Philippa Kaur

1.Otero, X. L., De La Peña-Lastra, S., Pérez-Alberti, A., Ferreira, T. O. & Huerta-Diaz, M. A. Seabird colonies as important global drivers in the nitrogen and phosphorus cycles. Nat. Commun. 9, 246 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Velarde, E., Anderson, D. W. & Ezcurra, E. Seabird clues to ecosystem health. Science 365, 116–117 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Piatt, J. F., Sydeman, W. J. & Wiese, F. Introduction: a modern role for seabirds as indicators. Mar. Ecol. Prog. Ser. 352, 199–204 (2007).Article 

Google Scholar 
4.Boersma, P. D., Clark, J. A. & Hillgarth, N. Seabird conservation. In Biology of Marine Birds (eds. Schreiber, E. & Burger, J.) 559–579 (CRC Press Boca Raton, 2002).5.Denlinger, L. & Wohl, K. Seabird harvest regimes in the circumpolar nations. Conservation of Arctic Flora and Fauna (CAFF), (2001).6.Merkel, F. & Barry, T. Seabird Harvest in the Arctic. Conservation of Arctic Flora and Fauna (CAFF), (2008).7.Croxall, J. P. et al. Seabird conservation status, threats and priority actions: a global assessment. Bird. Conserv. Int. 22, 1–34 (2012).Article 

Google Scholar 
8.Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950-2010. PLoS ONE 10, e0129342 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Frederiksen, M. Seabirds in the North East Atlantic. Summary of status, trends and anthropogenic impact. TemaNord 587, 21–24 (2010).
Google Scholar 
10.Chardine, J. & Mendenhall, V. Human Disturbance at Arctic Seabird Colonies. Conservation of Arctic Flora and Fauna (CAFF), (1998).11.Funk, W. C., McKay, J. K., Hohenlohe, P. A. & Allendorf, F. W. Harnessing genomics for delineating conservation units. Trends Ecol. Evol. 27, 489–496 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Moritz, C. Defining ‘Evolutionarily Significant Units’ for conservation. Trends Ecol. Evol. 9, 373–375 (1994).CAS 
PubMed 
Article 

Google Scholar 
13.Allendorf, F. W., Hohenlohe, P. A. & Luikart, G. Genomics and the future of conservation genetics. Nat. Rev. Genet. 11, 697 (2010).CAS 
PubMed 
Article 

Google Scholar 
14.Fraser, D. J. & Bernatchez, L. Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol. Ecol. 10, 2741–2752 (2001).CAS 
PubMed 
Article 

Google Scholar 
15.Friesen, V. L. Speciation in seabirds: why are there so many species… and why aren’t there more? J. Ornithol. 156, 27–39 (2015).Article 

Google Scholar 
16.Taylor, R. S. et al. Sympatric population divergence within a highly pelagic seabird species complex (Hydrobates spp.). J. Avian Biol. 49, 1–14 (2018).Article 

Google Scholar 
17.Rexer‐Huber, K. et al. Genomics detects population structure within and between ocean basins in a circumpolar seabird: the white‐chinned petrel. Mol. Ecol. 28, 4552–4572 (2019).PubMed 
Article 
CAS 

Google Scholar 
18.Clucas, G. V. et al. Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins. Mol. Ecol. 27, 4680–4697 (2018).CAS 
PubMed 
Article 

Google Scholar 
19.Frugone, M. J. et al. More than the eye can see: Genomic insights into the drivers of genetic differentiation in Royal/Macaroni penguins across the Southern Ocean. Mol. Phylogenet. Evol. 139, 106563 (2019).PubMed 
Article 

Google Scholar 
20.Cristofari, R. et al. Unexpected population fragmentation in an endangered seabird: the case of the Peruvian diving-petrel. Sci. Rep. 9, 2021 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Tigano, A., Shultz, A. J., Edwards, S. V., Robertson, G. J. & Friesen, V. L. Outlier analyses to test for local adaptation to breeding grounds in a migratory arctic seabird. Ecol. Evol. 7, 2370–2381 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).CAS 
PubMed 
Article 

Google Scholar 
23.Somvichian-Clausen, A. Behind the stunning photo of a puffin gorging on fish. Natl Geographic (2017).24.Huijbens, E. H. & Einarsson, N. Feasting on Friends: Whales, Puffins, and Tourism in Iceland. In Tourism Experiences and Animal Consumption (ed. Kline, C.) 10–27 (Routledge, 2018).25.Lund, K. A., Kjartansdóttir, K. & Loftsdóttir, K. ‘Puffin love’: performing and creating Arctic landscapes in Iceland through souvenirs. Tour. Stud. 18, 142–158 (2018).Article 

Google Scholar 
26.Hodgetts, L. M. Animal bones and human society in the late younger stone age of arctic Norway. (Durham University, 1999).27.Dove, C. J. & Wickler, S. Identification of bird species used to make a Viking age feather pillow. Arctic 69, 29–36 (2016).Article 

Google Scholar 
28.Harris, M. P. & Wanless, S. The puffin (T & AD Poyser, Bloomsbury Publishing, 2011).29.BirdLife International. Fratercula arctica. The IUCN Red List of Threatened Species 2017 (2017)30.Anker-Nilssen, T. & Aarvak, T. The population ecology of puffins at Røst. Status after the breeding season 2001. NINA Oppdragsmeld. 736, 1–40 (2002).
Google Scholar 
31.Anker-Nilssen, T. et al. Key-site monitoring in Norway 2019, including Svalbard and Jan Mayen. SEAPOP Short Report 1–2020 (2020).32.Lilliendahl, K. et al. Recruitment failure of Atlantic puffins Fratercula arctica and sandeels Ammodytes marinus in Vestmannaeyjar Islands. N.áttúrufræðingurinn 83, 65–79 (2013).
Google Scholar 
33.Walker, S. J. & Meijer, H. J. M. Size variation in mid-Holocene North Atlantic Puffins indicates a dynamic response to climate change. PLoS ONE 16, e0246888 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Burnham, K. K., Burnham, J. L. & Johnson, J. A. Morphological measurements of Atlantic puffin (Fratercula arctica naumanni) in High-Arctic Greenland. Polar Res. 39. https://doi.org/10.33265/polar.v39.5242 (2020).35.Gaston, A. J. & Provencher, J. F. A specimen of the high arctic subspecies of Atlantic Puffin, Fratercula arctica naumanni, in Canada. Can. Field-Nat. 126, 50–54 (2012).Article 

Google Scholar 
36.Salomonsen, F. The Atlantic Alcidae. vol. 6 (Elanders boktryckeri aktiebolag, 1944).37.Moen, S. M. Morphologic and genetic variation among breeding colonies of the Atlantic puffin (Fratercula arctica). Auk 108, 755–763 (1991).
Google Scholar 
38.Harris, M. P. Measurements and weights of British Puffins. Bird. Study 26, 179–186 (1979).Article 

Google Scholar 
39.Kim, J. A., Kang, S.-G., Yang, J. W., Hur, W.-H. & Kil, H.-J. Complete mitochondrial genome of Aethia cristatella (Charadriiformes: Alcidae). Mitochondrial DNA Part B 5, 31–32 (2020).Article 

Google Scholar 
40.Eo, S. H. & An, J. The complete mitochondrial genome sequence of Japanese murrelet (Aves: Alcidae) and its phylogenetic position in Charadriiformes. Mitochondrial DNA A DNA Mapp. Seq. Anal. 27, 4574–4575 (2016).CAS 
PubMed 

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

Google Scholar 
42.Sánchez-Barreiro, F. et al. Historical Population Declines Prompted Significant Genomic Erosion in the Northern and Southern White Rhinoceros (Ceratotherium Simum). Molecular Ecology. 1–15 https://doi.org/10.1111/mec.16043 (2021).43.Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94–100 (2016).CAS 
PubMed 
Article 

Google Scholar 
44.Lombal, A. J., O’dwyer, J. E., Friesen, V., Woehler, E. J. & Burridge, C. P. Identifying mechanisms of genetic differentiation among populations in vagile species: historical factors dominate genetic differentiation in seabirds. Biol. Rev. Camb. Philos. Soc. 95, 625–651 (2020).PubMed 
Article 

Google Scholar 
45.Friesen, V. L., Burg, T. M. & McCoy, K. D. Mechanisms of population differentiation in seabirds. Mol. Ecol. 16, 1765–1785 (2007).CAS 
PubMed 
Article 

Google Scholar 
46.Breton, A. R., Diamond, A. W. & Kress, S. W. Encounter, survival, and movement probabilities from an Atlantic puffin (Fratercula arctica) metapopulation. Ecol. Monogr. 75, 133–149 (2006).47.Fayet, A. L. et al. Ocean-wide drivers of migration strategies and their influence on population breeding performance in a declining seabird. Curr. Biol. 27, 3871–3878.e3 (2017).CAS 
PubMed 
Article 

