More stories

  • in

    The UN Environment Programme needs new powers

    Indian prime minister Indira Gandhi meets Maurice Strong, who chaired the 1972 Stockholm Conference on the Human Environment. Gandhi saw UNEP’s potential at a time when other countries doubted its value.Credit: Yutaka Nagata/UN Photo

    The United Nations Environment Programme (UNEP) will be 50 next year. But the globe’s green watchdog, which helped to create the Intergovernmental Panel on Climate Change (IPCC), very nearly didn’t exist.
    During talks hosted by Sweden in 1972, low- and middle-income countries were concerned that such a body would inhibit their industrial development. Some high-income countries also questioned its creation. UK representative Solly Zuckerman, a former chief scientific adviser to prime ministers including Winston Churchill, said the science did not justify warnings that human activities could have irreversible consequences for the planet. The view in London was that, on balance, environmental pollution was for individual nations to solve — not the UN.
    But the idea of UNEP had powerful supporters, too. India’s prime minister, Indira Gandhi, foresaw its potential in enabling industry to become cleaner and more humane. And the host nation made a wise choice in picking Canadian industrialist Maurice Strong to steer the often fractious talks to success. He would become UNEP’s first executive director. Two decades later, Strong re-emerged to chair the 1992 Earth Summit in Rio de Janeiro, Brazil, which created three landmark international agreements: to protect biodiversity, safeguard the climate and combat desertification.
    UNEP has chalked up some impressive achievements in science and legislation. In 1988, working with the World Meteorological Organization, it co-founded the IPCC, whose scientific assessments have been pivotal to global climate action. It also responded to scientists’ warnings about the hole in the ozone layer, leading to the creation of the 1987 Montreal Protocol, an international law to phase out ozone-depleting chemicals.
    Strong’s successors would go on to identify emerging green-policy issues and nudge them into the mainstream. UNEP has pushed the world of finance to think about how to stop funding polluting industries. It has also advocated working with China to green its rapid industrial growth — including the Belt and Road Initiative to develop global infrastructure. It is essential that this work continues.
    UNEP also accelerated the creation of environment ministries around the world. Their ministers sit on the programme’s governing council; at their annual meeting last week, they reflected on what UNEP must do to tackle the environmental crisis. Although the environment is a rising priority for governments, businesses and civil society, progress on the UN’s flagship Sustainable Development Goals — in biodiversity, climate, land degradation, pollution, finance and more — is next to non-existent. Moreover, the degradation of nature is putting hard-won gains at risk, argues a report that UNEP commissioned as part of its half-century commemorations.
    The report, Making Peace with Nature, assesses much of the same literature as would a climate- or land-degradation assessment, but its key strength is in how it brings together researchers from across environmental science. In doing so, UNEP is helping to accelerate a mode of working that should be standard. If, for example, there is to be an assessment of how climate change affects biodiversity, it makes much more sense for this to be carried out by a joint team from the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) than by researchers from just one of these organizations.
    The UNEP report’s authors stop short of recommending such changes to the architecture of the UN’s scientific advisory bodies. That is a missed opportunity. Also missing is a discussion and recommendations on how to make countries more accountable for their environmental pledges.
    Both these actions are sorely needed if the world is to take more meaningful steps to battle climate change and biodiversity loss. Countries have become expert in capturing data and reporting them to UN organizations. But there is no mechanism that holds nations to account. For example, there is no system to ensure compliance with targets for the Sustainable Development Goals.
    Last week, the UN produced a report in which countries published their progress towards commitments under the 2015 Paris climate agreement, known as nationally determined contributions. The agreement includes almost 200 countries, but just 75 reported their data. There are few incentives for success and no penalties for failure. Without such measures, it is hard to see how meaningful change could ever happen.
    In the past, researchers have proposed that UNEP’s member states upgrade its powers so it becomes more of a compliance body — a World Environment Organization that, like the World Trade Organization, has the power to censure countries for failing to keep to agreements. But this has been resisted as too radical a step, which would upend the autonomy of the UN biodiversity and climate organizations that UNEP itself helped to bring into being.
    Twenty years ago, there might have been some justification for such a view, but now, with the world on a path to extreme climate change, any action will need to be radical, including considering how to give UNEP more teeth.
    UNEP helped to lay the foundations for a scientific consensus on environmental decline, and it should be proud of the body of law that has been enacted globally. Alas, such measures risk being too little, too late. As it embarks on a year of reflection ahead of its anniversary, member states must consider what more they need to do to empower UNEP to tackle the planetary emergency. More

  • in

    Large-scale spatial patterns of small-mammal communities in the Mediterranean region revealed by Barn owl diet

    1.
    de Lattin, G. Grundriss der Zoogeographie (Gustav Fischer Verlag, 1976).
    2.
    Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. Lond. 68, 87–112. https://doi.org/10.1006/bijl.1999.0332 (1999).
    Article  Google Scholar 

    3.
    Wallace, A. R. The geographical distribution of animals; with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth’s surface (Harper & Brothers, 1876).

    4.
    Mittermeier, R. A., Myers, N., Mittermeier, C. G. & Robles Gil, P. Hotspots: Earth’s biologically richest and most endangered terrestrial ecoregions (CEMEX, 1999).
    Google Scholar 

    5.
    Médail, F. & Quézel, P. Biodiversity hotspots in the Mediterranean Basin: setting global conservation priorities. Conserv. Biol. 13(6), 1510–1513 (1999).
    Article  Google Scholar 

    6.
    Temple, H. J. & Cuttelod, A. (Compilers). The Status and Distribution of Mediterranean Mammals. Gland, Switzerland and Cambridge (UK: IUCN, vii+32pp, 2009).

    7.
    Blondel, J. The nature and origin of the vertebrate fauna. pp. 139–163 In: Woodward, C. J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

    8.
    Aulagnier, S., Hafner, P., Mitchell-Jones, A. J., Moutou, F. & Zima, J. Mammals of Europe, North Africa and the Middle East (A&C Black Publishers, 2009).
    Google Scholar 

    9.
    Horáček, I., Hanák, V. & Gaisler, J. Bats of the Palearctic region: a taxonomic and biogeographic review. In Proceedings of the VIIIth European bat research symposium (Vol. 1, pp. 11–157) (Kraków, CIC ISEZ PAN, 2000).

    10.
    Smith, C. H. A system of world mammal faunal regions. I. Logical and statistical derivation of the regions. J. Biogeogr. 10, 455–466. https://doi.org/10.2307/2844752 (1983).

    11.
    Dobson, M. Mammal distributions in the western Mediterranean: the role of human intervention. Mammal Rev. 28(2), 77–88 (1998).
    Article  Google Scholar 

    12.
    Sans-Fuentes, M. A. & Ventura, J. Distribution patterns of the small mammals (Insectivora and Rodentia) in a transitional zone between the Eurosiberian and the Mediterranean regions. J. Biogeogr. 27(3), 755–764 (2000).
    Article  Google Scholar 

    13.
    Kryštufek, B. & Vohralík, V. Mammals of Turkey and Cyprus: introduction, checklist, Insectivora (Zgodovinsko društvo za južno Primorsko, 2001).

    14.
    Kryštufek, B. A quantitative assessment of Balkan mammal diversity. In Balkan Biodiversity (pp. 79–108) (Springer, Dordrecht, 2004).

    15.
    Kryštufek, B., Vohralík, V. & Janžekovič, F. Mammals of Turkey and Cyprus: Rodentia I: Sciuridae, Dipodidae, Gliridae (Arvicolinae, 2005).
    Google Scholar 

    16.
    Kryštufek, B. & Vohralík, V. Mammals of Turkey and Cyprus, Rodentia II: Cricetinae, Murridae, Spalacidae, Calomyscidae, Capromyidae, Hystricidae Castoridae. J. Mammal. 96, 1–373 (2010).
    Google Scholar 

    17.
    Kryštufek, B., Donev, N. R. & Skok, J. Species richness and distribution of non-volant small mammals along an elevational gradient on a Mediterranean mountain. Mammalia 75(1), 3–11 (2011).
    Article  Google Scholar 

    18.
    Svenning, J. C., Fløjgaard, C. & Baselga, A. Climate, history and neutrality as drivers of mammal beta diversity in Europe: Insights from multiscale deconstruction. J. Anim. Ecol. 80(2), 393–402 (2011).
    Article  Google Scholar 

    19.
    Gaston, K., & Blackburn, T. Pattern and process in macroecology (John Wiley & Sons, 2008).

    20.
    Darwin, C. On the Origin of Species by Means of Natural Selection (J. Murray, 1859).

    21.
    Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).

    22.
    Hawkins, B. A. et al. Energy, water and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117. https://doi.org/10.1890/03-8006 (2002).
    Article  Google Scholar 

    23.
    Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163(2), 192–211 (2004).
    Article  Google Scholar 

    24.
    Kindlmann P, Schödelbauerová I, Dixon AF.G. Inverse latitudinal gradients in species diversity. pp. 246–257 in Storch D. et al. (eds.) Scaling Biodiversity (Cambridge University Press, 2007).

    25.
    Boone, R. B. & Krohn, W. B. Relationship between avian range limits and plant transition zones in Maine. J. Biogeogr. 27, 471–482 (2000).
    Article  Google Scholar 

    26.
    Storch, D., Evans, K. L. & Gaston, K. J. The species-area-energy relationship in orchids. Ecol. Lett. 8, 487–492. https://doi.org/10.15517/lank.v7i1-2.19504 (2005).
    Article  PubMed  Google Scholar 

    27.
    Valladares, F. et al. Global change and Mediterranean forests: current impacts and potential responses in Forests and Global Change (eds. Burslem, D. F. R. & Simonson, W. D.), 47–75 (Cambridge University Press, 2014).

    28.
    MacArthur, R. H. Patterns of Species Diversity. Geographical Ecology: Patterns in the Distributions of Species (Harper & Row, 1972).

    29.
    Whittaker, R. J. & Fernández-Palacios, J. M. Island biogeography: ecology, evolution, and conservation. Oxford University Press (2007).

    30.
    Sólymos, P. & Lele, S. R. Global pattern and local variation in species-area relationships. Glob. Ecol. Biogeogr. 21, 109–120. https://doi.org/10.1111/j.1466-8238.2011.00655.x (2012).
    Article  Google Scholar 

    31.
    Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: patterns, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309. https://doi.org/10.1146/annurev.ecolsys.34.012103.144032 (2003).
    Article  Google Scholar 

    32.
    Prevedello, J., Gotelli, N. J. & Metzger, J. A stochastic model for landscape patterns of biodiversity. Ecol. Monogr. 86, 462–479. https://doi.org/10.1002/ecm.1223 (2016).
    Article  Google Scholar 

    33.
    Blondel, J., Aronson, J., Bodiou, J. Y. & Boeuf, G. The Mediteranean region. Biological diversity in space and time (Oxford University Press, 2010).

    34.
    Vigne, J. D. The large “true” Mediterranean islands as a model for the Holocene human impact on the European vertebrate fauna? Recent data and new reflections. The Holocene history of the European vertebrate fauna. Modern aspects of research, 295–322 (1999).

    35.
    Harding, A.F., Palutikof, J. & Holt, T. The climate system. pp. 69–88 In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

    36.
    Zdruli, P. Desertification in the Mediterranean Region. Mediterranean year book 2011 (European Institute of the Mediterranean, 2012).

