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      Madrepora oculata forms large frameworks in hypoxic waters off Angola (SE Atlantic)

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      1.Roberts, J. M., Wheeler, A. J., Freiwald, A. & Cairns, S. D. Cold-Water Corals. The Biology and Geology of Deep-Sea Coral Habitats. (Cambridge University Press, 2009).2.Davies, A. J. & Guinotte, J. M. Global habitat suitability for framework-forming cold-water corals. Plos One 6, e18483 (2011).3.Morato, T. et al. Climate-induced changes in the suitable habitat of cold-water corals and commercially important deep-sea fishes in the North Atlantic. Glob. Chang. Biol. 26, 2181–2202. https://doi.org/10.1111/gcb.14996 (2020).ADS 
      Article 
      PubMed Central 

      Google Scholar 
      4.Arnaud-Haond, S. et al. Two “pillars” of cold-water coral reefs along Atlantic European margins: Prevalent association of Madrepora oculata with Lophelia pertusa, from reef to colony scale. Deep-Sea Res. Pt. II(145), 110–119 (2017).Article 

      Google Scholar 
      5.Buhl-Mortensen, L., Olafsdottir, S. H., Buhl-Mortensen, P., Burgos, J. M. & Ragnarsson, S. A. Distribution of nine cold-water coral species (Scleractinia and Gorgonacea) in the cold temperate North Atlantic: Effects of bathymetry and hydrography. Hydrobiologia 759, 39–61. https://doi.org/10.1007/s10750-014-2116-x (2015).CAS 
      Article 

      Google Scholar 
      6.Gori, A. et al. Bathymetrical distribution and size structure of cold-water coral populations in the Cap de Creus and Lacaze-Duthiers canyons (northwestern Mediterranean). Biogeosciences 10, 2049–2060. https://doi.org/10.5194/bg-10-2049-2013 (2013).ADS 
      Article 

      Google Scholar 
      7.Orejas, C. et al. Cold-water corals in the Cap de Creus canyon (north-western Mediterranean): Spatial distribution, density and anthropogenic impact. Mar. Ecol. Prog. Ser. 397, 37–51 (2009).ADS 
      Article 

      Google Scholar 
      8.Buhl-Mortensen, P. Coral reefs in the Southern Barents Sea: Habitat description and the effects of bottom fishing. Mar. Biol. Res. 13, 1027–1040. https://doi.org/10.1080/17451000.2017.1331040 (2017).Article 

      Google Scholar 
      9.Cairns, S. Antarctic and subantarctic Scleractinia. Antarctic Res. Ser. 34. https://doi.org/10.1029/AR034p0001 (1983).10.Cairns, S. D. & Zibrowius, H. Cnidaria Anthozoa: Azooxanthellate Scleractinia from the Philippine and Indonesian regions. Mém. Mus. Natl. Hist. Nat. 172, 27–243 (1997).
      Google Scholar 
      11.Tracey, D., Rowden, A., Mackay, K. & Compton, T. Habitat-forming cold-water corals show affinity for seamounts in the New Zealand region. Mar. Ecol. Prog. Ser. 430, 1–22. https://doi.org/10.3354/meps09164 (2011).ADS 
      Article 

      Google Scholar 
      12.Auscavitch, S. R. et al. Oceanographic drivers of deep-sea coral species distribution and community assembly on seamounts, islands, atolls, and reefs within the Phoenix Islands protected area. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00042 (2020).13.Angeletti, L., Castellan, G., Montagna, P., Remia, A. & Taviani, M. “The Corsica channel cold-water coral province” (Mediterranean Sea). Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00661 (2020).14.Chimienti, G., Bo, M., Taviani, M. & Mastrototaro, F. in Mediterranean Cold-Water Corals: Past, Present and Future, Springer Series: Coral Reefs of the World (eds. Covadonga Orejas Saco del Valle & C. Jiménez) 213–243 (Springer, 2019).15.Corbera, G. et al. Ecological characterisation of a Mediterranean cold-water coral reef: Cabliers Coral Mound Province (Alboran Sea, western Mediterranean). Prog. Oceanogr. 175, 245–262. https://doi.org/10.1016/j.pocean.2019.04.010 (2019).ADS 
      Article 

      Google Scholar 
      16.Freiwald, A. et al. The White Coral Community in the Central Mediterranean Sea revealed by ROV surveys. Oceanography 22, 58–74 (2009).Article 

      Google Scholar 
      17.Fabri, M. C. et al. Megafauna of vulnerable marine ecosystems in French Mediterranean submarine canyons: Spatial distribution and anthropogenic impacts. Deep-Sea Res. Pt. II(104), 184–207. https://doi.org/10.1016/j.dsr2.2013.06.016 (2014).Article 

      Google Scholar 
      18.Brooke, S. & Ross, S. W. First observations of the cold-water coral Lophelia pertusa in mid-Atlantic canyons of the USA. Deep-Sea Res. Pt. II(104), 245–251 (2014).Article 

      Google Scholar 
      19.Cordes, E. E. et al. Coral communities of the deep Gulf of Mexico. Deep-Sea Res. Pt. II(55), 777–787 (2008).Article 

      Google Scholar 
      20.Frederiksen, R., Jensen, A. & Westerberg, H. The distribution of scleratinian coral Lophelia pertusa around the Faroe Islands and the relation to intertidal mixing. Sarsia 77, 157–171 (1992).Article 

      Google Scholar 
      21.Hebbeln, D. et al. Environmental forcing of the Campeche cold-water coral province, southern Gulf of Mexico. Biogeosciences 11, 1799–1815. https://doi.org/10.5194/bg-11-1799-2014 (2014).ADS 
      Article 

      Google Scholar 
      22.Wienberg, C. et al. Franken Mound: Facies and biocoenoses on a newly-discovered “carbonate mound” on the western Rockall Bank, NE Atlantic. Facies 54, 1–24. https://doi.org/10.1007/s10347-007-0118-0 (2008).Article 

      Google Scholar 
      23.Purser, A. et al. Local variation in the distribution of benthic megafauna species associated with cold-water coral reefs on the Norwegian margin. Cont. Shelf Res. 54, 37–51. https://doi.org/10.1016/j.csr.2012.12.013 (2013).ADS 
      Article 

      Google Scholar 
      24.Fanelli, E. et al. Cold-water coral Madrepora oculata in the eastern Ligurian Sea (NW Mediterranean): Historical and recent findings. Aquat. Conserv. 27, 965–975. https://doi.org/10.1002/aqc.2751 (2017).Article 

      Google Scholar 
      25.Naumann, M. S., Orejas, C. & Ferrier-Pagès, C. Species-specific physiological response by the cold-water corals Lophelia pertusa and Madrepora oculata to variations within their natural temperature range. Deep-Sea Res. Pt. II(99), 36–41. https://doi.org/10.1016/j.dsr2.2013.05.025 (2014).CAS 
      Article 

      Google Scholar 
      26.Movilla, J. et al. Resistance of two mediterranean cold-water coral species to low-pH conditions. Water 6, 59–67 (2014).ADS 
      Article 

      Google Scholar 
      27.Dodds, L. A., Roberts, J. M., Taylor, A. C. & Marubini, F. Metabolic tolerance of the cold-water coral Lophelia pertusa (Scleractinia) to temperature and dissolved oxgen change. J. Exp. Mar. Biol. Ecol. 349, 205–214 (2007).CAS 
      Article 

      Google Scholar 
      28.Lunden, J. J., McNicholl, C. G., Sears, C. R., Morrison, C. L. & Cordes, E. E. Acute survivorship of the deep-sea coral Lophelia pertusa from the Gulf of Mexico under acidification, warming, and deoxygenation. Front. Mar. Sci. 1. https://doi.org/10.3389/fmars.2014.00078 (2014).29.Ramos, A., Sanz, J. L., Ramil, F., Agudo, L. M. & Presas-Navarro, C. in Deep-Sea Ecosystems Off Mauritania: Research of Marine Biodiversity and Habitats in the Northwest African Margin (eds. Ramos, A., Ramil, F., & Sanz, J.L.) 481–525 (Springer, 2017).30.Wienberg, C. et al. The giant Mauritanian cold-water coral mound province: Oxygen control on coral mound formation. Quat. Sci. Rev. 185, 135–152. https://doi.org/10.1016/j.quascirev.2018.02.012 (2018).ADS 
      Article 

      Google Scholar 
      31.Hanz, U. et al. Environmental factors influencing cold-water coral ecosystems in the oxygen minimum zones on the Angolan and Namibian margins. Biogeosciences 16, 4337–4356 (2019).ADS 
      CAS 
      Article 

      Google Scholar 
      32.Hebbeln, D. et al. Cold-water coral reefs thriving under hypoxia. Coral Reefs 39, 853–859. https://doi.org/10.1007/s00338-020-01934-6 (2020).Article 

      Google Scholar 
      33.Montero-Serrano, J.-C. et al. Decadal changes in the mid-depth water mass dynamic of the Northeastern Atlantic margin (Bay of Biscay). Earth Planet. Sci. Lett. 364, 134–144. https://doi.org/10.1016/j.epsl.2013.01.012 (2013).ADS 
      CAS 
      Article 

      Google Scholar 
      34.Orejas, C., Gori, A. & Gili, J. M. Growth rates of live Lophelia pertusa and Madrepora oculata cold-water coral species maintained in aquaria. Coral Reefs 27, 255 (2008).ADS 
      Article 

      Google Scholar 
      35.Sabatier, P. et al. 210Pb-226Ra chronology reveals rapid growth rate of Madrepora oculata and Lophelia pertusa on world’s largest cold-water coral reef. Biogeosciences 9, 1253–1265. https://doi.org/10.5194/bg-9-1253-2012 (2012).ADS 
      CAS 
      Article 

      Google Scholar 
      36.Sweetman, A. et al. Major impacts of climate change on deep-sea benthic ecosystems. Elementa-Sci. Anthrop. 5, 4. https://doi.org/10.1525/elementa.203 (2017).Article 

      Google Scholar 
      37.Lexerød, N. L. Recruitment models for different tree species in Norway. For. Ecol. Manag. 206, 91–108. https://doi.org/10.1016/j.foreco.2004.11.001 (2005).Article 

      Google Scholar 
      38.Georgian, S. et al. Biogeographic variability in the physiological response of the cold-water coral Lophelia pertusa to ocean acidification. Mar. Ecol. 37. https://doi.org/10.1111/maec.12373 (2016).39.Tamborrino, L. et al. Mid-Holocene extinction of cold-water corals on the Namibian shelf steered by the Benguela oxygen minimum zone. Geology 47, 1185–1188. https://doi.org/10.1130/g46672.1 (2019).ADS 
      Article 

      Google Scholar 
      40.Büscher, J., Form, A. & Riebesell, U. Interactive effects of ocean acidification and warming on growth, fitness and survival of the cold-water coral Lophelia pertusa under different food availabilities. Front. Mar. Sci. 4. https://doi.org/10.3389/fmars.2017.00101 (2017).41.Connolly, S., Lopez-Yglesias, M. & Anthony, K. Food availability promotes rapid recovery from thermal stress in a scleractinian coral. Coral Reefs 31. https://doi.org/10.1007/s00338-012-0925-9 (2012).42.Middelburg, J. J. et al. Discovery of symbiotic nitrogen fixation and chemoautotrophy in cold-water corals. Sci. Rep. 5, 17962. https://doi.org/10.1038/srep17962 (2015).ADS 
      CAS 
      Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      43.Wienberg, C. & Titschack, J. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds. Rossi, S., Bramanti, L., Gori, A., & del Valle, C.O.S.) 699–732 (Springer, 2017).44.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
      CAS 
      Article 

      Google Scholar 
      45.Levitus, S. & Mishonov, A. World Ocean Atlas 2013 (Vers. 2). NOAA Atlas NESDIS 73. National Oceanographic Data Center, Ocean Climate Laboratory United States, National Environmental Satellite Data Information Service (2013).46.Mienis, F. et al. Hydrodynamic controls on cold-water coral growth and carbonate-mound development at the SW and SE Rockall Trough Margin, NE Atlantic Ocean. Deep-Sea Res. Pt. I(54), 1655–1674 (2007).Article 

      Google Scholar 
      47.Sanfilippo, R. et al. Serpula aggregates and their role in deep-sea coral communities in the southern Adriatic Sea. Facies 59. https://doi.org/10.1007/s10347-012-0356-7 (2013).48.Hoey, J. A. & Pinsky, M. L. Genomic signatures of environmental selection despite near-panmixia in summer flounder. Evolut. Appl. 11, 1732–1747. https://doi.org/10.1111/eva.12676 (2018).CAS 
      Article 

      Google Scholar 
      49.Boavida, J., Becheler, R., Addamo, A. M., Sylvestre, F. & Arnaud-Haond, S. in Mediterranean Cold-Water Corals: Past, Present and Future, Springer Series: Coral Reefs of the World (eds. Covadonga Orejas Saco del Valle & C. Jiménez) (Springer, 2019).50.Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Ann. Rev. Mar. Sci. 3, 509–535. https://doi.org/10.1146/annurev-marine-120709-142756 (2011).Article 
      PubMed 

      Google Scholar 
      51.Frank, N. et al. Northeastern Atlantic cold-water coral reefs and climate. Geology 39, 743–746. https://doi.org/10.1130/g31825.1 (2011).ADS 
      Article 

      Google Scholar 
      52.Hebbeln, D. et al. ANNA cold-water coral ecosystems off Angola and Namibia. Cruise No. M122, December 30, 2015–January 31, 2016, Walvis Bay (Namibia) – Walvis Bay (Namibia). METEOR-Berichte, M122. DFG-Senatskommission Ozeanogr. 74. https://doi.org/10.2312/cr_m122 (2017).53.Vad, J., Orejas, C., Moreno-Navas, J., Findlay, H. S. & Roberts, J. M. Assessing the living and dead proportions of cold-water coral colonies: Implications for deep-water marine protected area monitoring in a changing ocean. PeerJ 5, e3705. https://doi.org/10.7717/peerj.3705 (2017).Article 
      PubMed 
      PubMed Central 

      Google Scholar  More

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      Computational sustainability meets materials science

