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    X-ray computed tomography (CT) and ESEM-EDS investigations of unusual subfossilized juniper cones

    1.Mohamed, W. & El-Rifai, E. An integrated approach for the documentation and virtual reconstruction of metal fragments. In Seventh World Archaeological Congress-WAC 7, Dead Sea, Jordan (2013).2.Birks, H. H. Plant macrofossil introduction. Encycl. Quat. Sci. 3, 2266–2288 (2007).
    Google Scholar 
    3.van der Veen, M. In The Science of Roman History (ed. Scheidel, W.) 53–94 (Princeton University Press, 2018).
    Google Scholar 
    4.Stanley, J.-D. Submergence and burial of ancient coastal sites on the subsiding Nile delta margin, Egypt. Méditer. Rev. Géogr. Pays Méditer./J. Mediter. Geogr. 104, 65–73 (2005).
    Google Scholar 
    5.Zhao, X. et al. Holocene climate change and its influence on early agriculture in the Nile Delta, Egypt. Palaeogeogr. Palaeoclimatol. Palaeoecol. 547, 109702. https://doi.org/10.1016/j.palaeo.2020.109702 (2020).Article 

    Google Scholar 
    6.Sestini, G. Nile Delta: A review of depositional environments and geological history. Geol. Soc. Lond. Spec. Publ. 41, 99–127 (1989).ADS 

    Google Scholar 
    7.Stanley, D. J. & Warne, A. G. Nile Delta: Recent geological evolution and human impact. Science 260, 628–634 (1993).ADS 
    CAS 
    PubMed 

    Google Scholar 
    8.Pennington, B. T., Sturt, F., Wilson, P., Rowland, J. & Brown, A. G. The fluvial evolution of the Holocene Nile Delta. Quatern. Sci. Rev. 170, 212–231. https://doi.org/10.1016/j.quascirev.2017.06.017 (2017).ADS 
    Article 

    Google Scholar 
    9.Björdal, C., Nilsson, T. & Daniel, G. Microbial decay of waterlogged archaeological wood found in Sweden applicable to archaeology and conservation. Int. Biodeterior. Biodegrad. 43, 63–73. https://doi.org/10.1016/S0964-8305(98)00070-5 (1999).Article 

    Google Scholar 
    10.Douterelo, I., Goulder, R. & Lillie, M. Soil microbial community response to land-management and depth, related to the degradation of organic matter in English wetlands: Implications for the in situ preservation of archaeological remains. Appl. Soil. Ecol. 44, 219–227. https://doi.org/10.1016/j.apsoil.2009.12.009 (2010).Article 

    Google Scholar 
    11.Weiss, E. & Kislev, M. E. Plant remains as a tool for reconstruction of the past environment, economy, and society: Archaeobotany in Israel. Israel J. Earth Sci. 56, 163–173 (2007).
    Google Scholar 
    12.Birks, H. J. B. Challenges in the presentation and analysis of plant-macrofossil stratigraphical data. Veg. Hist. Archaeobotany 23, 309–330 (2014).
    Google Scholar 
    13.Mauquoy, D., Hughes, P. & Van Geel, B. A protocol for plant macrofossil analysis of peat deposits. Mires Peat 7, 1–5 (2010).
    Google Scholar 
    14.Jacomet, S., Kreuz, A. & Rösch, M. Archäobotanik: Aufgaben Methoden, und Ergebnisse vegetations-und agrargeschichtlicher Forschung (Ulmer, 1999).
    Google Scholar 
    15.Jacomet, S. Plant macrofossil methods and studies: Use in environmental archaeology. In Encyclopedia of quaternary science 2384–2412 (Elsevier, Amsterdam, 2007).
    Google Scholar 
    16.Takahashi, M., Crane, P. R. & Ando, H. Fossil flowers and associated plant fossils from the Kamikitaba locality (Ashizawa Formation, Futaba Group, lower Coniacian, upper Cretaceous) of Northeast Japan. J. Plant. Res. 112, 187–206. https://doi.org/10.1007/PL00013872 (1999).Article 

    Google Scholar 
    17.Poppinga, S. et al. Hygroscopic motions of fossil conifer cones. Sci. Rep. 7, 40302. https://doi.org/10.1038/srep40302 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Crepet, W. L., Nixon, K. C., Grimaldi, D. & Riccio, M. A mosaic Lauralean flower from the Early Cretaceous of Myanmar. Am. J. Bot. 103, 290–297. https://doi.org/10.3732/ajb.1500393 (2016).Article 
    PubMed 

    Google Scholar 
    19.Feng, Z., Röβler, R., Annacker, V. & Yang, J.-Y. Micro-CT investigation of a seed fern (probable medullosan) fertile pinna from the Early Permian Petrified Forest in Chemnitz, Germany. Gondwana Res. 26, 1208–1215. https://doi.org/10.1016/j.gr.2013.08.005 (2014).ADS 
    Article 

    Google Scholar 
    20.Gee, C. T., Dayvault, R. D., Stockey, R. A. & Tidwell, W. D. Greater palaeobiodiversity in conifer seed cones in the Upper Jurassic Morrison Formation of Utah, USA. Palaeobiodivers. Palaeoenviron. 94, 363–375. https://doi.org/10.1007/s12549-014-0160-1 (2014).Article 

    Google Scholar 
    21.Herrera, F. et al. A new voltzian seed cone from the Early Cretaceous of Mongolia and its implications for the evolution of ancient conifers. Int. J. Plant Sci. 176, 791–809. https://doi.org/10.1086/683060 (2015).Article 

    Google Scholar 
    22.Rozefelds, A. et al. Traditional and computed tomographic (CT) techniques link modern and Cenozoic fruits of Pleiogynium (Anacardiaceae) from Australia. Alcheringa 39, 24–39. https://doi.org/10.1080/03115518.2014.951916 (2015).Article 

    Google Scholar 
    23.Su, T., Wilf, P., Huang, Y., Zhang, S. & Zhou, Z. Peaches Preceded Humans: Fossil Evidence from SW China. Sci. Rep. 5, 16794. https://doi.org/10.1038/srep16794 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Nishida, H. The frontier of fossil plant studies. Gakujutu Geppou 54, 1142–1144 (2001).
    Google Scholar 
    25.Collinson, M. E. et al. X-ray micro-computed tomography (micro-CT) of pyrite-permineralized fruits and seeds from the London Clay Formation (Ypresian) conserved in silicone oil: A critical evaluation. Botany 94, 697–711. https://doi.org/10.1139/cjb-2016-0078 (2016).CAS 
    Article 

    Google Scholar 
    26.Dilcher, D. L. & Manchester, S. R. Investigations of angiosperms from the Eocene of North America: A fruit belonging to the Euphorbiaceae. Tertiary Res. 9, 45–58 (1987).
    Google Scholar 
    27.Koch, B. E. & Friedrich, W. L. StereoskopischeRntgen-aufnahmen von fossilenFrüchten. Bull. Geol. Soc. Denmark. 21, 358–367 (1972).
    Google Scholar 
    28.Debussche, M. & Isenmann, P. Fleshy fruit characters and the choices of bird and mammal seed dispersers in a Mediterranean region. Oikos 56, 327–338 (1989).
    Google Scholar 
    29.Esteves, C. F., Costa, J. M., Vargas, P., Freitas, H. & Heleno, R. H. On the limited potential of Azorean fleshy fruits for oceanic dispersal. PLoS ONE 10, e0138882. https://doi.org/10.1371/journal.pone.0138882 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Manniche, L. Sacred Luxuries: Fragrance, Aromatherapy, and Cosmetics in Ancient Egypt (Cornell University Press, 1999).
    Google Scholar 
    31.Kendall, P. Trees for life Discover the forest, Mythology & folklore, Juniper (Iris Publisher, 2005).
    Google Scholar 
    32.Waltz, L. R. The Herbal Encyclopedia: A Practical Guide to the Many Uses of Herbs (iUniverse, 2004).
    Google Scholar 
    33.Tunon, H., Olavsdotter, C. & Bohlin, L. Evaluation of anti-inflammatory activity of some Swedish medicinal plants. Inhibition of prostaglandin biosynthesis and PAF-induced exocytosis. J. Ethnopharmacol. 48, 61–76 (1995).CAS 
    PubMed 

    Google Scholar 
    34.Modnicki, D. & Łabędzka, J. Estimation of the total phenolic compounds in juniper sprouts (Juniperus communis, Cupressaceae) from different places at the kujawsko-pomorskie province. Herba Pol. 55, 127–132 (2009).CAS 

    Google Scholar 
    35.Longe, J. L. The Gale Encyclopedia of Alternative Medicine Vol. 3 (Thomson Gale ((Thomson Gale, A Part of The Thomson Corporation), London, 2005).
    Google Scholar 
    36.Wurges, J. Juniper. In The Gale Encyclopedia of Alternative Medicine (ed. Longe, J. L.) (Thomson/Gale, 2005).
    Google Scholar 
    37.Larson, E. Dangerous Tastes: The Story of Spices. Northeast. Nat. 9, 124 (2002).
    Google Scholar 
    38.Dalby, A. Dangerous Tastes: The Story of Spices (University of California Press, 2000).
    Google Scholar 
    39.Lorman, J. Greek Life 76–77 (Gregory House, 1997).
    Google Scholar 
    40.El-Bana, M., Shaltout, K., Khalafallah, A. & Mosallam, H. Ecological status of the Mediterranean Juniperus phoenicea L. relicts in the desert mountains of North Sinai, Egypt. Flora 205, 171–178. https://doi.org/10.1016/j.flora.2009.04.004 (2010).Article 

    Google Scholar 
    41.Moustafa, A. et al. Ecological Prominence of Juniperus phoenicea L. growing in Gebel Halal, North Sinai, Egypt. Catrina 15, 11–23 (2016).
    Google Scholar 
    42.Dalby, A. Siren Feasts: A History of Food and Gastronomy in Greece (Routledge, 1997).
    Google Scholar 
    43.Klimko, M. et al. Morphological variation of Juniperus oxycedrus subsp. oxycedrus (Cupressaceae) in the Mediterranean region. Flora 202, 133–147. https://doi.org/10.1016/j.flora.2006.03.006 (2007).Article 

    Google Scholar 
    44.Farjon, A. A Monograph of Cupressaceae and Sciadopitys (Royal Botanic Gardens, 2005).
    Google Scholar 
    45.Farjon, A. A Handbook of the World’s Conifers (2 vols.) Vol. 1 (Brill, 2010).
    Google Scholar 
    46.Avci, M. & Zielinski, J. Juniperus oxycedrus f. yaltirikiana (Cupressaceae): A new form from NW Turkey. Phytol. Balcanica 14, 37–40 (2008).
    Google Scholar 
    47.Browicz, K. & Ielioski, J. Chorology of Trees and Shrubs in Southwest Asia and Adjacent Regions (PWN, 1984).
    Google Scholar 
    48.Adams, R. P. Junipers of the World: The Genus Juniperus (Trafford Publishing, 2014).
    Google Scholar 
    49.Liphschitz, N., Waisel, Y. & Lev-Yadun, S. Dendrochronological investigations in Iran. Tree-Ring. Bull. 39, 39–45 (1979).
    Google Scholar 
    50.Douaihy, B. et al. Morphological versus molecular markers to describe variability in Juniperus excelsa subsp. excelsa (Cupressaceae). AoB Plants https://doi.org/10.1093/aobpla/pls013 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Khajjak, M. H. et al. Seed and cone biometry of Juniperus excelsa from three Provenances in Balochistan. Int. J. Biosci. 10, 345–355. https://doi.org/10.12692/ijb/10.1.345-355 (2017).Article 

