Ecology

1.Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.European Centre for Disease Prevention and Control, European Medicines Agencies. The Bacterial Challenge: Time to React. A Call to Narrow the Gap Between Multidrug-Resistant Bacteria in the EU and the Development of New Antibacterial Agents https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/0909_TER_The_Bacterial_Challenge_Time_to_React.pdf (2009).3.Jevons, M. P. “Celbenin”—resistant Staphylococci. Br. Med. J. 1, 124–125 (1961).PubMed Central 

Google Scholar 
4.Harkins, C. P. et al. Methicillin-resistant Staphylococcus aureus emerged long before the introduction of methicillin into clinical practice. Genome Biol. 18, 130 (2017).PubMed 
PubMed Central 

Google Scholar 
5.Chambers, H. F. & DeLeo, F. R. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat. Rev. Microbiol. 7, 629–641 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Price, L. B. et al. Staphylococcus aureus CC398: host adaptation and emergence of methicillin resistance in livestock. mBio 3, e00305-11 (2012).PubMed 
PubMed Central 

Google Scholar 
7.Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1 (WHO, 2017).8.Rasmussen, S. L. et al. European hedgehogs (Erinaceus europaeus) as a natural reservoir of methicillin-resistant Staphylococcus aureus carrying mecC in Denmark. PLoS ONE 14, e0222031 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Bengtsson, B. et al. High occurrence of mecC-MRSA in wild hedgehogs (Erinaceus europaeus) in Sweden. Vet. Microbiol. 207, 103–107 (2017).PubMed 

Google Scholar 
10.García-Álvarez, L. et al. Methicillin-resistant Staphylococcus aureus with a novel mecA homologue in human and bovine populations in the UK and Denmark: a descriptive study. Lancet Infect. Dis. 11, 595–603 (2011).PubMed 
PubMed Central 

Google Scholar 
11.Paterson, G. K., Harrison, E. M. & Holmes, M. A. The emergence of mecC methicillin-resistant Staphylococcus aureus. Trends Microbiol. 22, 42–47 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Marples, M. J. & Smith, J. M. B. The hedgehog as a source of human ringworm. Nature 188, 867–868 (1960).ADS 
CAS 
PubMed 

Google Scholar 
13.English, M. P., Evans, C. D., Hewitt, M. & Warin, R. P. “Hedgehog ringworm”. Br. Med. J. 1, 149–151 (1962).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Smith, J. M. B. & Marples, M. J. A natural reservoir of penicillin-resistant strains of Staphylococcus aureus. Nature 201, 844 (1964).ADS 
CAS 
PubMed 

Google Scholar 
15.Smith, J. M. B. & Marples, M. J. Dermatophyte lesions in the hedgehog as a reservoir of penicillin-resistant staphylococci. J. Hyg. 63, 293–303 (1965).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Smith, J. M. B. Staphylococcus aureus strains associated with the hedgehog Erinaceus europaeus. J. Hyg. Camb. 63, 293–303 (1965).CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Morris, P. & English, M. P. Trichophyton mentagrophytes var. erinacei in British hedgehogs. Sabouraudia 7, 122–128 (1969).CAS 
PubMed 

Google Scholar 
18.Le Barzic, C. et al. Detection and control of dermatophytosis in wild European hedgehogs (Erinaceus europaeus) admitted to a French wildlife rehabilitation centre. J. Fungi 7, 74 (2021).
Google Scholar 
19.Dube, F., Söderlund, R., Salomonsson, M. L., Troell, K. & Börjesson, S. Benzylpenicillin-producing Trichophyton erinacei and methicillin resistant Staphylococcus aureus carrying the mecC gene on European hedgehogs: a pilot-study. BMC Microbiol. 21, 212 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).ADS 
CAS 
PubMed 

Google Scholar 
21.Brockie, R. E. Distribution and abundance of the hedgehog (Erinaceus europaeus) L. in New Zealand, 1869–1973. N. Z. J. Zool. 2, 445–462 (1975).
Google Scholar 
22.van den Berg, M. A. et al. Genome sequencing and analysis of the filamentous fungus Penicillium chrysogenum. Nat. Biotechnol. 26, 1161–1168 (2008).CAS 
PubMed 

Google Scholar 
23.Ullán, R. V., Campoy, S., Casqueiro, J., Fernández, F. J. & Martín, J. F. Deacetylcephalosporin C production in Penicillium chrysogenum by expression of the isopenicillin N epimerization, ring expansion, and acetylation genes. Chem. Biol. 14, 329–339 (2007).PubMed 

Google Scholar 
24.Kitano, K. et al. A novel penicillin produced by strains of the genus Paecilomyces. J. Ferment. Technol. 54, 705–711 (1976).CAS 

Google Scholar 
25.Petersen, A. et al. Epidemiology of methicillin-resistant Staphylococcus aureus carrying the novel mecC gene in Denmark corroborates a zoonotic reservoir with transmission to humans. Clin. Microbiol. Infect. 19, E16–E22 (2013).CAS 
PubMed 

Google Scholar 
26.Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Holden, M. T. G. et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res. 23, 653–664 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Strauß, L. et al. Origin, evolution, and global transmission of community-acquired Staphylococcus aureus ST8. Proc. Natl Acad. Sci. USA 114, E10596–E10604 (2017).PubMed 
PubMed Central 

Google Scholar 
29.Nübel, U. et al. Frequent emergence and limited geographic dispersal of methicillin-resistant Staphylococcus aureus. Proc. Natl Acad. Sci. USA 105, 14130–14135 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
30.Rasmussen, S. L., Nielsen, J. L., Jones, O. R., Berg, T. B. & Pertoldi, C. Genetic structure of the European hedgehog (Erinaceus europaeus) in Denmark. PLoS ONE 15, e0227205 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Hansen, J. E. et al. LA-MRSA CC398 in dairy cattle and veal calf farms indicates spillover from pig production. Front. Microbiol. 10, 2733 (2019).PubMed 
PubMed Central 

Google Scholar 
32.Eriksson, J. Espinosa-Gongora, C., Stamphøj, I., Larsen, A. R. & Guardabassi, L. Carriage frequency, diversity and methicillin resistance of in Danish small ruminants. Vet. Microbiol. 163, 110–115 (2013).CAS 
PubMed 

Google Scholar 
33.Danish Integrated Antimicrobial Resistance Monitoring and Research Programme. DANMAP 2019: Use of Antimicrobial Agents and Occurrence of Antimicrobial Resistance in Bacteria From Food Animals, Food, and Humans in DENMARK https://www.danmap.org/-/media/Sites/danmap/Downloads/Reports/2019/DANMAP_2019.ashx?la=da&hash=AA1939EB449203EF0684440AC1477FFCE2156BA5 (2020).34.Veterinary Medicines Directorate. UK Veterinary Antibiotic Resistance and Sales Surveillance Reporthttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/950126/UK-VARSS_2019_Report__2020-TPaccessible.pdf (2020).35.Harrison, E. M. et al. Whole genome sequencing identifies zoonotic transmission of MRSA isolates with the novel mecA homologue mecC. EMBO Mol. Med. 5, 509–515 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Loncaric, I. et al. Characterization of mecC gene-carrying coagulase-negative Staphylococcus spp. isolated from various animals. Vet. Microbiol. 230, 138–144 (2019).CAS 
PubMed 

Google Scholar 
37.Gómez, P. et al. Detection of MRSA ST3061-t843-mecC and ST398-t011-mecA in white stork nestlings exposed to human residues. J. Antimicrob. Chemother. 71, 53–57 (2016).PubMed 

Google Scholar 
38.Kim, C. et al. Properties of a novel PBP2A protein homolog from Staphylococcus aureus strain LGA251 and its contribution to the β-lactam-resistant phenotype. J. Biol. Chem. 287, 36854–36863 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Tahlan, K. & Jensen, S. E. Origins of the β-lactam rings in natural products. J. Antibiot. 66, 401–419 (2013).CAS 

Google Scholar 
40.Pantůček, R. et al. Staphylococcus edaphicus sp. nov. isolated in Antarctica harbors the mecC gene and genomic islands with a suspected role in adaptation to extreme environment. Appl. Environ. Microbiol. 84, e01746-17 (2018).PubMed 
PubMed Central 

Google Scholar 
41.D’Costa, V. M., et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).ADS 
PubMed 

Google Scholar 
42.Allen, H. K., Moe, L. A., Rodbumrer, J., Gaarder, A. & Handelsman, J. Functional metagenomics reveals diverse beta-lactamases in a remote Alaskan soil. ISME J. 3, 243–251 (2009).CAS 

Google Scholar 
43.Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Coll, F. et al. Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis. Lancet Microbe 1, e328–e335 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its application to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Enright, M. C., Day, N. P., Davies, C. E., Peacock, S. J., Spratt, B. G. Multilocus sequence typing for characterization of methicillin-resistant and methicillin-susceptible clones of Staphylococcus aureus. J. Clin. Microbiol. 38, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Van Wamel, W. J., Rooijakkers, S. H., Ruyken, M. van Kessel, K. P. & Strijp, J. A. The innate immune modulators staphylococcal complement inhibitor and chemotaxis inhibitory protein of Staphylococcus aureus are located on beta-hemolysin-converting bacteriophages. J. Bacteriol. 188, 1310–1315 (2006).PubMed 
PubMed Central 

