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    Equatorial pliosaurid from Venezuela marks the youngest South American occurrence of the clade

    1.Madzia, D. & Cau, A. Estimating the evolutionary rates in mosasauroids and plesiosaurs: Discussion of niche occupation in Late Cretaceous seas. PeerJ 8, e8941 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Benson, R. B. et al. A giant pliosaurid skull from the Late Jurassic of England. PLoS ONE 8, e65989. https://doi.org/10.1371/journal.pone.0065989 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Schumacher, B. A., Carpenter, K. & Everhart, M. J. A new Cretaceous pliosaurid (Reptilia, Plesiosauria) from the Carlile Shale (middle Turonian) of Russell County, Kansas. J. Vertebrate Paleontol. 33, 613–628 (2013).Article 

    Google Scholar 
    4.Madzia, D. A reappraisal of Polyptychodon (Plesiosauria) from the Cretaceous of England. PeerJ 4, e1998 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    5.Zverkov, N. G., Fischer, V., Madzia, D. & Benson, R. B. J. Increased pliosaurid dental disparity across the Jurassic–Cretaceous transition. Palaeontology 61, 825–846 (2018).Article 

    Google Scholar 
    6.Madzia, D., Sachs, S. & Lindgren, J. Morphological and phylogenetic aspects of the dentition of Megacephalosaurus eulerti, a pliosaurid from the Turonian of Kansas, USA, with remarks on the cranial anatomy of the taxon. Geol. Mag. 156, 1201–1216 (2019).Article 

    Google Scholar 
    7.Tarlo, L. B. A review of the Upper Jurassic pliosaurs. Bull. Br. Museum (Nat. Hist.) Geol. Lond. 4(5), 145–189 (1960).
    Google Scholar 
    8.Noè, L. F. A taxonomic and functional study of the Callovian (Middle Jurassic) Pliosauroidea (Reptilia, Sauropterygia), PhD thesis, University of Derby, 616 pp (2001).9.Ketchum, H. F. & Benson, R. B. J. The cranial anatomy and taxonomy of Peloneustes philarchus (Sauropterygia, Pliosauridae) from the Peterborough member (Callovian, Middle Jurassic) of the United Kingdom. Palaeontology 54(3), 639–665 (2011).Article 

    Google Scholar 
    10.Knutsen, E. M. A taxonomic revision of the genus Pliosaurus (Owen, 1841a) Owen, 1841b. Norw. J. Geol. 92, 259–276 (2012).
    Google Scholar 
    11.Knutsen, E. M., Druckenmiller, P. S. & Hurum, J. H. A new species of Pliosaurus (Sauropterygia: Plesiosauria) from the Middle Volgian of central Spitsbergen, Norway. Norw. J. Geol. 92, 235–258 (2012).
    Google Scholar 
    12.Williston, S. W. North American plesiosaurs. Field Columbian Museum, Pub. 73, Geological Series 2, 1–79 (1903).
    13.Williston, S. W. The skull of Brachauchenius, with special observations on the relationships of the plesiosaurs. US Natl. Museum Proc. 32, 477–489 (1907).Article 

    Google Scholar 
    14.Welles, S. P. & Slaughter, B. H. The first record of the plesiosaurian genus Polyptychodon (Pliosauridae) from the New World. J. Paleontol. 37, 131–133 (1963).
    Google Scholar 
    15.Hampe, O. Ein großwüchsiger Pliosauride (Reptilia: Plesiosauria) aus der Unterkreide (oberes Aptium) von Kolumbien. Courier Forschungs-Institut Senckenberg 145, 1–32 (1992).
    Google Scholar 
    16.Carpenter, K. A review of short-necked plesiosaurs from the Cretaceous of the Western Interior, North America. Neues Jb. Geol. Paläontol. Abh. 201, 259–287 (1996).Article 

    Google Scholar 
    17.VonLoh, J. P. & Bell, G. L. Jr. Fossil Reptiles from the Late Cretaceous Greenhorn Formation (Late Cenomanian-Middle Turonian) of the Black Hills Region, South Dakota. Dakoterra 5, 28–38 (1998).
    Google Scholar 
    18.Kear, B. P. Cretaceous marine reptiles of Australia: A review of taxonomy and distribution. Cretac. Res. 24, 277–303 (2003).Article 

    Google Scholar 
    19.Hampe, O. Considerations on a Brachauchenius skeleton (Pliosauroidea) from the lower Paja Formation (late Barremian) of Villa de Leyva area (Colombia). Fossil Record 8, 37–51 (2005).Article 

    Google Scholar 
    20.Liggett, G. A., Shimada, K., Bennett, S. C. & Schumacher, B. A. Cenomanian (Late Cretaceous) reptiles from northwestern Russell County, Kansas. PaleoBios 25, 9–17 (2005).
    Google Scholar 
    21.Schumacher, B. A. & Everhart, M. J. A stratigraphic and taxonomic review of plesiosaurs from the old “Fort Benton Group” of central Kansas: A new assessment of old records. Paludicola 5, 33–54 (2005).
    Google Scholar 
    22.Albright, B. L., Gillette, D. D. & Titus, A. L. Plesiosaurs from the Upper Cretaceous (Cenomanian-Turonian) tropic shale of southern Utah. Part 1: New records of the pliosaur Brachauchenius lucasi. J. Vertebrate Paleontol. 27, 41–58 (2007).Article 

    Google Scholar 
    23.Ketchum, H. F. & Benson, R. B. J. Global interrelationships of Plesiosauria (Reptilia, Sauropterygia) and the pivotal role of taxon sampling in determining the outcome of phylogenetic analyses. Biol. Rev. 85, 361–392 (2010).PubMed 
    Article 

    Google Scholar 
    24.Páramo-Fonseca, M. E., Gómez-Pérez, M., Noè, L. F. & Etayo-Serna, F. Stenorhynchosaurus munozi, gen. et sp. nov. a new pliosaurid from the Upper Barremian (Lower Cretaceous) of Villa de Leiva, Colombia, South America. Revista de la Academia Colombiana de Ciencias Exactas Físicas y Naturales 40, 84–103 (2016).Article 

    Google Scholar 
    25.Gómez-Pérez, M. & Noè, L. F. Cranial anatomy of a new pliosaurid Acostasaurus pavachoquensis from the Lower Cretaceous of Colombia, South America. Palaeontogr. Abt. A 310, 5–42 (2017).Article 

    Google Scholar 
    26.Páramo-Fonseca, M. E., Benavides-Cabra, C. D. & Gutiérrez, I. E. A new large pliosaurid from the Barremian (Lower Cretaceous) of Sáchica, Boyacá, Colombia. Earth Sci. Res. J. 22, 223–238 (2018).Article 

    Google Scholar 
    27.Fischer, V. et al. Peculiar macrophagous adaptations in a new Cretaceous pliosaurid. R. Soc. Open Sci. 2, 150552 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Fischer, V. et al. Plasticity and convergence in the evolution of short-necked plesiosaurs. Curr. Biol. 27, 1667-1676.e3 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Zverkov, N. G. On a typically Late Jurassic pliosaur from the Lower Cretaceous of Crimea. The International Scientific Conference on the Jurassic/Cretaceous boundary, Samara, Russia, 89–94 (2015).30.Angst, D. & Bardet, N. A new record of the pliosaur Brachauchenius lucasi [Williston, 1903] (Reptilia: Sauropterygia) of Turonian (Late Cretaceous) age, Morocco. Geol. Mag. 153, 449–459 (2016).CAS 
    Article 

    Google Scholar 
    31.Madzia, D. & Machalski, M. Isolated pliosaurid teeth from the Albian-Cenomanian (Cretaceous) of Annopol, Poland. Acta Geol. Pol. 67, 393–403 (2017).CAS 
    Article 

    Google Scholar 
    32.Lukeneder, A. & Zverkov, N. G. First evidence of a conical-toothed pliosaurid (Reptilia, Sauropterygia) in the Hauterivian of the Northern Calcareous Alps, Austria. Cretaceous Res. 106, 104248 (2020).Article 

    Google Scholar 
    33.Zverkov, N. G. & Pervushov, E. M. A gigantic pliosaurid from the Cenomanian (Upper Cretaceous) of the Volga Region, Russia. Cretaceous Res. 110, 104419 (2020).Article 

    Google Scholar 
    34.Kear, B. P., Ekrt, B., Prokop, J. & Georgalis, G. L. Turonian marine amniotes from the Bohemian Cretaceous Basin, Czech Republic. Geol. Mag. 151, 183–198 (2014).Article 

    Google Scholar 
    35.Benson, R. B. J. & Druckenmiller, P. S. Faunal turnover of marine tetrapods during the Jurassic–Cretaceous transition. Biol. Rev. 89, 1–23 (2014).PubMed 
    Article 

    Google Scholar 
    36.Páramo-Fonseca, M. E., Benavides-Cabra, C. D. & Gutiérrez, I. E. A new specimen of Stenorhynchosaurus munozi (Páramo-Fonseca et al., 2016) (Plesiosauria, Pliosauridae), from the Barremian of Colombia: new morphological features and ontogenetic implications. J. Vertebrate Paleontol. 39, e1663426 (2019).Article 

    Google Scholar 
    37.Albino, A. M., Carrillo-Briceño, J. D. & Neenan, J. M. An enigmatic snake from the Cenomanian of Northern South America. PeerJ 4, e2027. https://doi.org/10.7717/peerj.2027 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.de Juana, C. G., de Arocena, J. I. & Picard, X. Geologia de Venezuela y de sus Cuencas Petroliferas Vol. 103, 1 (Foninves, 1980).
    Google Scholar 
    39.Renz, O. Estratigrafía del Cretáceo en Venezuela occidental. Boletín de Geología 5(10), 3–48 (1959).
    Google Scholar 
    40.Guinot, G. & Carrillo-Briceño, J. D. Lamniform sharks from the Cenomanian (Upper Cretaceous) of Venezuela. Cretac. Res. 82, 1–20 (2018).Article 

    Google Scholar 
    41.Colbert, E. A new Cretaceous plesiosaur from Venezuela. Am. Mus. Novit. 1420, 1–22 (1949).
    Google Scholar 
    42.Bengston, P. & Kakabadze, M. V. Ammonites and the mid-Cretaceous saga. Cretac. Res. 88, 90–99 (2018).Article 

    Google Scholar 
    43.Páramo-Fonseca, M. E., O’Gorman, J. P., Gasparini, Z., Padilla, S. & Parra-Ruge, M. L. A new late Aptian elasmosaurid from the Paja Formation, Villa de Leiva, Colombia. Cretaceous Res. 99, 30–40 (2019).Article 

    Google Scholar 
    44.Welles, S. P. A new species of elasmosaur from the Aptian of Colombia, and a review of the Cretaceous plesiosaurs. Univ. California Publ. Geol. Sci. 44, 1–96 (1962).
    Google Scholar 
    45.Carpenter, K. Revision of North American elasmosaurs from the Cretaceous of the Western Interior. Paludicola 2, 148–173 (1999).
    Google Scholar 
    46.Jaimes, J. J. & Parra, E. N. Callawayasaurus colombiensis (Welles) Carpenter 1999 el plesiosaurio de Villa de Leyva (Boyacá, Colombia). ¿Un nuevo espécimen?. Boletín de Geología 23(38), 9–19 (2001).
    Google Scholar 
    47.Goñi, R. & Gasparini, Z. B. Nuevos restos de “Alzadasaurus colombiensis” (Reptilia, Plesiosauria) del Cretácico temprano de Colombia. Geologia Norandina 7, S.49–54 (1983).
    Google Scholar 
    48.Meza-Velez, I. & O’Gorman, J. E. registro fósil de plesiosaurios (Diapsida, Sauropterygia) en el Perú. Rev. Peru. Biol. 28(2), 1–8 (2021).Article 

