–1711 (1998).
    ADS  CAS  PubMed  Google Scholar 

    8.
    Fontaine, M. C. et al. Postglacial climate changes and rise of three ecotypes of harbour porpoises, Phocoena phocoena, in western Palearctic waters. Mol. Ecol. 23, 3306–3321 (2014).
    CAS  PubMed  Google Scholar 

    9.
    Louis, M. et al. Ecological opportunities and specializations shaped genetic divergence in a highly mobile marine top predator. Proc. Biol. Sci. 281, 20141558–20141558 (2014).
    PubMed  PubMed Central  Google Scholar 

    10.
    Foote, A. D. et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat. Commun. 7, 11693 (2016).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    11.
    Hare, M. P., Cipriano, F. & Palumbi, S. R. Genetic evidence on the demography of speciation in allopatric dolphin species. Evolution 56, 804–816 (2002).
    PubMed  Google Scholar 

    12.
    Pastene, L. A. et al. Radiation and speciation of pelagic organisms during periods of global warming: The case of the common minke whale, Balaenoptera acutorostrata. Mol. Ecol. 16, 1481–1495 (2007).
    CAS  PubMed  Google Scholar 

    13.
    Barnes, L. G. Evolution, taxonomy and antitropical distributions of the porpoises (Phocoenidae, Mammalia). Mar. Mammal Sci. 1, 149–165 (1985).
    Google Scholar 

    14.
    Burridge, C. P. Antitropicality of Pacific fishes: Molecular insights. Environ. Biol. Fishes 65, 151–164 (2002).

    15.
    Banguera-Hinestroza, E., Hayano, A., Crespo, E. & Hoelzel, A. R. Delphinid systematics and biogeography with a focus on the current genus Lagenorhynchus: Multiple pathways for antitropical and trans-oceanic radiation. Mol. Phylogenet. Evol. 80, 217–230 (2014).
    PubMed  Google Scholar 

    16.
    Marx, F. G. & Uhen, M. D. Climate, critters, and cetaceans: Cenozoic drivers of the evolution of modern whales. Science 327, 993–996 (2010).
    ADS  CAS  PubMed  Google Scholar 

    17.
    McGowen, M. R., Spaulding, M. & Gatesy, J. Divergence date estimation and a comprehensive molecular tree of extant cetaceans. Mol. Phylogenet. Evol. 53, 891–906 (2009).
    CAS  PubMed  Google Scholar 

    18.
    Gaskin, D. E. The ecology of whales and dolphins (Heinemann, London, 1982).
    Google Scholar 

    19.
    Zhou, X. et al. Population genomics of finless porpoises reveal an incipient cetacean species adapted to freshwater. Nat. Commun. 9, 1276 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    20.
    Teilmann, J. & Sveegaard, S. Porpoises the World over: Diversity in behavior and ecology. in Ethology and Behavioral Ecology of Odontocetes (ed. Würsig, B). Vol. 54, 449–464 (Springer International Publishing, New York, 2019).

    21.
    Ridgway, S. H. & Johnston, D. G. Blood oxygen and ecology of porpoises of three genera. Science 151, 456–458 (1966).
    ADS  CAS  PubMed  Google Scholar 

    22.
    Morell, V. World’s most endangered marine mammal down to 30. Science 355, 558–559 (2017).
    ADS  CAS  PubMed  Google Scholar 

    23.
    Amante, C. & Eatkins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC. https://doi.org/10.7289/V5C8276M.

    24.
    Berta, A., Sumich, J. L. & Kovacs, K. M. Chapter 6 – Evolution and geography. in Marine Mammals: Evolutionary Biology 131–166 (Elsevier, Amsterdam, 2015). https://doi.org/10.1016/B978-0-12-397002-2.00006-5.

    25.
    Chen, M. et al. Genetic footprint of population fragmentation and contemporary collapse in a freshwater cetacean. Sci. Rep. 7, 14449 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    26.
    Hayano, A., Amano, M. & Miyazaki, N. Phylogeography and population structure of the Dall’s porpoise, Phocoenoides dalli, in Japanese waters revealed by mitochondrial DNA. Genes Genet. Syst. 78, 81–91 (2003).
    CAS  PubMed  Google Scholar 

    27.
    Rosa, S. et al. Population structure of nuclear and mitochondrial DNA variation among South American Burmeister’s porpoises (Phocoena spinipinnis). Conserv. Genet. 6, 431–443 (2005).
    CAS  Google Scholar 

    28.
    Méndez-Fernandez, P. et al. Ecological niche segregation among five toothed whale species off the NW Iberian Peninsula using ecological tracers as multi-approach. Mar. Biol. 160, 2825–2840 (2013).
    Google Scholar 

    29.
    Galatius, A., Kinze, C. C. & Teilmann, J. Population structure of harbour porpoises in the Baltic region: Evidence of separation based on geometric morphometric comparisons. J. Mar. Biol. Ass. 92, 1669–1676 (2012).
    Google Scholar 

    30.
    Fontaine, M. C. Harbour porpoises, Phocoena phocoena, in the Mediterranean Sea and adjacent regions: Biogeographic relicts of the Last Glacial Period. Adv. Mar. Biol. 75, 333–358 (2016).
    CAS  PubMed  Google Scholar 

    31.
    Tezanos-Pinto, G. et al. A worldwide perspective on the population structure and genetic diversity of bottlenose dolphins (Tursiops truncatus) in New Zealand. J. Hered. 100, 11–24 (2009).
    CAS  PubMed  Google Scholar 

    32.
    Thomas, L. et al. Last call: Passive acoustic monitoring shows continued rapid decline of critically endangered vaquita. J. Acoust. Society Am. 142, EL512–EL517 (2017).
    Google Scholar 

    33.
    Jaramillo Legorreta, A. M. et al. Decline towards extinction of Mexico’s vaquita porpoise (Phocoena sinus). R. Soc. Open Sci. 6, 190598 (2019).
    ADS  PubMed  PubMed Central  Google Scholar 

    34.
    Wang, J. Y. & Reeves, R. R. Neophocaena phocaenoides. The IUCN Red List of Threatened Species. e.T198920A50386795. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T198920A50386795.en. Downloaded on 04 April 2019 (2017).

    35.
    Wang, D., Turvey, S. T., Zhao, X. & Mei, Z. Neophocaena asiaeorientalis ssp. asiaeorientalis. The IUCN Red List of Threatened Species. e.T43205774A45893487. https://doi.org/10.2305/IUCN.UK.2013-1.RLTS.T43205774A45893487.en. Downloaded on 04 April 2019. (2013).

    36.
    Birkun, A. A., Jr & Frantzis, A. Phocoena phocoena ssp. relicta. The IUCN Red List of Threatened Species. e.T17030A6737111. https://doi.org/10.2305/IUCN.UK.2008.RLTS.T17030A6737111.en. Downloaded on 04 April 2019 (2008).

    37.
    Read, F. L., Santos, M. B. & González, A. F. Understanding Harbour Porpoise (Phocoena phocoena) and Fishery Interactions in the North-West Iberian Peninsula. (Final report to ASCOBANS, 2012).