Google Scholar 
49.Viana, D. et al. Adaptation of Staphylococcus aureus to ruminant and equine hosts involved SaPI-carried variants of von Willebrand factor-binding protein. Mol. Microbiol. 77, 1583–1594 (2010).50.Rooijakkers, S. H. M. et al. Staphylococcal complement inhibitor: structure and active sites. J. Immunol. 179, 2989–2998 (2007).CAS 
PubMed 

Google Scholar 
51.Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75, 3491–3500 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Clausen, P. T. L. C., Aarestrup, F. M. & Lund, O. Rapid and precise alignment of raw reads against redundant database with KMA. BMC Bioinform. 19, 397 (2018).
Google Scholar 
54.Sahl, J. W. et al. NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microb. Genom. 2, e000074 (2016).PubMed 
PubMed Central 

Google Scholar 
55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrow-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.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 

Google Scholar 
57.DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation sequencing data. Nat. Genet. 43, 491–498 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Delcher, A. L., Phillippy, A., Carlton, J. & Salzberg, S. L. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res. 30, 2478–2483 (2002).PubMed 
PubMed Central 

Google Scholar 
59.Kurz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
Google Scholar 
60.Guindon, S. & Gasquel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 

Google Scholar 
61.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 

Google Scholar 
62.Didelot, X. & Wilson, D. J. ClonalFrameML: efficient inference of recombination in whole bacterial genome. PLoS Comput. Biol. 11, e1004041 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
63.Didelot, X. et al. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res. 46, e134 (2018).PubMed 
PubMed Central 

Google Scholar 
64.Didelot, X., Siveroni, I. & Volz, E. M. Additive uncorrelated relaxed clock models for the dating of genomic epidemiology phylogenies. Mol. Biol. Evol. 38, 307–317 (2021).CAS 
PubMed 

Google Scholar 
65.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).
Google Scholar 
66.Volz, E. M. & Frost, S. D. Scalable relaxed clock phylogenetic dating. Virus Evol. 3, vex025 (2017).
Google Scholar 
67.Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Adusumilli, R. & Mallick, P. Data conversion with ProteoWizard msConvert. Methods Mol. Biol. 1550, 339–368 (2017).CAS 
PubMed 

Google Scholar  More

Brown AHD, Feldman MW, Nevo E (1980) Multilocus structure of natural populations of Hordeum spontaneum. Genetics 96:523–536CAS 
Article 

Google Scholar 
Burow MD, Simpson CE, Starr JL, Paterson AH (2001) Transmission genetics of chromatin from a synthetic amphidiploid to cultivated peanut (Arachis hypogaea L.): broadening the gene pool of a monophyletic polyploid species. Genetics 159:823CAS 
Article 

Google Scholar 
Butruille DV, Boiteux LS (2000) Selection–mutation balance in polysomic tetraploids: Impact of double reduction and gametophytic selection on the frequency and subchromosomal localization of deleterious mutations. Proc Natl Acad Sci USA 97:6608–6613CAS 
Article 

Google Scholar 
Cockerham CC, Weir BS (1977) Digenic descent measures for finite populations. Genet Res 30:121–147Article 

Google Scholar 
Devlin B, Risch N (1995) A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29:311–322CAS 
Article 

Google Scholar 
Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
Article 

Google Scholar 
England PR, Cornuet J-M, Berthier P, Tallmon DA, Luikart G (2006) Estimating effective population size from linkage disequilibrium: severe bias in small samples. Conserv Genet 7:303Article 

Google Scholar 
Fisher RA (1947) The theory of linkage in polysomic inheritance. Philos Trans R Soc Lond Ser B Biol Sci 233:55–87
Google Scholar 
Gao XY, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32:361–369Article 

Google Scholar 
Hästbacka J, de la Chapelle A, Kaitila I, Sistonen P, Weaver A, Lander E (1992) Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nat Genet 2:204–211Article 

Google Scholar 
Hayes BJ, Visscher PM, McPartlan HC, Goddard ME (2003) Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res 13:635–643CAS 
Article 

Google Scholar 
Hill WG (1974) Disequilibrium among several linked neutral genes in finite population I. Mean changes in disequilibrium. Theor Popul Biol 5:366–392CAS 
Article 

Google Scholar 
Hill WG (1975) Linkage disequilibrium among multiple neutral alleles produced by mutation in finite population. Theor Popul Biol 8:117–126CAS 
Article 

Google Scholar 
Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium. Genet Res 38:209–216Article 

Google Scholar 
Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231CAS 
Article 

Google Scholar 
Hill WG, Weir BS (1994) Maximum-likelihood estimation of gene location by linkage disequilibrium. Am J Hum Genet 54:705CAS 

Google Scholar 
Hollenbeck C, Portnoy D, Gold J (2016) A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity 117:207–216CAS 
Article 

Google Scholar 
Hosking LK, Boyd PR, Xu CF, Nissum M, Cantone K, Purvis IJ, Khakhar R, Barnes MR, Liberwirth U, Hagen-Mann K (2002) Linkage disequilibrium mapping identifies a 390 kb region associated with CYP2D6 poor drug metabolising activity. Pharmacogenomics J 2:165CAS 
Article 

Google Scholar 
Huang K, Dunn DW, Ritland K, Li BG (2020) polygene: Population genetics analyses for autopolyploids based on allelic phenotypes. Methods Ecol Evol 11:448–456Article 

Google Scholar 
Jorde LB (1995) Linkage disequilibrium as a gene-mapping tool. Am J Hum Genet 56:11CAS 

Google Scholar 
Lewontin RC (1964) The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49:49CAS 
Article 

Google Scholar 
Maruyama T (1982) Stochastic integrals and their application to population genetics. In: Kimura M (ed) Molecular evolution, protein polymorphism and the neutral theory. Japan Scientific Societies Press, Tokyo, p 151–166
Google Scholar 
Nei M (1987) Molecular evolutionary genetics. Columbia university press, New YorkOhta T (1980) Linkage disequilibrium between amino acid sites in immunoglobulin genes and other multigene families. Genet Res 36:181–197CAS 
Article 

Google Scholar 
Ohta T, Kimura M (1969) Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics 63:229CAS 
Article 

Google Scholar 
Otto SP (2007) The evolutionary consequences of polyploidy. Cell 131:452–462CAS 
Article 

Google Scholar 
Rieger R, Michaelis A, Green MM (1968) A glossary of genetics and cytogenetics: classical and molecular. Springer-Verlag, New York, NYBook 

Google Scholar 
Robert C (1991) Generalized inverse normal distributions. Stat Probabil Lett 11:37–41Article 

Google Scholar 
Santiago E, Novo I, Pardiñas AF, Saura M, Wang J, Caballero A (2020) Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol Biol Evol 37:3642–3653CAS 
Article 

Google Scholar 
Sattler MC, Carvalho CR, Clarindo WR (2016) The polyploidy and its key role in plant breeding. Planta 243:281–296CAS 
Article 

Google Scholar 
Slatkin M (2008) Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9:477CAS 
Article 

Google Scholar 
Stift M, Berenos C, Kuperus P, van Tienderen PH (2008) Segregation models for disomic, tetrasomic and intermediate inheritance in tetraploids: a general procedure applied to Rorippa (yellow cress) microsatellite data. Genetics 179:2113–2123Article 

Google Scholar 
Sved JA (1964) The relationship between diploid and tetraploid recombination frequencies. Heredity 19:585–596CAS 
Article 

Google Scholar 
Sved JA, Cameron EC, Gilchrist AS (2013) Estimating effective population size from linkage disequilibrium between unlinked loci: theory and application to fruit fly outbreak populations. PLoS ONE 8:e69078CAS 
Article 

Google Scholar 
Sved JA, Feldman MW (1973) Correlation and probability methods for one and two loci. Theor Popul Biol 4:129–132CAS 
Article 

Google Scholar 
Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM (2007) Recent human effective population size estimated from linkage disequilibrium. Genome Res 17:520–526CAS 
Article 

Google Scholar 
Udall JA, Wendel JF (2006) Polyploidy and crop improvement. Crop Sci 46:S-3–S-14Article 

Google Scholar 
Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet 7:167–184. https://doi.org/10.1007/s10592-005-9100-yArticle 

Google Scholar 
Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780Article 

Google Scholar 
Waples RK, Larson WA, Waples RS (2016) Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117:233–240. https://doi.org/10.1038/hdy.2016.60CAS 
Article 

Google Scholar 
Weir BS (1979) Inferences about linkage disequilibrium. Biometrics 35:235–254Weir BS, Cockerham CC (1979) Estimation of linkage disequilibrium in randomly mating populations. Heredity 42:105Article 

Google Scholar 
Weir BS, Hill WG (1980) Effect of mating structure on variation in linkage disequilibrium. Genetics 95:477–488CAS 
Article 