    Google Scholar 
    49.Jaillard, E., Cordova, A., Mazin, J.-M. & Mourier, T. La transgression du Cénomanien supérieur-Turonien inférieur dans le région de Jaén (Nord du Pérou): donnés sédimentologique et stratigraphiques: Découverte du premier saurien marin du Pérou, Série II. C. R. Acad. Sc. Paris 301(20), 1429–1432 (1985).
    Google Scholar 
    50.Carvalho, I., Velas Bôas, I. & Bergqvist, L. Plesiossauros da regiao equatorial Brasileira Bacia de Sao Luís (Cretáceo Superior) Brazil. Acta Geologica Leopoldensia 23(51), S33–S41 (2000).
    Google Scholar 
    51.Velas Bôas, I. & Carvalho, I. Répteis Marinhos (Mosasauria e Plesiosauria) do Cretáceo Superior da Bacia de São Luís (Maranhão, Brasil). O Cretáceo na Bacia de São Luís-Grajaú, 223–233 (2001).52.O’Gorman, J. P. & Varela, A. N. The oldest lower Upper Cretaceous plesiosaurs (Reptilia, Sauropterygia) from southern Patagonia, Argentina. Ameghiniana 47, 447–459 (2010).Article 

    Google Scholar 
    53.Ameghino, F. Sobre la presencia de vertebrados de aspecto mesozoico en la formación Santacruceña de la Patagonia austral. Revista del Jardín Zoológico de Buenos Aires 1, 76–84 (1893).
    Google Scholar 
    54.Zumberge, J. E. Source rocks of the La Luna Formation (Upper Cretaceous) in the Middle Magdalena Valley, Colombia. Petroleum geochemistry and source rock potential of carbonate rocks. AAPG Stud. Geol. 18, 127133 (1984).
    Google Scholar 
    55.Tribovillard, N. P. et al. Cretaceous black shales of Venezuelan Andes: Preliminary results on stratigraphy and paleoenvironmental interpretations. Palaeogeogr. Palaeoclimatol. Palaeoecol. 81, 313–321 (1991).Article 

    Google Scholar 
    56.Bralower, T. J. & Lorente, M. A. Paleogeography and stratigraphy of the La Luna Formation and related Cretaceous anoxic depositional systems. Palaios 18(4–5), 301–304 (2003).Article 

    Google Scholar 
    57.Zapata, E. et al. Biostratigraphic, sedimentologic, and chemostratigraphic study of the La Luna Formation (Late Turonian-Campanian) in the San Miguel and Las Hernández sections, western Venezuela. Palaios 18, 367–377. https://doi.org/10.1669/0883-1351(2003)018%3c0367:BSACSO%3e2.0.CO;2 (2003).Article 

    Google Scholar 
    58.MINISTERIO DE ENERGIA Y MINAS. Lexico Estratigraifico de Venezuela, 3’d ed. Boletin de Geologia 12, 1–828 (1997).
    Google Scholar 
    59.Carrillo Briceño, J. D., Alvarado-Ortega, J. & Patiño Torres, C. Primer registro de Xiphactinus Leidy, 1870 (Teleostei, Ichthyodectiformes) en el Cretácico Superior de América del Sur (Formación La Luna, Venezuela). Revista Brasileira de Paleontologia. 15(3), 327–335 (2012).Article 

    Google Scholar 
    60.Albino, A. M., Rothschild, B., Carrillo-Briceño, J. D. & Neenan, J. M. Spondyloarthropathy in vertebrae of the aquatic Cretaceous snake Lunaophis aquaticus, and its first recognition in modern snakes. Sci. Nat. 105(9), 51 (2018).Article 
    CAS 

    Google Scholar 
    61.Renz, O. Die Ammonoidea im Stratotyp des Vraconien bei Sainte Croix (Kanton Waadt). Schweizerische palaontologische Abhandlungen 87, 1–97 (1968).
    Google Scholar 
    62.Erlich, R. N., Macsotay, O., Nederbragt, A. J. & Lorente, M. A. Palaeoceanography, palaeoecology, and depositional environments of Upper Cretaceous rocks of western Venezuela. Palaeogeogr. Palaeoclimatol. Palaeoecol. 153, 203–238 (1999).Article 

    Google Scholar 
    63.Renz, O. The Cretaceous Ammonites of Venezuela (Birkhäuser Verlag, 1982).
    Google Scholar 
    64.Moody, J. M. & Maisey, J. G. New Cretaceous marine vertebrate assemblages from North-Western Venezuela and their significance. J. Vertebr. Paleontol. 14(1), 1–8 (1994).Article 

    Google Scholar 
    65.Macsotay, O., Erlich, R. N. & Peraza, T. Sedimentary structures of the La Luna, Navay and Querecual Formations, Upper Cretaceous of Venezuela. Palaios 18(4–5), 334–348 (2003).Article 

    Google Scholar 
    66.Davis, C., Pratt, L. & Sliter, W. Factors influencing organic carbon and trace metal accumulation in the Upper Cretaceous La Luna Formation of the western Maracaibo Basin, Venezuela. In Evolution of the Cretaceous Ocean-Climate System Vol. 332 (eds Barrera, E. & Johnson, C. C.) 203–231 (Geological Society of America Special Paper Geological Society of America, 1999).
    Google Scholar 
    67.Martinez, J. I. & Hernandez, R. Evolution and drowning of the Late Cretaceous Venezuelan carbonate platform. J. S. Am. Earth Sci. 5(2), 197–210 (1992).Article 

    Google Scholar 
    68.Ford, A. B., & Houbolt, J. J. H. C. The microfacies of the Cretaceous of western Venezuela: International Sedimentary Petrographical Series 6. EJ Brill, Publisher, Leiden, Germany, 1–109 (1963).69.Caron, M. & Spezzaferri, S. Scanning electron microscope documentation of the lost holotypes of Mornod, 1949: Thalmanninella reicheli and Rotalipora montsalvensis. J. Foraminiferal Res. 36(4), 374–378 (2006).Article 

    Google Scholar 
    70.Veigal, R. & Dzelalija, F. A Regional Overview of the La Luna Formation and the Villeta Groups as Shale Gas/Shale Oil in the Catatumbo Magdalena Valley and Eastern Cordillera Regions, Colombia, Article #10565 21 (American Association of Petroleum Geologists, Search and Discovery, 2014).
    Google Scholar 
    71.Spickert, A. Petroleum System Analysis: Middle Magdalena Valley Basin, Colombia, South America. MSC Report 1–48 (University of Washington, 2014).
    Google Scholar 
    72.De Romero, L. M. et al. An integrated calcareous microfossil biostratigraphic and carbon-isotope stratigraphic framework for the La Luna Formation, western Venezuela. Palaios 18(4–5), 349–366 (2003).Article 

    Google Scholar 
    73.Mendez, C. E. La Formacion La Luna. Caracteristica de una cuenca anoxica en una plataforma de aguas someras. Proceedings of the 7th Congreso Geologico Venezolano, 852–866 (1981).74.Weiler, W. Fischreste aus der Umgebung von San Cristobal, SW. Venezuela. Zentralblatt für Mineralogie Geologie und Paläontologie (B). 1949, 240–255 (1940).
    Google Scholar 
    75.Sánchez-Villagra, M. R., Brinkmann, W. & Lozsán, R. The Paleozoic and Mesozoic vertebrate record of Venezuela: An overview, summary of previous discoveries and report of a mosasaur from the La Luna Formation (Cretaceous). Paläontol. Z. 82(2), 113–124 (2008).Article 

    Google Scholar 
    76.Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics 27(4), 857–871 (1971).Article 

    Google Scholar 
    77.RStudio Team. RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. http://www.rstudio.com/ (2019).78.Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    79.Smith, J. B. & Dodson, P. A proposal for a standard terminology of anatomical notation and orientation in fossil vertebrate dentitions. J. Vertebr. Paleontol. 23, 1–12 (2003).Article 

    Google Scholar 
    80.de Blainville, H. D. Description de quelques espècesde reptiles de la Californie, précédé de l’analyse d’unsystème général d’Erpétologie et d’Amphibiologie. Nouvelles Annales du Museum National d’Histoire Naturelle Paris 4, 233–296 (1835).
    Google Scholar 
    81.Seeley, H. G. Note on some of the generic modifications of the plesiosaurian pectoral arch. Q. J. Geol. Soc. Lond. 30, 436–449 (1874).Article 

    Google Scholar 
    82.McHenry, C. R. Devourer of gods: The palaeoecology of the Cretaceous pliosaur Kronosaurus queenslandicus. Doctoral dissertation, University of Newcastle (2009).83.Sato, T., Hasegawa, Y. & Manabe, M. A new elasmosaurid plesiosaur from the Upper Cretaceous of Fukushima, Japan. Palaeontology 49, 467–484 (2006).Article 

    Google Scholar 
    84.Sachs, S. Redescription of Elasmosaurus platyurus Cope 1868 (Plesiosauria: Elasmosauridae) from the Upper Cretaceous (lower Campanian) of Kansas, USA. Paludicola 5, 92–106 (2005).
    Google Scholar 
    85.Welles, S. P. Elasmosaurid plesiosaurs with a description of new material from California and Colorado. Univ. California Publ. Geol. Sci. 13, 125–215 (1943).
    Google Scholar 
    86.Welles, S. P. A new elasmosaur from the Eagle Ford Shale of Texas. Part I. Systematic description. Fondren Sci. Series 1, 1–28 (1949).
    Google Scholar 
    87.Sachs, S. Remarks on the pectoral girdle of Hydrotherosaurus alexandrae (Plesiosauria: Elasmosauridae). PalArch Vertebr. Palaeontol. 4(1), 1–6 (2005).
    Google Scholar 
    88.O’Gorman, J. P. Elasmosaurid phylogeny and paleobiogeography, with a reappraisal of Aphrosaurus furlongi from the Maastrichtian of the Moreno Formation. J. Vertebr. Paleontol. 39(5), e1692025 (2020).Article 

    Google Scholar 
    89.Sachs, S., Kear, B.P. & Lindgren, J. Re-description of Thalassomedon haningtoni—An elasmosaurid from the Cenomanian of North America. 5th Triennial Mosasaur Meeting—A Global Perspective on Mesozoic Marine Amniotes. Abstracts and Program (Sachs, S., Kear, B.P. & Lindgren, eds.), 38–40 (2016).90.Sachs, S. & Kear, B. P. Postcranium of the paradigm elasmosaurid plesiosaurian Libonectes morgani (Welles, 1949). Geol. Mag. 152, 694–710 (2015).Article 