    Google Scholar 
    52.Klimko, M. et al. Morphological variation of Juniperus oxycedrus subsp oxycedrus (Cupressaceae) in the Mediterranean region. Flora 202, 133–147. https://doi.org/10.1016/j.flora.2006.03.006 (2007).Article 

    Google Scholar 
    53.Schulz, C., Jagel, A. & Stützel, T. Cone morphology in Juniperus in the light of cone evolution in Cupressaceae s.l. Flora 198, 161–177. https://doi.org/10.1078/0367-2530-00088 (2003).Article 

    Google Scholar 
    54.Arista, M., Ortiz, P. L. & Talavera, S. Reproductive cycles of two allopatric subspecies of Juniperus oxycedrus (Cupressaceae). Flora 196, 114–120. https://doi.org/10.1016/S0367-2530(17)30026-9 (2001).Article 

    Google Scholar 
    55.Juan, R., Pastor, J., Fernández, I. & Diosdado, J. C. Relationships between mature cone traits and seed viability in Juniperus oxycedrus L. subsp macrocarpa (Sm.) Ball (Cupressaceae). Acta Biol. Cracov. Bot 45, 69–78 (2003).
    Google Scholar 
    56.Ward, L. & Shellswell, C. Looking After Juniper, Ecology, Conservation and Folklore (Plantlife Press, 2017).
    Google Scholar 
    57.García, D., Zamora, R., Gómez, J. M., Jordano, P. & Hódar, J. A. Geographical variation in seed production, predation and abortion in Juniperus communis throughout its range in Europe. J. Ecol. 88, 435–446. https://doi.org/10.1046/j.1365-2745.2000.00459.x (2000).Article 

    Google Scholar 
    58.Grzeskowiak, M. & Bednorz, L. Zmiennosc morfologiczna szyszkojagod jalowca pospolitego Juniperus communis L. subsp. communis w Nadlesnictwie Kaliska [Bory Tucholskie]. Roczniki Akademii Rolniczej w Poznaniu. Botanika 5, 71–78 (2002).
    Google Scholar 
    59.Shahi, A., Movafeghi, A., Hekmat-Shoar, H., Neishabouri, A. & Iranipour, S. Demographic study of Juniperus communis L. on Mishu-Dagh altitudes in North West of Iran. Asian J. Plant Sci. 6, 1080–1087. https://doi.org/10.3923/ajps.2007.1080.1087 (2007).Article 

    Google Scholar 
    60.Thomas, P. A., El-Barghathi, M. & Polwart, A. Biological flora of the British Isles: Juniperus communis L. J. Ecol. 95, 1404–1440. https://doi.org/10.1111/j.1365-2745.2007.01308.x (2007).Article 

    Google Scholar 
    61.McCartan, S. A. & Gosling, P. G. Guidelines for seed collection and stratification of common juniper (Juniperus communis L.). Tree Plant. Notes 56, 24–29 (2013).
    Google Scholar 
    62.García, D., Zamora, R., Gómez, J. M. & Hódar, J. A. Annual variability in reproduction of Juniperus communis L. in a Mediterranean mountain: Relationship to seed predation and weather. Écoscience 9, 251–255. https://doi.org/10.1080/11956860.2002.11682711 (2002).Article 

    Google Scholar 
    63.Raatikainen, N. & Tanska, T. Cone and seed yields of the juniper (Juniperus communis) in southern and central Finland. Acta Bot. Fenn. 149, 27–39 (1993).
    Google Scholar 
    64.McCartan, S., Gosling, P. G. & Ives, L. Seed fill determination in common juniper (Juniperus communis L.). In Procdings of IUFRO Tree Seed Symposium, Recent Advances in Seed Physiology and Technology (eds Beardmore, T. L. & Simpson, J. D.) 65 (Fredricton, 2007).
    Google Scholar 
    65.McCartan, S. & Gosling, P. G. Exposed! Predicting filled and empty seeds in juniper with x-radiographs. Ecotype 38, 7 (2007).
    Google Scholar 
    66.Pers-Kamczyc, E., Tyrała-Wierucka, Ż, Rabska, M., Wrońska-Pilarek, D. & Kamczyc, J. The higher availability of nutrients increases the production but decreases the quality of pollen grains in Juniperus communis L. J. Plant Physiol. 248, 153156. https://doi.org/10.1016/j.jplph.2020.153156 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    67.Verheyen, K. et al. Juniperus communis: Victim of the combined action of climate warming and nitrogen deposition?. Plant Biol. 11, 49–59. https://doi.org/10.1111/j.1438-8677.2009.00214.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Kormuťák, A., Bolecek, P., Galgóci, M. & Gömöry, D. Longevity and germination of Juniperus communis L. pollen after storage. Sci. Rep. 11, 12755. https://doi.org/10.1038/s41598-021-90942-9 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Yahaya, N., Lim, K. S., Noor, N. M., Othman, S. R. & Abdullah, A. Effects of clay and moisture content on soil-corrosion dynamic. Malays. J. Civ. Eng. 23, 24–32. https://doi.org/10.11113/mjce.v23.15809 (2011).Article 

    Google Scholar 
    70.Scott, D. A. (2002).71.Selwyn, L. S. ASM Handbook Volume 13C. Corrosion: Environments and Industries 306–322 (ASM International, 2006).
    Google Scholar 
    72.Ingo, G. M. et al. Large scale investigation of chemical composition, structure and corrosion mechanism of bronze archeological artefacts from Mediterranean basin. Appl. Phys. A 83, 513–520. https://doi.org/10.1007/s00339-006-3550-z (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    73.Papadopoulou, O., Vassiliou, P., Grassini, S., Angelini, E. & Gouda, V. Soil-induced corrosion of ancient Roman brass: A case study. Mater. Corros. 67, 160–169. https://doi.org/10.1002/maco.201408115 (2016).CAS 
    Article 

    Google Scholar 
    74.Robbiola, L. & Portier, R. A global approach to the authentication of ancient bronzes based on the characterization of the alloy–patina–environment system. J. Cult. Herit. 7, 1–12. https://doi.org/10.1016/j.culher.2005.11.001 (2006).Article 

    Google Scholar 
    75.Vuai, S. A., Nakamura, K. & Tokuyama, A. Geochemical characteristics of runoff from acid sulfate soils in the northern area of Okinawa Island, Japan. Geochem. J. 37, 579–592 (2003).ADS 
    CAS 

    Google Scholar 
    76.Marani, D., Patterson, J. W. & Anderson, P. R. Alkaline precipitation and aging of Cu(II) in the presence of sulfate. Water Res. 29, 1317–1326. https://doi.org/10.1016/0043-1354(94)00286-G (1995).CAS 
    Article 

    Google Scholar 
    77.Baboian, R. Corrosion Tests and Standards: Application and Interpretation Vol. 20 (ASTM International, 2005).
    Google Scholar 
    78.Strandberg, H. Reactions of copper patina compounds—II. Influence of sodium chloride in the presence of some air pollutants. Atmos. Environ. 32, 3521–3526. https://doi.org/10.1016/S1352-2310(98)00058-2 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    79.Borkow, G. & Gabbay, J. Copper, an ancient remedy returning to fight microbial, fungal and viral infections. Curr. Chem. Biol. 3, 272–278 (2009).CAS 

    Google Scholar 
    80.Dollwet, H. Historic uses of copper compounds in medicine. Trace Elem. Med. 2, 80–87 (1985).
    Google Scholar 
    81.Milanino, R. Copper in medicine and personal care: A historical overview. In Copper and the Skin 149–160 (Informa Healthcare, 2006).
    Google Scholar 
    82.Robinson, M. Environmental archaeology: Approaches, techniques & applications. Antiquity 79, 229–230 (2005).
    Google Scholar 
    83.Milanesi, C. et al. Ultrastructural study of archaeological Vitis vinifera L. seeds using rapid-freeze fixation and substitution. Tissue Cell 41, 443–447. https://doi.org/10.1016/j.tice.2009.03.002 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    84.Akahane, H., Furuno, T., Miyajima, H., Yoshikawa, T. & Yamamoto, S. Rapid wood silicification in hot spring water: An explanation of silicification of wood during the Earth’s history. Sed. Geol. 169, 219–228. https://doi.org/10.1016/j.sedgeo.2004.06.003 (2004).CAS 
    Article 

    Google Scholar 
    85.Leo, R. F. & Barghoorn, E. S. Silicification of wood. Bot. Mus. Leafl. Harv. Univ. 25, 1–47 (1976).CAS 

    Google Scholar 
    86.Hellawell, J. et al. Incipient silicification of recent conifer wood at a Yellowstone hot spring. Geochim. Cosmochim. Acta 149, 79–87. https://doi.org/10.1016/j.gca.2014.10.018 (2015).ADS 
    CAS 
    Article 

    Google Scholar  More

  • in

    Forest fires and climate-induced tree range shifts in the western US

    1.von Humboldt, A. & Bonpland, A. Essay on the Geography of Plants (Univ. of Chicago Press, 1807).2.Woodward, F. I. Climate and Plant Distribution (Cambridge Univ. Press, 1987).3.Pausas, J. G. & Bond, W. J. Alternative biome states in terrestrial ecosystems. Trends Plant Sci. 25, 250–263 (2020).CAS 
    PubMed 

    Google Scholar 
    4.Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. 105, 11823–11826 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Koide, D., Yoshida, K., Daehler, C. C. & Mueller-Dombois, D. An upward elevation shift of native and non-native vascular plants over 40 years on the island of Hawai’i. J. Veg. Sci. 28, 939–950 (2017).
    Google Scholar 
    6.Thomas, C. D. Climate, climate change and range boundaries: climate and range boundaries. Divers. Distrib. 16, 488–495 (2010).
    Google Scholar 
    7.Lenoir, J. & Svenning, J.-C. Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
    Google Scholar 
    8.Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    9.Grabherr, G., Gottfried, M. & Pauli, H. Climate change impacts in alpine environments: climate change impacts in alpine environments. Geogr. Compass 4, 1133–1153 (2010).
    Google Scholar 
    10.Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).ADS 

    Google Scholar 
    11.Im, S. T., Kharuk, V. I., Sukachev Institute of Forest SB RAS – subdivision of FSC KSC SB RAS; Siberian Federal University & Lee, V. G. Migration of the northern evergreen needleleaf timberline in Siberia in the 21st century. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 17, 176–187 (2020).
    Google Scholar 
    12.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    13.Murphy, H. T., VanDerWal, J. & Lovett-Doust, J. Signatures of range expansion and erosion in eastern North American trees: signatures of range expansion and erosion. Ecol. Lett. 13, 1233–1244 (2010).PubMed 

    Google Scholar 
    14.Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    15.Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change? Trends Ecol. Evol. 28, 482–488 (2013).PubMed 

    Google Scholar 
    16.Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111, 287–297 (2013).
    Google Scholar 
    17.Anderson, J. T. & Wadgymar, S. M. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23, 181–192 (2020).PubMed 

    Google Scholar 
    18.Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).PubMed 

    Google Scholar 
    19.Anderson, R. P. When and how should biotic interactions be considered in models of species niches and distributions? J. Biogeogr. 44, 8–17 (2017).
    Google Scholar 
    20.Wilkinson, D. M. Mycorrhizal fungi and quaternary plant migrations. Glob. Ecol. Biogeogr. Lett. 7, 137 (1998).
    Google Scholar 
    21.Wilkinson, D. M. Plant colonization: are wind dispersed seeds really dispersed by birds at larger spatial and temporal scales? J. Biogeogr. 24, 61–65 (1997).
    Google Scholar 
    22.MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species (Princeton Univ. Press, 1984).23.Pigot, A. L. & Tobias, J. A. Species interactions constrain geographic range expansion over evolutionary time. Ecol. Lett. 16, 330–338 (2013).PubMed 