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      Computational sustainability research has been supported by an Expedition in Computing from the US National Science Foundation (NSF; CCF-1522054). eBird has been supported by the Leon Levy Foundation, the Wolf Creek Foundation, and NSF (DBI-1939187). Materials science research has also been supported by the AFOSR Multidisciplinary University Research Initiative (MURI) Program FA9550-18-1-0136, US DOE Award No.DE-SC0020383, and an award from the Toyota Research Institute. More

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      Rapid ecosystem-scale consequences of acute deoxygenation on a Caribbean coral reef

      by

      1.Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, 6371 (2018).Article 
      CAS 

      Google Scholar 
      2.Laffoley, D. & Baxter, J. M. Ocean deoxygenation: everyone’s problem—causes, impacts, consequences and solutions (IUCN, 2019).3.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).ADS 
      CAS 
      PubMed 
      Article 

      Google Scholar 
      4.Altieri, A. H. et al. Tropical dead zones and mass mortalities on coral reefs. Proc. Natl Acad. Sci. USA 114, 3660–3665 (2017).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      5.Pandolfi, J. M. et al. Projecting coral reef futures under global warming and ocean acidification. Science 333, 418–422 (2011).ADS 
      CAS 
      PubMed 
      Article 

      Google Scholar 
      6.Hoegh-Guldberg, O. et al. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).7.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
      CAS 
      PubMed 
      Article 

      Google Scholar 
      8.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158 (2014).Article 

      Google Scholar 
      9.Wild, C. et al. Climate change impedes scleractinian corals as primary reef ecosystem engineers. Mar. Freshw. Res. 62, 205–215 (2011).CAS 
      Article 

      Google Scholar 
      10.Muscatine, L. & Porter, J. W. Reef corals-mutualistics symbioses adapted to nutrient-poor environments. Bioscience 27, 454–460 (1977).Article 

      Google Scholar 
      11.Ainsworth, T. D., Turber, R. V. & Gates, R. D. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240 (2010).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      12.Garren, M. et al. Resilience of coral-associated bacterial communities exposed to fish farm effluent. PLoS ONE 4, 10 (2009).Article 
      CAS 

      Google Scholar 
      13.Kelly, L. W. et al. Local genomic adaptation of coral reef-associated microbiomes to gradients of natural variability and anthropogenic stressors. Proc. Natl Acad. Sci. USA 111, 10227–10232 (2014).ADS 
      CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      14.Altieri, A. H., Johnson, M. D., Swaminathan, S. D., Nelson, H. & Gedan, K. Resilience of tropical ecosystems to ocean deoxygenation. Trends Ecol. Evol. 36, 227–238 (2021).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      15.Lucey, N. M., Collins, M. & Collin, R. Oxygen-mediated plasticity confers hypoxia tolerance in a corallivorous polychaete. Ecol. Evol. 10, 1145–1157 (2020).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      16.Kealoha, A. K. et al. Localized hypoxia may have caused coral reef mortality at the Flower Garden Banks. Coral Reefs 39, 119–132 (2020).Article 

      Google Scholar 
      17.Nelson, H. R. & Altieri, A. H. Oxygen: the universal currency on coral reefs. Coral Reefs 38, 177–198 (2019).ADS 
      Article 

      Google Scholar 
      18.Glynn, P. W. Coral-reef bleaching: ecological perspectives. Coral Reefs 12, 1–17 (1993).ADS 
      Article 

      Google Scholar 
      19.Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl Acad. Sci. USA 105, 17442–17446 (2008).ADS 
      CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      20.Alderice, R. et al. Divergent expression of hypoxia response systems under deoxygenation in reef-forming corals aligns with bleaching susceptibility. Glob. Change Biol. 27, 312–326 (2020).21.Cramer, K. L. et al. Anthropogenic mortality on coral reefs in Caribbean Panama predates coral disease and bleaching. Ecol. Lett. 15, 561–567 (2012).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      22.Warner, M. E., Fit, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: a determinant of coral bleaching. Proc. Natl Acad. Sci. USA 96, 8007–8012 (1999).ADS 
      CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      23.Guzmán, H. M. & Guevara, C. A. Coral reefs of Bocas del Toro, Panama: distribution, structure and state of conservation of the continental reefs of Laguna de Chiriquí and Bahía Almirante. Rev. Biol. Trop. 46, 601–623 (1998).
      Google Scholar 
      24.Prada, C. et al. Genetic species delineation among branching Caribbean Porites corals. Coral Reefs 33, 1019–1030 (2014).ADS 
      Article 

      Google Scholar 
      25.Wegley Kelly, L. et al. Diel population and functional synchrony of microbial communities on coral reefs. Nat. Comm. 10, 1691 (2019).26.Wegley Kelly, L., Haas, A. F. & Nelson, C. E. Ecosystem microbiology of coral reefs: Linking genomic, metabolomic, and biogeochemical dynamics from animal symbioses to reefscape processes. mSystems 3, e00162-17 (2018).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      27.Rohwer, F., Seguritan, V., Azam, F. & Knowlton, N. Diversity and distribution of coral-associated bacteria. Mar. Ecol. Prog. Ser. 243, 1–10 (2002).ADS 
      Article 

      Google Scholar 
      28.On, S. L. W. et al. A critical rebuttal of the proposed division of the genus Arcobacter into six genera using comparative genomic, phylogenetic, and phenotypic criteria. Syst. Appl. Microbiol. 43, 126108 (2020).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      29.Pérez-Cataluña, A. et al. Revisiting the taxonomy of the genus Arcobacter: Getting order from the chaos. Front. Microbiol. 9, 2077 (2018).PubMed 
      PubMed Central 
      Article 

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

      Google Scholar 
      31.Wang, Y. et al. Aliiroseovarius marinus sp. nov., isolated from seawater. Int. J. Syst. Evol. Microbiol. 70, 334–339 (2020).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      32.Park, S. et al. Aliiroseovarius pelagivivens gen. nov., sp. nov., isolated from seawater, and reclassification of three species of the genus Roseovarius as Aliiroseovarius crassostreae comb. nov., Aliiroseovarius halocynthiae comb. nov. and Aliiroseovarius sediminilitoris comb. nov. Int. J. Syst. Evol. Microbiol. 65, 2646–2652 (2015).33.Zhou, H. et al. Pyrene biodegradation and its potential pathway involving Roseobacter clade bacteria. Int. Biodeterio. Biodegrad. 150, 104961 (2020).CAS 
      Article 

      Google Scholar 
      34.Friedrich, C. G. et al. Prokaryotic sulfur oxidation. Curr. Opin. Microbiol. 8, 253–259 (2005).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      35.Wirsen, C. O. et al. Characterization of an autotrophic sulfide-oxidizing marine Arcobacter sp that produces filamentous sulfur. Appl. Environ. Microbiol. 68, 316–325 (2002).36.Hughes, D. J. et al. Coral reef survival under accelerating ocean deoxygenation. Nat. Clim. Change 10, 1–12 (2020).37.Seemann, J. et al. Assessing the ecological effects of human impacts on coral reefs in Bocas del Toro, Panama. Environ. Monit. Assess. 186, 747–1763 (2014).Article 
      CAS 

      Google Scholar 
      38.Hughes, T. P. & Tanner, J. E. Recruitment failure, life histories, and long-term decline of Caribbean corals. Ecology 81, 2250–2263 (2000).Article 

      Google Scholar 
      39.Sievert, S. M. et al. Growth and mechanism of filamentous-sulfur formation by Candidatus Arcobacter sulfidicus in opposing oxygen-sulfide gradients. Environ. Microbiol. 9, 271–276 (2007).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      40.Berg, C. et al. Acetate-utilizing bacteria at an oxic-anoxic interface in the Baltic Sea. FEMS Microbiol. Ecol. 85, 251–261 (2013).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      41.Broman, E. et al. Oxygenation of hypoxic coastal Baltic Sea sediments impacts on chemistry, microbial community composition, and metabolism. Front. Microbiol. 8, 2453 (2017).42.Bourlat, S. J. et al. Genomics in marine monitoring: new opportunities for assessing marine health status. Mar. Pollut. Bull. 74, 19–31 (2013).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      43.Altieri, A. H. & Gedan, K. B. Climate change and dead zones. Glob. Change Biol. 21, 1395–1406 (2015).ADS 
      Article 

      Google Scholar 
      44.Fitt, W. K. et al. Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65 (2001).Article 

      Google Scholar 
      45.Johnson, M. D. et al. Ecophysiology of coral reef primary producers across an upwelling gradient in the tropical central Pacific. PLoS ONE 15, e0228448 (2020).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      46.Stimson, J. & Kinzie, R. A. The temporal pattern and rate of release of zooxanthellae from the reef coral Pocillopora damicornis (Linnaeus) under nitrogen-enrichment and control conditions. J. Exp. Mar. Biol. Ecol. 153, 63–74 (1991).Article 

      Google Scholar 
      47.Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1, and c2 in higher-plants, algae, and natural phytolplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).48.Bates, D. et al. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).ADS 
      Article 

      Google Scholar 
      49.R Core Team. R: A language and environment for statistical computing (v3.6.2) (R Foundation for Statistical Computing, 2019).50.Kuznetsova, A., Brockhoff, P. B. & Christensen R. H. B. lmerTest package: tests in linear mixed effects models (2017).51.Oksanen, J. et al. The vegan package. Community ecology package. 631–637 (2007).52.Martinez Arbizu, P. Pairwiseadonis: pairwise multilevel comparison using adonis (2017).53.Nguyen, B. N. et al. Environmental DNA survey captures patterns of fish and invertebrate diversity across a tropical seascape. Sci. Rep. 10, 6729 (2020).ADS 
      CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      54.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2015).PubMed 
      Article 
      CAS 
      PubMed Central 

      Google Scholar 
      55.Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. Msystems 1, e00009-15 (2016).56.Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome helper: a custom and streamlined workflow for microbiome research. Msystems 2, e00127–00116 (2017).57.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).58.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      59.Wang, Q. et al. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
      CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      60.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      61.McMurdie, P. J. & Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).62.Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
      Google Scholar 
      63.Roberts, D. W. labdsv: ordination and multivariate analysis for ecology. R package (2017).64.Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platformfor ‘omics data. PeerJ 3, e1319 (2015).65.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      66.Koster, J. & Rahmann, S. Snakemake-a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).PubMed 
      Article 
      CAS 
      PubMed Central 

      Google Scholar 
      67.Eren, A. M. et al. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).68.Li, D. H. et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      69.Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).70.Breitwieser, F. P., Baker, D. N. & Salzberg, S. L. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 19, 198 (2018).71.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–U54 (2012).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      72.Menzel, P., Ng, K. L., & Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Comm. 7, 11257 (2016).73.Roux, S. et al. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).74.Buchfink, B., Xie, C. & Huson, D. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
      PubMed 
      Article 

      Google Scholar 
      75.Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).CAS 
      PubMed 
      Article 

      Google Scholar 
      76.Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2014).Article 
      CAS 

      Google Scholar 
      77.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      78.Kanehisa, M. et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      79.Jain, C. et al. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Comm. 9, 1–8 (2018).ADS 
      Article 
      CAS 

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

      Google Scholar 
      81.Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      82.Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      83.Hoang, D. T. et al. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      84.Johnson, M.D. et al. Rapid ecosystem-scale consequences of acute deoxygenation on a Caribbean coral reef. Zenodo. https://doi.org/10.5281/zenodo.4940132 (2021). More

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      Quantifying the effects of hydrogen on carbon assimilation in a seafloor microbial community associated with ultramafic rocks

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      1.Schink B. Energetics of syntrophic cooperation in methanogenic degradation. Microbiol Mol Biol Rev 1997;61:262–80.CAS 
      PubMed 
      PubMed Central 

      Google Scholar 
      2.Vignais PM, Billoud B. Occurrence, classification, and biological function of hydrogenases: an overview. Chem Rev 2007;107:4206–72.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      3.Wolf PG, Biswas A, Morales SE, Greening C, Gaskins HR. H2 metabolism is widespread and diverse among human colonic microbes. Gut Microbes. 2016;7:235–45.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      4.Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA, et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      5.Islam ZF, Welsh C, Bayly K, Grinter R, Southam G, Gagen EJ, et al. A widely distributed hydrogenase oxidises atmospheric H2 during bacterial growth. ISME J. 2020;14:2649–58.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      6.Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      7.Amend JP, McCollom TM, Hentscher M, Bach W. Catabolic and anabolic energy for chemolithoautotrophs in deep-sea hydrothermal systems hosted in different rock types. Geochim Cosmochim Acta. 2011;75:5736–48.CAS 
      Article 

      Google Scholar 
      8.Reveillaud J, Reddington E, McDermott J, Algar C, Meyer JL, Sylva S, et al. Subseafloor microbial communities in hydrogen-rich vent fluids from hydrothermal systems along the Mid-Cayman Rise. Environ Microbiol. 2016;18:1970–87.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      9.Perner M, Hansen M, Seifert R, Strauss H, Koschinsky A, Petersen S. Linking geology, fluid chemistry, and microbial activity of basalt- and ultramafic-hosted deep-sea hydrothermal vent environments. Geobiology. 2013;11:340–55.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      10.Schubotz F, Hays LE, Meyer-Dombard D, Gillespie A, Shock EL, Summons RE. Stable isotope labeling confirms mixotrophic nature of streamer biofilm communities at alkaline hot springs. Front Microbiol. 2015;6:42.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      11.Fortunato CS, Huber JA. Coupled RNA-SIP and metatranscriptomics of active chemolithoautotrophic communities at a deep-sea hydrothermal vent. ISME J. 2016;10:1925–38.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      12.McNichol J, Stryhanyuk H, Sylva SP, Thomas F, Musat N, Seewald JS, et al. Primary productivity below the seafloor at deep-sea hot springs. Proc Natl Acad Sci USA. 2018;115:6756–61.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      13.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microbiol. 2015;81:7570–81.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      14.Coskun ÖK, Pichler M, Vargas S, Gilder S, Orsi WD. Linking uncultivated microbial populations with benthic carbon turnover using quantitative stable isotope probing. Appl Environ Microbiol 2018;84:e01083–18.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      15.Tuorto SJ, Darias P, McGuinness LR, Panikov N, Zhang T, Häggblom MM, et al. Bacterial genome replication at subzero temperatures in permafrost. ISME J. 2014;8:139–49.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      16.Maia M, Sichel S, Briais A, Brunelli D, Ligi M, Ferreira N, et al. Extreme mantle uplift and exhumation along a transpressive transform fault. Nat Geosci. 2016;9:619–23.CAS 
      Article 