    Google Scholar 
    91.Sachs, S. & Kear, B. P. Redescription of the elasmosaurid plesiosaurian Libonectes atlasense from the Upper Cretaceous of Morocco. Cretac. Res. 74, 205–222 (2017).Article 

    Google Scholar 
    92.Sachs, S., Lindgren, J., Madzia, D. & Kear, B. P. Cranial osteology of the mid-Cretaceous elasmosaurid Thalassomedon haningtoni from the Western Interior Seaway of North America. Cretaceous Res. 123,104769 (2021).93.McKean, R. S. A new species of polycotylid plesiosaur (Reptilia: Sauropterygia) from the Lower Turonian of Utah: Extending the stratigraphic range of Dolichorhynchops. Cretac. Res. 34, 184–199 (2012).Article 

    Google Scholar 
    94.Sachs, S., Madzia, D., Püttmann, T. & Kear, B. P. Enigmatic plesiosaur vertebral remains from the middle Turonian of Germany. Cretaceous Res. 110, 104406 (2020).Article 

    Google Scholar  More

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    Effects of different returning method combined with decomposer on decomposition of organic components of straw and soil fertility

    Site descriptionThe experimental was conducted in Gengzhuang Town, Haicheng(40° 48′ N, 122° 37′ E), Liaoning Province from 2019 to 2020. This area is belonged to the continental monsoon climate zone of warm temperate zone, the annual average temperature is above 10 °C, the annual accumulated temperature is 3000–3100 °C, the frost-free period is about 170 days, and the annual rainfall is 600–800 mm. The soil at the experimental site is classified as brown earth29. Before the experiment, this test field had been in rotary tillage mode each year, with no straw return. The concentration of soil organic carbon, total nitrogen, available nitrogen, available phosphorus, available potassium, and soil bulk density in 0–20 cm surface layer were 12.50 g kg−1, 0.89 g kg−1, 129.6 mg kg−1, 25.96 mg kg−1, 117.94 mg kg−1, and 1.53 g cm−3, respectively. The components and nutrient contents of corn straw were shown in Table 1, The average temperature and precipitation from May 2019 to May 2020 are shown in Fig. 1.Table 1 Initial component content of corn straw.Full size tableFigure 1Daily precipitation and mean air temperature during the straw decomposition period from May 2019 to May 2020.Full size imageExperimental design and managementWe adopted a split plot design, with the main plot as the cultivation method, and with three cultivation options: no-tillage, deep loosening + deep rotary tillage and rotary tillage. Then, when adding straw as a decomposer, two methods were used: adding straw decomposer and not adding it. Our experimental approach included six treatments: No-tillage and straw mulching to the field + straw decomposer (NT + S); no-tillage and straw mulching to the field + no straw decomposer (NT); rotary tillage and straw mixed into the soil + straw decomposer (RT + S); rotary tillage and straw mixed into the soil + no straw decomposer (RT); deep loosening + deep rotary tillage and straw return to the field + straw decomposer (PT + S); and deep loosening + deep rotary tillage and straw return to the field + no straw decomposer (PT). Each treatment was replicated 3 times, located in random blocks, with a total plot area of 68.4 m2. At the same time as the previous year’s corn harvest, straw was returned to the field, crushed to about 10 cm long, spread evenly on the ground. No-tillage mulching and straw return to the field is the direct no-tillage maize sowing operation in spring; In the deep loosening + deep rotary tillage treatment, the soil is turned using a subsoiler to a depth of 35 cm, and then the straw is mixed into the soil through deep rotary tillage (a depth of 30 cm); and rotary tillage involves mixing straw into the soil with a rotary tiller to a depth of 20 cm and then raking it flat.Using the nylon net bag method (mesh bags were 5 cm × 6 cm, small; 15 cm × 20 cm, medium; and large, 25 cm × 35 cm; each size with an aperture of 100 mesh), we simulated three return modes. Soil added to the net bags was taken from the top 0–20 cm prior to sowing in 2019, in the corresponding plots of each treatment. Corn stalks were added at a ratio of 5:4 per stem and leaf (dry weight of stem and leaf of corn stalks in mature stage), and crushed to 2 cm long. In no-tillage treatment, 10 g straw was added to the medium mesh bag, in the rotary tillage and deep loosening + deep rotary tillage treatment, 10 g straw was evenly divided into five parts and put into five small net bags, then the five small net bags were evenly mixed into the soil of the outer large net bag and sealed, the weight of soil added to each large net bag is 2 kg, and the compactness between the inner net bag and the soil in the outer net bag was adjusted.Net bag layout was determined according to different treatment tillage patterns, and bags were placed in the field on seeding day in 2019. Deep loosening + deep rotary tillage was achieved by ploughing furrows 30 cm long, 15 cm wide, and 35 cm deep between corn rows in corresponding plots, large net bags were buried vertically in the furrows, filled with soil and compacted, so that return depth and straw distribution were basically the same as deep loosening + deep rotary tillage in the field. Rotary tillage mode was achieved by ploughing furrows 30 cm long, 15 cm wide, and 20 cm deep between corn rows in the corresponding treatment plot, the packed net bags were tilted in the furrows, filled with soil and moderately compacted, the top end of the net bags was level with the ground surface, which is basically consistent with the return depth of rotary tillage and straw distribution in actual field production. No-tillage mulching treatments involved laying the net bags containing straw on the ground and covering the four corners with soil to prevent the net bag from being blown away by the wind. The decomposer addition treatments involved evenly spraying c. 6.5 ml straw decomposer on the straw surface before bagging, in the treatment without decomposer, 6.5 ml water was sprayed on the surface of straw to maintain the same water content.In all treatments we applied the same amount of N, P and K (N 240 kg hm−2, P2O5 74 kg hm−2 and K2O 89 kg hm−2). The nitrogen fertilizer was urea, the phosphate fertilizer superphosphate, and the potassium fertilizer, potassium chloride. The brand of straw decomposing agent is Gainby and the model number is d-68 (created by NORDOX company and produced by Beijing Shifang Biotechnology Co., Ltd.). Straw decomposer dosage was 1.5 kg hm−2, diluted with water 100 times, and the effective viable bacteria number was ≥ 50 million g−1. The effective bacteria in the decomposer include: Bacillus licheniformis, Aspergillus niger and Saccharomyces cerevisiae and so on.Sampling and analysis methodsOn the 15th, 35th, 55th, 75th, 95th, 145th and 365th day after the nylon net bags were placed in the field plots, 3 bags were randomly sampled from each plot. For each net bag, we first washed the surface soil off with tap water, then washed the sample with distilled water 3 times, dried it at 60 °C, weighed it and then ground it to deter-mine the decomposition rate of straw and its components. At the same time, in the no-tillage treatments, 200 g soil was taken from 0 to 5 cm below the straw net bag, in rotary tillage and deep loosening + deep rotary tillage treatments, 200 g soil from net bag was taken for the determination of soil SOC, MBC and DOC. Content of cellulose, hemicellulose and lignin in straw were determined following Van’s method30, using a SLQ-6A semi-automatic crude fiber analyzer (Shanghai Fiber Testing Instrument Co., Ltd.).The following formula was used to calculate decomposition rate of straw and its components. M0 is the initial straw or cellulose (hemicellulose, lignin) mass, g, and Mt is the straw or cellulose (hemicellulose, lignin) mass at time t, g.$$mathrm{Decomposition ; proportion }left({%}right)= frac{{M}_{ 0}-{ M}_{ t}}{{M , }_{0}}times 100.$$
    (1)
    The following formula was used to calculate the straw carbon release proportion. C0 is the initial straw carbon content, g, Ct is the straw carbon content at time t, g.$$mathrm{Straw ; carbon ; release ; proportion }left({%}right)= frac{{C}_{ 0} -{ C }_{t}}{{C }_{0}}times 100.$$
    (2)
    The following formula was used to calculate the straw and its components decomposition rate. M365 is the quality of straw or cellulose (hemicellulose, lignin) mass on the 365th day, mg day−1.$$mathrm{Decomposition ; rate }left(mathrm{mg }{mathrm{day}}^{-1}right)= frac{{M}_{ 0 }- {M}_{365}}{365}.$$
    (3)
    The relationship of the straw decomposition proportion (%) changes over time was fitted as follows:$${y}_{t} = a+btimes exp left(-ktright),$$
    (4)
    where yt is the proportion of the straw decomposition proportion at time t, %; t is the decomposition time of straw; k is the decomposition rate constant calculated using the least-squares method; a and b are constants.SOC concentrations (g kg−1) was determined using the K2Cr2O7–H2SO4 digestion method31. Soil MBC content was determined using the Chloroform fumigation extraction method32. Two fresh soil samples were weighed, and then one of them was placed in a vacuum dryer with chloroform added, and pumped until the chloroform boiled violently, and after a period of time, the dryer cover was opened, the container containing chloroform removed, and the lid replaced. Another portion of soil was placed in a vacuum dryer without chloroform as a control. Then, 20 g each of fumigated and unfumigated soil samples were weighed, 50 mL 0.5 mg L−1 K2SO4 was added, extracted by vibration for 0.5 h, filtrate was pumped by 0.45 μm organic filter membrane, and then the filtrate was directly analyzed and detected using a TOC organic carbon analyzer. Based on the difference of organic C content between fumigated and unfumigated soil extracts, the microbial biomass carbon was obtained by multiplying the coefficient by 2.64. For the determination of soil DOC content, we used a slightly modified method of Jones33 and Hu Haiqing34. We made a leaching solution with 0.5 mol L K2SO4, weighed 10 g over 2 mm sieve of air dried soil, added the soil to the leaching solution to create a soil mass ratio of 2.5:1, and then applied a shock temperature for 1 h (220 r min−1). Then, after filtering, the filtrate was centrifuged for 20 min (3800 r min−1), filtered with a 0.45 μm organic membrane, and the filtrate subjected to TOC organic carbon analysis meter tests.Data analysisIn this experiment, Excel 2016 (Microsoft Corporation, New Mexico, USA) software was used to collate and analyze the data, and SPSS 19.0 (SPSS Inc., Chicago, Illinois, USA) statistical software was used to conduct variance analysis, LSD multiple analysis comparison and nonlinear regression analysis on the data. Duncan’s multiple range test was used to compare the treatment means at a 95% confidence level. Graphs were drawn using Origin 9.0 (Originlab, Northampton, USA). More

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    Honing in on bioluminescent milky seas from space