    38.
    Dufresnes, C. et al. Conservation phylogeography: Does historical diversity contribute to regional vulnerability in European tree frogs (Hyla arborea)?. Mol. Ecol. 22, 5669–5684 (2013).
    PubMed  Google Scholar 

    39.
    Malaney, J. L. & Cook, J. A. Using biogeographical history to inform conservation: The case of Preble’s meadow jumping mouse. Mol. Ecol. 22, 6000–6017 (2013).
    PubMed  Google Scholar 

    40.
    Moritz, C. C. & Potter, S. The importance of an evolutionary perspective in conservation policy planning. Mol. Ecol. 22, 5969–5971 (2013).
    PubMed  Google Scholar 

    41.
    Fajardo-Mellor, L. et al. The phylogenetic relationships and biogeography of true porpoises (Mammalia: Phocoenidae) based on morphological data. Mar. Mammal Sci. 22, 910–932 (2006).
    Google Scholar 

    42.
    Rosel, P. E., Haygood, M. G. & Perrin, W. F. Phylogenetic relationships among the true porpoises (Cetacea: Phocoenidae). Mol. Phylogenet. Evol. 4, 463–474 (1995).
    CAS  PubMed  Google Scholar 

    43.
    Torroni, A., Achilli, A., Macaulay, V., Richards, M. & Bandelt, H.-J. Harvesting the fruit of the human mtDNA tree. Trends Genet. 22, 339–345 (2006).
    CAS  PubMed  Google Scholar 

    44.
    Viricel, A. & Rosel, P. E. Evaluating the utility of cox1 for cetacean species identification. Mar. Mammal Sci. 28, 37–62 (2011).
    Google Scholar 

    45.
    Andrews, S. FastQC: A quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

    46.
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    47.
    Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
    PubMed  PubMed Central  Google Scholar 

    48.
    Arnason, U., Gullberg, A. & Janke, A. Mitogenomic analyses provide new insights into cetacean origin and evolution. Gene 333, 27–34 (2004).
    CAS  PubMed  Google Scholar 

    49.
    Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—A baiting and iterative mapping approach. Nucleic Acids Res. 41, e129–e129 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    50.
    Morin, P. A. et al. Complete mitochondrial genome phylogeographic analysis of killer whales (Orcinus orca) indicates multiple species. Genome Res. 20, 908–916 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    51.
    Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
    CAS  PubMed  PubMed Central  Google Scholar 

    52.
    Clayton, D. A. Transcription and replication of mitochondrial DNA. Hum. Reprod. 15(Suppl 2), 11–17 (2000).
    PubMed  Google Scholar 

    53.
    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
    CAS  Google Scholar 

    54.
    Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
    PubMed  PubMed Central  Google Scholar 

    55.
    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    56.
    Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v.1.6. (2014). https://tree.bio.ed.ac.uk/software/tracer/. Accessed 26 Feb 2017.

    57.
    Yu, G., Lam, T.T.-Y., Zhu, H. & Guan, Y. Two methods for mapping and visualizing associated data on phylogeny using Ggtree. Mol. Biol. Evol. 35, 3041–3043 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    58.
    Bouckaert, R. et al. BEAST 2: A software platform for bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537–e1003546 (2014).
    PubMed  PubMed Central  Google Scholar 

    59.
    Nabholz, B., Glemin, S. & Galtier, N. Strong variations of mitochondrial mutation rate across mammals—The longevity hypothesis. Mol. Biol. Evol. 25, 120–130 (2007).
    PubMed  Google Scholar 

    60.
    Fontaine, M. C. et al. Genetic and historic evidence for climate-driven population fragmentation in a top cetacean predator: The harbour porpoises in European water. Proc. Biol. Sci. 277, 2829–2837 (2010).
    PubMed  PubMed Central  Google Scholar 

    61.
    Rambaut, A. & Drummond, A. J. FigTree version 1.4.3. (tree.bio.ed.ac.uk/software/figtree, 2012).

    62.
    Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
    CAS  PubMed  Google Scholar 

    63.
    Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282 (1968).
    Google Scholar 

    64.
    McDonald, J. H. & Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991).
    ADS  CAS  PubMed  Google Scholar 

    65.
    Hervé, M. RVAideMemoire: Testing and plotting procedures for biostatistics. https://cran.r-project.org/web/packages/RVAideMemoire/index.html (2019).

    66.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).
    MathSciNet  MATH  Google Scholar 

    67.
    Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983). https://doi.org/10.1017/CBO9780511623486.
    Google Scholar 

    68.
    Hughes, A. L. Near neutrality: Leading edge of the neutral theory of molecular evolution. Ann. N. Y. Acad. Sci. 1133, 162–179 (2008).
    ADS  PubMed  PubMed Central  Google Scholar 

    69.
    Phifer-Rixey, M. et al. Adaptive evolution and effective population size in wild house mice. Mol. Biol. Evol. 29, 2949–2955 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    70.
    Eyre-Walker, A. Changing effective population size and the McDonald–Kreitman test. Genetics 162, 2017–2024 (2002).
    PubMed  PubMed Central  Google Scholar 

    71.
    Parsch, J., Zhang, Z. & Baines, J. F. The influence of demography and weak selection on the McDonald-Kreitman test: An empirical study in Drosophila. Mol. Biol. Evol. 26, 691–698 (2009).
    CAS  PubMed  Google Scholar 

    72.
    Romiguier, J. et al. Fast and robust characterization of time-heterogeneous sequence evolutionary processes using substitution mapping. PLoS ONE 7, e33852 (2012).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    73.
    Dutheil, J. & Boussau, B. Non-homogeneous models of sequence evolution in the Bio++ suite of libraries and programs. BMC Evol. Biol. 8, 255 (2008).
    PubMed  PubMed Central  Google Scholar 

    74.
    Dutheil, J. Y. et al. Efficient selection of branch-specific models of sequence evolution. Mol. Biol. Evol. 29, 1861–1874 (2012).
    CAS  PubMed  Google Scholar 

    75.
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria. https://www.R-project.org/ (2018).

    76.
    Figuet, E., Romiguier, J., Dutheil, J. Y. & Galtier, N. Mitochondrial DNA as a tool for reconstructing past life-history traits in mammals. J. Evol. Biol. 27, 899–910 (2014).
    CAS  PubMed  Google Scholar 

    77.
    Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).
    CAS  PubMed  PubMed Central  Google Scholar 

    78.
    Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).
    CAS  PubMed  PubMed Central  Google Scholar 

    79.
    Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Res. 10, 564–567 (2010).
    Google Scholar 

    80.
    Schneider, S. & Excoffier, L. Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA. Genetics 152, 1079–1089 (1999).
    CAS  PubMed  PubMed Central  Google Scholar 

    81.
    Drummond, A. J., Rambaut, A., Shapiro, B. & Pybus, O. G. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185–1192 (2005).
    CAS  PubMed  Google Scholar 

    82.
    Kingman, J. F. C. The coalescent. Stochastic Process. Appl. 13, 235–248 (1982).
    MathSciNet  MATH  Google Scholar 

    83.
    Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).
    MathSciNet  MATH  Google Scholar 

    84.
    Moura, A. E. et al. Phylogenomics of the genus Tursiops and closely related Delphininae reveals extensive reticulation among lineages and provides inference about eco-evolutionary drivers. Mol. Phylogenet. Evol. 146, 106756 (2020).
    PubMed  Google Scholar 

    85.
    Slater, G. J., Price, S. A., Santini, F. & Alfaro, M. E. Diversity versus disparity and the radiation of modern cetaceans. Proc. Biol. Sci. 277, 3097–3104 (2010).
    PubMed  PubMed Central  Google Scholar 

    86.
    McGowen, M. R. et al. Phylogenomic resolution of the cetacean tree of life using target sequence capture. Syst. Biol. 31, 2553 (2019).
    Google Scholar 

    87.
    Ho, S. Y. W., Saarma, U., Barnett, R., Haile, J. & Shapiro, B. The effect of inappropriate calibration: Three case studies in molecular ecology. PLoS ONE 3, e1615 (2008).
    ADS  PubMed  PubMed Central  Google Scholar 

    88.
    Zheng, Y. & Wiens, J. J. Do missing data influence the accuracy of divergence-time estimation with BEAST? Mol. Phylogenet. Evol. 85, 41–49 (2015).
    PubMed  Google Scholar 

    89.
    Lindberg, D. R. Marine biotic interchange between the northern and southern hemispheres. Paleobiology 17, 308–324 (1991).
    Google Scholar 

    90.
    Perrin, W. F. Coloration. in Encyclopedia of Marine Mammals (eds. Würsig, B., Perrin, W. & Thewissen, J. G. M.) 243–249 (Elsevier, 2009). https://doi.org/10.1016/B978-0-12-373553-9.00061-4.

