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    Deep genetic structure at a small spatial scale in the endangered land snail Xerocrassa montserratensis

    1.Cardoso, P., Erwin, T. L., Borges, P. A. V. & New, T. R. The seven impediments in invertebrate conservation and how to overcome them. Biol. Conserv. 144, 2647–2655 (2011).Article 

    Google Scholar 
    2.Lydeard, C. et al. The global decline of nonmarine mollusks. Bioscience 54, 321–330 (2004).Article 

    Google Scholar 
    3.Régnier, C. et al. Mass extinction in poorly known taxa. Proc. Natl. Acad. Sci. USA 112, 7761–7766 (2015).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    4.Cuttelod, A., Seddon, M. & Neubert, E. European Red List of Non-Marine Molluscs (2011).5.Aubry, S., Labaune, C., Magnin, F., Roche, P. & Kiss, L. Active and passive dispersal of an invading land snail in Mediterranean France. J. Anim. Ecol. 75, 802–813 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Guiller, A. & Madec, L. Historical biogeography of the land snail Cornu aspersum: A new scenario inferred from haplotype distribution in the Western Mediterranean basin. BMC Evol. Biol. 10, 18 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    7.Ochman, H., Jonest, J. S. & Selander, R. K. Molecular area effects in Cepaea. Proc. Natl. Acad. Sci. USA 80, 4189–4193 (1983).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Chueca, L. J., Gómez-Moliner, B. J., Madeira, M. J. & Pfenninger, M. Molecular phylogeny of Candidula (Geomitridae) land snails inferred from mitochondrial and nuclear markers reveals the polyphyly of the genus. Mol. Phylogenet. Evol. 118, 357–368 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Moreira, F., Calado, G. & Dias, S. Conservation status of a recently described endemic land snail, Candidula coudensis, from the Iberian peninsula. PLoS ONE 10, e0138464 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    10.Sauer, J. & Hausdorf, B. Reconstructing the evolutionary history of the radiation of the land snail genus Xerocrassa on Crete based on mitochondrial sequences and AFLP markers. BMC Evol. Biol. 10, 299 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    11.Davison, A. Land snails as a model to understand the role of history and selection in the origins of biodiversity. Popul. Ecol. 44, 129–136 (2002).Article 

    Google Scholar 
    12.Pfenninger, M., Posada, D. & Shaw, K. Phylogeographic history of the land snail Candidula unifasciata (Helicellinae, Stylommatophora): Fragmentation, corridor migration, and secondary contact. Evolution (N. Y). 56, 1776–1788 (2002).13.Madeira, P. M. et al. High unexpected genetic diversity of a narrow endemic terrestrial mollusc. PeerJ 2017, e3069 (2017).Article 

    Google Scholar 
    14.Sauer, J., Oldeland, J. & Hausdorf, B. Continuing fragmentation of a widespread species by geographical barriers as initial step in a land snail radiation on Crete. PLoS ONE 8, e62569 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Haig, S. M. Molecular contributions to conservation. Ecology 79, 413–425 (1998).Article 

    Google Scholar 
    16.Ezzine, I. K., Pfarrer, B., Dimassi, N., Said, K. & Neubert, E. At home at least: The taxonomic position of some North African Xerocrassa species (Pulmonata, Geomitridae). Zookeys 712, 1–27 (2017).Article 

    Google Scholar 
    17.Bank, R. A. & Neubert, E. Checklist of the Land and Freshwater Gastropoda of Europe. http://www.marinespecies.org/aphia.php?p=sourcedetails&id=279050 (2017).18.Chueca, L. J., Gómez-Moliner, B. J., Forés, M. & Madeira, M. J. Biogeography and radiation of the land snail genus Xerocrassa (Geomitridae) in the Balearic Islands. J. Biogeogr. 44, 760–772 (2017).Article 

    Google Scholar 
    19.Martínez-Ortí, A. Xerocrassa montserratensis. The IUCN Red List of Threatened Species e.T22254A9368348. https://doi.org/10.2305/IUCN.UK.2011-1.RLTS.T22254A9368348.en (2011).20.Martínez-Ortí, A. & Bros, V. Taxonomic clarification of three taxa of Iberian geomitrids, Helix montserratensis Hidalgo, 1870 and subspecies (Gastropoda, Pulmonata), based on morpho–anatomical data. Anim. Biodivers. Conserv. 40, 247–267 (2017).Article 

    Google Scholar 
    21.Bros, V. Composició de la comunitat de mol· luscs de les codines en el Parc Natural de Sant Llorenç del Munt i l’Obac, i l’impacte del trepig i l’erosió en el Montcau. In VII Monografies de Sant Llorenç del Munt i l’Obac 43–52 (2011).22.Santos, X., Bros, V. & Ros, E. Contrasting responses of two xerophilous land snails to fire and natural reforestation. Contrib. Zool. 81, 167–180 (2012).Article 

    Google Scholar 
    23.Hidalgo, J. G. Description de trois espèces nouvelles d’Helix d’Espagne. J. Conchyliol. 18, 298–299 (1870).
    Google Scholar 
    24.Bofill, A. Catálogo de los moluscos testáceos terrestres del llano de Barcelona. Crónica Científ. 3, 1–24 (1879).
    Google Scholar 
    25.Bofill, A. La Helix montserratensis. Su origen y su distribución en el tiempo y en el espacio. Mem. Real Acad. Cienc. Artes Barcelona 2, 331–343 (1898).26.Altimira, C. Notas malacológicas. Contribución al conocimiento de la fauna malacológica terrestre y de agua dulce de Cataluña. Misc. Zool. 3, 7–10 (1971).27.Van Riel, P. et al. Molecular systematics of the endemic Leptaxini (Gastropoda: Pulmonata) on the Azores islands. Mol. Phylogenet. Evol. 37, 132–143 (2005).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    28.Kruckenhauser, L. et al. Paraphyly and budding speciation in the hairy snail (Pulmonata, Hygromiidae). Zool. Scr. 43, 273–288 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Dempsey, Z. W., Goater, C. P. & Burg, T. M. Living on the edge: Comparative phylogeography and phylogenetics of Oreohelix land snails at their range edge in Western Canada. BMC Evol. Biol. 20, 3 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Ursenbacher, S., Alvarez, C., Armbruster, G. F. J. & Baur, B. High population differentiation in the rock-dwelling land snail (Trochulus caelatus) endemic to the Swiss Jura Mountains. Conserv. Genet. 11, 1265–1271 (2010).Article 

    Google Scholar 
    31.Jesse, R., Véla, E. & Pfenninger, M. Phylogeography of a land snail suggests trans-Mediterranean Neolithic transport. PLoS ONE 6, e20734 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Hausdorf, B. Biogeography of the Limacoidea sensu lato (Gastropoda: Stylommatophora): vicariance events and long-distance dispersal. J. Biogeogr. 27, 379–390 (2000).Article 

    Google Scholar 
    33.Neiber, M. T., Sagorny, C., Sauer, J., Walther, F. & Hausdorf, B. Phylogeographic analyses reveal Transpontic long distance dispersal in land snails belonging to the Caucasotachea atrolabiata complex (Gastropoda: Helicidae). Mol. Phylogenet. Evol. 103, 172–183 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Simonová, J., Simon, O. P., Kapic, Š, Nehasil, L. & Horsák, M. Medium-sized forest snails survive passage through birds’ digestive tract and adhere strongly to birds’ legs: More evidence for passive dispersal mechanisms. J. Molluscan Stud. 82, 422–426 (2016).Article 

    Google Scholar 
    35.Watanabe, Y. & Chiba, S. High within-population mitochondrial DNA variation due to microvicariance and population mixing in the land snail Euhadra quaesita (Pulmonata: Bradybaenidae). Mol. Ecol. 10, 2635–2645 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Nägele, K.-L. & Hausdorf, B. Comparative phylogeography of land snail species in mountain refugia in the European Southern Alps. J. Biogeogr. 42, 821–832 (2015).Article 

    Google Scholar 
    37.Shakun, J. D., Lea, D. W., Lisiecki, L. E. & Raymo, M. E. An 800-kyr record of global surface ocean δ18O and implications for ice volume-temperature coupling. Earth Planet. Sci. Lett. 426, 58–68 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    38.Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ 18O records. Paleoceanography 20, 1–17 (2005).
    Google Scholar 
    39.Santos, X., Bros, V. & Miño, À. Recolonization of a burned Mediterranean area by terrestrial gastropods. Biodivers. Conserv. 18, 3153–3165 (2009).Article 

    Google Scholar 
    40.Bishop, P. Drainage rearrangement by river capture, beheading and diversion. Prog. Phys. Geogr. Earth Environ. 19, 449–473 (1995).Article 

    Google Scholar 
    41.Castelltort, F. X., Balasch, J. C., Cirés, J. & Colombo, F. Consecuencias de la migración lateral de una cuenca de drenaje (Homoclinal shifting) en la formación de la cuenca erosiva de la Plana de Vic. NE de la Cuenca del Ebro. Geogaceta 61, 55–58 (2017).42.Irwin, D. E. Phylogeographic breaks without geographic barriers to gene flow. Evolution (N. Y). 56, 2383–2394 (2002).43.Falniowski, A. et al. Melanopsidae (Caenogastropoda: Cerithioidea) from the eastern Mediterranean: Another case of morphostatic speciation. Zool. J. Linn. Soc. 190, 483–507 (2020).Article 

    Google Scholar 
    44.Proćków, M., Strzała, T., Kuźnik-Kowalska, E., Proćków, J. & Mackiewicz, P. Ongoing speciation and gene flow between taxonomically challenging Trochulus species complex (Gastropoda: Hygromiidae). PLoS ONE 12, e0170460 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Fiorentino, V., Manganelli, G., Giusti, F., Tiedemann, R. & Ketmaier, V. A question of time: The land snail Murella muralis (Gastropoda: Pulmonata) reveals constraints on past ecological speciation. Mol. Ecol. 22, 170–186 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Bamberger, S. et al. Genome‐wide nuclear data confirm two species in the Alpine endemic land snail Noricella oreinos s.l. (Gastropoda, Hygromiidae). J. Zool. Syst. Evol. Res. 00, 1–23 (2020).47.Torrado, H., Carreras, C., Raventos, N., Macpherson, E. & Pascual, M. Individual-based population genomics reveal different drivers of adaptation in sympatric fish. Sci. Rep. 10, 12683 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Alexander, A. et al. What influences the worldwide genetic structure of sperm whales (Physeter macrocephalus)?. Mol. Ecol. 25, 2754–2772 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Petit, R. J., El Mousadik, A. & Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855 (1998).Article 

    Google Scholar 
    53.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).54.Narum, S. R. Beyond Bonferroni: Less conservative analyses for conservation genetics. Conserv. Genet. 7, 783–787 (2006).CAS 
    Article 

    Google Scholar 
    55.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).56.Miller, M. P. Alleles in space (AIS): Computer software for the joint analysis of interindividual spatial and genetic information. J. Hered. 96, 722–724 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).59.Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).60.Xia, X. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018). More

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    Projected shifts in loggerhead sea turtle thermal habitat in the Northwest Atlantic Ocean due to climate change

    1.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. (2014).2.Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: Scaling from organisms to communities. Ann. Rev. Mar. Sci. 12, 153–179 (2020).PubMed 
    Article 

    Google Scholar 
    3.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3(10), 919–925 (2013).ADS 
    Article 

    Google Scholar 
    4.Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430(7002), 881–884 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Weatherdon, L. V., Magnan, A. K., Rogers, A. D., Sumaila, U. R. & Cheung, W. W. Observed and projected impacts of climate change on marine fisheries, aquaculture, coastal tourism, and human health: an update. Front. Mar. Sci. 3, 48 (2016).Article 

    Google Scholar 
    6.Mawdsley, J. R., O’Malley, R. & Ojima, D. S. A review of climate-change adaptation strategies for wildlife management and biodiversity conservation. Conserv. Biol. 23(5), 1080–1089 (2009).PubMed 
    Article 

    Google Scholar 
    7.Cañadas, A. & Hammond, P. S. Abundance and habitat preferences of the short-beaked common dolphin Delphinus delphis in the southwestern Mediterranean: Implications for conservation. Endanger. Species Res. 4(3), 309–331 (2008).Article 

    Google Scholar 
    8.Franco, A. M., Catry, I., Sutherland, W. J. & Palmeirim, J. M. Do different habitat preference survey methods produce the same conservation recommendations for lesser kestrels?. Anim. Conserv. 7(3), 291–300 (2004).Article 

    Google Scholar 
    9.Spotila, J. R., Reina, R. D., Steyermark, A. C., Plotkin, P. T. & Paladino, F. V. Pacific leatherback turtles face extinction. Nature 405(6786), 529–530 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Wallace, B. P. et al. Impacts of fisheries bycatch on marine turtle populations worldwide: Toward conservation and research priorities. Ecosphere 4(3), 1–49 (2013).Article 

    Google Scholar 
    11.Dunn, D. C., Boustany, A. M. & Halpin, P. N. Spatio-temporal management of fisheries to reduce by-catch and increase fishing selectivity. Fish Fish. 12(1), 110–119 (2011).Article 

    Google Scholar 
    12.Senko, J., White, E. R., Heppell, S. S. & Gerber, L. R. Comparing bycatch mitigation strategies for vulnerable marine megafauna. Anim. Conserv. 17(1), 5–18 (2014).Article 

    Google Scholar 
    13.Howell, E. A., Kobayashi, D. R., Parker, D. M., Balazs, G. H. & Polovina, J. J. TurtleWatch: A tool to aid in the bycatch reduction of loggerhead turtles Caretta caretta in the Hawaii-based pelagic longline fishery. Endanger. Species Res. 5(2–3), 267–278 (2008).Article 

    Google Scholar 
    14.Swimmer, Y. et al. Sea turtle bycatch mitigation in US longline fisheries. Front. Mar. Sci. 4, 260 (2017).Article 

    Google Scholar 
    15.Saba, V. S., Stock, C. A., Spotila, J. R., Paladino, F. V. & Tomillo, P. S. Projected response of an endangered marine turtle population to climate change. Nat. Clim. Change 2(11), 814–820 (2012).ADS 
    Article 

    Google Scholar 
    16.Santidrián Tomillo, P. et al. Global analysis of the effect of local climate on the hatchling output of leatherback turtles. Sci. Rep. 5, 16789 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Patel, S. H. et al. Climate impacts on sea turtle breeding phenology in Greece and associated foraging habitats in the wider Mediterranean region. PLoS ONE 11(6), e0157170 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    18.Shoop, C. R. & Kenney, R. D. Seasonal distributions and abundances of loggerhead and leatherback sea turtles in waters of the northeastern United States. Herpetol. Monogr. 6, 43–67 (1992).Article 

    Google Scholar 
    19.Coles, W. & Musick, J. A. Satellite sea surface temperature analysis and correlation with sea turtle distribution off North Carolina. Copeia 2000(2), 551–554 (2000).Article 

    Google Scholar 
    20.Kleisner, K. M. et al. Marine species distribution shifts on the US Northeast Continental Shelf under continued ocean warming. Prog. Oceanogr. 153, 24–36 (2017).ADS 
    Article 