    We processed DNB imagery from three key regions per the historical record of mariner sightings2—the northwest Indian Ocean (5° S–20° N, 40–70° E) and Indonesian waters surrounding Java (15° S–0°, 100–115° E) and the Banda Sea (11–1° S, 120–135° E). Our search window spanned 2012–2021, during the periods December–March and July–September corresponding to the peak modes of ship sightings. Data processing and detection criteria are described in “Methods” section.Our search yielded 12 DNB-detected events (listed in Table 1) whose properties met the strict criteria for milky seas. Physically unexplainable in terms of thermal emissions (which would require scene temperatures exceeding 600 K), uncorrelated with clouds/airglow, invisible during the day, and persistent over multiple consecutive nights, these luminous bodies drifted and evolved in ways that were consistent with the analyzed ocean surface currents. The start and end dates of detection were in many cases bound by the observable periods as defined by the lunar cycle. Here, we highlight three exemplary cases, with additional details for all cases summarized in Supplementary Discussion 2.Table 1 Day/Night Band detected milky sea events identified in this study.Full size tableSocotra, July/August 2013On 31 July 2013, the Suomi NPP DNB detected a luminous body with well-defined boundaries (Fig. 2), located east of Socotra in the northwest Indian Ocean, at (14.0° N, 57.0° E). Uncorrelated with the observed cloud field (Fig. 2a–c), the body drifted northeast with the currents at ~ 0.44 m s−1, stretching and curving in a manner consistent with the analysed ocean-surface currents (Fig. 2d–f), which showed a clockwise-rotating eddy located to its south.Figure 2Three-night sequence over 2–4 August 2013 of a bioluminescent milky sea in the Arabian Sea for (a–c) DNB log10—scaled radiance imagery (W cm−2 sr−1), showing a ~ 9000 km2 luminous body persisting amidst the ephemeral cloud cover, and (d–f) a pan-out of HYCOM sea surface currents (magenta box in (d) corresponds to domain of (a–c), with approximate location of the luminous body noted) shown for comparison against the body’s observed structural evolution and drift.Full size imageWhereas DNB imagery showed only dark ocean during the daytime overpasses of the same location, these glowing waters persisted on successive nights over a two-week period. By 2 August the milky sea covered ~ 9000 km2 (involving roughly 5 × 1021 to 5 × 1022 luminous bacteria, per “Methods” section). The DNB lost sight of the milky sea on 14 August due to moonlight, and it was not seen again in the following moon-free period.Suomi NPP’s daytime chlorophyll-a (Chla) retrievals, a proxy for the amount of organic material in the surface waters, showed structural similarities to the milky sea, but were more widespread (Supplementary Fig. S1). Moreover, the most elevated regions of Chla ( > 1 mg m−3) occurred not directly atop, but adjacent to the most luminous waters—a recurring property among the cases documented in this research which may indicate regions of algal stress where (potentially luminous) bacteria would proliferate. On several nights, a faint signature of the luminous body was detectable beneath analyzed cloud cover; its light scattering upward through the clouds in a way similar to the behaviour of city lights in DNB imagery.Somali Sea, January 2018On 12 January 2018, both Suomi NPP and NOAA-20 captured a luminous structure offshore of southern Somalia. Over the next 5 days it stretched into a narrow filament that paralleled the Somali coast, mirroring the behaviour of other winter-mode Somali Sea cases described in Supplementary Discussion 2. Over 18–23 January, the luminous filament extended east/northeast, forming a comma-shape (Fig. 3a–c), with a sharply-defined southeastern edge and gradually fading brightness on its northwestern side. By 20 January, it spanned ~ 15,000 km2, suggesting involvement of roughly 8 × 1021 to 8 × 1022 bacteria. Its scale, shape, time, and location were similar to the Lima-sighted milky sea4,14, as well as to a subset of the surface reports in Supplementary Discussion 1.Figure 3Three-night sequence over 20–22 January 2018 of a bioluminescent milky sea in the Somali Sea for (a–c) DNB log10 – scaled radiance imagery (W cm−2 sr−1), showing a ~ 15,000 km2 luminous feature persistent amidst the variable cloud field. Focusing on 22 January, (d) VIIRS-derived night time SST (K; with cloud cover in black) at 2156Z, (e) daytime VIIRS-retrieved Chla (mg m−3) at 1007Z, and (f) pan-out of HYCOM sea surface currents at 2100Z with approximate location of DNB-observed luminous body.Full size imageComparing the luminous body to satellite retrievals of Sea Surface Temperature (SST; Fig. 3d) showed its eastern boundary aligned with the edge of an oceanic front, residing within relatively cool (298–299 K) waters that extended northeast from the Somali coastal upwelling zone. These cooler SSTs corresponded to elevated Chla values in the range of ~ 0.5–1.0 mg m−3 (Fig. 3e). As in the 2013 Socotra case, the area of elevated Chla was more extensive than the luminous region. Ocean surface currents (Fig. 3f) showed the body’s eastern boundary embedded within counter-clockwise flow and drifting north/northwest at ~ 0.8 m s−1.Java, July–September 2019The DNB detected a large milky sea in the east Indian Ocean, immediately south of Java, Indonesia in 2019. The event spanned two complete moon-free cycles (26 July–9 August, and 25 August–7 September). On the night of 25 July, the DNB detected a luminous anomaly south of Surakarta, Java, near 9.5° S, 111° E. The detection amidst moderate moonlight conditions suggested a particularly strong source of emission. Imagery on subsequent moonless nights confirmed that the initial detection was in fact part of a much larger milky sea, spanning ~ 100,000 km2—approximately the same size as Iceland. A milky sea of this scale suggests involvement of roughly 6 × 1022 to 6 × 1023 luminous bacteria, which would qualify as the largest event on record. Undetectable during the day, the contiguous feature reappeared in nightly imagery throughout the two observable moon-free periods (Fig. 4).Figure 4Day/night comparison of DNB log10—scaled radiance imagery (W cm−2 sr−1) of a bioluminescent milky sea near Java for the period 2–4 August 2019 for (a–c) daytime imagery, and (d–f) night time imagery. The amorphous luminous body, located immediately south of Java and detectable only at night, covered ~ 100,000 km2 of ocean surface. Bright patches seen over Java in (d–f) are city lights.Full size imageSituated within quiescent, low-shear waters (Supplementary Fig. S2) between counter-clockwise-spinning warm-core eddies to its southeast and southwest (Fig. 4), this massive milky sea rotated clockwise like a cog between gears, its centre near 9.0° S, 110.0° E. By 30 July, its northern boundary approached within 25 km of the Java coast, and a 500 km2 area of its core was so bright that certain infrared-detected cumulus clouds appeared in the DNB imagery as dark, attenuating objects in contrast to the glowing waters below them (Supplementary Fig. S3).The DNB radiances measured in the brightest areas of these luminous waters approached Crescent- to Quarter-Moon illumination levels. Based on scotopic vision sensitivity to bioluminescent emission (Supplementary Fig. S4) and direct comparisons against legacy OLS imagery (Supplementary Fig. S5), portions of this milky sea may have appeared visually bright to dark-adapted human vision—perhaps even attaining the classical snowfield effect described in the historical mariner accounts.After losing sight of the luminous body on 10 August due to moonlight contamination, the DNB recaptured it on 25 August and tracked it for 2 weeks thereafter, as described in Supplementary Discussion 2. The longevity of this event, which lasted for at least 45 nights, by far eclipsed all other cases encountered in this study—indicating that significant milky seas offer a reasonable time window for reaching them if a rapid-response team is on the ready.Satellite retrievals of ocean surface properties for the 2019 Java case (Fig. 5) showed cooler waters and elevated Chla along the Java coast. A stream of higher Chla ( > 2 mg m−3), embedded within a tongue of these cooler waters, extended southward and immediately east of the luminous body, following the flow of the south-eastern eddy (Fig. 5b). At this time, the luminous waters were confined to a narrow range of SST over 298 ± 1 K (~ 25 ± 1 °C) and moderate Chla over 1 ± 0.5 mg m−3, demarcated from the surrounding waters at abrupt boundaries defined by coastal upwelling to the north and the two eddies to the south.Figure 5Multi-parameter analysis of the 2019 Java milky sea on 2 August 2019 for (a) night time DNB log10—scaled radiance imagery (W cm−2 sr−1) at 1752Z, (b) pan-out of HYCOM sea surface currents valid at 1800Z (magenta box shows domain of (a), with approximate location of luminous body shaded), (c) VIIRS-retrieved SST (with cloud cover and land surfaces in black) valid at 1752Z, and (d) daytime VIIRS-retrieved Chla at 0554Z.Full size imageFigure 6 relates DNB radiance, SST, and Chla for 10 nights (27 July–5 August) of the 2019 Java case, centred on the luminous body. DNB radiances correlated positively with Chla over 0.5–1.5 mg m−3, and negatively with SST over 297–299 K. The brightest milky sea waters corresponded to asymptotic SST values of ~ 298 K and Chla of ~ 1.2 mg m−3, respectively, and notably, did not overlap with the strongest parts of the algal bloom. The SST values, hovering around 298 K for most DNB-detected cases in this study, may hold significance, as this temperature regime promotes rapid growth of V. harveyi and P. leiognathi over a wide range of ocean salinity values20.Figure 6Relationship between DNB-measured milky sea radiances and ocean surface fields for the 2019 Java case (27 July–5 August, centred on the luminous body). Radiance-specific distributions (i.e., for a given radiance level, each row sums to 100%) are shown as a function of (a) SST and (b) Chla. The DNB noise floor (where SNR = 1) is drawn as a horizontal dashed line.Full size image More

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    Colour and motion affect a dune wasp’s ability to detect its cryptic spider predators

    1.Smith, M. Q. R. P. & Ruxton, G. D. Camouflage in predators. Biol. Rev. 63, 178–216 (2020).
    Google Scholar 
    2.Anderson, A. G. & Dodson, G. N. Colour change ability and its effect on prey capture success in female Misumenoides formosipes crab spiders. Ecol. Entomol. 40, 106–113 (2015).Article 

    Google Scholar 
    3.Gonzálvez, F. G. & Rodríguez-Gironés, M. A. Seeing is believing: information content and behavioural response to visual and chemical cues. Proc. R. Soc. Lond. Ser. B Biol. Sci. 280, 20130886–20130888 (2013).
    Google Scholar 
    4.Schwantes, C. J., Carper, A. L. & Bowers, M. D. Solitary floral specialists do not respond to cryptic flower-occupying predators. J. Insect Behav. 31, 642–655 (2018).Article 

    Google Scholar 
    5.Cronin, T. W., Johnsen, S., Marshall, N. J. & Warrant, E. J. Visual Ecology (Princeton University Press, Princeton, 2014).Book 

    Google Scholar 
    6.Caves, E. M., Brandley, N. C. & Johnsen, S. Visual acuity and the evolution of signals. Trends Ecol. Evol. 33, 1–15 (2018).Article 

    Google Scholar 
    7.Burnett, N. P., Badger, M. A. & Combes, S. A. Wind and obstacle motion affect honeybee flight strategies in cluttered environments. J. Exp. Biol. 223, jeb222471-9 (2020).
    Google Scholar 
    8.Hennessy, G. et al. Gone with the wind: effects of wind on honey bee visit rate and foraging behaviour. Anim. Behav. 161, 23–31 (2020).Article 

    Google Scholar 
    9.Thery, M. & Casas, J. The multiple disguises of spiders: web colour and decorations, body colour and movement. Philos. Trans. R. Soc. B Biol. Sci. 364, 471–480 (2009).Article 

    Google Scholar 
    10.Oxford, G. & Gillespie, R. Evolution and ecology of spider coloration. Annu. Rev. Entomol. 43, 619–643 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Rodríguez-Morales, D. et al. Context-dependent crypsis: a prey’s perspective of a color polymorphic predator. Sci. Nat. 105, 81 (2018).Article 
    CAS 

    Google Scholar 
    12.Gavini, S. S., Quintero, C. & Tadey, M. Ecological role of a flower-dwelling predator in a tri-trophic interaction in northwestern Patagonia. Acta Oecol. 95, 100–107 (2019).ADS 
    Article 