    Google Scholar 
    24.Svenning, J.-C. et al. The influence of interspecific interactions on species range expansion rates. Ecography 37, 1198–1209 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    25.Liang, Y., Duveneck, M. J., Gustafson, E. J., Serra-Diaz, J. M. & Thompson, J. R. How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change. Glob. Chang. Biol. 24, e335–e351 (2018).ADS 
    PubMed 

    Google Scholar 
    26.Moorcroft, P. R., Pacala, S. W. & Lewis, M. A. Potential role of natural enemies during tree range expansions following climate change. J. Theor. Biol. 241, 601–616 (2006).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    27.Moran, E. V. & Ormond, R. A. Simulating the interacting effects of intraspecific variation, disturbance, and competition on climate-driven range shifts in trees. PLoS ONE 10, e0142369 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    28.Stralberg, D. et al. Wildfire-mediated vegetation change in boreal forests of Alberta. Can. Ecosphere 9, e02156 (2018).
    Google Scholar 
    29.Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    30.Ettinger, A. & HilleRisLambers, J. Competition and facilitation may lead to asymmetric range shift dynamics with climate change. Glob. Chang. Biol. 23, 3921–3933 (2017).ADS 
    PubMed 

    Google Scholar 
    31.Caplat, P., Anand, M. & Bauch, C. Interactions between climate change, competition, dispersal, and disturbances in a tree migration model. Theor. Ecol. 1, 209–220 (2008).
    Google Scholar 
    32.Serra-Diaz, J. M., Scheller, R. M., Syphard, A. D. & Franklin, J. Disturbance and climate microrefugia mediate tree range shifts during climate change. Landsc. Ecol. 30, 1039–1053 (2015).
    Google Scholar 
    33.Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B Biol. Sci. 279, 2072–2080 (2012).
    Google Scholar 
    34.Pausas, J. G. & Keeley, J. E. Wildfires as an ecosystem service. Front. Ecol. Environ. 17, 289–295 (2019).
    Google Scholar 
    35.Harvey, B. J., Donato, D. C. & Turner, M. G. High and dry: post-fire tree seedling establishment in subalpine forests decreases with post-fire drought and large stand-replacing burn patches: Drought and post-fire tree seedlings. Glob. Ecol. Biogeogr. 25, 655–669 (2016).
    Google Scholar 
    36.Coop, J. D. et al. Wildfire-driven forest conversion in western north American landscapes. BioScience 70, 659–673 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    37.Turner, M. G., Braziunas, K. H., Hansen, W. D. & Harvey, B. J. Short-interval severe fire erodes the resilience of subalpine lodgepole pine forests. Proc. Natl Acad. Sci. 116, 11319–11328 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Stevens‐Rumann, C. S. et al. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 21, 243–252 (2018).PubMed 

    Google Scholar 
    39.Hanes, T. L. Succession after fire in the Chaparral of southern California. Ecol. Monogr. 41, 27–52 (1971).
    Google Scholar 
    40.McKenzie, D. A. & Tinker, D. B. Fire-induced shifts in overstory tree species composition and associated understory plant composition in Glacier National Park, Montana. Plant Ecol. 213, 207–224 (2012).
    Google Scholar 
    41.Walker, X. J., Mack, M. C. & Johnstone, J. F. Predicting ecosystem resilience to fire from tree ring analysis in black spruce forests. Ecosystems 20, 1137–1150 (2017).
    Google Scholar 
    42.Hart, S. J. et al. Examining forest resilience to changing fire frequency in a fire-prone region of boreal forest. Glob. Change Biol. 25, 869–884 (2019).ADS 

    Google Scholar 
    43.Davis, K. T. et al. Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proc. Natl Acad. Sci. 116, 6193–6198 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 

    Google Scholar 
    45.Enright, N. J., Fontaine, J. B., Bowman, D. M., Bradstock, R. A. & Williams, R. J. Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes. Front. Ecol. Environ. 13, 265–272 (2015).
    Google Scholar 
    46.Dobrowski, S. Z. et al. Forest structure and species traits mediate projected recruitment declines in western US tree species: tree recruitment patterns in the western US. Glob. Ecol. Biogeogr. 24, 917–927 (2015).
    Google Scholar 
    47.Anderson, T. W. An Introduction to Multivariate Statistical Analysis (Wiley-Interscience, 2003).48.Keeley, J. E. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildland Fire 18, 116 (2009).
    Google Scholar 
    49.Tollefson, J. Quercus chrysolepis. https://www.fs.fed.us/database/feis/plants/tree/quechr/all.html (2008).50.Fryer, J. Quercus kelloggii. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2007).51.Meyer, R. Chrysolepis chrysophylla. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2012).52.Michelle, A. Pinus contorta var. latifolia. https://www.fs.fed.us/database/feis/plants/tree/pinconl/all.html (2003).53.Cope, A. Pinus contorta var. murrayana. https://www.fs.fed.us/database/feis/plants/tree/pinconm/all.html (1993).54.Cope, A. Pinus contorta var. contorta. https://www.fs.fed.us/database/feis/plants/tree/pinconc/all.html (1993).55.Rodman, K. C. et al. A trait‐based approach to assessing resistance and resilience to wildfire in two iconic North American conifers. J. Ecol. https://doi.org/10.1111/1365-2745.13480 (2020).56.Davis, K. T., Higuera, P. E. & Sala, A. Anticipating fire‐mediated impacts of climate change using a demographic framework. Funct. Ecol. 32, 1729–1745 (2018).
    Google Scholar 
    57.Gutzler, D. S. & Robbins, T. O. Climate variability and projected change in the western United States: regional downscaling and drought statistics. Clim. Dyn. 37, 835–849 (2011).
    Google Scholar 
    58.Leung, L. R. et al. Mid-century ensemble regional climate change scenarios for the western United States. Clim. Chang. 62, 75–113 (2004).
    Google Scholar 
    59.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Ecol. Manag. 259, 660–684 (2010).
    Google Scholar 
    60.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).ADS 

    Google Scholar 
    61.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 
    PubMed 

    Google Scholar 
    62.Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    63.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).64.RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, PBC, 2020).65.U.S. Forest Service. Forest Inventory and Analysis National Core Field Guide. https://www.fia.fs.fed.us/library/field-guides-methods-proc/docs/2017/core_ver7-2_10_2017_final.pdf (2017).66.U.S. EPA. Level I Ecoregions of North America Shapefile. (2010).67.Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for north America. PLoS ONE 11, e0156720 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    68.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).
    Google Scholar 
    69.Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data: measuring niche overlap. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
    Google Scholar 
    70.Hill, A. avephill/wildfire-plant_RS: Forest fires and climate-induced tree range shifts in the western US. https://doi.org/10.5281/ZENODO.5555390 (2021). More

  • in

    Raised seasonal temperatures reinforce autumn Varroa destructor infestation in honey bee colonies

    1.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC (IPCC, 2014).2.Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
    PubMed 
    ADS 

    Google Scholar 
    3.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Peñuelas, J. & Filella, I. Responses to a warming world. Science (80-). 294, 793–795 (2001).
    Google Scholar 
    5.Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).ADS 

    Google Scholar 
    6.Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
    Google Scholar 
    7.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    8.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
    Google Scholar 
    9.Vanbergen, A. J. et al. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).
    Google Scholar 
    10.Hung, K. L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Biol. Sci. 285, 20172140 (2018).
    Google Scholar 
    11.Watanabe, M. E. Pollination worries rise as honey bees decline. Science (80-). 265, 1170 (1994).CAS 
    ADS 

    Google Scholar 
    12.Chauzat, M.-P. et al. Demographics of the European apicultural industry. PLoS ONE 8, e79018 (2013).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    13.Conte, Y. L. & Navajas, M. Climate change: Impact on honey bee populations and diseases. OIE Rev. Sci. Tech. 27, 485–510 (2008).
    Google Scholar 
    14.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).
    Google Scholar 
    15.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. Seasonal timing in honey bee colonies: Phenology shifts affect honey stores and Varroa infestation levels. Oecologia 189, 1121–1131 (2019).PubMed 
    ADS 

    Google Scholar 
    16.Traynor, K. S. et al. Multiyear survey targeting disease incidence in US honey bees. Apidologie https://doi.org/10.1007/s13592-016-0431-0 (2016).Article 

    Google Scholar 
    17.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U. S. A. 116, 1792–1801 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 

    Google Scholar 
    19.Switanek, M., Crailsheim, K., Truhetz, H. & Brodschneider, R. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 579, 1581–1587 (2017).CAS 
    PubMed 
    ADS 

    Google Scholar 
    20.Genersch, E. et al. The German bee monitoring project: A long term study to understand periodically high winter losses of honey bee colonies. Apidologie 41, 332–352 (2010).CAS 

    Google Scholar 
    21.van Dooremalen, C. et al. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation. PLoS One 7, e36285 (2012).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    22.Morawetz, L. et al. Health status of honey bee colonies (Apis mellifera) and disease-related risk factors for colony losses in Austria. PLoS One 14, e0219293 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Fries, I., Imdorf, A. & Rosenkranz, P. Survival of mite infested (Varroa destructor) honey bee (Apis mellifera) colonies in a Nordic climate. Apidologie 37, 564–570 (2006).
    Google Scholar 
    24.Guzmán-Novoa, E. et al. Varroa destructor is the main culprit for the death and reduced populations of overwintered honey bee (Apis mellifera) colonies in Ontario, Canada. Apidologie 41, 443–450 (2010).
    Google Scholar 
    25.Giacobino, A. et al. Environment or beekeeping management: What explains better the prevalence of honey bee colonies with high levels of Varroa destructor?. Res. Vet. Sci. 112, 1–6 (2017).PubMed 

    Google Scholar 
    26.van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).
    Google Scholar 
    27.Leza, M. M., Miranda-Chueca, M. A. & Purse, B. V. Patterns in Varroa destructor depend on bee host abundance, availability of natural resources, and climate in Mediterranean apiaries. Ecol. Entomol. 41, 542–553 (2016).
    Google Scholar 
    28.Dietemann, V. et al. Standard methods for Varroa research. J. Apic. Res. 52, 1–54 (2013).
    Google Scholar 
    29.Branco, M. R., Kidd, N. A. C. & Pickard, R. S. A comparative evaluation of sampling methods for Varroa destructor (Acari: Varroidae) population estimation. Apidologie 37, 452–461 (2006).
    Google Scholar 
    30.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20119 (2008).ADS 