    91.
    Koopman, H. N., Pabst, D. A., McLellan, W. A., Dillaman, R. M. & Read, A. J. Changes in blubber distribution and morphology associated with starvation in the harbor porpoise (Phocoena phocoena): Evidence for regional differences in blubber structure and function. Physiol. Biochem. Zool. 75, 498–512 (2002).
    CAS  PubMed  Google Scholar 

    92.
    Hoekendijk, J. P. A., Spitz, J., Read, A. J., Leopold, M. F. & Fontaine, M. C. Resilience of harbor porpoises to anthropogenic disturbance: Must they really feed continuously? Mar. Mammal Sci. 34, 258–264 (2018).
    Google Scholar 

    93.
    Escorza-Treviño, S. & Dizon, A. E. Phylogeography, intraspecific structure and sex-biased dispersal of Dall’s porpoise, Phocoenoides dalli, revealed by mitochondrial and microsatellite DNA analyses. Mol. Ecol. 9, 1049–1060 (2000).
    PubMed  Google Scholar 

    94.
    Wang, J. Y., Frasier, T. R., Yang, S. C. & White, B. N. Detecting recent speciation events: The case of the finless porpoise (genus Neophocaena). Heredity (Edinb) 101, 145–155 (2008).
    CAS  Google Scholar 

    95.
    Lin, W. et al. Phylogeography of the finless porpoise (genus Neophocaena): Testing the stepwise divergence hypothesis in the northwestern Pacific. Sci. Rep. 4, 6572 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    96.
    Rosel, P. E., Dizon, A. E. & Haygood, M. G. Variability of the mitochondrial control region in populations of the harbour porpoise, Phocoena, on interoceanic and regional scales. Can. J. Fish. Aquat. Sci. 52, 1210–1219 (1995).
    CAS  Google Scholar 

    97.
    Harris, S. A. Thermal history of the Arctic Ocean environs adjacent to North America during the last 3.5 Ma and a possible mechanism for the cause of the cold events (major glaciations and permafrost events). Progress Phys. Geogr. Earth Environ. 29, 218–237 (2005).
    Google Scholar 

    98.
    Chivers, S. J., Dizon, A. E. & Gearin, P. J. Small-scale population structure of eastern North Pacific harbour porpoises (Phocoena phocoena) indicated by molecular genetic analyses. J. Cetacean Res. Manag. 4, 111–122 (2002).
    Google Scholar 

    99.
    Pimper, L. E., Goodall, R. N. P. & Remis, M. I. First mitochondrial DNA analysis of the spectacled porpoise (Phocoena dioptrica) from Tierra del Fuego, Argentina. Mamm. Biol. Zeitschrift für Säugetierkunde 77, 459–462 (2012).
    Google Scholar 

    100.
    Lundmark, C. Science sings the blues: Other words for Nothin’ left to lose. Bioscience 57, 208–208 (2007).
    Google Scholar 

    101.
    Ehlers, J. R. & Gibbard, P. Quaternary glaciation. in Encyclopedia of Snow, Ice and Glaciers 873–882 (Springer, Dordrecht, 2014). https://doi.org/10.1007/978-90-481-2642-2_423

    102.
    Norris, K. S. & McFarland, W. N. A new harbor porpoise of the genus Phocoena from the Gulf of California. J. Mammal. 39, 22 (1958).
    Google Scholar 

    103.
    Rosel, P. E. & Rojas-Bracho, L. Mitochondrial DNA variation in the critically endangered Vaquita Phocoena Sinus Norris and Macfarland, 1958. Mar. Mammal Sci. 15, 990–1003 (1999).
    Google Scholar 

    104.
    Allendorf, F. W., Luikart, G. H. & Aitken, S. N. Conservation and the Genetics of Populations (Wiley, New York, 2012).
    Google Scholar 

    105.
    Moritz, C. Defining ‘Evolutionarily Significant Units’ for conservation. Trends Ecol. Evol. 9, 373–375 (1994).
    CAS  PubMed  Google Scholar 

    106.
    Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).
    PubMed  PubMed Central  Google Scholar 

    107.
    Bazin, E., Glemin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).
    ADS  CAS  PubMed  Google Scholar 

    108.
    Degnan, J. H. & Rosenberg, N. A. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol. Evol. 24, 332–340 (2009).
    PubMed  Google Scholar 

    109.
    Fontaine, M. C. et al. Mosquito genomics. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258524 (2015).
    PubMed  Google Scholar 

    110.
    Heliconius Genome Consortium. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).
    ADS  Google Scholar 

    111.
    Miles, A. et al. Genetic diversity of the African malaria vector Anopheles gambiae. Nature 552, 96–100 (2017).
    ADS  Google Scholar  More

  • in

    Benchmarking microbial growth rate predictions from metagenomes

    1.
    Kirchman DL. Growth rates of microbes in the oceans. Annu Rev Mar Sci. 2016;8:285–309.
    Google Scholar 
    2.
    Koch BJ, McHough TA, Hayer M, Schwartz E, Blazewicz SJ, Dijkstra P, et al. Estimating taxon-specific population dynamics in diverse microbial communities. Ecosphere. 2018;9:e02090.
    Google Scholar 

    3.
    Vieira-Silva S, Rocha EPC. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 2010;6:e1000808.
    PubMed  PubMed Central  Google Scholar 

    4.
    Korem T, Zeevi D, Suez J, Weinberger A, Avnit-Sagi T, Pompan-Lotan M, et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science. 2015;349:1101–6.
    CAS  PubMed  PubMed Central  Google Scholar 

    5.
    Brown CT, Olm MR, Thomas BC, Banfield JF. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol. 2016;34:1256–63.
    CAS  PubMed  PubMed Central  Google Scholar 

    6.
    Emiola A, Oh J. High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage. Nat Commun. 2018;9:4956.
    PubMed  PubMed Central  Google Scholar 

    7.
    Gao Y, Li H. Quantifying and comparing bacterial growth dynamics in multiple metagenomic samples. Nat Methods. 2018;15:1041–4.
    CAS  PubMed  PubMed Central  Google Scholar 

    8.
    Noble RT, Fuhrman JA. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat Microb Ecol. 1998;14:113–8.
    Google Scholar 

    9.
    Hobbie JE, Daley RJ, Jasper S. Use of nuclepore filters for counting bacteria by fluorescence microscopy. Appl Environ Microbiol. 1977;33:1225–8.
    CAS  PubMed  PubMed Central  Google Scholar 

    10.
    Didion JP, Martin M, Collins FS. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ. 2017;5:e3720.
    PubMed  PubMed Central  Google Scholar 

    11.
    Joshi NA, Fass JN. Sickle: asliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. 2011. https://github.com/najoshi/sickle.

    12.
    Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.

    13.
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
    CAS  PubMed  PubMed Central  Google Scholar 

    14.
    Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE. 2016;11:e0163962.
    PubMed  PubMed Central  Google Scholar 

    15.
    Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005;437:376–80.
    CAS  PubMed  PubMed Central  Google Scholar 

    16.
    Treangen TJ, Sommer DD, Angly FE, Koren S, Pop M. Next generation sequence assembly with AMOS. Curr Protoc Bioinformatics. 2011;11:11.8.

    17.
    Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.
    CAS  PubMed  Google Scholar 

    18.
    Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM.  arXiv. 2013;1303.3997v2.

    19.
    Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.
    PubMed  PubMed Central  Google Scholar 

    20.
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359. https://doi.org/10.7717/peerj.7359.
    PubMed  PubMed Central  Google Scholar 

    21.
    Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144.
    CAS  PubMed  Google Scholar 

    22.
    Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
    CAS  PubMed  Google Scholar 

    23.
    Eren AM, Murat Eren A, Esen ÖC, Quince C, Vineis JH, Morrison HG, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
    PubMed  PubMed Central  Google Scholar 

    24.
    Nissen JN, Sonderby CK, Armenteros JJA, Groenbech CH, Nielsen HB, Petersen TN, et al. Binning microbial genomes using deep learning. bioRxiv. 2018:490078. bioRxiv preprint https://doi.org/10.1101/490078.

    25.
    Graham ED, Heidelberg JF, Tully BJ. BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ. 2017;5:e3035.
    PubMed  PubMed Central  Google Scholar 

    26.
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836.
    CAS  PubMed  PubMed Central  Google Scholar 

    27.
    Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
    CAS  PubMed  Google Scholar 

    28.
    Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.
    CAS  PubMed  PubMed Central  Google Scholar 

    29.
    Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable, and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics. 2019;35:4453–5.
    CAS  PubMed  PubMed Central  Google Scholar 

    30.
    Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
    Google Scholar 

    31.
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
    CAS  PubMed  PubMed Central  Google Scholar 

    32.
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
    CAS  PubMed  PubMed Central  Google Scholar 

    33.
    Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.
    PubMed  PubMed Central  Google Scholar 

    34.
    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    35.
    Harrel FE, Dupont C. Hmisc: Harrell Miscellaneous. R package version 4.2-0. 2019. https://CRAN.R-project.org/package=Hmisc.