    Google Scholar 
    13.Morse, D. H. Predatory risk to insects foraging at flowers. Oikos 46, 223–228 (1986).Article 

    Google Scholar 
    14.Brechbuhl, R., Casas, J. & Bacher, S. Ineffective crypsis in a crab spider: a prey community perspective. Proc. R. Soc. Lond. Ser. B Biol. Sci. 277, 739–746 (2010).
    Google Scholar 
    15.Rodríguez-Gironés, M. A. & Maldonado, M. Detectable but unseen: imperfect crypsis protects crab spiders from predators. Anim. Behav. 164, 83–90 (2020).Article 

    Google Scholar 
    16.Heiling, A., Herberstein, M. & Chittka, L. Pollinator attraction: crab-spiders manipulate flower signals. Nature 421, 334–334 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Llandres, A. L. & Rodríguez-Gironés, M. A. Spider movement, UV reflectance and size, but not spider Crypsis, affect the response of honeybees to Australian crab spiders. PLoS ONE 6, e17136–e17211 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Vieira, C., Ramires, E. N., Vasconcellos-Neto, J., Poppi, R. J. & Romero, G. Q. Crab spider lures prey in flowerless neighborhoods. Sci. Rep. 7, 1–7 (2017).Article 
    CAS 

    Google Scholar 
    19.Robertson, I. C. & Maguire, D. K. Crab spiders deter insect visitations to slickspot peppergrass flowers. Oikos 109, 577–582 (2005).Article 

    Google Scholar 
    20.Yokoi, T. & Fujisaki, K. Hesitation behaviour of hoverflies Sphaerophoria spp. to avoid ambush by crab spiders. Sci. Nat. 96, 195–200 (2008).Article 
    CAS 

    Google Scholar 
    21.Defrize, J., Thery, M. & Casas, J. Background colour matching by a crab spider in the field: a community sensory ecology perspective. J. Exp. Biol. 213, 1425–1435 (2010).PubMed 
    Article 

    Google Scholar 
    22.Reader, T., Higginson, A. D., Barnard, C. J. & Gilbert, F. S. The effects of predation risk from crab spiders on bee foraging behavior. Behav. Ecol. 17, 933–939 (2006).Article 

    Google Scholar 
    23.Ings, T. & Chittka, L. Speed-accuracy tradeoffs and false alarms in bee responses to cryptic predators. Curr. Biol. 18, 1520–1524 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Ings, T. C., Wang, M. Y. & Chittka, L. Colour-independent shape recognition of cryptic predators by bumblebees. Behav. Ecol. Sociobiol. 66, 487–496 (2011).Article 

    Google Scholar 
    25.Collett, T. S. & Zeil, J. Flights of learning. Curr. Dir. Psychol. Sci. 5, 149–155 (1996).Article 

    Google Scholar 
    26.Stürzl, W., Zeil, J., Boeddeker, N. & Hemmi, J. M. How wasps acquire and use views for homing. Curr. Biol. 26, 470–482 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    27.Zeil, J., Kelber, A. & Voss, R. Structure and function of learning flights in bees and wasps. J. Exp. Zool. A Ecol. Genet. Physiol. 199, 245–252 (1996).CAS 

    Google Scholar 
    28.Egelhaaf, M., Boeddeker, N., Kern, R., Kurtz, R., & Lindemann, J. P. Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action. Front. Neural Circuits. 6, 1–23 (2012).Article 

    Google Scholar 
    29.Lehrer, M. Small-scale navigation in the honeybee: active acquisition of visual information about the goal. J. Evol. Biol. 199, 253–261 (1996).CAS 

    Google Scholar 
    30.Lehrer, M. & Campan, R. Shape discrimination by wasps (Paravespula germanica) at the food source: generalization among various types of contrast. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 190, 1–13 (2004).Article 

    Google Scholar 
    31.Nityananda, V., Skorupski, P. & Chittka, L. Can bees see at a glance?. J. Exp. Biol. 217, 1933–1939 (2014).PubMed 

    Google Scholar 
    32.Kral, K. & Poteser, M. Motion parallax as a source of distance information in locusts and mantids. J. Insect Behav. 10, 145–163 (1997).Article 

    Google Scholar 
    33.Dukas, R. Effects of predation risk on pollinators and plants. in Cognitive ecology of pollination 214–236 (Cambridge University Press, Cambridge, 2019).
    Google Scholar 
    34.Rodríguez-Morales, D. et al.. Response of flower visitors to the morphology and color of crab spiders in a coastal environment of the Gulf of Mexico. Isr. J. Ecol. Evol. 66, 32–40 (2019).Article 

    Google Scholar 
    35.Uexküll, J. V. A Foray Into the Worlds of Animals and Humans: With a Theory of Meaning Vol. 12 (University of Minnesota Press, Minnesota, 2013).
    Google Scholar 
    36.Caves, E. M., Nowicki, S. & Johnsen, S. V. Uexküll revisited: addressing human biases in the study of animal perception. Integr. Comp. Biol. 215, 1184–1212 (2019).
    Google Scholar 
    37.Álvarez-Molina, L. L. et al. Biological flora of coastal dunes and wetlands: Palafoxia lindenii A. Gray. J. Coast. Res. 29, 680–693 (2013).
    Google Scholar 
    38.Evans, H. E., O’Neill, K. M. & Evans, H. E. The Sand Wasps: Natural History and Behavior (Harvard University Press, Harvard, 2009).
    Google Scholar 
    39.Alcock, J. & Ryan, A. F. The behavior of microbembex nigrifons. Pan-Pac. Entomol. 49, 144–148 (1973).
    Google Scholar 
    40.Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).CAS 
    Article 

    Google Scholar 
    42.Peitsch, D. et al. The spectral input systems of hymenopteran insects and their receptor-based colour vision. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 170, 23–40 (1992).CAS 
    Article 

    Google Scholar 
    43.Feller, K. D. et al. Surf and turf vision: patterns and predictors of visual acuity in compound eye evolution. Arthropod Struct. Dev. 60, 101002 (2021).PubMed 
    Article 

    Google Scholar 
    44.van den Berg, C. P., Troscianko, J., Endler, J. A., Marshall, N. J. & Cheney, K. L. Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature. Methods Ecol. Evol. 11, 316–332 (2019).Article 

    Google Scholar 
    45.Meijering, E., Dzyubachyk, O. & Smal, I. Methods for cell and particle tracking. Methods Enzymol. 504, 183–200 (2012).PubMed 
    Article 

    Google Scholar 
    46.McLean, D. J. & Volponi, M. A. S. trajr: An R package for characterisation of animal trajectories. Ethology 124, 440–448 (2018).Article 

    Google Scholar 
    47.Fu, A.W.-C., Keogh, E., Lau, L. Y. H., Ratanamahatana, C. A. & Wong, R.C.-W. Scaling and time warping in time series querying. VLDB J. 17, 899–921 (2008).Article 

    Google Scholar 
    48.Hu, B., Chen, Y., & Keogh, E. Time series classification under more realistic assumptions. in Proceedings of the 2013 SIAM international conference on data mining 578–586 (Society for Industrial and Applied Mathematics, 2013).
    Google Scholar 
    49.Keogh, E. & Ratanamahatana, C. A. Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005).Article 

    Google Scholar 
    50.Pewsey, A., Neuhäuser, M. & Ruxton, G. D. Circular Statistics in R (Oxford University Press, Oxford, 2013).MATH 

    Google Scholar  More

  • in

    Habitat monitoring and conservation prioritization of Western Hoolock Gibbon in upper Brahmaputra Valley, Assam, India

    1.Brown, J. H., Mehlman, D. W. & Stevens, G. C. Spatial variation in abundance. Ecology 76, 2028–2043 (1985).Article 

    Google Scholar 
    2.Rylands, A. B. Primate communities in Amazonian forests: Their habitats and food resources. Experientia 43, 267–279 (1987).Article 

    Google Scholar 
    3.Chapman, C. A. & Peres, C. A. Primate conservation in the new millennium: The role of scientists. Evol. Anthropol. 10, 16–33 (2001).Article 

    Google Scholar 
    4.Anderson, J., Cowlishaw, G. & Rowcliff, J. M. Effects of forest fragmentation on the abundance of Colobus angolensis palliates in Kenya’s coastal forests. Int. J. Primatol. 28, 637–655 (2007).Article 

    Google Scholar 
    5.Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportion of suitable habitat: A review. Oikos 7, 340–346 (1994).
    Google Scholar 
    6.Marsh, L. K. Primates in Fragments: Ecology and Conservation (Kluwer/Plenum, 2003).Book 

    Google Scholar 
    7.Harcourt, A. H. Ecological indicators of risk for primates, as judged by susceptibility to logging. In Behavioral Ecology and Conservation Biology (ed Caro, T. M.) pp. 56–79. (Oxford University Press, 1998).8.Harcourt, A. H. Empirical estimates of minimum viable population sizes for primates: Tens to tens of thousands?. Anim. Conserv. 5, 237–244 (2002).Article 

    Google Scholar 
    9.Lindenmayer, D. B. Future directions for biodiversity conservation in managed forests: Indicator species, impact studies and monitoring programs. For. Ecol. Manag. 115, 277–287 (1999).Article 

    Google Scholar 
    10.Das, J. et al. Distribution of hoolock gibbon (Bunopithecus hoolock hoolock) in India and Bangladesh. Zoos Print J. 18, 969–976 (2003).Article 

    Google Scholar 
    11.Das, J., Biswas, J., Bhattacherjee, P. C. & Mohnot, S. M. The distribution and abundance of hoolock gibbons in India. In The Gibbons: New Perspectives on Small Ape Socioecology and Population Biology (eds Lappan, S. & Whittacker, D. J.) 409–433 (Springer, 2009).Chapter 

    Google Scholar 
    12.Islam, M. A. & Feeroz, M. M. Ecology of hoolock gibbons in Bangladesh. Primates 33, 451–464 (1992).Article 

    Google Scholar 
    13.Brockelman, W. Y. et al. Census of eastern hoolock gibbons (Hoolock leuconedys) in Mahamyaing Wildlife Sanctuary, Sagaing Division, Myanmar. In The Gibbons: New Perspectives on Small Ape Socioecology and Population Biology (eds Lappan, S. & Whittaker, D. J.) 435–452 (Springer, 2009).Chapter 

    Google Scholar 
    14.Fan, F. P. et al. Distribution and conservation status of the vulnerable eastern hoolock gibbon Hoolock leuconedys in China. Oryx 45, 129–134 (2011).Article 

    Google Scholar 
    15.Kumar, A., Devi, A., Gupta, A.K., & Sarma, K. Population and Behavioural Ecology and Conservation of Hoolock Gibbon in Northeast India. In: Rare Animals of India (ed Singaravelan, N) 242–266 (Bentham Science Publisher, 2013).16.Kakati, K. Impact on Forest Fragmentation on the Hoolock Gibbon in Assam, India. PhD thesis, University of Cambridge.17.Ray, P. C. et al. Habitat characteristics and their effects on the density of groups of western hoolock gibbon (Hoolock hoolock) in Namdapha National Park, Arunachal Pradesh, India. Int. J. Primatol. 36(3), 445–459 (2015).Article 