    Google Scholar 
    21.Tyberghein, L. et al. Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21(2), 272–281 (2012).Article 

    Google Scholar 
    22.Stoneburner, D. L. Satellite telemetry of loggerhead sea turtle movement in the Georgia Bight. Copeia 1982, 400–408 (1982).Article 

    Google Scholar 
    23.Hart, K. M. & Hyrenbach, K. D. Satellite telemetry of marine megavertebrates: The coming of age of an experimental science. Endanger. Species Res. 10, 9–20 (2009).Article 

    Google Scholar 
    24.Hebblewhite, M. & Haydon, D. T. Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philos. Trans. R. Soc. B Biol. Sci. 365(1550), 2303–2312 (2010).Article 

    Google Scholar 
    25.Hays, G. C. & Hawkes, L. A. Satellite tracking sea turtles: Opportunities and challenges to address key questions. Front. Mar. Sci. 5, 432 (2018).Article 

    Google Scholar 
    26.Hawkes, L. A., Broderick, A. C., Coyne, M. S., Godfrey, M. H. & Godley, B. J. Only some like it hot—Quantifying the environmental niche of the loggerhead sea turtle. Divers. Distrib. 13(4), 447–457 (2007).Article 

    Google Scholar 
    27.Hazen, E. L. et al. Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Clim. Chang. 3(3), 234–238 (2013).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    28.Roe, J. H. et al. Predicting bycatch hotspots for endangered leatherback turtles on longlines in the Pacific Ocean. Proc. R. Soc. B Biol. Sci. 281(1777), 20132559 (2014).Article 

    Google Scholar 
    29.Winton, M. V. et al. Estimating the distribution and relative density of satellite-tagged loggerhead sea turtles using geostatistical mixed effects models. Mar. Ecol. Prog. Ser. 586, 217–232 (2018).ADS 
    Article 

    Google Scholar 
    30.Araújo, M. B. & Townsend, P. A. Uses and misuses of bioclimatic envelope modeling. Ecology 93(7), 1527–1539 (2012).PubMed 
    Article 

    Google Scholar 
    31.Gilman P, et al. National offshore wind strategy: facilitating the development of the offshore wind industry in the United States. National Renewable Energy Lab. (NREL), Golden, CO (United States) (2016).32.Northeast Fisheries Science Center (NEFSC) and Southeast Fisheries Science Center (SEFSC). Preliminary summer 2010 regional abundance estimate of loggerhead turtles (Caretta caretta) in northwestern Atlantic Ocean continental shelf waters. US Dept Commer, Northeast Fish Sci Cent Ref Doc. 11–03; 33 p (2011).33.Ceriani, S. A., Weishampel, J. F., Ehrhart, L. M., Mansfield, K. L. & Wunder, M. B. Foraging and recruitment hotspot dynamics for the largest Atlantic loggerhead turtle rookery. Sci. Rep. 7(1), 1–3 (2017).CAS 
    Article 

    Google Scholar 
    34.Fofonoff, N. P. The Gulf Stream. In Evolution of Physical Oceanography: Scientific Surveys in Honor of Henry Stommel (eds. Warren, B. A., & Wunsch, C.) 112–139 (MIT Press, 1981) Cambridge, MA.35.Patel, S. H., Miller, S. & Smolowitz, R. J. Understanding impacts of the sea scallop fishery on loggerhead sea turtles through satellite tagging. Final report for 2015 Sea Scallop Research Set-Aside (RSA). NOAA grant: NA15 NMF 4540055. Coonamessett Farm Foundation, East Falmouth, MA (2016).36.Patel, S. H. et al. Loggerhead turtles are good ocean-observers in stratified mid-latitude regions. Estuar. Coast. Shelf Sci. 213, 128–136 (2018).ADS 
    Article 

    Google Scholar 
    37.Crowe, L. M., Hatch, J. M., Patel, S. H., Smolowitz, R. J. & Haas, H. L. Riders on the storm: loggerhead sea turtles detect and respond to a major hurricane in the Northwest Atlantic Ocean. Mov. Ecol. 8(1), 1–3 (2020).Article 

    Google Scholar 
    38.Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. & Bell, B. M. TMB: Automatic differentiation and Laplace approximation. J. Stat. Softw. 70(5), 1–21 (2016).Article 

    Google Scholar 
    39.R Core Team. R: A language and environment for statistical computing (2017).40.Johnson, D. S., London, J. M., Lea, M.-A. & Durban, J. W. Continuous-time correlated random walk model for animal telemetry data. Ecology 89(5), 1208–1215 (2008).PubMed 
    Article 

    Google Scholar 
    41.Albertsen, C. M., Whoriskey, K., Yurkowski, D., Nielsen, A. & Flemming, J. M. Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder. Ecology 96(10), 2598–2604 (2015).PubMed 
    Article 

    Google Scholar 
    42.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. American Statistical Association (2015).43.Turtle Expert Working Group (TEWG). An assessment of the loggerhead turtle population in the western North Atlantic Ocean. NOAA Tech. Mem. NMFS-SEFSC. 575(131), 744 (2009).
    Google Scholar 
    44.Clay, P. M. Management regions, statistical areas and fishing grounds: Criteria for dividing up the sea. J. Northwest Atl. Fish. Sci. 19, 103–126 (1996).Article 

    Google Scholar 
    45.Murray, K. T. & Orphanides, C. D. Estimating the risk of loggerhead turtle Caretta caretta bycatch in the US mid-Atlantic using fishery-independent and-dependent data. Mar. Ecol. Prog. Ser. 477, 259–270 (2013).ADS 
    Article 

    Google Scholar 
    46.Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate. J. Geophys. Res. Oceans 121(1), 118–132 (2016).ADS 
    Article 

    Google Scholar 
    47.Amante, C. & Eakins, B. W. ETOPO1 arc-minute global relief model: procedures, data sources and analysis. NOAA Technical Memorandum NESDIS NGDC-24 (2009).48.Reynolds, R. W. & Smith, T. M. Improved global sea surface temperature analyses using optimum interpolation. J. Clim. 7(6), 929–948 (1994).ADS 
    Article 

    Google Scholar 
    49.Chamberlain, S. rerddap – General purpose client for ‘ERDDAP’ servers. R Package (2016).50.Akaike, H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60(2), 255–265 (1973).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    51.Maunder, M. N. & Punt, A. E. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70(2–3), 141–159 (2004).Article 

    Google Scholar 
    52.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 
    Book 

    Google Scholar 
    53.Benjamin, M. A., Rigby, R. A. & Stasinopoulos, D. M. Generalized autoregressive moving average models. J. Am. Stat. Assoc. 98(461), 214–223 (2003).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    54.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4(43), 1686 (2019).ADS 
    Article 

    Google Scholar 
    55.Tanaka, K. R., Torre, M. P., Saba, V. S., Stock, C. A. & Chen, Y. An ensemble high‐resolution projection of changes in the future habitat of American lobster and sea scallop in the Northeast US continental shelf. Diversity and Distributions (2020).56.McHenry, J., Welch, H., Lester, S. E. & Saba, V. Projecting marine species range shifts from only temperature can mask climate vulnerability. Glob. Change Biol. 25(12), 4208–4221 (2019).ADS 
    Article 

    Google Scholar 
    57.Selden, R. L., Batt, R. D., Saba, V. S. & Pinsky, M. L. Diversity in thermal affinity among key piscivores buffers impacts of ocean warming on predator–prey interactions. Glob. Change Biol. 24(1), 117–131 (2018).ADS 
    Article 

    Google Scholar 
    58.Griffin, D. B. et al. Foraging habitats and migration corridors utilized by a recovering subpopulation of adult female loggerhead sea turtles: Implications for conservation. Mar. Biol. 160(12), 3071–3086 (2013).Article 

    Google Scholar 
    59.Unal I. Defining an optimal cut-point value in ROC analysis: an alternative approach. Computational and mathematical methods in medicine (2017).60.Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21(20), 7881 (2005).Article 
    CAS 

    Google Scholar 
    61.Link, J. et al. The Northeast US continental shelf Energy Modeling and Analysis exercise (EMAX): Ecological network model development and basic ecosystem metrics. J. Mar. Syst. 74(1–2), 453–474 (2008).Article 

    Google Scholar 
    62.Bane, J. M. Jr., Brown, O. B., Evans, R. H. & Hamilton, P. Gulf Stream remote forcing of shelfbreak currents in the Mid-Atlantic Bight. Geophys. Res. Lett. 15(5), 405–407 (1988).ADS 
    Article 

    Google Scholar 
    63.Hawkes, L. A. et al. Home on the range: spatial ecology of loggerhead turtles in Atlantic waters of the USA. Divers. Distrib. 17(4), 624–640 (2011).Article 

    Google Scholar 
    64.Mansfield, K. L., Saba, V. S., Keinath, J. A. & Musick, J. A. Satellite tracking reveals a dichotomy in migration strategies among juvenile loggerhead turtles in the Northwest Atlantic. Mar. Biol. 156(12), 2555–2570 (2009).Article 

    Google Scholar 
    65.Lentz, S. J. Seasonal warming of the Middle Atlantic Bight Cold Pool. J. Geophys. Res. Oceans 122(2), 941–954 (2017).ADS 
    Article 

    Google Scholar 
    66.Iverson, A. R., Fujisaki, I., Lamont, M. M. & Hart, K. M. Loggerhead sea turtle (Caretta caretta) diving changes with productivity, behavioral mode, and sea surface temperature. PLoS ONE 14(8), e0220372 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Braun-McNeill, J., Sasso, C. R., Epperly, S. P. & Rivero, C. Feasibility of using sea surface temperature imagery to mitigate cheloniid sea turtle–fishery interactions off the coast of northeastern USA. Endanger. Species Res. 5(2–3), 257–266 (2008).Article 

    Google Scholar 
    68.Murray, K. T. Characteristics and magnitude of sea turtle bycatch in US mid-Atlantic gillnet gear. Endanger. Species Res. 8(3), 211–224 (2009).Article 

    Google Scholar 
    69.Murray, K. T. Interactions between sea turtles and dredge gear in the US sea scallop (Placopecten magellanicus) fishery, 2001–2008. Fish. Res. 107(1–3), 137–146 (2011).Article 

    Google Scholar 
    70.Witt, M. J., Hawkes, L. A., Godfrey, M. H., Godley, B. J. & Broderick, A. C. Predicting the impacts of climate change on a globally distributed species: The case of the loggerhead turtle. J. Exp. Biol. 213(6), 901–911 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    71.Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103(2), 247–260 (2003).Article 

    Google Scholar 
    72.Saunders, M. A. & Lea, A. S. Large contribution of sea surface warming to recent increase in Atlantic hurricane activity. Nature 451(7178), 557–560 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    73.McClellan, C. M. & Read, A. J. Complexity and variation in loggerhead sea turtle life history. Biol. Lett. 3(6), 592–594 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.McClellan, C. M., Braun-McNeill, J., Avens, L., Wallace, B. P. & Read, A. J. Stable isotopes confirm a foraging dichotomy in juvenile loggerhead sea turtles. J. Exp. Mar. Biol. Ecol. 387(1–2), 44–51 (2010).Article 

    Google Scholar 
    75.Hatase, H. et al. Size-related differences in feeding habitat use of adult female loggerhead turtles Caretta caretta around Japan determined by stable isotope analyses and satellite telemetry. Mar. Ecol. Prog. Ser. 233, 273–281 (2002).ADS 
    Article 

    Google Scholar 
    76.Hatase, H., Omuta, K. & Tsukamoto, K. Bottom or midwater: Alternative foraging behaviours in adult female loggerhead sea turtles. J. Zool. 273(1), 46–55 (2007).Article 

    Google Scholar 
    77.Hawkes, L. A. et al. Phenotypically linked dichotomy in sea turtle foraging requires multiple conservation approaches. Curr. Biol. 16(10), 990–995 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    78.Reich, K. J. et al. Polymodal foraging in adult female loggerheads (Caretta caretta). Mar. Biol. 157(1), 113–121 (2010).Article 

    Google Scholar 
    79.Smolowitz, R. J., Patel, S. H., Haas, H. L. & Miller, S. A. Using a remotely operated vehicle (ROV) to observe loggerhead sea turtle (Caretta caretta) behavior on foraging grounds off the mid-Atlantic United States. J. Exp. Mar. Biol. Ecol. 471, 84–91 (2015).Article 

    Google Scholar 
    80.Patel, S. H., Dodge, K. L., Haas, H. L. & Smolowitz, R. J. Videography reveals in-water behavior of loggerhead turtles (Caretta caretta) at a foraging ground. Front. Mar. Sci. 3, 254 (2016).Article 

    Google Scholar 
    81.James, M. C., Andrea Ottensmeyer, C. & Myers, R. A. Identification of high-use habitat and threats to leatherback sea turtles in northern waters: new directions for conservation. Ecol. Lett. 8(2), 195–201 (2005).Article 

    Google Scholar 
    82.Dodge, K. L., Galuardi, B., Miller, T. J. & Lutcavage, M. E. Leatherback turtle movements, dive behavior, and habitat characteristics in ecoregions of the Northwest Atlantic Ocean. PLoS ONE 9(3), e91726 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    83.Smolowitz, R., Milliken, H. O. & Weeks, M. Design, evolution, and assessment of a sea turtle deflector dredge for the US Northwest Atlantic Sea scallop fishery: Impacts on fish bycatch. North Am. J. Fish. Manag. 32(1), 65–76 (2012).Article 

    Google Scholar 
    84.Hart, D. R. & Chute, A. S. Essential fish habitat source document: Sea scallop, Placopecten magellanicus, life history and habitat characteristics. NOAA Tech. Mem. NMFS NE 189, 21 (2004).
    Google Scholar 
    85.Rheuban, J. E., Doney, S. C., Cooley, S. R. & Hart, D. R. Projected impacts of future climate change, ocean acidification, and management on the US Atlantic sea scallop (Placopecten magellanicus) fishery. PLoS ONE 13(9), e0203536 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    86.Framework Adjustment 23 to the Scallop Fisheries Management Plan. NOAA-NMFS-2011-0255 (2012).87.Murray, K. T. Estimated magnitude of sea turtle interactions and mortality in US Bottom Trawl Gear, 2014–2018 (2020).88.Houghton, J. D., Doyle, T. K., Wilson, M. W., Davenport, J. & Hays, G. C. Jellyfish aggregations and leatherback turtle foraging patterns in a temperate coastal environment. Ecology 87(8), 1967–1972 (2006).PubMed 
    Article 

    Google Scholar 
    89.Nelson, D. A. Life history and environmental requirements of loggerhead turtles. Fish and Wildlife Service, US Department of the Interior (1988). More

  • in

    Zinc oxide nanoparticles using plant Lawsonia inermis and their mosquitocidal, antimicrobial, anticancer applications showing moderate side effects

    1.Benelli, G. Green synthesized nanoparticles in the fight against mosquito-borne diseases and cancer—a brief review. Enzyme Microbial Technol 95, 58–68 (2016).CAS 
    Article 