Google Scholar 
48.Burg, T. M. & Croxall, J. P. Global relationships amongst black-browed and grey-headed albatrosses: analysis of population structure using mitochondrial DNA and microsatellites. Mol. Ecol. 10, 2647–2660 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Lowther, P. E., Diamond, T., Kress, S. W., Robertson, G. J. & Gill, F. Atlantic Puffin (Fratercula arctica). The Birds of North America Online 18, (2002).50.Wojczulanis-Jakubas, K. et al. Weak population genetic differentiation in the most numerous Arctic seabird, the little auk. Polar Biol. 37, 621–630 (2014).Article 

Google Scholar 
51.Smith, A. L., Monteiro, L., Hasegawa, O. & Friesen, V. L. Global phylogeography of the band-rumped storm-petrel (Oceanodroma castro; Procellariiformes: Hydrobatidae). Mol. Phylogenet. Evol. 43, 755–773 (2007).PubMed 
Article 

Google Scholar 
52.Bergmann, C. Über die Verhältnisse der Wärmeökonomie der Tiere zu ihrer Grösse. Gottinger Stud. 3, 595–708 (1847).
Google Scholar 
53.James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51, 365–390 (1970).Article 

Google Scholar 
54.Yamamoto, T. et al. Geographical variation in body size of a pelagic seabird, the streaked shearwater Calonectris leucomelas. J. Biogeogr. 43, 801–808 (2016).Article 

Google Scholar 
55.Barrett, R. T., Anker-Nilssen, T. & Krasnov, Y. V. Can Norwegian and Russian razorbills (Alca torda) be identified by their measurements? Mar. Ornithol. 25, 5–8 (1997).
Google Scholar 
56.Anker-Nilssen, T., Aarvak, T. & Bangjord, G. Mass mortality of Atlantic Puffins Fratercula arctica off Central Norway, spring 2002: causes and consequences. Atl. Seab. 5, 57–72 (2003).
Google Scholar 
57.Pearce, R. L. et al. Mitochondrial DNA suggests high gene flow in ancient murrelets. Condor 104, 84–91 (2002).Article 

Google Scholar 
58.Thomas, J. E. et al. Demographic reconstruction from ancient DNA supports rapid extinction of the great auk. eLife 8, e47509 (2019).59.Milot, E., Weimerskirch, H. & Bernatchez, L. The seabird paradox: dispersal, genetic structure and population dynamics in a highly mobile, but philopatric albatross species. Mol. Ecol. 17, 1658–1673 (2008).CAS 
PubMed 
Article 

Google Scholar 
60.Edwards, S. & Bensch, S. Looking forwards or looking backwards in avian phylogeography? A comment on Zink and Barrowclough 2008. Mol. Ecol. 18, 2930–2936 (2009).CAS 
PubMed 
Article 

Google Scholar 
61.IPCC. Global Warming of 1.5 °C—Summary for Policy Makers. (2018).62.Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
Article 

Google Scholar 
64.Schubert, M. et al. Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. Nat. Protoc. 9, 1056–1082 (2014).CAS 
PubMed 
Article 

Google Scholar 
65.McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinforma. 43, 11.10.1–33 (2013).Article 

Google Scholar 
67.Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
70.Matschiner, M. Fitchi: haplotype genealogy graphs based on the Fitch algorithm. Bioinformatics 32, 1250–1252 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Watterson, G. A. Heterosis or neutrality? Genetics 85, 789–814 (1977).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Chakraborty, R. & Mitochondrial, D. N. A. polymorphism reveals hidden heterogeneity within some Asian populations. Am. J. Hum. Genet. 47, 87–94 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 356 (2014).Article 

Google Scholar 
77.Orlando, L. & Librado, P. Origin and evolution of deleterious mutations in horses. Genes 10, 649 (2019).78.Meisner, J. & Albrechtsen, A. Inferring population structure and admixture proportions in low-depth NGS data. Genetics 210, 719–731 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Lefort, V., Desper, R. & Gascuel, O. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. Mol. Biol. Evol. 32, 2798–2800 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 
PubMed 

Google Scholar 
84.Lichstein, J. W. Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecol. 188, 117–131 (2007).Article 

Google Scholar 
85.Slatkin, M. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Pante, E., Simon-Bouhet, B. & Irisson, J.-O. marmap—R package. (2019).87.Goslee, S. & Urban, D. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw., Artic. 22, 1–19 (2007).
Google Scholar 
88.Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).Article 

Google Scholar 
89.Blanchet, F. G., Legendre, P. & Borcard, D. Modelling directional spatial processes in ecological data. Ecol. Modell. 215, 325–336 (2008).Article 

Google Scholar 
90.Benestan, L. M. et al. Population genomics and history of speciation reveal fishery management gaps in two related redfish species (Sebastes mentella and Sebastes fasciatus). Evol. Appl. 14, 588–606 (2021).CAS 
PubMed 
Article 

Google Scholar 
91.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-statistic on low-coverage whole-genome data. G3 8, 551–566 (2018).PubMed 
Article 

Google Scholar 
92.Kersten, O. Code for Population Genomics Analyses of Atlantic Puffin (Fratercula arctica) using Whole Genome Sequencing (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.4899575 (2021). More

1.Orlando, L., Gilbert, M. T. & Willerslev, E. Reconstructing ancient genomes and epigenomes. Nat. Rev. Genet. 16, 395–408 (2015).CAS 
PubMed 
Article 

Google Scholar 
2.Smith, Z. D. & Meissner, A. DNA methylation: Roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).CAS 
PubMed 
Article 

Google Scholar 
3.Schmidt, M., Maie, T., Dahl, E., Costa, I. G. & Wagner, W. Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites. BMC Biol. 18, 178 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Moss, J. et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 5068 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Shen, H. & Laird, P. W. Interplay between the cancer genome and epigenome. Cell 153, 38–55 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Koch, C. M. & Wagner, W. Epigenetic-aging-signature to determine age in different tissues. Aging (Albany N.Y.) 3, 1018–1027 (2011).CAS 

Google Scholar 
7.Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Weidner, C. I. et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 15, R24 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Dabney, J., Meyer, M. & Paabo, S. Ancient DNA damage. Cold Spring Harb. Perspect Biol. 5, a012567 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Gokhman, D. et al. Reconstructing the DNA methylation maps of the Neandertal and the Denisovan. Science 344, 523–527 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Briggs, A. W. et al. Removal of deaminated cytosines and detection of in vivo methylation in ancient DNA. Nucleic Acids Res. 38, e87 (2010).PubMed 
Article 
CAS 

Google Scholar 
12.Bibikova, M. et al. High density DNA methylation array with single CpG site resolution. Genomics 98, 288–295 (2011).CAS 
PubMed 
Article 

Google Scholar 
13.Pap, I., Susa, E. & Joszsa, L. Mummies from the 18–19th century Domanical Church of Vác, Hungary. Acta Biol. Szegediensis 42, 107–112 (1997).
Google Scholar 
14.Donoghue, H. D., Pap, I., Szikossy, I. & Spigelman, M. The Vác Mummy Project: Investigation of 265 eighteenth-century mummified remains from the TB pandemic era. In The Handbook of Mummy Studies (eds Shin, D. H. & Bianucci, R.) 1–30 (Springer, 2021).
Google Scholar 
15.Hotz, G. et al. Der rätselhafte Mumienfund aus der Barfüsserkirche in Basel. Ein aussergewöhnliches Beispiel interdisziplinärer Familienforschung. Jahrbuch der Schweizerischen Gesellschaft für Familienforschung 2018, 1–30 (2018).
Google Scholar 
16.Hotz, G. Das Rätsel der Anna Catharina Bischoff. Spektrum der Wissenschaft 3, 76–81 (2018).
Google Scholar 
17.Zhou, W., Triche, T. J. Jr., Laird, P. W. & Shen, H. SeSAMe: Reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).PubMed 
PubMed Central 

Google Scholar 
18.Triche, T. J., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. low-level processing of illumina infinium DNA methylation beadarrays. Nucleic Acids Res. 41, e90 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Ruiz-Hernandez, A. et al. Environmental chemicals and DNA methylation in adults: A systematic review of the epidemiologic evidence. Clin. Epigenet. 7, 55 (2015).Article 
CAS 

Google Scholar 
20.Pedersen, J. S. et al. Genome-wide nucleosome map and cytosine methylation levels of an ancient human genome. Genome Res. 24, 454–466 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Gaudin, M. & Desnues, C. Hybrid capture-based next generation sequencing and its application to human infectious diseases. Front. Microbiol. 9, 2924 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Knapp, M. & Hofreiter, M. Next generation sequencing of ancient DNA: Requirements, strategies and perspectives. Genes (Basel) 1, 227–243 (2010).CAS 
Article 

Google Scholar 
23.Koop, B. E. et al. Postmortem age estimation via DNA methylation analysis in buccal swabs from corpses in different stages of decomposition—A “proof of principle” study. Int. J. Legal Med. 135, 167–173 (2021).PubMed 
Article 

Google Scholar 
24.Joehanes, R. et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9, 436–447 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bozic, T. et al. Investigation of measurable residual disease in acute myeloid leukemia by DNA methylation patterns. Leukemia https://doi.org/10.1038/s41375-021-01316-z (2021).Article 
PubMed 