Google Scholar  More

1.Tilman, D. et al. Science 277, 1300–1302 (1997).CAS 
Article 

Google Scholar 
2.Hughes, T. P. et al. Nature 556, 492–496 (2018).CAS 
Article 

Google Scholar 
3.Eddy, T. D. et al. One Earth 4, 1278–1285 (2021).Article 

Google Scholar 
4.Stoeckl, N., Condie, S. & Anthony, K. Ecosyst. Serv. 51, 101352 (2021).Article 

Google Scholar 
5.Hoegh-Guldberg, O., Pendleton, L. & Kaup, A. Reg. Stud. Mar. Sci. 30, 100699 (2019).Article 

Google Scholar 
6.Teh, L. S. L., Teh, L. C. L. & Sumaila, U. R. PLoS ONE 8, e65397 (2013).CAS 
Article 

Google Scholar 
7.Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar 
8.Crain, C. M., Kroeker, K. & Halpern, B. S. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar 
9.Schröter, M. et al. Conserv. Lett. 7, 514–523 (2014).Article 

Google Scholar 
10.Glanemann, N., Willner, S. N. & Levermann, A. Nat. Commun. 11, 110 (2020).CAS 
Article 

Google Scholar 
11.Emissions Gap Report 2021: The Heat is On – A World of Climate Promises Not Yet Delivered (UNEP, 2021).12.Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Sci. Adv. 4, eaar5809 (2018).Article 

Google Scholar 
13.Berrang-Ford, L. et al. Nat. Clim. Change 11, 989–1000 (2021).Article 

Google Scholar 
14.Roberts, J. T. et al. Nat. Clim. Change 11, 180–182 (2021).Article 

Google Scholar  More

1.Muellner-Riehl, A. N. et al. Origins of global mountain plant biodiversity: Testing the ‘mountain-geobiodiversity hypothesis’. J. Biogeogr. 46, 2826–2838 (2019).
Google Scholar 
2.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).ADS 
CAS 

Google Scholar 
3.Schrodt, F. et al. Opinion: To advance sustainable stewardship, we must document not only biodiversity but geodiversity. Proc. Natl. Acad. Sci. 116, 16155–16158 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Alahuhta, J. et al. The role of geodiversity in providing ecosystem services at broad scales. Ecol. Indic. 91, 47–56 (2018).
Google Scholar 
5.Read, Q. D. et al. Beyond counts and averages: Relating geodiversity to dimensions of biodiversity. Glob. Ecol. Biogeogr. 29, 696–710 (2020).
Google Scholar 
6.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).PubMed 

Google Scholar 
7.Alahuhta, J., Toivanen, M. & Hjort, J. Geodiversity–biodiversity relationship needs more empirical evidence. Nat. Ecol. Evol. 4, 2–3 (2020).PubMed 

Google Scholar 
8.Boothroyd, A. & McHenry, M. Old processes, new movements: the inclusion of geodiversity in biological and ecological discourse. Diversity 11, 216 (2019).
Google Scholar 
9.Hunter, M. L., Jacobson, G. L. & Webb, T. Paleoecology and the coarse-filter approach to maintaining biological diversity. Conserv. Biol. 2, 375–385 (1988).
Google Scholar 
10.Hjort, J. & Luoto, M. Can geodiversity be predicted from space?. Geomorphology 153–154, 74–80 (2012).ADS 

Google Scholar 
11.Benito-Calvo, A., Pérez-González, A., Magri, O. & Meza, P. Assessing regional geodiversity: the Iberian Peninsula. Earth Surf. Process. Landf. 34, 1433–1445 (2009).ADS 

Google Scholar 
12.dos Santos, F. M., de La Corte Bacci, D., Saad, A. R. & da Silva Ferreira, A. T. Geodiversity index weighted by multivariate statistical analysis. Appl. Geomat. 12, 361–370 (2020).
Google Scholar 
13.Crisp, J. R., Ellison, J. C. & Fischer, A. Current trends and future directions in quantitative geodiversity assessment. Prog. Phys. Geogr. Earth Environ. https://doi.org/10.1177/0309133320967219 (2020).Article 

Google Scholar 
14.Pereira, D. I., Pereira, P., Brilha, J. & Santos, L. Geodiversity assessment of Paraná State (Brazil): An innovative approach. Environ. Manag. 52, 541–552 (2013).ADS 

Google Scholar 
15.Gray, M. Geodiversity and geoconservation: What, why, and how?. George Wright Forum 22, 4–12 (2005).
Google Scholar 
16.Ruban, D. A. Quantification of geodiversity and its loss. Proc. Geol. Assoc. 121, 326–333 (2010).
Google Scholar 
17.Hjort, J., Gordon, J. E., Gray, M. & Hunter, M. L. Why geodiversity matters in valuing nature’s stage: Why geodiversity matters. Conserv. Biol. 29, 630–639 (2015).PubMed 

Google Scholar 
18.Beier, P. & Brost, B. Use of land facets to plan for climate change: Conserving the arenas, not the actors. Conserv. Biol. J. Soc. Conserv. Biol. 24, 701–710 (2010).
Google Scholar 
19.Anderson, M. G. & Ferree, C. E. Conserving the stage: Climate change and the geophysical underpinnings of species diversity. PLoS ONE 5, e11554 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Knudson, C., Kay, K. & Fisher, S. Appraising geodiversity and cultural diversity approaches to building resilience through conservation. Nat. Clim. Change 8, 678–685 (2018).ADS 

Google Scholar 
21.Turner, J. A. Geodiversity: The natural support system of ecosystems. In Landscape Planning with Ecosystem Services: Theories and Methods for Application in Europe 253–265 (eds von Haaren, C. et al.) (Springer, 2019). https://doi.org/10.1007/978-94-024-1681-7_16.Chapter 

Google Scholar 
22.Fox, N., Graham, L. J., Eigenbrod, F., Bullock, J. M. & Parks, K. E. Incorporating geodiversity in ecosystem service decisions. Ecosyst. People 16, 151–159 (2020).
Google Scholar 
23.Parks, K. E. & Mulligan, M. On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns. Biodivers. Conserv. 19, 2751–2766 (2010).
Google Scholar 
24.Comer, P. J. et al. Incorporating geodiversity into conservation decisions: Geodiversity and conservation decisions. Conserv. Biol. 29, 692–701 (2015).PubMed 

Google Scholar 
25.Chakraborty, A. & Gray, M. A call for mainstreaming geodiversity in nature conservation research and praxis. J. Nat. Conserv. 56, 125862 (2020).
Google Scholar 
26.Lawler, J. et al. The theory behind, and the challenges of, conserving nature’s stage in a time of rapid change. Conserv. Biol. 29, 618–629 (2015).PubMed 

Google Scholar 
27.Beier, P. et al. A review of selection-based tests of abiotic surrogates for species representation. Conserv. Biol. J. Soc. Conserv. Biol. 29, 668–679 (2015).
Google Scholar 
28.Purvis, A. & Hector, A. Getting the Measure of Biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
29.Moreno, C. et al. Measuring biodiversity in the Anthropocene: A simple guide to helpful methods. Biodivers. Conserv. 26, 2993–2998 (2017).
Google Scholar 
30.Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).
Google Scholar 
31.Chiarucci, A., Bacaro, G. & Scheiner, S. M. Old and new challenges in using species diversity for assessing biodiversity. Philos. Trans. R. Soc. B Biol. Sci. 366, 2426–2437 (2011).
Google Scholar 
32.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
33.Hjort, J., Heikkinen, R. K. & Luoto, M. Inclusion of explicit measures of geodiversity improve biodiversity models in a boreal landscape. Biodivers. Conserv. 21, 3487–3506 (2012).
Google Scholar 
34.Bailey, J. J., Boyd, D. S., Hjort, J., Lavers, C. P. & Field, R. Modelling native and alien vascular plant species richness: At which scales is geodiversity most relevant?. Glob. Ecol. Biogeogr. 26, 763–776 (2017).
Google Scholar 
35.Zarnetske, P. L. et al. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing. Glob. Ecol. Biogeogr. 28, 548–556 (2019).PubMed 
PubMed Central 

Google Scholar 
36.Bétard, F. Patch-scale relationships between geodiversity and biodiversity in hard rock quarries: Case study from a disused quartzite quarry in NW France. Geoheritage 5, 59–71 (2013).
Google Scholar 
37.Tukiainen, H. et al. Spatial relationship between biodiversity and geodiversity across a gradient of land-use intensity in high-latitude landscapes. Landsc. Ecol. 32, 1049–1063 (2017).
Google Scholar 
38.Anderson, M. G. et al. Case studies of conservation plans that incorporate geodiversity. Conserv. Biol. 29, 680–691 (2015).CAS 
PubMed 

Google Scholar 
39.Ren, Y., Lü, Y., Hu, J. & Yin, L. Geodiversity underpins biodiversity but the relations can be complex: Implications from two biodiversity proxies. Glob. Ecol. Conserv. 31, e01830 (2021).
Google Scholar 
40.Homeier, J., Breckle, S.-W., Günter, S., Rollenbeck, R. T. & Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest: Ecuadorian Montane forest diversity and structure. Biotropica 42, 140–148 (2010).
Google Scholar 
41.Krashevska, V., Bonkowski, M., Maraun, M. & Scheu, S. Testate amoebae (protista) of an elevational gradient in the tropical mountain rain forest of Ecuador. Pedobiologia 51, 319–331 (2007).
Google Scholar 
42.Zhalnina, K. et al. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395–406 (2015).CAS 
PubMed 