    37.
    Bilton, D. T. et al. Mediterranean Europe as an area of endemism for small mammals rather than a source for northwards postglacial colonization. Proc. Royal Soc. B 265(1402), 1219–1226 (1998).
    CAS  Article  Google Scholar 

    38.
    Hewitt, G. M. Mediterranean peninsulas: The evolution of hotspots. In Biodiversity hotspots (pp. 123–147) (Springer, Berlin, Heidelberg, 2011).

    39.
    Bilgin, R. Back to the suture: the distribution of intraspecific genetic diversity in and around Anatolia. Int. J. Mol. Sci. 12, 4080–4103. https://doi.org/10.3390/ijms12064080 (2011).
    Article  PubMed  PubMed Central  Google Scholar 

    40.
    Vigne, J. D. The origins of mammals on the Mediterranean islands as an indicator of early voyaging. Euras. Prehistory 10(1–2), 45–56 (2014).
    Google Scholar 

    41.
    Masseti, M. Mammals of the Mediterranean islands: Homogenisation and the loss of biodiversity. Mammalia 73, 169–202. https://doi.org/10.1515/MAMM.2009.029 (2009).
    Article  Google Scholar 

    42.
    Angelici, F. M., Laurenti, A. & Nappi, A. A. checklist of the mammals of small Italian islands. Hystrix 20, 3–27. https://doi.org/10.4404/hystrix-20.1-4429 (2009).
    Article  Google Scholar 

    43.
    Cunningham, P. L. & Aspinall, S. The diet of Little Owl Athene noctua in the UAE, with notes on Barn Owl Tyto alba and Desert Eagle Owl Bubo (b.) ascalaphus. Tribulus 11, 13–15 (2001).

    44.
    Taylor, I. R. How owls select their prey: A study of Barn owls Tyto alba and their small mammal prey. Ardea 97, 635–644. https://doi.org/10.5253/078.097.0433 (2009).
    Article  Google Scholar 

    45.
    Yom-Tov, Y. & Wool, D. Do the contents of barn owl pellets accurately represent the proportion of prey species in the field?. Condor 99, 972–976. https://doi.org/10.2307/1370149 (1997).
    Article  Google Scholar 

    46.
    Dodson, P. & Wexlar, D. Taphonomic investigations of owl pellets. Paleobiology 5, 275–284 (1979).
    Article  Google Scholar 

    47.
    Heisler, L., Somers, C. & Poulin, R. Owl pellets: A more effective alternative to conventional trapping for broad-scale studies of small mammal communities. Methods Ecol. Evol. 7, 96–103. https://doi.org/10.1111/2041-210X.12454 (2015).
    Article  Google Scholar 

    48.
    Torre, I., Arrizabalaga, A. & Flaquer, C. Three methods for assessing richness and composition of small mammal communities. J. Mammal. 85, 524–530. https://doi.org/10.1644/BJK-112 (2004).
    Article  Google Scholar 

    49.
    Yalden, D. W. & Morris, P. A. The analysis of owl pellet (Occasional publications)(The Mammal Society, 1990).

    50.
    Williams, D. F. & Braun, S. E. Comparison of pitfall and conventional traps for sampling small mammal populations. J. Wildl. Manage. 47, 841–845 (1983).
    Article  Google Scholar 

    51.
    Glennon, M. J., Porter, W. F. & Demers, C. L. An alternative field technique for estimating diversity of small-mammal populations. J. Mammal. 83, 734–742. https://doi.org/10.1644/1545-1542 (2002).
    Article  Google Scholar 

    52.
    Morris, P. A., Burgis, M. J., Morris, P. A. & Holloway, R. A method for estimating total body weight of avian prey items in the diet of owls. J. Zool. 210, 642–644 (1986).
    Article  Google Scholar 

    53.
    Vukićević Radić, O., Jovanović, T. B., Matić, R. & Katarinovski, D. Age structure of yellow-necked mouse (Apodemus flavicollis Melchior 1834) in two samples obtained from live traps and owl pellets. Arch. Biol. Sci. 57, 53–56 (2005).

    54.
    Coda, J., Gomez, D., Steinmann, A. R. & Priotto, J. Small mammals in farmlands of Argentina: Responses to organic and conventional farming. Agric. Ecosyst. Environ. 211, 17–23 (2015).
    Article  Google Scholar 

    55.
    Andrade, A., de Menezes, J. F. S. & Monjeau, A. Are owl pellets good estimators of prey abundance?. J. King Saud Univ. Sci. 28, 239–244. https://doi.org/10.1016/j.jksus.2015.10.007 (2016).
    Article  Google Scholar 

    56.
    Moysi, M., Christou, M., Goutner, V., Kassinis, N. & Iezekiel, S. Spatial and temporal patterns in the diet of barn owl (Tyto alba) in Cyprus. J. Biol. Res-Thessalon. 25(1), 9 (2018).
    Article  Google Scholar 

    57.
    Romano, A., Séchaud, R. & Roulin, A. Global biogeographical patterns in the diet of a cosmopolitan predator. J. Biogeogr. 47, 1467–1481. https://doi.org/10.1111/jbi.13829 (2020).
    Article  Google Scholar 

    58.
    Baquero, R. A. & Tellería, J. L. Species richness, rarity and endemicity of European mammals: A biogeographical approach. Biodivers. Conserv. 10(1), 29–44 (2001).
    Article  Google Scholar 

    59.
    Mitchell-Jones, A. J. et al. The Atlas of European Mammals (T & AD Poyser, 1999).

    60.
    Kross, S. M., Bourbour, R. P. & Martinico, B. L. Agricultural land use, arn owl diet, and vertebrate pest control implications. Agric. Ecosyst. Environ. 223, 167–174. https://doi.org/10.1016/j.agee.2016.03.002 (2016).
    Article  Google Scholar 

    61.
    Krishnapriya, T. & Ramakrishnan, U. Higher speciation and lower extinction rates influence mammal diversity gradients in Asia. BMC Evol. Biol. 15, 11. https://doi.org/10.1186/s12862-015-0289-1 (2015).
    Article  Google Scholar 

    62.
    Kouki, J., Niemela, P. & Viitasaari, M. Reversed latitudinal gradient in species richness of sawflies (Hymenoptera, Symphyta). Ann. Zool. Fenn. 31, 83–88 (1994).
    Google Scholar 

    63.
    Rabenold, K. N. A reversed latitudinal diversity gradient in avian communities of eastern deciduous forests. Am. Nat. 114, 275–286. https://doi.org/10.1086/283474 (1979).
    Article  Google Scholar 

    64.
    Ruffino, L. & Vidal, E. Early colonization of Mediterranean islands by Rattus rattus: A review of zooarcheological data. Biol. Invasions 12(8), 2389–2394 (2010).
    Article  Google Scholar 

    65.
    Thomes, J. B. Land degradation. pp. 563–581. In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

    66.
    Allen, H. D. Vegetation and ecosystem dynamics. pp. 203–227. In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

    67.
    Dov Por, F. & Dimentman, C. Mare Nostrum. Neogene and anthropic natural history of the Mediterranean basin, with emphasis on the Levant (Pensoft, Sofia-Moscow, 2006).

    68.
    Zohary, D., Hopi, M. & Weiss, E. Domestication of Plants in the Old World 4th edn. (Oxford University Press, 2012).
    Google Scholar 

    69.
    Roulin, A. Spatial variation in the decline of European birds as shown by the Barn Owl Tyto alba diet. Bird Study 62, 271–275. https://doi.org/10.1080/00063657.2015.1012043 (2015).
    Article  Google Scholar 

    70.
    Pezzo, F. & Morimando, F. Food habits of the barn owl, Tyto alba, in a mediterranean rural area: Comparison with the diet of two sympatric carnivores. Boll. Zool. 62, 369–373. https://doi.org/10.1080/11250009509356091 (1995).
    Article  Google Scholar 

    71.
    Soranzo, N., Alia, R., Provan, J. & Powell, W. Patterns of variation at a mitochondrial sequence-tagged-site locus provides new insights into the postglacial history of European Pinus sylvestris populations. Mol. Ecol. 9, 1205–1211. https://doi.org/10.1046/j.1365-294x.2000.00994.x (2000).
    CAS  Article  PubMed  Google Scholar 

    72.
    van Andel, T. H. The climate and landscape of the middle part of the Weichselian Glaciation in Europe: The stage 3 project. Q. Res. 57, 2–8. https://doi.org/10.1006/qres.2001.2294 (2002).
    ADS  Article  Google Scholar 

    73.
    Johnston, D. W. & Hill, J. M. Prey selection of Common Barn-owls on islands and mainland sites. J. Raptor. Res. 21(1), 3–7 (1987).
    Google Scholar 

    74.
    Sommer, R., Zoller, H., Kock, D., Böhme, W. & Griesau, A. Feeding of the barn owl, Tyto alba with first record of the European free-tailed bat, Tadarida teniotis on the island of Ibiza (Spain, Balearics). Fol. Zool. 54, 364–370 (2005).
    Google Scholar 

    75.
    Kryštufek, B., Reed, J. Pattern and process in Balkan biodiversity – an overview in A quantitative assesment of Balkan mammal diversity (eds. Griffiths, H. I., Kryštufek, B. & Reed, J. M.) 79–108 (Kluwer Academic, 2004).

    76.
    Ricklefs, R. E. & Lovette, I. J. The roles of island area per se and habitat diversity in the species-area relationships of four Lesser Antillean faunal groups. J. Anim. Ecol. 68, 1142–1160 (1999).
    Article  Google Scholar 

    77.
    Heaney, L. R. Mammalian species richness on islands on the Sunda Shelf Southeast Asia. Oecologia 61, 11–17 (1984).
    ADS  Article  Google Scholar 

    78.
    Carvajal, A. & Adler, G. H. Biogeography of mammals on tropical Pacific islands. J. Biogeogr. 32, 1561–1569. https://doi.org/10.1111/j.1365-2699.2005.01302.x (2005).
    Article  Google Scholar 

    79.
    Millien-Parra, V. & Jaeger, J. J. Island biogeography of the Japanese terrestrial mammal assemblages: An example of a relict fauna. J. Biogeogr. 26, 959–972. https://doi.org/10.1046/j.1365-2699.1999.00346.x (1999).
    Article  Google Scholar 

    80.
    Amori, G., Rizzo Pinna, V., Sammuri, G. & Luiselli, L. Diversity of small mammal communities of the tuscan archipelago: Testing the effects of island size, distance from mainland and human density. Fol. Zool. 64, 161–166. https://doi.org/10.25225/fozo.v64.i2.a9.2015 (2015).

    81.
    Audoin-Rouzeau, F. & La Vigne, J. D. colonisation de l’Europe par le rat noir (Rattus rattus). Rev. de Paléobiologie 13, 125–145. https://doi.org/10.1134/S1062359011020130 (1994).
    Article  Google Scholar 

    82.
    Towns, D. R., Atkinson, I. A. E. & Daugherty, Ch. H. Have the harmful effects of introduced rats on islands been exaggerated?. Biol. Invasions 8, 863–891. https://doi.org/10.1007/s10530-005-0421-z (2006).
    Article  Google Scholar 

    83.
    Martin, J. L., Thibault, J. C. & Bretagnolle, V. Black rats, island characteristics, and colonial nesting birds in the Mediterranean: Consequences of an ancient introduction. Conserv. Biol. 14, 1452–1466. https://doi.org/10.1046/j.1523-1739.2000.99190.x (2000).
    Article  Google Scholar 

    84.
    Landová, E., Horáček, I. & Frynta, D. Have black rats evolved a culturally-transmitted technique of pinecone opening independently in Cyprus and Israel?. Isr. J. Ecol. Evol. 52(2), 151–158 (2006).
    Article  Google Scholar 

    85.
    Sarà, M. & Morand, S. Island incidence and mainland population density: Mammals from Mediterranean islands. Divers. Distrib. 8, 1–9 (2002).
    Article  Google Scholar 

    86.
    Libois, M. R., Fons, R., Saint Girons, M. C. Le régime alimentaire de la chouette effraie Tyto alba, dans les Pyrénées-orientales. Etude des variations ecogéographiques. Rev. Ecol.-Terre Vie 37, 187–217 (1983).