      Google Scholar 
      17.Klein F, Tarnas JD, Bach W. Abiotic sources of molecular hydrogen on Earth. Elements. 2020;16:19–24.CAS 
      Article 

      Google Scholar 
      18.Seewald JS, Doherty KW, Hammar TR, Liberatore SP. A new gas-tight isobaric sampler for hydrothermal fluids. Deep Sea Res Part I. 2002;49:189–96.CAS 
      Article 

      Google Scholar 
      19.Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      20.Vuillemin A, Wankel SD, Coskun OK, Magritsch T, Vargas S, Estes ER, et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv. 2019;5:eaaw4108.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      21.Oremland RS, Miller LG, Whiticar MJ. Sources and flux of natural gases from Mono Lake, California. Geochim Cosmochim Acta. 1987;51:2915–29.CAS 
      Article 

      Google Scholar 
      22.Lang SQ, Butterfield DA, Schulte M, Kelley DS, Lilley MD. Elevated concentrations of formate, acetate and dissolved organic carbon found at the Lost City hydrothermal field. Geochim Cosmochim Acta. 2010;74:941–52.CAS 
      Article 

      Google Scholar 
      23.Butler IB, Schoonen MA, Rickard DT. Removal of dissolved oxygen from water: a comparison of four common techniques. Talanta. 1994;41:211–5.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      24.Ortega-Arbulu AS, Pichler M, Vuillemin A, Orsi WD. Effects of organic matter and low oxygen on the mycobenthos in a coastal lagoon. Environ Microbiol. 2019;21:374–88.CAS 
      PubMed 
      Article 

      Google Scholar 
      25.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 2016;18:1403–14.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      26.Coskun ÖK, Özen V, SD Wankel SD, Orsi WD. Quantifying population-specific growth in benthic bacterial communities under low oxygen using H218O. ISME J. 2019;13:1546–59.27.Pichler M, Coskun ÖK, Ortega-Arbulú A-S, Conci N, Wörheide G, Vargas S, et al. A 16S rRNA gene sequencing and analysis protocol for the Illumina MiniSeq platform. Microbiologyopen 2018:7;e00611.28.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
      Article 

      Google Scholar 
      29.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
      Article 

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

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

      Google Scholar 
      32.Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014;12:87.PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      33.Morrissey EM, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Phylogenetic organization of bacterial activity. ISME J. 2016;10:2336.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      34.Youngblut ND, Barnett SE, Buckley DH. HTSSIP: an R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments. PLoS ONE. 2018;13:e0189616.PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      35.R. Team. Others, RStudio: integrated development for R. vol. 42. Boston, MA: RStudio, Inc; 2015. P. 14.
      Google Scholar 
      36.Blomberg SP, Garland T Jr, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 2003;57:717–45.PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      37.Pagel M. Inferring the historical patterns of biological evolution. Nature. 1999;401:877–84.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      38.Orsi WD, Morard R, Vuillemin A, Eitel M, Worheide G, Milucka J, et al. Anaerobic metabolism of Foraminifera thriving below the seafloor. ISME J. 2020;14:2580–94.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      39.Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38:e191.PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      40.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      41.Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      42.Sieradzki ET, Koch BJ, Greenlon A, Sachdeva R, Malmstrom RR, Mau RL, et al. Measurement error and resolution in quantitative stable isotope probing: implications for experimental design. mSystems. 2020;5:e00151–20.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      43.Youngblut ND, Barnett SE, Buckley DH. SIPSim: a modeling toolkit to predict accuracy and aid design of DNA-SIP experiments. Front Microbiol 2018;9:570.PubMed 
      PubMed Central 
      Article 

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

      Google Scholar 
      45.Gouy M, Guindon S, Gascuel O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol 2010;27:221–4.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      46.Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–235.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      47.Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      48.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      49.Keck F, Rimet F, Bouchez A, Franc A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol Evol 2016;6:2774–80.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      50.Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–90.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      51.Meier DV, Pjevac P, Bach W, Markert S, Schweder T, Jamieson J, et al. Microbial metal-sulfide oxidation in inactive hydrothermal vent chimneys suggested by metagenomic and metaproteomic analyses. Environ Microbiol. 2019;21:682–701.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      52.Lecoeuvre A, Menez B, Cannat M, Chavagnac V, Gerard E. Microbial ecology of the newly discovered serpentinite-hosted Old City hydrothermal field (southwest Indian ridge). ISME J. 2021;15:818–32.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      53.Mason OU, Di Meo-Savoie CA, Van Nostrand JD, Zhou J, Fisk MR, Giovannoni SJ. Prokaryotic diversity, distribution, and insights into their role in biogeochemical cycling in marine basalts. ISME J. 2009;3:231–42.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      54.Koch H, Galushko A, Albertsen M, Schintlmeister A, Gruber-Dorninger C, Lucker S, et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science. 2014;345:1052–4.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      55.Santelli CM, Orcutt BN, Banning E, Bach W, Moyer CL, Sogin ML, et al. Abundance and diversity of microbial life in ocean crust. Nature. 2008;453:653–7.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      56.Schrenk MO, Brazelton WJ, Lang SQ. Serpentinization, carbon, and deep life. Rev Mineral Geochem 2013;75:575–606.CAS 
      Article 

      Google Scholar 
      57.Klein F, Bach W, Humphris SE, Kahl W-A, Jöns N, Moskowitz B, et al. Magnetite in seafloor serpentinite—some like it hot. Geology. 2014;42:135–8.CAS 
      Article 

      Google Scholar 
      58.Kelley DS, Karson JA, Früh-Green GL, Yoerger DR, Shank TM, Butterfield DA, et al. A serpentinite-hosted ecosystem: the Lost City hydrothermal field. Science. 2005;307:1428–34.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      59.Wankel SD, Germanovich LN, Lilley MD, Genc G, DiPerna CJ, Bradley AS, et al. Influence of subsurface biosphere on geochemical fluxes from diffuse hydrothermal fluids. Nat Geosci. 2011;4:461–8.CAS 
      Article 

      Google Scholar 
      60.McDowall JS, Murphy BJ, Haumann M, Palmer T, Armstrong FA, Sargent F. Bacterial formate hydrogenlyase complex. Proc Natl Acad Sci USA. 2014;111:E3948–3956.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      61.Fones EM, Colman DR, Kraus EA, Stepanauskas R, Templeton AS, Spear JR, et al. Diversification of methanogens into hyperalkaline serpentinizing environments through adaptations to minimize oxidant limitation. ISME J. 2021;15:1121–35.CAS 
      PubMed 
      Article 

      Google Scholar 
      62.Carr SA, Orcutt BN, Mandernack KW, Spear JR. Abundant Atribacteria in deep marine sediment from the Adélie Basin, Antarctica. Front Microbiol 2015;6:872.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      63.Nobu MK, Dodsworth JA, Murugapiran SK, Rinke C, Gies EA, Webster G, et al. Phylogeny and physiology of candidate phylum ‘Atribacteria’ (OP9/JS1) inferred from cultivation-independent genomics. ISME J. 2016;10:273–86.CAS 
      PubMed 
      Article 

      Google Scholar 
      64.Schuchmann K, Müller V. Energetics and application of heterotrophy in acetogenic bacteria. Appl Environ Microbiol 2016;82:4056–69.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      65.Vuillemin A, Vargas S, Coskun OK, Pockalny R, Murray RW, Smith DC, et al. Atribacteria reproducing over millions of years in the Atlantic Abyssal subseafloor. mBio. 2020;11:e01937–20.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      66.Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.CAS 
      PubMed 
      Article 

      Google Scholar 
      67.Bryant FO, Adams MW. Characterization of hydrogenase from the hyperthermophilic archaebacterium, Pyrococcus furiosus. J Biol Chem 1989;264:5070–9.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      68.Berney M, Greening C, Conrad R, Jacobs WR Jr, Cook GM. An obligately aerobic soil bacterium activates fermentative hydrogen production to survive reductive stress during hypoxia. Proc Natl Acad Sci USA 2014;111:11479–84.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      69.Kwan P, McIntosh CL, Jennings DP, Hopkins RC, Chandrayan SK, Wu C-H, et al. The [NiFe]-hydrogenase of Pyrococcus furiosus exhibits a new type of oxygen tolerance. J Am Chem Soc. 2015;137:13556–65.CAS 
      PubMed 
      Article 

      Google Scholar 
      70.Daebeler A, Herbold CW, Vierheilig J, Sedlacek CJ, Pjevac P, Albertsen M, et al. Cultivation and genomic analysis of “Candidatus Nitrosocaldus islandicus,” an obligately thermophilic, ammonia-oxidizing Thaumarchaeon from a hot spring biofilm in Graendalur Valley, Iceland. Front Microbiol. 2018;9:193.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      71.W Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.Article 
      CAS 

      Google Scholar 
      72.Seyler LM, McGuinness LR, Gilbert JA, Biddle JF, Gong D, Kerkhof LJ. Discerning autotrophy, mixotrophy and heterotrophy in marine TACK archaea from the North Atlantic. FEMS Microbiol Ecol 2018;94:fiy014.73.Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      74.Diaz R, Rosenberg R. Marine benthic hypoxia: a review of its ecological effects and the behavioural response of benthic macrofauna. Oceanogr Mar Biol. 1995;33:245–303.
      Google Scholar 
      75.Jenkins MC, Kemp WM. The coupling of nitrification and denitrification in two estuarine sediments. Limnol Oceanogr. 1984;29:609–19.CAS 
      Article 

      Google Scholar 
      76.Rempfert KR, Miller HM, Bompard N, Nothaft D, Matter JM, Kelemen P, et al. Geological and geochemical controls on subsurface microbial life in the Samail Ophiolite, Oman. Front Microbiol. 2017;8:56.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      77.Ragsdale SW. Life with carbon monoxide. Crit Rev Biochem Mol Biol. 2004;39:165–95.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      78.Fones EM, Colman DR, Kraus EA, Nothaft DB, Poudel S, Rempfert KR, et al. Physiological adaptations to serpentinization in the Samail Ophiolite, Oman. ISME J. 2019;13:1750–62.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      79.Morrill PL, Brazelton WJ, Kohl L, Rietze A, Miles SM, Kavanagh H, et al. Investigations of potential microbial methanogenic and carbon monoxide utilization pathways in ultra-basic reducing springs associated with present-day continental serpentinization: the Tablelands, NL, CAN. Front Microbiol. 2014;5:613.PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      80.Wilcoxen J, Zhang B, Hille R. Reaction of the molybdenum- and copper-containing carbon monoxide dehydrogenase from Oligotropha carboxydovorans with quinones. Biochemistry. 2011;50:1910–6.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      81.Cordero PRF, Bayly K, Man Leung P, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      82.Seewald JS, Zolotov MY, McCollom T. Experimental investigation of single carbon compounds under hydrothermal conditions. Geochim Cosmochim Acta. 2006;70:446–60.CAS 
      Article 

      Google Scholar 
      83.Can M, Armstrong FA, Ragsdale SW. Structure, function, and mechanism of the nickel metalloenzymes, CO dehydrogenase, and acetyl-CoA synthase. Chem Rev. 2014;114:4149–74.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      84.Gudasz C, Bastviken D, Steger K, Premke K, Sobek S, Tranvik LJ. Temperature-controlled organic carbon mineralization in lake sediments. Nature. 2010;466:478–81.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      85.Katayama T, Nobu MK, Kusada H, Meng XY, Hosogi N, Uematsu K, et al. Isolation of a member of the candidate phylum ‘Atribacteria’ reveals a unique cell membrane structure. Nat Commun. 2020;11:6381.CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      86.Brisbarre N, Fardeau M-L, Cueff V, Cayol J-L, Barbier G, Cilia V, et al. Clostridium caminithermale sp. nov., a slightly halophilic and moderately thermophilic bacterium isolated from an Atlantic deep-sea hydrothermal chimney. Int J Syst Evol Microbiol. 2003;53:1043–9.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      87.Roslev P, Larsen MB, Jørgensen D, Hesselsoe M. Use of heterotrophic CO2 assimilation as a measure of metabolic activity in planktonic and sessile bacteria. J Microbiol Methods. 2004;59:381–93.CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      88.Spona-Friedl M, Braun A, Huber C, Eisenreich W, Griebler C, Kappler A, et al. Substrate-dependent CO2 fixation in heterotrophic bacteria revealed by stable isotope labelling. FEMS Microbiol Ecol 2020;96:fiaa080.89.Jansen K, Thauer RK, Widdel F, Fuchs G. Carbon assimilation pathways in sulfate reducing bacteria. Formate, carbon dioxide, carbon monoxide, and acetate assimilation by Desulfovibrio baarsii. Arch Microbiol. 1984;138:257–62.CAS 
      Article 

      Google Scholar 
      90.Braun A, Spona-Friedl M, Avramov M, Elsner M, Baltar F, Reinthaler T, et al. Reviews and syntheses: heterotrophic fixation of inorganic carbon—significant but invisible flux in global carbon cycling. Biogeosciences 2020;18:3689–3700.91.Russell MJ, Hall AJ, Martin W. Serpentinization as a source of energy at the origin of life. Geobiology. 2010;8:355–71. https://doi.org/10.1111/j.1472-4669.2010.00249.x92.Martin W, Baross J, Kelley D, Russell MJ. Hydrothermal vents and the origin of life. Nat Rev Microbiol. 2008;6:805–14. 10.1038/nrmicro1991. More

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      Geographically targeted surveillance of livestock could help prioritize intervention against antimicrobial resistance in China