Google Scholar 
43.Fierer, N., Craine, J. M., McLauchlan, K. & Schimel, J. P. Litter quality and the temperature sensiticity of decomposition. Ecology 86, 320–326 (2005).
Google Scholar 
44.Gibb, H. et al. Climate mediates the effects of disturbance on ant assemblage structure. Proc. R. Soc. B Biol. Sci. 282, 20150418 (2015).
Google Scholar 
45.Sanders, N. J., Lessard, J.-P., Fitzpatrick, M. C. & Dunn, R. R. Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains. Glob. Ecol. Biogeogr. 16, 640–649 (2007).
Google Scholar 
46.Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).ADS 

Google Scholar 
47.McCain, C. M. Global analysis of bird elevational diversity. Glob. Ecol. Biogeogr. 18, 346–360 (2009).
Google Scholar 
48.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures: Animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).
Google Scholar 
49.Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 
PubMed 

Google Scholar 
50.Hofhansl, F. et al. Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage. Sci. Rep. 10, 5066 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B Biol. Sci. 282, 20142620 (2015).
Google Scholar 
53.Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. 112, 797–802 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).PubMed 

Google Scholar 
55.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
Google Scholar 
56.Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 213–251 (1972).
Google Scholar 
57.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).PubMed 

Google Scholar 
58.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 

Google Scholar 
59.Lichstein, J. W. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecol. 188, 117–131 (2007).
Google Scholar 
60.Tuomisto, H. & Ruokolainen, K. Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87, 2697–2708 (2006).PubMed 

Google Scholar 
61.Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).ADS 
PubMed 

Google Scholar 
62.Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).PubMed 

Google Scholar 
63.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: Consolidation and progress in functional biodiversity research: Consolidation and progress in BDEF research. Ecol. Lett. 12, 1405–1419 (2009).PubMed 

Google Scholar 
64.Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
65.Bendix, J. et al. A research framework for projecting ecosystem change in highly diverse tropical mountain ecosystems. Oecologia 195, 589–600 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
66.Beck, E., Bendix, J., Kottke, I., Makeschin, F. & Mosandl, R. Gradients in a Tropical Mountain Ecosystem of Ecuador. ISBN: 978-3-540-73525-0
(Springer, 2008).
Google Scholar 
67.Landscape Restoration, Sustainable Use and Cross-Scale Monitoring of Biodiversity and Ecosystem Functions – A Science-directed Approach for South Ecuador (PAK823–825 Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador, 2017).68.Beck, E. et al. Ecosystem Services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador. ISBN: 978-3-642-38136-2 (Springer, 2013).
Google Scholar 
69.Homeier, J. & Leuschner, C. Factors controlling the productivity of tropical Andean forests: Climate and soil are more important than tree diversity. Biogeosciences 18, 1525–1541 (2021).ADS 
CAS 

Google Scholar 
70.Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Consequences of exclusion of precipitation on microorganisms and microbial consumers in montane tropical rainforests. Oecologia 170, 1067–1076 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Moderate changes in nutrient input alter tropical microbial and protist communities and belowground linkages. ISME J. 8, 1126–1134 (2014).CAS 
PubMed 

Google Scholar 
72.Tiede, Y. et al. Ants as indicators of environmental change and ecosystem processes. Ecol. Indic. 83, 527–537 (2017).
Google Scholar 
73.Santillán, V. et al. Spatio-temporal variation in bird assemblages is associated with fluctuations in temperature and precipitation along a tropical elevational gradient. PLoS ONE 13, e0196179 (2018).PubMed 
PubMed Central 

Google Scholar 
74.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
75.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20, http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2020).76.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Google Scholar 
77.Wallis, C. I. B. et al. Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sens. Environ. 225, 77–92 (2019).ADS 

Google Scholar 
78.Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: A novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).
Google Scholar 
79.Quitián, M. et al. Elevation-dependent effects of forest fragmentation on plant-bird interaction networks in the tropical Andes. Ecography 41, 1497–1506 (2018).
Google Scholar 
80.Fries, A. et al. Thermal structure of a megadiverse Andean mountain ecosystem in southern Ecuador and its regionalization. Erdkunde 63, 321–335 (2009).
Google Scholar 
81.Fries, A., Rollenbeck, R., Nauß, T., Peters, T. & Bendix, J. Near surface air humidity in a megadiverse Andean mountain ecosystem of southern Ecuador and its regionalization. Agric. For. Meteorol. 152, 17–30 (2012).ADS 

Google Scholar 
82.Zvoleff, A. glcm: calculate textures from grey-level co-occurrence matrices (GLCMs). R package version 1.6.1 (2016).83.Wallis, C. I. B. et al. Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa. Ecol. Indic. 83, 538–549 (2017).
Google Scholar 
84.Wolf, K., Veldkamp, E., Homeier, J. & Martinson, G. O. Nitrogen availability links forest productivity, soil nitrous oxide and nitric oxide fluxes of a tropical montane forest in southern Ecuador: N2 O + NO flux of tropical montane forests. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003876 (2011).Article 

Google Scholar 
85.Fisher, W. D. On Grouping for Maximum Homogeneity. J. Am. Stat. Assoc. 53, 789–798 (1958).MathSciNet 
MATH 

Google Scholar 
86.Bivand, R. classInt: Choose Univariate Class Intervals (2020).87.Oksanen, J. et al. vegan: Community Ecology Package (2020).88.vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan.89.Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).MATH 

Google Scholar 
90.Barbosa, A. M., Real, R., Munoz, A. R. & Brown, J. A. New measures for assessing model equilibrium and prediction mismatch in species distribution models. Divers. Distrib. 19, 1333–1338 (2013).
Google Scholar 
91.Lotz, T., Nieschulze, J., Bendix, J., Dobbermann, M. & König-Ries, B. Diverse or uniform? Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research. Ecol. Inform. 8, 10–19 (2012).
Google Scholar 
92.Göttlicher, D. et al. Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling. Int. J. Remote Sens. 30, 1867–1886 (2009).
Google Scholar 
93.Deng, Y., Wilson, J. P. & DEM Bauer, B. O. resolution dependencies of terrain attributes across a landscape. Int. J. Geogr. Inf. Sci. 21, 187–213 (2007).
Google Scholar 
94.Weiss, M. & Baret, F. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER Version 1.1. in S2 Toolbox Level 2 Product algorithms v1.1 53.95.Unger, M., Homeier, J. & Leuschner, C. Relationships among leaf area index, below-canopy light availability and tree diversity along a transect from tropical lowland to montane forests in NE Ecuador. Trop. Ecol. 54, 33–45 (2013).
Google Scholar 
96.Krashevska, V., Maraun, M. & Scheu, S. Micro- and macroscale changes in density and diversity of Testate amoebae of tropical montane rain forests of southern Ecuador. Acta Protozool. 49, 17–28 (2010).
Google Scholar  More

1.Frolking, S. et al. Peatlands in the earth’s 21st century climate system. Environ. Rev. 19, 371–396 (2011).CAS 

Google Scholar 
2.Loisel, J. et al. A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation. Holocene 24(9), 1028–1042 (2014).ADS 

Google Scholar 
3.Belyea, L. R. & Malmer, N. Carbon sequestration in peatland: Patterns and mechanisms of response to climate change. Glob. Change Biol. 10(7), 1043–1052 (2004).ADS 

Google Scholar 
4.Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8(10), 907–913 (2018).ADS 
CAS 

Google Scholar 
5.Harden, J. W., Sundquist, E. T., Stallard, R. F. & Mark, R. K. Dynamics of soil carbon during deglaciation of the Laurentide ice-sheet. Science 258(5090), 1921–1924 (1992).ADS 
CAS 
PubMed 

Google Scholar 
6.Gorham, E., Lehman, C., Dyke, A., Janssens, J. & Dyke, L. Temporal and spatial aspects of peatland initiation following deglaciation in North America. Quat. Sci. Rev. 26(3–4), 300–311 (2007).ADS 

Google Scholar 
7.Indermühle, A. et al. Holocene carbon-cycle dynamics based on CO2 trapped in ice at Taylor Dome, Antarctica. Nature 398(6723), 121–126 (1999).ADS 

Google Scholar 
8.IPCC. In Climate Change (2013): The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker T.F. et al.) (Cambridge University Press, 2013).9.Charman, D. J. et al. Climate-related changes in peatland carbon accumulation during the last millennium. Biogeosciences 10(2), 929–944 (2013).ADS 

Google Scholar 
10.Loisel, J. & Yu, Z. Recent acceleration of carbon accumulation in a boreal peatland, south central Alaska. J. Geophys. Res. Biogeosci. 118, 41–53 (2013).CAS 

Google Scholar 
11.Lund, M. et al. Variability in exchange of CO2 across 12 northern peatland and tundra sites. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2009.02104.x (2010).Article 