    Google Scholar 
    31.Bailey, L. D. & van de Pol, M. climwin: An R toolbox for climate window analysis. PLoS One 11, 1–27 (2016).
    Google Scholar 
    32.Hartig, F. Residual Diagnostics for Hierachical (Multi-Level/Mixed) Regression Models. (2021).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 51 (2014).
    Google Scholar 
    34.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    35.R Core Team. R: A Language and Environment for Statistical Computing. (2021).36.Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
    Google Scholar 
    37.Calis, J. N. M., Fries, I. & Ryrie, S. C. Population modelling of Varroa jacobsoni Oud. Apidologie 30, 111–124 (1999).
    Google Scholar 
    38.Fries, I., Hansen, H., Imdorf, A. & Rosenkranz, P. Swarming in honey bees (Apis mellifera) and Varroa destructor population development in Sweden. Apidologie 34, 389–397 (2003).
    Google Scholar 
    39.Wilde, J., Fuchs, S., Bratkowski, J. & Siuda, M. Distribution of Varroa destructor between swarms and colonies. J. Apic. Res. 44, 190–194 (2005).
    Google Scholar 
    40.Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS One 11, 1–11 (2016).
    Google Scholar 
    41.Moretto, G., Goncalves, L. S., De Jong, D. & Bichuette, M. Z. The effects of climate and bee race on Varroa jacobsoni Oud infestations in Brazil. Apidologie 22, 197–203 (1991).
    Google Scholar 
    42.Guzmán-Novoa, E., Vandame, R. & Arechavaleta, M. E. Susceptibility of European and Africanized honey bees (Apis mellifera L.) to Varroa jacobsoni Oud. in Mexico. Apidologie 30, 173–182 (1999).
    Google Scholar 
    43.Ruttner, F. Biogeography and Taxonomy of Honeybees (Springer, 1988). https://doi.org/10.1007/978-3-642-72649-1.Book 

    Google Scholar 
    44.Adam, B. Breeding the Honeybee: A Contribution to the Science of Bee Breeding (Northern Bee Books, 2013).
    Google Scholar 
    45.Tarpy, D. R., Hatch, S. & Fletcher, D. J. C. The influence of queen age and quality during queen replacement in honeybee colonies. Anim. Behav. 59, 97–101 (2000).CAS 
    PubMed 

    Google Scholar 
    46.Simeunovic, P. et al. Nosema ceranae and queen age influence the reproduction and productivity of the honey bee colony. J. Apic. Res. 53, 545–554 (2014).
    Google Scholar 
    47.Akyol, E., Yeninar, H., Karatepe, M., Karatepe, B. & Özkök, D. Effects of queen ages on Varroa (Varroa destructor) infestation level in honey bee (Apis mellifera caucasica) colonies and colony performance. Ital. J. Anim. Sci. 6, 143–149 (2007).
    Google Scholar 
    48.Harris, J. W., Harbo, J. R., Villa, J. D. & Danka, R. G. Variable population growth of Varroa destructor (Mesostigmata: Varroidae) in colonies of honey bees (Hymenoptera: Apidae) during a 10-year period. Environ. Entomol. 32, 1305–1312 (2003).
    Google Scholar 
    49.Kruuk, L. E. B., Osmond, H. L. & Cockburn, A. Contrasting effects of climate on juvenile body size in a Southern Hemisphere passerine bird. Glob. Change Biol. 21, 2929–2941 (2015).ADS 

    Google Scholar 
    50.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Predictive markers of honey bee colony collapse. PLoS One 7, e32151 (2012).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    51.Peck, D. T., Smith, M. L. & Seeley, T. D. Varroa destructor mites can nimbly climb from flowers onto foraging honey bees. PLoS One 11, e0167798 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    52.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS One 14, e0218392 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Seeley, T. D. & Smith, M. L. Crowding honeybee colonies in apiaries can increase their vulnerability to the deadly ectoparasite Varroa destructor. Apidologie 46, 716–727 (2015).
    Google Scholar 
    54.Vetharaniam, I. Predicting reproduction rate of Varroa. Ecol. Model. 224, 11–17 (2012).
    Google Scholar 
    55.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. The influence of temperature and photoperiod on the timing of brood onset in hibernating honey bee colonies. PeerJ 6, e4801. https://doi.org/10.7717/peerj.4801 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Seeley, T. D. & Visscher, P. K. Survival of honeybees in cold climates: The critical timing of colony growth and reproduction. Ecol. Entomol. 10, 81–88 (1985).
    Google Scholar 
    57.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in worker brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. https://doi.org/10.1007/BF00055033 (1994).Article 

    Google Scholar 
    58.Martin, S. J. Reproduction of Varroa jacobsoni in cells of Apis mellifera containing one or more mother mites and the distribution of these cells. J. Apic. Res. 34, 187–196 (1995).
    Google Scholar 
    59.Sparks, T. H. et al. Advances in the timing of spring cleaning by the honeybee Apis mellifera in Poland. Ecol. Entomol. 35, 788–791 (2010).
    Google Scholar 
    60.Langowska, A. et al. Long-term effect of temperature on honey yield and honeybee phenology. Int. J. Biometeorol. 61, 1125–1132 (2017).PubMed 
    ADS 

    Google Scholar 
    61.Bordier, C. et al. Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera). Sci. Rep. 7, 1–11 (2017).CAS 

    Google Scholar 
    62.Fahrenholz, L., Lamprecht, I. & Schricker, B. Thermal investigations of a honey bee colony: Thermoregulation of the hive during summer and winter and heat production of members of different bee castes. J. Comp. Physiol. B 159, 551–560 (1989).
    Google Scholar 
    63.Villa, J. D., Gentry, C. & Taylor, O. R. Jr. Preliminary observations on thermoregulation, clustering, and energy utilization in African and European Honey Bees. J. Kansas Entomol. Soc. 60, 4–14 (1987).
    Google Scholar 
    64.Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189 (2000).CAS 
    PubMed 

    Google Scholar 
    65.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 

    Google Scholar 
    66.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A Sens. Neural Behav. Physiol. 187, 541–547 (2001).CAS 

    Google Scholar 
    67.Requier, F., Odoux, J. F., Henry, M. & Bretagnolle, V. The carry-over effects of pollen shortage decrease the survival of honeybee colonies in farmlands. J. Appl. Ecol. 54, 1161–1170 (2017).
    Google Scholar 
    68.Seeley, T. D. Honeybee Ecology. A Study of Adaptation in Social Life (Princeton University Press, 1985).
    Google Scholar 
    69.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in drone brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. 19, 199–210 (1995).ADS 

    Google Scholar 
    70.Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects 8, 48 (2017).PubMed Central 

    Google Scholar 
    71.Giacobino, A. et al. Risk factors associated with failures of Varroa treatments in honey bee colonies without broodless period. Apidologie 46, 573–582 (2015).
    Google Scholar 
    72.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).
    Google Scholar 
    73.FAO. Good beekeeping practices: Practical manual on how to identify and control the main diseases of the honeybee (Apis mellifera). TECA—Technologies and practices for small agricultural producers. (2020).74.Harbo, J. R. Effect of population size on brood production, worker survival and honey gain in colonies of honeybees. J. Apic. Res. 25, 22–29 (1986).
    Google Scholar 
    75.Döke, M. A., McGrady, C. M., Otieno, M., Grozinger, C. M. & Frazier, M. Colony size, rather than geographic origin of stocks, predicts overwintering success in honey bees (Hymenoptera: Apidae) in the Northeastern United States. J. Econ. Entomol. 112, 525–533 (2019).PubMed 

    Google Scholar 
    76.Martin, S. J. The role of Varroa and viral pathogens in the collapse of honeybee colonies: A modelling approach. J. Appl. Ecol. 38, 1082–1093 (2001).
    Google Scholar  More

  • in

    Winter distribution of juvenile and sub-adult male Antarctic fur seals (Arctocephalus gazella) along the western Antarctic Peninsula

    1.Knox, G. A. Biology of the Southern Ocean (CRC Press, 2006). https://doi.org/10.1201/9781420005134Book 

    Google Scholar 
    2.Thomas, D. N. et al. The Biology of Polar Regions: The Biology of Polar Regions (Oxford University Press, 2008).
    Google Scholar 
    3.Trathan, P. N. & Hill, S. L. The Importance of Krill Predation in the Southern Ocean. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 321–350 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_9.Chapter 

    Google Scholar 
    4.Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).ADS 
    CAS 

    Google Scholar 
    5.Siegel, V. & Watkins, J. L. Distribution, biomass and demography of antarctic krill, Euphausia superba. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 21–100 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_2.Chapter 

    Google Scholar 
    6.Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16 (2017).ADS 
    CAS 

    Google Scholar 
    7.Andrews-Goff, V. et al. Humpback whale migrations to Antarctic summer foraging grounds through the southwest Pacific Ocean. Sci. Rep. 8, 12333 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Ribic, C. A., Ainley, D. G. & Fraser, W. R. Habitat selection by marine mammals in the marginal ice zone. Antarct. Sci. 3, 181–186 (1991).ADS 

    Google Scholar 
    9.Takahashi, A. et al. Migratory movements and winter diving activity of Adélie penguins in East Antarctica. Mar. Ecol. Prog. Ser. 589, 227–239 (2018).ADS 

    Google Scholar 
    10.Hückstädt, L. A. et al. Projected shifts in the foraging habitat of crabeater seals along the Antarctic Peninsula. Nat. Clim. Change 10, 472–477 (2020).ADS 

    Google Scholar 
    11.Lowther, A. D., Staniland, I., Lydersen, C. & Kovacs, K. M. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Sci. Rep. 10, 18436 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Forcada, J. & Staniland, I. J. Antarctic fur seal Arctocephalus gazella. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 36–42 (Academic Press, 2009).
    Google Scholar 
    13.Boyd, I. L., McCafferty, D. J., Reid, K., Taylor, R. & Walker, T. R. Dispersal of male and female Antarctic fur seals (Arctocephalus gazella). Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/f97-314 (1998).Article 

    Google Scholar 
    14.Cherel, Y., Kernaléguen, L., Richard, P. & Guinet, C. Whisker isotopic signature depicts migration patterns and multi-year intra- and inter-individual foraging strategies in fur seals. Biol. Lett. 5, 830–832 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Kernaléguen, L. et al. Long-term species, sexual and individual variations in foraging strategies of fur seals revealed by stable isotopes in whiskers. PLoS ONE 7, e32916 (2012).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Kernaléguen, L., Arnould, J. P. Y., Guinet, C. & Cherel, Y. Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals. J. Anim. Ecol. 84, 1081–1091 (2015).PubMed 

    Google Scholar 
    17.Arthur, B. et al. Winter habitat predictions of a key Southern Ocean predator, the Antarctic fur seal (Arctocephalus gazella). Deep Sea Res. Part II Top. Stud. Oceanogr. 140, 171–181 (2017).ADS 

    Google Scholar 
    18.Arthur, B. et al. Managing for change: Using vertebrate at sea habitat use to direct management efforts. Ecol. Indic. 91, 338–349 (2018).
    Google Scholar 
    19.Reisinger, R. R. et al. Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean. Divers. Distrib. 24, 535–550 (2018).MathSciNet 

    Google Scholar 
    20.Wege, M., de Bruyn, P. J. N., Hindell, M. A., Lea, M.-A. & Bester, M. N. Preferred, small-scale foraging areas of two Southern Ocean fur seal species are not determined by habitat characteristics. BMC Ecol. 19, 36 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    21.Jones, K. A. et al. Intra-specific niche partitioning in antarctic fur seals, Arctocephalus gazella. Sci. Rep. 10, 3238 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.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 
    23.Raymond, B. et al. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography 38, 121–129 (2015).
    Google Scholar 
    24.Bestley, S., Jonsen, I. D., Hindell, M. A., Harcourt, R. G. & Gales, N. J. Taking animal tracking to new depths: Synthesizing horizontal–vertical movement relationships for four marine predators. Ecology 96, 417–427 (2015).PubMed 