    Google Scholar 
    2.Dash, A. P., Valecha, N. & Anvikar, A. R. Malaria in India: challenges and opportunities. J. Biosci 33(4), 583–928 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.World Malaria Report: Geneva: World Health Organization. Accessed 18th July 2017.4.Olotu, A. et al. Seven-year efficacy of RTS, S/AS01 malaria vaccine among young African children. N. Engl. J. Med 374, 2519–2529 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Solomona, S., Plattnerb, G. K., Knuttic, R. & Friedlingsteind, P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. U.S.A. 106, 1704–1709 (2009).ADS 
    Article 

    Google Scholar 
    6.Shaalan, E. A. S., Canyonb, D., Younesc, M. W. F., Abdel-Wahaba, H. & Mansoura, A. H. A review of botanical phytochemicals with mosquitocidal potential. Environ. Int. 3, 1149–1166 (2005).Article 
    CAS 

    Google Scholar 
    7.Sundukov, Y. N. First record of the ground beetle Trechoblemus postilenatus (Coleoptera, Carabidae) in Primorskii krai. Far East Entomol. 165, 16 (2006).
    Google Scholar 
    8.Soni, N. & Prakash, S. Green nanoparticles for mosquito control. Sci. World J. 214, 1–6 (2014).Article 

    Google Scholar 
    9.Abinaya, M. et al. Structural characterization of Bacillus licheniformis Dahb1 exopolysaccharide antimicrobial potential and larvicidal activity on malaria and Zika virus mosquito vectors. Environ. Sci. Pollut. Res 25, 5 (2018).Article 
    CAS 

    Google Scholar 
    10.Shawkey, A. M., Rabeh, M. A., Abdulall, A. K. & Abdellatif, A. O. Green nanotechnology: anticancer activity of silver nanoparticles using Citrullus colocynthis aqueous extracts. Adv. Life Sci. Technol. 13, 60–70 (2013).
    Google Scholar 
    11.Thomas, S., Ravishankaran, S. & Johnson Amala Justin, N. A. Resting and feeding preferences of Anopheles stephensi in an urban setting, perennial for malaria. Malar. J. 16(11), 1–7 (2017).
    Google Scholar 
    12.Murugan, K. et al. Sargassum wightii-synthesized ZnO nanoparticles reduce the fitness and reproduction of the malaria vector Anopheles stephensi and cotton bollworm Helicoverpa armigera. Physiol. Mol. Plant Pathol. 101, 202–213 (2018).CAS 
    Article 

    Google Scholar 
    13.Kalimuthu, K., Panneerselvam, C., Murugan, K. & Hwang, J. S. Green synthesis of silver nanoparticles using Cadaba indica Lam leaf extract and its larvicidal and pupicidal activity against Anopheles stephensi and Culex quinquefasciatus. J. Entomol. Acarol. Res. 45(2), e11 (2013).Article 

    Google Scholar 
    14.Patra, A., Raja, A. S. M. & Shah, N. Current developments in (Malaria) mosquito protective methods: a review paper. Int. J. Mosquito Res. 6(1), 38–45 (2019).
    Google Scholar 
    15.Wahab, R., Ahmad, J. & Ahmad, N. Application of multi-dimensional (0D, 1D, 2D) nanostructures for the cytological evaluation of cancer cells and their bacterial response. Colloids Surf. A Physicochem. Eng. Asp. 583, 123953 (2019).CAS 
    Article 

    Google Scholar 
    16.Bhadra, J., Alkareem, A. & Al-Thani, N. A review of advances in the preparation and application of polyaniline based thermoset blends and composites. J. Polym. Res. 27(5), 1–20 (2020).Article 
    CAS 

    Google Scholar 
    17.Jaganathana, A. et al. (+16), Earthworm-mediated synthesis of silver nanoparticles: a potent toolagainst hepatocellular carcinoma, Plasmodium falciparum parasites and malaria mosquitoes. Parasitol. Int. 65(2016), 276–284 (2016).Article 
    CAS 

    Google Scholar 
    18.Abdelkhalek, A. & Al-Askar, A. A. Green synthesized ZnO nanoparticles mediated by Mentha spicata extract induce plant systemic resistance against Tobacco mosaic virus. Appl. Sci. 10, 15 (2020).Article 
    CAS 

    Google Scholar 
    19.Ishwarya, R. et al. Facile green synthesis of zinc oxide nanoparticles using Ulva lactuca seaweed extract and evaluation of their photocatalytic, antibiofilm and insecticidal activity. J. Photochem. Photobiol. 2018(178), 249–258 (2018).Article 
    CAS 

    Google Scholar 
    20.Murugan, K. et al. Nano-insecticides for the control of human and crop pests. In Short Views on Insect Genomics and Proteomics. Entomology in Focus (eds Raman, C. et al.) 229–251 (Springer, 2016).
    Google Scholar 
    21.Bauer, A. W., Kirby, W. M., Sherris, J. C. & Turck, M. Antibiotic susceptibility testing by a standardized single disk method. Am. J. Clin. Pathol. 45(4), 493–496 (1966).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Anitha, J. et al. Earthworm-mediated synthesis of silver nanoparticles: a potent tool against hepatocellular carcinoma, Plasmodium falciparum parasites and malaria mosquitoes. Parasitol. Int. 65, 276–284 (2016).Article 
    CAS 

    Google Scholar 
    23.Wahab, R., Khan, F. & Al-Khedhairy, A. A. Hematite iron oxide nanoparticles: apoptosis of myoblast cancer cells and their arithmetical assessment. RSC Adv. 8(44), 24750–24759 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Ashley, E. A. et al. Spread of artemisinin resistance in Plasmodium falciparum malaria. N. Engl. J. Med. 371, 411–423 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Rajan, R., Chandran, K., Harper, S. L., Yun, S. I. & Kalaichelvan, P. T. Plant extract synthesized nanoparticles: an ongoing source of novel biocompatible materials. Ind. Crop Prod. 70, 356–373 (2015).CAS 
    Article 

    Google Scholar 
    26.Suresh, U. et al. Tackling the growing threat of dengue: Phyllanthus niruri-mediated synthesis of silver nanoparticles and their mosquitocidal properties against the dengue vector Aedes aegypti (Diptera: Culicidae). Parasitol. Res. 114, 1551–1562 (2015).PubMed 
    Article 

    Google Scholar 
    27.Natarajan, K., Selvaraj, S. & Murty, V. R. Microbial production of silver nanoparticle. Digest J. Nanomat. Biostruct. 5, 135–140 (2010).
    Google Scholar 
    28.Song, Y. J., Jang, H. K. & Kim, S. B. Biological synthesis of gold nanoparticles using Magnolia kobus and Diopyros kaki leaf extract. Process Biochem. 44, 1133–1138 (2009).CAS 
    Article 

    Google Scholar 
    29.Krishnan, R. & Maru, G. B. Isolation and analysis of polymeric polyphenol fractions from black tea. Food Chem. 94, 331–340 (2006).CAS 
    Article 

    Google Scholar 
    30.Shankar, S., Rai, A., Ahmad, A. & Sastry, M. Rapid synthesis of Au, Ag and bimetallic Au core-Ag shell nanoparticles using Neem (Azadirachta indica) leaf broth. J. Colloid Interface Sci. 275, 496–550 (2004).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Chandran, S. P., Chaudhary, M., Pasricha, R., Ahmad, A. & Sastry, M. Synthesis of gold nanotriangles and silver nanoparticles using Aloe vera plant extract. Biotechnol. Prog. 22, 577–583 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Benelli, G. Plant-synthesized nanoparticles: an eco-friendly tool against mosquito vectors? In Nanoparticles in the Fight Against Parasites Parasitology Research Monographs (ed. Mehlhorn, H.) 155–172 (Springer, 2015).
    Google Scholar 
    33.Sadraei, R. A simple method for preparation of nano-sized ZnO. Res. Rev. J. Chem. 5(2), 45–49 (2016).CAS 

    Google Scholar 
    34.Priyadarshini, K. A. et al. Biolarvicidal and pupicidal potential of silver nanoparticles synthesized using Euphorbia hirta against Anopheles stephensi Liston (Diptera: Culicidae). Parasitol. Res. 111(3), 997–1006 (2012).PubMed 
    Article 

    Google Scholar 
    35.Satheeshkumar, K. & Kathireswari, P. Biological synthesis of Silver nanoparticles (Ag-NPS) by Lawsonia inermis (Henna) plant aqueous extract and its antimicrobial activity against human pathogens. Int. J. Curr. Microbiol. Appl. Sci. 5, 926–937 (2016).
    Google Scholar 
    36.Nareshkumar, G. et al. Electron channeling contrast imaging for III-nitride thin film structures. Mat. Sci. Semicon. Proc. 2016(47), 44–50 (2016).Article 
    CAS 

    Google Scholar 
    37.Gandhi, S. & Madhusudhan, N. Retrieval of exoplanet emission spectra with HyDRA. Mon. Not. R. Astron. Soc. 47, 1–20 (2017).
    Google Scholar 
    38.Murugan, K. et al. Mosquitocidal and antiplasmodial activity of Senna occidentalis (Cassiae) and Ocimum basilicum (Lamiaceae) from Maruthamalai hills against Anopheles stephensi and Plasmodium falciparum. Parasitol. Res. 114, 3657–3664 (2015).PubMed 
    Article 

    Google Scholar 
    39.Dinesh, D. et al. Mosquitocidal and antibacterial activity of green-synthesized silver nanoparticles from Aloe vera extracts: towards an effective tool against the malaria vector Anopheles stephensi?. Parasitol. Res. 114, 1519–1529 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Pati, F. et al. Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink. Nat. Commun. 5, 3935 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Baxter, J. B. & Aydil, E. S. Nanowire based dye sensitized solar cells. Appl. Phys. Lett. 86, 53114 (2005).ADS 
    Article 
    CAS 

    Google Scholar 
    42.Reddy, K. M. et al. Selective toxicity of zinc oxide nanoparticles to prokaryotic and eukaryotic systems. Appl. Phys. Lett. 90(21), 213902–213903 (2007).ADS 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.Chwalibog, A. et al. Visualization of interaction between inorganic nano-particles and bacteria or fungi. Int. J. Nanomedicine. 2010(5), 1085–1094 (2010).Article 
    CAS 

    Google Scholar 
    44.Saha, S., Dhanasekaran, D., Chandraleka, S. & Panneerselvam, C. A Synthesis, characterization and antimicrobial activity of cobalt metal complex against multi drug resistant bacterial and fungal pathogen Facta universitatis series. Phys. Chem. Technol. 7(1), 73–80 (2009).CAS 

    Google Scholar 
    45.Vivek, M., Kumar, P. S., Steffi, S. & Sudha, S. Biogenic silver nanoparticles by Gelidiella acerosa extract and their antifungal effects Avicenna. J. Med. Biotechnol. 3(3), 143 (2011).CAS 

    Google Scholar 
    46.Chobu, M., Nkwengulila, G., Mahande, A. M., Mwangonde, B. J. & Kweka, E. J. Direct and indirect effect of predators on Anopheles gambiae sensu stricto. Acta Trop. 142, 131–137 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Murugan, K. et al. Hydrothermal synthesis of titanium dioxide nanoparticles: mosquitocidal potential and anticancer activity on human breast cancer cells (MCF-7). Parasitol. Res. 115, 1085–1096 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Subramaniam, J. et al. Eco-friendly control of malaria and arbovirus vectors using the mosquitofish Gambusia affinis and ultra-low dosages of Mimusops elengi-synthesized silver nanoparticles: towards an integrative approach?. Environ. Sci. Pollut. Res. Int. 22(24), 20067–20083 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Murugan, K. et al. Predation by Asian bullfrog tadpoles, Hoplobatrachus tigerinus, against the dengue vector, Aedes aegypti, in an aquatic environment treated with mosquitocidal nanoparticles. Parasitol. Res. 114, 3601–3610 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Mahesh Kumar, P. et al. Mosquitocidal activity of Solanum xanthocarpum fruit extract and copepod Mesocyclops thermocyclopoides for the control of dengue vector Aedes aegypti. Parasitol. Res. 111, 609–618 (2012).PubMed 
    Article 

    Google Scholar 
    51.Khooshe-Bast, Z., Sahebzadeh, N., Ghaffari-Moghaddam, M. & Mirshekar, A. Insecticidal effects of zinc oxide nanoparticles and Beauveria bassiana TS11 on Trialeurodes vaporariorum (Westwood, 1856) (Hemiptera: Aleyrodidae). Acta Agric Slov. 107(2), 299 (2016).CAS 
    Article 

    Google Scholar 
    52.Ahmad, J., Wahab, R., Siddiqui, M. A., Saquib, Q. & Al-Khedhairy, A. A. Cytotoxicity and cell death induced by engineered nanostructures (quantum dots and nanoparticles) in human cell lines. J. Biol. Inorg. Chem. 25(2), 325–338 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Wahab, R. et al. Gold quantum dots impair the tumorigenic potential of glioma stem-like cells via β-catenin downregulation in vitro. Int. J. Nanomed. 14, 1131–1148 (2019).CAS 
    Article 

    Google Scholar 
    54.Wahab, R., Saquib, Q. & Faisal, M. Zinc oxide nanostructures: a motivated dynamism against cancer cells. Process Biochem. 98(June), 83–92 (2020).CAS 
    Article 

    Google Scholar 
    55.Wahab, R. et al. Microwave plasma-assisted silicon nanoparticles: cytotoxic, molecular, and numerical responses against cancer cells. RSC Adv. 9(23), 13336–13347 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    56.Anitha, J., Selvakumar, R. & Murugan, K. Chitosan capped ZnO nanoparticles with cell specific apoptosis induction through P53 activation and G2/M arrest in breast cancer cells—In vitro approaches. Int. J. Biol. Macromol. 136, 686–696 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Wahab, R. et al. Zinc oxide quantum dots: Multifunctional candidates for arresting C2C12 cancer cells and their role towards caspase 3 and 7 genes. RSC Adv. 6(31), 26111–26120 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    58.Liu, J. & Wang, Z. Increased oxidative stress a selective anticancer therapy. Oxid. Med. Cell. Longev. 2015, 294303 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    59.Droese, S. & Brandt, U. Molecular mechanisms of superoxide production by the mitochondrial respiratory chain. Adv. Exp. Med. Biol. 748, 145–169 (2012).CAS 
    Article 

    Google Scholar 
    60.Gupta, S. C. et al. Upsides and downsides of reactive oxygen species for cancer: the roles of reactive oxygen species in tumorigenesis, prevention, and therapy. Antioxid. Redox Signal. 16, 1295–1322 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Rearing experience with ramps improves specific learning and behaviour and welfare on a commercial laying farm