Google Scholar 
12.Yang, G. et al. Responses of CO2 emission and pore water DOC concentration to soil warming and water table drawdown in Zoige Peatlands. Atmos. Environ. 152, 323–329 (2017).ADS 
CAS 

Google Scholar 
13.Laine, A. M. et al. Warming impacts on boreal fen CO2 exchange under wet and dry conditions. Glob. Change Biol. 25(6), 1995–2008 (2019).ADS 

Google Scholar 
14.Pancotto, V., Holl, D., Escobar, J., Castagnani, M. F. & Kutzbach, L. Cushion bog plant community responses to passive warming in southern Patagonia. Biogeosciences 18(16), 4817–4839 (2020).ADS 

Google Scholar 
15.Gunnarsson, U. Global patterns of Sphagnum productivity. J. Bryol. 27, 269–279 (2005).
Google Scholar 
16.Limpens, J. & Berendse, F. How litter quality affects mass loss and N loss from decomposing Sphagnum. Oikos 103(3), 537–547 (2003).CAS 

Google Scholar 
17.Hajek, T., Ballance, S., Limpens, J., Zijlstra, M. & Verhoeven, J. T. A. Cell-wall polysaccharides play an important role in decay resistance of Sphagnum and actively depressed decomposition in vitro. Biogeochemistry 103, 45–57 (2011).CAS 

Google Scholar 
18.Dorrepaal, E. et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature 460, 616–619. https://doi.org/10.1038/nature08216 (2009).ADS 
CAS 
Article 

Google Scholar 
19.Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Change 11, 70–77 (2021).ADS 

Google Scholar 
20.Van der Heijden, E., Verbeek, S. K. & Kuiper, P. J. C. Elevated atmospheric CO2 and increased nitrogen deposition: Effects on C and N metabolism and growth of the peat moss Sphagnum recurvum P. Beauv. Var. mucronatum (Russ.) Warnst. Glob. Change Biol. 6(2), 201–212 (2000).ADS 

Google Scholar 
21.Berendse, F. et al. Raised atmospheric CO2 levels and increased N deposition cause shifts in plant species composition and production in Sphagnum bogs. Glob. Change Biol. 7(5), 591–598 (2001).ADS 

Google Scholar 
22.Heijmans, M. M. P. D. et al. Effects of elevated carbon dioxide and increased nitrogen deposition on bog vegetation in the Netherlands. J. Ecol. 89(2), 268–279 (2001).CAS 

Google Scholar 
23.Heijmans, M. M. P. D., Klees, H., de Visser, W. & Berendse, F. Response of a Sphagnum bog plant community to elevated CO2 and N supply. Plant Ecol. 162(1), 123–134 (2002).
Google Scholar 
24.Mitchell, E. A. D. et al. Contrasted effects of increased N and CO2 supply on two keystone species in peatland restoration and implications for global change. J. Ecol. 90(3), 529–533 (2002).CAS 

Google Scholar 
25.Toet, S. et al. Moss responses to elevated CO2 and variation in hydrology in a temperate lowland peatland. Plant Ecol. 182(1–2), 27–40 (2006).
Google Scholar 
26.Ehlers, I. et al. Detecting long-term metabolic shifts using isotopomers: CO2-driven suppression of photorespiration in C3 plants over the 20th century. Proc. Natl. Acad. Sci. USA 112(51), 15585–15590 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Serk, H., Nilsson, M. B., Figueira, J., Wieloch, T. & Schleucher, J. CO2 fertilization of Sphagnum peat mosses is modulated by water table level and other environmental factors. Plant Cell Environ. https://doi.org/10.1111/pce.14043 (2021).Article 
PubMed 

Google Scholar 
28.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. https://doi.org/10.1111/nph.16866 (2020).Article 
PubMed 

Google Scholar 
29.Schipperges, B. & Rydin, H. Response of photosynthesis of Sphagnum species from contrasting microhabitats to tissue water content and repeated desiccation. New Phytol. 140(4), 677–684 (1998).CAS 
PubMed 

Google Scholar 
30.Robroek, B. J. M., Schouten, M. G. C., Limpens, J., Berendse, F. & Poorter, H. Interactive effects of water table and precipitation on net CO2 assimilation of three co-occurring Sphagnum mosses differing in distribution above the water table. Glob. Change Biol. 15, 680–691 (2009).ADS 

Google Scholar 
31.Weston, D. J. et al. Sphagnum physiology in the context of changing climate: Emergent influences of genomics, modelling and host–microbiome interactions on understanding ecosystem function. Plant Cell Environ. 38(9), 1737–1751 (2015).PubMed 

Google Scholar 
32.Bengtsson, F., Granath, G. & Rydin, H. Photosynthesis, growth, and decay traits in Sphagnum—A multispecies comparison. Ecol. Evol. 6(19), 3325–3341 (2016).PubMed 
PubMed Central 

Google Scholar 
33.Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).ADS 
CAS 
PubMed 

Google Scholar 
34.Pauling, A., Luterbacher, J., Casty, C. & Wanner, H. Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation. Clim. Dyn. 26, 387–405 (2006).
Google Scholar 
35.Willmot, C.J., & Matsuura, K. Terrestrial air temperature and precipitation: Gridded monthly time series (1900–2017), (V 5.01 added 6/1/18). http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsT2017.html and README.GlobalTsP2017.html (2018).36.Loisel, J., Garneau, M. & Hélie, J.-F. Sphagnum δ13C values as indicators of palaeohydrological changes in a peat bog. Holocene 20(2), 285–291 (2010).ADS 

Google Scholar 
37.Swindles, G. T. et al. Widespread drying of European peatlands in recent centuries. Nat. Geosci. 12, 922–928 (2019).ADS 
CAS 

Google Scholar 
38.Pelletier, N. et al. Influence of Holocene permafrost aggradation and thaw on the paleoecology and carbon storage of a peatland complex in northwestern Canada. Holocene 27(9), 1391–1405 (2017).ADS 

Google Scholar 
39.Talbot, J., Richard, P. J. H., Roulet, N. T. & Booth, R. K. Assessing long-term hydrological and ecological responses to drainage in a raised bog using paleoecology and a hydrosequence. J. Veg. Sci. 21, 143–156 (2010).
Google Scholar 
40.Kopp, B. J. et al. Impact of long-term drainage on summer groundwater flow patterns in the Mer Bleue peatland, Ontario, Canada. Hydrol. Earth Sci. 17, 3485–3498 (2013).
Google Scholar 
41.Van Bellen, S. et al. Late-Holocene climate dynamics recorded in the peat bogs of Tierra del Fuego, South America. Holocene 26(3), 489–501 (2016).ADS 

Google Scholar 
42.De Jong, R., Schoning, K. & Björck, S. Increased aeolian acitivty during humidity shifts as recorded in a raised bog in south-west Sweden during the past 1700 years. Clim. Past 3, 411–422 (2007).
Google Scholar 
43.Kunshan, B. et al. A 100-year history of water level change and driving mechanism in Heilongjiang River basin wetlands. Quat. Sci. 38(4), 981–995 (2018).
Google Scholar 
44.Zheng, X. The reconstruction of moisture availability in south-eastern Australia during the Holocene. PhD thesis, University of New South Wales, Sydney (2018).45.Loader, N. J. et al. Measurements of hydrogen, oxygen and carbon isotope variability in Sphagnum moss along a micro-topographical gradient in a southern Patagonian peatland. J. Quat. Sci. 31(4), 426–435 (2016).
Google Scholar 
46.Xia, Z. et al. Environmental controls on the carbon and water (H and O) isotopes in peatland Sphagnum mosses. Geochim. Cosmochim. Acta 277, 265–284 (2020).ADS 
CAS 

Google Scholar 
47.Sharkey, T. D. Estimating the rate of photorespiration in leaves. Physiol. Plant. 73, 147–152 (1988).CAS 

Google Scholar 
48.Flamholz, A. I. et al. Revisiting trade-offs between Rubisco kinetic properties. Biochemistry 58, 3365–3376 (2019).CAS 
PubMed 

Google Scholar 
49.Wu, J. H. & Roulet, N. T. Climate change reduces the capacity of northern peatlands to absorb the atmospheric carbon dioxide: The different responses of bogs and fens. Glob. Biogeochem. Cycles https://doi.org/10.1002/2014GB004845 (2014).Article 

Google Scholar 
50.Fenner, N. & Freeman, C. Drought-induced carbon loss in peatlands. Nat. Geosci. 4, 895–900 (2011).ADS 
CAS 

Google Scholar 
51.Lund, M., Chrsitensen, T. R., Lindroth, A. & Schubert, P. Effects of drought conditions on the carbon dioxide dynamics in a temperate peatland. Environ. Res. Lett. 7, 045704. https://doi.org/10.1088/1748-9326/7/4/045704 (2012).ADS 
Article 

Google Scholar 
52.Chong, M., Humphreys, E. R. & Moore, T. R. Microclimatic response to increasing shrub cover and its effect on Sphagnum CO2 exchange in a bog. Ecoscience 19, 89–97 (2012).
Google Scholar 
53.Fritz, C. et al. Nutrient additions in pristine Patagonian Sphagnum bog vegetation: Can phosphorus addition alleviate (the effects of) increased nitrogen loads. Plant Biol. 14, 491–499 (2012).CAS 
PubMed 