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      DataWe reviewed PPS reporting rates of AMR in healthy animals and animal food products in China between 2000 and 2019 (Supplementary Text S1). We focused on three common food animal species: chicken, pigs and cattle. Dairy cattle and meat cattle were pooled in this study, in consistency with the categorization adopted in the maps of livestock created by the Food and Agriculture Organization27. The review focused on four common foodborne bacteria: E. coli, non-typhoidal Salmonella, S. aureus and Campylobacter. We recorded resistance rates reported in PPSs, defined as the percentage of isolates tested resistant to an antimicrobial compound. In addition, we extracted the anatomical therapeutic chemical classification codes of the drugs tested, the year of publication, the guidelines used for susceptibility testing, the latitude and longitude of sampling sites, the number of samples collected and the host animals. We recorded sample types for each survey, including live animals, slaughtered animals, animal products and faecal samples. Each sample was taken from one animal or animal product. These sample types were pooled in the current analysis. In total, 10,747 rates of AMR were extracted from 446 surveys (Supplementary Fig. 8), including 318 surveys from China’s National Knowledge Infrastructure (CNKI), the leading Chinese-language academic search engine. All data extracted in the review are available at https://resistancebank.org.Two steps were taken to ensure comparability of the resistance rates extracted from the surveys. First, the panel of drug–bacteria combinations extracted from each survey was that recommended for susceptibility testing by the World Health Organization Advisory Group on Integrated Surveillance of Antimicrobial Resistance40. This resulted in the extraction of 6,295 resistance rates for 76 drug–bacteria combinations. Second, resistance rates were harmonized using a methodology4 accounting for potential variations in the clinical breakpoints used for antimicrobial susceptibility testing (Supplementary Text S1). There are two major families of methods used for susceptibility testing in this dataset: diffusion methods (for example, disc diffusion) and dilution methods (for example, broth dilution). Previous works have shown good agreement between the two approaches in measuring resistance in foodborne bacteria4,46. For each family of methods, variations of breakpoints may result from differences between laboratory guidelines systems (European Committee on Antimicrobial Susceptibility Testing vs Clinical and Laboratory Standards Institute), or from variations over time of clinical breakpoints within a laboratory guidelines system (Clinical and Laboratory Standards Institute or European Committee on Antimicrobial Susceptibility Testing). Here we accounted for both situations using distributions of minimum inhibitory concentrations and inhibition zones obtained from eucast.org (Supplementary Text S1).Trends in AMRWe defined a composite metric of AMR to summarize trends in resistance across multiple drugs and bacterial species. For each survey, we calculated the proportion of antimicrobial compounds with resistance higher than 50% (P50). For each animal–bacteria combination, we assessed the significance of the temporal trends of P50 between 2000 to 2019 using a logistic regression model, weighted by the log10-transformed number of samples in each survey.For each bacteria–drug (antimicrobial class) combination, we estimated prevalence of resistance by calculating a curve of the distribution of resistance rates across all surveys (Fig. 2). The analysis was conducted for surveys published between 2000 and 2009, and between 2010 and 2019, respectively. The distribution was estimated at 100 equally spaced intervals from resistance rates of 0% to 100%, using kernel density estimation. We used the centre of mass of the density distribution to estimate prevalence of resistance. The calculation was conducted for six animal–bacteria combinations. This included E. coli in chicken, pigs and cattle, Salmonella in chicken and pigs, and S. aureus in cattle. The remaining animal–bacteria combinations were excluded due to limited sample size, only represented in 32 out of 446 PPSs. The analysis was restricted to antimicrobial classes represented by at least 10 resistance rates. In addition, we estimated the association between resistance rates and the ease of obtaining antimicrobials from the market, using data from online stores (Supplementary Text S3).Geospatial modellingWe interpolated P50 values from the survey locations to create a map of P50 at a resolution of 10 × 10 km across China. The approach followed a two-step procedure47. In step 1, three ‘child models’ were trained using four-fold spatial cross-validation to quantify the relation between P50 and environmental and anthropogenic covariates (Supplementary Text S2 and Supplementary Table 1). In step 2, the predictions of the child models were stacked using universal kriging (Supplementary Text S2). This approach combined the ability of the child models to capture interactions and non-linear relationships between P50 and environmental and anthropogenic covariates, as well as the ability to account for spatial autocorrelation in the distribution of P50.The outputs of the two-step procedure were a map of P50 (Fig. 3) and a map of uncertainty on the P50 predictions (Supplementary Fig. 9 and Supplementary Text S2). The overall accuracy of the geospatial model was evaluated using the area under the AUC. The contribution of each covariate was evaluated by permuting sequentially all covariates, and calculating the reduction in AUC compared with a full model including all covariates (Supplementary Fig. 4). The administrative boundaries used in all maps were obtained from the Global Administrative Areas database (http://www.gadm.org).Identifying (optimal) locations for future surveys on AMRWe identified the locations of 50 hypothetical new surveys—the rounded average number of surveys conducted per year (54 surveys per year) between 2014 and 2019 in China. The location of each new survey was determined recursively such that it minimized the overall uncertainty levels on the geographical trends in AMR across the country. This process took into account the locations of existing surveys and the location of each additional hypothetical survey. The objective of this approach was to maximize gain in information about AMR given the resource invested in conducting surveys.The map of uncertainty consisted of the variance in the child model predictions Var(PBRT,PLASSO–GLM,PFFNN) (step 1) across 10 Monte Carlo simulations, where PBRT, PLASSO–GLM, and PFFNN were the predictions of P50 using boosted regression trees, logistic regression with LASSO regularization, and feed-forward neural network, and the kriging variance VarK (step 2):Vartotal = Var(PBRT,PLASSO–GLM,PFFNN) + VarKIn this study, the location of hypothetical surveys was solely based on VarK, instead of the sum of both terms. This approach was preferred because including both terms would have required to hypothesize P50 values associated with surveys to be conducted in the future, adding an additional source of uncertainty that cannot be quantified. In any case, the uncertainty attributable to VarK was 4.1 times the Var(PBRT,PLASSO–GLM,PFFNN) (Supplementary Text S2).The allocation of new surveys was based on a map of ‘necessity for additional surveillance’ (NS), defined as:NS = VarK × Wwhere VarK reflects the uncertainty of the spatial interpolation, and W is the log10-transformed population density of humans48, animals27 in total, and in chicken, pigs and cattle, separately, which reflected exposure (Supplementary Fig. 10). Animal population density was calculated here as the sum of population-corrected units of pigs, chicken and cattle, using methods described by Van Boeckel et al.7. We adjusted the values of W such that its density distribution equals that of VarK. Concretely, for each pixel i, we calculated the quantile of Wi on the map of W, and replaced the value by the corresponding value of VarK at the same quantile. VarK and W were both standardized to range [0,1], thus giving each term equal weight in the need for surveillance.Four approaches were used to distribute 50 surveys across China based on the map of NS. The reduction in uncertainty on AMR level associated with each of the four spatial configurations of the hypothetical surveys was evaluated by calculating the reduction in the mean values of NS across 7,857 possible pixels on the map of China.First, we used a ‘greedy’ approach where all possible locations for additional surveys were tested. Concretely, the first hypothetical survey was placed at each of the 7,857 possible pixel locations, and a revised map of NS(+1 survey) was calculated for each of the placements. The survey was eventually placed in the pixel that led to the largest reduction in NS(+1 survey). The map of NS was then revised to account for the reduction in uncertainty in the neighbourhood of the new survey. The process was repeated recursively for the next hypothetical surveys (2nd–50th). This approach, by definition, yields the optimal set of locations to reduce uncertainty, but it also bears a considerable computational burden, because every possible location is tested (Npixels = 7,857) by the geospatial model for each hypothetical survey.The second approach developed was a computational approximation to the greedy approach, hereafter referred to as the ‘overlap approach’. This approach exploits a key feature of the kriging procedure: the decrease of the kriging variance (VarK) with increasing proximity to existing survey locations. Each additional survey reduces the variance of the geospatial model at its own location, but also in its surrounding area (Supplementary Fig. 11). The ‘overlap approach’ selects an optimal set of locations that reflect a compromise between high local NS and distance to other surveys. It iteratively selects new locations based on the highest local NS penalized by the degree of overlap between the hypothetical new surveys and existing surveys (Supplementary Fig. 12). The first survey was placed at the location Xp,Yp with the highest local NS (Supplementary Fig. 12, part 1). Then the value of NS at each pixel location Xi,Yi was recalculated as ({mathrm{NS}}_{{(+1,{mathrm{survey}})}X_i,,Y_i}={mathrm{NS}}_{X_i,,Y_i}times(1-{mathrm{overlap}},{mathrm{area}}/{mathrm{neighborhood}},{mathrm{area}})) (Supplementary Fig. 12, Part 2), where the neighbourhood area was the circular area of decreased kriging variance around a new survey, and its radius was the distance until which NS decreased due to this new survey; the ‘overlap area’ is the shared area of the neighbourhoods of location Xp,Yp and of location Xi,Yi. The radius of the neighbourhood was determined using a sensitivity analysis, optimized by approximate Bayesian computation (sequential Monte Carlo)49 (Supplementary Text S5). The optimal neighbourhood radius was chosen such that it minimizes reduction in NS across all pixels. The procedure (Supplementary Fig. 12, parts 1 and 2) was repeated recursively for the hypothetical surveys (2nd–50th).The third approach tested consisted of distributing surveys equally between provinces to reflect a common approach to disease surveillance based on equal allocation of resources between administrative entities. Twenty-two provinces with the highest human population were assigned two surveys, and the remaining six provinces were assigned one survey per province. The exact location of each survey was randomly selected inside a province. Finally, all approaches were compared with the fourth approach (the random approach) as a ‘null model’, in which the 50 hypothetical surveys were located randomly across the country without any geographic weighting criteria. The reduction in NS associated with the third and fourth approaches, which was compared to the greedy approach and overlap approach, was the average over 50 simulations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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      Spatial models of giant pandas under current and future conditions reveal extinction risks

      by

      1.Tang, X. et al. Scheme design and main result analysis of the fourth national survey on giant pandas. For. Resour. Manag. 1, 11–16 (2015).
      Google Scholar 
      2.Swaisgood, R. R., Wang, D. & Wei, F. Panda downlisted but not out of the woods. Conserv. Lett. 11, 1 (2018).Article 

      Google Scholar 
      3.Xu, W. et al. Reassessing the conservation status of the giant panda using remote sensing. Nat. Ecol. Evol. 1, 1635–1638 (2017).PubMed 
      Article 

      Google Scholar 
      4.Shen, G. et al. Climate change challenges the current conservation strategy for the giant panda. Biol. Conserv. 190, 43–50 (2015).Article 

      Google Scholar 
      5.Tian, Z. et al. The next widespread bamboo flowering poses a massive risk to the giant panda. Biol. Conserv. 234, 180–187 (2019).Article 

      Google Scholar 
      6.Lu, Z. et al. Patterns of genetic diversity in remaining giant panda populations. Conserv. Biol. 15, 1596–1607 (2001).Article 

      Google Scholar 
      7.Pimm, S. L. The Balance of Nature (Univ. Chicago Press, 1991).8.Pimm, S. L., Dollar, L. & Bass, O. L. Jr The genetic rescue of the Florida panther. Anim. Conserv. 9, 115–122 (2006).Article 

      Google Scholar 
      9.Qing, J. et al. The minimum area requirements (MAR) for giant panda: an empirical study. Sci. Rep. 6, 37715 (2016).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      10.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      11.Fahrig, L. Relative effects of habitat loss and fragmentation on population extinction. J. Wildl. Manag. 61, 603–610 (1997).Article 

      Google Scholar 
      12.Simberloff, D. Habitat fragmentation and population extinction of birds. IBIS 137, S105–S111 (1995).Article 

      Google Scholar 
      13.Viña, A. et al. Range-wide analysis of wildlife habitat: implications for conservation. Biol. Conserv. 143, 1960–1969 (2010).Article 

      Google Scholar 
      14.Wang, T., Ye, X., Skidmore, A. K. & Toxopeus, A. G. Characterising the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes. J. Biogeogr. 37, 865–878 (2010).CAS 
      Article 

      Google Scholar 
      15.Xu, W. et al. Conservation of giant panda habitat in South Minshan, China, after the May 2008 earthquake. Front. Ecol. Environ. 7, 353–358 (2009).Article 

      Google Scholar 
      16.Gong, M., Guan, T., Hou, M., Liu, G. & Zhou, T. Hopes and challenges for giant panda conservation under climate change in the Qinling Mountains of China. Ecol. Evol. 7, 596–605 (2017).PubMed 
      Article 

      Google Scholar 
      17.Songer, M., Delion, M., Biggs, A. & Huang, Q. Modeling impacts of climate change on giant panda habitat. Int. J. Ecol. 2012, 108752 (2012).Article 

      Google Scholar 
      18.Tuanmu, M. N. et al. Climate-change impacts on understorey bamboo species and giant pandas in China’s Qinling Mountains. Nat. Clim. Change 3, 249–253 (2013).Article 

      Google Scholar 
      19.He, K. et al. Effects of roads on giant panda distribution: a mountain range scale evaluation. Sci. Rep. 9, 1110 (2019).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      20.Li, H. et al. Application of least-cost path model to identify a giant panda dispersal corridor network after the Wenchuan earthquake—case study of Wolong Nature Reserve in China. Ecol. Model. 221, 944–952 (2010).Article 

      Google Scholar 
      21.Qi, D. et al. Quantifying landscape linkages among giant panda subpopulations in regional scale conservation. Integr. Zool. 7, 165–174 (2012).PubMed 
      Article 

      Google Scholar 
      22.Shen, G. et al. Proposed conservation landscape for giant pandas in the Minshan Mountains, China. Conserv. Biol. 22, 1144–1153 (2008).PubMed 
      Article 

      Google Scholar 
      23.Kong, L. et al. Habitat conservation redlines for the giant pandas in China. Biol. Conserv. 210, 83–88 (2017).Article 

      Google Scholar 
      24.Zhang, J. et al. Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake. Ecol. Manag. 319, 1–9 (2014).Article 

      Google Scholar 
      25.Boyce, M. S. Population viability analysis. Annu. Rev. Ecol. Syst. 23, 481–497 (1992).Article 

      Google Scholar 
      26.Possingham, H. in Conservation of Australia’s Forest Fauna (ed. Lunney, D.) Ch. 3, 35–40 (Royal Zoological Society of New South Wales, 1991).27.Lacy, R. C. VORTEX: a computer simulation model for population viability analysis. Wildl. Res. 20, 45–65 (1993).Article 