    Google Scholar 
    18.Leighton, D.R. Gibbons: Territoriality and monogamy. In Primate Societies (ed Smuts, B. B. et al.) 135–145 (University of Chicago Press, 1987).19.Palombit, R. A. A preliminary study of vocal communication in wild long-tailed macaques (Macaca fascicularis). II. Potential of calls to regulate intragroup spacing. Int. J. Primatol. 13, 183–207 (1992).Article 

    Google Scholar 
    20.Das, J. Socioecology of hoolock gibbon Hylobates hoolock hoolock (Harlan, 1834) in Response to Habitat Change. PhD thesis. Department of Zoology, Gauhati University, Guwahati, India (2002).21.Sarma, K. Studies on Population Status, Behavioural and Habitat Ecology of Eastern Hoolock gibbon (Hoolock leuconedys) in Arunachal Pradesh, India. PhD thesis. Department of Forestry, North Eastern Regional Institute of Science & Technology (NERIST), Itanagar, India (2015).22.Kakati, K. Food Selection and Ranging in the Hoolock Gibbon (Hylobates hoolock) in Borajan Reserve Forest, Assam. MSc dissertation. Wildlife Institute of India, Dehradun, India (1997).23.Sharma, N., Madhusudan, M. D. & Sinha, A. Local and landscape correlates of primate distribution and persistence in the remnant lowland rainforests of the Upper Brahmaputra valley, northeastern India. Conserv. Biol. 28, 95–106 (2013).PubMed 
    Article 

    Google Scholar 
    24.Hanson, J.O., Schuster, R., Morrell, N., Strimas-Mackey, M., Watts, M.E., Arcese, P., Bennett, J., & Possingham, H.P. prioritizr: Systematic conservation prioritization in R. Available at https://github.com/prioritizr/prioritizr (2018).25.Champion, H. G. & Seth, S. K. Revised Survey of Forest Types of India (Manager of Publications, 1968).
    Google Scholar 
    26.Deka, R. L., Mahanta, C., Pathak, H., Nath, K. K. & Das, S. Trends and fluctuations of rainfall regime in the Brahmaputra and Barak basins of Assam, India. Theor. Appl. Climatol. 114, 61–71 (2013).ADS 
    Article 

    Google Scholar 
    27.Nath, K. K. & Deka, R. L. Climate change and agriculture over Assam. In Climate Change and Agriculture Over India (eds Rao, G. S. L. H. V. et al.) 224–243 (PHI Learning Private Ltd., 2010).
    Google Scholar 
    28.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    29.Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

    Google Scholar 
    30.Phillips, S.J., Dudík, M., & Schapire, R.E. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning 655–662 (2004).31.Flory, A. R., Kumar, S., Stohlgren, T. J. & Cryan, P. M. Environmental conditions associated with bat whitenose syndrome mortality in the north-eastern United States. J. Appl. Ecol. 49, 680–689 (2012).
    Google Scholar 
    32.Mas, J. Monitoring land-cover changes: A comparison of change detection techniques. Int. J. Remote Sens. 20, 139–152 (1999).ADS 
    Article 

    Google Scholar 
    33.Hazarika, N., Das, A. & Borah, S. Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra plains, India, using RS-GIS techniques. Egypt. J. Remote. Sens. 39, 107–118 (2015).
    Google Scholar 
    34.Twisa, S. & Buchroithner, M. F. Land-use and land-cover (LULC) change detection in Wami River Basin, Tanzania. Land 8, 1–15 (2019).Article 

    Google Scholar 
    35.Garcia, M. & Alvarez, R. TM digital processing of a tropical forest region in southern Mexico. Int. J. Remote Sens. 15, 1611–1632 (1994).ADS 
    Article 

    Google Scholar 
    36.Xiao, H. & Weng, Q. The impact of land use and land cover changes on land surface temperature in a karst area of China. J. Environ. Manag. 85, 245–257 (2007).Article 

    Google Scholar 
    37.Gao, J. & Liu, Y. Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. Int J Appl Earth Obs Geoinf 12, 9–16 (2010).Article 

    Google Scholar 
    38.Richards, J. A. & Jia, X. Interpretation of hyperspectral image data. In Remote Sensing Digital Image Analysis: An Introduction 359–388 (Springer, 2006).
    Google Scholar 
    39.Rosenfield, G. H. & Fitzpatrick-Lins, K. A coefficient of agreement as a measure of thematic classification accuracy. PhotogrammEng Remote Sens. 52, 223–227 (1986).
    Google Scholar 
    40.Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46 (1991).ADS 
    Article 

    Google Scholar 
    41.Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
    Article 

    Google Scholar 
    42.McGarigal, K., Cushman, S.A., Neel, M.C., & Ene, E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at www.umass.edu/landeco/research/fragstats/fragstats.html (2002).43.Hanson, J.O., Schuster, R., Morrell, N., Strimas-Mackey, M., Watts, M.E., Arcese, P., Bennett, J., & Possingham, H.P. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0.3. Available at https://CRAN.R-project.org/package=prioritizr (2020).44.Sharma, N., Madhusudan, M. D., Sarkar, P., Bawri, M. & Sinha, A. Trends in extinction and persistence of diurnal primates in the fragmented lowland rainforests of the Upper Brahmaputra Valley, northeastern India. Oryx 46, 308–311 (2012).Article 

    Google Scholar 
    45.Turner, W. et al. Remote sensing for biodiversity science and conservation. Trends Ecol. Evol. 18, 306–314 (2003).Article 

    Google Scholar 
    46.Corbane, C. Remote sensing for mapping natural habitats and their conservation status—New opportunities and challenges. Int. J. Appl. Earth Obs. 37, 7–16 (2015).Article 

    Google Scholar 
    47.Kakati, K., Raghavan, R., Chellam, R., Qureshi, Q. & Chivers, D. J. Status of western hoolock gibbon (Hoolock hoolock) populations in non-protected forests of eastern Assam. Primate Conserv. 24, 127–137 (2009).Article 

    Google Scholar 
    48.Peterson, A. T., Soberon, J. & Sanchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiv. Inform. 2, 1–10 (2005).Article 

    Google Scholar 
    50.Sarma, K., Kumar, A., Krishna, M., Medhi, M. & Tripathi, O. P. Predicting suitable habitats for the Vulnerable Eastern Hoolock Gibbon Hoolock leuconedys, in India using the Maxent model. Folia Primatol. 86, 387–397 (2015).Article 

    Google Scholar 
    51.Sharma, N., Madhusudan, M. D. & Sinha, A. Socio-economic drivers of forest cover change in Assam: A historical perspective. Econ. Polit. Wkly. 47, 64–72 (2012).
    Google Scholar 
    52.Sarma, K., Kumar, A., Krishna, M., Tripathi, O. P. & Gajurel, P. R. Ground feeding observations on corn (Zea mays) by eastern hoolock gibbon (Hoolock leuconedys). Curr. Sci. 104, 587–589 (2013).
    Google Scholar 
    53.Chetry, D., Chetry, R., & Bhattacharjee, P.C. Hoolock: The Ape of India. Gibbon Conservation Centre, Assam, India (2007). More

  • in

    Unraveling negative biotic interactions determining soil microbial community assembly and functioning

    1.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Le Chatelier E, Nielsen T, Qin JJ, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500:541–6.PubMed 
    Article 
    CAS 

    Google Scholar 
    3.Philippot L, Raaijmakers JM, Lemanceau P, van der Putten WH. Going back to the roots: the microbial ecology of the rhizosphere. Nat Rev Microbiol. 2013;11:789–99.CAS 
    Article 

    Google Scholar 
    4.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Mol Biol Rev. 2013;77:342–56.Article 

    Google Scholar 
    5.Jones RT, Robeson MS, Lauber CL, Hamady M, Knight R, Fierer N. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 2009;3:442–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Rasche F, Knapp D, Kaiser C, Koranda M, Kitzler B, Zechmeister-Boltenstern S, et al. Seasonality and resource availability control bacterial and archaeal communities in soils of a temperate beech forest. ISME J. 2011;5:389–402.CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Goberna M, Garcia C, Verdu M. A role for biotic filtering in driving phylogenetic clustering in soil bacterial communities. Glob Ecol Biogeogr. 2014;23:1346–55.Article 

    Google Scholar 
    8.Zhou JZ, Ning DL. Stochastic community assembly: does it matter in microbial ecology? Mol Biol Rev. 2017;81:e00002–17.9.Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Griffin AS, West SA, Buckling A. Cooperation and competition in pathogenic bacteria. Nature. 2004;430:1024–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.West SA, Cooper GA. Division of labour in microorganisms: an evolutionary perspective. Nat Rev Microbiol. 2016;14:716–23.CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Garcia-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:eaat2456.16.Braga LPP, Spor A, Kot W, Breuil MC, Hansen LH, Setubal JC, et al. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome. 2020;8:52.17.Saleem M, Fetzer I, Harms H, Chatzinotas A. Diversity of protists and bacteria determines predation performance and stability. ISME J. 2013;7:1912–21.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Nair RR, Vasse M, Wielgoss S, Sun L, Yu YTN, Velicer GJ. Bacterial predator-prey coevolution accelerates genome evolution and selects on virulence-associated prey defences. Nat Commun. 2019;10:4301.19.Perez J, Moraleda-Munoz A, Marcos-Torres FJ, Munoz-Dorado J. Bacterial predation: 75 years and counting! Environ Microbiol. 2016;18:766–79.PubMed 
    Article 

    Google Scholar 
    20.Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1:109.21.Goldford JE, Lu NX, Bajic D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Russel J, Roder HL, Madsen JS, Burmolle M, Sorensen SJ. Antagonism correlates with metabolic similarity in diverse bacteria. Proc Natl Acad Sci USA. 2017;114:10684–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Zhang JJ, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.26.Engelhardt IC, Welty A, Blazewicz SJ, Bru D, Rouard N, Breuil MC, et al. Depth matters: effects of precipitation regime on soil microbial activity upon rewetting of a plant-soil system. ISME J. 2018;12:1061–71.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.29.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.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 
    PubMed Central 

    Google Scholar 
    31.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Abarenkov K, Nilsson RH, Larsson KH, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi—recent updates and future perspectives. N Phytol. 2010;186:281–5.Article 

    Google Scholar 
    33.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.Article 