Google Scholar 
54.Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).CAS 

Google Scholar 
55.Bengtsson, F., Granath, G., Cronberg, N. & Rydin, H. Mechanisms behind species-specific water economy responses to water level drawdown in peat mosses. Ann. Bot. 126(2), 219–230 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Nijp, J. J. et al. Can frequent precipitation moderate the impact of drought on peatmoss carbon uptake in northern peatlands?. New Phytol. 203(1), 70–80 (2014).PubMed 

Google Scholar 
57.Limpens, J., Berendse, F. & Klees, H. How phosphorous availability affects the impact of nitrogen deposition on Sphagnum and vascular plants in bogs. Ecosystems 7, 793–804 (2004).CAS 

Google Scholar 
58.Wu, J. H., Roulet, N. T., Nilsson, M., Lafleur, P. & Humphreys, E. Simulating the carbon cycling of Northern peat lands using a land surface scheme coupled to a Wetland Carbon Model (CLASS3W-MWM). Atmos. Ocean 50(4), 487–506 (2012).CAS 

Google Scholar 
59.Etheridge D. M., Steele L. P., Langenfelds R. L., Francey R. J., Barnola J. M., & Morgan V. I. Historical CO2 records from the law dome DE08, DE08-2, and DSS ice cores (1006 A.D.–1978 A.D). https://doi.org/10.3334/CDIAC/ATG.011 (Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), 1998).60.Laine, J. et al. The intrinsic beauty of Sphagnum Mosses—A Finnish guide to Identification. University of Helsinki. Dept. For. Sci. Publ. 2, 1–191 (2011).
Google Scholar 
61.Grover, S. P. P., Baldock, J. A. & Jacobsen, G. E. Accumulation and attrition of peat soils in the Australian Alps: Isotopic dating evidence. Austral. Ecol. 37, 510–517 (2012).
Google Scholar 
62.Kleinbecker, T., Hölzel, N. & Vogel, A. Gradients of continentality and moisture in south Patagonian ombrotrophic peatland vegetation. Folia Geobotanica 42, 363–382 (2007).
Google Scholar 
63.Hassel, K. et al. Sphagnum divinum (sp. nov.) and S. medium Limpr. and their relationship to S. magellanicum Brid. J. Bryol. 40, 197–222 (2018).
Google Scholar 
64.Betson, T. R., Augusti, A. & Schleucher, J. Quantification of deuterium isotopomers of tree-ring cellulose using nuclear magnetic resonance. Anal. Chem. 78(24), 8406–8411 (2006).CAS 
PubMed 

Google Scholar 
65.Schleucher, J., Vanderveer, P., Markley, J. L. & Sharkey, T. D. Intramolecular deuterium distributions reveal disequilibrium of chloroplast phosphoglucose isomerase. Plant Cell Environ. 22(5), 525–533 (1999).CAS 

Google Scholar 
66.Werner, R. A., Bruch, B. A. & Brand, W. A. ConFlo III—An interface for high precision δ13C and δ15N analysis with an extended dynamic range. Rapid Commun. Mass Spectrom. 13(13), 1237–1241 (1999).ADS 
CAS 
PubMed 

Google Scholar 
67.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015).
Google Scholar 
68.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
69.Gareth, J., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer Science+Business Media, 2013).MATH 

Google Scholar 
70.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).MATH 

Google Scholar 
71.Lüning, S., Galka, M., Bamonte, F. P., Rodríguez, F. G. & Vahrenholt, F. The medieval climate anomaly in South America. Quat. Int. 508, 70–87 (2019).
Google Scholar 
72.Schimpf, D. et al. The significance of chemical isotopic and detrital components in three coeval stalagmites from the superhumid southernmost Andes (53°S) as high-resolution paleo-climate proxies. Quat. Sci. Rev. 30, 443–459 (2011).ADS 

Google Scholar  More

Recent analyses have found that, despite high and increasing levels of community turnover, there is no clear overall trend in local-scale species richness1,2,3,4. However, it remains unclear how this result translates into functional changes. One of the most fundamental functional traits of a species is its size5,6 and there is an expectation that a warming climate will lead to a shift towards smaller species7,8,9,10,11, drawing upon metabolic theory12 and the observed distributional patterns described by Bergmann’s rule13,14. Temperature-driven shifts towards smaller species have been observed in tundra plant communities15 and some7,9,16, but not all11, aquatic systems. Furthermore, larger species have been more extinction prone during some previous mass extinctions17,18 and are more likely to show strong recent population declines19. Although relationships are threat dependent20,21, larger species tend to be assessed at a higher risk of extinction due to longer generational intervals and increased threat from habitat loss, fragmentation and hunting22.One might therefore expect a detectable signal of shifts in community trait values beneath the apparent underlying consistency in taxonomic diversity. To examine this, we tested whether the size of a species is correlated with the change in abundance through time using the publicly available BioTIME database23. This database is the largest collection of time series of ecological communities and, despite considerable biases that we discuss below, has wide geographic and taxonomic scope23. It consists of ‘studies’ defined by a consistent sampling methodology and taxonomic focus. After cleaning and standardizing the names associated with the records, we linked six fundamental ‘size’ traits from four openly accessible trait databases representing four broad guilds: adult body mass from a database of amniote life history traits24, adult body length and qualitative body size of marine species from the World Register of Marine Species (WoRMS) database25, plant maximum height and seed mass from the TRY database26 and maximum body length of fish from a compilation27 based on data in the FishBase repository28.Observations from single-location studies were combined, whilst widely dispersed studies were separately binned into a global grid of cells, each approximately 10 km wide, and data from each study and cell were treated as discrete assemblages, following previous analyses1,29. Selecting only assemblages with quantitative observations of ≥10 species, over ≥5 years and with ≥40% of the species having records for at least one size trait, we generated 12,956 assemblage time series from 144 studies (Fig. 1). This filtered dataset represented 2,109,593 observations of 10,286 species, of which 7,234 could be linked to at least one size trait (representing 84.02% of observations). Equally weighting studies, the average time series length was 18.2 years (range 5–71.8 years), and the average number of species per included assemblage was 65.4 (range 10–337). The log10 ratio between the largest and smallest species in each study averaged 2.49 (range 0.55–6.73) across the ‘mass’ traits and 1.06 (range 0.3–3.15) across the ‘length’ traits.Fig. 1: Global distribution of studies in our dataset, showing average τ for each study–trait combination and divided into aquatic and terrestrial realms.The aquatic realm is principally marine but includes three freshwater studies. Note that the locations are shown as the centre point of each study, which can cause oceanic studies to be ‘located’ on land. See Extended Data Fig. 1 for full details of study-level results.Full size imageFor each trait and community assemblage time series for which there were sufficient data, we first square-root transformed and standardized each time series following previous approaches3 and calculated βi, the slope of a regression of abundance of species i against time. We then calculated, for each assemblage, τ (the Kendall rank correlation coefficient between the trait in question) and β, across the species for which we had trait data. This gives a non-parametric measure of whether larger species are more or less likely than smaller species to have increased through time and, importantly, can be calculated where trait values for only a fraction of the observed species are available. To weight each study within BioTIME equally, where there were multiple assemblages per study, these were averaged to generate a τ value for each possible study–trait combination. To provide a reference distribution against which to evaluate the statistical significance of this multistage analysis, we repeated the procedure with 10,000 trait randomizations within each assemblage.Certain individual studies showed significant relationships between size traits and population trends (coloured dots in Fig. 2 and Extended Data Fig. 1). However, for five of the six tested size traits, the overall mean τ values did not differ significantly from the null model (Fig. 2). For one trait (amniote body mass, Fig. 2d) we found a marginally significant (unadjusted for multiple comparisons) overall average positive relationship between size and the slope of population trends (β). Alternative population data transformations gave highly concordant results (Extended Data Figs. 2 and 3). Possible confounding factors for the value of τ associated with each study, namely the total span of the time series, the number of sample points, the species richness, the range of traits in the assemblage, the average size trait completeness, the number of assemblages within the study, the grain of the study and the absolute latitude, did not consistently predict either τ or τ2 (Extended Data Figs. 4 and 5 and Supplementary Tables 1 and 2). Further, the likelihood of an individual species showing either a statistically significant positive or negative population trend was not linked to its relative size trait value within the assemblage (all P  > 0.05; Extended Data Fig. 6 and Supplementary Table 3).Fig. 2: Correlation between six body-size traits and changes in abundance through time (τ).a–f, Distribution of Kendall rank correlation coefficient between body-size traits for body length (a) and qualitative body size (b) of marine species, maximum length of fish (c), adult body mass of amniotes (d) and seed mass (e) and maximum height (f) of plants versus changes in abundance through time. Each dot represents one study, averaging across the constituent assembly time series for studies of large spatial extent. Study-level results are binned into classes 0.05 units of τ wide. Coloured dots highlight studies that were individually identified as showing a significant trend (yellow for negative, blue for positive; see Extended Data Fig. 1 for study-level intervals). The error bar below each plot displays the distribution (central 95% and 66%) of mean τ values over 10,000 permutations of the size trait data, whilst the red line indicates the observed mean τ value within that panel. Displayed P values are calculated from permutation tests. Equivalent results using alternative approaches to transforming the community data are given in Extended Data Fig. 3.Full size imageThese results indicate that there is not yet evidence for widely pervasive within-assemblage trends in a core functional trait, size. Importantly however, this study should not be seen as a refutation or diminishment of the heightened threats faced by the very largest apex species30,31, which constitute only a minor component of the BioTIME database. Rather, against a background of considerable turnover2,3 across whole observed community assemblages, on average, species positions in communities are being taken up by species of comparable size. Our results suggest that previously identified shifts towards smaller species found in some aquatic systems9,16 may not be as universal as currently expected7,11 and align with the divergent changes in global body-size abundance distributions observed between mammal guilds32 and the apparent stability of trait diversity in North American birds despite declines in abundance33.The tendency towards an overall positive association between body-size and population trends across the amniote studies could have a number of drivers that would benefit from further investigation. One putative explanation that has been put forward for positive size trends is that anthropogenic dispersal limitations (generally considered to act more strongly against smaller species) may be having a greater immediate impact than climate change34. There are also indications of differences between terrestrial and marine systems. Previous work with the same datasets1,29 has found greater species richness and abundance changes in marine than terrestrial systems, whilst here we see a signal of greater trait changes in the (largely terrestrial) amniotes.In our dataset, the fish length trait studies displayed a particularly skewed distribution of τ values (Fig. 2c), with a modal peak of studies showing small negative values then a tail of strongly positive relationships. This guild is also the most likely to have experienced sustained anthropogenic pressure35, and many of the ‘fish’ datasets in BioTIME include data from surveys of actively fished and managed areas. Accurately quantifying marine community trends is a challenge36,37, but this pattern could reflect the imposition or relaxation of anthropogenic pressure across marine systems38,39. Positive τ values could represent recoveries from past pressures on larger species, and positive τ values were associated with shorter study durations in the fish studies (Extended Data Fig. 4).Our analysis necessarily sacrifices fine resolution for global scale. Technically, BioTIME studies represent assemblages defined by taxonomy and sampling protocol rather than complete ecological communities. We must implicitly assume that the scope of each study within BioTIME strikes a reasonable balance between the need to include a sufficiently diverse set of species to be able to observe any potential impact of trait differences whilst maintaining meaningful comparability. Limitations to total time series lengths and the limited range of sizes recorded within each dataset inevitably constrain our capacity to detect gradual changes or subtle influences of size. Although the lack of consistent study-level drivers of τ suggests that the results are unlikely to be solely determined by the inevitable spatial and temporal limitations of the BioTIME database, future work should seek to improve the scope and resolution of available data to enable more strongly parametric analyses and examine additional measures of community change.Whilst available trait databases of amniotes and fish are carefully curated, checked and taxonomically tidy, the other databases pose more problems in terms of taxonomic matching and consistency of trait measurements. Without direct correspondence between the sources of dynamics and trait data, it is necessary to take traits as fixed values for each species, despite known differences in traits in time8,40,41,42 and space43 that may themselves represent responses to global change. However, in Celtic Sea fish, within-species shifts have been shown to contribute less to community-level size shifts than changes in species composition44. We also note that ‘size’ traits for indeterminately growing plants have a less clear meaning than for animals. However, both seed size and maximum height are linked to environmental variables45,46, plant size is linked to life history47,48 and changes in community height driven by species turnover have been observed in tundra plants15.Many of the criticisms and defences regarding earlier studies using the BioTIME dataset, and indeed other analyses of large collections of time series, also apply to this work49,50. The consistency between the alternative approaches we tested to determine population trends (Extended Data Fig. 3) demonstrates that our conclusions are not dependent on particular data transformation choices. However, a largely non-parametric statistical approach was necessitated by the unevenness of the available data, and it must be noted that it could lack the power and resolution to identify subtle changes. Biases in the underlying BioTIME database towards vertebrate taxa, particular biomes and temperate North American and European sites23 are further exaggerated when crossed with trait data availability (Fig. 1). One particularly concerning gap is the absence of any insect studies in our dataset due to a paucity of usable trait information. Observations suggest that there have been considerable changes in the structure of insect communities34,51,52. Developing comprehensive insect trait datasets, including using proxies and data imputation, will be crucial to address this deficit53,54,55.In conclusion, despite necessary reservations, this global analysis suggests that examples of relative increases of larger species11,34 may in fact be as frequent as shifts towards smaller-sized species16. Community responses appear to be considerably more nuanced and localized than previously considered based on theoretical macro-ecological expectations7. More