    36.
    Mouriño-Pérez RR, Worden AZ, Azam F. Growth of Vibrio cholerae O1 in red tide waters off California. Appl Environ Microbiol. 2003;69:6923–31.
    PubMed  PubMed Central  Google Scholar 

    37.
    Liu H, Campbell L, Landry MR. Growth and mortality rates of Prochlorococcus and Synechococcus measured with a selective inhibitor technique. Mar Ecol Prog Ser. 1995;116:277–87.
    Google Scholar 

    38.
    Liu H, Nolla HA, Campbell L. Prochlorococcus growth rate and contribution to primary production in the equatorial and subtropical North Pacific Ocean. Aquat Microb Ecol. 1997;12:39–47.
    Google Scholar 

    39.
    Johnson ZI, Zinser ER, Coe A, McNulty NP, Woodward EMS, Chisholm SW. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science. 2006;311:1737–40.
    CAS  PubMed  Google Scholar 

    40.
    Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson A, Yooseph S, et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 2007;5:e77.
    PubMed  PubMed Central  Google Scholar 

    41.
    Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science. 2014;344:416–20.
    CAS  PubMed  Google Scholar 

    42.
    Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. PNAS. 2015;112:5443–8.
    CAS  PubMed  Google Scholar 

    43.
    Roux S, Trubl G, Goudeau D, Nath N, Couradeau E, Ahlgren NA, et al. Optimizing de novo genome assembly from PCR-amplified metagenomes. PeerJ. 2019;7:e6902.
    PubMed  PubMed Central  Google Scholar 

    44.
    Ross A, Somssich IE. A DNA-based real-time PCR assay for robust growth quantification of the bacterial pathogen Pseudomonas syringae on Arabidopsis thaliana. Plant Methods. 2016;12:48.
    PubMed  PubMed Central  Google Scholar 

    45.
    Thrash JC, Seitz KW, Baker BJ, Temperton B, Gillies LE, Rabalais NN, et al. Metabolic roles of uncultivated bacterioplankton lineages in the Northern Gulf of Mexico ‘Dead Zone’. MBio. 2017;8:e01017–17.
    CAS  PubMed  PubMed Central  Google Scholar 

    46.
    Campbell BJ, Yu L, Straza TRA, Kirchman DL. Temporal changes in bacterial rRNA and rRNA genes in Delaware (USA) coastal waters. Aquat Microb Ecol. 2009;57:123–35.
    Google Scholar 

    47.
    Nikrad MP, Cottrell MT, Kirchman DL. Growth activity of gammaproteobacterial subgroups in waters off the west Antarctic Peninsula in summer and fall. Environ Microbiol. 2014;16:1513–23.
    CAS  PubMed  Google Scholar 

    48.
    Teira E, Martinez-Garcia S, Lonborg C, Ãlvarez-Salgado XA. Growth rates of different phylogenetic bacterioplankton groups in a coastal upwelling system. Environ Microbiol Rep. 2009;1:545–54.
    PubMed  Google Scholar 

    49.
    Alderkamp AC, Sintes E, Herndl GJ. Abundance and activity of major groups of prokaryotic plankton in the coastal North Sea during spring and summer. Aquat Microb Ecol. 2006;45:237–46.
    Google Scholar 

    50.
    Suzuki MT, Béjà O, Taylor LT, Delong EF. Phylogenetic analysis of ribosomal RNA operons from uncultivated coastal marine bacterioplankton. Environ Microbiol. 2001;3:323–31.
    CAS  PubMed  Google Scholar 

    51.
    Dupont CL, Rusch DB, Yooseph S, Lombardo M-J, Richter RA, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 2012;6:1186–99.
    CAS  PubMed  Google Scholar 

    52.
    Frigaard N-U, Martinez A, Mincer TJ, DeLong EF. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. Nature. 2006;439:847–50.
    CAS  PubMed  Google Scholar 

    53.
    Hugoni M, Taib N, Debroas D, Domaizon I, Jouan Dufournel I, Bronner G, et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc Natl Acad Sci USA. 2013;110:6004–9.
    CAS  PubMed  Google Scholar 

    54.
    Galand PE, Gutiérrez-Provecho C, Massana R, Gasol JM, Casamayor EO. Inter-annual recurrence of archaeal assemblages in the coastal NW Mediterranean Sea (Blanes Bay Microbial Observatory). Limnol Oceanogr. 2010;55:2117–25.
    Google Scholar 

    55.
    Martin-Cuadrado A-B, Garcia-Heredia I, Moltó AG, López-Úbeda R, Kimes N, López-García P, et al. A new class of marine Euryarchaeota group II from the Mediterranean deep chlorophyll maximum. ISME J. 2015;9:1619–34.
    CAS  PubMed  Google Scholar 

    56.
    Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417–32.
    CAS  PubMed  PubMed Central  Google Scholar 

    57.
    Mincer TJ, Church MJ, Taylor LT, Preston C, Karl DM, DeLong EF. Quantitative distribution of presumptive archaeal and bacterial nitrifiers in Monterey Bay and the North Pacific Subtropical Gyre. Environ Microbiol. 2007;9:1162–75.
    CAS  PubMed  Google Scholar  More

  • in

    New insights into the biodiversity of coliphages in the intestine of poultry

    Phage isolation
    In this study, 38 coliphages were isolated from poultry faecal samples collected from 27 Belgian poultry farms located in five different regions, including West Flanders, East Flanders, Antwerp, and Limburg. Between one and seven phages were isolated from each farm using E. coli C600 or K514 as host strain.
    Phage morphological analysis
    Based on a sequencing cut-off value of ≤ 95% nucleotide similarity, 18 coliphages were selected and subjected to TEM to determine phage morphology and confirm phage classification. Based on the morphological features, the phages were classified into the Caudovirales order and either the Siphoviridae family or the Myoviridae family. Analysis of the isolated Siphoviridae phages showed a long flexible non-contractile tail with a length varying between ~ 100 and ~ 200 nm and icosahedral heads with widths ranging from ~ 52 to ~ 77 nm (Fig. 1a–h). Among the isolated Myoviridae phages a long straight contractile tail was observed with a tailed length varied between ~ 100 and ~ 120 nm, head widths ranging from ~ 65 to ~ 84 nm, and head lengths from ~ 60 to ~ 110 nm (Fig. 1i–r). Taxonomic classification of each of the coliphages is shown in Table 1.
    Figure 1

    Negative staining electron microscopy images of Siphoviridae and Myoviridae coliphages. Siphoviridae phages: (a) Phage 17. (b) Phage 53. (c) Phage 54. (d) Phage 61. (e) Phage 70. (f) Phage 74. (g) Phage 76. (h) Phage 77. Myoviridae phages: (i) Phage 10. (j) Phage 11. (k) Phage 15. (l) Phage 18. (m) Phage 30. (n) Phage 55. (o) Phage 60. (p) Phage 62 (q) Phage 78. (r) Phage 79. The black bars represent 100 nm.