Google Scholar 
26.Pap, I. et al. 18–19th century tuberculosis in naturally mummified individuals (Vác, Hungary). In Tuberculosis Past and Present (eds Pálfi, G. et al.) 421–428 (Golden Books/Tuberculosis Foundation, 1999).
Google Scholar 
27.Kreissl Lonfat, B. M., Kaufmann, I. M. & Ruhli, F. A code of ethics for evidence-based research with ancient human remains. Anat. Rec. (Hoboken) 298, 1175–1181 (2015).Article 

Google Scholar 
28.Maixner, F. et al. The Iceman’s last meal consisted of fat, wild meat, and cereals. Curr. Biol. 28, 2348–2355 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Tang, J. N. et al. An effective method for isolation of DNA from pig faeces and comparison of five different methods. J. Microbiol. Methods 75, 432–436 (2008).CAS 
PubMed 
Article 

Google Scholar 
30.Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).CAS 
PubMed 
Article 

Google Scholar 
31.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, 5448 (2010).Article 

Google Scholar 
32.Rosenbloom, K. R. et al. The UCSC genome browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).CAS 
PubMed 
Article 

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

Google Scholar 
34.Peltzer, A. et al. EAGER: Efficient ancient genome reconstruction. Genome Biol. 17, 60 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. & Orlando, L. mapDamage2.0: Fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Huson, D. H. et al. MEGAN Community edition—Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 12, 385 (2011).Article 

Google Scholar 
39.Xu, Z., Langie, S. A., De Boever, P., Taylor, J. A. & Niu, L. RELIC: A novel dye-bias correction method for illumina methylation beadchip. BMC Genomics 18, 4 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Lee, D. D. & Seung, H. S. Algorithms for non-negative matrix factorization. Adv. Neural. Inf. Process. Syst. 13(13), 556–562 (2001).
Google Scholar 
41.Schmidt, M., Maié, T., Dahl, E., Costa, I. G. & Wagner, W. Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites. BMC Biol. 34, 1969 (2020).
Google Scholar 
42.Frobel, J. et al. Leukocyte counts based on DNA methylation at individual cytosines. Clin. Chem. 64, 566–575 (2018).CAS 
PubMed 
Article 

Google Scholar  More

1.Moritz, C., Patton, J. L., Schneider, C. J. & Smith, T. B. Diversification of rainforest faunas: an integrated molecular approach. Annu. Rev. Ecol. Syst. 31, 533–563 (2000).Article 

Google Scholar 
2.Haffer, J. Speciation in Amazonian forest birds. Science 165, 131–137 (1969).Article 

Google Scholar 
3.Carnaval, A. C. & Moritz, C. Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. J. Biogeogr. 35, 1187–1201 (2008).Article 

Google Scholar 
4.Colinvaux, P. A., De Oliveira, P. E., Moreno, J. E., Miller, M. C. & Bush, M. B. A long pollen record from lowland Amazonia: forest and cooling in glacial times. Science 274, 85 (1996).Article 

Google Scholar 
5.Burbridge, R. E., Mayle, F. E. & Killeen, T. J. Fifty-thousand-year vegetation and climate history of Noel Kempff Mercado National Park, Bolivian Amazon. Quat. Res. 61, 215–230 (2004).Article 

Google Scholar 
6.Bush, M. B. & Silman, M. R. Observations on Late Pleistocene cooling and precipitation in the lowland Neotropics. J. Quat. Sci. 19, 677–684 (2004).Article 

Google Scholar 
7.Cowling, S. A., Maslin, M. A. & Sykes, M. T. Paleovegetation simulations of lowland Amazonia and implications for neotropical allopatry and speciation. Quat. Res. 55, 140–149 (2001).Article 

Google Scholar 
8.Claussen, M., Selent, K., Brovkin, V., Raddatz, T. & Gayler, V. Impact of CO2 and climate on Last Glacial Maximum vegetation—a factor separation. Biogeosciences 10, 3593–3604 (2013).Article 

Google Scholar 
9.O’ishi, R. & Abe-Ouchi, A. Influence of dynamic vegetation on climate change and terrestrial carbon storage in the Last Glacial Maximum. Clim. Past 9, 1571–1587 (2013).Article 

Google Scholar 
10.Hopcroft, P. O. & Valdes, P. J. Last Glacial Maximum constraints on the Earth system model HadGEM2-ES. Clim. Dyn. 45, 1657–1672 (2015).Article 

Google Scholar 
11.Hermanowski, B., da Costa, M. L. & Behling, H. Environmental changes in southeastern Amazonia during the last 25,000 yr revealed from a paleoecological record. Quat. Res. 77, 138–148 (2012).Article 

Google Scholar 
12.Fontes, D. et al. Paleoenvironmental dynamics in South Amazonia, Brazil, during the last 35,000 years inferred from pollen and geochemical records of Lago do Saci. Quat. Sci. Rev. 173, 161–180 (2017).Article 

Google Scholar 
13.D’Apolito, C., Absy, M. L. & Latrubesse, E. M. The Hill of Six Lakes revisited: new data and re-evaluation of a key Pleistocene Amazon site. Quat. Sci. Rev. 76, 140–155 (2013).Article 

Google Scholar 
14.AdrianQuijada-Mascareñas, J. et al. Phylogeographic patterns of trans-Amazonian vicariants and Amazonian biogeography: the Neotropical rattlesnake (Crotalus durissus complex) as an example. J. Biogeogr. 34, 1296–1312 (2007).Article 

Google Scholar 
15.Prado, D. E. & Gibbs, P. E. Patterns of species distributions in the dry seasonal forests of South America. Ann. MO Bot. Gard. 80, 902–927 (1993).Article 

Google Scholar 
16.Cardoso Da Silva, J. M. & Bates, J. M. Biogeographic patterns and conservation in the South American Cerrado: a tropical savanna hotspot: the Cerrado, which includes both forest and savanna habitats, is the second largest South American biome, and among the most threatened on the continent. AIBS Bull. 52, 225–234 (2002).
Google Scholar 
17.da Silva, J. M. C. Biogeographic analysis of the South American Cerrado avifauna. Steenstrupia 21, 49–67 (1995).
Google Scholar 
18.Werneck, F. P., Nogueira, C., Colli, G. R., Sites, J. W. & Costa, G. C. Climatic stability in the Brazilian Cerrado: implications for biogeographical connections of South American savannas, species richness and conservation in a biodiversity hotspot. J. Biogeogr. 39, 1695–1706 (2012).Article 

Google Scholar 
19.Wuster, W. et al. Tracing an invasion: landbridges, refugia, and the phylogeography of the Neotropical rattlesnake (Serpentes: Viperidae: Crotalus durissus). Mol. Ecol. 14, 1095–1108 (2005).Article 

Google Scholar 
20.Prentice, I. C. et al. Modeling fire and the terrestrial carbon balance. Glob. Biogeochem. Cycles 25, GB3005 (2011).Article 

Google Scholar 
21.Colinvaux, P. A., De Oliveira, P. E. & Bush, M. B. Amazonian and neotropical plant communities on glacial time-scales: the failure of the aridity and refuge hypotheses. Quat. Sci. Rev. 19, 141–169 (2000).Article 

Google Scholar 
22.Bush, M. B. Climate science: the resilience of Amazonian forests. Nature 541, 167 (2017).Article 

Google Scholar 
23.Mayle, F. E., Beerling, D. J., Gosling, W. D. & Bush, M. B. Responses of Amazonian ecosystems to climatic and atmospheric carbon dioxide changes since the Last Glacial Maximum. Philos. Trans. R. Soc. Lond. B 359, 499–514 (2004).Article 

Google Scholar 
24.Costa, G. C. et al. Biome stability in South America over the last 30 kyr: inferences from long-term vegetation dynamics and habitat modelling. Glob. Ecol. Biogeogr. 27, 285–297 (2018).Article 

Google Scholar 
25.Wilson, J. B. & Agnew, A. D. in Advances in Ecological Research Vol. 23 (eds Begon, M. & Fitter, A. H.) 263–336 (Academic Press, 1992).26.Moncrieff, G. R., Scheiter, S., Bond, W. J. & Higgins, S. I. Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa. New. Phytol. 201, 908–915 (2014).Article 

Google Scholar 
27.Aleixo, A. & de Fátima Rossetti, D. Avian gene trees, landscape evolution, and geology: towards a modern synthesis of Amazonian historical biogeography? J. Ornithol. 148, 443–453 (2007).Article 

Google Scholar 
28.Pennington, R. T. & Dick, C. W. Diversification of the Amazonian Flora and Its Relation to Key Geological and Environmental Events: A Molecular Perspective (Blackwell, 2010).29.Leite, R. N. & Rogers, D. S. Revisiting Amazonian phylogeography: insights into diversification hypotheses and novel perspectives. Org. Divers. Evol. 13, 639–664 (2013).Article 

Google Scholar 
30.Haffer, J. R. Alternative models of vertebrate speciation in Amazonia: an overview. Biodivers. Conserv. 6, 451–476 (1997).Article 

Google Scholar 
31.Garzón-Orduña, I. J., Benetti-Longhini, J. E. & Brower, A. V. Timing the diversification of the Amazonian biota: butterfly divergences are consistent with Pleistocene refugia. J. Biogeogr. 41, 1631–1638 (2014).Article 