    87.
    Di Russo, C. Dati sui micromammiferi da borre di barbacianni, Tyto alba, di un Sito della Sardegna Centro-orientale. Hystrix 2, 57–62. https://doi.org/10.4404/hystrix-2.1-3885 (1987).
    Article  Google Scholar 

    88.
    Guerra, C., García, D. & Alcover, J. A. Unusual foraging patterns of the barn owl, Tyto alba (Strigiformes: Tytonidae), on small islets from the Pityusic archipelago (Western Mediterranean Sea). Fol. Zool. 63, 180–187. https://doi.org/10.25225/fozo.v63.i3.a5.2014 (2014).

    89.
    Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linn. Soc. Lond. 28, 65–82. https://doi.org/10.1111/j.1095-8312.1986.tb01749.x (1986).
    Article  Google Scholar 

    90.
    Kutiel, P., Peled, Y. & Geffen, E. The effect of removing shrub cover on annual plants and small mammals in a coastal sand dune ecosystem. Biol. Conserv. 94, 235–242. https://doi.org/10.1016/S0006-3207(99)00172-X (2000).
    Article  Google Scholar 

    91.
    Tores, M., Motro, Y., Motro, U. & Yom-Tov, Y. The barn owl-a selective opportunist predator. Israel J. Zool. 51, 349–360. https://doi.org/10.1560/7862-9E5G-RQJJ-15BE (2005).
    Article  Google Scholar 

    92.
    Obuch, J. & Benda, P. Food of the Barn Owl (Tyto alba) in the Eastern Mediterranean. Slovak Raptor J. 3, 41–50. https://doi.org/10.2478/v10262-012-0032-4 (2009).
    Article  Google Scholar 

    93.
    Anděra, M. & Horáček, I. Determining our mammals (Sobotáles, 2005).

    94.
    Dor, M. Observations sur les Micromammiferes trouves dans les Pelotes de la Chouette effraye (Tyto alba) en Palestine. Mammalia 11, 50–54 (1947).
    Article  Google Scholar 

    95.
    De Pablo, F. Alimentación de la Lechuza Común (Tyto alba) en Menorca. Bolleti Soc. Hist. Nat. Balear. 43, 15–26 (2000).
    Google Scholar 

    96.
    Rihane, A. Contribution to the study of the diet of Barn Owl Tyto alba in the semi-arid plains of Atlantic Morocco. Alauda 71, 363–369 (2003).
    Google Scholar 

    97.
    Kennedy, C. M., J. R. Oakleaf, D. M. Theobald, Baruch-Mordo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biol. 25(3), 811–826. https://doi.org/10.1111/gcb.14549 (2019).

    98.
    Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Global Human Modification of Terrestrial Systems. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/edbc-3z60. Accessed DAY MONTH YEAR (2020).

    99.
    Shannon, C. & Weaver, W. The Mathematical Theory of Communication (The University of Illinois Press, 1964).

    100.
    R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Found Stat Comp (2011).

    101.
    Anderson, D. R. & Burnham, K. P. Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manag. 66, 912–918 (2002).
    Article  Google Scholar 

    102.
    Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189. https://doi.org/10.1111/j.1365-2656.2006.01141.x (2006).
    Article  PubMed  Google Scholar 

    103.
    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35. https://doi.org/10.1007/s00265-010-1039-4 (2011).
    Article  Google Scholar 

    104.
    ter Braak, C. & Šmilauer, P. Canoco reference manual and user’s quide: software for ordination, version 5.0 (Microcomputer Power, 2012).

    105.
    StatSoft Inc. Statistica (data analysis software system), version 12. http://www.statsoft.com (2013). More

  • in

    The interplay of labile organic carbon, enzyme activities and microbial communities of two forest soils across seasons

    1.
    Dixon, R. K. et al. Carbon pools and flux of global forest ecosystems. Science 263, 185–190 (1994).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Siles, J. A., Cajthaml, T., Filipová, A., Minerbi, S. & Margesin, R. Altitudinal, seasonal and interannual shifts in microbial communities and chemical composition of soil organic matter in Alpine forest soils. Soil Biol. Biochem. 112, 1–13 (2017).
    CAS  Article  Google Scholar 

    3.
    Sedjo, R. A. The carbon cycle and global forest ecosystem. Water Air Soil Pollut. 70, 295–307 (1993).
    ADS  CAS  Article  Google Scholar 

    4.
    Flato, G. & Marotzke, J. Evaluation of climate models. In Climate Change 2013: The physical science basis. contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change (2013).

    5.
    Zhao, W. et al. Effect of different vegetation cover on the vertical distribution of soil organic and inorganic carbon in the Zhifanggou Watershed on the loess plateau. CATENA 139, 191–198 (2016).
    CAS  Article  Google Scholar 

    6.
    Lal, R. Soil carbon sequestration to mitigate climate change. Geoderma 123(1–2), 1–22 (2004).
    ADS  CAS  Article  Google Scholar 

    7.
    Yang, Y. & Tilman, D. Soil and root carbon storage is key to climate benefits of bioenergy crops. Biofuel Res. J. 7(2), 1143–1148 (2020).
    Article  Google Scholar 

    8.
    Rovira, P. & Vallejo, V. R. Labile and recalcitrant pools of carbon and nitrogen in organic matter decomposing at different depths in soil: An acid hydrolysis approach. Geoderma 107, 109–141 (2002).
    ADS  CAS  Article  Google Scholar 

    9.
    Zou, X., Ruan, H., Fu, Y., Yang, X. & Sha, L. Estimating soil labile organic carbon and potential turnover rates using a sequential fumigation-incubation procedure. Soil Biol. Biochem. 37, 1923–1928 (2005).
    CAS  Article  Google Scholar 

    10.
    Liang, B. C. et al. Management-induced change in labile soil organic matter under continuous corn in eastern Canadian soils. Biol. Fertil. Soils 26, 88–94 (1997).
    Article  Google Scholar 

    11.
    Xu, G. et al. Labile, recalcitrant, microbial carbon and nitrogen and the microbial community composition at two Abies faxoniana forest elevations under elevated temperatures. Soil Biol. Biochem. 91, 1–13 (2015).
    CAS  Article  Google Scholar 

    12.
    Wolters, V. Invertebrate control of soil organic matter stability. Biol. Fertil. Soils 31, 1–19 (2000).
    MathSciNet  CAS  Article  Google Scholar 

    13.
    Marschner, P., Kandelerb, E. & Marschnerc, B. Structure and function of the soil microbial community in a long-term fertilizer experiment. Soil Biol. Biochem. 35, 453–461 (2003).
    CAS  Article  Google Scholar 

    14.
    Xiao, Y., Huang, Z. & Lu, X. Changes of soil labile organic carbon fractions and their relation to soil microbial characteristics in four typical wetlands of Sanjiang Plain, Northeast China. Ecol. Eng. 82, 381–389 (2015).
    Article  Google Scholar 

    15.
    Burke, D. J., Weintraub, M. N., Hewins, C. R. & Kalisz, S. Relationship between soil enzyme activities, nutrient cycling and soil fungal communities in a northern hardwood forest. Soil Biol. Biochem. 43, 795–803 (2011).
    CAS  Article  Google Scholar 

    16.
    Ljungdahl, L. G. & Eriksson, K. E. Ecology of microbial cellulose degradation. Adv. Microb. Ecol. 8, 237–299 (1985).
    CAS  Article  Google Scholar 

    17.
    Sinsabaugh, R. L., Hill, B. H. & Follstad-Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 468, 122–122 (2010).
    ADS  CAS  Article  Google Scholar 

    18.
    Bowles, T. M., Acosta-Martínez, V., Calderón, F. & Jackson, L. E. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262 (2014).
    CAS  Article  Google Scholar 

    19.
    Chen, X. et al. Soil labile organic carbon and carbon-cycle enzyme activities under different thinning intensities in Chinese fir plantations. Appl. Soil Ecol. 107, 162–169 (2016).
    Article  Google Scholar 

    20.
    Qi, R. et al. Temperature effects on soil organic carbon, soil labile organic carbon fractions, and soil enzyme activities under long-term fertilization regimes. Appl. Soil Ecol. 102, 36–45 (2016).
    Article  Google Scholar 

    21.
    Rasche, F. et al. Seasonality and resource availability control bacterial and archaeal communities in soils of a temperate beech forest. ISME J 5, 389–402 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Piao, H., Hong, Y. & Yuan, Z. Seasonal changes of microbial biomass carbon related to climatic factors in soils from karst areas of southwest China. Biol. Fertil. Soils 30, 294–297 (2000).
    CAS  Article  Google Scholar 

    23.
    Zhou, G., Xu, J. & Jiang, P. Effect of management practices on seasonal dynamics of organic carbon in soils under bamboo plantations. Pedosphere 16, 525–531 (2006).
    CAS  Article  Google Scholar 

    24.
    Thomas, G. W. Soil pH and soil acidity. Soil Sci. Soc. Am. J. 5, 475–490 (1996).
    Google Scholar 

    25.
    Walkley, A. An examination of methods for determining organic carbon and nitrogen in soils (with one text-figure). Indian. J. Agric. Sci. 25, 598–609 (1935).
    CAS  Article  Google Scholar 

    26.
    Jenkinson, D. S. & Powlson, D. S. The effects of biocidal treatments on metabolism in soil: A method for measuring soil biomass. Soil Biol. Biochem. 8, 209–213 (1976).
    CAS  Article  Google Scholar 

    27.
    Blair, G. J., Lefroy, R. & Lisle, L. Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems. Aust. J. Agric. Res. 46, 393–406 (1995).
    Article  Google Scholar 

    28.
    Mcgill, W. B., Cannon, K. R., Robertson, J. A. & Cook, F. D. Dynamics of soil microbial biomass and water-soluble organic C in Breton L after 50 years of cropping to two rotations. Can. J. Soil Sci. 66, 1–19 (1986).
    Article  Google Scholar 

    29.
    Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).
    CAS  Article  Google Scholar 

    30.
    Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16s rrna gene sequencing on the illumina miseq platform. Microbiome 2, 1–7 (2014).
    Article  Google Scholar 

    31.
    Mukherjee, P. K. et al. Oral mycobiome analysis of HIV-infected patients: Identification of Pichia as an antagonist of opportunistic fungi. PLoS Pathog 10, e1003996 (2014).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    32.
    Masella, A. P., Bartram, A. K., Truszkowski, J. M. & Brown, D. G. Neufeld JD (2012) PANDAseq: Paired-end assembler for illumina sequences. BMC Bioinform. 13, 31 (2014).
    Article  CAS  Google Scholar 

    33.
    Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Kemp, P. F. & Aller, J. Y. Bacterial diversity in aquatic and other environments: What 16S rDNA libraries can tell us. FEMS Microbiol. Ecol. 47, 161–177 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Cole, J. R. et al. Ribosomal Database Project, data and tools for high throughput rRNA analysis. Nucleic Acids. Res. 42, 633–642 (2014).
    Article  CAS  Google Scholar 