    Google Scholar 
    34.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Ning DL, Deng Y, Tiedje JM, Zhou JZ. A general framework for quantitatively assessing ecological stochasticity. Proc Natl Acad Sci USA. 2019;116:16892–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Muyzer G, Dewaal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.White TJ, Bruns TD, Lee SB, Taylor JWI. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR-protocols and applications: a laboratory manual. New York, NY: Academic Press; 1990. p. 315–22.39.Bru D, Ramette A, Saby NPA, Dequiedt S, Ranjard L, Jolivet C, et al. Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale. ISME J. 2011;5:532–42.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Campbell CD, Chapman SJ, Cameron CM, Davidson MS, Potts JM. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl Environ Microbiol. 2003;69:3593–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.42.de Mendiburu F. Agricolae: statistical procedures for agricultural research. R Package Version. 2017;1:2–8.
    Google Scholar 
    43.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. 2018.44.Soetaert K. plot3D: plotting multi-dimensional data. R package version 1.0. 2013.45.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.46.Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Letunic I, Bork P. Interactive Tree of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 2011;39:W475–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Chiquet J, Mariadassou M, S. R. Variational inference for sparse network reconstruction from count data. ICML. 2018;97:1162–71.
    Google Scholar 
    50.Liu H, Roeder K, Wasserman L. Stability Approach to Regularization Selection (StARS) for high dimensional graphical models. Adv Neural Inf Process Syst. 2010;31:1432–40.
    Google Scholar 
    51.Chen L, Reeve J, Zhang LJ, Huang SB, Wang XF, Chen J. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;6:e4600.52.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 
    53.Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.54.Singh A, Gautier B, Shannon CP, Rohart F, Vacher M, Tebutt SJ, et al. DIABLO: from multi-omics assays to biomarker discovery, an integrative approach. Bioinformatics. 2019;35:3055–62.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Calderon K, Spor A, Breuil MC, Bru D, Bizouard F, Violle C, et al. Effectiveness of ecological rescue for altered soil microbial communities and functions. ISME J. 2017;11:272–83.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Hol WHG, de Boer W, de Hollander M, Kuramae EE, Meisner A, van der Putten WH. Context dependency and saturating effects of loss of rare soil microbes on plant productivity. Front Plant Sci. 2015;6:485.57.Weber MF, Poxleitner G, Hebisch E, Frey E, Opitz M. Chemical warfare and survival strategies in bacterial range expansions. J Royal Soc Interface. 2014;11:20140172.58.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
    Article 

    Google Scholar 
    59.Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.CAS 
    Article 

    Google Scholar 
    60.Kurm V, van der Putten WH, de Boer W, Naus-Wiezer S, Hol WHG. Low abundant soil bacteria can be metabolically versatile and fast growing. Ecology. 2017;98:555–64.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Berns AE, Philipp H, Narres HD, Burauel P, Vereecken H, Tappe W. Effect of gamma-sterilization and autoclaving on soil organic matter structure as studied by solid state NMR, UV and fluorescence spectroscopy. Eur J Soil Sci. 2008;59:540–50.CAS 
    Article 

    Google Scholar 
    62.Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-Gonzalez A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
    Article 

    Google Scholar 
    64.Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USa. 2010;107:5881–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Kurm V, Geisen S, Hol WHG. A low proportion of rare bacterial taxa responds to abiotic changes compared with dominant taxa. Environ Microbiol. 2019;21:750–8.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Garbeva P, Hordijk C, Gerards S, de Boer W. Volatile-mediated interactions between phylogenetically different soil bacteria. Front Microbiol. 2014;5:289.67.Karimi B, Terrat S, Dequiedt S, Saby NPA, Horriguel W, Lelievre M, et al. Biogeography of soil bacteria and archaea across France. Sci Adv. 2018;4:eaat1808.68.Lewin GR, Carlos C, Chevrette MG, Horn HA, McDonald BR, Stankey RJ, et al. Evolution and ecology of actinobacteria and their bioenergy applications. Annu Rev Microbiol. 2016;70:235–54.69.Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, et al. Complete nitrification by Nitrospira bacteria. Nature. 2015;528:504–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Sorokin DY, Luecker S, Vejmelkova D, Kostrikina NA, Kleerebezem R, Rijpstra WIC, et al. Nitrification expanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J. 2012;6:2245–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Bell T. Next-generation experiments linking community structure and ecosystem functioning. Environ Microbiol Rep. 2019;11:20–2.PubMed 
    Article 

    Google Scholar  More

  • in

    309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula

    Marine microbiome is considered as the largest environment on earth which has many secrets concealed into it1,2. Many marine microbes play a key role in biogeochemical cycles. However, high proportions of microbes remain uncultured in vitro3 and so instead of analysing the microbes individually, cultivation-independent genome-level characterization methods notably single-cell genomics and metagenomics are frequently being applied for microbiome analysis4. Amplicon sequencing based cultivation-independent studies are enriching the microbial diversity knowledge of various hitherto less studied environmental niche, specifically within the marine resources. However, amplicon analysis is just a preliminary step in metagenomics as it focuses only on one gene for the community diversity assessment.With the view of studying the marine microbial community for determination of its composition in terms of diversity as well as function, whole metagenomics has become the preferred approach. Recently, it has been realized that the actual understanding of metagenomics data can be obtained by individual genome binning, which eventually also enhances the microbial genome database5. This requires use of various complex computational algorithms including those relying on previous data findings viz., the supervised classifiers and the unsupervised classifiers that rely on sequence specific features like the GC content, k-mer frequency and coverage estimation for binning the genomes. Most of the recently developed tools for binning include a combined approach of both the algorithms6. Binning aids in revealing the link between the potential functional genes in a given microbiome to its taxonomy.The unique properties of the Gulfs of Kathiawar Peninsula like extreme tidal variations, different sediment texture and physicochemical variations make them an ideal place for studying the microbial diversity. Varied onshore anthropogenic activities may have imparted unique features to the microflora of the Gulfs. Study of microbial diversity and functions in the mentioned Gulfs have largely been focused on cultivation based approaches and very few molecular studies have been conducted on the shore sediments. Additionally, the presence of several on-shore industries like fertilizer, chemicals, oil refineries, power plants and ASSBRY (Alang Ship Breaking Yard) may have also influenced the deeper sediment microbiome leading to their variable gene profile7. Our previous insights into the pelagic sediment resistome profile by metagenomics approach have shown that the deeper sediments, earlier thought to be primeval are actually hosting microbes with a concerning number of resistance genes7,8. This acted as a propeller to the present study wherein we tried to look deeper into the metagenomics data of the samples collected from the Gulfs of Kathiawar Peninsula and a sample from the Arabian Sea by sorting individual prokaryoplankton genomes from the data using the binning approach.We successfully reconstructed 309 Metagenome Assembled Genomes (MAGs) from the nine sediment metagenomics sequences (Table 1) from Gulf of Khambhat (GOC), Gulf of Kutch (GOK) and Arabian Sea (A) by differential coverage approach and considering the GC percent and tetranucleotide frequencies. Out of the 309 MAGs, 39 were archaeal genomes (Online-only Table 1) and 270 were bacterial genomes (Online-only Table 2). Seventy-one were high quality drafts with a completeness of ≥90% and contamination More

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    Microbial drivers of methane emissions from unrestored industrial salt ponds

    1.Costanza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, et al. The value of the world’s ecosystem services and natural capital. Ecol Econ. 1998;25:3–15.Article 

    Google Scholar 
    2.Grimsditch G, Alder J, Nakamura T, Kenchington R, Tamelander J. The blue carbon special edition—introduction and overview. Ocean Coast Manag. 2013;83:1–4.Article 

    Google Scholar 
    3.Duarte CM, Losada IJ, Hendriks IE, Mazarrasa I, Marbà N. The role of coastal plant communities for climate change mitigation and adaptation. Nat Clim Change. 2013;3:961–8.CAS 
    Article 

    Google Scholar 
    4.Mcleod E, Chmura GL, Bouillon S, Salm R, Björk M, Duarte CM, et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front Ecol Environ. 2011;9:552–60.Article 

    Google Scholar 
    5.Neef L, Weele M van, Velthoven P van. Optimal estimation of the present-day global methane budget. Glob Biogeochem Cycles. 2010;24:GB4024.6.Schlesinger WH, Bernhardt ES. Biogeochemistry: an analysis of global change. 3rd ed. Waltham, MA: Academic Press; 2013.7.Lessner DJ. Methanogenesis biochemistry. eLS. John Wiley & Sons, Hoboken, NJ, USA; 2009.8.Conrad R. Importance of hydrogenotrophic, aceticlastic and methylotrophic methanogenesis for methane production in terrestrial, aquatic and other anoxic environments: a mini review. Pedosphere. 2020;30:25–39.Article 

    Google Scholar 
    9.Herbert ER, Boon P, Burgin AJ, Neubauer SC, Franklin RB, Ardón M, et al. A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere. 2015;6:art206.Article 

    Google Scholar 
    10.Wicke B, Smeets E, Dornburg V, Vashev B, Gaiser T, Turkenburg W, et al. The global technical and economic potential of bioenergy from salt-affected soils. Energy Environ Sci. 2011;4:2669–81.Article 

    Google Scholar 
    11.Kristjansson JK, Schönheit P. Why do sulfate-reducing bacteria outcompete methanogenic bacteria for substrates? Oecologia. 1983;60:264–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Karl DM, Beversdorf L, Björkman KM, Church MJ, Martinez A, Delong EF. Aerobic production of methane in the sea. Nat Geosci. 2008;1:473–8.CAS 
    Article 

    Google Scholar 
    13.Mcgenity T, Sorokin D. Methanogens and methanogenesis in hypersaline environments. Biogenesis of hydrocarbons. Springer International Publishing, New York, NY, USA; 2018. p. 1–27.14.Repeta DJ, Ferrón S, Sosa OA, Johnson CG, Repeta LD, Acker M, et al. Marine methane paradox explained by bacterial degradation of dissolved organic matter. Nat Geosci. 2016;9:884–7.CAS 
    Article 

    Google Scholar 
    15.Oremland RS, Polcin S. Methanogenesis and sulfate reduction: competitive and noncompetitive substrates in estuarine sediments. Appl Environ Microbiol. 1982;44:1270–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.van der Gon HACD, Neue H-U. Methane emission from a wetland rice field as affected by salinity. Plant Soil. 1995;170:307–13.Article 

    Google Scholar 
    17.Gómez-Villegas P, Vigara J, León R. Characterization of the microbial population inhabiting a solar saltern pond of the Odiel Marshlands (SW Spain). Mar Drugs. 2018;16:332.PubMed Central 
    Article 
    CAS 
    PubMed 

    Google Scholar 
    18.Ley RE, Harris JK, Wilcox J, Spear JR, Miller SR, Bebout BM, et al. Unexpected diversity and complexity of the Guerrero Negro hypersaline microbial mat. Appl Environ Microbiol. 2006;72:3685–95.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Thombre RS, Shinde VD, Oke RS, Dhar SK, Shouche YS. Biology and survival of extremely halophilic archaeon Haloarcula marismortui RR12 isolated from Mumbai salterns, India in response to salinity stress. Sci Rep. 2016;6:25642.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Takekawa JY, Miles AK, Schoellhamer DH, Athearn ND, Saiki MK, Duffy WD, et al. Trophic structure and avian communities across a salinity gradient in evaporation ponds of the San Francisco Bay estuary. Hydrobiologia. 2006;567:307–27.CAS 
    Article 