Google Scholar 
32.Smith, B. T., Amei, A. & Klicka, J. Evaluating the role of contracting and expanding rainforest in initiating cycles of speciation across the Isthmus of Panama. Proc. R. Soc. B 279, 3520–3526 (2012).Article 

Google Scholar 
33.Cramer, W. et al. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob. Change Biol. 7, 357–373 (2001).Article 

Google Scholar 
34.Sitch, S. et al. Evaluation of the terrestrial carbon cycle, future plant geography and climate–carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Glob. Change Biol. 14, 2015–2039 (2008).Article 

Google Scholar 
35.McMahon, S. M. et al. Improving assessment and modelling of climate change impacts on global terrestrial biodiversity. Trends Ecol. Evol. 26, 249–259 (2011).Article 

Google Scholar 
36.Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).Article 

Google Scholar 
37.Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991 (2010).Article 

Google Scholar 
38.Monteith, J. L. A reinterpretation of stomatal responses to humidity. Plant Cell Environ. 18, 357–364 (1995).Article 

Google Scholar 
39.Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels Research Paper INT-115 (USDA, 1972).40.Prentice, I. C., Harrison, S. P. & Bartlein, P. J. Global vegetation and terrestrial carbon cycle changes after the last ice age. New Phytol. 189, 988–998 (2011).Article 

Google Scholar 
41.Kelley, D. I. et al. A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences 10, 3313–3340 (2013).Article 

Google Scholar 
42.Kelley, D. I., Harrison, S. P. & Prentice, I. C. Improved simulation of fire–vegetation interactions in the land surface processes and exchanges dynamic global vegetation model (LPX-Mv1). Geosci. Model Dev. 7, 2411–2433 (2014).Article 

Google Scholar 
43.Kelley, D. I. & Harrison, S. P. Enhanced Australian carbon sink despite increased wildfire during the 21st century. Environ. Res. Lett. 9, 104015 (2014).Article 

Google Scholar 
44.Braconnot, P. et al. Results of PMIP2 coupled simulations of the mid-Holocene and Last Glacial Maximum—part 1: experiments and large-scale features. Climate 3, 261–277 (2007).
Google Scholar 
45.Martin Calvo, M. & Prentice, I. C. Effects of fire and CO2 on biogeography and primary production in glacial and modern climates. New Phytol. 208, 987–994 (2015).Article 

Google Scholar 
46.Braconnot, P. et al. Results of PMIP2 coupled simulations of the mid-Holocene and Last Glacial Maximum—part 2: feedbacks with emphasis on the location of the ITCZ and mid- and high latitudes heat budget. Climate 3, 279–296 (2007).
Google Scholar 
47.Harris, I. P. D. J., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
48.Mayle, F. E., Burn, M. J., Power, M. & Urrego, D. H. in Past Climate Variability in South America and Surrounding Regions (eds Vimeux, F. et al.) 89–112 (Springer, 2009).49.Marchant, R. et al. Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbon years ago. Climate 5, 725–767 (2009).
Google Scholar 
50.Stein, U. & Alpert, P. I. N. H. A. S. Factor separation in numerical simulations. J. Atmos. Sci. 50, 2107–2115 (1993).Article 

Google Scholar 
51.Argollo, J. & Mourguiart, P. Late Quaternary climate history of the Bolivian Altiplano. Quat. Int. 72, 37–51 (2000).Article 

Google Scholar 
52.Watts, W. A. & Bradbury, J. P. Paleoecological studies at Lake Patzcuaro on the west-central Mexican Plateau and at Chalco in the Basin of Mexico. Quat. Res. 17, 56–70 (1982).Article 

Google Scholar 
53.del Socorro Lozano-Garcia, M. & Ortega-Guerrero, B. Palynological and magnetic susceptibility records of Lake Chalco, central Mexico. Palaeogeogr. Palaeoclimatol. Palaeoecol. 109, 177–191 (1994).Article 

Google Scholar 
54.del Socorro Lozano-García, M. & Ortega-Guerrero, B. Late Quaternary environmental changes of the central part of the Basin of Mexico; correlation between Texcoco and Chalco basins. Rev. Palaeobot. Palynol. 99, 77–93 (1998).Article 

Google Scholar 
55.Leyden, B. W. Guatemalan forest synthesis after Pleistocene aridity. Proc. Natl Acad. Sci. USA 81, 4856–4859 (1984).Article 

Google Scholar 
56.Piperno, D. R., Bush, M. B. & Colinvaux, P. A. Paleoecological perspectives on human adaptation in central Panama. I. Pleistocene. Geoarchaeology 6, 201–226 (1991).Article 

Google Scholar 
57.Hooghiemstra, H., Cleef, A. M., Noldus, C. W. & Kappelle, M. Upper Quaternary vegetation dynamics and palaeoclimatology of the La Chonta bog area (Cordillera de Talamanca, Costa Rica). J. Quat. Sci. 7, 205–225 (1992).Article 

Google Scholar 
58.van der Hammen, T. & Hooghiemstra, H. Interglacial–glacial Fuquene-3 pollen record from Colombia: an Eemian to Holocene climate record. Glob. Planet. Change 36, 181–199 (2003).Article 

Google Scholar 
59.Graf, K. Pollendiagramme aus den Anden: Eine Synthese zur Klimageschichte und Vegetationsentwicklung seit der letzten Eiszeit (Universität Zürich-Irchel-Geographisches Institut, 1992).60.Van Geel, B. & Van der Hammen, T. Upper Quaternary vegetational and climatic sequence of the Fuquene area (Eastern Cordillera, Colombia). Palaeogeogr. Palaeoclimatol. Palaeoecol. 14, 9–92 (1973).Article 

Google Scholar 
61.Behling, H. & Hooghiemstra, H. Environmental history of the Colombian savannas of the Llanos Orientales since the Last Glacial Maximum from lake records El Pinal and Carimagua. J. Paleolimnol. 21, 461–476 (1999).Article 

Google Scholar 
62.Wille, M., Negret, J. A. & Hooghiemstra, H. Paleoenvironmental history of the Popayán area since 27 000 yr BP at Timbio, southern Colombia. Rev. Palaeobot. Palynol. 109, 45–63 (2000).Article 

Google Scholar 
63.Oliveira, P. E. D. A Palynological Record of Late Quaternary Vegetational and Climatic Change in Southeastern Brazil. PhD dissertation, The Ohio State Univ. (1992).64.Ledru, M. P. et al. Late-glacial cooling in Amazonia inferred from pollen at Lagoa do Caçó, Northern Brazil. Quat. Res. 55, 47–56 (2001).Article 

Google Scholar 
65.Behling, H., Arz, H. W., Pätzold, J. & Wefer, G. Late Quaternary vegetational and climate dynamics in southeastern Brazil, inferences from marine cores GeoB 3229-2 and GeoB 3202-1. Palaeogeogr. Palaeoclimatol. Palaeoecol. 179, 227–243 (2002).Article 

Google Scholar 
66.Van der Hammen, T. & González, E. Upper Pleistocene and Holocene climate and vegetation of the ‘Sabana de Bogota’ (Colombia, South America). Leidse Geologische Mededelingen 25, 261–315 (1960).
Google Scholar 
67.Guimarães, J. T. F. et al. Modern pollen rain as a background for palaeoenvironmental studies in the Serra dos Carajás, southeastern Amazonia. Holocene 27, 1055–1066 (2017).Article 

Google Scholar 
68.Van der Hammen, T. & Absy, M. L. Amazonia during the last glacial. Palaeogeogr. Palaeoclimatol. Palaeoecol. 109, 247–261 (1994).Article 

Google Scholar 
69.Hansen, B. C. S. et al. Late-glacial and Holocene vegetational history from two sites in the western Cordillera of southwestern Ecuador. Palaeogeogr. Palaeoclimatol. Palaeoecol. 194, 79–108 (2003).Article 

Google Scholar 
70.Mayle, F. E., Burbridge, R. & Killeen, T. J. Millennial-scale dynamics of southern Amazonian rain forests. Science 290, 2291–2294 (2000).Article 

Google Scholar 
71.Urrego, D. H., Bush, M. B. & Silman, M. R. A long history of cloud and forest migration from Lake Consuelo, Peru. Quat. Res. 73, 364–373 (2010).Article 

Google Scholar 
72.Barberi, M., Salgado-Labouriau, M. L. & Suguio, K. Paleovegetation and paleoclimate of ‘Vereda de Águas Emendadas’, central Brazil. J. South Am. Earth Sci. 13, 241–254 (2000).Article 

Google Scholar 
73.Mourguiart, P., Argollo, J. & Wirrmann, D. In Climas Cuaternarios en America del Sur = Quaternary Climates of South America. 157–171 (ORSTOM, 1995).74.Mourguiart, P. & Ledru, M. P. Last Glacial Maximum in an Andean cloud forest environment (Eastern Cordillera, Bolivia). Geology 31, 195–198 (2003).Article 

Google Scholar 
75.Salgado-Labouriau, M. L., Barberi, M., Ferraz-Vicentini, K. R. & Parizzi, M. G. A dry climatic event during the late Quaternary of tropical Brazil. Rev. Palaeobot. Palynol. 99, 115–129 (1998).Article 