    36.
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. App. Environ. Microbiol. 73, 5261–5267 (2007).
    CAS  Article  Google Scholar 

    37.
    Haynes, R. J. Labile organic matter fractions as central components of the quality of agricultural soils: An pverview. Adv. Agron. 85, 221–268 (2005).
    CAS  Article  Google Scholar 

    38.
    Wang, J., Song, C., Wang, X. & Song, Y. Changes in labile soil organic carbon fractions in wetland ecosystems along a latitudinal gradient in northeast china. CATENA 96, 83–89 (2012).
    CAS  Article  Google Scholar 

    39.
    Ma, W., Li, G., Wu, J., Xu, G. & Wu, J. Response of soil labile organic carbon fractions and carbon-cycle enzyme activities to vegetation degradation in a wet meadow on the Qinghai-Tibet Plateau. Geoderma 377, 114565 (2020).
    ADS  CAS  Article  Google Scholar 

    40.
    Smolander, A. & Kitunen, V. Soil microbial activities and characteristics of dissolved organic C and N in relation to tree species. Soil Biol. Biochem. 34, 651–660 (2002).
    CAS  Article  Google Scholar 

    41.
    Wang, Q. & Wang, S. Soil organic matter under different forest types in Southern China. Geoderma 142, 349–356 (2007).
    ADS  CAS  Article  Google Scholar 

    42.
    Kalbitz, K., Solinger, S., Park, J. H., Michalzik, B. & Matzner, E. Controls on the dynamics of dissolved organic matter in soils: A review. Soil Sci. 165, 277–304 (2000).
    ADS  CAS  Article  Google Scholar 

    43.
    Quideau, S. A. et al. Vegetation control on soil organic matter dynamics. Org. Geochem. 32, 247–252 (2001).
    CAS  Article  Google Scholar 

    44.
    Liu, C. et al. Standing fine root mass and production in four Chinese subtropical forests along a succession and species diversity gradient. Plant Soil 376, 445–459 (2014).
    CAS  Article  Google Scholar 

    45.
    Jiang, P., Xu, Q., Xu, Z. & Cao, Z. Seasonal changes in soil labile organic carbon pools within a Phyllostachys praecox stand under high rate fertilization and winter mulch in subtropical China. Forest Ecol. Manag. 236, 30–36 (2006).
    Article  Google Scholar 

    46.
    Hu, Y. et al. Climate change affects soil labile organic carbon fractions in a Tibetan alpine meadow. J. Soil Sediment 17, 326–339 (2016).
    Article  CAS  Google Scholar 

    47.
    Liu, G. et al. Seasonal changes in labile organic matter as a function of environmental factors in a relict permafrost region on the Qinghai-Tibetan Plateau. CATENA 180, 194–202 (2019).
    CAS  Article  Google Scholar 

    48.
    Mcdowell, W. H., Currie, W. S., Aber, J. D. & Yano, Y. Effects of chronic nitrogen amendments on production of dissolved organic carbon and nitrogen in forest soils. Water Air Soil Pollut. 105, 175–182 (1998).
    ADS  CAS  Article  Google Scholar 

    49.
    Kurka, A. M., Starr, M., Heikinheimo, M. & Salkinojasalonen, M. Decomposition of cellulose strips in relation to climate, litterfall nitrogen, phosphorus and C/N ratio in natural boreal forests. Plant Soil 219, 91–101 (2000).
    CAS  Article  Google Scholar 

    50.
    Waldrop, M. P. & Firestone, M. K. Response of microbial community composition and function to soil climate change. Microb. Ecol. 52, 716–724 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Uselman, S. M., Qualls, R. G. & Thomas, R. B. Effects of increased atmospheric CO2, temperature, and soil N availability on root exudation of dissolved organic carbon by a N-fixing tree. Plant Soil 222, 191–202 (2000).
    CAS  Article  Google Scholar 

    52.
    Ziegler, S. E., Billings, S. A., Lane, C. S., Li, J. & Fogel, M. L. Warming alters routing of labile and slower-turnover carbon through distinct microbial groups in boreal forest organic soils. Soil Biol. Biochem. 60, 23–32 (2013).
    CAS  Article  Google Scholar 

    53.
    Mondal, I. K. et al. Seasonal variation of soil enzymes in areas of fluoride stress in Birbhum District, West Bengal, India. J. Taibah. Univ. Sci. 9, 133–142 (2015).
    Article  Google Scholar 

    54.
    Wang, C., Lü, Y., Wang, L., Liu, X. & Tian, X. Insights into seasonal variation of litter decomposition and related soil degradative enzyme activities in subtropical forest in China. J. Forest Res. 24, 683–689 (2013).
    CAS  Article  Google Scholar 

    55.
    Baldrian, P., Merhautová, V., Petránková, M., Cajthaml, T. & Šnajdr, J. Distribution of microbial biomass and activity of extracellular enzymes in a hardwood forest soil reflect soil moisture content. Appl. Soil Ecol. 46, 177–182 (2010).
    Article  Google Scholar 

    56.
    Song, Y. et al. Changes in labile organic carbon fractions and soil enzyme activities after marshland reclamation and restoration in the Sanjiang Plain in northeast China. Environ. Manag. 50, 418–426 (2012).
    ADS  Article  Google Scholar 

    57.
    Shi, W., Dell, E., Bowman, D. & Iyyemperumal, K. Soil enzyme activities and organic matter composition in a turfgrass chronosequence. Plant Soil 288, 285–296 (2006).
    CAS  Article  Google Scholar 

    58.
    Salazar, S. et al. Correlation among soil enzyme activities under different forest system management practices. Ecol. Eng. 37, 1123–1131 (2011).
    Article  Google Scholar 

    59.
    Waldrop, M. P. & Zak, D. R. Response of oxidative enzyme activities to nitrogen deposition affects soil concentrations of dissolved organic carbon. Ecosystems 9, 921–933 (2006).
    CAS  Article  Google Scholar 

    60.
    Stursova, M., Zifcakova, L., Leigh, M. B., Burgess, R. & Baldrian, P. Cellulose utilization in forest litter and soil: Identification of bacterial and fungal decomposers. FEMS Microbiol. Ecol. 80, 735–746 (2012).
    CAS  PubMed  Article  Google Scholar 

    61.
    Pankratov, T. A., Ivanova, A. O., Dedysh, S. N. & Liesack, W. Bacterial populations and environmental factors controlling cellulose degradation in an acidic Sphagnum peat. Environ. Microbiol. 13, 1800–1814 (2011).
    CAS  PubMed  Article  Google Scholar 

    62.
    Eichorst, S. A., Kuske, C. R. & Schmidt, T. M. Influence of plant polymers on the distribution and cultivation of bacteria in the phylum Acidobacteria. Appl. Environ. Microbiol. 77, 586–596 (2011).
    CAS  PubMed  Article  Google Scholar 

    63.
    Ward, N. L., Challacombe, J. F., Janssen, P. H., Henrissat, B. & Coutinho, P. M. Three genomes from the phylum Acidobacteria provide insight into the lifestyles of these microorganisms in soils. App. Environ. Microbiol. 75, 2046–2056 (2009).
    CAS  Article  Google Scholar 

    64.
    Bastida, F., Hernández, T., Albaladejo, J. & García, C. Phylogenetic and functional changes in the microbial community of long-term restored soils under semiarid climate. Soil Biol. Biochem. 65, 12–21 (2013).
    CAS  Article  Google Scholar 

    65.
    Hannula, S. E., Boschker, H. T. S., Boer, W. D. & Veen, J. A. V. 13C pulse-labeling assessment of the community structure of active fungi in the rhizosphere of a genetically starch-modified potato (Solanum tuberosum) cultivar and its parental isoline. New Phytol. 194, 784–799 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    66.
    Edwards, I. P., Zak, D. R., Kellner, H., Eisenlord, S. D. & Pregitzer, K. S. Simulated atmospheric N deposition alters fungal community composition and suppresses ligninolytic gene expression in a northern hardwood forest. PLoS ONE 6, e20421 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: A question of microbial competition?. Soil Biol. Biochem. 35, 837–843 (2003).
    CAS  Article  Google Scholar  More

  • in

    Iron limitation by transferrin promotes simultaneous cheating of pyoverdine and exoprotease in Pseudomonas aeruginosa

    1.
    Smith P, Schuster M. Public goods and cheating in microbes. Curr Biol. 2019;29:R442–7.
    2.
    Harrison F, McNally A, Da Silva AC, Heeb S, Diggle SP. Optimised chronic infection models demonstrate that siderophore ‘cheating’ in Pseudomonas aeruginosa is context specific. ISME J. 2017;11:2492–509.

    3.
    Kümmerli R, Santorelli LA, Granato ET, Dumas Z, Dobay A, Griffin AS, et al. Co-evolutionary dynamics between public good producers and cheats in the bacterium Pseudomonas aeruginosa. J Evol Biol. 2015;28:2264–74.

    4.
    Stilwell P, Lowe C, Buckling A. The effect of cheats on siderophore diversity in Pseudomonas aeruginosa. J Evol Biol. 2018;31:1330–9.

    5.
    Butaite E, Baumgartner M, Wyder S, Kümmerli R. Siderophore cheating and cheating resistance shape competition for iron in soil and freshwater Pseudomonas communities. Nat Commun. 2017;8:414.

    6.
    Jin Z, Li J, Ni L, Zhang R, Xia A, Jin F. Conditional privatization of a public siderophore enables Pseudomonas aeruginosa to resist cheater invasion. Nat Commun. 2018;9:1383.

    7.
    Leinweber A, Fredrik Inglis R, Kümmerli R. Cheating fosters species co-existence in well-mixed bacterial communities. ISME J. 2017;11:1179–88.

    8.
    Özkaya Ö, Balbontín R, Gordo I, Xavier KB. Cheating on cheaters stabilizes cooperation in Pseudomonas aeruginosa. Curr Biol. 2018;28:2070–80.

    9.
    O’Brien S, Kümmerli R, Paterson S, Winstanley C, Brockhurst MA. Transposable temperate phages promote the evolution of divergent social strategies in Pseudomonas aeruginosa populations. Proc R Soc B Biol Sci. 2019;286:20191794.

    10.
    Wolz C, Hohloch K, Ocaktan A, Poole K, Evans RW, Rochel N, et al. Iron release from transferrin by pyoverdin and elastase from Pseudomonas aeruginosa. Infect Immun. 1994;62:4021–7.

    11.
    Kim SJ, Park RY, Kang SM, Choi MH, Kim CM, Shin SH. Pseudomonas aeruginosa alkaline protease can facilitate siderophore-mediated iron-uptake via the proteolytic cleavage of transferrins. Biol Pharm Bull. 2006;29:2295–300.