    Full size image

    Table 1 Characteristics of the 38 E. coli phages investigated in this study.
    Full size table

    Phage genome sequence analysis and annotation
    All 38 coliphages isolated in this study were characterized based on WGS data. An overview of the genomic characteristics and properties are listed in Table 1. According to FastQC parameters, good quality of the raw sequence data for all phages was confirmed. The phage genomes ranged in size between 44,324 and 173,384 bp, with a G+C content between 35.5 and 46.4%. Genomes smaller than 90,000 bp had a G+C content between 38.9 and 46.4%, whereas the larger genomes had a G+C content of 35.5–38%. For each coliphage, 72–275 putative CDSs were identified using both automatic and manual annotation. CDSs encoding the phage terminase small subunit, the phage terminase large subunit, the phage portal protein, and phage capsid and scaffold proteins were identified within all 38 coliphage genome sequences. They presented the same conserved genome structure with a general gene order: the terminase small subunit upstream from the terminase large subunit, the phage portal protein and two genes encoding phage capsid and scaffold proteins. In general, one phage terminase small subunit, one phage portal protein, and up to four phage capsid and scaffold proteins were found within each of the phage genomes. Besides, phage exonucleases were identified in all phage genomes. For each phage, one to three CDSs for exonucleases were found. No gene encoding for an integrase was found, indicating that these phages are strictly virulent/lytic phages. No known acquired resistance or virulence genes were detected in any of the 38 phage genomes.
    Phage phylogeny and taxonomy
    Taxonomic classification of the 38 isolated coliphages was performed through multiple WGS genome comparisons. These coliphages included 27 (71%) Siphoviridae coliphages and 11 (29%) Myoviridae coliphages. The Siphoviridae phages were compared with 146 published phages from this family. The Myoviridae phages were compared with 171 published Myoviridae phages. According to ICTV guidelines, phage family, subfamily and genus were predicted based on genome similarity. Results are shown in Table 1. All Siphoviridae phages belonged to the Tunavirinae subfamily, except for Phage 61. This phage was predicted to belong to the Tequintavirus genus, which do not have any ICTV subfamily. The ten phages, Phage 8, 53, 54, 63, 65, 68, 69, 71, 72, and 75 all belonged to the Hanrivervirus genus. Three phages belonged to the Rtpvirus genus, including Phage 17, 70, and 73. No existing ICTV genus could be assigned to the remaining 13 coliphages. Phage 28, 56_1 and 76 could be assigned to the same unknown genus. Phage 58 and Phage 74 were found to be in the same genus. Phage 47, 48, 59, 64, and 77 were predicted to belong to the same genus. Phage 52, 56_2, and 80 were predicted to belong to the same genus. The 11 Myoviridae belonged either to the Tevenvirinae or the Ounavirinae subfamily. Tevenvirinae phages included the six phages: Phage 10, 11, 15, 18, 30 and 55. Phage 10, 11 and 55 belonged to the Tequatrovirus genus, and Phage 18 and 30 belonged to the Mosigvirus genus. Ounavirinae phages included the remaining five phages; Phage 60, 62, 66, 78 and 79. All phages belonged to the Felixounavirus genus.
    Phage diversity
    To investigate the diversity of the coliphages, phage genomes were first clustered based on whole genome sequence. A total of 173 Siphoviridae and 182 Myoviridae coliphage genomes were included. Characteristics of selected reference genomes are listed in Supplementary Table S1. Siphoviridae phages isolated in this study were found in five different (sub)clusters, cluster A1–3, B and C, with a cut-off value of 0.82 (Fig. 2). Cluster A was divided into three subclusters. Fourteen of our phages, formed subcluster A1 together with the three pSf-1-like reference phages from the NCBI database. Phage 80, 28, 56_1, and 76 formed subcluster A2 with the three Swan01-like reference phages. Phages 69, 52, and 56_2 formed subcluster A3 with phage Jahat_MG145. Phage 73, 70, 17, 58 and 74 formed cluster B without any known reference phages. Phage 61 was placed in cluster C with 13 T5-like reference phages. For the Myoviridae phages, the resulting phylogeny placed phages isolated in this study in three different clusters with a cut-off height of 0.52 (Fig. 3). Phages 62, 78, 66, 60 and 79 formed cluster D with Felix01-like reference phage Alf5. Phage 30, 15 and 18 formed cluster E with 19 T4-like reference phages (cut-off height of 0.36). Phages 55, 11 and 10 were placed in cluster F with 57 reference phages. At the cut-off height of 0.39 Phage 55 was found in a different subcluster than Phage 10 and 11.
    Figure 2

    Phylogenetic analysis of Siphoviridae coliphages based on WGS sequence. Phages isolated in this study are highlighted. Each colour represents a cluster: Cluster A (blue), cluster B (green), and cluster C (red). Cluster A subclusters include A1 (light blue), A2 (blue), and A3 (dark blue). Distance matrices and clustering are based on kmer length = 10.

    Full size image

    Figure 3

    Phylogenetic analysis of Myoviridae coliphages based on WGS sequence. Phages isolated in this study are highlighted. Each colour represents a cluster: Cluster D (orange), cluster E (purple), and cluster F (brown). Distance matrices and clustering are based on kmer length = 10.

    Full size image

    Coliphages were further assessed based on the presence/absence of families of orthologues genes in their pan genome. Similar clusters were observed with only minor changes. For the Siphoviridae phage analysis, 5,227 gene groups were included (Supplementary Fig. S1). The resulting phylogenetic analysis placed phages isolated in this study in the same five clusters, cluster A1–3, B and C, with a cut-off height of 0.81 (Supplementary Fig. S2). One additional reference phage was found in cluster B and C, including the T1-like reference phage CEB_EC3a and the T5-like reference phage EPS7, respectively. For the Myoviridae phage analysis, 9,420 gene groups were included (Supplementary Fig. S3). The resulting phylogeny placed phages isolated in this study in the same three clusters, cluster D, E, and F, with a cut-off height of 0.58 (Supplementary Fig. S4). For cluster D, additionally 13 Felix01-like reference phages were found. In contrast to the WGS-based analysis, at a cut-off height of 0.39, all cluster F phages isolated in this study, were found in one subcluster with 10 T4-like reference phages. The degree of topological and branch length agreement between the different phylogenetic methods were compared (Supplementary Table S2).
    The coliphage diversity was further assessed based on three phage marker genes: the terminase large subunit and phage portal protein, and the phage exonuclease. Selected gene sequences from known phages were included for reference. Results are summarized in Table 1. For all three marker genes, cluster formation was in accordance with resulting clusters of the pan genome- and WGS-based phylogeny, cluster A–F, only with minor differences. Results based on the terminase large subunit analysis are shown below (Fig. 4).
    Figure 4

    Maximum likelihood tree based on the nucleotide sequences of the phage terminase large subunit. The analysis resulted in six clusters: A–F, according to phage family and subfamily. Cluster A and B: Siphoviridae, Tunavirinae, cluster C: Siphoviridae and Tequintavirus genus, cluster D: Myoviridae, Ounavirinae, and cluster E and F: Myoviridae, Tevenvirinae. Cluster A was divided into three subclusters: A1, A2 and A3. The tree was constructed using the MEGA X software54. The percent of data coverage for internal nodes is indicated. The scale bar indicates the number of nucleotide sequence substitutions per site. The analysis included 62 nucleotide sequences, including 24 reference phages listed in Supplementary Table S1 for comparison.

    Full size image

    For cluster A, all coliphages isolated in this study were found within same subclusters as for the WGS-based phylogeny except for Phage 63, which was found in the A2 subcluster instead of A1. Analysis based on the phage portal protein resulted in the division of our A2 subcluster phages into two groups: Phage 56_1, 80 and 28 in one group and Phage 76 in the other group (Supplementary Fig. S5). Analysis based on the exonuclease resulted in multiple clusters of cluster C and F, as phages from these clusters encoded 2 or 2–3 exonuclease genes, respectively (Supplementary Fig. S6). Comparison of the cluster construction of the three single genes analysis showed only minor topological and branch length differences (Supplementary Table S2). Moreover, cluster construction was in accordance with phage subfamily defined based on the whole genome. Siphoviridae phages from cluster A and B belonged to the Tunavirinae subfamily, and Siphoviridae phages form cluster C had no defined ICTV subfamily. Myoviridae phages from cluster D belonged to the Ounavirinae subfamily, and Myoviridae phages from cluster E and F belonged to the Tevenvirinae subfamily.
    Phage comparative genomics
    Pan genome analysis of Siphoviridae and Myoviridae phages isolated in this study revealed that neither of the two groups had any core genes. Analysis of coliphage genomes from each of the six clusters, A–F, identified core genes (core and softcore) and accessory genes (shell and cloud). As cluster A phages had only five core genes (2% of the total genome), analysis of subclusters, A1, A2 and A3, were performed additionally. Results are summarized in Table 2. The pan genome included between 81 and 333 genes, and core genes constituted between 22 and 73% of the pan genome.
    Table 2 Overview of comparative genomics analysis.
    Full size table