    Experimental designOver 3 years, six paired organic British Blacktail flocks with intact beaks (i.e. not beak-trimmed) were visited between 1 and 40 weeks of age. Within each pair, one flock was ramp reared (RR) and one flock was control reared without ramps (CR). All flocks were kept on one farm which possessed two rearing houses and six laying sheds of approximately 2000 birds per flock. The site was multi-age, meaning that of the six laying sheds there were three different ages on the site at one time.The availability of this commercial facility enabled us to design an experiment whereby we allocated two rearing treatments, one with ramps provided to access elevated structures and a control with elevated structures but no ramps and to alternate these treatments between the two rearing houses available to avoid treatment x house confounds. Each rearing flock was moved independently to a laying house with no mixing, so we were able to continue data collection and examine any long-term effects of the rearing treatment during the laying period. Rearing flocks were systematically allocated so that each laying house received one RR flock and one CR flock during the experiment.Observations were made in the mornings at three time points during the rearing period at 1, 3 and 15–16 weeks, and three in the laying period at 16–17, 24 and 40 weeks of age. See Table 5 for a summary of experimental design, flock and housing information.Table 5 Experimental design for each ramp reared and control reared flock for the 6 replicates. There were two rearing sheds used, Rear1 (R1) and Rear2 (R2), with 6 different laying sheds named A1, A2, B1, B2, C1 and C2.Full size tableThe rearing sheds were static with 142.7 m2 of floor space covered with wood shavings. Rearing sheds were both set up with feed tracks giving mini pellet feed up to 11 weeks of age then pellet grower feed and 7 nipple drinker lines. The lighting schedule was 23 h light in the first day reducing gradually over the rearing period to 10 h light at 7 weeks of age. A minimum light intensity of 10 lx is required, but with windows and pop-holes light intensity was higher in the houses. The temperature was maintained at 30 °C during the first few days then slowly reduced to match the temperature in the laying sheds. Shed heating was provided by gas spot lamps, whole shed heating through hot pipes running along the length of the shed and hot air fans run by a biomass boiler. All flocks had access to the outside range by 10 weeks of age through two pop holes (each L: 2 m by H: 0.4 m). Flocks were moved the short distance from the rearing to the laying house at between 15 to 16 weeks of age in one night using transport modules.All rearing flocks had access to six elevated structures (ES) (see Fig. 3) from four days of age when the chicks were released from the brooding circles. Each ES comprised nine metal perches (length 302 cm, width 3.5 cm), with three perches (25 cm apart) at three different heights (43 cm, 73 cm and 103 cm). Two plastic grids (width 60 cm, length 115 cm) were fixed within the ES to provide platforms at different heights (Fig. 3). In each replicate, the RR flock had one ramp attached to each ES. Three of the ES were fitted with plastic grid ramps (width 60 cm, length 74 cm, angle 35.5°) leading up to the low perch and three ES had ramps (width 60 cm, length 115 cm, angle 40°) leading up to the middle perch. The CR flock had six ES without ramps.Figure 3Elevated structure dimensions used in the ramp reared sheds, (a) shows the high ramp (b) shows the low ramp. The control sheds elevated structures were identical to these but without ramps.Full size imageThe six single-tier laying houses on-site were mobile organic units with approximately 345m2 of floor space. See Fig. 4 for a schematic plan of their layout. All had a raised area comprising plastic slats over supports (approx. 70 cm from the litter) and a ground-level litter area covered with wood chip. Four of the sheds (Fig. 4a) were set up with the slatted area spanning the whole width of the shed and halfway down the length. In two of the sheds (Fig. 4b) the litter area was either side of the elevated slatted area. Nest boxes ran down the centre of the slatted area, dividing this into two sections. Intermittent ramps were installed at the level change, resulting in 4 m of ramp access and 4 m without ramps in the shed with litter at the end and 8 m of ramp access and 13 m without ramps in the shed with litter at the sides. In sheds A1 and A2 the height of the slatted area resulted in a steeper ramp angle of 45° compared to 30° in the other sheds. There were four pop holes at ground level with two on each side of the house (L: 2.35 m by H: 0.4 m) leading to the range from the litter area on both sides of the sheds. All sheds had aerial perches at 1 m high with 18 cm of perching space per bird resulting in approximately 360 m of perch length running the length of the slatted area. Feed tracks and drinker lines matched those in the rearing sheds. The lighting schedule was 16 h of light and varied between summer and winter with the lights set to turn off at the same time as natural dusk. The birds were fed on organic mini pellets throughout lay. Enrichment was provided to the flocks in the form of pecking objects such as buckets and boots. Replicates 5 and 6 were provided with pecking blocks and alfalfa hay nets hung on the litter area.Figure 4Plan view of the laying house layout (a) for replicates 1, 2, 4 and 5 and (b) for replicates 3 and 6. Images not to scale.Full size imageAssessments of behaviourObservations were made at three time points during the rearing period at 1, 3 and 15–16 weeks. On the first visit at 1 week of age, the total number of chicks on each ES was counted once in spot counts in the morning. At 3 and 15–16 weeks, observations of the movements up and down the ES were made. Three of the 6 structures were chosen at random in each shed. The number of chicks present on the different parts of the ES was counted at the beginning and end of the recording period to allow a comparison with the 1-week counts. The recordings involved 5-min continuous sampling where all movements down the ES were recorded and the area the chicks moved down from was noted. This was then repeated for movements up the ES. Focal bird recordings were taken at 3 and 15–16 weeks of age. Records were made for each of 3 randomly selected ES. When 10 focal birds had been observed (approximately 30 birds per flock), or 10 min had passed recordings stopped. A focal bird was chosen if it was performing orientation behaviour, indicating a downwards or upwards transition. This was described as the bird rotating its head to look in the direction of movement. Behaviours performed after the orientation behaviour were tallied, thus recorded as counts per behaviour (see Table 6). Recordings were stopped if birds completed a transition or moved away from transitioning.Table 6 An ethogram of behaviours of focal birds during up and down movements.Full size tableAt 15–16 weeks of age, three types of interactions were recorded for feather pecking. These included severe feather pecking (SFP), gentle feather pecking (GFP) and aggressive pecks (AP)28. A quadrat area 2 m by 2 m was randomly selected, with the number of birds in each quadrat counted at the beginning and end of the recording period. The number of SFP, GFP and AP were recorded over three minutes of continuous recordings in three different areas of the house, selected randomly at each end and the middle of the shed. Feather pecks and aggressive pecks were recorded as bouts: a series of pecks not separated by more than 5 s28. Rates of pecking were calculated as the number of pecks per bird per second.In the laying shed around 16–17 and at 24 weeks of age 3-min continuous sampling and focal bird recordings were taken for transitions between the slats and litter. Four recordings were made at 2-m lengths along the elevated slatted area: two areas with ramps (RA) and two areas without ramps (NRA) were selected. Separate recordings were taken for upwards and downwards movements and the number of birds in the recording area were counted at the start and end of the scans. At 16–17 and 24 weeks of age, feather pecking observations were taken using the same procedure as for the 15–16-week observations during rear.Welfare assessments and production data rearing phaseFeather scores of 20 birds per flock were recorded at 16–17 weeks of age by walking in a straight line down the centre of the shed, selecting a bird at random then counting two birds to the left of this and visually feather scoring that bird. Birds were not handled to minimise disturbance and plumage was scored using the method from Bright et al.33. The neck, back, rump, tail and wings were scored using a four-point scale 0 (best) to 4 (worst). Data were obtained from the farm records for percentage cumulative mortality and body weight.Welfare assessments and production data laying phaseAt 16–17 and 24 weeks of age, the attitude of the flocks was assessed using the approach distance and reactions to novel objects methodology developed by Whay et al.34. Distance to approach birds before they moved away was recorded by walking through the house selecting a bird at random and counting two birds to the left. The bird had to be standing up and facing the researcher, who approached the bird at a steady pace and recorded the distance before the bird moved away. This was repeated on 20 birds in each flock. Reactions to a novel object (blue folder at 17 weeks of age and a white and blue tub at 24 weeks of age) were assessed by placing a novel object on the ground and recording the time taken for the first bird to interact with it and then how many birds were within a 30 cm radius after 60 s. The novel object test was repeated in 4 areas per flock. Range use was recorded by counting the number of birds near to the house (5 m) in the middle range (5–20 m) and far (the rest of the range). Feather scores of 20 birds per flock were recorded at 17 and 24 weeks of age using the same procedure as for the 16-week assessment for birds at rear.At 40 weeks of age, feather cover and keel bone fractures were scored. Up to 100 birds per shed were caught from four different locations (25 litter, 25 slats, 25 perches, 25 nest boxes). In four sheds only 50 birds were caught as the birds were fearful and showed signs of distress. Feather cover was scored by picking the bird up and scoring the body and flight feathers separately using a the AssureWel three-point scale 0 (best) to 2 (worst)35. The keel damage was then scored using a 0 (no damage) to 2 scale based on the technique used by Wilkins et al.36. Validation for keel bone palpations was conducted. A score of 94% matched scores compared to an experienced gold standard assessor and 85% match at dissection for scoring a break. At 24 weeks of age, the number of floor eggs were counted over 1 day.Data were collected from the farm records on laying house percentage of daily eggs, average egg weight (grams), average hen body weight and feed conversion ratio.During the 16 week recordings in the final rearing flocks, the lighting inside the shed was considerably reduced compared to previous flocks. This resulted in poor visibility for feather cover and feather pecking observations, so these were not taken during this visit. Data were not obtained on keel fractures and feather cover scores at 40 weeks for the first laying flocks visited as their sheds were destroyed by strong winds.Statistical analysisData were analysed using SPSS 24 (IMB) or MLwiN 3.0. The statistical package MLwiN was chosen as it is designed for multilevel modelling and can therefore accommodate data nested within levels with repeated measures. Such models account for dependence between responses caused by grouping of birds within sheds, and repeated measures taken from the same sheds on different visits within and between replicates. Including visit and replicate as nested effects ensures that dependences (e.g. due to differing times of year when data were collected) are accounted for. All residuals were checked for normal distributions using a Shapiro-Wilks test or plotted graphically and no transformations were needed to meet the assumptions of the tests. All results are reported in the format mean ± SD unless when stated as the percentage of birds performing a behaviour during transitions.Assessments of behaviourAt rear, from the counts of chicks on structures and counts of transitions up and down the structures, a normal model (generalised linear model) was used with a four-level hierarchy (bird within shed within visit within replicate). The same normal model and four level hierarchy were used for the counts of transitions in the laying shed.For the focal bird behaviours of birds transitioning at rear and lay, the data were presented as the percentage of each behaviour calculated for the birds in the recording session for the two rearing treatments. The direction (up or down) was analysed separately. For the focal birds at lay, all were included in the analysis for the pre-transitioning behaviours, only birds that attempted a transition were included for analysis of the transitioning behaviours. Pre-transition behaviours for birds that moved-away and did not transition were analysed separately. Owing to the low occurrence of behaviours during the focal recordings for transitions up and down the ramps, data were coded as yes or no, and a Binomial model was used for analysis for both the rear and lay focal transition data with four hierarchical levels (bird within shed within visit within replicate).Welfare and production dataFor the Novel object test, human approach, feather pecking and feather cover data a normal model was used in MLwiN with four-levels (Bird within Shed within visit within replicate). Floor eggs were analysed using a two-tailed t-test in SPSS, due to limited data. Ordinal data such a keel bone fracture scores and feather cover recorded at 40 weeks of age were converted to binomial data due to a lack of data for some scores, these were therefore analysed using a binomial model in MLwiN with two levels (Bird within shed).Production data at rear (body weight in grams) and lay (% eggs daily, egg weight in grams, body weight in grams and feed conversion ratio) were obtained from farm records and analysed in SPSS using a general linear model with treatment (CR and RR) as a fixed factor and age (3, 8 and 14 weeks at rear and 20, 30 and 70 weeks at lay) as a random factor to account for repeated results. Cumulative percentage mortality was analysed at 14 weeks of age using a t-test to compare the treatment groups.Ethical approvalEthical approval for this project was granted by the University of Bristol’s Animal Welfare and Ethical review body under UIN: UB/16/040 and all methods were conducted in accordance with the review body and UK legislation. 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    Multiple pygmy blue whale acoustic populations in the Indian Ocean: whale song identifies a possible new population