    12.
    Sandoz KM, Mitzimberg SM, Schuster M. Social cheating in Pseudomonas aeruginosa quorum sensing. Proc Natl Acad Sci USA. 2007;104:15876–81.
    CAS  Article  Google Scholar 

    13.
    Diggle SP, Griffin AS, Campbell GS, West SA. Cooperation and conflict in quorum-sensing bacterial populations. Nature. 2007;450:411–4.
    CAS  Article  Google Scholar 

    14.
    Dandekar AA, Chugani S, Greenberg EP. Bacterial quorum sensing and metabolic incentives to cooperate. Science. 2012;338:264–6.
    CAS  Article  Google Scholar 

    15.
    Loarca D, Díaz D, Quezada H, Guzmán-Ortiz AL, Rebollar-Ruiz A, Presas AMF, et al. Seeding public goods is essential for maintaining cooperation in Pseudomonas aeruginosa. Front Microbiol. 2019;10:1–8.
    Article  Google Scholar 

    16.
    García-Contreras R, Loarca D, Pérez-González C, Jiménez-Cortés JG, Gonzalez-Valdez A, Soberón-Chávez G. Rhamnolipids stabilize quorum sensing mediated cooperation in Pseudomonas aeruginosa. FEMS Microbiol Lett. 2020;367:1–5.

    17.
    García-Contreras R, Lira-Silva E, Jasso-Chávez R, Hernández-González IL, Maeda T, Hashimoto T, et al. Isolation and characterization of gallium resistant Pseudomonas aeruginosa mutants. Int J Med Microbiol. 2013;303:574–82.

    18.
    Castañeda-Tamez P, Ramírez-Peris J, Pérez-Velázquez J, Kuttler C, Jalalimanesh A, Saucedo-Mora M, et al. Pyocyanin restricts social cheating in Pseudomonas aeruginosa. Front Microbiol. 2018;9:1–10.
    Article  Google Scholar 

    19.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    20.
    Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013;00:1–3.

    21.
    Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing — Free bayes — Variant Calling — Longranger. arXiv Prepr arXiv12073907 2012.

    22.
    Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly. 2012;6:80–92.

    23.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    24.
    Quinlan AR, Hall IM BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.

    25.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.

    26.
    Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA. Artemis: An integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics. 2012;28:464–9.

    27.
    Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, Smith JA, et al. Current protocols in molecular biology: preface. Curr Protoc Mol Biol. 2010;1:178–89.

    28.
    King EO, Ward MK, Raney DE. Two simple media for the demonstration of pyocyanin and fluorescin. J Lab Clin Med. 1954;44:301–7.

    29.
    López-Jácome LE, Garza-Ramos G, Hernández-Durán M, Franco-Cendejas R, Loarca D, Romero-Martínez D, et al. AiiM lactonase strongly reduces quorum sensing controlled virulence factors in clinical strains of Pseudomonas aeruginosa isolated from burned patients. Front Microbiol. 2019;10:1–11.
    Article  Google Scholar 

    30.
    Sandoz KM, Mitzimberg SM, Schuster M. Social cheating in Pseudomonas aeruginosa quorum sensing. Proc Natl Acad Sci USA. 2007;104:15876–81.

    31.
    D’Onofrio A, Crawford JM, Stewart EJ, Witt K, Gavrish E, Epstein S, et al. Siderophores from neighboring organisms promote the growth of uncultured bacteria. Chem Biol. 2010;17:254–64.

    32.
    Wang Y, Gao L, Rao X, Wang J, Yu H, Jiang J, et al. Characterization of lasR-deficient clinical isolates of Pseudomonas aeruginosa. Sci Rep. 2018;8:13344.

    33.
    Wilder CN, Allada G, Schuster M. Instantaneous within-patient diversity of Pseudomonas aeruginosa quorum-sensing populations from cystic fibrosis lung infections. Infect Immun. 2009;77:5631–9.
    CAS  Article  Google Scholar 

    34.
    Brown SP, West SA, Diggle SP, Griffin AS. Social evolution in micro-organisms and a Trojan horse approach to medical intervention strategies. Philos Trans R Soc B Biol Sci. 2009;364:3157–68.

    35.
    Rumbaugh KP, Diggle SP, Watters CM, Ross-Gillespie A, Griffin AS, West SA. Quorum sensing and the social evolution of bacterial virulence. Curr Biol. 2009;19:341–5.

    36.
    Bonchi C, Frangipani E, Imperi F, Visca P. Pyoverdine and proteases affect the response of Pseudomonas aeruginosa to gallium in human serum. Antimicrob Agents Chemother. 2015;59:5641–6.

    37.
    Sathe S, Mathew A, Agnoli K, Eberl L, Kümmerli R. Genetic architecture constrains exploitation of siderophore cooperation in the bacterium Burkholderia cenocepacia. Evol Lett. 2019;3:610–22.

    38.
    Liberati NT, Urbach JM, Miyata S, Lee DG, Drenkard E, Wu G, et al. An ordered, nonredundant library of Pseudomonas aeruginosa strain PA14 transposon insertion mutants. Proc Natl Acad Sci USA. 2006;103:2833–8.

    39.
    Chandler CE, Horspool AM, Hill PJ, Wozniak DJ, Schertzer JW, Rasko DA, et al. Genomic and phenotypic diversity among ten laboratory isolates of Pseudomonas aeruginosa PAO1. J Bacteriol. 2019;201. More

  • in

    Expansion of the mangrove species Rhizophora mucronata in the Western Indian Ocean launched contrasting genetic patterns

    1.
    Bryan-Brown, D. N., Brown, C. J., Hughes, J. M. & Connolly, R. M. Patterns and trends in marine population connectivity research. Mar. Ecol. Prog. Ser. 585, 243–256 (2017).
    ADS  Article  Google Scholar 
    2.
    Tomlinson, P. B. The Botany of Mangroves (Cambridge University Press, Cambridge, 2016).
    Google Scholar 

    3.
    Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669. https://doi.org/10.3390/rs10101669 (2018).
    ADS  Article  Google Scholar 

    4.
    Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosyst. Health Sustain. 2, 01211. https://doi.org/10.1002/ehs2.1211 (2016).
    Article  Google Scholar 

    5.
    Richards, D. R. & Friess, D. A. Rates of drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. USA 113, 344–349 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 

    6.
    Hermansen, T. D., Britton, D. R., Ayre, D. J. & Minchonton, T. E. Identifying the real pollinators? Exotic honeybees are the dominant flower visitors and only effective pollinators of Avicennia marina in Australian temperate mangroves. Estuar. Coast. 37, 621–635 (2014).
    Article  Google Scholar 

    7.
    Wee, A. K. S., Low, S. Y. & Webb, E. L. Pollen limitation affects reproductive outcome in the bird-pollinated mangrove Bruguiera gymnorrhiza (Lam.) in a highly urbanized environment. Aquat. Bot. 120, 240–243 (2015).
    Article  Google Scholar 

    8.
    Rabinowitz, D. Dispersal properties of mangrove propagules. Biotropica 10, 47–57 (1978).
    Article  Google Scholar 

    9.
    Drexler, J. Z. Maximum longevities of Rhizophora apiculataand R. mucronatapropagules. Pac. Sci. 55, 17–22 (2001).
    Article  Google Scholar 

    10.
    Nettel, A. & Dodd, R. S. Drifting propagules and receding swamps: genetic footprints of mangrove recolonization and dispersal along tropical coasts. Evolution 61, 958–971 (2007).
    CAS  PubMed  Article  Google Scholar 

    11.
    Takayama, K., Tamura, M., Tateshi, Y., Webb, E. L. & Kajita, T. Strong genetic structure over the American continents and transoceanic dispersal in red mangroves Rhizophora (Rhizophoraceae), revealed by broad-scale nuclear and chloroplast DNA analysis. Am. J. Bot. 100, 1191–1201 (2013).
    CAS  PubMed  Article  Google Scholar 

    12.
    Lo, E. Y., Duke, N. C. & Sun, M. Phylogeographic pattern of Rhizophora(Rhizophoraceae) reveals the importance of both vicariance and long-distance oceanic dispersal to modern mangrove distribution. BMC Evol. Biol. 14, 83. https://doi.org/10.1186/1471-2148-14-83 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    13.
    Van der Stocken, T. et al. A general framework for propagule dispersal in mangroves. Biol. Rev. 94, 1547–1575 (2019).
    PubMed  Article  Google Scholar 

    14.
    Thomas, L. et al. Isolation by resistance across a complex coral reef seascape. Proc. R. Soc. B Biol. Sci. 282, 20151217. https://doi.org/10.1098/rspb.2015.1217 (2015).
    CAS  Article  Google Scholar 

    15.
    Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Contrasting effects of historical sea level rise and contemporary ocean currents on regional gene flow of Rhizophora racemosain eastern Atlantic mangroves. PLoS ONE 11, e0150950. https://doi.org/10.1371/journal.pone.0150950 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    16.
    Wee, A. K. S. et al. Oceanic currents, not land masses, maintain the genetic structure of the mangrove Rhizophora mucronataLam. (Rhizophoraceae) in Southeast Asia. J. Biogeogr. 41, 954–964 (2014).
    Article  Google Scholar 

    17.
    Wee, A. K. S. et al. Genetic structures across a biogeographical barrier reflect dispersal potential of four Southeast Asian mangrove plant species. J. Biogeogr. 47, 1258–1271 (2020).
    Article  Google Scholar 

    18.
    Lessios, H. A. & Robertson, D. R. Crossing the impassable: genetic connections in 20 reef fishes across the eastern Pacific barrier. Proc. R. Soc. B: Biol. Sci. 273, 2201–2208 (2006).
    CAS  Article  Google Scholar 

    19.
    Ng, W. L., Chan, H. T. & Szmidt, A. E. Molecular identification of natural mangrove hybrids of Rhizophora in Peninsular Malaysia. Tree Genet. Genomes 9, 1151–1160 (2013).
    Article  Google Scholar 

    20.
    Guo, Z. et al. Genetic discontinuities in a dominant mangrove Rhizophora apiculata (Rhizophoraceae) in the Indo-Malaysian region. J. Biogeogr. 43, 1856–1868 (2016).
    Article  Google Scholar 

    21.
    Yan, Y.-B., Duke, N. & Sun, M. Comparative analysis of the pattern of population genetic diversity in three Indo-West Pacific Rhizophora mangrove species. Front. Plant Sci. 7, 1434. https://doi.org/10.3389/fpls.2016.01434 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    22.
    Triest, L., Hasan, S., Motro, P. R. & De Ryck, D. J. R. Geographical distance and large rivers shape genetic structure of Avicennia officinalis in the highly dynamic Sundarbans mangrove forest and Ganges Delta region. Estuar. Coast. 41, 908–920 (2018).
    Article  Google Scholar 

    23.
    Do, B. T. N., Koedam, N. & Triest, L. Avicennia marina maintains genetic structure whereas Rhizophora stylosa connects mangroves in a flooded, former inner sea (Vietnam). Estuar. Coast. Shelf Sci. 222, 195–204 (2019).
    ADS  Article  Google Scholar 

    24.
    He, Z. et al. Speciation with gene flow via cycles of isolation and migration: insights from multiple mangrove taxa. Natl. Sci. Rev. 6, 272–288 (2019).
    Google Scholar 

    25.
    Pil, M. W. et al. Postglacial north-south expansion of populations of Rhizophora mangle (Rhizophoraceae) along the Brazilian coast revealed by microsatellite analysis. Am. J. Bot. 98, 1031–1039 (2011).
    PubMed  Article  Google Scholar 

    26.
    Cerón-Souza, I. et al. Contrasting demographic history and gene flow patterns of two mangrove species on either side of the Central American Isthmus. Ecol. Evol. 5, 3486–3499 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    27.
    Sandoval-Castro, E. et al. Post-glacial expansion and population genetic divergence of mangrove species Avicennia germinans (L.) Stearn and Rhizophora mangle L. along the Mexican coast. PLoS ONE 9, 93358. https://doi.org/10.1371/journal.pone.0093358 (2014).
    ADS  CAS  Article  Google Scholar 