    Google Scholar 
    25.Kernaléguen, L. et al. Early-life sexual segregation: Ontogeny of isotopic niche differentiation in the Antarctic fur seal. Sci. Rep. 6, 33211 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Payne, M. R. Growth in the Antarctic fur seal Arctocephalus gazella. J. Zool. 187, 1–20 (1979).
    Google Scholar 
    27.Costa, D., Goebel, M. E. & Sterling, J. T. Foraging energetics and diving behavior of the Antarctic fur seal, Arctocephalus gazzella at Cape Shirreff, Livingston Island. In Antarctic Ecosystems: Models for Wider Ecological Understanding (eds Davision, W. et al.) 77–84 (New Zealand Natural Science Press, 2000).
    Google Scholar 
    28.Staniland, I. J. et al. Geographical variation in the behaviour of a central place forager: Antarctic fur seals foraging in contrasting environments. Mar. Biol. 157, 2383–2396 (2010).
    Google Scholar 
    29.Blanchet, M.-A. et al. At-sea behaviour of three krill predators breeding at Bouvetøya—Antarctic fur seals, macaroni penguins and chinstrap penguins. Mar. Ecol. Prog. Ser. 477, 285–302 (2013).ADS 

    Google Scholar 
    30.Jeanniard-du-Dot, T., Trites, A. W., Arnould, J. P. Y. & Guinet, C. Reproductive success is energetically linked to foraging efficiency in Antarctic fur seals. PLoS ONE 12, e0174001 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    31.Favilla, A. B. & Costa, D. P. Thermoregulatory strategies of diving air-breathing marine vertebrates: A review. Front. Ecol. Evol. 8, 292 (2020).
    Google Scholar 
    32.Staniland, I. J. & Robinson, S. L. Segregation between the sexes: Antarctic fur seals, Arctocephalus gazella, foraging at South Georgia. Anim. Behav. 75, 1581–1590 (2008).
    Google Scholar 
    33.Reid, K. The diet of Antarctic fur seals (Arctocephalus gazella Peters 1875) during winter at South Georgia. Antarct. Sci. 7, 241–249 (1995).ADS 

    Google Scholar 
    34.Kirkman, S. P., Wilson, W., Klages, N. T. W., Bester, M. N. & Isaksen, K. Diet and estimated food consumption of Antarctic fur seals at Bouvetøya during summer. Polar Biol. 23, 745–752 (2000).
    Google Scholar 
    35.Casaux, R., Baroni, A., Arrighetti, F., Ramón, A. & Carlini, A. Geographical variation in the diet of the Antarctic fur seal Arctocephalus gazella. Polar Biol. 26, 753–758 (2003).
    Google Scholar 
    36.Casaux, R., Baroni, A. & Ramón, A. Diet of Antarctic fur seals Arctocephalus gazella at the Danco Coast, Antarctic Peninsula. Polar Biol. 26, 49–54 (2003).
    Google Scholar 
    37.Davis, D., Staniland, I. J. & Reid, K. Spatial and temporal variability in the fish diet of Antarctic fur seal (Arctocephalus gazella) in the Atlantic sector of the Southern Ocean. Can. J. Zool. https://doi.org/10.1139/z06-071 (2006).Article 

    Google Scholar 
    38.Casaux, R., Juares, M., Carlini, A. & Corbalán, A. The diet of the Antarctic fur seal Arctocephalus gazella at the South Orkney Islands in ten consecutive years. Polar Biol. 39, 1197–1206 (2016).
    Google Scholar 
    39.Tarroux, A., Lowther, A. D., Lydersen, C. & Kovacs, K. M. Temporal shift in the isotopic niche of female Antarctic fur seals from Bouvetøya. Polar Res. 35, 31335 (2016).
    Google Scholar 
    40.Garcia-Garin, O. et al. No evidence of microplastics in Antarctic fur seal scats from a hotspot of human activity in Western Antarctica. Sci. Total Environ. 737, 140210 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    41.Boyd, I. L. Estimating food consumption of marine predators: Antarctic fur seals and macaroni penguins. J. Appl. Ecol. 39, 103–119 (2002).
    Google Scholar 
    42.Wilson, D. E. & Mittermeier, R. A. Handbook of the mammals of the world : vol. 4 : Sea mammals. (2014).43.Melin, S. R. et al. Reversible immobilization of free-ranging adult male California sea lions (Zalophus californianus). Mar. Mammal Sci. 29, E529–E536 (2013).
    Google Scholar 
    44.Pussini, N. & Goebel, M. E. A safer protocol for field immobilization of leopard seals (Hydrurga leptonyx). Mar. Mammal Sci. 31, 1549–1558 (2015).
    Google Scholar 
    45.Spelman, L. H. Reversible anesthesia of captive California sea lions (Zalophus californianus) with medetomidine, midazolam, butorphanol, and isoflurane. J. Zoo Wildl. Med. Off. Publ. Am. Assoc. Zoo Vet. 35, 65–69 (2004).
    Google Scholar 
    46.Cook, T. A. Butorphanol tartrate: An intravenous analgesic for outpatient surgery. Otolaryngol. Head Neck Surg. J. Am. Acad. Otolaryngol. Head Nexk Surg. 91, 251–254 (1983).CAS 

    Google Scholar 
    47.Ropert-Coudert, Y. et al. The retrospective analysis of Antarctic tracking data project. Sci. Data 7, 94 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    48.Freitas, C., Lydersen, C., Fedak, M. A. & Kovacs, K. M. A simple new algorithm to filter marine mammal Argos locations. Mar. Mammal Sci. 24, 315–325 (2008).
    Google Scholar 
    49.Bonadonna, F., Lea, M.-A., Dehorter, O. & Guinet, C. Foraging ground fidelity and route-choice tactics of a marine predator: The Antarctic fur seal Arctocephalus gazella. Mar. Ecol. Prog. Ser. 223, 287–297 (2001).ADS 

    Google Scholar 
    50.Lea, M.-A. & Dubroca, L. Fine-scale linkages between the diving behaviour of Antarctic fur seals and oceanographic features in the southern Indian Ocean. ICES J. Mar. Sci. 60, 990–1002 (2003).
    Google Scholar 
    51.Jonsen, I. D. et al. Movement responses to environment: Fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100, e02566 (2019).CAS 
    PubMed 

    Google Scholar 
    52.Jonsen, I. D. et al. A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags. Mov. Ecol. 8, 31 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    53.Hazen, E. L. et al. Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models. Mov. Ecol. 9, 5 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    54.O’Toole, M., Queiroz, N., Humphries, N. E., Sims, D. W. & Sequeira, A. M. M. Quantifying effects of tracking data bias on species distribution models. Methods Ecol. Evol. 12, 170–181 (2021).
    Google Scholar 
    55.Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelemetry 5, 23 (2017).
    Google Scholar 
    56.Labrousse, S. et al. Under the sea ice: Exploring the relationship between sea ice and the foraging behaviour of southern elephant seals in East Antarctica. Prog. Oceanogr. 156, 17–40 (2017).ADS 

    Google Scholar 
    57.Hazen, E. L. et al. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Sci. Adv. 4, eaar3001 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    59.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
    Google Scholar 
    60.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    61.Hijmans, R. J., Phillips, S. & Elith, J. L. dismo: Species Distribution Modeling. (2020).62.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
    PubMed 

    Google Scholar 
    63.Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
    Google Scholar 
    64.Scales, K. L. et al. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol. Appl. 27, 2313–2329 (2017).PubMed 

    Google Scholar 
    65.Pya, N. & Wood, S. N. Shape constrained additive models. Stat. Comput. 25, 543–559 (2015).MathSciNet 
    MATH 

    Google Scholar 
    66.R Core Team. R: A Language and Environment for Statistical Computing. (2019).67.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).ADS 

    Google Scholar 
    68.Brodie, S. et al. Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. (2018).69.Becker, E. A. et al. Moving Towards dynamic ocean management: How well do modeled ocean products predict species distributions?. Remote Sens. 8, 149 (2016).ADS 

    Google Scholar 
    70.Lellouche, J.-M. et al. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Sci. 14, 1093–1126 (2018).ADS 

    Google Scholar 
    71.Handcock, M. S. & Raphael, M. N. Modeling the annual cycle of daily Antarctic sea ice extent. Cryosphere 14, 2159–2172 (2020).ADS 

    Google Scholar 
    72.Smith, G. C. et al. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Front. Mar. Sci. (2019).73.March, D., Boehme, L., Tintoré, J., Vélez-Belchi, P. J. & Godley, B. J. Towards the integration of animal-borne instruments into global ocean observing systems. Glob. Change Biol. 26, 586–596 (2020).ADS 

    Google Scholar 
    74.Santora, J. A. Dynamic intra-seasonal habitat use by Antarctic fur seals suggests migratory hotspots near the Antarctic Peninsula. Mar. Biol. 160, 1383–1393 (2013).
    Google Scholar 
    75.Vergani, D. F. & Coria, N. R. Increase in numbers of male fur seals Arctocephalus gazella during the summer autumn period at Mossman Peninsula (Laurie Island). Polar Biol. 9, 487–488 (1989).
    Google Scholar 
    76.Rutishauser, M. R., Costa, D. P., Goebel, M. E. & Williams, T. M. Ecological implications of body composition and thermal capabilities in young antarctic fur seals (Arctocephalus gazella). Physiol. Biochem. Zool. PBZ 77, 669–681 (2004).PubMed 

    Google Scholar 
    77.Vales, D. G., Cardona, L., García, N. A., Zenteno, L. & Crespo, E. A. Ontogenetic dietary changes in male South American fur seals Arctocephalus australis in Patagonia. Mar. Ecol. Prog. Ser. 525, 245–260 (2015).ADS 
    CAS 

    Google Scholar 
    78.Cardona, L., Vales, D., Aguilar, A., Crespo, E. & Zenteno, L. Temporal variability in stable isotope ratios of C and N in the vibrissa of captive and wild adult South American sea lions Otaria byronia: More than just diet shifts. Mar. Mammal Sci. 33, 975–990 (2017).CAS 

    Google Scholar 
    79.Costa, D. P., Gales, N. J. & Goebel, M. E. Aerobic dive limit: How often does it occur in nature?. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 129, 771–783 (2001).CAS 
    PubMed 

    Google Scholar 
    80.Biuw, M., Krafft, B. A., Hofmeyr, G. J. G., Lydersen, C. & Kovacs, K. M. Time budgets and at-sea behaviour of lactating female Antarctic fur seals Arctocephalus gazella at Bouvetøya. Mar. Ecol. Prog. Ser. 385, 271–284 (2009).ADS 

    Google Scholar 
    81.Lascara, C. M., Hofmann, E. E., Ross, R. M. & Quetin, L. B. Seasonal variability in the distribution of Antarctic krill, Euphausia superba, west of the Antarctic Peninsula. Deep Sea Res. Part Oceanogr. Res. Pap. 46, 951–984 (1999).ADS 

    Google Scholar 
    82.Lea, M.-A., Hindell, M., Guinet, C. & Goldsworthy, S. Variability in the diving activity of Antarctic fur seals, Arctocephalus gazella, at Iles Kerguelen. Polar Biol. 25, 269–279 (2002).
    Google Scholar 
    83.Vaughan, D. G. et al. Recent rapid regional climate warming on the antarctic peninsula. Clim. Change 60, 243–274 (2003).
    Google Scholar 
    84.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 
    85.Forcada, J. & Hoffman, J. I. Climate change selects for heterozygosity in a declining fur seal population. Nature 511, 462–465 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    86.Schwarz, L. K., Goebel, M. E., Costa, D. P. & Kilpatrick, A. M. Top-down and bottom-up influences on demographic rates of Antarctic fur seals Arctocephalus gazella. J. Anim. Ecol. 82, 903–911 (2013).PubMed 