Google Scholar 
76.Ledru, M. P. et al. The last 50,000 years in the Neotropics (Southern Brazil): evolution of vegetation and climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 123, 239–257 (1996).Article 

Google Scholar 
77.Chepstow-Lusty, A. et al. Vegetation and climate change on the Bolivian Altiplano between 108,000 and 18,000 yr ago. Quat. Res. 63, 90–98 (2005).Article 

Google Scholar 
78.Behling, H. & Lichte, M. Evidence of dry and cold climatic conditions at glacial times in tropical southeastern Brazil. Quat. Res. 48, 348–358 (1997).Article 

Google Scholar 
79.Behling, H. South and southeast Brazilian grasslands during late Quaternary times: a synthesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 177, 19–27 (2002).Article 

Google Scholar 
80.Behling, H. Late Quaternary vegetation, climate and fire history from the tropical mountain region of Morro de Itapeva, SE Brazil. Palaeogeogr. Palaeoclimatol. Palaeoecol. 129, 407–422 (1997).Article 

Google Scholar 
81.Ledru, M. P., Mourguiart, P. & Riccomini, C. Related changes in biodiversity, insolation and climate in the Atlantic rainforest since the last interglacial. Palaeogeogr. Palaeoclimatol. Palaeoecol. 271, 140–152 (2009).Article 

Google Scholar 
82.Pessenda, L. C. R. et al. The evolution of a tropical rainforest/grassland mosaic in southeastern Brazil since 28,000 14C yr BP based on carbon isotopes and pollen records. Quat. Res. 71, 437–452 (2009).Article 

Google Scholar 
83.Behling, H. & Negrelle, R. R. Tropical rain forest and climate dynamics of the Atlantic lowland, Southern Brazil, during the Late Quaternary. Quat. Res. 56, 383–389 (2001).Article 

Google Scholar 
84.Behling, H., Pillar, V. D., Orlóci, L. & Bauermann, S. G. Late Quaternary Araucaria forest, grassland (Campos), fire and climate dynamics, studied by high-resolution pollen, charcoal and multivariate analysis of the Cambará do Sul core in southern Brazil. Palaeogeogr. Palaeoclimatol. Palaeoecol. 203, 277–297 (2004).Article 

Google Scholar 
85.Behling, H., Pillar, V. D. & Bauermann, S. G. Late Quaternary grassland (Campos), gallery forest, fire and climate dynamics, studied by pollen, charcoal and multivariate analysis of the São Francisco de Assis core in western Rio Grande do Sul (southern Brazil). Rev. Palaeobot. Palynol. 133, 235–248 (2005).Article 

Google Scholar  More

1.Brown, C. R. The ecology and evolution of colony-size variation. Behav. Ecol. Sociobiol. 70, 1613–1632 (2016).Article 

Google Scholar 
2.Brown, C. R., Stutchbury, B. J. & Walsh, P. D. Choice of colony size in birds. Trends Ecol. Evol. 5, 398–403 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Wittenberger, J. F. & Hunt, G. L. The adaptive significance of coloniality in birds. Avian Biol. 8, 1–78 (1985).
Google Scholar 
4.Ainley, D. G., Nur, N. & Woehler, E. J. Factors affecting the distribution and size of Pygoscelid penguin colonies in the Antarctic. Auk 112, 171–182 (1995).Article 

Google Scholar 
5.Forero, M. G., Tella, J. L., Hobson, K. A., Bertellotti, M. & Blanco, G. Conspecific food competition explains variability in colony size: A test in Magellanic Penguins. Ecology 83, 3466–3475 (2002).Article 

Google Scholar 
6.Hunt, G. L., Eppley, Z. A. & Schneider, D. C. Reproductive performance of seabirds: The importance of population and colony size. Auk 103, 306–317 (1986).Article 

Google Scholar 
7.Brunton, D. ‘Optimal’ colony size for least terns: An inter-colony study of opposing selective pressures by predators. Condor 101, 607–615 (1999).Article 

Google Scholar 
8.Lyver, P. O. et al. Trends in the breeding population of Adélie penguins in the Ross Sea, 1981–2012: A coincidence of climate and resource extraction effects. PLoS ONE 9, e91188 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Croxall, J. P. et al. Seabird conservation status, threats and priority actions: A global assessment. Bird Conserv. Int. 22, 1–34 (2012).Article 

Google Scholar 
10.Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950–2010. PLoS ONE 10, e0129342 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Hinke, J., Polito, M., Reiss, C., Trivelpiece, S. & Trivelpiece, W. Flexible reproductive timing can buffer reproductive success of Pygoscelis spp. penguins in the Antarctic Peninsula region. Mar. Ecol. Prog. Ser. 454, 91–104 (2012).ADS 
Article 

Google Scholar 
12.Elliott, M. L. et al. Brandt’s cormorant diet (1994–2012) indicates the importance of fall ocean conditions for northern anchovy in central California. Fish. Oceanogr. 25, 515–528 (2016).Article 

Google Scholar 
13.Cairns, D. K. Population regulation of seabird colonies. In Current Ornithology (ed. Power, D. M.) 37–61 (Springer US, 1992).Chapter 

Google Scholar 
14.Aebischer, N. J., Coulson, J. C. & Colebrook, J. M. Parallel long-term trends across four marine trophic levels and weather. Nature 347, 753–755 (1990).ADS 
Article 

Google Scholar 
15.Saether, B. E. & Bakke, O. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).Article 

Google Scholar 
16.Jenouvrier, S., Barbraud, C., Cazelles, B. & Weimerskirch, H. Modelling population dynamics of seabirds: Importance of the effects of climate fluctuations on breeding proportions. Oikos 108, 511–522 (2005).Article 

Google Scholar 
17.Schmidt, A. E. et al. Changing environmental spectra influence age-structured populations: Increasing ENSO frequency could diminish variance and extinction risk in long-lived seabirds. Theor. Ecol. 11, 367–377 (2018).Article 

Google Scholar 
18.Kokko, H., Harris, M. P. & Wanless, S. Competition for breeding sites and site-dependent, population regulation in a highly colonial seabird, the common guillemot Uria aalge. J. Anim. Ecol. 73, 367–376 (2004).Article 

Google Scholar 
19.Oro, D. Living in a ghetto within a local population: An empirical example of an ideal despotic distribution. Ecology 89, 838–846 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Stokes, D. L. & Boersma, P. D. Nest-site characteristics and reproductive success in Magellanic Penguins (Spheniscus magellanicus). Auk 115, 34–49 (1998).Article 

Google Scholar 
21.Velando, A. & Freire, J. Nest site characteristics, occupation, and breeding success in the European Shag. Waterbirds 26, 473 (2003).Article 

Google Scholar 
22.Coulson, J. C. Colonial breeding in seabirds. In Biology of Marine Birds (eds Schreiber, E. A. & Burger, J.) 87–113 (CRC Press, 2002).
Google Scholar 
23.Liljesthröm, M., Emslie, S. D., Frierson, D. & Schiavini, A. Avian predation at a Southern Rockhopper Penguin colony on Staten Island, Argentina. Polar Biol. 31, 465–474 (2007).Article 

Google Scholar 
24.Frere, E., Gandini, P. & Boersma, P. D. Effects of nest type on reproductive success of the Magellanic penguin Spenishcus magellanicus. Mar. Ornithol. 20, 1–6 (1992).
Google Scholar 
25.Emslie, S. D., Karnovsky, N. & Trivelpiece, W. Avian predation at penguin colonies on King George Island, Antarctica. Wilson Bull. 107, 317–327 (1995).
Google Scholar 
26.Gaston, A. J. & Elliot, R. D. Predation by Ravens Corvus corax on Brunnich’s Guillemot Uria lomvia eggs and chicks and its possible impact on breeding site selection. Ibis 138, 742–748 (1996).Article 

Google Scholar 
27.Taylor, R. H. The Adélie penguin Pygoscelis adeliae at Cape Royds. Ibis 104, 176–204 (1962).Article 

Google Scholar 
28.Votier, S. C., Heubeck, M. & Furness, R. W. Using inter-colony variation in demographic parameters to assess the impact of skua predation on seabird populations. Ibis 150, 45–53 (2008).Article 

Google Scholar 
29.Hamilton, W. D. Geometry for the selfish herd. J. Theor. Biol. 31, 295–311 (1971).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Weidinger, K. Effect of predation by skuas on breeding success of the Cape petrel Daption capense at Nelson Island, Antarctica. Polar Biol. 20, 170–177 (1998).Article 

Google Scholar 
31.Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).Article 

Google Scholar 
32.Ainley, D. The Adélie Penguin: Bellwether of Climate Change (Columbia University Press, 2002).Book 

Google Scholar 
33.Borowicz, A. et al. Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Bracegirdle, T. J., Connolley, W. M. & Turner, J. Antarctic climate change over the twenty first century. J. Geophys. Res. 113, D03103 (2008).ADS 