    28.
    Kennedy, J. P. et al. Contrasting genetic effects of red mangrove (Rhizophora mangleL.) range expansion along West and East Florida. J. Biogeogr. 44, 335–347 (2017).
    Article  Google Scholar 

    29.
    Francisco, P. M., Mori, G. M., Alves, F. A., Tambarussi, E. V. & de Souza, A. P. Population genetic structure, introgression, and hybridization in the genus Rhizophora along the Brazilian coast. Ecol. Evol. 8, 3491–3504. https://doi.org/10.1002/ece3.3900 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    30.
    Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Hidden founders? Strong bottlenecks and fine-scale genetic structure in mangrove populations of the Cameroon Estuary complex. Hydrobiologia 803, 189–207 (2017).
    Article  Google Scholar 

    31.
    Ngeve, M. N., Van der Stocken, T., Sierens, T., Koedam, N. & Triest, L. Bidirectional gene flow on a mangrove river landscape and between-catchment dispersal of Rhizophora racemosa (Rhizophoraceae). Hydrobiologia 790, 93–108 (2017).
    Article  Google Scholar 

    32.
    De Ryck, D. J. R. et al. Dispersal limitation of the mangrove Avicennia marina at its South African range limit in strong contrast to connectivity in its core East African region. Mar. Ecol. Prog. Ser. 545, 123–134 (2016).
    ADS  Article  CAS  Google Scholar 

    33.
    Duke, N. C., Lo, E. Y. Y. & Sun, M. Global distribution and genetic discontinuities of mangroves—emerging patterns in the evolution of Rhizophora. Trees Struct. Funct. 16, 65–79 (2002).
    Article  Google Scholar 

    34.
    Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves (Earthscan and James & James, 2010).

    35.
    Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017).
    Article  Google Scholar 

    36.
    Duke, N. et al. Rhizophora mucronata. The IUCN Red List of Threatened Species 2010: e.T178825A7618520.https://doi.org/10.2305/IUCN.UK.2010-2.RLTS.T178825A7618520.en (2010). Downloaded on 27 January 2020.

    37.
    Schouten, M. W., de Ruijter, W. P. M., van Leeuwen, P. J. & Ridderinkhof, H. Eddies and variability in the Mozambique Channel. Deep-Sea Res. II(50), 1987–2003 (2003).
    ADS  Google Scholar 

    38.
    Ternon, J. F., Roberts, M. J., Morris, T., Hancke, L. & Backeberg, B. In situ measured current structures of the eddy field in the Mozambique Channel. Deep-Sea Res. II 100, 10–26 (2014).
    Article  Google Scholar 

    39.
    Yokoyama, Y., Lambeck, K., De Deckker, P., Johnston, P. & Fifield, K. L. Timing of the Last Glacial Maximum from observed sea-level minima. Nature 406, 713–716 (2000).
    ADS  CAS  PubMed  Article  Google Scholar 

    40.
    Van der Stocken, T., Carroll, D., Menemenlis, D., Simard, M. & Koedam, N. Global-scale dispersal and connectivity in mangroves. Proc. Natl. Acad. Sci. USA 116, 915–922 (2019).
    PubMed  Article  CAS  Google Scholar 

    41.
    Schott, F. A., Shang-Ping, X. & McCreary, J. P. Jr. Indian Ocean circulation and climate variability. Rev. Geophys. 47, RG1002. https://doi.org/10.1029/2007RG000245 (2009).
    ADS  Article  Google Scholar 

    42.
    Hume, J. P., Martill, D. & Hing, R. A. Terrestrial vertebrate palaeontological review of Aldabra Atoll, Aldabra Group. Seychelles. PLoS ONE 13, e0192675. https://doi.org/10.1371/journal.pone.0192675 (2018).
    CAS  Article  PubMed  Google Scholar 

    43.
    Braithwaite, C. J. R., Taylor, J. D. & Kennedy, W. J. The evolution of an atoll: the depositional and erosional history of Aldabra. Philos. Trans. R. Soc. Lond. B. 266, 307–340 (1973).
    ADS  Article  Google Scholar 

    44.
    Obura, D. The diversity and biogeography of Western Indian Ocean reef-building corals. PLoS ONE 7, e45013. https://doi.org/10.1371/journal.pone.0045013 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    45.
    Urashi, C., Teshima, K. M., Minobe, S., Koizumi, O. & Inomata, N. Inferences of evolutionary history of a widely distributed mangrove species, Bruguiera gymnorrhiza, in the Indo-West Pacific region. Ecol. Evol. 3, 2251–2261 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    46.
    Tomizawa, Y. et al. Genetic structure and population demographic history of a widespread mangrove plant Xylocarpus granatum J. Koenig across the Indo-West Pacific region. Forests 8, 480 (2017).
    Article  Google Scholar 

    47.
    van der Ven, R. M. et al. Population genetic structure of the stony coral Acropora tenius shows high but variable connectivity in East Africa. J. Biogeogr. 43, 510–519 (2016).
    Article  Google Scholar 

    48.
    Jahnke, M. et al. Population genetic structure and connectivity of the seagrass Thalassia hemprichii in the Western Indian Ocean is influenced by predominant ocean currents. Ecol. Evol. 9, 8953–8964 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    49.
    Muths, D., Tessier, E. & Bourjea, J. Genetic structure of the reef grouper Epinephelus merra in the West Indian Ocean appears congruent with biogeographic and oceanographic boundaries. Mar. Ecol. 36, 447–461 (2015).
    ADS  Article  Google Scholar 

    50.
    Mori, G. M., Zucchi, M. I. & Souza, A. P. Multiple-geographic-scale genetic structure of two mangrove tree species: the roles of mating system, hybridization, limited dispersal and extrinsic factors. PLoS ONE 10, 0118710. https://doi.org/10.1371/journal.pone.0118710 (2015).
    CAS  Article  Google Scholar 

    51.
    Hancke, L., Roberts, M. J. & Ternon, J. F. Surface drifter trajectories highlight flow pathways in the Mozambique Channel. Deep-Sea Res. II(100), 27–37 (2014).
    Google Scholar 

    52.
    Gamoyo, M., Obura, D. & Reason, C. J. C. Estimating connectivity through larval dispersal in the Western Indian Ocean. J. Geophys. Res. Biogeo. 124, 2446–2459. https://doi.org/10.1029/2019JG005128 (2019).
    Article  Google Scholar 

    53.
    Silva, I., Mesquita, N. & Paula, J. Genetic and morphological differentiation of the mangrove crab Perisesarma guttatum (Brachyura Sesarmidae) along an East African latitudinal gradient. Biol. J. Linn. Soc. 99, 28–46 (2010).
    Article  Google Scholar 

    54.
    Madeira, C., Alves, M. J., Mesquita, N., Silva, I. & Paula, J. Tracing geographical patterns of population differentiation in a widespread mangrove gastropod: genetic and geometric morphometrics surveys along the eastern African coast. Biol. J. Linn. Soc. 107, 647–663 (2012).
    Article  Google Scholar 

    55.
    Fatoyinbo, E. T., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeo. 113, G02S06. https://doi.org/10.1029/2007JG000551 (2008).
    ADS  Article  Google Scholar 

    56.
    Lutjeharms, J. R. E. & Da Silva, A. J. The Delagoa bight eddy. Deep-Sea Res. 35, 619–634 (1988).
    ADS  Article  Google Scholar 

    57.
    Quartly, G. D. & Srokosz, M. A. Eddies in the southern Mozambique Channel. Dee-Sea Res. II: Top. Stud. Oceanogr. 51, 69–83 (2004).
    ADS  CAS  Article  Google Scholar 

    58.
    Paula, J., Dray, T. & Queiroga, H. Interaction of offshore and inshore processes controlling settlement of brachyuran megalopae in Saco mangrove creek, Inhaca Island (South Mozambique). Mar. Ecol. Prog. Ser. 215, 251–260 (2001).
    ADS  Article  Google Scholar 

    59.
    Singh, S. P., Groeneveld, J. C., Hart-Davis, M. G., Backeberg, B. C. & Willows-Munro, S. Seascape genetics of the spiny lobster Panulirus homarus in the Western Indian Ocean: understanding how oceanographic features shape the genetic structure of species with high larval dispersal potential. Ecol. Evol. 8, 12221–12237 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Ngeve, M., Koedam, N. & Triest, L. Runaway fathers? Limited pollen dispersal and mating system in Rhizophora racemosa populations of a disturbed mangrove estuary. Aquat. Bot. 165, 103241. https://doi.org/10.1016/j.aquabot.2020.103241 (2020).
    Article  Google Scholar 

    61.
    Kondo, K., Nakamura, T., Tsuruda, K., Saito, N. & Yaguchi, Y. Pollination in Bruguiera gymnorrhiza and Rhizophora mucronata (Rhizophoraceae) in Ishigaki Island, The Ryukyu Islands, Japan. Biotropica 19, 377–380 (1987).
    Article  Google Scholar 

    62.
    Islam, M. S., Lian, C., Kameyama, N., Wu, B. & Hogetsu, T. Development of microsatellite markers in Rhizophora stylosa using a dual-suppression-polymerase chain reaction technique. Mol. Ecol. Notes 4, 110–112 (2004).
    CAS  Article  Google Scholar 

    63.
    Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in the red mangrove Rhizophora mangle (Rhizophoraceae) and its related species. Conserv. Genet. 9, 1323–1325 (2008).
    CAS  Article  Google Scholar 

    64.
    Takayama, K. et al. Isolation and characterization of microsatellite loci in a mangrove species, Rhizophora stylosa (Rhizophoraceae). Conserv. Genet. Resour. 1, 175. https://doi.org/10.1007/s12686-009-9042-7 (2009).
    Article  Google Scholar 

    65.
    Shinmura, Y. et al. Isolation and characterization of 14 microsatellite markers for Rhizophora mucronata (Rhizophoraceae) and their potential use in range-wide population studies. Conserv. Genet. Resour. 4, 951–954 (2012).
    Article  Google Scholar 

    66.
    Wee, A. K. S., Takayama, K., Kajita, T. & Webb, E. L. Microsatellite loci for Avicennia alba (Acanthaceae), Sonneratia alba (Lythraceae) and Rhizophora mucronata (Rhizophoraceae). J. Trop. For. Sci. 25, 131–136 (2013).
    Google Scholar 

    67.
    Ribeiro, D. O. et al. Isolation of microsatellite markers for the red mangrove, Rhizophora mangle (Rhizophoraceae). Appl. Plant Sci. 1, 1300003. https://doi.org/10.3732/apps.1300003 (2013).
    Article  Google Scholar 

    68.
    Goudet, J. FSTAT, version 2.9.3, a program to estimate and test gene diversities and fixation indices. (2001).

    69.
    van Oosterhout, C., Hutchison, W. F., Wills, D. P. M. & Shipley, P. Micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
    Article  CAS  Google Scholar 

    70.
    Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Hered. 100, 106113 (2009).
    Article  CAS  Google Scholar 

    71.
    Campagne, P., Smouse, P. E., Varouchas, G., Silvain, J.-F. & Leru, B. Comparing the van Oosterhout and Chybicki-Burczyk methods of estimating null allele frequencies for inbred populations. Mol. Ecol. Resour. 12, 975–982 (2012).
    CAS  PubMed  Article  Google Scholar 

    72.
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    73.
    Hardy, O. & Vekemans, X. spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).
    Article  CAS  Google Scholar 