    Google Scholar 
    87.Hoffman, J. I. & Forcada, J. Extreme natal philopatry in female Antarctic fur seals (Arctocephalus gazella). Mamm. Biol. 77, 71–73 (2012).
    Google Scholar 
    88.Hucke-Gaete, R., Osman, L. P., Moreno, C. A. & Torres, D. Examining natural population growth from near extinction: The case of the Antarctic fur seal at the South Shetlands, Antarctica. Polar Biol. 27, 304–311 (2004).
    Google Scholar  More

  • in

    Lion and spotted hyena distributions within a buffer area of the Serengeti-Mara ecosystem

    1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

    Google Scholar 
    2.Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2013).
    Google Scholar 
    3.Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2276 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    4.Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
    PubMed 

    Google Scholar 
    6.Tanner, E. et al. Wolves contribute to disease control in a multi-host system. Sci. Rep. 9, 1–12 (2019).ADS 

    Google Scholar 
    7.O’Bryan, C. J. et al. The contribution of predators and scavengers to human well-being. Nat. Ecol. Evol. 2, 229–236 (2018).PubMed 

    Google Scholar 
    8.Prugh, L. R. & Sivy, K. J. Enemies with benefits: integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 

    Google Scholar 
    9.Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).ADS 
    CAS 
    PubMed 

    Google Scholar 
    10.Wilfred, P. Towards sustainable wildlife management areas in Tanzania. Trop. Conserv. Sci. 3, 103–116 (2010).
    Google Scholar 
    11.Sinclair, A. R., Metzger, K. L., Mduma, S. A. & Fryxell, J. M. Serengeti IV: Sustaining Biodiversity in a Coupled Human-Natural System (University of Chicago Press, 2015).
    Google Scholar 
    12.Crooks, K. R. & Sanjayan, M. Connectivity Conservation Vol. 14 (Cambridge University Press, 2006).
    Google Scholar 
    13.Balme, G. A., Slotow, R. & Hunter, L. T. Edge effects and the impact of non-protected areas in carnivore conservation: Leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
    Google Scholar 
    14.Lindsey, P. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).
    Google Scholar 
    15.Elliot, N. B. & Gopalaswamy, A. M. Toward accurate and precise estimates of lion density. Conserv. Biol. 31, 934–943 (2017).PubMed 

    Google Scholar 
    16.Masenga, E. et al. Strychnine poisoning in African wild dogs (Lycaon pictus) in the Loliondo game controlled area, Tanzania. Int. J. Biodivers. Conserv. 5, 367–370 (2013).
    Google Scholar 
    17.Metzger, K., Sinclair, A., Hilborn, R., Hopcraft, J. G. C. & Mduma, S. A. Evaluating the protection of wildlife in parks: The case of African buffalo in Serengeti. Biodivers. Conserv. 19, 3431–3444 (2010).
    Google Scholar 
    18.Mogensen, N. L., Ogutu, J. O. & Dabelsteen, T. The effects of pastoralism and protection on lion behaviour, demography and space use in the Mara Region of Kenya. Afr. Zool. 46, 78–87 (2011).
    Google Scholar 
    19.Kiffner, C., Meyer, B., Mühlenberg, M. & Waltert, M. Plenty of prey, few predators: what limits lions Panthera leo in Katavi National Park, western Tanzania?. Oryx 43, 52–59 (2009).
    Google Scholar 
    20.Kiffner, C., Stoner, C. & Caro, T. Edge effects and large mammal distributions in a national park. Anim. Conserv. 16, 97–107 (2013).
    Google Scholar 
    21.Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).
    Google Scholar 
    22.Hofer, H. & East, M. Population dynamics, population size, and the commuting system of Serengeti spotted hyenas. Serengeti II Dyn. Manag. Conserv. Ecosyst. 2, 332 (1995).
    Google Scholar 
    23.Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).
    Google Scholar 
    24.Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. biol. 16, 488–502 (2002).
    Google Scholar 
    25.Martin, J. et al. Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach. Methods Ecol. Evol. 2, 595–601 (2011).ADS 

    Google Scholar 
    26.Prins, H. H., Grootenhuis, J. G. & Dolan, T. T. Wildlife Conservation by Sustainable Use Vol. 12 (Springer Science & Business Media, 2012).
    Google Scholar 
    27.Knapp, E. J. Why poaching pays: a summary of risks and benefits illegal hunters face in Western Serengeti, Tanzania. Trop. Conserv. Sci. 5, 434–445 (2012).ADS 

    Google Scholar 
    28.Revilla, E., Palomares, F. & Delibes, M. Edge-core effects and the effectiveness of traditional reserves in conservation: Eurasian badgers in Doñana National Park. Conserv. Biol. 15, 148–158 (2001).
    Google Scholar 
    29.Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96 (2013).
    Google Scholar 
    30.Ikanda, D. & Packer, C. Ritual vs. retaliatory killing of African lions in the Ngorongoro Conservation Area, Tanzania. Endanger. Species Res. 6, 67–74 (2008).
    Google Scholar 
    31.Belant, J. L. et al. Estimating lion abundance using N-mixture models for social species. Sci. Rep. 6, 1–9 (2016).
    Google Scholar 
    32.Hofer, H. & East, M. L. The commuting system of Serengeti spotted hyaenas: how a predator copes with migratory prey I. Social organization. Anim. Behav. 46, 547–557 (1993).
    Google Scholar 
    33.Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania. J. Appl. Ecol. 48, 1490–1500 (2011).
    Google Scholar 
    34.Swanson, A. B. Living with Lions: Spatiotemporal Aspects of Coexistence in Savanna Carnivores (University of Minnesota, 2014).
    Google Scholar 
    35.Masenga, E. H., Lyamuya, R. D., Mjingo, E. E., Fyumagwa, R. D. & Røskaft, E. Communal knowledge and perceptions of African wild dog (Lycaon pictus) reintroduction in the western part of Serengeti National Park, Tanzania. Int. J. Biodivers. Conserv. 9, 122–129 (2017).
    Google Scholar 
    36.Hopcraft, J. G. C., Sinclair, A. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).
    Google Scholar 
    37.Packer, C. & Pusey, A. E. Adaptations of female lions to infanticide by incoming males. Am. Nat. 121, 716–728 (1983).
    Google Scholar 
    38.Kruuk, H. & Turner, M. Comparative notes on predation by lion, leopard, cheetah and wild dog in the Serengeti area, East Africa. Mammalia 31, 1–27 (1967).
    Google Scholar 
    39.Green, D. S., Johnson-Ulrich, L., Couraud, H. E. & Holekamp, K. E. Anthropogenic disturbance induces opposing population trends in spotted hyenas and African lions. Biodiver. Conserv. 27, 871–889. https://doi.org/10.1007/s10531-017-1469-7 (2018).Article 

    Google Scholar 
    40.Kolowski, J. M., Katan, D., Theis, K. R. & Holekamp, K. E. Daily patterns of activity in the spotted hyena. J. Mamm. 88, 1017–1028 (2007).
    Google Scholar 
    41.Šálek, M., Kreisinger, J., Sedláček, F. & Albrecht, T. Do prey densities determine preferences of mammalian predators for habitat edges in an agricultural landscape?. Landsc. Urban Plan. 98, 86–91 (2010).
    Google Scholar 
    42.Mosser, A., Fryxell, J. M., Eberly, L. & Packer, C. Serengeti real estate: density vs. fitness-based indicators of lion habitat quality. Ecol. Lett. 12, 1050–1060 (2009).PubMed 

    Google Scholar 
    43.Schmitt, J. A. Improving Conservation Efforts in the Serengeti Ecosystem, Tanzania: An Examination of Knowledge, Benefits, Costs, and Attitudes (University of Minnesota, 2010).
    Google Scholar 
    44.Makacha, S., Msingwa, M. J. & Frame, G. W. Threats to the Serengeti herds. Oryx 16, 437–444 (1982).
    Google Scholar 
    45.Crosmary, W.-G. et al. Lion densities in selous game reserve, Tanzania. Afr. J. Wildl. Res. 48, 1–6 (2018).
    Google Scholar 
    46.Belant, J. L. et al. Track surveys do not provide accurate or precise lion density estimates in serengeti. Glob. Ecol. 19, e00651 (2019).
    Google Scholar 
    47.Midlane, N., O’Riain, M. J., Balme, G. A. & Hunter, L. T. B. To track or to call: comparing methods for estimating population abundance of African lions Panthera leo in Kafue National Park. Biodiver. Conserv. 24, 1311–1327. https://doi.org/10.1007/s10531-015-0858-z (2015).Article 

    Google Scholar 
    48.Ogutu, J. O. & Dublin, H. T. The response of lions and spotted hyaenas to sound playbacks as a technique for estimating population size. Afr. J. Ecol. 36, 83–95. https://doi.org/10.1046/j.1365-2028.1998.113-89113.x (1998).Article 

    Google Scholar 
    49.Belant, J. L. et al. Temporal and spatial variation of broadcasted vocalizations does not reduce lion Panthera leo habituation. Wildl. Biol. wlb. 00287 (2017).50.Cozzi, G., Broekhuis, F., McNutt, J. & Schmid, B. Density and habitat use of lions and spotted hyenas in northern Botswana and the influence of survey and ecological variables on call-in survey estimation. Biodiver. Conserv. 22, 2937–2956 (2013).
    Google Scholar 
    51.M’soka, J., Creel, S., Becker, M. S. & Droge, E. Spotted hyaena survival and density in a lion depleted ecosystem: The effects of prey availability, humans and competition between large carnivores in African savannahs. Biol. Conserv. 201, 348–355 (2016).
    Google Scholar 
    52.Croes, B. et al. The impact of trophy hunting on lions (Panthera leo) and other large carnivores in the Bénoué Complex, northern Cameroon. Biol. Conserv. 144, 3064–3072 (2011).
    Google Scholar 
    53.Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    54.National Bureau of Statistics. Tanzania in Figures 2012 (The United Republic of Tanzania, 2013).55.McNaughton, S. Serengeti grassland ecology: The role of composite environmental factors and contingency in community organization. Ecol. Monograph. 53, 291–320 (1983).
    Google Scholar 
    56.Reed, D., Anderson, T., Dempewolf, J., Metzger, K. & Serneels, S. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J. Biogeogr. 36, 770–782 (2009).
    Google Scholar 
    57.Sollmann, R., Gardner, B., Belant, J. L., Wilton, C. M. & Beringer, J. Habitat associations in a recolonizing, low‐density black bear population. Ecosphere 7 (2016).58.Royle, J. A. & Dorazio, R. M. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities (Elsevier, 2008).
    Google Scholar 
    59.Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435 (2011).PubMed 

    Google Scholar 
    60.Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115 (2004).MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    61.Kellner, K. & Meredith, M. Package ‘jagsUI’. (2021).62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).63.Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).MathSciNet 
    MATH 

    Google Scholar 
    64.Kuo, L. & Mallick, B. Variable selection for regression models. Indian J. Stat. 65–81 (1998).65.Congdon, P. Bayesian Models for Categorical Data (John Wiley and Sons, 2005).MATH 

    Google Scholar  More

  • in

    Investing wisely in land restoration

    1.Mirzabaev, A., Sacande, M., Motlagh, F., Shyrokaya, A. & Martucci, A. Nat. Sustain. https://doi.org/10.1038/s41893-021-00801-8 (2021).2.Barbier, E. B. & Hochard, J. P. Nat. Sustain. 1, 623–631 (2018).Article 

    Google Scholar 
    3.Deininger, K. & Jin, S. Eur. Econ. Rev. 50, 1245–1277 (2006).Article 

    Google Scholar 
    4.Barbier, E. B. Environ. Dev. Econ. 15, 635–660 (2010).Article 