    Song description: terminologySong organisationBlue whale vocal sequences are traditionally referred to as ‘calls’19,20,21, however, as they meet the criterion of ‘song’ as used in the bioacoustic community22, in this study we use the term ‘song’ to refer to regularly-repeated whale vocalisations. The song is repeated in a sequence with regular intervals, defined as the Inter-Call Interval (ICI), measured as the time interval between the beginning of the one song and the beginning of the following song. Note that although we use the term ‘song’, we chose to keep the definition ‘ICI’ as this nomenclature is used traditionally in the whale literature, rather than ‘ISI’, which usually designates Inter-Series (or Sequence) Interval. Songs are composed of units and we used the term ‘unit’ to designate parts of the song that are separated by a silence (see reviewed criteria in23). Units were divided into subunits: subunits are defined as such when there is a sudden change in the sound structure for instance becoming harmonic or noisy.Sound typesA sound can be of different types: (1) the simpler one is the simple tone, which is either pure, with the same frequency all along, or showing frequency and/or amplitude modulations; (2) harmonic sounds are sounds with multiple tones at frequencies that are integer multiples of the frequency of the original wave, called the fundamental frequency ((F_{0})). When one of the harmonics has a greater amplitude than the others, it is called ‘resonance frequency’; (3) pulsed sounds are, as defined in24, the repetition of similar “pulses” or short signals with a constant pulse rate, often aurally perceived by humans as amplitude modulated sounds. On spectrogram representation, using a long analysis time window, these sounds are characterized by sidebands with regular spacing. The frequency difference ((Delta f)) between each sideband is the pulse rate of the sound. In their recent study, Patris et al. made the difference between what they defined as ‘tonal pulsed sounds’ and ‘non-tonal pulsed sounds’24. Following their criterium, the sidebands of the tonal pulsed sounds show a harmonic relationship, meaning that the frequency of each sideband divided by the pulsed rate is a positive integer. If it is not the case, then the sound is a non-tonal pulsed sound.Nonlinear phenomenaNonlinear phenomena are observed in a variety of birds25, anurans26 and mammals27,28, including marine mammals (e.g., manatee29) and more particularly cetaceans (right whales30,31, killer whales30,32 and humpback whales33). They have been well described by a variety of authors27,34 and include: (1) frequency jumps, that are characterized by sudden (F_{0}) changes which moves up or down abruptly and discontinuously, and is different from continuous, smooth modulation27; (2) subharmonics, that are additional spectral components and can suddenly appear at integer fractional values of an identifiable (F_{0}) (e.g., (F_{0}/2), (F_{0}/3, ldots)) and as harmonics of these values. On a spectrogram, it results as bands of energy evenly spaced below (F_{0}) and between its harmonics throughout the spectrum; (3) biphonation, that is the simultaneous occurrence of two independent fundamental frequencies (F_{0}) and (G_{0}). Biphonation can be visible on a spectrogram as two distinct frequency contours35. Alternatively, if one source ((F_{0})) vibrates at a much lower frequency than the other ((G_{0})), biphonation will appear as visible sidebands at linear combinations of (F_{0}) and (G_{0}) (m(G_{0}) ± n(F_{0}), where m and n are integers), because the airflow is then modulated by the frequency difference. This is equivalent to considering that the lower (F_{0}) amplitude-modulates the higher frequency (G_{0}) (carrier frequency)28; (4) finally, deterministic chaos are broadband, noise-like segments. These episodes of non-random noise appear via abrupt transitions and can also contain some periodic energy, which appears as banding in a spectrogram. In extreme cases there are no repeating periods at all27,34.Analysis of the Chagos song and comparison with the Indian Ocean pygmy blue whale song types and Omura’s whale song typesChagos songThe Chagos song was composed of 3 units (Fig. 3). The 3-unit song was repeated in stereotyped series with an ICI of (190.79 pm 1.49) s (Fig. 7b).The first unit of the Chagos song is divided into 3 subunits (Fig. 3): in 2017, subunit 1 was pulsed with a rate (Delta f_{u1su1}) = 3.22 ± 0.01 Hz. Using Patris et al. ’s criterion24, we concluded that this subunit is a non-tonal pulsed sound, since the sidebands do not have a harmonic relationship. The carrier frequency (where the peak of energy lies) was 35.74 ± 0.02 Hz for 73% of the measured songs, 32.47 ± 0.05 Hz for 23% of the songs and 38.9 ± 0.06 Hz for 3% of the measured songs. One song had a carrier frequency of 29.18 Hz. This subunit 1 lasted 3.02 ± 0.03 s in duration. Subunit 2 was often less obvious (likely due to propagation effects, lower source level or possibly to deterministic chaos) so that it could not be measured for all of the songs sampled; it is also a short (1.53 ± 0.05 s) non-tonal pulsed unit with a pulse rate ((Delta f_{u1su2})) of approximately 3 Hz and a slightly different carrier frequency, induced by a frequency jump. The carrier frequency was of 36.02 ± 0.03 Hz for 87% of the measurements, 39.16 ± 0.05 Hz for 8% of the measured songs and 32.97 ± 0.1 Hz for 5%. Finally, subunit 3 was a tonal unit showing a frequency modulation. The subunit started at 29.55 ± 0.02 Hz down to 29.35 ± 0.02 Hz over approximately 3.5 s, then down to 28.10 ± 0.09 Hz as a decrease to 27.62 ± 0.04 Hz over 3 s. The total duration of this subunit was 6.40 ± 0.07 s, and the total duration of the unit 1 was 11.36 ± 0.08 s.Unit 2 was a pure tone following after a silence of 3.06 ± 0.1 s. Its peak frequency was 22.34 ± 0.05 Hz and its duration was 3.24 ± 0.07 s. Finally, unit 3, also a pure tone, followed after a silence of 14.38 ± 0.23 s. It had a peak frequency of 17.44 ± 0.05 Hz and lasted 2.94 ± 0.15 s. The third unit was sometimes absent. This could be due to a variation in the song or due to propagation losses. When unit 3 was present, the total song duration was 34.38 ± 0.4 s.The frequency for the beginning of the third subunit of the unit 1 of the Chagos song (point 1 in Fig. 3a) decreased by approximatively 0.33 Hz/year across years (Fig. 4).This phenomenon will be examined in details in a further study.Figure 3Spectrogram (a) and waveforms (b) of a Chagos song recorded on the eastern side of the Chagos Archipelago (DGS) in August 2017. Detailed waveforms show the signal structure of the units within the song. Spectrogram parameters: Hamming window, 1024-point FFT length, 90% overlap. Note that the axes differ among plots. (c) Measurements (mean ± standard error (s.e.)) of the acoustic features. N is the number of measurements, (u_{i}su_{j}) stands for (unit_{i} subunit_{j}) where i and j are the unit and subunit numbers, (Delta f) designates the frequency difference between the sidebands, f and d are the frequency and duration of the feature indicated in subscript, and when present, the number in brackets refers to the point measured as indicated on the spectrogram. (F_{x}) or (G_{x}) designate the xth harmonic of a sound, and Cf designates the carrier frequency of a sound.Full size imageFigure 4The decline in frequency of the Chagos song from 2002 to 2017: spectrogram representation of five songs recorded at Diego Garcia in years 2002, 2005, 2012, 2015 and 2017. Spectrogram parameters: Hamming window, 1024-point FFT length, 90% overlap.Full size imageIndian Ocean pygmy blue whale songsThis section describes the structural, temporal and frequency features of the pygmy blue whale song-types commonly reported in the Indian Ocean. Note that as the frequency of at least parts of these songs are known to vary within and across years36,37,38,39,40,41, the frequency values obtained here are only valid for the years sampled.Madagascan pygmy blue whale The Madagascan pygmy blue whale song had 2 units (Fig. 5a). Unit 1 was divided into 2 subunits. In 2004, subunit 1 was a noisy pulsed sound, characteristic of deterministic chaos, with a pulse rate (Delta f_ {u1su1}) = 1.44 ± 0.01 Hz and of 4.76 ± 0.005 s duration. Subunit 2 was a tonal sound with harmonics. Its (F_ {0}), estimated as the mean frequency difference between the harmonics, was 7.04 ± 0.005 Hz. The maximum energy was in the (F_ {5}) (resonance frequency), which commenced at 35.31 ± 0.02 Hz and remained stable over 10.65 ± 0.13 s ((F_{5_{u1su2}}) in Fig. 5a). The frequency then remained stable over another 3.00 ± 0.16 s or in some songs increased to 35.91 ± 0.05 Hz [range = 34.84–37.05 Hz]. The total duration of subunit 2 was 13.65 ± 0.12 s, and unit 1 was 18.41 ± 0.15 s.Unit 2 followed after 27.74 ± 0.13 s. It had 2 subunits. Subunit 1 was a noisy pulsed sound, identified as deterministic chaos, it had a pulsed rate of (Delta f_ {u1su1}) = 1.25 ± 0.017 Hz, and a duration of 3.30 ± 0.05 s. Subunit 2 was a complex harmonic-like signal, with sidebands spaced by (Delta f_ {u2su2}) = 1.39 ± 0.003 Hz. Calculations of the ratio of the sideband frequencies over (Delta f) show that these 1.39 Hz-spaced bands do not have a harmonic relationship. However, relatively higher energy lies in frequency bands that have a harmonic relationship, where the band with the greatest energy started at 25.11 ± 0.02 Hz and ended at 24.33 ± 0.02 Hz ((G_{3_{u2su2}}) on Fig. 5). On the low signal-to-noise ratio (SNR) songs, only the harmonic bands were visible, this explains why this unit has been described previously as a harmonic signal when it is not7. The complex structure of subunit 2 can be explained by a phenomenon of biphonation, where there are two concurrent frequencies, with a lower fundamental frequency ((F_{0})) of 1.39 Hz, a higher fundamental frequency ((G_{0})) of 8.37 Hz (resonance frequency (G_{3}) starting at 25.11 Hz), and the sidebands at m(G_{0}) ± n(F_{0}) consistent with the amplitude modulation of (G_{0}) by (F_{0}). This biphonation event lasted for 16.04 ± 0.19 s. Finally, subunit 2 ended in a tonal sound with the harmonics ((G_{0}) = 7.94 Hz ± 0.003 Hz), that decreased in frequency from 24.30 ± 0.02 Hz to 23.05 ± 0.04 Hz over 4.88 ± 0.11 s (measured for the harmonic where there is the greatest energy ((G_{3_{u2su2}}))). Unit 2 was 23.63 ± 0.73 s in duration. The total duration of the Madagascan pygmy blue whale song was 68.68 ± 0.34 s.Sri Lankan pygmy blue whale The Sri Lankan pygmy blue whale song had 3 units (Fig. 5b). In 2009, unit 1 was a pulsed, non-tonal sound of a duration of 22.25 ± 0.11 s. The pulse rate was (Delta f_{u1}) = 3.28 ± 0.09 Hz. The carrier frequency of unit 1 started at 29.87 ± 0.09 Hz (‘Cf’ on Fig. 5b), and slightly down swept to 29.68 ± 0.09 Hz over 4.57 ± 0.06 s, then the frequency decreased to 25.85 ± 0.09 Hz over 17.68 ± 0.09 s.Unit 2 followed after 16.45 ± 0.12 s of silence. Unit 2 was a tonal sound with harmonics spaced by 12.21 ± 0.08 Hz. The maximum of energy was in the (F_{5_{u2}}) and started at 56.55 ± 0.12 Hz, increased to 60.63 ± 0.03 Hz over 4.87 ± 0.09 s, then increased to 60.80 ± 0.03 Hz over 8.80 ±0.09 s, and finally increased sharply to 70.13 ± 0.18 Hz overe 0.92 ± 0.06 s. Unit 2 was 14.60 ± 0.07 s in duration.Unit 3 followed after 2.20 ± 0.06 s of silence. It started as a non-tonal pulsed sound lasting 4.46 ± 0.01 s, with a pulse rate (Delta f_{u3}) = 3.29 ± 0.12 Hz and a carrier frequency starting at 103.47 ± 0.05 Hz and slightly decreasing to 102.91 ± 0.03 Hz. It then continued as a pure tone starting at 102.63 ± 0.05 Hz down to 102.41 ± 0.04 Hz during 24.19 ± 0.14 s and then suddenly peaked to 108.08 ± 0.06 Hz. Unit 3 lasted 29.25 ± 0.10 s in total, and the entire song was 84.76 ± 0.16 s in duration.Australian pygmy blue whale The Australian pygmy blue whale song is the most complex of the pygmy blue whale songs. It is traditionally described as a 3-unit signal, although multiple variations in the unit order (or syntax) are found42. The song variants change the order and repetition of the unit types. Here, for simplicity, we selected and thus described only the common traditional 3-unit song (Fig. 5c).Unit 1 was 48.83 ± 0.20 s in duration. It had 2 subunits: subunit 1 was a pulsed sound, with a pulse rate (Delta f^{s}_{u1su1}) = 1.21 ± 0.01 Hz at the beginning of the subunit, pulsing accelerated to reach (Delta f^{e}_{u1su1}) = 1.71 ± 0.01 Hz at the end of the unit. Following the ratio “band frequency/pulse rate” criterion, this unit is a non-tonal pulsed sound. However, it is a biphonation sound, as higher energy bands, which do have a harmonic relationship and are spaced by approximately 9 Hz, are obvious on the spectrogram (grey arrows on Fig. 5). The higher fundamental frequency (G_{0}) was at (sim) 9.10 Hz. The resonance frequency of this harmonic sound was the (G_{1_{u1su1}}). It started at 18.20 ± 0.02 Hz and ended at 18.47 ± 0.02  Hz, and was 23.85 ± 0.16 s in duration. Subunit 2 is also a biphonation sound, with a (F_{0}) at 2.80 ± 0.03 Hz at the beginning of the unit ((Delta f^{s}_{u1su2}) in Fig. 5c), decreasing to 1.78 ± 0.01 Hz at the end of the subunit ((Delta f^{e}_{u1su2})), which gives an impression of a decreasing pulse rate when listening to the song. This change in (F_{0}) frequency creates the complicated pattern of intersecting sidebands toward the end of unit 2. The harmonic bands are spaced by approximately 20 Hz (= (G_{0}), precise measurements are given below). Subunit 2 had two variations: subunit 2 was continuous in 42.9% of the sampled songs, but was interrupted by a short gap in 57.1%. In the continuous subunit case (N = 48), the fundamental frequency ((G_{0_{u1su2}})), which is here the band with the most energy, started at 20.22 ± 0.03 Hz and ended at 20.71 ± 0.02 Hz. The subunit lasted 23.26 ± 0.2 s. In the interrupted subunit case (N = 64), the fundamental frequency ((G_{0_{u1su2}})) started at 20.12 ± 0.03 Hz and slightly increased to 20.44 ± 0.02 Hz over 15.27 ± 0.21 s. Then, there was a silence of 3.32 ± 0.08 s followed by the resumption of the subunit at 20.29 ± 0.03 Hz increasing to 20.48 ± 0.17 Hz over 5.71 ± 0.17 s. In this case, the total duration of the subunit (gap included) was 24.31 ± 0.14 s.Unit 2 followed after 7.30 ± 0.09 s. It started as a slightly noisy pulsed sound (possibly deterministic chaos) with a rate (Delta f_{u2}) = 2.77 ± 0.06 Hz during 4.54 ± 0.07 s, then continued as a tonal sound with harmonics. The (F_{0_{u2}}) started at 20.11 ± 0.06 Hz, increased to 22.61 ± 0.02 Hz over 5.14 ± 0.10 s, and then slowly increased to 23.84 ± 0.02 Hz over 23.84 ± 0.02 s. Unit 2 was 23.12 ± 0.12 s in duration.Unit 3 followed after 24.28 ± 0.09 s of silence. It started as a tonal sound with harmonics spaced by 8.93 ± 0.05 Hz. The resonance frequency ((F_{1_{u3}})) started at 7.59 ± 0.02 Hz then increased to 18.26 ± 0.01 Hz over 3.76 ± 0.05 s, with the appearance of sidebands with non-harmonic relationship, spaced by (Delta f_{u3}) = 3.19 ± 0.09 Hz. These non-tonal pulses stopped approximately 3.5 s before the end of the unit, which ends on the harmonic sound, slightly down swept to 18.05 ± 0.02 Hz. These sidebands could be subharmonics, ((F_{0}/3, 2F_{0}/3), etc). Alternatively, they could suggest a biphonation sound. This third unit lasted 18.82 ± 0.12 s in duration, and the whole 3-unit song was 123.54 ± 0.29 s in duration.Figure 5Spectrograms (upper panels) and waveforms (middle panels) of the song of the Madagascan, Sri Lankan and Australian pygmy blue whales, including detailed waveforms to show the internal signal structure. The Madagascan song was recorded off Crozet Island (CTBTO records, site H04S1) in April 2004, the Sri Lankan song was recorded at DGN (CTBTO records, site H08N1) in April 2009 and the Australian song was recorded at Perth Canyon in March 2008 (IMOS records). (Spectrogram parameters: Hamming window, 1024-point FFT length, 90% overlap. Note that the axes differ among plots.) And measurements (mean ± s.e., lower panels) of the acoustic features of the different song types. N is the number of measurements, (u_{i}su_{j}) stands for (unit_{i} subunit_{j}) where i and j are the unit and subunit