    74.
    Loiselle, B., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
    Article  Google Scholar 

    75.
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
    CAS  PubMed  PubMed Central  Google Scholar 

    76.
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
    CAS  PubMed  Article  Google Scholar 

    77.
    Earl, D. M. & von Holdt, B. M. Structure harvester: a website and program for visualizing Structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
    Article  Google Scholar 

    78.
    Li, Y. L. & Liu, J. X. Structureselector: a web based software to select and visualize the optimal number of clusters using multiple methods. Mol. Ecol. Resour. 18, 176–177 (2018).
    PubMed  Article  Google Scholar 

    79.
    Manni, F., Guerard, E. & Heyer, E. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier’s algorithm. Hum. Biol. 76, 173190 (2004).
    Article  Google Scholar 

    80.
    Beerli, P. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22, 341–345 (2006).
    CAS  PubMed  Article  Google Scholar 

    81.
    Beerli, P. & Palczewski, M. Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185, 313–326 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    82.
    Cornuet, J. M. et al. DIYABC v2.0: a software to make approximate bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
    CAS  PubMed  Article  Google Scholar 

    83.
    Lutjeharms, J. R. E., Biastoch, A., Van der Werf, P. M., Ridderinkhof, H. & De Ruijter, W. P. M. On the discontinuous nature of the Mozambique Current. S. Afr. J. Sci. https://doi.org/10.4102/sajs.v108i1/2.428 (2012).
    Article  Google Scholar  More

  • in

    My race against time to capture the sounds of ancient rainforests

    Natural soundscapes have always called to me. As an eco- and electro-acoustics researcher, with a background in sound engineering and electronic music composition, I have always tried to strike a balance between art and science in my work.
    In 1998, when I first heard about the extinction crisis — more than 35,500 species of flora and fauna are endangered — the idea for the Fragments of Extinction project came to me very quickly. My vision was to build a collection of 24-hour-long ‘acoustic fragments’, recorded at the highest definition possible, capturing the sonic heritage of ancient, biodiverse, untouched tropical rainforests — before climate change damages them irreversibly.
    In these forests, some species vocalize from the canopy, some from the ground and others from big tree trunks that act like sound diffusers. To capture a 3D acoustic portrait of the forest, we simultaneously record on 38 audio channels and microphones.
    In this photograph, I am standing in the Sonosfera, a geodesic theatre in Pesaro, Italy, in which audiences can experience rainforest soundscapes captured in the Amazon, Africa and Borneo. Forty-five high-definition loudspeakers are positioned in an isolated, acoustically perfect space, realistically reproducing the ecosystems’ natural sounds.
    For the first 15 minutes of the performance, the Sonosfera is completely dark. Sound helps listeners to ‘build’ the forest space around them — the position of every insect and amphibian; the birds and mammals moving through the canopy. My team then projects the spectrograms shown here to explain the sounds, and present data showing that these ecosystems are disappearing.
    We have captured the deep infrasound calls of elephants and have recorded insects that sound exactly like violins or trumpets. Our ecosystem recordings are very different. But I don’t have a favourite — they’re a collection. More

  • in

    The population sizes and global extinction risk of reef-building coral species at biogeographic scales

    1.
    Wilkinson, C. Status of Coral Reefs of the World: 2008 (Global Coral Reef Monitoring Network and Reef and Rainforest Research Centre, 2008).
    2.
    Jackson, J. B. C., Donovan, M. K., Cramer, K. L. & Lam, V. V. Status and Trends of Caribbean Coral Reefs: 1970–2012 (Global Coral Reef Monitoring Network, 2014).

    3.
    Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    4.
    Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).
    Article  Google Scholar 

    5.
    Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
    CAS  Article  PubMed  Google Scholar 

    6.
    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
    CAS  PubMed  Article  Google Scholar 

    7.
    De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl Acad. Sci. USA 109, 17995–17999 (2012).
    PubMed  Article  Google Scholar 

    8.
    Gardner, T. A. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).
    CAS  PubMed  Article  Google Scholar 

    9.
    Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).
    CAS  PubMed  Article  Google Scholar 

    10.
    ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    11.
    Fauset, S. et al. Hyperdominance in Amazonian forest carbon cycling. Nat. Commun. 6, 6857 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).
    CAS  PubMed  Article  Google Scholar 

    13.
    Connell, J., Hughes, T. & Wallace, C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).
    Article  Google Scholar 

    14.
    Hughes, T. P. & Jackson, J. B. C. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).
    Article  Google Scholar 

    15.
    ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).
    PubMed  Article  CAS  Google Scholar 

    16.
    Gaston, K. J. & Blackburn, T. M. How many birds are there? Biodivers. Conserv. 6, 615–625 (1997).
    Article  Google Scholar 

    17.
    Kerry, J. T. & Bellwood, D. R. Do tabular corals constitute keystone structures for fishes on coral reefs? Coral Reefs 34, 41–50 (2015).
    Article  Google Scholar 

    18.
    Connolly, S. R., Hughes, T. P., Bellwood, D. R. & Karlson, R. H. Community structure of corals and reef fishes at multiple scales. Science 309, 1363–1365 (2005).
    CAS  PubMed  Article  Google Scholar 

    19.
    Connolly, S. R., Hughes, T. P. & Bellwood, D. R. A unified model explains commonness and rarity on coral reefs. Ecol. Lett. 20, 477–486 (2017).
    PubMed  Article  Google Scholar 

    20.
    Hubbell, S. P. Estimating the global number of tropical tree species, and Fisher’s paradox. Proc. Natl Acad. Sci. USA 112, 7343–7344 (2015).
    CAS  PubMed  Article  Google Scholar 

    21.
    Hughes, T. P., Bellwood, D. R. & Connolly, S. R. Biodiversity hotspots, centres of endemicity, and the conservation of coral reefs. Ecol. Lett. 5, 775–784 (2002).
    Article  Google Scholar 

    22.
    Hughes, T. P., Bellwood, D. R., Connolly, S. R. & Cornell, H. V. Double jeopardy and global extinction risk in corals and reef fishes. Curr. Biol. 24, 2946–2951 (2014).
    CAS  PubMed  Article  Google Scholar 

    23.
    Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84, 2007–2020 (2003).
    Article  Google Scholar 

    24.
    Hull, P. M., Darroch, S. A. F. & Erwin, D. H. Rarity in mass extinctions and the future of ecosystems. Nature 528, 345–351 (2015).
    CAS  PubMed  Article  Google Scholar 

    25.
    Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. Adapting the IUCN Red List criteria for invertebrates. Biol. Conserv. 144, 2432–2440 (2011).
    Article  Google Scholar 

    26.
    Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. The underrepresentation and misrepresentation of invertebrates in the IUCN Red List. Biol. Conserv. 149, 147–148 (2012).
    Article  Google Scholar 

    27.
    Estes, J. A., Duggins, D. O. & Rathbun, G. B. The ecology of extinctions in kelp forest communities. Conserv. Biol. 3, 252–264 (1989).
    Article  Google Scholar 

    28.
    Oliver, J. & Babcock, R. Aspects of the fertilization ecology of broadcast spawning corals: sperm dilution effects and in situ measurements of fertilization. Biol. Bull. 183, 409–417 (1992).
    CAS  PubMed  Article  Google Scholar 

    29.
    Knowlton, N., Lang, J. C. & Keller, B. D. Case study of natural population collapse: post-hurricane predation on Jamaican staghorn corals. Smithson. Contrib. Mar. Sci. 31, 1–25 (1990).
    Google Scholar 

    30.
    Gaston, K. J. & Fuller, R. A. Commonness, population depletion and conservation biology. Trends Ecol. Evol. 23, 14–19 (2008).
    PubMed  Article  Google Scholar 

    31.
    Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).
    PubMed  Article  CAS  Google Scholar 

    32.
    Pratchett, M. S. Dietary overlap among coral-feeding butterflyfishes (Chaetodontidae) at Lizard Island, northern Great Barrier Reef. Mar. Biol. 148, 373–382 (2005).
    Article  Google Scholar 

    33.
    Huang, D., Licuanan, W. Y., Baird, A. H. & Fukami, H. Cleaning up the ‘Bigmessidae’: molecular phylogeny of scleractinian corals from Faviidae, Merulinidae, Pectiniidae and Trachyphylliidae. BMC Evol. Biol. 11, 37 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Knowlton, N. & Jackson, J. B. C. New taxonomy and niche partitioning on coral reefs: jack of all trades or master of some? Trends Ecol. Evol. 9, 7–9 (1994).
    CAS  PubMed  Article  Google Scholar 

    35.
    Gilpin, M. E. & Soulé, M. E. in Conservation Biology: The Science of Scarcity and Diversity (ed, Soulé, M. E.) 19–34 (Sinauer Associates, 1986).

    36.
    Bak, R. P. M. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).
    Article  Google Scholar 

    37.
    McClanahan, T. R., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).
    Article  Google Scholar 

    38.
    Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).
    CAS  Article  Google Scholar 

    40.
    Global Distribution of Coral Reefs (UNEP-WCMC, WorldFish Centre, WRI & TNC, 2018); https://data.unep-wcmc.org/datasets/

    41.
    Bruno, J. F. & Valdivia, A. Coral reef degradation is not correlated with local human population density. Sci. Rep. 6, 29778 (2016).

    42.
    Bruno, J. Data from: Coral reef degradation is not correlated with local human population density. Dryad Digital Repository https://doi.org/10.5061/dryad.48r68 (2016).

    43.
    Karlson, R. H., Cornell, H. V. & Hughes, T. P. Coral communities are regionally enriched along an oceanic biodiversity gradient. Nature 429, 867–870 (2004).
    CAS  PubMed  Article  Google Scholar 

    44.
    Cornell, H. V., Karlson, R. H. & Hughes, T. P. Scale-dependent variation in coral community similarity across sites, islands, and island groups. Ecology 88, 1707–1715 (2007).
    PubMed  Article  Google Scholar 

    45.
    Cornell, H. V., Karlson, R. H. & Hughes, T. P. Local-regional species richness relationships are linear at very small to large scales in west-central Pacific corals. Coral Reefs 27, 145–151 (2008).
    Article  Google Scholar 

    46.
    Connolly, S. R., Dornelas, M., Bellwood, D. R. & Hughes, T. P. Testing species abundance models: a new bootstrap approach applied to Indo-Pacific coral reefs. Ecology 90, 3138–3149 (2009).
    PubMed  Article  Google Scholar 

    47.
    Reef Habitat Maps (NOAA-NCCOS, accessed 10 November 2017); https://products.coastalscience.noaa.gov/collections/benthic/default.aspx

    48.
    Purkis, S. J. et al. High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs. Coral Reefs 38, 467–488 (2019).
    Article  Google Scholar 

    49.
    Roelfsema, C., Phinn, S., Jupiter, S., Comley, J. & Albert, S. Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis. Int. J. Remote Sens. 34, 6367–6388 (2013).
    Article  Google Scholar 

    50.
    Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Article  Google Scholar 

    51.
    Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92, 3–10 (2011).
    PubMed  Article  Google Scholar 

    52.
    Marsh, L. M., Bradbury, R. H. & Reichelt, R. E. Determination of the physical parameters of coral distributions using line transect data. Coral Reefs 2, 175–180 (1984).
    Google Scholar 

    53.
    Hughes, T. P. Population dynamics based on individual size rather than age: a general model with a reef coral example. Am. Nat. 123, 778–795 (1984).
    Article  Google Scholar 