      Google Scholar 
      28.Wei, F., Fgeng, Z. & Hu, J. Population viability analysis computer model of giant panda population in Wuyipeng, Wolong Natural Reserve, China. Int. Conf. Bear. Res. Manag. 9, 19–23 (1997).
      Google Scholar 
      29.Guo, J., Chen, Y. & Hu, J. Population viability analysis of giant pandas in the Yele Nature Reserve. J. Nat. Conserv. 10, 35–40 (2002).Article 

      Google Scholar 
      30.Jiang, H. & Hu, J. Population viability analysis for the Giant Panda in Baoxing County, Sichuan. Sichuan J. Zool. 29, 161–165 (2010).
      Google Scholar 
      31.Li, X., Li, D., Yong, Y. & Zhang, J. A preliminary analysis on population viability analysis for Giant Panda in Foping. Acta Zool. Sin. 43, 285–293 (1997).
      Google Scholar 
      32.Yang, Z., Hu, J. & Liu, N. The influence of dispersal on the metapopulation viability of Giant Panda (Aliuropoda melanoleuca) in the Minshan Mountains. Acta Zool. Acad. Sci. Hung. 53, 169–184 (2007).
      Google Scholar 
      33.Leslie, P. H. On the use of matrices in certain population mathematics. Biometrika 33, 183–212 (1945).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      34.Carter, J., Ackleh, A. S., Leonard, B. P. & Wang, H. Giant panda (Ailuropoda melanoleuca) population dynamics and bamboo (subfamily Bambusoideae) life history: a structured population approach to examining carrying capacity when the prey are semelparous. Ecol. Model. 123, 207–223 (1999).Article 

      Google Scholar 
      35.Xia, W. & Hu, J. On the trend of population dynamics in giant panda based on age structure. Acta Theriologica Sin. 9, 87–93 (1989).
      Google Scholar 
      36.Zhu, L. et al. Conservation implications of drastic reductions in the smallest and most isolated populations of giant pandas. Conserv. Biol. 24, 1299–1306 (2010).PubMed 
      Article 

      Google Scholar 
      37.State Forestry Administration P. R. C. Report of the Third National Giant Panda Census (Science Publishing House, 2006).38.Zhu, L., Hu, Y., Zhang, Z. & Wei, F. Effect of China’s rapid development on its iconic giant panda. Chin. Sci. Bull. 58, 2134–2139 (2013).Article 

      Google Scholar 
      39.Hu, J. Research on the Giant Panda (Shanghai Science and Technology Education Press, 2001).40.Li, R. et al. Climate change threatens giant panda protection in the 21st century. Biol. Conserv. 182, 93–101 (2015).Article 

      Google Scholar 
      41.Yang, B. et al. China’s collective forest tenure reform and the future of the giant panda. Conserv. Lett. 8, 251–261 (2015).Article 

      Google Scholar 
      42.Linderman, M. et al. The effects of understory bamboo on broad-scale estimates of giant panda habitat. Biol. Conserv. 121, 383–390 (2005).Article 

      Google Scholar 
      43.Yang, Z. et al. Reintroduction of the giant panda into the wild: a good start suggests a bright future. Biol. Conserv. 217, 181–186 (2018).CAS 
      Article 

      Google Scholar 
      44.Kaiser, H. The dynamics of populations as result of the properties of individual animals. Fortschr. D. Zool. 25, 109–136 (1979).
      Google Scholar 
      45.Huston, M., DeAngelis, D. & Post, W. New computer models unify ecological theory. BioScience 38, 682–691 (1988).Article 

      Google Scholar 
      46.DeAngelis, D. L. Individual-Based Models and Approaches In Ecology: Populations, Communities and Ecosystems (CRC Press, 2017)47.Uchmański, J. & Grimm, V. Individual-based modelling in ecology: what makes the difference? Trends Ecol. Evol. 11, 437–441 (1996).PubMed 
      Article 

      Google Scholar 
      48.Wei, F. et al. A study on the life table of wild giant pandas. Acta Theriologica Sin. 9, 81–86 (1989).
      Google Scholar 
      49.Hou, W. Revision of the giant panda life table and related data indicators. Zool. Res. 21, 361–366 (2000).
      Google Scholar  More

    • 163 Shares159 Views

      in Ecology

      Cuticular hydrocarbons are associated with mating success and insecticide resistance in malaria vectors

      by

      1.Tripet, F., Toure, Y. T., Dolo, G. & Lanzaro, G. C. Frequency of multiple inseminations in field-collected Anopheles gambiae females revealed by DNA analysis of transferred sperm. Am. J. Tropical Med. Hyg. 68, 1–5 (2003).Article 

      Google Scholar 
      2.Beehler, B. M. & Foster, M. S. Hotshots, hotspots, and female preference in the organization of lek mating systems. Am. Nat. 131, 203–219 (1988).Article 

      Google Scholar 
      3.Cator, L. J., Wyer, C. A. S. & Harrington, L. C. Mosquito sexual selection and reproductive control programs. Trends Parasitol. 37, 330–339 (2021).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      4.Charlwood, J. D. & Jones, M. D. R. Mating behaviour in the mosquito, Anopheles gambiae s.1.save. Physiol. Entomol. 4, 111–120 (1979).Article 

      Google Scholar 
      5.Charlwood, J. D. et al. The swarming and mating behaviour of Anopheles gambiae s.s. (Diptera: Culicidae) from São Tomé Island. J. Vector Ecol. 27, 178–183 (2002).CAS 
      PubMed 
      PubMed Central 

      Google Scholar 
      6.Mozūraitis, R. et al. Male swarming aggregation pheromones increase female attraction and mating success among multiple African malaria vector mosquito species. Nat. Ecol. Evol. 1395–1401 (2020).7.Wang, G. et al. Clock genes and environmental cues coordinate Anopheles pheromone synthesis, swarming, and mating. Science 371, 411–415 (2021).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      8.Cator, L. J., Ng’Habi, K. R., Hoy, R. R. & Harrington, L. C. Sizing up a mate: variation in production and response to acoustic signals in Anopheles gambiae. Behav. Ecol. 21, 1033–1039 (2010).Article 

      Google Scholar 
      9.Pennetier, C., Warren, B., Dabiré, K. R., Russell, I. J. & Gibson, G. “Singing on the wing” as a mechanism for species recognition in the malarial mosquito Anopheles gambiae. Curr. Biol. 20, 131–136 (2010).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      10.Simões, P. M., Gibson, G. & Russell, I. J. Pre-copula acoustic behaviour of males in the malarial mosquitoes Anopheles coluzzii and Anopheles gambiae s.s. does not contribute to reproductive isolation. J. Exp. Biol. 220, 379–385 (2017).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      11.Maïga, H., Dabiré, R. K., Lehmann, T., Tripet, F. & Diabaté, A. Variation in energy reserves and role of body size in the mating system of Anopheles gambiae. J. Vector Ecol. 37, 289–297 (2012).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      12.Sawadogo, S. P. et al. Effects of age and size on Anopheles gambiae s.s. male mosquito mating success. J. Med. Entomol. 50, 285–293 (2013).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      13.Ng’habi, K. R. et al. Sexual selection in mosquito swarms: may the best man lose? Anim. Behav. 76, 105–112 (2008).Article 

      Google Scholar 
      14.Howell, P. I. & Knols, B. G. J. Male mating biology. Malar. J. 8, S8-S8, https://doi.org/10.1186/1475-2875-8-S2-S8 (2009).CAS 
      Article 

      Google Scholar 
      15.Aldersley, A. & Cator, L. J. Female resistance and harmonic convergence influence male mating success in Aedes aegypti. Sci. Rep. 9, 2145 (2019).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      16.Pantoja-Sánchez, H., Gomez, S., Velez, V., Avila, F. W. & Alfonso-Parra, C. Precopulatory acoustic interactions of the New World malaria vector Anopheles albimanus (Diptera: Culicidae). Parasites Vectors 12, 386–386 (2019).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      17.Ferveur, J.-F. & Cobb, M. Insect Hydrocarbons: Biology, Biochemistry, and Chemical Ecology. Cambridge University Press 325–343 (2010).18.Theresa, L. S. Roles of hydrocarbons in the recognition systems of insects. Am. Zool. 38, 394–405 (1998).Article 

      Google Scholar 
      19.Chung, H. et al. A single gene affects both ecological divergence and mate choice in Drosophila. Science 343, 1148–1151 (2014).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      20.Grigoraki, L., Grau-Bové, X., Carrington Yates, H., Lycett, G. J. & Ranson, H. Isolation and transcriptomic analysis of Anopheles gambiae oenocytes enables the delineation of hydrocarbon biosynthesis. eLife 9, e58019 (2020).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      21.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      22.Ingleby, F. C. Insect cuticular hydrocarbons as dynamic traits in sexual communication. Insects 6, 732–742 (2015).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      23.Lang, J. T. & Foster, W. A. Is there a female sex pheromone in the mosquito Culiseta inornata? Environ. Entomol. 5, 1109–1115 (1976).Article 

      Google Scholar 
      24.Nijout, H. F. C. J. & George, B. Reproductive isolation in Stepgomyia mosquitoes. III Evidence for a sexual pheromone. Entomol. Exp. Appl. 14, 399–412 (1971).Article 

      Google Scholar 
      25.Lang, J. T. Contact sex pheromone in the mosquito Culiseta inornata (Diptera: Culicidae). J. Med. Entomol. 14, 448–454 (1977).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      26.Polerstock, A. R., Eigenbrode, S. D. & Klowden, M. J. Mating alters the cuticular hydrocarbons of female Anopheles gambiae sensu stricto and aedes Aegypti (Diptera: Culicidae). J. Med. Entomol. 39, 545–552 (2002).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      27.Balabanidou, V. et al. Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. Proc. Natl Acad. Sci. USA 113, 9268–9273 (2016).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      28.Balabanidou, V. et al. Mosquitoes cloak their legs to resist insecticides. Proc. Biol. Sci. 286, 20191091 (2019).CAS 
      PubMed 
      PubMed Central 

      Google Scholar 
      29.Yahouedo, G. A. et al. Contributions of cuticle permeability and enzyme detoxification to pyrethroid resistance in the major malaria vector Anopheles gambiae. Sci. Rep. 7, 11091 (2017).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      30.Baeshen, R. et al. Differential effects of inbreeding and selection on male reproductive phenotype associated with the colonization and laboratory maintenance of Anopheles gambiae. Malar. J. 13, 19 (2014).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      31.Toe, K. H. et al. Increased pyrethroid resistance in malaria vectors and decreased bed net effectiveness, Burkina Faso. Emerg. Infect. Dis. 20, 1691–1696 (2014).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      32.World Health Organization. Test Procedures for Insecticide Resistance Monitoring in Malaria Vector Mosquitoes. Geneva, Switzerland: World Health Organization (2013).33.Toe, K. H., N’Fale, S., Dabire, R. K., Ranson, H. & Jones, C. M. The recent escalation in strength of pyrethroid resistance in Anopheles coluzzi in West Africa is linked to increased expression of multiple gene families. BMC Genomics 16, 146 (2015).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      34.Kwiatkowska, R. M. et al. Dissecting the mechanisms responsible for the multiple insecticide resistance phenotype in Anopheles gambiae s.s., M form, from Vallee du Kou, Burkina Faso. Gene 519, 98–106 (2013).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      35.Ingham, V. A. et al. Dissecting the organ specificity of insecticide resistance candidate genes in Anopheles gambiae: known and novel candidate genes. BMC Genomics 15, 1018 (2014).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      36.Blows, M. W. Interaction between natural and sexual selection during the evolution of mate recognition. Proc. Biol. Sci. 269, 1113–1118 (2002).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      37.Lane, S. M., Dickinson, A. W., Tregenza, T. & House, C. M. Sexual selection on male cuticular hydrocarbons via male-male competition and female choice. J. Evol. Biol. 29, 1346–1355 (2016).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      38.Steiger, S. et al. Sexual selection on cuticular hydrocarbons of male sagebrush crickets in the wild. Proc. Biol. Sci. 280, 20132353–20132353 (2013).PubMed 
      PubMed Central 

      Google Scholar 
      39.Chung, H. & Carroll, S. B. Wax, sex and the origin of species: dual roles of insect cuticular hydrocarbons in adaptation and mating. Bioessays 37, 822–830, https://doi.org/10.1002/bies.201500014 (2015).CAS 
      Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      40.Sawadogo, S. P. et al. Differences in timing of mating swarms in sympatric populations of Anopheles coluzzii and Anopheles gambiae s.s. (formerly An. gambiae M and S molecular forms) in Burkina Faso, West Africa. Parasit. Vectors 6, 275 (2013).PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      41.Arcaz, A. C. et al. Desiccation tolerance in Anopheles coluzzii: the effects of spiracle size and cuticular hydrocarbons. J. Exp. Biol. 219, 1675–1688 (2016).PubMed 
      PubMed Central 

      Google Scholar 
      42.Hidalgo, K. et al. Distinct physiological, biochemical and morphometric adjustments in the malaria vectors Anopheles gambiae and A. coluzzii as means to survive dry season conditions in Burkina Faso. J. Exp. Biol. 70, 102–116 (2018).43.Wagoner, K. M. et al. Identification of morphological and chemical markers of dry- and wet-season conditions in female Anopheles gambiae mosquitoes. Parasit. Vectors 7, 294 (2014).PubMed 
      PubMed Central 
      Article 
      CAS 

      Google Scholar 
      44.Wicker, C. & Jallon, J. M. Influence of ovary and ecdysteroids on pheromone biosynthesis in Drosophila melanogaster (Diptera: Drosophilidae). EJE 92, 197–202 (1995).CAS 

      Google Scholar 
      45.Andersson, M. Sexual Selection. Princeton University Press (1994).46.Fisher, R. The Genetical Theory of Natural Selection. The Clarendon Press, Oxford (1930).47.Weatherhead, P. J. & Robertson, R. J. Offspring quality and the polygyny threshold: “The Sexy Son Hypothesis”. Am. Nat. 113, 201–208 (1979).Article 