1.Cesar, H., Burke, L. & Pet-Soede L. The Economics of Worldwide Coral Reef Degradation (Cesar Environmental Economics Consulting, 2003).2.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science 301, 955–958 (2003).CAS 
Article 

Google Scholar 
3.Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).Article 

Google Scholar 
4.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Science 359, 80–83 (2018).CAS 
Article 

Google Scholar 
5.Grottoli, A. G., Rodrigues, L. J. & Juarez, C. Lipids and stable carbon isotopes in two species of Hawaiian corals, Porites compressa and Montipora verrucosa, following a bleaching event. Mar. Biol. 145, 621–631 (2004).CAS 
Article 

Google Scholar 
6.Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Change Biol. 20, 3823–3833 (2014).Article 

Google Scholar 
7.Underwood, J. N., Smith, L. D., van Oppen, M. J. H. & Gilmour, J. P. Ecologically relevant dispersal of corals on isolated reefs: implications for managing resilience. Ecol. Appl. 19, 18–29 (2009).Article 

Google Scholar 
8.Nozawa, Y. & Harrison, P. L. Effects of elevated temperature on larval settlement and post-settlement survival in scleractinian corals, Acropora solitaryensis and Favites chinensis. Mar. Biol. 152, 1181–1185 (2007).Article 

Google Scholar 
9.Heyward, A. J. & Negri, A. P. Plasticity of larval pre-competency in response to temperature: observations on multiple broadcast spawning coral species. Coral Reefs 29, 631–636 (2010).Article 

Google Scholar 
10.Figueiredo, J., Baird, A. H., Harii, S. & Connolly, S. R. Increased local retention of reef coral larvae as a result of ocean warming. Nat. Clim. Change 4, 498–502 (2014).Article 

Google Scholar 
11.Munday, P. L. et al. Climate change and coral reef connectivity. Coral Reefs 28, 379–395 (2009).Article 

Google Scholar 
12.van Gennip, S. J. et al. Going with the flow: the role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23, 2602–2617 (2017).Article 

Google Scholar 
13.IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).14.Nishikawa, A. & Sakai, K. Settlement-competency period of planulae and genetic differentiation of the scleractinian coral Acropora digitifera. Zool. Sci. 22, 391–399 (2005).Article 

Google Scholar 
15.Connolly, S. R. & Baird, A. H. Estimating dispersal potential for marine larvae: dynamic models applied to scleractinian corals. Ecology 91, 3572–3583 (2010).Article 

Google Scholar 
16.Figueiredo, J., Baird, A. H. & Connolly, S. R. Synthesizing larval competence dynamics and reef-scale retention reveals a high potential for self-recruitment in corals. Ecology 94, 650–659 (2013).Article 

Google Scholar 
17.Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the elkhorn coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).Article 

Google Scholar 
18.Randall, C. J. & Szmant, A. M. Elevated temperature reduces survivorship and settlement of the larvae of the Caribbean scleractinian coral, Favia fragum (Esper). Coral Reefs 28, 537–545 (2009).Article 

Google Scholar 
19.Burgess, S. C. et al. Beyond connectivity: how empirical methods can quantify population persistence to improve marine protected-area design. Ecol. Appl. 24, 257–270 (2014).Article 

Google Scholar 
20.Woolsey, E. S., Keith, S. A., Byrne, M., Schmidt-Roach, S. & Baird, A. H. Latitudinal variation in thermal tolerance thresholds of early life stages of corals. Coral Reefs 34, 471–478 (2015).Article 

Google Scholar 
21.Rodriguez-Lanetty, M., Harii, S. & Hoegh-Guldberg, O. Early molecular responses of coral larvae to hyperthermal stress. Mol. Ecol. 18, 5101–5114 (2009).CAS 
Article 

Google Scholar 
22.Andutta, F. P., Kingsford, M. J. & Wolanski, E. ‘Sticky water’ enables the retention of larvae in a reef mosaic. Estuar. Coast. Shelf Sci. 54, 655–668 (2012).
Google Scholar 
23.Hock, K. et al. Connectivity and systemic resilience of the Great Barrier Reef. PLoS Biol. 15, e2003355 (2017).Article 

Google Scholar 
24.Bode, M., Bode, L., Choukroun, S., James, M. K. & Mason, L. B. Resilient reefs may exist, but can larval dispersal models find them? PLoS Biol. 16, e2005964 (2018).Article 

Google Scholar 
25.Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).Article 