    54.
    Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: comparative studies of reef-building corals. Ecology 77, 950–963 (1996).
    Article  Google Scholar 

    55.
    Hughes, T. P., Connolly, S. R. & Keith, S. A. Geographic ranges of reef corals (Cnidaria: Anthozoa: Scleractinia) in the Indo-Pacific. Ecology 94, 1659 (2013).
    Article  Google Scholar 

    56.
    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
    CAS  PubMed  Article  Google Scholar 

    57.
    van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).
    PubMed  Article  CAS  Google Scholar 

    58.
    Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).
    CAS  PubMed  Article  Google Scholar 

    59.
    Atkinson, A., Siegel, V., Pakhomov, E. A., Jessopp, M. J. & Loeb, V. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep-Sea Res. I 56, 727–740 (2009).
    Article  Google Scholar 

    60.
    Current World Population (Worldometer, accessed 13 May 2020); https://www.worldometers.info/world-population/

    61.
    California Condor Recovery Program: 2017 Annual Population Status (US Fish and Wildlife Service, 2017).

    62.
    Goodrich, J. M. et al. Panthera tigris. The IUCN Red List of Threatened Species 2015 Report number e.T15955A50659951 (IUCN, 2015). More

  • in

    Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity

    1.
    Suess E. Marine cold seeps and their manifestations: geological control, biogeochemical criteria and environmental conditions. Int J Earth Sci. 2014;103:1889–916.
    CAS  Article  Google Scholar 
    2.
    Joye SB. The geology and biogeochemistry of hydrocarbon seeps. Annu Rev Earth Planet Sci. 2020;48:205–31.
    CAS  Article  Google Scholar 

    3.
    Etiope G, Panieri G, Fattorini D, Regoli F, Vannoli P, Italiano F, et al. A thermogenic hydrocarbon seep in shallow Adriatic Sea (Italy): Gas origin, sediment contamination and benthic foraminifera. Mar Pet Geol. 2014;57:283–93.
    CAS  Article  Google Scholar 

    4.
    Kennicutt, MC Habitats and biota of the Gulf of Mexico: before the deepwater horizon oil spill. Ward CH, editor. New York, NY: Springer New York; 2017. p. 275–358.

    5.
    Ruppel CD, Kessler JD. The interaction of climate change and methane hydrates. Rev Geophys. 2017;55:126–68.
    Article  Google Scholar 

    6.
    Kniemeyer O, Musat F, Sievert SM, Knittel K, Wilkes H, Blumenberg M, et al. Anaerobic oxidation of short-chain hydrocarbons by marine sulphate-reducing bacteria. Nature. 2007;449:898–901.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Jaekel U, Musat N, Adam B, Kuypers M, Grundmann O, Musat F. Anaerobic degradation of propane and butane by sulfate-reducing bacteria enriched from marine hydrocarbon cold seeps. ISME J. 2013;7:885–95.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Teske A, Carvalho V. Marine hydrocarbon seeps: microbiology and biogeochemistry of a global marine habitat. Cham, Switzerland: Springer Nature; 2020.

    9.
    Kellogg CA. Enumeration of viruses and prokaryotes in deep-sea sediments and cold seeps of the Gulf of Mexico. Deep Sea Res Part II Top Stud Oceanogr. 2010;57:2002–7.
    Article  Google Scholar 

    10.
    Bryson SJ, Thurber AR, Correa AM, Orphan VJ, Vega Thurber R. A novel sister clade to the enterobacteria microviruses (family Microviridae) identified in methane seep sediments. Environ Microbiol. 2015;17:3708–21.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Paul BG, Bagby SC, Czornyj E, Arambula D, Handa S, Sczyrba A, et al. Targeted diversity generation by intraterrestrial archaea and archaeal viruses. Nat Commun. 2015;6:6585.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Pan D, Morono Y, Inagaki F, Takai K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol. 2019;10:878.
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.
    PubMed  PubMed Central  Article  Google Scholar 

    15.
    Labbe M, Girard C, Vincent WF, Culley AI. Extreme viral partitioning in a marine-derived high arctic lake. mSphere. 2020;5:e00334–00320.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Okazaki Y, Nishimura Y, Yoshida T, Ogata H, Nakano SI. Genome-resolved viral and cellular metagenomes revealed potential key virus-host interactions in a deep freshwater lake. Environ Microbiol. 2019;21:4740–54.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Backstrom D, Yutin N, Jorgensen SL, Dharamshi J, Homa F, Zaremba-Niedwiedzka K, et al. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism. mBio. 2019;10:e02497–02418.
    PubMed  PubMed Central  Article  Google Scholar 

    18.
    Daly RA, Roux S, Borton MA, Morgan DM, Johnston MD, Booker AE, et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat Microbiol. 2019;4:352–61.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Daly RA, Borton MA, Wilkins MJ, Hoyt DW, Kountz DJ, Wolfe RA, et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat Microbiol. 2016;1:16146.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    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.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Gregory AC, Zayed AA, Conceicao-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.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA, Brussaard CPD, et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun. 2017;8:15955.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    23.
    Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Chen LX, Meheust R, Crits-Christoph A, McMahon KD, Nelson TC, Slater GF, et al. Large freshwater phages with the potential to augment aerobic methane oxidation. Nat Microbiol. 2020;5:1504–15.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    25.
    Cai L, Jorgensen BB, Suttle CA, He M, Cragg BA, Jiao N, et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 2019;13:1857–64.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Danovaro R, Dell’Anno A, Corinaldesi C, Magagnini M, Noble R, Tamburini C, et al. Major viral impact on the functioning of benthic deep-sea ecosystems. Nature. 2008;454:1084–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Middelboe M, Glud RN, Wenzhöfer F, Oguri K, Kitazato H. Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay. Jpn Deep Sea Res Part 1 Oceanogr Res Pap. 2006;53:1–13.
    Article  Google Scholar 

    28.
    Danovaro R, Serresi M. Viral density and virus-to-bacterium ratio in deep-sea sediments of the Eastern Mediterranean. Appl Environ Microbiol. 2000;66:1857–61.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Hewson I, Fuhrman JA. Viriobenthos production and virioplankton sorptive scavenging by suspended sediment particles in coastal and pelagic waters. Micro Ecol. 2003;46:337–47.
    CAS  Article  Google Scholar 

    30.
    Corinaldesi C, Dell’Anno A, Danovaro R. Viral infection plays a key role in extracellular DNA dynamics in marine anoxic systems. Limnol Oceanogr. 2007;52:508–16.
    CAS  Article  Google Scholar 

    31.
    Dong X, Greening C, Rattray JE, Chakraborty A, Chuvochina M, Mayumi D, et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat Commun. 2019;10:1816.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    32.
    Dong X, Rattray JE, Campbell DC, Webb J, Chakraborty A, Adebayo O, et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nat Commun. 2020;11:5825.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:e00920–00920.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.
    PubMed  PubMed Central  Article  Google Scholar 

    36.
    Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    37.
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36:1925–7.
    PubMed  PubMed Central  Google Scholar 

    39.
    Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    Federhen S. The NCBI taxonomy database. Nucleic Acids Res. 2012;40:D136–43.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Marquet M, Hölzer M, Pletz MW, Viehweger A, Makarewicz O, Ehricht R, et al. What the phage: a scalable workflow for the identification and analysis of phage sequences. 2020. https://www.biorxiv.org/content/10.1101/2020.07.24.219899v1.

    46.
    Kieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome. 2020;8:90.
    PubMed  PubMed Central  Article  Google Scholar 

    47.
    Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol. 2020. https://doi.org/10.1101/2020.1105.1106.081778.

    48.
    Dalcin Martins P, Danczak RE, Roux S, Frank J, Borton MA, Wolfe RA, et al. Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems. Microbiome. 2018;6:138.
    PubMed  PubMed Central  Article  Google Scholar 

    49.
    Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
    Article  CAS  Google Scholar 

    50.
    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.
    Article  CAS  Google Scholar 

    51.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Roux S, Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2020;49:D764–75.

    53.
    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.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Castelan-Sanchez HG, Lopez-Rosas I, Garcia-Suastegui WA, Peralta R, Dobson ADW, Batista-Garcia RA, et al. Extremophile deep-sea viral communities from hydrothermal vents: structural and functional analysis. Mar Genom. 2019;46:16–28.
    Article  Google Scholar 

    55.
    Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17:377–86.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Tominaga K, Morimoto D, Nishimura Y, Ogata H, Yoshida T. In silico prediction of virus-host interactions for marine bacteroidetes with the use of metagenome-assembled genomes. Front Microbiol. 2020;11:738.
    PubMed  PubMed Central  Article  Google Scholar 

    57.
    Ahlgren NA, Ren J, Lu YY, Fuhrman JA, Sun F. Alignment-free d2*oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res. 2017;45:39–53.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32:11–16.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Skennerton CT, Imelfort M, Tyson GW. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 2013;41:e105.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Dong X, Strous M. An integrated pipeline for annotation and visualization of metagenomic contigs. Front Genet. 2019;10:999.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    61.
    Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. 2020. https://www.biorxiv.org/content/10.1101/761643v1.

    62.
    Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113.
    Article  CAS  Google Scholar 

    63.
    Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.
    PubMed  PubMed Central  Article  Google Scholar 

    66.
    Vik D, Gazitua MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15313.

    67.
    ter Horst AM, Santos-Medellin C, Sorensen JW, Zinke LA, Wilson RM, Johnston ER, et al. Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations. 2020. https://www.biorxiv.org/content/10.1101/2020.12.15.422944v1.full.

    68.
    Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 2020;48:D265–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    69.
    Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
    Article  Google Scholar 

    71.
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    72.
    Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    73.
    Al-Shayeb B, Sachdeva R, Chen LX, Ward F, Munk P, Devoto A, et al. Clades of huge phages from across Earth’s ecosystems. Nature. 2020;578:425–31.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci USA. 2015;112:4015–20.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    75.
    Trubl G, Jang HB, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–00018.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    76.
    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.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    77.
    Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. elife. 2015;4:e08490.
    PubMed Central  Article  Google Scholar 

    78.
    Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol. 2018;16:629–45.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    79.
    Jarett JK, Dzunkova M, Schulz F, Roux S, Paez-Espino D, Eloe-Fadrosh E, et al. Insights into the dynamics between viruses and their hosts in a hot spring microbial mat. ISME J. 2020;14:2527–41.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    80.
    Orsi WD. Ecology and evolution of seafloor and subseafloor microbial communities. Nat Rev Microbiol. 2018;16:671–83.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    81.
    Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    82.
    Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    83.
    Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    84.
    Anderson CL, Sullivan MB, Fernando SC. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome. 2017;5:155.
    PubMed  PubMed Central  Article  Google Scholar 

    85.
    Gao SM, Schippers A, Chen N, Yuan Y, Zhang MM, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.
    PubMed  PubMed Central  Article  Google Scholar 

    86.
    Zhao R, Summers ZM, Christman GD, Yoshimura KM, Biddle JF. Metagenomic views of microbial dynamics influenced by hydrocarbon seepage in sediments of the Gulf of Mexico. Sci Rep. 2020;10:5772.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    87.
    Dekas AE, Poretsky RS, Orphan VJ. Deep-sea archaea fix and share nitrogen in methane-consuming microbial consortia. Science. 2009;326:422–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    88.
    Zheng, X, Liu, W, Dai, X, Zhu, Y, Wang, J, Zhu, Y et al. Extraordinary diversity of viruses in deep-sea sediments as revealed by metagenomics without prior virion separation. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15154. More