      Google Scholar 
      48.Ryan, M. J. Sexual selection, receiver biases, and the evolution of sex differences. Science 281, 1999–2003 (1998).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      49.Rundle, H. D., Chenoweth, S. F. & Blows, M. W. The roles of natural and sexual selection during adaptation to a novel environment. Evolution 60, 2218–2225 (2006).PubMed 
      Article 
      PubMed Central 

      Google Scholar 
      50.Thailayil, J., Magnusson, K., Godfray, H. C. J., Crisanti, A. & Catteruccia, F. Spermless males elicit large-scale female responses to mating in the malaria mosquito Anopheles gambiae. Proc. Natl Acad. Sci. USA 108, 13677–13681, https://doi.org/10.1073/pnas.1104738108 (2011).Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      51.Charlwood, J. D. Studies on the bionomics of male Anopheles gambiae Giles and male Anopheles funestus Giles from southern Mozambique. J. Vector Ecol. 36, 382–394, https://doi.org/10.1111/j.1948-7134.2011.00179.x (2011).CAS 
      Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      52.Glunt, K. D., Thomas, M. B. & Read, A. F. The effects of age, exposure history and malaria infection on the susceptibility of Anopheles mosquitoes to low concentrations of pyrethroid. PLoS ONE 6, e24968–e24968 (2011).CAS 
      PubMed 
      PubMed Central 
      Article 

      Google Scholar 
      53.Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163, https://doi.org/10.1186/1475-2875-7-163 (2008).Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      54.Diabaté, A. et al. Spatial distribution and male mating success of Anopheles gambiae swarms. BMC Evol. Biol. 11, 184–184, https://doi.org/10.1186/1471-2148-11-184 (2011).Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      55.Niang, A. et al. Does extreme asymmetric dominance promote hybridization between Anopheles coluzzii and Anopheles gambiae s.s. in seasonal malaria mosquito communities of West Africa? Parasit. Vectors 8, 586–586, https://doi.org/10.1186/s13071-015-1190-x (2015).CAS 
      Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      56.Caputo, B. et al. Identification and composition of cuticular hydrocarbons of the major Afrotropical malaria vector Anopheles gambiae s.s. (Diptera: Culicidae): analysis of sexual dimorphism and age-related changes. J. Mass Spectrom. 40, 1595–1604, https://doi.org/10.1002/jms.961 (2005).CAS 
      Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      57.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
      Article 

      Google Scholar 
      58.Charlwood, J. Biological variation in Anopheles darlingi root. Mem. Inst. Oswaldo Cruz. 91, 391–398 (1996).CAS 
      PubMed 
      Article 
      PubMed Central 

      Google Scholar  More

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      First modern human settlement recorded in the Iberian hinterland occurred during Heinrich Stadial 2 within harsh environmental conditions

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      1.Zilhão, J. Neandertal-modern human contact in Western Eurasia: issues of dating, taxonomy, and cultural associations. In Dynamics of Learning in Neanderthals and Modern humans Volume 1: Cultural Perspectives (eds Akazawa, T., Nishiaki, K. & Aoko. Y.), 21–57 (Springer, 2013).2.Welker, F. et al. Palaeoproteomic evidence identifies archaic hominins associated with the Châtelperronian at the Grotte du Renne. Proc. Natl Acad Sci. 113(40), 11162–11167. https://doi.org/10.1073/pnas.1605834113 (2016).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      3.Gravina, B. et al. No reliable evidence for a Neanderthal-Châtelperronian Association at La Roche-à-Pierrot, Saint-Césaire. Sci. Rep. 8, 15134. https://doi.org/10.1038/s41598-018-33084-9 (2018).ADS 
      Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      4.Wood, R. et al. The chronology of the earliest Upper Palaeolithic in northern Iberia: New insights from L’Arbreda, Labeko Koba and La Viña. J. Hum Evol. 69, 91–109. https://doi.org/10.1016/j.jhevol.2013.12.017 (2014).Article 
      PubMed 
      CAS 

      Google Scholar 
      5.Marín-Arroyo, A. B. et al. Chronological reassessment of the Middle to Upper Paleolithic transition and Early Upper Paleolithic cultures in Cantabrian Spain. PLoS ONE 13(4), e0194708. https://doi.org/10.1371/journal.pone.0194708 (2018).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      6.Wood, R. et al. El Castillo (Cantabria, northern Iberia) and the Transitional Aurignacian: Using radiocarbon dating to assess site taphonomy. Quat. Int. 474(A), 56–70. https://doi.org/10.1016/j.quaint.2016.03.005 (2018).Article 

      Google Scholar 
      7.Teyssandier, N. & Zilhão, J. On the entity and antiquity of the Aurignacian at Willendorf (Austria): Implications for modern human emergence in Europe. J. Paleolithic Archaeol. 1, 107–138. https://doi.org/10.1007/s41982-017-0004-4 (2018).Article 

      Google Scholar 
      8.Dinnis, R., Bessudnov, A., Chiotti, L., Flas, D. & Michel, A. Thoughts on the structure of the European Aurignacian, with Particular Focus on Hohle Fels IV. Proc. Prehist. Soc. 85, 29–60. https://doi.org/10.1017/ppr.2019.11 (2019).Article 

      Google Scholar 
      9.Hublin, J. J. et al. Initial Upper Palaeolithic Homo sapiens from Bacho Kiro Cave, Bulgaria. Nature 581, 299–302. https://doi.org/10.1038/s41586-020-2259-z (2020).ADS 
      Article 
      CAS 

      Google Scholar 
      10.Fewlass, H. et al. A 14C chronology for the Middle to Upper Palaeolithic transition at Bacho Kiro Cave, Bulgaria. Nat. Ecol. Evol. 4, 794–801. https://doi.org/10.1038/s41559-020-1136-3 (2020).Article 

      Google Scholar 
      11.Straus, L. G. The Upper Paleolithic of Iberia. Trab. de Prehist. 75(1), 9–51. https://doi.org/10.3989/tp.2018.12202 (2018).Article 

      Google Scholar 
      12.Zilhão, J. et al. Precise dating of the Middle-to-Upper Paleolithic transition in Murcia (Spain) supports late Neandertal persistence in Iberia. Heliyon 3, e00435. https://doi.org/10.1016/j.heliyon.2017.e00435 (2017).Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      13.Zilhão, J. et al. A revised, Last Interglacial chronology for the Middle Palaeolithic sequence of Gruta da Oliveira (Almonda karst system, Torres Novas, Portugal). Quat. Sci. Rev. 258, 106885. https://doi.org/10.1016/j.quascirev.2021.106885 (2021).Article 

      Google Scholar 
      14.Aubry, T. et al. Timing of the Middle-to-Upper Palaeolithic transition in the Iberian inland (Cardina-Salto do Boi, Côa Valley, Portugal). Quat. Res. 98, 81–101. https://doi.org/10.1017/qua.2020.43 (2020).15.Cortés-Sánchez, M. et al. An early Aurignacian arrival in southwestern Europe. Nat. Ecol. Evol. 3, 207–212. https://doi.org/10.1038/s41559-018-0753-6 (2019).Article 
      PubMed 

      Google Scholar 
      16.Haws, J. A. et al. The early Aurignacian dispersal of modern humans into westernmost Eurasia. Proc. Natl Acad. Sci. USA 117(41), 25414–25422. https://doi.org/10.1073/pnas.2016062117 (2020).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      17.Anderson, L., Reynolds, N. & Teyssandier, N. No reliable evidence for a very early Aurignacian in Southern Iberia. Nat. Ecol. Evol. 3, 713. https://doi.org/10.1038/s41559-019-0885-3 (2019).Article 
      PubMed 

      Google Scholar 
      18.de la Peña, P. Dating on its own cannot resolve hominin occupation patterns. Nat. Ecol. Evol. 3, 712. https://doi.org/10.1038/s41559-019-0886-2 (2019).Article 
      PubMed 

      Google Scholar 
      19.Morales, J. I. et al. The Middle-to-Upper Paleolithic transition occupations from Cova Foradada (Calafell, NE Iberia). PLoS ONE 14(5), e0215832. https://doi.org/10.1371/journal.pone.0215832 (2019).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      20.Alcaraz-Castaño, M. Central Iberia around the Last Glacial Maximum. Hopes and prospects. J. Anthropol. Res. 71(4), 565–578. https://doi.org/10.3998/jar.0521004.0071.406 (2015).Article 

      Google Scholar 
      21.Mosquera, M. et al. Valle de las Orquídeas: un yacimiento al aire libre del Pleistoceno Superior en la Sierra de Atapuerca (Burgos). Trab. de Prehist. 64(2), 143–155. https://doi.org/10.3989/tp.2007.v64.i2.113 (2007).Article 

      Google Scholar 
      22.Carretero, J. M. et al. A Late Pleistocene-Early Holocene archaeological sequence of Portalón de Cueva Mayor (Sierra de Atapuerca, Burgos, Spain). Munibe (Antropologia-Arkeologia). 59, 67–80 (2008).23.Aubry, T., Luis, L., Mangado Llach, J. & Matías, H. We will be known by the tracks we leave behind: Exotic lithic raw materials, mobility and social networking among the Côa Valley foragers (Portugal). J. Anthropol. Archaeol. 31, 528–550. https://doi.org/10.1016/j.jaa.2012.05.003 (2012).Article 

      Google Scholar 
      24.Gaspar, R., Ferreira, J., Carrondo, J., Silva, M. J. & García-Vadillo, F. J. Open-air Gravettian lithic assemblages from Northeast Portugal: The Foz do Medal site (Sabor valley). Quat. Int. 406, 44–64. https://doi.org/10.1016/j.quaint.2015.12.054 (2016).Article 

      Google Scholar 
      25.Alcaraz-Castaño, M. et al. Los orígenes del Solutrense y la ocupación pleniglaciar del interior de la Península Ibérica: implicaciones del nivel 3 de Peña Capón (valle del Sorbe, Guadalajara). Trab. de Prehist. 70(1), 28–53. http://tp.revistas.csic.es/index.php/tp/article/view/637/659(2013).26.Alcaraz-Castaño, M., Alcolea-González, J.J., Balbín Behrmann, R. de, Kehl, M., Weniger, G.C. Recurrent Human Occupations in Central Iberia around the Last Glacial Maximum. The Solutrean Sequence of Peña Capón Updated. In Human Adaptations to the Last Glacial Maximum: The Solutrean and Its Neighbors (eds. I. Schmidt & J. Cascalheira), 148–170 (Cambridge Scholars Publishing, Cambridge, UK, 2019).27.Straus, L. G. The human occupations of southwestern Europe during the Last Glacial Maximum: Solutrean cultural adaptations in France and Iberia. J. Anthropol. Res. 71(4), 465–492. https://doi.org/10.3998/jar.0521004.0071.401 (2015).Article 

      Google Scholar 
      28.Schmidt, I. et al. Rapid climate change and variability of settlement patterns in Iberia during the Late Pleistocene. Quat. Int. 274(1), 179–204. https://doi.org/10.1016/j.quaint.2012.01.018 (2012).Article 

      Google Scholar 
      29.Burke, A. et al. Exploring the impact of climate variability during the Last Glacial Maximum on the pattern of human occupation of Iberia. J. Hum. Evol. 73, 35–46. https://doi.org/10.1016/j.jhevol.2014.06.003 (2014).Article 
      PubMed 

      Google Scholar 
      30.Burke, A. et al. Risky business: The impact of climate and climate variability on human population dynamics in Western Europe during the Last Glacial Maximum. Quat. Sci. Rev. 164, 217–229. https://doi.org/10.1016/j.quascirev.2017.04.001 (2017).ADS 
      Article 

      Google Scholar 
      31.Lüdwig, P., Shao, Y., Kehl, M. & Weniger, G.-C. The Last Glacial Maximum and Heinrich event I on the Iberian Peninsula: A regional climate modelling study for understanding human settlement patterns. Glob. Planet. Change. 170, 34–47. https://doi.org/10.1016/j.gloplacha.2018.08.006 (2018).ADS 
      Article 

      Google Scholar 
      32.Wren, C. D. & Burke, A. Habitat suitability and the genetic structure of human populations during the Last Glacial Maximum (LGM) in Western Europe. PLoS ONE 14(6), e0217996. https://doi.org/10.1371/journal.pone.0217996 (2019).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      33.Klein, K. et al. Human existence potential in Europe during the Last Glacial Maximum. Quat. Int. https://doi.org/10.1016/j.quaint.2020.07.046 (2020).Article 

      Google Scholar 
      34.Mix, A. C., Bard, E. & Schneider, R. Environmental processes of the ice age: land, oceans, glaciers (EPILOG). Quat. Sci. Rev. 20, 627–657. https://doi.org/10.1016/S0277-3791(00)00145-1 (2001).ADS 
      Article 

      Google Scholar 
      35.Maier, A. et al. Demographic estimates of hunter–gatherers during the Last Glacial Maximum in Europe against the background of palaeoenvironmental data. Quat. Int. 425, 49–61. https://doi.org/10.1016/j.quaint.2016.04.009 (2016).Article 

      Google Scholar 
      36.Banks, W. E. et al. Human ecological niches and ranges during the LGM in Europe derived from an application of eco-cultural niche modeling. J. Archaeol. Sci. 35, 481–491. https://doi.org/10.1016/j.jas.2007.05.011 (2008).Article 

      Google Scholar 
      37.Tallavaara, M., Luoto, M., Korhonen, N., Järvinen, H. & Seppä, H. Human population dynamics in Europe over the last glacial maximum. Proc. Natl. Acad. Sci. 112, 8232–8237. https://doi.org/10.1073/pnas.1503784112 (2015).ADS 
      Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      38.Sala, N. et al. Central Iberia in the Middle MIS 3. Paleoecological inferences during the period 34–40 cal kyr BP. Quat. Sci. Rev. 228, 106027 (2020).Article 

      Google Scholar 
      39.Sala, N. et al. Cueva de los Torrejones revisited. New insights on the paleoecology of inland Iberia during the Late Pleistocene. Quat. Sci. Rev. 253, 106765 (2021).Article 