numbers, (Delta f) designates the frequency difference between the sidebands, f and d are the frequency and duration of the feature indicated in subscript, and when present, the number in brackets refers to the point measured as indicated on the corresponding spectrogram. (F_{x}) or (G_{x}) designate the xth harmonic of a sound, and Cf designates the carrier frequency of a sound.Full size imageOmura’s whale songsAll Omura’s whale songs showed energy between 15 and 55 Hz and peaks of energy around 20 and 40–45 Hz (Fig. 6 lower panels).Ascension Island Omura’s whale Omura’s whale songs recorded in 2005 off Ascension Island started as a tonal sound at 19.84 ± 0.03 Hz. This tone was 3.21 ± 0.08 s in duration but less than 1 s after its beginning, it was overlapped by a noisy pulsed sound, typical of deterministic chaos. The pulse rate was estimated at (Delta f) = 1.44 ± 0.05 Hz. This deterministic chaos lasted for 5.20 ± 0.07 s. Finally, 2.65 ± 0.06 s after the beginning of the song, three tonal components appeared at harmonically independent frequencies, characteristic of triphonation: two tones starting simultaneously, one at 20.88 ± 0.02 Hz and the other at 21.85 ± 0.03 Hz, lasting respectively 4.08 ± 0.23  s and 3.65 ± 0.16 s, and a third tone starting a bit later, 4.48 ± 0.07 s after the beginning of the song, at a frequency of 47.22 ± 0.03 Hz and lasting 3.33 ± 0.09 s. The duration of the total component was 7.64 ± 0.11 s (Fig. 6a).Madagascan Omura’s whale song The following description of the Madagascan Omura’s whale song uses the description provided by Moreira et al.18 and observation from the spectrogram (Fig. 6b). In 2015, Cerchio et al. described the Madagascan Omura’s whale song recorded in 2013–2014 as a single-unit amplitude-modulated low frequency vocalization, with a 15–50 Hz bandwidth15. More recently, Moreira et al. reported a 2-unit song, with the first unit commencing as an amplitude-modulated component with bimodal energy at 20.75 Hz and 40.04 Hz, followed by a harmonic component with a low harmonic at 20.0 Hz and an upper harmonic at 41.0 Hz, as well as an additional tone at (sim) 30 Hz. Unit 1 was characterized as sometimes followed by a tonal unit at 16 Hz18. The ICI was 189.7 s (s.d. 16.47 s, measured from 118 series with (ge) 20 consecutive songs) and ranged from 145.5 to 237.6 s43.Based on the song example recorded in December 2015 in Nosy Be, Madagascar, and provided by S. Cerchio, we observed a 2-unit song (Fig. 6b). The first unit started as chaotic, with no visible sidebands. After (sim) 3 s the signal had a bi- or triphonation event (whilst the deterministic chaos still continues), with first a tone at 40.04 Hz, another tone with a harmonic relationship at 20.02 Hz but starting circa 2.6 s later and a third one at 27.8 Hz starting 4.4 s after the beginning of the first tone, whilst the chaotic sound ends (the chaotic sound lasted circa 9.3 s). The tones of the bi- or triphonic sound all ended at the same time, 11.7 s after the beginning of the song. The second unit seems to be optional15,17,18,44. It followed after 2.8 s silence. It was a tonal sound of 4.9 s in duration with a peak frequency of 16.6 Hz. (Note that the observations here are purely qualitative since only based on 1 song).Diego Garcia Omura’s whale song (DGC) The ‘Diego Garcia Croak’—DGC—recently attributed to the Omura’s whales17 was comprised of one unit (Fig. 6c), although sometimes a second unit was present. The first unit was tonal at the start, with a frequency of 17.91 ± 0.03 Hz, quickly becoming a noisy pulsed sound, characteristic of deterministic chaos, with a pulsed rate of 2.09 ± 0.07 Hz estimated on 41 songs. This chaotic component was 2.76 ± 0.06 s in duration to then became pulsed, although still slightly noisy, with a pulse rate of 2.21 ± 0.005 Hz. This part showed a peak of energy around 19.46 ± 0.08 Hz, and another one around 43.51 ± 0.11 Hz (Fig. 6c, lower panel), and lasted 4.07 ± 0.05 s. Finally, the unit ended as a tonal sound at 17.62 ± 0.04 Hz lasting 5.29 ± 0.12 s. This whole unit had a duration of 10.56 ± 0.14 s. In some occurrences (N = 12), a second tonal unit was present after a silence of 39.89 ± 0.5 s. Unit 2 started at 13.51 ± 0.06 to 13.46 ± 0.04 Hz and lasted for 3.81 ± 0.20 s. When the second unit was present, the entire song was 54.74 ± 0.19 s in duration. Note that in our study, out of the 80 songs measured only 12 had unit 2.Australian Omura’s whale song The Omura’s whale song recorded in 2013 off western Australia had two units (Fig. 6d). Unit 1 was a noisy pulsed sound with a pulse rate of 1.65 ± 0.06 Hz with deterministic chaos, and a duration of 6.28 ± 0.06 s.The peak in energy was at 25.32 ± 0.14 Hz followed by a gap of 2.53 ± 0.04 s, and then a second noisy pulsed unit, with a pulsed rate of 1.80 ± 0.02 Hz estimated on 83 songs. This unit lasted 4.08 ± 0.03 s and had a peak of energy at 25.25 ± 0.18 Hz and another one at 41.20 ± 0.18 Hz (Fig. 6d, lower panel). During the last third of unit 2, the song transitioned to a tonal sound, starting at 25.15 ± 0.02 Hz and swept down to 25.07 ± 0.02 Hz over 3.28 ± 0.04 s, then abruptly decreased to 19.8 ± 0.02 Hz and became tonal for 4.90 ± 0.07 s, forming a z-shape on the spectrogram representation. The whole song was 16.39 ± 0.08 s in duration.Figure 6Spectrograms (a–d), waveforms (e–h), acoustic measurements (mean ± (s.e.)—i–l), and Power Spectral Density (PSD—m–p) of the songs of the Omura’s whales from Ascension Island, Madagascar, Diego Garcia and Australia. The stars on the PSD (m–p) outline the peaks of energy. The Ascension Island song was recorded off Ascension Island (CTBTO records, site H10N1) in November 2005, the Madagascar song was recorded off Madagascar in December 2015 and provided by S. Cerchio, the Diego Garcia DGC song was recorded at DGN (CTBTO records, site H08N1) in October 2003 and the Australian song was recorded at Kimberley site in March 2013 (IMOS records). For the panels (a–d) and (i–j): N is the number of measurements, (u_{i}su_{j}) stands for (unit_{i} subunit_{j}) where i and j are the unit and subunit numbers, (Delta f) designates the frequency difference between the sidebands, f and d are the frequency and duration of the feature indicated in subscript, and when present, the number in brackets refers to the point measured as indicated on the corresponding spectrogram. (Spectrogram parameters: Hamming window, 1024-point FFT length, 90% overlap. Note that the axes differ among plots).Full size imageDeterministic chaosWe classified deterministic chaos as: ‘slight’, where sidebands were easily distinguished but the sound was noisy; ‘moderate’, where the sidebands were visible but difficult to measure; and ‘strong’, where the sound had no discernible structure. Where deterministic chaos was present, we identified its persistence, defined as the proportion of deterministic chaos over the duration of a song31.It was difficult to characterize the presence of deterministic chaos where the song (sub)unit was short and the pulse rate was low, as it is difficult to ascertain if the noisy structure (i.e., lack of structure) was part of the whale’s song (i.e., deterministic chaos) or whether it was due to an artefact, such as a sound propagation issue. This was the condition for the subunit 2 of unit 1 of the Chagos song. If this subunit had indeed a chaotic structure, this chaos was slight, and represented 4.5% of the entire duration of the song (Fig. 7a).Pygmy blue whale songs had only slight deterministic chaos, and of the entire song, it represented: 11.7% of the duration of the Madagascan song; 3.7% of the Australian song; and it was not present in the Sri Lankan pygmy blue whale song (Fig. 7a). In the Madagascan pygmy blue whale songs, slight deterministic chaos was in subunits 1 of both units 1 and 2, and in the Australian pygmy blue whale songs, deterministic chaos was present in subunit 1 of unit 2.In contrast, deterministic chaos was a significant proportion of all Omura’s whale songs (Figs. 6a–d and 7a). For the song of the Ascension Island Omura’s whale, moderate deterministic chaos was present across 68% of the duration of their song. For the Australian Omura’s whales, deterministic chaos was present across 63.2% of their song, it was moderate-to-strong in the first unit and slight in the second unit. The Madagascan Omura’s whales had strong deterministic chaos across 72% of their song, which excludes the tonal unit as the tonal part was not always present. The Diego Garcia DGC Omura’s whale song had a total chaos persistence of 65.2% (Fig. 7a), with a moderate deterministic chaos present in the first 2.7 s of the song, which represents 26.3% of the song duration (Fig. 7a medium grey section). The song then evolved to a more clearly pulsed sound, with a slightly noisy structure, classified as slight deterministic chaos. Here again, it was difficult to ascertain whether this lack of structure was a characteristic of the song or an artefact of the propagation. Yet, the slight lack of structure was consistently observed across the sampled songs.Inter-call-intervalsWhilst the Madagascan pygmy blue whale had a shorter ICI, all the other acoustic groups studied here had a similar ICI duration (Fig. 7b). Thus, ICI is not a key parameter in the distinction among species and cannot be used to determine whether Chagos-whales are a blue or an Omura’s whale.Figure 7(a) Proportion of deterministic chaos (i.e., chaos persistence) in the Chagos song compared with the three Indian Ocean pygmy blue whale song types (Madagascan, Sri Lankan and Australian) and the four Omura’s whale song types (Madagascan, Diego-Garcia DGC, Australian and Ascension Island). Chaos persistence is defined as the proportion of deterministic chaos across the entire song duration (given as a percentage). Shades of grey indicate the strength of the chaos: slight (light grey), moderate (medium grey) and strong (dark grey)). (b) Boxplot representation of the Inter-call Intervals (ICI expressed in s) for the different song-types measured in this study. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points considered to be not outliers, and the outliers are plotted individually.Full size imageGeographic distributionChagos song was detected at 5 of our 6 recording sites at disparate locations across the Indian Ocean, from: the northern Indian Ocean, off Sri Lanka; on both sides of the central Indian Ocean, off the Chagos Archipelago; and in the far eastern Indian Ocean, off northern Western Australia (Fig. 2). The Chagos song was recorded off Sri Lanka (i.e., Trincomalee) in April. Blue whales were observed at the time the recordings were made, and the songs of the Sri Lankan pygmy blue whale were also recorded at the time. The acoustic recording had become degraded as they were made nearly forty years before, on 19 April 1984, and only six distinct Chagos songs were found. Unfortunately, these recordings were of poor SNR which prevented detailed acoustic measurement. The songs, however, had the distinct structure of the Chagos song (Fig. 3) and an ICI of (simeq) 200 s (range 200 to 209 s), consistent with the ICI rate measured for the Chagos song off the Chagos Archipelago (Fig. 7b). Further south in the northern Indian Ocean, 6,984 Chagos songs were detected in 2013 (from January to early December) at our recording site RAMA, but no songs were detected at this site in 2012, although recording had been made over a shorter period, from May to December, in that year. In the central Indian Ocean, a total of 486,316 Chagos songs were detected from January 2002 to March 2014 at DGN, and 737,089 Chagos songs from January 2002 to August 2018 at DGS. In the far eastern Indian Ocean, off Kimberley, northern Western Australia, low SNR Chagos songs were manually detected from January to May, in 41 out of the 331 recording days in 2012–2013. In the south-central Indian Ocean, at our recording site RTJ, no Chagos songs were detected in 2018.Figure 8 shows the average number of Chagos songs detected per day for each year of data at the sites located on: (a) the western (DGN); and (b) eastern (DGS) sides of the Chagos Archipelago; as well as (c) further north-east, at RAMA site. The number of songs varied over the years; fewer songs were recorded at both DGN and DGS sites in 2008. In comparison with the Chagos Archipelago sites, the number of songs detected at north-eastern RAMA was low in 2013, with an average of only 20 songs/day.Figure 8Average number of Chagos songs per day detected in each year of data on the (a) western (DGN) and (b) eastern (DGS) sides of the Chagos Archipelago, and (c) further north-east, at RAMA site.Full size imageSeasonalityFigure 9b shows the average seasonality of Chagos song occurrence on both sides of the Chagos Archipelago. On the western side of the central Indian Ocean (DGN site), Chagos songs were heard predominantly from September to January, with detections peaking in December and January. On the eastern side of the central Indian Ocean (DGS site), songs were detected from June to November, with detection peaks in August to October, depending on the year. In 2013, at the RAMA site (further north-east of the Chagos Archipelago), Chagos songs were detected from January to June (with peaks in May), and in November (Fig. 9a). Off Kimberley, in the north of Western Australia, low SNR Chagos songs were found from the 22 January 2012 to the 20 May 2012, with a peak in March (Fig. 9c).Figure 9(a) Seasonality of Chagos songs at RAMA in 2013, presented as a percentage of songs per month (i.e. monthly number of songs divided by total number of songs detected in the year); (b) Seasonality of Chagos song averaged over the years (±SE) on the western (DGN—gray) and eastern (DGS—orange) sides of the Chagos Archipelago. This average seasonality is calculated as such: the monthly number of songs is divided by the total number of songs detected in the corresponding year, and averaged over the years. Note that due to the low number of recording days at DGN in 2007 and 2014, and in 2007 at DGS, these years were removed from the averaging (DGN: 11 years and DGS: 15.5 years); (c) Hourly presence of Chagos songs in Kimberley (Western Australia) in 2012–2013. Note that the metric and thus the graphic representation used for this site is different from that for RAMA and DGN/DGS: in the Kimberley data set, Chagos songs were logged upon visual inspection of the spectrograms, and a metric of hourly presence/absence of the song per day was used (see the Methods section for details).Full size imageWe found strong evidence at both Chagos Archipelago sites (DGN and DGS) that the number of Chagos songs changes not only across months (Table 1; (p=0.02417), Table 2; (p < 0.001)) and years (Table 2; (p < 0.001), Table 2; (p < 0.001)), but also that there is an interaction between months and years (Table 1; (p < 0.001), Table 2; (p < 0.001); Fig. 10). This provides evidence to suggest that there is variation in the pattern of whale songs across years at both sites. Although Chagos songs were detected throughout the year, there were more songs detected at restricted times (Fig. 10). The timing of peaks in song detection was different between the sites. At DGN most songs were detected in 2 to 3 months, whereas at DGS songs were detected over a longer period, from 2 to 6 months. At DGN, where the Chagos song distribution in most years shows clear peaks towards December and January, in a few years, peaks were outside this time (e.g., 2005 in March and September, 2006 in September and 2008 in July and August; Fig. 10). Conversely, in DGS most songs were observed between June and November, although there were inter-annual differences (Fig. 10).Table 1 Assessing the likelihood of an effect on the number of Chagos songs per day at site DGN (n = 3917 days).Full size tableTable 2 Assessing the likelihood of an effect on the number of Chagos songs per day at site DGS (n = 5557 days).Full size tableFigure 10Number of Chagos songs per month for each year at DGN and DGS. Note that the scale of the y-axis differs among years to highlight the seasonal patterns. Months without data are indicated by ‘No Data’, and months with more than 50% of missing days are indicated by a black dot.Full size image More