    The level of synteny and genomic rearrangement within each cluster or subcluster of related phages was assessed by genome comparison. Results are summarized in Table 2. Eight comparisons were performed, corresponding to the eight (sub)clusters, A1, A2, A3, B, C, D, E, and F resulting from the phage diversity analysis above (Supplementary Fig. S7–S14). Genome comparison of the phages resulted in identification of local collinear blocks (LCBs), indicating homologues DNA regions shared by two or more genomes without sequence rearrangements. The LCBs comprised different modules of genes with different functions, including modules for DNA packaging, structural proteins, head and tail morphogenesis, and host cell lysis. Several modules comprised only hypothetical proteins with unknown function. The average level of conservation varied between the different type of genes.
    Genes encoding the terminase large and small subunit, the major capsid protein, DNA primase, single-stranded protein, portal protein, recombinase, specific tail protein and holin were the most conserved genes between all phages, whereas genes with the lowest level of conservation included, specific tail fiber proteins, tail tape measure proteins and HNH homing endonucleases. Hypothetical proteins were found with large variation in level of conservation. Each phage genome comprised between four and 17 LCBs. Genome comparison of phages belonging to subcluster A1, A2 and A3 identified 16, seven and four LCBs, respectively. All phages in each cluster comprised all LCBs. All cluster B phages comprised all six LCBs. For the cluster C phages, between 6 and 10 LCBs were identified for each phage. Phage 61 isolated in this study comprised all nine regions. Variation in number of LCBs was due to a variable repeat region comprising multiple LCBs, which was found only in some of the cluster C phages. For the cluster D comparison, 14–17 different LCBs were identified for each phage. Variation in number of LCBs was due to four different small variable regions of which some of all were missing in the majority of the phages. Phages isolated in this study, including Phage 79, Phage 78, Phage 60, Phage 66, and Phage 62, comprised 17, 17, 16, 15, and 14 LCBs, respectively. Comparison of phages belonging to cluster E identified 18 LCBs. All phages lacked one or both of the same two LCBs. Phages isolated in this study, including Phage 30, Phage 15, and Phage 18, comprised 16, 17, and 17 LCBs, respectively. All 13 cluster F phages included in the comparison comprised all five LCBs. The comparison confirmed the presence of homologue regions between the phages within the clusters but also highlighted that re-arrangement and/or gain/loss of LCBs must have occurred at some point during the evolution of the phages. The region encoding the terminase large subunit and portal protein were present in a conserved region all genomes in all eight comparisons. More

  • in

    Livestock enclosures in drylands of Sub-Saharan Africa are overlooked hotspots of N2O emissions

    Field N2O flux measurements
    At seven different semi-arid and arid savanna regions in Kenya (Fig. 1, Supplementary Fig. 9), N2O measurements from soils at 46 boma sites and 22 adjacent reference sites (undisturbed savanna) were carried out. At each boma and control site, 3–7 plots were chosen randomly for flux measurements. Local members of the pastoral communities, including herders and/or community elders were interviewed for information on the time since abandonment of each boma.
    N2O fluxes from bomas were measured using the fast-box chamber method15, deploying an ultra-portable greenhouse gas analyzer of ABB-Los Gatos Research Inc. (Modell 909–0041). A gas-tight, vented chamber (0.3 × 0.2 × 0.15 m) was pressed against the ground on foam frames for 4–7 min, during which time sample air was pumped from the headspace of the chamber to the analyzer and returned to the chamber thereafter. In this way, changes in headspace N2O concentrations were continuously measured over the sample period, with a running average of every 5 s. Linear regression over the sample period was used to calculate fluxes. The detection limit for N2O fluxes was More

  • in

    Temperature transcends partner specificity in the symbiosis establishment of a cnidarian

    1.
    Hoegh-Guldberg O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar Freshw Res. 1999;50:839–66.
    Google Scholar 
    2.
    Hoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S. Coral reef ecosystems under climate change and ocean acidification. Front Mar Sci. 2017;4:158.
    Google Scholar 

    3.
    Kenkel CD, Goodbody-Gringley G, Caillaud D, Davies SW, Bartels E, Matz MV. Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments. Mol Ecol. 2013;22:4335–48.
    PubMed  CAS  Google Scholar 

    4.
    Bay RA, Palumbi SR. Multilocus adaptation associated with heat resistance in reef-building corals. Curr Biol. 2014;24:2952–6.
    PubMed  CAS  Google Scholar 

    5.
    Dixon GB, Davies SW, Aglyamova GV, Meyer E, Bay LK, Matz MV. Genomic determinants of coral heat tolerance across latitudes. Science. 2015;348:1460.
    PubMed  CAS  Google Scholar 

    6.
    Howells EJ, Abrego D, Meyer E, Kirk NL, Burt JA. Host adaptation and unexpected symbiont partners enable reef-building corals to tolerate extreme temperatures. Glob Change Biol. 2016;22:2702–14.
    Google Scholar 

    7.
    Morikawa MK, Palumbi SR. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc Natl Acad Sci USA. 2019;116:10586.
    PubMed  CAS  Google Scholar 

    8.
    Berkelmans R, van Oppen MJH. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc Biol Sci. 2006;273:2305–12.
    PubMed  PubMed Central  Google Scholar 

    9.
    Sampayo EM, Ridgway T, Bongaerts P, Hoegh-Guldberg O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc Natl Acad Sci USA. 2008;105:10444.
    PubMed  CAS  Google Scholar 

    10.
    LaJeunesse TC, Smith RT, Finney J, Oxenford H. Outbreak and persistence of opportunistic symbiotic dinoflagellates during the 2005 Caribbean mass coral ‘bleaching’ event. Proc R Soc B: Biol Sci. 2009;276:4139–48.
    Google Scholar 

    11.
    Howells EJ, Beltran VH, Larsen NW, Bay LK, Willis BL, van Oppen MJH. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat Clim Change. 2011;2:116.
    Google Scholar 

    12.
    LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–.e6.
    PubMed  CAS  Google Scholar 

    13.
    Bellantuono AJ, Granados-Cifuentes C, Miller DJ, Hoegh-Guldberg O, Rodriguez-Lanetty M. Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE. 2012;7:e50685.
    PubMed  PubMed Central  CAS  Google Scholar 

    14.
    Palumbi SR, Barshis DJ, Traylor-Knowles N, Bay RA. Mechanisms of reef coral resistance to future climate change. Science. 2014;344:895.
    PubMed  CAS  Google Scholar 

    15.
    Sawall Y, Al-Sofyani A, Hohn S, Banguera-Hinestroza E, Voolstra CR, Wahl M. Extensive phenotypic plasticity of a Red Sea coral over a strong latitudinal temperature gradient suggests limited acclimatization potential to warming. Sci Rep. 2015;5:8940.
    PubMed  PubMed Central  CAS  Google Scholar 

    16.
    Torda G, Donelson JM, Aranda M, Barshis DJ, Bay L, Berumen ML, et al. Rapid adaptive responses to climate change in corals. Nat Clim Change. 2017;7:627.
    Google Scholar 

    17.
    Baker AC. Flexibility and specificity in coral-algal symbiosis: diversity, ecology, and biogeography of Symbiodinium. Annu Rev Ecol, Evolution, Syst. 2003;34:661–89.
    Google Scholar 

    18.
    Boulotte NM, Dalton SJ, Carroll AG, Harrison PL, Putnam HM, Peplow LM, et al. Exploring the Symbiodinium rare biosphere provides evidence for symbiont switching in reef-building corals. ISME J. 2016;10:2693–701.
    PubMed  PubMed Central  CAS  Google Scholar 

    19.
    Cunning R, Silverstein RN, Baker AC. Symbiont shuffling linked to differential photochemical dynamics of Symbiodinium in three Caribbean reef corals. Coral Reefs. 2018;37:145–52.
    Google Scholar 