      Google Scholar 
      40.Wolf, D. et al. Climate deteriorations and Neanderthal demise in interior Iberia. Sci. Rep. 8, 7048. https://doi.org/10.1038/s41598-018-25343-6 (2018).ADS 
      Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      41.Wolf, D. et al. Evidence for strong relations between the upper Tagus loess formation (central Iberia) and the marine atmosphere off the Iberian margin during the last glacial period. Quat. Res. 101, 84-113.https://doi.org/10.1017/qua.2020.119(2021).42.Zilhão, J. et al. Pego do Diabo (Loures, Portugal). Dating the emergence of anatomical modernity in westernmost Eurasia. PLoS ONE 5, e8880. https://doi.org/10.1371/journal.pone.0008880(2010).43.Cascalheira, J. et al. Paleoenvironments and human adaptations during the Last Glacial Maximum in the Iberian Peninsula: A review. Quat. Int. 581-582, 28-51. https://doi.org/10.1016/j.quaint.2020.08.005(2021).44.Binford, L. R. Constructing Frames of Reference: An Analytical Method for Archaeological Theory Building Using Ethnographic and Environmental Data Sets (University of California Press, 2001).45.Kelly, R. L. The Lifeways of Hunter-Gatherers. The Foraging Spectrum (Cambridge University Press, 2013, 2nd edition).46.Bettinger, R. L., Garvey, R. & Tushingham, S. Hunter-Gatherers: Archaeological and Evolutionary Theory (Springer, 2015, 2nd edition).47.Gamble, C., Davies, W., Pettitt, P. & Richards, M. Climate change and evolving human diversity in Europe during the last glacial. Phil. Trans. R. Soc. B 359, 243–254. https://doi.org/10.1098/rstb.2003.1396 (2014).Article 

      Google Scholar 
      48.d’Errico, F. & Banks, W. Identifying Mechanisms behind Middle Paleolithic and Middle Stone Age Cultural Trajectories. Curr. Anthropol. 54(S8), S371–S387. https://doi.org/10.1086/673388 (2013).Article 

      Google Scholar 
      49.Collard, M., Vaesen K., Cosgrove, R. & Roebroeks, W. The empirical case against the ‘demographic turn’ in Palaeolithic archaeology. Phil. Trans. R. Soc. B 371, 20150242. https://doi.org/10.1098/rstb.2015.0242 (2016).50.Banks, W. E. et al. Investigating links between ecology and bifacial tool types in Western Europe during the Last Glacial Maximum. J. Archaeol. Sci. 36(12), 2853–2867. https://doi.org/10.1016/j.jas.2009.09.014 (2009).Article 

      Google Scholar 
      51.Bradtmöller, M., Pastoors, A., Weninger, B. & Weniger, G.-C. The repeated replacement model—Rapid climate change and population dynamics in Late Pleistocene Europe. Quat. Int. 247, 38–49. https://doi.org/10.1016/j.quaint.2010.10.015 (2012).Article 

      Google Scholar 
      52.Cascalheira, J. & Bicho, N. Hunter–gatherer ecodynamics and the impact of the Heinrich event 2 in Central and Southern Portugal. Quat. Int. 318, 117–127 (2013).Article 

      Google Scholar 
      53.Bicho, N., Cascalheira, J., Marreiros, J. & Pereira, T. Rapid climatic events and long term cultural change: The case of the Portuguese Upper Paleolithic. Quat. Int. 428, 3–16. https://doi.org/10.1016/j.quaint.2015.05.044 (2017).Article 

      Google Scholar 
      54.Cascalheira, J. & Bicho, N. Testing the impact of environmental change on hunter-gatherer settlement organization during the Upper Paleolithic in western Iberia. J. Quat. Sci. 33(3), 323–334 (2018).Article 

      Google Scholar 
      55.Weniger, G-C. et al. Late Glacial rapid climate change and human response in the Westernmost Mediterranean (Iberia and Morocco). PLoS ONE 14(12), e0225049. https://doi.org/10.1371/journal.pone.0225049(2019).56.McLaughlin, T. R., Gómez-Puche, M., Cascalheira, J., Bicho, N. & Fernández-López de Pablo, J. Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia. Phil. Trans. R. Soc. B 376, 20190724. (2021).57.Portero García, J. M. et al. Mapa Geológico de España 1:50.000, MAGNA n. 486, Jadraque. (Instituto Geológico y Minero de España, Madrid, 1994).58.Portero García, J. M., et al. Mapa Geológico de España 1:50.000, MAGNA n. 485, Valdepeñas de la Sierra, (Instituto Geológico y Minero de España, Madrid, 1995).59.Pérez-González, A. Depresión del Tajo. In Geomorfología de España (ed. Gutiérrez Elorza, M.), 389–436 (Rueda, Madrid, 1994).60.Silva, P. G., Roquero, E., López-Recio, M., Huerta, P. & Martínez-Graña, A. M. Chronology of fluvial terrace sequences for large Atlantic rivers in the Iberian Peninsula (Upper Tagus and Duero basins, Central Spain). Quat. Sci. Rev. 166, 188–203. https://doi.org/10.1016/j.quascirev.2016.05.027 (2017).ADS 
      Article 

      Google Scholar 
      61.Angelucci, D. E. The recognition and description of knapped lithic artifacts in thin section. Geoarchaeology 25(2), 220–232. https://doi.org/10.1002/gea.20303 (2010).Article 

      Google Scholar 
      62.Kooistra, M. J. & Pulleman, M. M. Features Related to Faunal Activity. In Interpretation of Micromorphological Features of Soils and Regoliths (eds. Stoops, G., Marcelino, V. & Mess, F.), 397–440 (Elsevier, Amsterdam, 2010).63.Higham, T. et al. The timing and spatiotemporal patterning of Neanderthal disappearance. Nature 512, 306–309. https://doi.org/10.1038/nature13621 (2014).ADS 
      Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      64.Cascalheira, J. & Bicho, N. On the chronological structure of the Solutrean in Southern Iberia. PLoS ONE 10(9), e0137308. https://doi.org/10.1371/journal.pone.0137308 (2015).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      65.Pettitt, P. & Zilhão, J. Problematizing Bayesian approaches to prehistoric chronologies. World Archaeol. 47(4), 525–542. https://doi.org/10.1080/00438243.2015.1070082 (2015).Article 

      Google Scholar 
      66.Douka, K., Chiotti, L., Nespoulet, R. & Higham, T. A refined chronology for the Gravettian sequence of Abri Pataud. J. Hum. Evol. 141, 10230. https://doi.org/10.1016/j.jhevol.2019.102730 (2020).Article 

      Google Scholar 
      67.Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy. Quat. Sci. Rev. 106, 14–28. https://doi.org/10.1016/j.quascirev.2014.09.007 (2014).ADS 
      Article 

      Google Scholar 
      68.Sánchez Goñi, M. F. & Harrison, S. P. Millennial-scale climate variability and vegetation changes during the Last Glacial: Concepts and terminology. Quat. Sci. Rev., 29(21–22), 2823–2827. https://doi.org/10.1016/j.quascirev.2009.11.014(2010).69.Clark, P. U. et al. The last glacial maximum. Science 325(5941), 710–714. https://doi.org/10.1126/science.1172873 (2009).ADS 
      Article 
      PubMed 
      CAS 

      Google Scholar 
      70.López-Sáez, J. A. et al. Discrimination of Scots pine forests in the Iberian Central System (Pinus sylvestris var. iberica) by means of pollen analysis. Phytosociological considerations. Lazaroa, 34, 191–208. https://doi.org/10.5209/rev_LAZA.2013.v34.n1.43599 (2013).71.Aranbarri, J. et al. Rapid climatic changes and resilient vegetation during the Lateglacial and Holocene in a continental region of southwestern Europe. Glob. Planet. Change 114, 50–65. https://doi.org/10.1016/j.gloplacha.2014.01.003 (2014).ADS 
      Article 

      Google Scholar 
      72.Broothaerts, N. et al. Reconstructing past arboreal cover based on modern and fossil pollen data: A statistical approach for the Gredos Range (Central Spain). Rev. Palaeobot. Palynol. 255, 1–13. https://doi.org/10.1016/j.revpalbo.2018.04.007 (2018).Article 

      Google Scholar 
      73.López-Sáez, J. A., et al. Vegetation history, climate and human impact in the Spanish Central System over the last 9,000 years. Quat. Int. 353, 98–122. https://doi.org/10.1016/j.quaint.2013.06.034 (2014).74.López-Sáez, J. A., Sánchez-Mata, D. & Gavilán, R. G. Syntaxonomical update on the relict groves of Scots pine (Pinus sylvestris L. var. iberica Svoboda) and Spanish black pine (Pinus nigra Arnold subsp. salzmannii (Dunal) Franco) in the Gredos range (central Spain). Lazaroa 37, 153–172. https://doi.org/10.5209/LAZA.54043 (2016).75.López-Sáez, J. A., Alba-Sánchez, F., López-Merino, L. & Pérez-Díaz, S. Modern pollen analysis: A reliable tool for discriminating Quercus rotundifolia communities in Central Spain. Phytocoenologia 40, 57–72. https://doi.org/10.1127/0340-269X/2010/0040-0430 (2010).Article 

      Google Scholar 
      76.Sánchez-Mata, D., Gavilán, R. G. & de la Fuente, V. The Sistema Central (Central Range). In The Vegetation of the Iberian Peninsula (ed. Loidi, J.), vol. 1, 549–588 (Springer, Utrecht, 2017).77.López-Sáez, J. A. et al. A palynological approach to the study of Quercus pyrenaica forest communities in the Spanish Central System. Phytocoenologia 45, 107–124. https://doi.org/10.1127/0340-269X/2014/0044-0572 (2015).Article 

      Google Scholar 
      78.van der Knaap, W. O. et al. Migration and population expansion of Abies, Fagus, Picea, and Quercus since 15000 years in and across the Alps, based on pollen percentage threshold values. Quat. Sci. Rev. 24(645–680), 2005. https://doi.org/10.1016/j.quascirev.2004.06.013 (2005).Article 

      Google Scholar 
      79.Abel-Schaad, D. et al. Persistence of tree relicts through the Holocene in the Spanish Central System. Lazaroa 35, 107–131. https://doi.org/10.5209/rev_LAZA.2014.v35.41932 (2014).Article 

      Google Scholar 
      80.López-Sáez, J. A. et al. Late Glacial-early Holocene vegetation and environmental changes in the western Iberian Central System inferred from a key site: The Navamuño record, Béjar range (Spain). Quat. Sci. Rev. 230, 106167. https://doi.org/10.1016/j.quascirev.2020.106167 (2020).Article 

      Google Scholar 
      81.López-Merino, L., López-Sáez, J. A., Ruiz-Zapata, M.B. & Gil-García, M. J. Reconstructing the history of beech (Fagus sylvatica L.) in north-western Iberian Range (Spain): From Late-Glacial refugia to Holocene anthropic induced forests. Rev. Palaeobot. Palynol. 152, 58–65. https://doi.org/10.1016/j.revpalbo.2008.04.003 (2008).82.Ruiz-Alonso, M., Pérez-Díaz, S. & López-Sáez, J. A. From glacial refugia to the current landscape configuration: permanence, expansion and forest management of Fagus sylvatica L. in the Western Pyrenean Region (Northern Iberian Peninsula). Veget. Hist. Archaeobot. 28, 481–496. https://doi.org/10.1007/s00334-018-0707-6(2019). 83.Cuenca-Bescós G, Straus, L. G., González Morales, M. R & García Pimienta J.C. The reconstruction of past environments through small mammals: from the Mousterian to the Bronze Age in El Mirón Cave (Cantabria Spain). J. Arch. Sci., 36, 947–955. https://doi.org/10.1016/j.jas.2008.09.025 (2009).84.López-García, J. M, Cuenca-Bescós, G., Finlayson, C., Brown, K. & Giles Pacheco, F. Palaeoenvironmental and palaeoclimatic proxies of the Gorham’s cave small mammal sequence Gibraltar southern Iberia. Quat. Int., 243, 137–142. https://doi.org/10.1016/j.quaint.2010.12.032 (2011).85.Garcia-Ibaibarriaga, N. et al. A palaeoenvironmental estimate in Askondo (Bizkaia, Spain) using small vertebrates. Quat. Int. 364, 244–254. https://doi.org/10.1016/j.quaint.2014.09.069 (2015).Article 

      Google Scholar 
      86.Baca, M. et al. Diverse responses of common vole (Microtus arvalis) populations to Late Glacial and Early Holocene climate changes – Evidence from ancient DNA. Quat. Sci. Rev. 233, 106239. https://doi.org/10.1016/j.quascirev.2020.106239 (2020).Article 

      Google Scholar 
      87.Yravedra, J. et al. Not so deserted…paleoecology and human subsistence in Central Iberia (Guadalajara, Spain) around the Last Glacial Maximum. Quat. Sci. Rev. 140, 21–38. https://doi.org/10.1016/j.quascirev.2016.03.021 (2016).88.Alcolea-González, J. J. et al. Avance al estudio del poblamiento paleolítico del Alto Valle del Sorbe (Muriel, Guadalajara). In Il Congreso de Arqueología Peninsular I, Paleolítico y Epipaleolítico (eds. Balbín R. de & Bueno, P.), 201–218 (Fundacion Rei Afonso Henriques, Zamora, 1997).89.Smith P. Le Solutréen en France (Delmas, Bordeaux, 1966).90.Fullola, J. M. El Solutreo-Gravetiense o Parpallense, industria mediterránea. Zephyrvs XXVIII–XXIX, 125–33 (1978).91.Villaverde, V. & Peña, J. L. Piezas con escotadura del Paleolitico Superior Valenciano. (Servicio de Investigación Prehistórica, Valencia, 1981).92.de la Rasilla, M. Secuencia y cronoestratigrafía del solutrense cantábrico. Trab. de Prehist. 46, 35–46. https://doi.org/10.3989/tp.1989.v46.i0.585 (1989).Article 