    Google Scholar 
    21.Ver Planck WE. Salt in California. State of California Deparment of Natural Resources, Division of Mines. Mines Bull 175. San Francisco, CA, USA: 1958.22.Johnck EJ. The South Bay Salt Pond Restoration Project: a cultural landscape approach for the resource management plan. Sonoma State University, Rohnert Park, CA, USA; 2008.23.Ackerman JT, Marvin-DiPasquale M, Slotton D, Eagles-Smith CA, Hartman A, Agee JL, et al. The South Bay Mercury Project: using biosentinels to monitor effects of wetland restoration for the South Bay Salt Pond Restoration Project. South Bay Salt Pond Restoration Project and Resources Legacy Fund, San Francisco, CA, USA; 2013.24.Valoppi L. Phase 1 studies summary of major findings of the South Bay Salt Pond Restoration Project, South San Francisco Bay, California. Phase 1 studies summary of major findings of the South Bay Salt Pond Restoration Project, South San Francisco Bay, California. Reston, VA: U.S. Geological Survey; 2018.25.Callaway JC, Parker VT, Vasey MC, Schile LM, Herbert ER. Tidal wetland restoration in San Francisco Bay: history and current issues. San Franc Estuary Watershed Sci. 2011;9: Article 2.26.Cargill. San Francisco Bay salt ponds. Cargill, Newark, CA, USA; 2020. https://www.cargill.com/page/sf/sf-bay-salt-ponds.27.Levey JR, Vasicek P, Fricke H, Archer J, Henry RF. Salt pond SF2 restoration, wildlife, and habitat protection. American Society of Civil Engineers, Reston, VA; 2012.520−9.28.Dugan HA, Summers JC, Skaff NK, Krivak-Tetley FE, Doubek JP, Burke SM, et al. Long-term chloride concentrations in North American and European freshwater lakes. Sci Data. 2017;4:170101.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, et al. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol 2015;6:771.30.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:D590–96.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Wang Q, Garrity GM, Tiedje JM, Cole JR. A naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;73:5264−67.32.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

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

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

    Google Scholar 
    35.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.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    39.Lin H-H, Liao Y-C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:24175.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.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 
    43.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 
    44.Yu FB, Blainey PC, Schulz F, Woyke T, Horowitz MA, Quake SR. Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. eLife. 2017;6:e26580.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 2019;20:217.Article 
    CAS 

    Google Scholar 
    46.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5-6. 2019. https://CRAN.R-project.org/package=vegan.47.Vu VQ. ggbiplot: a ggplot2 based biplot. R package version 0.55. 2011. http://github.com/vqv/ggbiplot.48.De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.PubMed 
    Article 

    Google Scholar 
    49.Prestat E, David MM, Hultman J, Taş N, Lamendella R, Dvornik J, et al. FOAM (functional ontology assignments for metagenomes): a hidden Markov model (HMM) database with environmental focus. Nucleic Acids Res. 2014;42:e145.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    50.Liu J, Cade-Menun BJ, Yang J, Hu Y, Liu CW, Tremblay J, et al. Long-term land use affects phosphorus speciation and the composition of phosphorus cycling genes in agricultural soils. Front Microbiol. 2018;9:1643.51.Manor O, Borenstein E. MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome. Genome Biol. 2015;16:53.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Girvan M, Newman MEJ. Community structure in social and biological networks. Proc Natl Acad Sci USA. 2002;99:7821–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Jurasinski G, Koebsch F, Guenther A, Beetz S. flux: flux rate calculation from dynamic closed chamber measurements. R package version 0.3-0. 2014. https://CRAN.R-project.org/package=flux.55.Culkin F, Smith N. Determination of the concentration of potassium chloride solution having the same electrical conductivity, at 15 °C and infinite frequency, as standard seawater of salinity 35.0000 ‰ (chlorinity 19.37394 ‰). IEEE J Ocean Eng. 1980;5:22–23.Article 

    Google Scholar 
    56.Kuever J. The Family Desulfohalobiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 87–95.57.López-Pérez M, Rodriguez-Valera F. The Family Alteromonadaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Gammaproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 69–92.58.Oren A. The Order Halanaerobiales, and the Families Halanaerobiaceae and Halobacteroidaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Firmicutes and Tenericutes. Berlin, Heidelberg: Springer; 2014. p. 153−77.59.Pujalte MJ, Lucena T, Ruvira MA, Arahal DR, Macián MC. The Family Rhodobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Alphaproteobacteria and Betaproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 439–512.60.Kuever J. The Family Desulfobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 45–73.61.Kuever J. The Family Desulfobulbaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 75–86.62.Kuever J. The Family Syntrophobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 289−99.63.Oren A. The Family Methanosarcinaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: other major lineages of bacteria and the archaea. Berlin, Heidelberg: Springer; 2014. p. 259−81.64.Bonin AS, Boone DR. The Order Methanobacteriales. In: Dworkin M, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E (eds). The Prokaryotes: Volume 3: Archaea. Bacteria: Firmicutes, Actinomycetes. New York, NY: Springer; 2006. p. 231−43.65.Kathuria S, Martiny AC. Prevalence of a calcium-based alkaline phosphatase associated with the marine cyanobacterium Prochlorococcus and other ocean bacteria. Environ Microbiol. 2011;13:74–83.CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Kamat SS, Williams HJ, Dangott LJ, Chakrabarti M, Raushel FM. The catalytic mechanism for aerobic formation of methane by bacteria. Nature. 2013;497:132–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Yu X, Doroghazi JR, Janga SC, Zhang JK, Circello B, Griffin BM, et al. Diversity and abundance of phosphonate biosynthetic genes in nature. Proc Natl Acad Sci USA. 2013;110:20759–64.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.69.Metcalf WW, Griffin BM, Cicchillo RM, Gao J, Janga SC, Cooke HA, et al. Synthesis of methylphosphonic acid by marine microbes: a source for methane in the aerobic ocean. Science. 2012;337:1104–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Poffenbarger HJ, Needelman BA, Megonigal JP. Salinity influence on methane emissions from tidal marshes. Wetlands. 2011;31:831–42.Article 

    Google Scholar 
    71.Oremland RS, Boone DR. Methanolobus taylorii sp. nov., a new methylotrophic, estuarine methanogen. Int J Syst Bacteriol. 1994;44:573–5.Article 

    Google Scholar 
    72.Zhang G, Jiang N, Liu X, Dong X. Methanogenesis from methanol at low temperatures by a novel psychrophilic methanogen, “Methanolobus psychrophilus” sp. nov., prevalent in Zoige Wetland of the Tibetan Plateau. Appl Environ Microbiol. 2008;74:6114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Antony CP, Murrell JC, Shouche YS. Molecular diversity of methanogens and identification of Methanolobus sp. as active methylotrophic Archaea in Lonar crater lake sediments. FEMS Microbiol Ecol. 2012;81:43–51.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.König H, Stetter KO. Isolation and characterization of Methanolobus tindarius, sp. nov., a coccoid methanogen growing only on methanol and methylamines. Zentralblatt Für Bakteriol Mikrobiol Hyg Abt Orig C Allg Angew Ökol Mikrobiol. 1982;3:478–90.
    Google Scholar 
    75.Doerfert SN, Reichlen M, Iyer P, Wang M, Ferry JG. Methanolobus zinderi sp. nov., a methylotrophic methanogen isolated from a deep subsurface coal seam. Int J Syst Evol Microbiol. 2009;59:1064–9.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Ni S, Boone DR. Isolation and characterization of a dimethyl sulfide-degrading methanogen, methanolobus siciliae HI350, from an oil well, characterization of M. siciliae T4/MT, and emendation of M. siciliae. Int J Syst Bacteriol. 1991;41:410–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Mochimaru H, Tamaki H, Hanada S, Imachi H, Nakamura K, Sakata S, et al. Methanolobus profundi sp. nov., a methylotrophic methanogen isolated from deep subsurface sediments in a natural gas field. Int J Syst Evol Microbiol. 2009;59:714–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Orphan VJ, Jahnke LL, Embaye T, Turk KA, Pernthaler A, Summons RE, et al. Characterization and spatial distribution of methanogens and methanogenic biosignatures in hypersaline microbial mats of Baja California. Geobiology. 2008;6:376–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Smith JM, Green SJ, Kelley CA, Prufert‐Bebout L, Bebout BM. Shifts in methanogen community structure and function associated with long-term manipulation of sulfate and salinity in a hypersaline microbial mat. Environ Microbiol. 2008;10:386–94.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Zhuang G-C, Elling FJ, Nigro LM, Samarkin V, Joye SB, Teske A, et al. Multiple evidence for methylotrophic methanogenesis as the dominant methanogenic pathway in hypersaline sediments from the Orca Basin, Gulf of Mexico. Geochim Cosmochim Acta. 2016;187:1–20.CAS 
    Article 

    Google Scholar 
    81.Zhuang G-C, Heuer VB, Lazar CS, Goldhammer T, Wendt J, Samarkin VA, et al. Relative importance of methylotrophic methanogenesis in sediments of the Western Mediterranean Sea. Geochim Cosmochim Acta. 2018;224:171–86.CAS 
    Article 

    Google Scholar 
    82.Oremland RS, Marsh LM, Polcin S. Methane production and simultaneous sulphate reduction in anoxic, salt marsh sediments. Nature. 1982;296:143–5.CAS 
    Article 

    Google Scholar 
    83.Wanner BL, Metcalf WW. Molecular genetic studies of a 10.9 kb operon in Escherichia coli for phosphonate uptake and biodegradation. FEMS Microbiol Lett. 1992;100:133–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Dyhrman ST, Chappell PD, Haley ST, Moffett JW, Orchard ED, Waterbury JB, et al. Phosphonate utilization by the globally important marine diazotroph Trichodesmium. Nature. 2006;439:68–71.CAS 
    PubMed 
    Article 

    Google Scholar 
    85.White AK, Metcalf WW. Microbial metabolism of reduced phosphorus compounds. Annu Rev Microbiol. 2007;61:379–400.CAS 
    PubMed 
    Article 

    Google Scholar 
    86.Carini P, White AE, Campbell EO, Giovannoni SJ. Methane production by phosphate-starved SAR11 chemoheterotrophic marine bacteria. Nat Commun. 2014;5:4346.CAS 
    PubMed 
    Article 

    Google Scholar 
    87.Damm E, Helmke E, Thoms S, Schauer U, Nothig E, Bakker K, et al. Methane production in aerobic oligotrophic surface water in the central Arctic Ocean. Biogeosciences. 2010;7:1099–108.CAS 
    Article 

    Google Scholar 
    88.Martínez A, Ventouras L-A, Wilson ST, Karl DM, Delong EF. Metatranscriptomic and functional metagenomic analysis of methylphosphonate utilization by marine bacteria. Front Microbiol. 2013;4:340.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Yao M, Henny C, Maresca JA. Freshwater bacteria release methane as a by-product of phosphorus acquisition. Appl Environ Microbiol. 2016;82:6994–7003.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Sosa OA, Repeta DJ, DeLong EF, Ashkezari MD, Karl DM. Phosphate-limited ocean regions select for bacterial populations enriched in the carbon–phosphorus lyase pathway for phosphonate degradation. Environ Microbiol. 2019;21:2402–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Fisher J, Acreman MC. Wetland nutrient removal: a review of evidence. Hydrol Earth Syst Sci Discuss Eur Geosci Union. 2004;8:673–85.CAS 
    Article 

    Google Scholar 
    92.Kadlec RH. Constructed marshes for nitrate removal. Crit Rev Environ Sci Technol. 2012;42:934–1005.CAS 
    Article 

    Google Scholar 
    93.He S, Malfatti SA, McFarland JW, Anderson FE, Pati A, Huntemann M, et al. Patterns in wetland microbial community composition and functional gene repertoire associated with methane emissions. mBio. 2015;6:e00066–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More