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    Declining phenology observations by the Japan Meteorological Agency

    1.Koike, S., Fujita, G. & Higuchi, H. Glob. Environ. Res. 10, 167–174 (2006).
    Google Scholar 
    2.Doi, H. & Takahashi, M. Glob. Ecol. Biogeogr. 17, 556–561 (2008).Article 

    Google Scholar 
    3.Primack, R. B. et al. Biol. Conserv. 142, 2569–2577 (2009).Article 

    Google Scholar 
    4.Changes in the events and phenomena of seasonal biological observations (in Japanese). Japan Meteorological Agency (10 November 2020); https://go.nature.com/3fLGlCw5.Ellwood, E. R. et al. Oecologia 168, 1161–1171 (2012).Article 

    Google Scholar 
    6.Ibáñez, I. et al. Phil. Trans. R. Soc. B 365, 3247–3260 (2010).Article 

    Google Scholar 
    7.Primack, R. B. & Miller-Rushing, A. J. New Phytol. 182, 303–313 (2009).Article 

    Google Scholar 
    8.Piao, S. et al. Glob. Change Biol. 25, 1922–1940 (2019).Article 

    Google Scholar 
    9.Radchuk, V. et al. Nat. Commun. 10, 3109 (2019).Article 

    Google Scholar 
    10.Menzel, A. et al. Glob. Change Biol. 26, 2599–2612 (2020).Article 

    Google Scholar 
    11.Kim, M., Lee, S., Lee, H. & Lee, S. Int. J. Environ. Res. Public Health 18, 1086 (2021).Article 

    Google Scholar  More

  • in

    Cooperation-based concept formation in male bottlenose dolphins

    1.Gross, J. & De Dreu, C. K. W. The rise and fall of cooperation through reputation and group polarization. Nat. Commun. 10, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    2.Nowak, M. A. & Sigmund, K. Evolution of indirect reciprocity by image scoring. Nature 393, 573–577 (1998).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Melis, A. P. & Semmann, D. How is human cooperation different? Philos. Trans. R. Soc. B Biol. Sci. 365, 2663–2674 (2010).Article 

    Google Scholar 
    4.Tibbetts, E. A. & Dale, J. Individual recognition: it is good to be different. Trends Ecol. Evol. 22, 529–537 (2007).PubMed 
    Article 

    Google Scholar 
    5.Tebbich, S., Bshary, R. & Grutter, A. Cleaner fish Labroides dimidiatus recognise familiar clients. Anim. Cogn. 5, 139–145 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Seyfarth, R. M. & Cheney, D. L. in Conceptual Mind: New Directions in the Study of Concepts (eds Margolis, E. & Laurence, S.) 57–76 (MIT Press, 2015).7.Cheney, D. L. & Seyfarth, R. M. Recognition of other individuals’ social relationships by female baboons. Anim. Behav. 58, 67–75 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Cheney, D. L., Seyfarth, R. M. & Silk, J. B. The responses of female baboons (Papio cynocephalus ursinus) to anomalous social interactions: evidence for causal reasoning? J. Comp. Psychol. 109, 134–141 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Borgeaud, C., van de Waal, E. & Bshary, R. Third-party ranks knowledge in wild vervet monkeys (Chlorocebus aethiops pygerythrus). PLoS ONE 8, 8–11 (2013).
    Google Scholar 
    10.Paz-Y-Miño, C. G., Bond, A. B., Kamil, A. C. & Balda, R. P. Pinyon jays use transitive inference to predict social dominance. Nature 430, 778–781 (2004).ADS 
    Article 
    CAS 

    Google Scholar 
    11.Massen, J. J. M., Pašukonis, A., Schmidt, J. & Bugnyar, T. Ravens notice dominance reversals among conspecifics within and outside their social group. Nat. Commun. 5, 1–7 (2014).Article 

    Google Scholar 
    12.Engh, A. L., Siebert, E. R., Greenberg, D. A. & Holekamp, K. E. Patterns of alliance formation and postconflict aggression indicate spotted hyaenas recognize third-party relationships. Anim. Behav. 69, 209–217 (2005).Article 

    Google Scholar 
    13.Bergman, T. J., Beehner, J. C., Cheney, D. L. & Seyfarth, R. M. Hierarchical classification by-rank and kinship in baboons. Science 302, 1234–1236 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Schino, G., Tiddi, B. & Di Sorrentino, E. P. Simultaneous classification by rank and kinship in Japanese macaques. Anim. Behav. 71, 1069–1074 (2006).Article 

    Google Scholar 
    15.Connor, R. C. Dolphin social intelligence: complex alliance relationships in bottlenose dolphins and a consideration of selective environments for extreme brain size evolution in mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 587–602 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Randić, S., Connor, R. C., Sherwin, W. B. & Krützen, M. A novel mammalian social structure in Indo-Pacific bottlenose dolphins (Tursiops sp.): complex male alliances in an open social network. Proc. R. Soc. B Biol. Sci. 279, 3083–3090 (2012).Article 

    Google Scholar 
    17.Connor, R. C. & Krützen, M. Male dolphin alliances in Shark Bay: changing perspectives in a 30-year study. Anim. Behav. 103, 223–235 (2015).Article 

    Google Scholar 
    18.Connor, R., Wells, R., Mann, J. & Read, A. in Cetacean Societies: Field Studies of Dolphins and Whales (eds Mann, J., Connor, R. C., Tyack, P. L. & Whitehead, H.) 91–126 (The University of Chicago Press, 2000).19.Frère, C. H. et al. Home range overlap, matrilineal and biparental kinship drive female associations in bottlenose dolphins. Anim. Behav. 80, 481–486 (2010).Article 

    Google Scholar 
    20.Gerber, L. et al. Affiliation history and age similarity predict alliance formation in adult male bottlenose dolphins. Behav. Ecol. 31, 361–370 (2020).PubMed 
    Article 

    Google Scholar 
    21.Connor, R. C., Smolker, R. A. & Richards, A. F. Two levels of alliance formation among male bottlenose dolphins (Tursiops sp.). Proc. Natl Acad. Sci. USA 89, 987–990 (1992).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Connor, R. C., Heithaus, M. R. & Barre, L. M. Superalliance of bottlenose dolphins. Nature 397, 571–572 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    23.Connor, R. C., Heithaus, M. R. & Barre, L. M. Complex social structure, alliance stability and mating access in a bottlenose dolphin ‘super-alliance’. Proc. Biol. Sci. 268, 263–267 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Whitehead, H. SOCPROG programs: analysing animal social structures. Behav. Ecol. Sociobiol. 63, 765–778 (2009).Article 

    Google Scholar 
    25.Connor, R. C., Watson-Capps, J. J., Sherwin, W. B. & Krützen, M. A new level of complexity in the male alliance networks of Indian Ocean bottlenose dolphins (Tursiops sp.). Biol. Lett. 7, 623–626 (2011).PubMed 
    Article 

    Google Scholar 
    26.King, S. L. & Janik, V. M. Bottlenose dolphins use learned vocal labels to address each other. Proc. Natl Acad. Sci. USA 110, 13216–13221 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Bruck, J. N. Decades-long social memory in bottlenose dolphins. Proc. Biol. Sci. 280, 20131726 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    28.Janik, V. M., Sayigh, L. S. & Wells, R. S. Signature whistle shape conveys identity information to bottlenose dolphins. Proc. Natl Acad. Sci. USA 103, 8293–8297 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Janik, V. M. & Sayigh, L. S. Communication in bottlenose dolphins: 50 years of signature whistle research. J. Comp. Physiol. A 199, 479–489 (2013).Article 

    Google Scholar 
    30.Sayigh, L. S. et al. Individual recognition in wild bottlenose dolphins: a field test using playback experiments. Anim. Behav. 57, 42–50 (1999).Article 

    Google Scholar 
    31.King, S. L. et al. Bottlenose dolphins retain individual vocal labels in multi-level alliances. Curr. Biol. 28, 1993–1999 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Cheney, D. L. & Seyfarth, R. M. Recognition of individuals within and between groups of free-ranging vervet monkeys. Am. Zool. 22, 519–529 (1982).Article 

    Google Scholar 
    33.Boeckle, M. & Bugnyar, T. Long-term memory for affiliates in ravens. Curr. Biol. 22, 801–806 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Kern, J. M. & Radford, A. N. Social-bond strength influences vocally mediated recruitment to mobbing. Biol. Lett. 12, 20160648 (2016).35.Micheletta, J. et al. Social bonds affect anti-predator behaviour in a tolerant species of macaque, Macaca nigra. Proc. R. Soc. B Biol. Sci. 279, 4042–4050 (2012).Article 

    Google Scholar 
    36.Connor, R. C. Pseudo-reciprocity: investing in mutualism. Anim. Behav. 34, 1562–1566 (1986).Article 

    Google Scholar 
    37.Connor, R. C. Cooperation beyond the dyad: on simple models and a complex society. Philos. Trans. R. Soc. B Biol. Sci. 365, 2687–2697 (2010).Article 

    Google Scholar 
    38.Trivers, R. L. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35–57 (1971).Article 

    Google Scholar 
    39.Taborsky, M., Frommen, J. G. & Riehl, C. Correlated pay-offs are key to cooperation. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150084 (2016).40.Dakin, R., Clunis, P. & Ryder, T. Reciprocal social ties drive stable cooperation within a social network. Preprint at bioRxiv https://doi.org/10.1101/2020.11.06.371567 (2020).41.Carter, G. G. et al. Development of new food-sharing relationships in vampire bats. Curr. Biol. 30, 1275.e3–1279.e3 (2020).Article 
    CAS 

    Google Scholar 
    42.Kokko, H., Johnstone, R. A. & Clutton-Brock, T. H. The evolution of cooperative breeding through group augmentation. Proc. R. Soc. B Biol. Sci. 268, 187–196 (2001).CAS 
    Article 

    Google Scholar 
    43.Kern, J. M. & Radford, A. N. Experimental evidence for delayed contingent cooperation among wild dwarf mongooses. Proc. Natl Acad. Sci. USA 115, 6255–6260 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Wittig, R. M., Crockford, C., Langergraber, K. E. & Zuberbühler, K. Triadic social interactions operate across time: a field experiment with wild chimpanzees. Proc. R. Soc. B Biol. Sci. 281, 20133155 (2014).45.Carter, G. G. & Wilkinson, G. S. Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment. Proc. R. Soc. B Biol. Sci. 280, 20122573 (2013).46.Choleris, D., Pfaff, W. & Kavaliers, M. Oxytocin, Vasopressin and Related Peptides in the Regulation of Behavior (Cambridge University Press, 2013).47.Brunnlieb, C. et al. Vasopressin increases human risky cooperative behavior. Proc. Natl Acad. Sci. USA 113, 2051–2056 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Spengler, F. B. et al. Oxytocin facilitates reciprocity in social communication. Soc. Cogn. Affect. Neurosci. 12, 1325–1333 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.De Dreu, C. K. W., Greer, L. L., Van Kleef, G. A., Shalvi, S. & Handgraaf, M. J. J. Oxytocin promotes human ethnocentrism. Proc. Natl Acad. Sci. USA 108, 1262–1266 (2011).ADS 
    PubMed 
    Article 

    Google Scholar 
    50.De Dreu, C. K. W. et al. The neuropeptide oxytocin regulates parochial altruism in intergroup conflict among humans. Science 328, 1408–1411 (2010).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    51.Connor, R. C., Smolker, R. & Bejder, L. Synchrony, social behaviour and alliance affiliation in Indian Ocean bottlenose dolphins, Tursiops truncatus. Anim. Behav. 72, 1371–1378 (2006).Article 

    Google Scholar 
    52.Moore, B. M., Connor, R. C., Allen, S. J., Krützen, M. & King, S. L. Acoustic coordination by allied male dolphins in a cooperative context. Proc. R. Soc. B Biol. Sci.287, 20192944 (2020).53.Madden, J. R. & Clutton-Brock, T. H. Experimental peripheral administration of oxytocin elevates a suite of cooperative behaviours in a wild social mammal. Proc. R. Soc. B Biol. Sci. 278, 1189–1194 (2011).CAS 
    Article 

    Google Scholar 
    54.Crockford, C. et al. Urinary oxytocin and social bonding in related and unrelated wild chimpanzees. Proc. R. Soc. B Biol. Sci. 280, 20122765 (2013).CAS 
    Article 

    Google Scholar 
    55.Robinson, K. J. et al. Positive social behaviours are induced and retained after oxytocin manipulations mimicking endogenous concentrations in a wild mammal. Proc. R. Soc. B Biol. Sci. 284, 20170554 (2017).Article 
    CAS 

    Google Scholar 
    56.Zentall, T. R., Wasserman, E. A. & Urcuioli, P. J. Associative concept learning in animals. J. Exp. Anal. Behav. 101, 130–151 (2014).PubMed 
    Article 

    Google Scholar 
    57.Bhatt, R. S., Wasserman, E. A., Reynolds, W. F. & Knauss, K. S. Conceptual behavior in pigeons: categorization of both familiar and novel examples from four classes of natural and artificial stimuli. J. Exp. Psychol. Anim. Behav. Process. 14, 219–234 (1988).Article 

    Google Scholar 
    58.Magnotti, J. F., Katz, J. S., Wright, A. A. & Kelly, D. M. Superior abstract-concept learning by Clark’s nutcrackers (Nucifraga columbiana). Biol. Lett. 11, 1–4 (2015).Article 

    Google Scholar 
    59.Byosiere, S. E., Feng, L. C., Chouinard, P. A., Howell, T. J. & Bennett, P. C. Relational concept learning in domestic dogs: performance on a two-choice size discrimination task generalises to novel stimuli. Behav. Process. 145, 93–101 (2017).Article 

    Google Scholar 
    60.Miller, N. & Dollard, J. Social Learning and Imitation (Yale University Press, 1941).61.Wild, S., Hoppitt, W. J. E., Allen, S. J. & Krützen, M. Integrating genetic, environmental, and social networks to reveal transmission pathways of a dolphin foraging innovation. Curr. Biol. 30, 3024–3030.e4 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Krützen, M. et al. Cultural transmission of tool use by Indo-Pacific bottlenose dolphins (Tursiops sp.) provides access to a novel foraging niche. Proc. R. Soc. B Biol. Sci. 281, 20140374 (2014).63.Carter, G. G. & Wilkinson, G. S. Common vampire bat contact calls attract past food-sharing partners. Anim. Behav. 116, 45–51 (2016).Article 

    Google Scholar 
    64.King, S., Allen, S., Krützen, M. & Connor, R. Vocal behaviour of allied male dolphins during cooperative mate guarding. Anim. Cogn. 22, 991–1000 (2019).65.Quick, N. J. & Janik, V. M. Bottlenose dolphins exchange signature whistles when meeting at sea. Proc. R. Soc. B Biol. Sci. 279, 2539–2545 (2012).Article 

    Google Scholar 
    66.Richards, D. G., Wolz, J. P. & Herman, L. M. Vocal mimicry of computer-generated sounds and vocal labeling of objects by a bottlenose dolphin, Tursiops truncatus. J. Comp. Psychol. 98, 10–28 (1984).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Herman, L. M. in Rational Animals? (eds Hurley, S. & Nudds, M.) 439–467 (Oxford University Press, 2006).68.Herman, L. M., Pack, A. A. & Wood, A. M. Bottlenose dolphins can generalize rules and develop abstract concepts. Mar. Mammal. Sci. 10, 70–80 (1994).Article 

    Google Scholar 
    69.Galizio, M. & Bruce, K. E. Abstraction, multiple exemplar training and the search for derived stimulus relations in animals. Perspect. Behav. Sci. 41, 45–67 (2018).PubMed 
    Article 