Google Scholar 
35.Smith, W. O., Ainley, D. G., Arrigo, K. R. & Dinniman, M. S. The oceanography and ecology of the Ross Sea. Ann. Rev. Mar. Sci. 6, 469–487 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Ainley, D. et al. Antarctic penguin response to habitat change as Earth’s troposphere reaches 2 C above pre industrial levels. Ecol. Monogr. 80, 49–66 (2010).Article 

Google Scholar 
37.Cimino, M. A., Lynch, H. J., Saba, V. S. & Oliver, M. J. Projected asymmetric response of Adélie penguins to Antarctic climate change. Sci. Rep. 6, 28785 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Fraser, W. R., Patterson-Fraser, D. L., Ribic, C. A., Schofield, O. & Ducklow, H. A nonmarine source of variability in Adélie penguin demography. Oceanography 26, 207–209 (2013).Article 

Google Scholar 
39.Cimino, M. A., Patterson-Fraser, D. L., Stammerjohn, S. & Fraser, W. R. The interaction between island geomorphology and environmental parameters drives Adélie penguin breeding phenology on neighboring islands near Palmer Station, Antarctica. Ecol. Evol. 9, 9334–9349 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Patterson, D. L., Easter-Pilcher, A. L. & Fraser, W. R. The effects of human activity and environmental variability on long-term changes in Adélie penguin populations at Palmer Station, Antarctica. In Antarctic Biology in a Global Context (eds. van der Vies, S. M. et al.) 301–307 (2003).
Google Scholar 
41.Bricher, P. K., Lucieer, A. & Woehler, E. J. Population trends of Adélie penguin (Pygoscelis adeliae) breeding colonies: A spatial analysis of the effects of snow accumulation and human activities. Polar Biol. 31, 1397–1407 (2008).Article 

Google Scholar 
42.Ainley, D. G., LeResche, R. E. & Sladen, W. J. L. Breeding Biology of the Adélie Penguin (1983).
Google Scholar 
43.Stonehouse, B. Observations on Adélie penguins (Pygoscelis adeliae) at Cape Royds, Antarctica. In Proc. XIIIth Internatl. Ornith. Congr. Vol. 1963, 766–779 (1963).44.Ainley, D. G. et al. Diet and foraging effort of Adélie penguins in relation to pack-ice conditions in the southern Ross Sea. Polar Biol. 20, 311–319 (1998).Article 

Google Scholar 
45.Ballard, G., Ainley, D. G., Ribic, C. A. & Barton, K. R. Effect of instrument attachment and other factors on foraging trip duration and nesting success of Adélie penguins. Condor 103, 481–490 (2001).Article 

Google Scholar 
46.Ainley, D. G. et al. Post-fledging survival of Adélie penguins at multiple colonies: Chicks raised on fish do well. Mar. Ecol. Prog. Ser. 601, 239–251 (2018).ADS 
Article 

Google Scholar 
47.Dugger, K. M., Ballard, G., Ainley, D. G., Lyver, P. O. & Schine, C. Adélie penguins coping with environmental change: Results from a natural experiment at the edge of their breeding range. Front. Ecol. Evol. 2, 1–12 (2014).Article 

Google Scholar 
48.Ainley, D. G. et al. Decadal-scale changes in the climate and biota of the Pacific sector of the Southern Ocean, 1950s to the 1990s. Antarct. Sci. 17, 171–182 (2005).ADS 
Article 

Google Scholar 
49.Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547, 49–54 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.LaRue, M. A. et al. Climate change winners: Receding ice fields facilitate colony expansion and altered dynamics in an Adélie penguin metapopulation. PLoS ONE 8, e60568 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Emslie, S. D., Berkman, P. A., Ainley, D. G., Coats, L. & Polito, M. Late-Holocene initiation of ice-free ecosystems in the southern Ross Sea, Antarctica. Mar. Ecol. Prog. Ser. 262, 19–25 (2003).ADS 
Article 

Google Scholar 
52.Emslie, S. D., Coats, L. & Licht, K. A 45,000 yr record of Adélie penguins and climate change in the Ross Sea, Antarctica. Geology 35, 61–64 (2007).ADS 
Article 

Google Scholar 
53.Penney, R. L. Territorial and social behavior in the Adélie Penguin. Antarct. Bird Stud. 12, 83–131 (1968).
Google Scholar 
54.LaRue, M. A. et al. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biol. 37, 507–517 (2014).Article 

Google Scholar 
55.De Neve, L., Fargallo, J. A., Polo, V., Martin, J. & Soler, M. Subcolony characteristics and breeding performance in the Chinstrap Penguin Pygoscelis antarctica. Ardeola 53, 19–29 (2006).
Google Scholar 
56.Winstral, A., Elder, K. & Davis, R. E. Spatial snow modeling of wind-redistributed snow using terrain-based parameters. J. Hdyrometeorol. 3, 524–538 (2002).ADS 
Article 

Google Scholar 
57.Plattner, C. H., Braun, L. N. & Brenning, A. Spatial variability of snow accumulation on Vernagtferner, Austrian Alps, in winter 2003/04. Z. Gletscherkd. Glazialgeol. 39, 43–57 (2006).
Google Scholar 
58.Young, E. Skua and Penguin: Predator and Prey (Cambridge University Press, 1994).Book 

Google Scholar 
59.Trillmich, F. Feeding Territories and breeding success of South Polar Skuas. Auk 95, 23–33 (1978).Article 

Google Scholar 
60.Moret, G. J. M. & Huerta, A. D. Correcting GIS-based slope aspect calculations for the Polar Regions. Antarct. Sci. 19, 129–130 (2007).ADS 
Article 

Google Scholar 
61.Seefeldt, M. W., Tripoli, G. J. & Stearns, C. R. A high-resolution numerical simulation of the wind flow in the Ross Island region, Antarctica. Mon. Weather Rev. 131, 435–458 (2003).ADS 
Article 

Google Scholar 
62.Jammalamadaka, S. R., Rao Jammalamadaka, S. & SenGupta, A. Topics in circular statistics. Ser. Multivariate Anal. https://doi.org/10.1142/4031 (2001).Article 
MATH 

Google Scholar 
63.Watson, G. S. Goodness-of-fit tests on a circle. II.. Biometrika 49, 57–63 (1962).MathSciNet 
MATH 
Article 

Google Scholar 
64.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).MATH 
Book 

Google Scholar 
65.Marra, G. & Wood, S. N. Practical variable selection for generalized additive models. Comput. Stat. Data Anal. 55, 2372–2387 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
66.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel inference: A Practical Information-Theoretic Approach Vol. 2 (Springer Science, 2002).MATH 

Google Scholar 
67.Ferrer, M., Belliure, J., Minguez, E., Casado, E. & Bildstein, K. Heat loss and site-dependent fecundity in chinstrap penguins (Pygoscelis antarctica). Polar Biol. 37, 1031–1039 (2014).Article 

Google Scholar 
68.Tenaza, R. Behavior and nesting success relative to nest location in Adélie Penguins (Pygoscelis adeliae). Condor 73, 81–92 (1971).Article 

Google Scholar 
69.Wilson, D. J. et al. South Polar Skua breeding populations in the Ross Sea assessed from demonstrated relationship with Adélie Penguin numbers. Polar Biol. 40, 577–592 (2017).Article 

Google Scholar 
70.Ballard, G. et al. Responding to climate change: Adélie Penguins confront astronomical and ocean boundaries. Ecology 91, 2056–2069 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Shepherd, L. D. et al. Microevolution and mega-icebergs in the Antarctic. Proc. Natl. Acad. Sci. USA. 102, 16717–16722 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Dugger, K. M., Ainley, D. G., Lyver, P. O., Barton, K. & Ballard, G. Survival differences and the effect of environmental instability on breeding dispersal in an Adélie penguin meta-population. Proc. Natl. Acad. Sci. USA. 107, 12375–12380 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).Article 

Google Scholar 
74.Jackson, A. L., Bearhop, S. & Thompson, D. R. Shape can influence the rate of colony fragmentation in ground nesting seabirds. Oikos 111, 473–478 (2005).Article 

Google Scholar 
75.McDowall, P. S. & Lynch, H. J. When the ‘selfish herd’ becomes the ‘frozen herd’: Spatial dynamics and population persistence in a colonial seabird. Ecology 100, e02823 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Gilchrist, H. G. Declining thick-billed murre Uria lomvia colonies experience higher gull predation rates: An inter-colony comparison. Biol. Conserv. 87, 21–29 (1999).Article 

Google Scholar 
77.Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: Implications for the study of coloniality. Ecology 79, 2415–2428 (1998).Article 

Google Scholar 
78.Valone, T. J. & Templeton, J. J. Public information for the assessment of quality: A widespread social phenomenon. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 357, 1549–1557 (2002).Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar 
49.Chiquet J, Mariadassou M, S. R. Variational inference for sparse network reconstruction from count data. ICML. 2018;97:1162–71.
Google Scholar 
50.Liu H, Roeder K, Wasserman L. Stability Approach to Regularization Selection (StARS) for high dimensional graphical models. Adv Neural Inf Process Syst. 2010;31:1432–40.
Google Scholar 
51.Chen L, Reeve J, Zhang LJ, Huang SB, Wang XF, Chen J. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;6:e4600.52.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.54.Singh A, Gautier B, Shannon CP, Rohart F, Vacher M, Tebutt SJ, et al. DIABLO: from multi-omics assays to biomarker discovery, an integrative approach. Bioinformatics. 2019;35:3055–62.CAS 
PubMed 
PubMed Central 
Article 