    20.
    Stat M, Loh WKW, LaJeunesse TC, Hoegh-Guldberg O, Carter DA. Stability of coral–endosymbiont associations during and after a thermal stress event in the southern Great Barrier Reef. Coral Reefs. 2009;28:709–13.
    Google Scholar 

    21.
    Putnam HM, Stat M, Pochon X, Gates RG. Endosymbiotic flexibility associates with environmental sensitivity in scleractinian corals. Proc R Soc B: Biol Sci. 2012;279:4352–61.
    Google Scholar 

    22.
    Stat M, Morris E, Gates RD. Functional diversity in coral–dinoflagellate symbiosis. Proc Natl Acad Sci USA. 2008;105:9256.
    PubMed  CAS  Google Scholar 

    23.
    Starzak DE, Quinnell RG, Nitschke MR, Davy SK. The influence of symbiont type on photosynthetic carbon flux in a model cnidarian–dinoflagellate symbiosis. Mar Biol. 2014;161:711–24.
    CAS  Google Scholar 

    24.
    Gabay Y, Weis VM, Davy SK. Symbiont identity influences patterns of symbiosis establishment, host growth, and asexual reproduction in a model cnidarian-dinoflagellate symbiosis. Biol Bull. 2018;234:1–10.
    PubMed  Google Scholar 

    25.
    Quigley KM, Bay LK, Willis BL. Temperature and water quality-related patterns in sediment-associated Symbiodinium communities impact symbiont uptake and fitness of juveniles in the genus acropora. Front Mar Sci. 2017;4:401.
    Google Scholar 

    26.
    Cumbo VR, vanOppen MJH, Baird AH. Temperature and Symbiodinium physiology affect the establishment and development of symbiosis in corals. Mar Ecol Prog Ser. 2018;587:117–27.
    CAS  Google Scholar 

    27.
    Ali A, Kriefall NG, Emery LE, Kenkel CD, Matz MV, Davies SW. Recruit symbiosis establishment and Symbiodiniaceae composition influenced by adult corals and reef sediment. Coral Reefs. 2019;38:405–15.
    Google Scholar 

    28.
    McIlroy SE, Cunning R, Baker AC, Coffroth MA. Competition and succession among coral endosymbionts. Ecol Evolution. 2019;9:12767–78.
    Google Scholar 

    29.
    Abrego D, Willis BL, van Oppen MJH. Impact of light and temperature on the uptake of algal symbionts by coral juveniles. PLoS ONE. 2012;7:e50311.
    PubMed  PubMed Central  CAS  Google Scholar 

    30.
    Schnitzler CE, Hollingsworth LL, Krupp DA, Weis VM. Elevated temperature impairs onset of symbiosis and reduces survivorship in larvae of the Hawaiian coral, Fungia scutaria. Mar Biol. 2012;159:633–42.
    Google Scholar 

    31.
    Hawkins TD, Hagemeyer JCG, Warner ME. Temperature moderates the infectiousness of two conspecific Symbiodinium strains isolated from the same host population. Environ Microbiol. 2016;18:5204–17.
    PubMed  CAS  Google Scholar 

    32.
    Swain TD, Chandler J, Backman V, Marcelino L. Consensus thermotolerance ranking for 110 Symbiodinium phylotypes: an exemplar utilization of a novel iterative partial-rank aggregation tool with broad application potential. Funct Ecol. 2017;31:172–83.
    Google Scholar 

    33.
    Gabay Y, Parkinson JE, Wilkinson SP, Weis VM, Davy SK. Inter-partner specificity limits the acquisition of thermotolerant symbionts in a model cnidarian-dinoflagellate symbiosis. ISME J. 2019;13:2489–99.
    PubMed  Google Scholar 

    34.
    Poland DM, Coffroth MA. Trans-generational specificity within a cnidarian–algal symbiosis. Coral Reefs. 2017;36:119–29.
    Google Scholar 

    35.
    Fraune S, Bosch TCG. Long-term maintenance of species-specific bacterial microbiota in the basal metazoan Hydra. Proc Natl Acad Sci USA. 2007;104:13146.
    PubMed  CAS  Google Scholar 

    36.
    Herrera M, Ziegler M, Voolstra CR, Aranda M. Laboratory-cultured strains of the sea anemone exaiptasia reveal distinct bacterial communities. Front Mar Sci. 2017;4:115.
    Google Scholar 

    37.
    Grajales A, Rodríguez E. Morphological revision of the genus Aiptasia and the family Aiptasiidae (Cnidaria, Actinaria, Metridioidea). Zootaxa. 2014;3826:55–100.
    PubMed  Google Scholar 

    38.
    Xiang T, Hambleton EA, DeNofrio JC, Pringle JR, Grossman AR. Isolation of clonal axenic strains of the symbiotic dinoflagellate Symbiodinium and their growth and host specificity. J Phycol. 2013;49:447–58.
    PubMed  CAS  Google Scholar 

    39.
    Bieri T, Onishi M, Xiang T, Grossman AR, Pringle JR. Relative contributions of various cellular mechanisms to loss of algae during cnidarian bleaching. PLoS ONE. 2016;11:e0152693.
    PubMed  PubMed Central  Google Scholar 

    40.
    Sunagawa S, Wilson EC, Thaler M, Smith ML, Caruso C, Pringle JR, et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics. 2009;10:258.
    PubMed  PubMed Central  Google Scholar 

    41.
    Cziesielski MJ, Liew YJ, Cui G, Schmidt-Roach S, Campana S, Marondedze C, et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc Biol Sci. 2018;285:20172654.
    PubMed  PubMed Central  Google Scholar 

    42.
    Belda-Baillie CA, Baillie BK, Maruyama T. Specificity of a model cnidarian-dinoflagellate symbiosis. Biol Bull. 2002;202:74–85.
    PubMed  CAS  Google Scholar 

    43.
    Rodriguez-Lanetty M, Chang S-J, Song J-I. Specificity of two temperate dinoflagellate–anthozoan associations from the north-western Pacific Ocean. Mar Biol. 2003;143:1193–9.
    Google Scholar 

    44.
    Cziesielski MJ, Schmidt-Roach S, Aranda M. The past, present, and future of coral heat stress studies. Ecol Evolution. 2019;9:10055–66.
    Google Scholar 

    45.
    Lehnert EM, Mouchka ME, Burriesci MS, Gallo ND, Schwarz JA, Pringle JR. Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians. G3 (Bethesda). 2014;4:277–95.
    CAS  Google Scholar 

    46.
    Gegner HM, Ziegler M, Rädecker N, Buitrago-López C, Aranda M, Voolstra CR. High salinity conveys thermotolerance in the coral model Aiptasia. Biol Open. 2017;6:1943.
    PubMed  PubMed Central  CAS  Google Scholar 

    47.
    Cui G, Liew YJ, Li Y, Kharbatia N, Zahran NI, Emwas A-H, et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLoS Genet. 2019;15:e1008189.
    PubMed  PubMed Central  CAS  Google Scholar 

    48.
    Röthig T, Costa RM, Simona F, Baumgarten S, Torres AF, Radhakrishnan A, et al. Distinct bacterial communities associated with the coral model aiptasia in aposymbiotic and symbiotic states with symbiodinium. Front Mar Sci. 2016;3:234.
    Google Scholar 

    49.
    Matthews JL, Sproles AE, Oakley CA, Grossman AR, Weis VM, Davy SK. Menthol-induced bleaching rapidly and effectively provides experimental aposymbiotic sea anemones (Aiptasia sp.) for symbiosis investigations. J Exp Biol. 2016;219:306.
    PubMed  Google Scholar 

    50.
    Hume BCC, Ziegler M, Poulain J, Pochon X, Romac S, Boissin E, et al. An improved primer set and amplification protocol with increased specificity and sensitivity targeting the Symbiodinium ITS2 region. PeerJ. 2018;6:e4816.
    PubMed  PubMed Central  Google Scholar 

    51.
    Hume BCC, Smith EG, Ziegler M, Warrington HJM, Burt JA, LaJeunesse TC, et al. SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol Ecol Resour. 2019;19:1063–80.
    PubMed  PubMed Central  CAS  Google Scholar 

    52.
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing; 2018.

    53.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Community Ecology Package. 2019.