      Google Scholar 
      93.Zilhão J. O Paleolítico Superior da Estremadura portuguesa. (Edições Colibri, Lisboa, II vols., 1997).94.Straus, L. G. Once more into the breach: Solutrean chronology. Munibe 38, 35–38 (1986).
      Google Scholar 
      95.Calvo, A. & Prieto, A. El final del Gravetiense y el comienzo del Solutrense en la Península Ibérica. Un estado de la cuestión acerca de la cronología radiocarbónica en 2012. Espacio, Tiempo y Forma 5, 131–148. https://doi.org/10.5944/etfi.5.2012.5377 (2012). 96.Zilhão, J. Seeing the leaves and not missing the forest: a Portuguese perspective of the Solutrean. In Pleistocene foragers on the Iberian Peninsula: their culture and environment (eds. Pastoors A, Auffermann B.), 201–216 (Neanderthal Museum, Mettmann, 2013).97.González-Sampériz, P. et al. Steppes, savannahs, forests and phytodiversity reservoirs during the Pleistocene in the Iberian Peninsula. Rev. Palaeobot. Palynol. 162(3), 427–457. https://doi.org/10.1016/j.revpalbo.2010.03.009 (2010).Article 

      Google Scholar 
      98.González-Sampériz, P. et al. Strong continentality and effective moisture drove unforeseen vegetation dynamics since the last interglacial at inland Mediterranean areas: The Villarquemado sequence in NE Iberia. Quat. Sci. Rev. 242, 106425. https://doi.org/10.1016/j.quascirev.2020.106425 (2020).Article 

      Google Scholar 
      99.Cacho, I. et al. Variability of the western Mediterranean Sea surface temperature during the last 25,000 years and its connection with the Northern Hemisphere climatic changes. Paleoceanogr. Paleoclimatol. 16, 40–52. https://doi.org/10.1029/2000PA000502 (2001).100.Fletcher, W. J. & Sánchez Goñi, M. F. Orbital and sub-orbital climate impacts on vegetation of the western Mediterranean basin over the last 48,000 yr. Quat. Sci. Rev. 70, 451–464. https://doi.org/10.1016/j.yqres.2008.07.002 (2008).101.Sánchez Goñi, M. F. et al. Contrasting impacts of Dansgaard–Oeschger events over a western European latitudinal transect modulated by orbital parameters. Quat. Sci. Rev. 27, 1136–1151. https://doi.org/10.1016/j.quascirev.2008.03.003(2008).102.Domínguez-Villar, D. et al. Early maximum extent of paleoglaciers from Mediterranean mountains during the last glaciation. Sci. Rep. 3, 2034. https://doi.org/10.1038/srep02034 (2013).Article 
      PubMed 
      PubMed Central 

      Google Scholar 
      103.Carrasco, R., Pedraza, J., Domínguez-Villar, D., Willenbring, J. & Villa, J. Sequence and chronology of the Cuerpo de Hombre paleoglacier (Iberian Central System) during the last glacial cycle. Quat. Sci. Rev. 129, 163–177. https://doi.org/10.1016/j.quascirev.2015.09.021 (2015).ADS 
      Article 

      Google Scholar 
      104.Valdeolmillos, A., Dorado-Valiño, M., Ruiz-Zapata, B., Bardají, T. & Bustamante, I. Palaeoclimatic record of the Last Glacial Cycle at las Tablas de Daimiel National Park (Southern Iberian Meseta, Spain). In Quaternary climatic changes and environmental crises in the Mediterranean region (eds. Ruiz-Zapata, B. et al.), 221–228 (Universidad de Alcalá, Alcalá de Henares, 2003).105.Vegas, J. et al. Identification of arid phases during the last 50 kyr Cal BP from the Fuentillejo maar lacustrine record (Campo de Calatrava Volcanic Field, Spain). J. Quat. Sci. 25, 1051–1062. https://doi.org/10.1002/jqs.1262 (2010).Article 

      Google Scholar 
      106.Alcolea-González, J. J. & Balbín-Behrmann, R. de. El Arte rupestre Paleolítico del interior peninsular. In Arte sin artistas. Una mirada al Paleolítico, 187–207 (Museo Arqueológico Regional, Comunidad de Madrid, Madrid, 2012).107.Alcaraz-Castaño, M. et al. The human settlement of Central Iberia during MIS 2: New technological, chronological and environmental data from the Solutrean workshop of Las Delicias (Manzanares River valley, Spain). Quat. Int. 431, 104–124. https://doi.org/10.1016/j.quaint.2015.06.069 (2017).108.Aubry, T., Luis, L., Mangado Llach, J. & Matias, H. Adaptation to resources and environments during the last glacial maximum by hunter-gatherer societies in Atlantic Europe. J. Anthropol. Res. 71, 521–544. https://doi.org/10.3998/jar.0521004.0071.404 (2015).109.Aubry, T. et al. Upper Paleolithic lithic raw material sourcing in Central and Northern Portugal as an aid to reconstructing hunter-gatherer societies. J. Lithic Stud. 3(2), 7–28. https://doi.org/10.2218/jls.v3i2.1436 (2016).Article 

      Google Scholar 
      110.Alcaraz-Castaño, M. et al. A context for the last Neandertals of interior Iberia: Los Casares cave revisited. PLoS ONE 12(7), e0180823. https://doi.org/10.1371/journal.pone.0180823 (2017).Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      111.Kehl, M. et al. The rock shelter Abrigo del Molino (Segovia, Spain) and the timing of the late Middle Palaeolithic in Central Iberia. Quat. Res. 90, 180–200. https://doi.org/10.1017/qua.2018.13 (2018).Article 
      CAS 

      Google Scholar 
      112.Hoffecker, J. Desolate landscapes. Ice-age settlement in Eastern Europe. (Rutgers University Press, London, 2002).113.Nigst, P. et al. Early modern human settlement of Europe north of the Alps occurred 43,500 years ago in a cold steppe-type environment. Proc. Natl. Acad. Sci. 111(40), 14394–14399. https://doi.org/10.1073/pnas.1412201111 (2014).ADS 
      Article 
      PubMed 
      PubMed Central 
      CAS 

      Google Scholar 
      114.Terberger T. Le Dernier Maximum glaciaire entre le Rhin et le Danube, un réexamen critique. In Le Paléolithique supérieur ancien de l’Europe du Nord-Ouest (dirs. Bodu, P. et al.), 415–443 (Société Préhistorique Française, Mémoire LVI, Paris, 2013).  115.Terberger, T. & Street, M. Hiatus or continuity? New results for the question of pleniglacial settlement in Central Europe. Antiquity 76, 691–698 (2002).Article 

      Google Scholar 
      116.Verpoorte, A. Limiting factors on early modern human dispersals: The human biogeography of late Pleniglacial Europe. Quatern. Int. 201(1–2), 77–85 (2009).Article 

      Google Scholar 
      117.Slimak, L. Late Mousterian Persistence near the Arctic Circle. Science 332(6031), 841–845. https://doi.org/10.1126/science.1203866 (2011).ADS 
      Article 
      PubMed 
      CAS 

      Google Scholar 
      118.Rethemeyer, J. et al. Status report on sample preparation facilities for 14C analysis at the new CologneAMS center. Nucl. Instrum. Methods Phys. Res. B 294, 168–172. https://doi.org/10.1016/j.nimb.2012.02.012 (2013).ADS 
      Article 
      CAS 

      Google Scholar 
      119.Brock, F., Higham, T., Ditchfield, P. & Ramsey, C. B. Current pretreatment methods for AMS radiocarbon dating at the Oxford Radiocarbon Accelerator Unit (ORAU). Radiocarbon 52(1), 103–112. https://doi.org/10.1017/S0033822200045069 (2010).Article 
      CAS 

      Google Scholar 
      120.Wood, R. E. et al. Testing the ABOx-SC method: dating known age charcoals associated with the Campanian Ignimbrite. Quat. Geochronol. 9, 16–26. https://doi.org/10.1016/j.quageo.2012.02.003 (2012).Article 

      Google Scholar 
      121.Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51(1), 337–360. https://doi.org/10.1017/S0033822200033865 (2009).Article 

      Google Scholar 
      122.Reimer, P. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62(4), 725–757. https://doi.org/10.1017/RDC.2020.41 (2020).Article 
      CAS 

      Google Scholar 
      123.Bronk Ramsey, C. Dealing with outliers and offsets in radiocarbon dating. Radiocarbon 51(3), 1023–1045. https://doi.org/10.1017/S0033822200034093 (2009).Article 

      Google Scholar 
      124.Burjachs, F., López-Sáez, J. A. & Iriarte, M. J. Metodología Arqueopalinológica. In La recogida de muestras en Arqueobotánica: objetivos y propuestas metodológicas. La gestión de los recursos vegetales y la transformación del paleopaisaje en el Mediterráneo occidental (eds. Buxó, R. & Piqué, R.), 11–18 (Museu d’Arqueologia de Catalunya, Barcelona, 2003).125.López-Sáez, J. A., López-García, P. & Burjachs, F. Arqueopalinología: Síntesis Crítica. Polen 12, 5–35 (2003).
      Google Scholar 
      126.Moore, P. D., Webb, J. A. & Collinson, M. E. Pollen analysis (Blackwell Scientific Publications, 1991).
      Google Scholar 
      127.Reille, M. Pollen et spores d’Europe et d’Afrique du Nord. (Marseille: Laboratoire de Botanique Historique et Palynologie, 1999).128.Desprat, S. et al. Pinus nigra (Spanish black pine) as the dominant species of the last glacial pinewoods in south-western to central Iberia: a morphological study of modern and fossil pollen. J. Biogeogr. 42, 1998–2009. https://doi.org/10.1111/jbi.12566 (2015).Article 

      Google Scholar 
      129.Grimm, E. C. CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Comput. Geosci. 13, 13–35. https://doi.org/10.1016/0098-3004(87)90022-7 (1987).ADS 
      Article 

      Google Scholar 
      130.Bennett, K. D. Determination of the number of zones in a biostratigraphical sequence. New. Phytol. 132, 155–170. https://doi.org/10.1111/j.1469-8137.1996.tb04521.x (1996).Article 
      PubMed 
      CAS 

      Google Scholar 
      131.Grimm, E. C. Tilia version 2. (Illinois State Museum. Research and Collection Center, Springfield, 1992).132.Grimm, E. D. TGView. (Illinois State Museum, Research and Collection Center, Springfield, 2004).133.Schweingruber F.H. Microscopic Wood Anatomy; Structural variability of stems and twigs in recent and subfossil woods from Central Europe. (Birmensdorf: Swiss Federal Institute for Forest, Snow and Landscape Research, 1990, 3rd edition).134.Hather, J. G. The Identification of the Northern European Woods. A Guide for Archaeologists and Conservators (Archetype Publications, 2000).
      Google Scholar 
      135.Vernet, J. L., Ogereau, P., Figueiral, I., Machado, C., Uzquiano, C. Guide d’identification des charbons de bois préhistoriques et récents. Sud-Ouest de l’Europe: France, Péninsule Ibérique et Îles Canaries (CNRS Éditions, Paris, 2001).136.Cuenca-Bescós, G., Straus, L. G., González Morales, M. R. & García Pimienta, J. C. The reconstruction of past environments through small mammals: from the Mousterian to the Bronze Age in El Mirón Cave (Cantabria Spain). J. Arch. Sci. 36, 947–955. https://doi.org/10.1016/j.jas.2008.09.025 (2009).Article 

      Google Scholar 
      137.López-García, J. M. Los micromamíferos del Pleistoceno Superior en la Península Ibérica. Evoluión de la diversidad taxonómica y cambios paleoambientales y paleoclimáticos (Editorial Académica Española, 2011).138.Moya-Sola, R. & Cuenca-Bescós, G. Biometría mandibular y dentaria de las musarañas del género Sorex Linnaeus, 1758 en la región central y occidental de los Pirineos. Galemys 31, 11–25. https://doi.org/10.7325/Galemys.2019.A2 (2019).Article 

      Google Scholar 
      139.Lyman, R. L. Relative abundance of skeletal specimens and taphonomic analysis of vertebrate remains. Palaios 9(3), 288–298. https://doi.org/10.2307/3515203 (1994).ADS 
      Article 

      Google Scholar 
      140.Brain, C. K. The contribution of Namib Desert Hottentot to understanding of Australopithecus bone accumulations. Sci. Pap. Namibian Desert Res. Station 32, 1–11 (1969).
      Google Scholar 
      141.Barba, R. & Domínguez-Rodrigo M. The Taphonomic Relevance of the Analysis of Bovid Long Limb Bone Shaft Features and Their Application to Element Identification. Study of Bone Thickness and Morphology of the Medullar Cavity. J. Taphon. 3, 29–42 (2005).142.Yravedra, J. & Domínguez-Rodrigo, M. The shaft-based methodological approach to the quantification of long limb bones and its relevance to understanding hominid subsistence in the Pleistocene: application to four Palaeolithic sites. J. Quat. Sci. 24(1), 85–96. https://doi.org/10.1002/jqs.1164 (2009).Article 

      Google Scholar 
      143.Inizan, M. L., Reduron, M., Roche, H. & Tixier J. Préhistoire de la Pierre Taillée. T. 4. Technologie de la pierre taillée. (CREP, Meudon, Paris, 1995).144.Soressi, M. & Geneste, J.-M. The history and efficacy of the Chaîne Opératoire approach to lithic analysis: Studying techniques to reveal past societies in an evolutionary perspective. PaleoAnthropology 2011, 334–350. https://doi.org/10.4207/PA.2011.ART63 (2011).Article 

      Google Scholar 
      145.Nelson, M. C. The study of technological organization. Archaeol. Method. Theory 3, 57–100 (1991).
      Google Scholar 
      146.Kelly, R. L. The three sides of a biface. Am. Antiq. 53(4), 717–734. https://doi.org/10.2307/281115 (1998).Article 

      Google Scholar 
      147.Robinson, E. & Sellet, F. (eds.). Lithic Technological Organization and Paleoenvironmental Change. Global and Diachronic Perspectives. https://doi.org/10.1007/978-3-319-64407-3(Springer, 2018). 148.Pelegrin, J. & Chauchat, C. Tecnología y función de las puntas de Paijan: el aporte de la experimentación. Lat. Am. Antiq. 4(4), 367–382 (1993).Article 

      Google Scholar 
      149.Callahan, E. The Basics of Biface Knapping in the Eastern Fluted Point Tradition: a Manual for Flintknappers and Lithic Analysts. (Piltdown Productions, 2000, 4th edition).
      Google Scholar 
      150.Aubry, T. et al. Solutrean laurel leaf production at Maîtreaux: An experimental approach guided by techno-economic analysis. World. Archaeol. 40, 48–66 (2008).Article 

      Google Scholar  More