    Google Scholar 
    5.Nkonya, E., Mirzabaev, A. & von Braun, J. in Economics of Land Degradation and Improvement—A Global Assessment for Sustainable Development (eds. Nkonya, E., Mirzabaev, A. & von Braun, J.) 1–14 (Springer, Cham, 2016).6.Barbier, E. B. & Hochard, J. P. Rev. Environ. Econ. Policy 12, 26–47 (2018).Article 

    Google Scholar  More

  • in

    Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean

    1.IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (in the press).2.Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Meltofte, H. (ed.) Arctic Biodiversity Assessment: Status and Trends in Arctic Biodiversity (CAFF International Secretariat, 2013).4.Wassmann, P. & Reigstad, M. Future Arctic Ocean seasonal ice zones and implications for pelagic-benthic coupling. Oceanography 24, 220–231 (2011).
    Google Scholar 
    5.Bunse, C. & Pinhassi, J. Marine bacterioplankton seasonal succession dynamics. Trends Microbiol. 25, 494–505 (2017).CAS 
    PubMed 

    Google Scholar 
    6.Olli, K. et al. Seasonal variation in vertical flux of biogenic matter in the marginal ice zone and the central Barents Sea. J. Mar. Syst. 38, 189–204 (2002).
    Google Scholar 
    7.Riedel, A., Michel, C., Gosselin, M. & LeBlanc, B. Winter–spring dynamics in sea-ice carbon cycling in the coastal Arctic Ocean. J. Mar. Syst. 74, 918–932 (2008).
    Google Scholar 
    8.Joli, N., Monier, A., Logares, R. & Lovejoy, C. Seasonal patterns in Arctic prasinophytes and inferred ecology of Bathycoccus unveiled in an Arctic winter metagenome. ISME J. 11, 1372–1385 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    9.Alonso-Sáez, L., Sánchez, O., Gasol, J. M., Balagué, V. & Pedrós-Alio, C. Winter-to-summer changes in the composition and single-cell activity of near-surface Arctic prokaryotes. Environ. Microbiol. 10, 2444–2454 (2008).PubMed 

    Google Scholar 
    10.Alonso-Sáez, L. et al. Role for urea in nitrification by polar marine Archaea. Proc. Natl Acad. Sci. USA 109, 17989–17994 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    11.Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).CAS 
    PubMed 

    Google Scholar 
    12.Circumpolar Biodiversity Monitoring Program, Conservation of Arctic Flora and Fauna. State of the Arctic Marine Biodiversity Report (Conservation of Arctic Flora and Fauna International Secretariat, 2017).13.Kirchman, D. L., Cottrell, M. T. & Lovejoy, C. The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes. Environ. Microbiol. 12, 1132–1143 (2010).CAS 
    PubMed 

    Google Scholar 
    14.Galand, P. E., Casamayor, E. O., Kirchman, D. L., Potvin, M. & Lovejoy, C. Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing. ISME J. 3, 860–869 (2009).CAS 
    PubMed 

    Google Scholar 
    15.Pedrós-Alió, C., Potvin, M. & Lovejoy, C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog. Oceanogr. 139, 233–243 (2015).
    Google Scholar 
    16.Amaral-Zettler, L. et al. in Life in the World’s Oceans: Diversity, Distribution, and Abundance (ed. McIntyre, A. D.) 221–245 (Blackwell Publishing Ltd, 2010).17.Christman, G. D., Cottrell, M. T., Popp, B. N., Gier, E. & Kirchman, D. L. Abundance, diversity, and activity of ammonia-oxidizing prokaryotes in the coastal Arctic Ocean in summer and winter. Appl. Environ. Microbiol. 77, 2026–2034 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Alonso-Sáez, L., Galand, P. E., Casamayor, E. O., Pedrós-Alió, C. & Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. ISME J. 4, 1581–1590 (2010).PubMed 

    Google Scholar 
    19.Galand, P. E., Lovejoy, C., Pouliot, J., Garneau, M.-È. & Vincent, W. F. Microbial community diversity and heterotrophic production in a coastal Arctic ecosystem: a stamukhi lake and its source waters. Limnol. Oceanogr. 53, 813–823 (2008).
    Google Scholar 
    20.Nguyen, D. et al. Winter diversity and expression of proteorhodopsin genes in a polar ocean. ISME J. 9, 1835–1845 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    21.Cifuentes-Anticevic, J. et al. Proteorhodopsin phototrophy in Antarctic coastal waters. mSphere 6, e00525–21 (2021).CAS 
    PubMed Central 

    Google Scholar 
    22.Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl Acad. Sci. USA 109, 17633–17638 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Kraemer, S., Ramachandran, A., Colatriano, D., Lovejoy, C. & Walsh, D. A. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. 14, 79–90 (2020).PubMed 

    Google Scholar 
    25.Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    26.Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).CAS 
    PubMed 

    Google Scholar 
    27.Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).CAS 
    PubMed 

    Google Scholar 
    29.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Aagaard, K., Swift, J. H. & Carmack, E. C. Thermohaline circulation in the Arctic Mediterranean Seas. J. Geophys. Res. Oceans 90, 4833–4846 (1985).
    Google Scholar 
    32.Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).CAS 
    PubMed 

    Google Scholar 
    33.Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Jones, S. E. & Lennon, J. T. Dormancy contributes to the maintenance of microbial diversity. Proc. Natl Acad. Sci. USA 107, 5881–5886 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Mestre, M. & Höfer, J. The microbial conveyor belt: connecting the globe through dispersion and dormancy. Trends Microbiol. 29, 482–492 (2021).CAS 
    PubMed 

    Google Scholar 
    36.Ciufo, S. et al. Using average nucleotide identity to improve taxonomic assignments in prokaryotic genomes at the NCBI. Int. J. Syst. Evol. Microbiol. 68, 2386–2392 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    37.Chaumeil, P-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

    Google Scholar 
    38.Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    39.Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun. Biol. 3, 119 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    40.Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Christensen, M. & Nilsson, A. E. Arctic sea ice and the communication of climate change. Pop. Commun. 15, 249–268 (2017).
    Google Scholar 
    42.Jaffe, A. L., Castelle, C. J., Dupont, C. L. & Banfield, J. F. Lateral gene transfer shapes the distribution of RuBisCO among candidate phyla radiation bacteria and DPANN Archaea. Mol. Biol. Evol. 36, 435–446 (2019).CAS 
    PubMed 

    Google Scholar 
    43.Kono, T. et al. A RuBisCO-mediated carbon metabolic pathway in methanogenic archaea. Nat. Commun. 8, 14007 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Sato, T., Atomi, H. & Imanaka, T. Archaeal type III RuBisCOs function in a pathway for AMP metabolism. Science 315, 1003–1006 (2007).CAS 
    PubMed 

    Google Scholar 
    45.Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E. & Scott, S. S. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships. J. Exp. Bot. 59, 1515–1524 (2008).CAS 
    PubMed 

    Google Scholar 
    46.Yelton, A. P. et al. Global genetic capacity for mixotrophy in marine picocyanobacteria. ISME J. 10, 2946–2957 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Cordero, P. R. F. et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 13, 2868–2881 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.King, G. M. & Weber, C. F. Distribution, diversity and ecology of aerobic CO-oxidizing bacteria. Nat. Rev. Microbiol. 5, 107–118 (2007).CAS 
    PubMed 

    Google Scholar 
    49.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

    Google Scholar 
    50.Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl Acad. Sci. USA 110, 2342–2347 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton Univ. Press, 1968).
    Google Scholar 
    53.Colwell, R. K. & Futuyma, D. J. On the measurement of niche breadth and overlap. Ecology 52, 567–576 (1971).PubMed 

    Google Scholar 
    54.Massana, R. & Logares, R. Eukaryotic versus prokaryotic marine picoplankton ecology. Environ. Microbiol. 15, 1254–1261 (2013).PubMed 

    Google Scholar 
    55.Székely, A. J., Berga, M. & Langenheder, S. Mechanisms determining the fate of dispersed bacterial communities in new environments. ISME J. 7, 61–71 (2013).PubMed 

    Google Scholar 
    56.Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 66 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    57.Logares, R. et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 7, 937–948 (2013).CAS 
    PubMed 

    Google Scholar 
    58.Ruiz-González, C. et al. Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean. Mol. Ecol. 28, 1930–1945 (2019).PubMed 

    Google Scholar 
    59.Staley, J. T. & Gosink, J. J. Poles apart: biodiversity and biogeography of sea ice bacteria. Annu. Rev. Microbiol. 53, 189–215 (1999).CAS 
    PubMed 

    Google Scholar 
    60.Chaffron, S. et al. Environmental vulnerability of the global ocean epipelagic plankton community interactome. Sci. Adv. 7, eabg1921 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Estrada, E. Characterization of topological keystone species: local, global and “meso-scale” centralities in food webs. Ecol. Complex. 4, 48–57 (2007).
    Google Scholar 
    62.Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
    PubMed 

    Google Scholar 
    63.Tully, B. J., Sachdeva, R., Graham, E. D. & Heidelberg, J. F. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 2017, e3558 (2017).
    Google Scholar 
    64.Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun. Biol. 4, 604 (2021).65.Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    71.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
    PubMed Central 

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

    Google Scholar 
    73.Huang, X. & Madan, A. CAP3: a DNA sequence assembly program. Genome Res. 9, 868–877 (1999).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 

    Google Scholar 
    77.Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487–2489 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Jain, C., Rodriguez-R, L. M., Phillipy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    79.Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    81.Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Res. 9, ISCB Comm J-304 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    82.Aylward, F. O. & Santoro, A. E. Heterotrophic Thaumarchaeota with ultrasmall genomes are widespread in the ocean. mSystems 5, e00415–20 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    84.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2––approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    85.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Louca, S., Doebeli, M. & Parfrey, L. W. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 6, 41 (2018).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Unexpected myriad of co-occurring viral strains and species in one of the most abundant and microdiverse viruses on Earth

    1.Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUVIG). Nat Biotechnol 2019;37:29–37.PubMed 

    Google Scholar 
    2.Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.PubMed 

    Google Scholar 
    3.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.PubMed 
    PubMed Central 

    Google Scholar 
    4.Kavagutti VS, Andrei AŞ, Mehrshad M, Salcher MM, Ghai R. Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics. Microbiome. 2019;7:1–15.
    Google Scholar 
    5.Schulz F, Alteio L, Goudeau D, Ryan EM, Yu FB, Malmstrom RR, et al. Hidden diversity of soil giant viruses. Nat Commun 2018;9:1–9.
    Google Scholar 
    6.Trubl G, Jang H Bin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 2018;3:e00076–18.PubMed 
    PubMed Central 

    Google Scholar 
    7.Guerin E, Shkoporov A, Stockdale SR, Clooney AG, Ryan FJ, Sutton TDS, et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. Cell Host Microbe. 2018;24:653–664.e6.PubMed 

    Google Scholar 
    8.Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Peña MJ, Martínez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun 2017;8:1–13.
    Google Scholar 
    9.Aguirre de Cárcer D, Angly FE, Alcamí A. Evaluation of viral genome assembly and diversity estimation in deep metagenomes. BMC Genomics. 2014;15:1–12.
    Google Scholar 
    10.Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
    PubMed Central 

    Google Scholar 
    11.Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature. 2011;474:604–8.PubMed 

    Google Scholar 
    12.Rodriguez-Valera F, Martin-Cuadrado A-B, Rodriguez-Brito B, Pasic L, Thingstad TF, Rohwer F, et al. Explaining microbial population genomics through phage predation. Nat Rev Microbiol 2009;7:828–36.PubMed 

    Google Scholar 
    13.Marston MF, Pierciey FJ, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.PubMed 
    PubMed Central 