    54.
    Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 

    55.
    Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. American Cancer Society; 2017. p. 1–15.

    56.
    Anderson MJ, Willis TJ. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84:511–25.
    Google Scholar 

    57.
    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    PubMed  PubMed Central  Google Scholar 

    58.
    IBM SPSS Statistics (Version 22.0). Armonk, NY, USA: IBM Corporation; 2013.

    59.
    Robison JD, Warner ME. Differential impacts of photoacclimation and thermal stress on the photobiology of four different phylotypes of Symbiodinium (Pyrrhophyta). J Phycol. 2006;42:568–79.
    CAS  Google Scholar 

    60.
    McGinty ES, Pieczonka J, Mydlarz LD. Variations in reactive oxygen release and antioxidant activity in multiple Symbiodinium types in response to elevated temperature. Microb Ecol. 2012;64:1000–7.
    PubMed  CAS  Google Scholar 

    61.
    Grégoire V, Schmacka F, Coffroth MA, Karsten U. Photophysiological and thermal tolerance of various genotypes of the coral endosymbiont Symbiodinium sp. (Dinophyceae). J Appl Phycol. 2017;29:1893–905.
    Google Scholar 

    62.
    Lesser MP. Phylogenetic signature of light and thermal stress for the endosymbiotic dinoflagellates of corals (Family Symbiodiniaceae). Limnol Oceanogr. 2019;64:1852–63.
    Google Scholar 

    63.
    Hambleton EA, Guse A, Pringle JR. Similar specificities of symbiont uptake by adults and larvae in an anemone model system for coral biology. J Exp Biol. 2014;217:1613.
    PubMed  PubMed Central  Google Scholar 

    64.
    Wolfowicz I, Baumgarten S, Voss PA, Hambleton EA, Voolstra CR, Hatta M, et al. Aiptasia sp. larvae as a model to reveal mechanisms of symbiont selection in cnidarians. Sci Rep. 2016;6:32366.
    PubMed  PubMed Central  CAS  Google Scholar 

    65.
    Thornhill DJ, Xiang Y, Pettay DT, Zhong M, Santos SR. Population genetic data of a model symbiotic cnidarian system reveal remarkable symbiotic specificity and vectored introductions across ocean basins. Mol Ecol. 2013;22:4499–515.
    PubMed  CAS  Google Scholar 

    66.
    Little AF, van Oppen MJH, Willis BL. Flexibility in algal endosymbioses shapes growth in reef corals. Science. 2004;304:1492.
    PubMed  CAS  Google Scholar 

    67.
    Pettay DT, Wham DC, Smith RT, Iglesias-Prieto R, LaJeunesse TC. Microbial invasion of the Caribbean by an Indo-Pacific coral zooxanthella. Proc Natl Acad Sci USA. 2015;112:7513.
    PubMed  CAS  Google Scholar 

    68.
    Baums IB, Devlin-Durante MK, LaJeunesse TC. New insights into the dynamics between reef corals and their associated dinoflagellate endosymbionts from population genetic studies. Mol Ecol. 2014;23:4203–15.
    PubMed  Google Scholar 

    69.
    Nyamukondiwa C, Terblanche JS. Thermal tolerance in adult Mediterranean and Natal fruit flies (Ceratitis capitata and Ceratitis rosa): effects of age, gender and feeding status. J Therm Biol. 2009;34:406–14.
    Google Scholar 

    70.
    Dowd WW, King FA, Denny MW. Thermal variation, thermal extremes and the physiological performance of individuals. J Exp Biol. 2015;218:1956.
    PubMed  Google Scholar 

    71.
    Chidawanyika F, Nyamukondiwa C, Strathie L, Fischer K. Effects of thermal regimes, starvation and age on heat tolerance of the Parthenium Beetle Zygogramma bicolorata (Coleoptera: Chrysomelidae) following dynamic and static protocols. PLoS ONE. 2017;12:e0169371.
    PubMed  PubMed Central  Google Scholar 

    72.
    Hoadley KD, Lewis AM, Wham DC, Pettay DT, Grasso C, Smith R, et al. Host–symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress. Sci Rep. 2019;9:9985.
    PubMed  PubMed Central  Google Scholar 

    73.
    Rädecker N, Raina J-B, Pernice M, Perna G, Guagliardo P, Kilburn MR, et al. Using aiptasia as a model to study metabolic interactions in cnidarian-symbiodinium symbioses. Front Physiol. 2018;9:214–214.
    PubMed  PubMed Central  Google Scholar 

    74.
    Osman EO, Smith DJ, Ziegler M, Kürten B, Conrad C, El-Haddad KM, et al. Thermal refugia against coral bleaching throughout the northern Red Sea. Glob Change Biol. 2018;24:e474–84.
    Google Scholar 

    75.
    Berumen ML, Voolstra CR, Daffonchio D, Agusti S, Aranda M, Irigoien X, et al. The Red Sea: environmental gradients shape a natural laboratory in a nascent ocean. In: Voolstra CR, Berumen ML, editors. Coral Reefs of the Red Sea. Cham: Springer International Publishing; 2019. p. 1–10.

    76.
    Hawkins TD, Hagemeyer JCG, Hoadley KD, Marsh AG, Warner ME. Partitioning of respiration in an animal-algal symbiosis: implications for different aerobic capacity between Symbiodinium spp. Front Physiol. 2016;7:128.
    PubMed  PubMed Central  Google Scholar 

    77.
    Hoadley KD, Rollison D, Pettay DT, Warner ME. Differential carbon utilization and asexual reproduction under elevated pCO2 conditions in the model anemone, Exaiptasia pallida, hosting different symbionts. Limnol Oceanogr. 2015;60:2108–20.
    CAS  Google Scholar 

    78.
    Borell EM, Yuliantri AR, Bischof K, Richter C. The effect of heterotrophy on photosynthesis and tissue composition of two scleractinian corals under elevated temperature. J Exp Mar Biol Ecol. 2008;364:116–23.
    Google Scholar 

    79.
    Ferrier-Pagès C, Rottier C, Beraud E, Levy O. Experimental assessment of the feeding effort of three scleractinian coral species during a thermal stress: effect on the rates of photosynthesis. J Exp Mar Biol Ecol. 2010;390:118–24.
    Google Scholar 

    80.
    Connolly SR, Lopez-Yglesias MA, Anthony KRN. Food availability promotes rapid recovery from thermal stress in a scleractinian coral. Coral Reefs. 2012;31:951–60.
    Google Scholar 

    81.
    Lyndby NH, Holm JB, Wangpraseurt D, Grover R, Rottier C, Kühl M, et al. Effect of feeding and thermal stress on photosynthesis, respiration and the carbon budget of the scleractinian coral Pocillopora damicornis. bioRxiv. 2018:378059.

    82.
    Borell EM, Bischof K. Feeding sustains photosynthetic quantum yield of a scleractinian coral during thermal stress. Oecologia. 2008;157:593.
    PubMed  Google Scholar 

    83.
    Weng L-C, Pasaribu B, Ping Lin I, Tsai C-H, Chen C-S, Jiang P-L. Nitrogen deprivation induces lipid droplet accumulation and alters fatty acid metabolism in symbiotic dinoflagellates isolated from aiptasia pulchella. Sci Rep. 2014;4:5777.
    PubMed  PubMed Central  CAS  Google Scholar 

    84.
    Fitt W, Cook C. The effects of feeding or addition of dissolved inorganic nutrients in maintaining the symbiosis between dinoflagellates and a tropical marine cnidarian. Mar Biol. 2001;139:507–17.
    Google Scholar 

    85.
    Ferrier-Pagès C, Witting J, Tambutté E, Sebens KP. Effect of natural zooplankton feeding on the tissue and skeletal growth of the scleractinian coral Stylophora pistillata. Coral Reefs. 2003;22:229–40.
    Google Scholar 

    86.
    van der Merwe R, Röthig T, Voolstra CR, Ochsenkühn MA, Lattemann S, Amy GL. High salinity tolerance of the Red Sea coral Fungia granulosa under desalination concentrate discharge conditions: an in situ photophysiology experiment. Front Mar Sci. 2014;1:58.
    Google Scholar  More