    Google Scholar 
    70.Hayes, S. C. & Sanford, B. T. Cooperation came first: evolution and human cognition. J. Exp. Anal. Behav. 101, 112–129 (2014).PubMed 
    Article 

    Google Scholar 
    71.Allen, S. J. et al. Genetic isolation between coastal and fishery-impacted, offshore bottlenose dolphin (Tursiops spp.) populations. Mol. Ecol. 25, 2735–2753 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    72.Smolker, R. A., Richards, A. F., Connor, R. C. & Pepper, J. W. Sex differences in patterns of association among Indian Ocean bottlenose dolphins. Behaviour 123, 38–69 (1992).Article 

    Google Scholar 
    73.Hoppitt, W. J. E. & Farine, D. R. Association indices for quantifying social relationships: How to deal with missing observations of individuals or groups. Anim. Behav. 136, 227–238 (2017).Article 

    Google Scholar 
    74.Cairns, S. & Schwager, S. A comparison of association indices. Anim. Behav. 35, 1454–1469 (1987).Article 

    Google Scholar 
    75.Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

    Google Scholar 
    76.Galezo, A. A., Foroughirad, V., Krzyszczyk, E., Frère, C. H. & Mann, J. Juvenile social dynamics reflect adult reproductive strategies in bottlenose dolphins. Behav. Ecol. 31, 1159–1171 (2020).Article 

    Google Scholar 
    77.Whitehead, H. Analysing Animal Societies: Quantitative Methods for Vertebrate Social Analysis (Chicago University Press, 2008).78.Connor, R. C. et al. Male alliance behaviour and mating access varies with habitat in a dolphin social network. Sci. Rep. 7, 46354 (2017).79.Deecke, V. B. & Janik, V. M. Automated categorization of bioacoustic signals: avoiding perceptual pitfalls. J. Acoust. Soc. Am. 119, 645–653 (2006).ADS 
    PubMed 
    Article 

    Google Scholar 
    80.Janik, V. M., King, S. L., Sayigh, L. S. & Wells, R. S. Identifying signature whistles from recordings of groups of unrestrained bottlenose dolphins (Tursiops truncatus). Mar. Mammal. Sci. 29, 109–122 (2013).Article 

    Google Scholar 
    81.Quick, N. J., Rendell, L. E. & Janik, V. M. A mobile acoustic localisation system for the study of free-ranging dolphins during focal follows. Mar. Mammal. Sci. 24, 979–989 (2008).
    Google Scholar 
    82.Wahlberg, M., Møhl, B. & Madsen, P. T. Estimating source position accuracy of a large-aperture hydrophone array for bioacoustics. J. Acoust. Soc. Am. 109, 397–406 (2001).ADS 
    Article 

    Google Scholar 
    83.Schulz, T. M., Whitehead, H. & Rendell, L. E. A remotely-piloted acoustic array for studying sperm whale vocal behaviour. Can. Acoust. 34, 54–55 (2006).
    Google Scholar 
    84.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    85.Nieuwenhuis, R., te Grotenhuis, M. & Pelzer, B. Influence.ME: tools for detecting influential data in mixed effects models. R J. 4, 38–47 (2012).Article 

    Google Scholar  More

  • in

    Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin

    1.Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).Article 

    Google Scholar 
    2.Merino, N. et al. Living at the extremes: extremophiles and the limits of life in a planetary context. Front. Microbiol. 10, 780 (2019).Article 

    Google Scholar 
    3.Colman, D. R. et al. Geobiological feedbacks and the evolution of thermoacidophiles. ISME J. 12, 225–236 (2018).Article 

    Google Scholar 
    4.Reveillaud, J. et al. Subseafloor microbial communities in hydrogen-rich vent fluids from hydrothermal systems along the Mid-Cayman Rise. Environ. Microbiol. 18, 1970–1987 (2016).Article 

    Google Scholar 
    5.Lau, M. C. Y. et al. An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers. Proc. Natl Acad. Sci. USA 113, 7927–7936 (2016).Article 

    Google Scholar 
    6.Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).Article 

    Google Scholar 
    7.Brazelton, W. J. et al. Metagenomic identification of active methanogens and methanotrophs in serpentinite springs of the Voltri Massif, Italy. PeerJ 5, e2945 (2017).Article 

    Google Scholar 
    8.Havig, J. R., Raymond, J., Meyer-Dombard, D. R., Zolotova, N. & Shock, E. L. Merging isotopes and community genomics in a siliceous sinter-depositing hot spring. J. Geophys. Res. Biogeosci. 116, G01005 (2011).Article 

    Google Scholar 
    9.Power, J. F. et al. Microbial biogeography of 925 geothermal springs in New Zealand. Nat. Commun. 9, 2876 (2018).Article 

    Google Scholar 
    10.Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).Article 

    Google Scholar 
    11.Kelemen, P. B. & Manning, C. E. Reevaluating carbon fluxes in subduction zones, what goes down, mostly comes up. Proc. Natl Acad. Sci. USA 112, E3997–E4006 (2015).Article 

    Google Scholar 
    12.Brovarone, A. V. et al. Subduction hides high-pressure sources of energy that may feed the deep subsurface biosphere. Nat. Commun. 11, 3880 (2020).Article 

    Google Scholar 
    13.Plümper, O. et al. Subduction zone forearc serpentinites as incubators for deep microbial life. Proc. Natl Acad. Sci. USA 114, 4324–4329 (2017).Article 

    Google Scholar 
    14.Syracuse, E. M. & Abers, G. A. Global compilation of variations in slab depth beneath arc volcanoes and implications. Geochem. Geophys. Geosyst. 7, Q05017 (2006).Article 

    Google Scholar 
    15.Shaw, A. M., Hilton, D. R., Fischer, T. P., Walker, J. A. & Alvarado, G. E. Contrasting He–C relationships in Nicaragua and Costa Rica: insights into C cycling through subduction zones. Earth Planet. Sci. Lett. 214, 499–513 (2003).Article 

    Google Scholar 
    16.Barry, P. H. et al. Forearc carbon sink reduces long-term volatile recycling into the mantle. Nature 568, 487–492 (2019).Article 

    Google Scholar 
    17.Arce-Rodríguez, A. et al. Thermoplasmatales and sulfur-oxidizing bacteria dominate the microbial community at the surface water of a CO2-rich hydrothermal spring located in Tenorio Volcano National Park, Costa Rica. Extremophiles 23, 177–187 (2019).Article 

    Google Scholar 
    18.Crespo-Medina, M. et al. Methane dynamics in a tropical serpentinizing environment: the Santa Elena ophiolite, Costa Rica. Front. Microbiol. 8, 916 (2017).Article 

    Google Scholar 
    19.Probst, A. J. & Moissl-Eichinger, C. “Altiarchaeales”: uncultivated Archaea from the subsurface. Life https://doi.org/10.3390/life5021381 (2015).20.Giggenbach, W. F. Geothermal solute equilibria, derivation of Na-K-Mg-Ca geoindicators. Geochim. Cosmochim. Acta 52, 2749–2765 (1988).Article 

    Google Scholar 
    21.Giggenbach, W. F. & Soto, R. C. Isotopic and chemical composition of water and steam discharges from volcanic–magmatic–hydrothermal systems of the Guanacaste Geothermal Province, Costa Rica. Appl. Geochem. 7, 309–332 (1992).Article 

    Google Scholar 
    22.Rodríguez, A. & van Bergen, M. J. Superficial alteration mineralogy in active volcanic systems: an example of Poás volcano, Costa Rica. J. Volcanol. Geotherm. Res. 346, 54–80 (2017).Article 

    Google Scholar 
    23.Chan, C. S., Fakra, S. C., Emerson, D., Fleming, E. J. & Edwards, K. J. Lithotrophic iron-oxidizing bacteria produce organic stalks to control mineral growth: implications for biosignature formation. ISME J. 5, 717–727 (2011).Article 

    Google Scholar 
    24.Lücke, O. H. & Arroyo, I. G. Density structure and geometry of the Costa Rican subduction zone from 3-D gravity modeling and local earthquake data. Solid Earth 6, 1169–1183 (2015).Article 

    Google Scholar 
    25.Protti, M., Gündel, F. & McNally, K. The geometry of the Wadati–Benioff zone under southern Central America and its tectonic significance: results from a high-resolution local seismographic network. Phys. Earth Planet. Inter. 84, 271–287 (1994).Article 

    Google Scholar 
    26.de Moor, J. M. et al. A new sulfur and carbon degassing inventory for the Southern Central American Volcanic Arc: the importance of accurate time-series data sets and possible tectonic processes responsible for temporal variations in arc-scale volatile emissions: new volatile budget for Central America. Geochem. Geophys. Geosyst. 18, 4437–4468 (2017).Article 

    Google Scholar 
    27.Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).Article 

    Google Scholar 
    28.Kim, M. S., Jo, S. K., Roh, S. W. & Bae, J. W. Alishewanella agri sp. nov., isolated from landfill soil. Int. J. Syst. Evol. Microbiol. 60, 2199–2203 (2010).Article 

    Google Scholar 
    29.Chen, W. M. et al. Aquabacterium limnoticum sp. nov., isolated from a freshwater spring. Int. J. Syst. Evol. Microbiol. 62, 698–704 (2012).Article 

    Google Scholar 
    30.Garrity, G. M. & Bell, J. A. Bergey’s Manual of Systematics of Archaea and Bacteria (Bergey’s Manual Trust, 2015).31.Hayashi, N. R., Ishida, T., Yokota, A., Kodama, T. & Igarashi, Y. Hydrogenophilus thermoluteolus gen. nov., sp. nov., a thermophilic, facultatively chemolithoautotrophic, hydrogen-oxidizing bacterium. Int. J. Syst. Evol. Microbiol. 49, 783–786 (1999).Article 

    Google Scholar 
    32.Berg, I. A. et al. Autotrophic carbon fixation in archaea. Nat. Rev. Microbiol. 8, 447–460 (2010).Article 

    Google Scholar 
    33.Giovannelli, D. et al. Insight into the evolution of microbial metabolism from the deep-branching bacterium, Thermovibrio ammonificans. eLife 6, e18990 (2017).Article 

    Google Scholar 
    34.Yokochi, R. et al. Noble gas radionuclides in Yellowstone geothermal gas emissions: a reconnaissance. Chem. Geol. 339, 43–51 (2013).Article 

    Google Scholar 
    35.Harris, R. N. & Wang, K. Thermal models of the Middle America Trench at the Nicoya Peninsula, Costa Rica. Geophys. Res. Lett. 29, 6-1–6-4 (2010).
    Google Scholar 
    36.Jelen, B. I., Giovannelli, D. & Falkowski, P. G. The role of microbial electron transfer in the coevolution of the biosphere and geosphere. Annu. Rev. Microbiol. 70, 45–62 (2016).Article 

    Google Scholar 
    37.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).Article 

    Google Scholar 
    38.Tassi, F. et al. The geothermal resource in the Guanacaste region (Costa Rica): new hints from the geochemistry of naturally discharging fluids. Front. Earth Sci. 6, 69 (2018).Article 

    Google Scholar 
    39.Tassi, F., Vaselli, O., Barboza, V., Fernandez, E. & Duarte, E. Fluid geochemistry and seismic activity in the period 1998–2002 at Turrialba Volcano (Costa Rica). Ann. Geophys. 47, 4 (2004).
    Google Scholar 
    40.Barry, P. H. et al. Helium, inorganic and organic carbon isotopes of fluids and gases across the Costa Rica convergent margin. Sci. Data https://doi.org/10.1038/s41597-019-0302-4 (2019).41.Vetriani, C., Jannasch, H. W., MacGregor, B. J., Stahl, D. A. & Reysenbach, A.-L. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl. Environ. Microbiol. 65, 4375–4384 (1999).Article 

    Google Scholar 
    42.Wright, J. J., Lee, S., Zaikova, E., Walsh, D. A. & Hallam, S. J. DNA extraction from 0.22 μm Sterivex filters and cesium chloride density gradient centrifugation. JOVE https://doi.org/10.3791/1352 (2009).43.Teare, J. M. et al. Measurement of nucleic acid concentrations using the DyNA QuantTM and the GeneQuantTM. Biotechniques 22, 1170–1174 (1997).Article 

    Google Scholar 
    44.Simbolo, M. et al. DNA qualification workflow for next generation sequencing of histopathological samples. PLoS ONE 8, e62692 (2013).Article 

    Google Scholar 
    45.Giovannelli, D. et al. Diversity and distribution of prokaryotes within a shallow-water pockmark field. Front. Microbiol. 7, 941 (2016).Article 

    Google Scholar 
    46.Huse, S. M. et al. VAMPS: a website for visualization and analysis of microbial population structures. BMC Bioinformatics 15, 41 (2014).Article 

    Google Scholar 
    47.Huse, S. M. et al. Comparison of brush and biopsy sampling methods of the ileal pouch for assessment of mucosa-associated microbiota of human subjects. Microbiome 2, 5 (2014).Article 

    Google Scholar 
    48.Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 

    Google Scholar 
    49.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).Article 

    Google Scholar 
    50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 

    Google Scholar 
    51.Zhu, C. et al. Functional sequencing read annotation for high precision microbiome analysis. Nucleic Acids Res. 46, e23 (2018).Article 

    Google Scholar 
    52.R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).53.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 

    Google Scholar 
    54.vegan (CRAN, 2019).55.Hamilton, N. E. & Ferry, M. ggtern: ternary diagrams using ggplot2. J. Stat. Softw. 87, 1–17 (2018).Article 

    Google Scholar 
    56.Stekhoven, D. J. & Buhlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).Article 

    Google Scholar 
    57.Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: an R package for variable selection using random forests. R J. 7, 1–19 (2015).Article 

    Google Scholar 
    58.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).59.Sheik, C. S. et al. Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the Census of Deep Life. Front. Microbiol. 9, 840 (2018).Article 

    Google Scholar 
    60.Sugimori, K. et al. Microbial life in the acid lake and hot springs of Poas Volcano, Costa Rica. In Proc. Colima Volcano International Meeting (2002).61.Mcmurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).Article 

    Google Scholar 
    62.Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).Article 

    Google Scholar 
    63.Giovannelli, D. et al. Large-scale distribution and activity of prokaryotes in deep-sea surface sediments of the Mediterranean Sea and the adjacent Atlantic Ocean. PLoS ONE 8, e72996 (2013).64.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
    Google Scholar 
    65.Schruben, P. G. Geology and Resource Assessment of Costa Rica DDS-19-R (USGS, 1987).66.Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).Article 

    Google Scholar 
    67.Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).Article 

    Google Scholar 
    68.Schwager, E., Mallick, H., Ventz, S. & Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput. Biol. 13, e1005852 (2017).Article 

    Google Scholar 
    69.Zar, J. H. Significance testing of the spearman rank correlation coefficient. J. Am. Stat. Assoc. 67, 578–580 (1972).Article 

    Google Scholar 
    70.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).
    Google Scholar 
    71.Braun, S. et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci. Rep. 7, 5680 (2017).Article 

    Google Scholar 
    72.Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).Article 

    Google Scholar 
    73.McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2013).Article 

    Google Scholar  More