    Google Scholar 
    14.Enav H, Kirzner S, Lindell D, Mandel-Gutfreund Y, Béjà O. Adapt sub-Optim hosts is a Driv viral Diversif ocean Nat Comm 2018;9:1–11.
    Google Scholar 
    15.Boon M, Holtappels D, Lood C, van Noort V, Lavigne R. Host range expansion of pseudomonas virus LUZ7 is driven by a conserved tail fiber mutation. PHAGE. 2020;1:87–90.
    Google Scholar 
    16.Bernheim A, Sorek R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat Rev Microbiol 2020;18:113–9.PubMed 

    Google Scholar 
    17.Sørensen MA, Kurland CG, Pedersen S. Codon usage determines translation rate in Escherichia coli. J Mol Biol 1989;207:365–77.PubMed 

    Google Scholar 
    18.Varenne S, Buc J, Lloubes R, Lazdunski C. Translation is a non-uniform process. Effect of tRNA availability on the rate of elongation of nascent polypeptide chains. J Mol Biol 1984;180:549–76.PubMed 

    Google Scholar 
    19.Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, et al. Codon Usage Influences the Local Rate of Translation Elongation to Regulate Co-translational Protein Folding. Mol Cell. 2015;59:744–54.PubMed 
    PubMed Central 

    Google Scholar 
    20.Plotkin JB, Kudla G. Synonymous but not the same: The causes and consequences of codon bias. Nat Rev Genet 2011;12:32–42.PubMed 

    Google Scholar 
    21.Chu D, Wei L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 2019;19:359.PubMed 
    PubMed Central 

    Google Scholar 
    22.Deng L, Ignacio-Espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–5.PubMed 

    Google Scholar 
    23.Edwards RA, Vega AA, Norman HM, Ohaeri M, Levi K, Dinsdale EA, et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat Microbiol 2019;4:1727–36.PubMed 
    PubMed Central 

    Google Scholar 
    24.Ignacio-Espinoza JC, Ahlgren NA, Fuhrman JA. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat. Microbiol. 2019;5:1–7.25.Coutinho FH, Rosselli R, Rodríguez-Valera F. Trends of microdiversity reveal depth-dependent evolutionary strategies of viruses in the Mediterranean. mSystems. 2019;4:1–17.
    Google Scholar 
    26.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
    PubMed Central 

    Google Scholar 
    27.Martinez-Hernandez F, Fornas Ò, Lluesma Gomez M, Garcia-Heredia I, Maestre-Carballa L, López-Pérez M, et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 2019;13:232–6.PubMed 

    Google Scholar 
    28.McMullen A, Martinez‐Hernandez F, Martinez‐Garcia M. Absolute quantification of infecting viral particles by chip‐based digital polymerase chain reaction. Environ Microbiol Rep. 2019;11:855–60.PubMed 

    Google Scholar 
    29.Marston MF, Amrich CG. Recombination and microdiversity in coastal marine cyanophages. Environ Microbiol. 2009;11:2893–903.PubMed 

    Google Scholar 
    30.Marston MF, Martiny JBH. Genomic diversification of marine cyanophages into stable ecotypes. Environ Microbiol 2016;18:4240–53.PubMed 

    Google Scholar 
    31.Cordero OX. Endemic cyanophages and the puzzle of phage-bacteria coevolution. Environ Microbiol 2017;19:420–2.PubMed 

    Google Scholar 
    32.Shannon CE. The mathematical theory of communication. 1963. MD Comput. 1997;14:306–17.PubMed 

    Google Scholar 
    33.Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.PubMed 

    Google Scholar 
    34.Bobay L-M, Ochman H. Biological species in the viral world. Proc Natl Acad Sci USA 2018;115:6040–5.PubMed 
    PubMed Central 

    Google Scholar 
    35.Henson MW, Lanclos VC, Faircloth BC, Thrash JC. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 2018;12:1846–60.PubMed 
    PubMed Central 

    Google Scholar 
    36.Paez-Espino D, Roux S, Chen I-MA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 2019;47:D678–D686.PubMed 

    Google Scholar 
    37.Brum JR, Ignacio-Espinoza JC, Kim E-H, Trubl G, Jones RM, Roux S, et al. Illuminating structural proteins in viral ‘dark matter’ with metaproteomics. Proc Natl Acad Sci USA 2016;113:2436–41.PubMed 
    PubMed Central 

    Google Scholar 
    38.Sakowski EG, Arora-Williams K, Tian F, Zayed AA, Zablocki O, Sullivan MB, et al. Interaction dynamics and virus–host range for estuarine actinophages captured by epicPCR. Nat. Microbiol. 2021;6:1–13.39.Alonso-Sáez L, Morán XAG, Clokie MR. Low activity of lytic pelagiphages in coastal marine waters. ISME J. 2018;12:2100–2.PubMed 
    PubMed Central 

    Google Scholar 
    40.Martinez‐Hernandez F, Luo E, Tominaga K, Ogata H, Yoshida T, DeLong EF, et al. Diel cycling of the cosmopolitan abundant Pelagibacter virus 37‐F6: one of the most abundant viruses in Earth. Environ Microbiol Rep. 2020;12:214–21941.Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.PubMed 

    Google Scholar 
    42.de Avila e Silva S, Echeverrigaray S, Gerhardt GJL. BacPP: bacterial promoter prediction-A tool for accurate sigma-factor specific assignment in enterobacteria. J Theor Biol 2011;287:92–99.PubMed 

    Google Scholar 
    43.Sampaio M, Rocha M, Oliveira H, Dias O. Predicting promoters in phage genomes using PhagePromoter. Bioinformatics. 2019;35:5301–2.PubMed 

    Google Scholar 
    44.Allert M, Cox JC, Hellinga HW. Multifactorial determinants of protein expression in prokaryotic open reading frames. J Mol Biol. 2010;402:905–18.PubMed 
    PubMed Central 

    Google Scholar 
    45.Dressaire C, Picard F, Redon E, Loubière P, Queinnec I, Girbal L, et al. Role of mRNA stability during bacterial adaptation. PLoS ONE 2013;8:e59059.PubMed 
    PubMed Central 

    Google Scholar 
    46.Deana A, Belasco JG. Lost in translation: The influence of ribosomes on bacterial mRNA decay. Genes Dev. 2005;19:2526–33.PubMed 

    Google Scholar 
    47.Zhao Y, Temperton B, Thrash JC, Schwalbach MS, Vergin KL, Landry ZC, et al. Abundant SAR11 viruses in the ocean. Nature. 2013;494:357–60.PubMed 

    Google Scholar 
    48.Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2020;1462-2920:15272.
    Google Scholar 
    49.Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2018;21:1989–2001.50.Morris RM, Cain KR, Hvorecny KL, Kollman JM. Lysogenic host–virus interactions in SAR11 marine bacteria. Nat Microbiol 2020;5:1011–5.PubMed 
    PubMed Central 

    Google Scholar 
    51.Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trans R Soc Lond, B, Biol Sci 2006;361:1929–40.
    Google Scholar 
    52.Rosselló-Mora R. Updating prokaryotic taxonomy. J Bacteriol. 2005;187:6255–7.PubMed 
    PubMed Central 

    Google Scholar 
    53.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017;2:1533–42.PubMed 

    Google Scholar 
    54.Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci 2009;106:19126–31.PubMed 
    PubMed Central 

    Google Scholar 
    55.Pope WH, Bowman CA, Russell DA, Jacobs-Sera D, Asai DJ, Cresawn SG, et al. Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity. eLife 2015;4:e06416.PubMed 
    PubMed Central 

    Google Scholar 
    56.Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC genomics. 2016;17:930.PubMed 
    PubMed Central 

    Google Scholar 
    57.Martinez-Hernandez F, Garcia-Heredia I, Lluesma Gomez M, Maestre-Carballa L, Martínez Martínez J, Martinez-Garcia M. Droplet digital PCR for estimating absolute abundances of widespread Pelagibacter viruses. Front Microbiol 2019;10:1226.PubMed 
    PubMed Central 

    Google Scholar 
    58.Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.PubMed 
    PubMed Central 

    Google Scholar 
    59.Beaulaurier J, Luo E, Eppley JM, Uyl P Den, Dai X, Burger A, et al. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities. Genome Res. 2020;30:437–46.PubMed 
    PubMed Central 

    Google Scholar 
    60.Murigneux V, Rai SK, Furtado A, Bruxner TJC, Tian W, Harliwong I, et al. Comparison of long-read methods for sequencing and assembly of a plant genome. GigaScience 2020;9:giaa146.61.Wenger AM, Peluso P, Rowell WJ, Chang PC, Hall RJ, Concepcion GT, et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 2019;37:1155–62.PubMed 
    PubMed Central 

    Google Scholar 
    62.Martínez Martínez J, Martinez-Hernandez F, Martinez-Garcia M. Single-virus genomics and beyond. Nat Rev Microbiol. 2020;18:705–16.PubMed 

    Google Scholar 
    63.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
    PubMed Central 

    Google Scholar 
    64.Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R. Expanding the marine virosphere using metagenomics. PLoS Genet. 2013;9:e1003987.PubMed 
    PubMed Central 

    Google Scholar 
    65.Mizuno CM, Ghai R, Saghaï A, López-García P, Rodriguez-Valera F. Genomes of abundant and widespread viruses from the deep ocean. mBio. 2016;7:e00805–16.PubMed 
    PubMed Central 

    Google Scholar 
    66.Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma. 2012;13:134.
    Google Scholar 
    67.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.PubMed 
    PubMed Central 

    Google Scholar 
    68.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.PubMed 

    Google Scholar 
    69.Philosof A, Yutin N, Flores-Uribe J, Sharon I, Koonin EV, Béjà O. Novel abundant oceanic viruses of uncultured marine group II Euryarchaeota. Curr Biol. 2017;27:1362–8.PubMed 
    PubMed Central 

    Google Scholar 
    70.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
    PubMed Central 

    Google Scholar 
    71.Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019;37:632–9.
    Google Scholar 
    72.Bobay L-M, Ellis BS-H, Ochman H. ConSpeciFix: classifying prokaryotic species based on gene flow. Bioinformatics. 2018;34:3738–40.PubMed 
    PubMed Central 

    Google Scholar 
    73.Bobay L-M, Ochman H. Biological species are universal across life’s domains. Genome Biol Evol. 2017;9:491–501.PubMed Central 

    Google Scholar 
    74.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 
    75.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.PubMed 
    PubMed Central 

    Google Scholar 
    76.Harris CD, Torrance EL, Raymann K, Bobay L-M. CoreCruncher: Fast and robust construction of core genomes in large prokaryotic data sets. Mol. Biol. Evol. 2020;38:727–734.77.Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.PubMed 
    PubMed Central 

    Google Scholar 
    78.Rice P, Longden L, Bleasby A EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000. Elsevier Ltd., 16: 276–779.Džunková M, Low SJ, Daly JN, Deng L, Rinke C, Hugenholtz P. Defining the human gut host–phage network through single-cell viral tagging. Nat Microbiol 2019;4:2192–203.PubMed 

    Google Scholar 
    80.Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
    PubMed Central 

    Google Scholar 
    81.Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
    PubMed Central 

    Google Scholar 
    82.Swan BK, Ehrhardt CJ, Reifel KM, Moreno LI, Valentine DL. Archaeal and bacterial communities respond differently to environmental gradients in anoxic sediments of a california hypersaline lake, the Salton Sea. Appl Environ Microbiol 2010;76:757–68.PubMed 

    Google Scholar 
    83.Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol 2018;3:62–72.PubMed 

    Google Scholar  More