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar 
29.Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, et al. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol 2015;6:771.30.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–96.CAS 
PubMed 
Article 
PubMed Central 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

1.Fuller, A. et al. Physiological mechanisms in coping with climate change. Phys. Biochem. Zool. 83, 713–720. https://doi.org/10.1086/652242 (2010).Article 

Google Scholar 
2.Raubenheimer, D., Simpson, S. J. & Tait, A. H. Match and mismatch: Conservation physiology, nutritional ecology and the timescales of biological adaptation. Philos. Trans. R. Soc. B 367, 1628–1646. https://doi.org/10.1098/rstb.2012.0007 (2012).CAS 
Article 

Google Scholar 
3.Tracy, C. R. et al. The importance of physiological ecology in conservation biology. Integr. Comp. Biol. 46, 1191–1205. https://doi.org/10.1093/icb/icl054 (2006).Article 
PubMed 

Google Scholar 
4.Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69. https://doi.org/10.1111/j.1365-2435.2009.01528.x (2009).Article 

Google Scholar 
5.Morris, J. G. Idiosyncratic nutrient requirements of cats appear to be diet-induced evolutionary adaptations. Nutr. Res. Rev. 15, 153–168. https://doi.org/10.1079/NRR200238 (2002).CAS 
Article 
PubMed 

Google Scholar 
6.Hofmann, R. R. Evolutionary steps of ecophysiological adaptation and diversification of ruminants: A comparative view of their digestive system. Oecologia 78, 443–457. https://doi.org/10.1007/BF00378733 (1989).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Rode, K. D., Chapman, C. A., McDowell, L. R. & Stickler, C. Nutritional correlates of population density across habitats and logging intensities in redtail monkeys (Cercopithecus Ascanius). Biotropica 38, 625–634. https://doi.org/10.1111/j.1744-7429.2006.00183.x (2006).Article 

Google Scholar 
8.Birnie-Gauvin, K., Peiman, K. S., Raubenheimer, D. & Cooke, S. J. Nutritional physiology and ecology of wildlife in a changing world. Cons Phys. 5, cox030. https://doi.org/10.1093/conphys/cox030 (2017).CAS 
Article 

Google Scholar 
9.Rode, K. D. & Robbins, C. T. Why bears consume mixed diets during fruit abundance. Can. J. Zool. 78, 1640–1645. https://doi.org/10.1139/z00-082 (2000).Article 

Google Scholar 
10.Robbins, C. T. et al. Optimizing protein intake as a foraging strategy to maximize mass gain in an omnivore. Oikos 116, 1675–1683. https://doi.org/10.1111/j.0030-1299.2007.16140.x (2007).Article 

Google Scholar 
11.Erlenbach, J. A., Rode, K. D., Raubenheimer, D. & Robbins, C. T. Macronutrient optimization and energy maximization determine diets of brown bears. J. Mamm. 95, 160–168. https://doi.org/10.1644/13-MAMM-A-161 (2014).Article 

Google Scholar 
12.Nie, Y. et al. Giant pandas are macronutritional carnivores. Curr. Biol. 29, 1677–1682. https://doi.org/10.1016/j.cub.2019.03.067 (2019).CAS 
Article 
PubMed 

Google Scholar 
13.Sponheimer, M., Clauss, M. & Codron, D. Dietary evolution: The panda paradox. Curr. Biol. 29, R417–R419. https://doi.org/10.1016/j.cub.2019.04.045 (2019).CAS 
Article 
PubMed 

Google Scholar 
14.Stirling, I. & McEwan, E. H. The caloric value of whole ringed seals (Phoca hispida) in relation to polar bear (Ursus maritimus) ecology and hunting behavior. Can. J. Zool. 53, 1021–1027. https://doi.org/10.1139/z75-117 (1975).CAS 
Article 
PubMed 

Google Scholar 
15.Liu, S. P. et al. Population genomics reveal recent speciation and rapid evolutionary adaptation in Polar Bears. Cell 157, 785–794. https://doi.org/10.1016/j.cell.2014.03.054 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Kohl, K. D., Coogan, S. C. P. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrient? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37, 701–709. https://doi.org/10.1002/bies.201400171 (2015).Article 
PubMed 

Google Scholar 
17.Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387. https://doi.org/10.1146/annurev-marine-010318-095411 (2020).Article 
PubMed 

Google Scholar 
18.Hewson-Hughes, A. K., Colyer, A., Simpson, S. J. & Raubenheimer, D. Balancing macronutrient intake in a mammalian carnivore: Disentangling the influences of flavor and nutrition. R. Soc. Open 3, 160081. https://doi.org/10.1098/rsos.160081 (2016).ADS 
CAS 
Article 

Google Scholar 
19.McKinney, M. A., Atwood, T. C., Iverson, S. J. & Peacock, E. Temporal complexity of southern Beaufort Sea polar bear diets during a period of increasing land use. Ecosphere 8, e01633. https://doi.org/10.1002/ecs2.1633 (2017).Article 

Google Scholar 
20.Rode, K. D. et al. Spring fasting behavior in a marine apex predator provides an index of ecosystem productivity. Glob. Change Biol. 24, 410–423. https://doi.org/10.1111/gcb.13933 (2018).ADS 
Article 

Google Scholar 
21.Rode, K. D. et al. Variation in the response of an arctic top predator experiencing habitat loss: Feeding and reproductive ecology of two polar bear populations. Glob. Change Biol. 20, 76–88. https://doi.org/10.1111/gcb.12339 (2014).ADS 
Article 

Google Scholar 
22.Rode, K. D. et al. Seal body condition and atmospheric circulation patterns in the Chukchi Sea influence polar bear body condition, recruitment, and feeding ecology. Glob. Change Biol. https://doi.org/10.1111/gcb.15572 (2021).Article 

Google Scholar 
23.Yurkowski, D. J., Hussey, N. E., Semeniuk, C., Ferguson, S. H. & Fisk, A. T. Effects of fat extraction and the utility of fat normalization models on δ13C and δ15N values in Arctic marine mammal tissues. Pol. Biol. 38, 131–143. https://doi.org/10.1007/s00300-014-1571-1 (2014).Article 

Google Scholar 
24.Hilderbrand, G. V., Jenkins, S. G., Schwartz, C. C., Hanley, T. A. & Robbins, C. T. Effect of seasonal differences in dietary meat intake on changes in body mass and composition in wild and captive brown bears. Can. J. Zool. 77, 1623–1630. https://doi.org/10.1139/z99-133 (1999).Article 

Google Scholar 
25.McCullough, D. R. & Ullrey, D. E. Proximate mineral and gross energy composition of white-tailed deer. J. Wildl. Manag. 47, 430–441. https://doi.org/10.2307/3808516 (1983).Article 

Google Scholar 
26.Pritchard, G. T. & Robbins, C. T. Digestive and metabolic efficiencies of grizzly and black bears. Can. J. Zool. 68, 1645–1651. https://doi.org/10.1139/z90-244 (1990).Article 

Google Scholar 
27.LaDouceur, E. E. B., Garner, M. M., Davis, B. & Tseng, F. A retrospective study of end-stage renal disease in captive polar bears (Ursus maritimus). J. Zoo Wildl. Med. 45, 69–77. https://doi.org/10.1638/2013-0071R.1 (2014).Article 
PubMed 

Google Scholar 
28.Derocher, A. E. & Stirling, I. Aspects of survival in juvenile polar bears. Can. J. Zool. 74, 1246–1252. https://doi.org/10.1139/z96-138 (1996).Article 

Google Scholar 
29.Hedberg, G. E. et al. Milk composition in free-ranging polar bears (Ursus maritimus) as a model for captive rearing milk formula. Zoo Biol. 30, 550–565. https://doi.org/10.1002/zoo.20375 (2011).CAS 
Article 
PubMed 

Google Scholar 
30.Jensen, K. et al. Nutrient-specific compensatory feeding in a mammalian carnivore, the mink, Neovison vison. Br. J. Nutr. 112, 1226–1233. https://doi.org/10.1017/S0007114514001664 (2014).CAS 
Article 
PubMed 

Google Scholar 
31.Rosen, D. A. S. & Trites, A. W. Examining the potential for nutritional stress in young Stellar sea lions: Physiological effects of prey composition. J. Comp. Phys. B 175, 265–273. https://doi.org/10.1007/s00360-005-0481-5 (2005).CAS 
Article 

Google Scholar 
32.Kirsch, P. E., Iverson, S. J. & Bowen, W. D. Effect of a low-fat diet on body composition and blubber fatty acids of captive juvenile harp seals (Phoca groenlandica). Phys. Biochem. Zool. 73, 45–59. https://doi.org/10.1086/316723 (2000).CAS 
Article 

Google Scholar 
33.Zhao, L., Schell, D. M. & Castellini, M. A. Dietary macronutrients influence 13C and 15N signatures of pinnipeds: Captive feeding studies with harbor seals (Phoca vitulina). Physiol. Part A Mol. Integr. Phys. 143, 469–478. https://doi.org/10.1016/j.cbpa.2005.12.032 (2006).CAS 
Article 

Google Scholar 
34.Diaz Gomez, M., Rosen, D. A. S. & Trites, A. W. Net energy gained by northern fur seals (Callorhinus ursinus) is impacted more by diet quality than diet diversity. Can. J. Zool. 94, 12–135. https://doi.org/10.1139/cjz-2015-0143 (2016).CAS 
Article 

Google Scholar 
35.Le Bellego, L., van Milgen, J. & Noblet, J. Effect of high temperature and low-protein diets on performance of growing pigs. J. Anim. Sci. 79, 1259–1271. https://doi.org/10.2527/2001.7951259x (2002).Article 

Google Scholar 
36.Anton, S. D. et al. Effects of popular diets without specific calorie targets on weight loss outcomes: Systematic review of findings from clinical trials. Nutrients 9, 822. https://doi.org/10.3390/nu9080822 (2017).Article 
PubMed Central 

Google Scholar 
37.Bininda-Emonds, O. R. P., Gittleman, J. L. & Purvis, A. Building large trees by combining phylogenetic information: A complete phylogeny of the extant Carnivora (Mammalia). Biol. Rev. 74, 143–175. https://doi.org/10.1017/S0006323199005307 (1999).CAS 
Article 
PubMed 

Google Scholar 
38.Plantinga, E. A., Bosch, G. & Hendriks, W. H. Estimation of the dietary nutrient profile of free-roaming feral cats: Possible implications for nutrition of domestic cats. Br. J. Nutr. 106, S35–S48. https://doi.org/10.1017/S0007114511002285 (2011).CAS 
Article 
PubMed 

Google Scholar 
39.Hewson-Hughes, A. K. et al. Geometric analysis of macronutrient selection in breeds of the domestic dog, Canis lupus familiaris. Behav. Ecol. 24, 293–304. https://doi.org/10.1093/beheco/ars168 (2013).Article 
PubMed 

Google Scholar 
40.Trites, A. W. & Donnelly, C. P. The decline of Steller sea lions Eumetopias jubatus in Alaska: A review of the nutritional stress hypothesis. Mamm. Rev. 33, 3–28. https://doi.org/10.1046/j.1365-2907.2003.00009.x (2003).Article 

Google Scholar 
41.Hauser, D. D. W., Allen, C. S., Rich, H. B. Jr. & Quinn, T. P. Resident harbor seals (Phoca vitulina) in Iliamna Lake, Alaska: Summer diet and partial consumption of adult sockeye salmon (Oncorhynchus nerka). Aquat. Mamm. 34, 303–309. https://doi.org/10.1578/AM.34.3.2008.303 (2008).Article 

Google Scholar 
42.Jia, Y. et al. Long-term high intake of whole proteins results in renal damage in pigs. J. Nutr. 140, 1646–1652. https://doi.org/10.3945/jn.110.123034 (2010).CAS 
Article 
PubMed 

Google Scholar 
43.Wakefield, A. P., House, J. D., Ogborn, M. R., Weiler, H. A. & Aukema, H. M. A diet of 35% of energy from protein leads to kidney damage in female Sprague–Dawley rats. Br. J. Nutr. 106, 656–663. https://doi.org/10.1017/S0007114511000730 (2011).CAS 
Article 
PubMed 

Google Scholar 
44.Ko, G.-J., Rhee, C. M., Kalantar-Zadeh, K. & Joshi, S. The effects of high-protein diets on kidney health and longevity. J. Am. Soc. Nephrol. 31, 1667–1679. https://doi.org/10.1681/ASN.2020010028 (2020).CAS 
Article 
PubMed 

Google Scholar 
45.Bӧswald, L. F., Kienzle, E. & Dobenecker, B. Observation about phosphorus and protein supply in cats and dogs prior to the diagnosis of chronic kidney disease. J. Phys. Anim. Nutr. 102, 31–36. https://doi.org/10.1111/jpn.12886 (2017).CAS 
Article 

Google Scholar 
46.Ioannou, G. N., Morrow, O. B., Connole, M. L. & Lee, S. P. Association between dietary nutrient composition and the incidence of cirrhosis or liver cancer in the united states population. Hepatology 50, 175–184. https://doi.org/10.1002/hep.22941 (2009).CAS 
Article 
PubMed 

Google Scholar 
47.Tryland, M. et al. Plasma biochemical values from apparently healthy free-ranging polar bears from Svalbard. J. Wildl. Dis. 38, 566–575. https://doi.org/10.7589/0090-3558-38.3.566 (2002).CAS 
Article 
PubMed 

Google Scholar 
48.Thiemann, G. W., Iverson, S. J. & Stirling, I. Polar bear diets and Arctic marine food webs: Insights from fatty acid analysis. Ecol. Monogr. 78, 591–613. https://doi.org/10.1890/07-1050.1 (2008).Article 

Google Scholar 
49.Ryg, M., Smith, T. G. & Oritsland, N. A. Seasonal changes in body mass and body composition of ringed seals (Phoca hispida) on Svalbard. Can. J. Zool. 68, 470–475. https://doi.org/10.1139/z90-069 (1990).Article 

Google Scholar 
50.Ferguson, S. H. et al. Demographic, ecological, and physiological responses of ringed seals to an abrupt decline in sea ice availability. Peer J. 5, e2957. https://doi.org/10.7717/peerj.2957 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Atwood, T. C. et al. Rapid environmental change drives increased land use by an Arctic marine predator. PLoS One 11, 30155932. https://doi.org/10.1371/journal.pone.0155932 (2016).ADS 
CAS 
Article 

Google Scholar 
52.Molnar, P. K. et al. Fasting season length sets temporal limits for global polar bear persistence. Nat. Clim. Change 10, 732–738. https://doi.org/10.1038/s41558-020-0818-9 (2020).ADS 
Article 

Google Scholar 
53.Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities?. Front. Ecol. Environ. 13, 138–145. https://doi.org/10.1890/140202 (2015).Article 

Google Scholar 
54.McArt, S. H. et al. Summer nitrogen availability as a bottom-up constraint on moose in south-central Alaska. Ecology 90, 1400–1411. https://doi.org/10.1890/08-1435.1 (2009).Article 
PubMed 

Google Scholar 
55.Lahtinen, M., Clinnick, D., Mannermaa, K., Salonen, J. S. & Viranta, S. Excess protein enabled dog domestication during severe Ice Age winters. Sci. Rep. 11, 7. https://doi.org/10.1038/s41598-020-78214-4 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Regehr, E. V., Hostetter, N. J., Wilson, R. R. & Rode, K. D. Integrated population modeling provides the first empirical estimates of vital rates and abundance for polar bears in the Chukchi Sea. Sci. Rep. 8, 16780. https://doi.org/10.1038/s41598-018-34824-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Crawford, J. A., Quakenbush, L. T. & Citta, J. J. A comparison of ringed seal and bearded seal diet, condition, and productivity between historical (1975–19480 and recent (2003–2012) periods in the Alaskan Bering and Chukchi Seas. Progr. Oceanogr. 136, 133–150. https://doi.org/10.1016/j.pocean.2015.05.011 (2015).ADS 
Article 

Google Scholar 
58.Germain, L. R., McCarthy, M. D., Koch, P. L. & Harvey, J. T. Stable carbon and nitrogen isotopes in multiple tissues of wild and captive harbor seals (Phoca vitulina) off the California coast. Mar. Mamm. Sci. 28, 542–560. https://doi.org/10.1111/j.1748-7692.2011.00516.x (2011).CAS 
Article 

Google Scholar 
59.Erlenbach, J. A. Nutritional and landscape ecology of brown bears (Ursus arctos). PhD dissertation. Washington State University, Pullman, WA, USA (2020).60.Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524. https://doi.org/10.1002/fee.1963 (2018).Article 

Google Scholar 
61.Newsome, S. D., Koch, P. L., Etnier, M. A. & Aurioles-Gamboa, D. Using carbon and nitrogen isotope values to investigate maternal strategies in northeast Pacific otariids. Mar. Mamm. Sci. 22, 556–572. https://doi.org/10.1111/j.1748-7692.2006.00043.x (2006).Article 

Google Scholar 
62.Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracker mixing models. Peer J. 6, e5096. https://doi.org/10.7717/peerj.5096 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Rode, K. D. et al. Isotopic incorporation and the effects of fasting and dietary fat content on isotopic discrimination in large carnivorous mammals. Phys. Biochem. Zool. 89, 182–197. https://doi.org/10.1086/686490 (2016).CAS 
Article 

Google Scholar 
64.Merrill, A. L. & Watt, B. K. Energy Value of Foods: Basis and Derivation, Revised. Agriculture Handbook 74 (United States Department of Agriculture, 1973).65.Dyck, M. G. & Morin, P. In vivo digestibility trials of a captive polar bear (Ursus maritimus) feeding on harp seal (Pagophilus growenlandicus) and Arctic charr (Salvelinus alpinus). Pak. J. Zool. 43, 759–767 (2011).CAS 

Google Scholar  More