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

Google Scholar 
27.Strathmann, R. R. et al. Evolution of local recruitment and its consequences for marine populations. Bull. Mar. Sci. 70, 377–396 (2002).
Google Scholar 
28.Marshall, D. J., Monro, K., Bode, M., Keough, M. J. & Swearer, S. Phenotype–environment mismatches reduce connectivity in the sea. Ecol. Lett. 13, 128–140 (2010).CAS 
Article 

Google Scholar 
29.Szmant, A. M. & Gassman, N. J. The effects of prolonged “bleaching” on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8, 217–224 (1990).Article 

Google Scholar 
30.Leis, J. M. Nearshore distributional gradients of larval fish (15 taxa) and planktonic crustaceans (6 taxa) in Hawaii. Mar. Biol. 72, 89–97 (1982).Article 

Google Scholar 
31.Kraines, S. B., Yanagi, T., Isobe, M. & Komiyama, H. Wind-wave driven circulation on the coral reef at Bora Bay, Miyako Island. Coral Reefs 17, 133–143 (1998).Article 

Google Scholar 
32.Paris, C. B. & Cowen, R. K. Direct evidence of a biophysical retention mechanism for coral reef fish larvae. Limnol. Oceanogr. 49, 1964–1979 (2004).Article 

Google Scholar 
33.Keshavmurthy, S., Fontana, S., Mezaki, T., Gonzalez, L. C. & Chen, C. A. Doors are closing on early development in corals facing climate change. Sci. Rep. 4, 5633 (2014).CAS 
Article 

Google Scholar 
34.Thomas, C. J. et al. Numerical modelling and graph theory tools to study ecological connectivity in the Great Barrier Reef. Ecol. Model. 272, 160–174 (2014).Article 

Google Scholar 
35.Holstein, D. M., Paris, C. B., Vaz, A. C. & Smith, T. B. Modeling vertical coral connectivity and mesophotic refugia. Coral Reefs 35, 23–37 (2016).Article 

Google Scholar 
36.Hata, T. et al. Coral larvae are poor swimmers and require fine-scale reef structure to settle. Sci. Rep. 7, 2249 (2017).Article 

Google Scholar 
37.Gleason, D. F. & Hofmann, D. K. Coral larvae: from gametes to recruits. J. Exp. Mar. Biol. Ecol. 408, 42–57 (2011).Article 

Google Scholar  More

1.Haq, B. U. Jurassic sea-level variations: a reappraisal. GSA Today 28, 4–10 (2017).
Google Scholar 
2.Sahagian, D., Pinous, O., Olferiev, A. & Zakharov, V. Eustatic curve for the Middle Jurassic–Cretaceous based on Russian platform and Siberian stratigraphy: zonal resolution. Am. Assoc. Pet. Geol. Bull. 80, 1433–1458 (1996).
Google Scholar 
3.Donnadieu, Y. et al. A mechanism for brief glacial episodes in the Mesozoic greenhouse. Paleoceanography 26, PA3212 (2011).
Google Scholar 
4.Korte, C. & Hesselbo, S. P. Shallow marine carbon and oxygen isotope and elemental records indicate icehouse–greenhouse cycles during the Early Jurassic. Paleoceanography 26, PA4219 (2011).
Google Scholar 
5.Dromart, G. et al. Ice age at the Middle–Late Jurassic transition? Earth Planet. Sci. Lett. 213, 205–220 (2003).
Google Scholar 
6.Price, G. D. The evidence and implications of polar ice during the Mesozoic. Earth Sci. Rev. 48, 183–210 (1999).
Google Scholar 
7.Rogov, M. A. & Zakharov, V. A. Jurassic and Lower Cretaceous glendonite occurrences and their implication for Arctic paleoclimate reconstructions and stratigraphy. Earth Sci. Front. 17, 345–347 (2010).
Google Scholar 
8.Teichert, B. M. A. & Luppold, F. W. Glendonites from an Early Jurassic methane seep—climate or methane indicators? Palaeogeogr. Palaeoclimatol. Palaeoecol. 390, 81–93 (2013).
Google Scholar 
9.Suan, G. et al. Polar record of Early Jurassic massive carbon injection. Earth Planet. Sci. Lett. 312, 102–113 (2011).
Google Scholar 
10.Brandt, K. Glacioeustatic cycles in the Early Jurassic? Neues Jahrb. Geol. Palaontol. Abh. 5, 257–274 (1986).
Google Scholar 
11.Woolfe, K. J. & Francis, J. E. An Early to Middle Jurassic glaciation-evidence from Allan Hills, Transantarctic Mountains. In Proc. 6th International Symposium on Antarctic Earth Sciences, Japan 652–653 (1991).12.Dera, G. & Donnadieu, Y. Modeling evidences for global warming, Arctic seawater freshening, and sluggish oceanic circulation during the early Toarcian anoxic event. Paleoceanography 27, PA2211 (2012).
Google Scholar 
13.Silva, R. C. & Duarte, L. V. Organic matter production and preservation in the Lusitanian Basin (Portugal) and Pliensbachian climatic hots snaps. Glob. Planet. Change 131, 24–34 (2015).
Google Scholar 
14.Gómez, J. J., Comas-Rengifo, M. J. & Goy, A. Paleoclimatic oscillations in the Pliensbachian (Early Jurassic) of the Asturian Basin (Northern Spain). Clim. Past 12, 1199–1274 (2016).
Google Scholar 
15.Suan, G. et al. Secular environmental precursors to early Toarcian (Jurassic) extreme climate changes. Earth Planet. Sci. 290, 448–458 (2010).
Google Scholar 
16.Fantasia, A. et al. Global versus local processes during the Pliensbachian–Toarcian transition at the Peniche GSSP, Portugal: a multi-proxy record. Earth Sci. Rev. 198, 2932 (2019).
Google Scholar 
17.Sell, B. et al. Evaluating the temporal link between the Karoo LIP and climatic–biologic events of the Toarcian Stage with high-precision U-Pb geochronology. Earth Planet. Sci. Lett. 408, 48–56 (2014).
Google Scholar 
18.Hesselbo, S. P. et al. Massive dissociation of gas hydrate during a Jurassic oceanic anoxic event. Nature 406, 392–395 (2000).
Google Scholar 
19.Hesselbo, S. P., Jenkyns, H. C., Duarte, L. V. & Oliveira, L. C. V. Carbon-isotope record of the Early Jurassic (Toarcian) ocean anoxic event from fossil wood and marine carbonate (Lusitanian Basin, Portugal). Earth Planet. Sci. Lett. 253, 455–470 (2007).
Google Scholar 
20.Schubert, B. A. & Jahren, A. H. Incorporating the effects of photorespiration into terrestrial paleoclimate reconstruction. Earth Sci. Rev. 177, 637–642 (2018).
Google Scholar 
21.Miller, K. G., Wright, J. D. & Browning, J. V. Visions of ice sheets in a greenhouse world. Mar. Geol. 217, 215–231 (2005).
Google Scholar 
22.Gómez, J. J., Goy, A. & Canales, M. L. Seawater temperature and carbon isotope variations in belemnites linked to mass extinction during the Toarcian (Early Jurassic) in Central and Northern Spain. Comparison with other European sections. Palaeogeogr. Palaeoclimatol. Palaeoecol. 258, 28–58 (2008).
Google Scholar 
23.Rosales, I., Quesada, S. & Robles, S. Paleotemperature variations of Early Jurassic seawater recorded in geochemical trends of belemnites from the Basque–Cantabrian basin, northern Spain. Palaeogeogr. Palaeoclimatol. Palaeoecol. 203, 253–275 (2004).
Google Scholar 
24.van de Schootbrugge, B. et al. Early Jurassic climate change and the radiation of organic-walled phytoplankton in the Tethys Ocean. Paleobiology 31, 73–97 (2010).
Google Scholar 
25.Vera, E. I. & Césari, S. N. New species of conifer wood from the Baqueró Group (Early Cretaceous) of Patagonia. Ameghiniana 52, 468–471 (2015).
Google Scholar 
26.Wilson, J. P. et al. Dynamic Carboniferous tropical forests: new views of plant function and physiological forcing of climate. New Phytol. 215, 1333–1353 (2017).
Google Scholar 
27.Lomax, B. H., Lake, J. A., Leng, M. J. & Jardine, P. E. An experimental evaluation of the use of Δ13C as a proxy for palaeoatmospheric CO2. Geochim. Cosmochim. Acta 247, 162–174 (2019).
Google Scholar 
28.Tipple, B. J., Meyers, S. R. & Pagani, M. Carbon isotope ratio of Cenozoic CO2: a comparative evaluation of available geochemical proxies. Paleoceanography 25, PA3202 (2010).
Google Scholar 
29.Diefendorf, A. F., Freeman, K. H. & Wing, S. L. Distribution and carbon isotope patterns of diterpenoids and triterpenoids in modern temperate C3 trees and their geochemical substantial. Geochim. Cosmochim. Acta 85, 342–356 (2012).
Google Scholar 
30.Lenton, T. M., Daines, S. J. & Mills, B. J. W. COPSE reloaded: an improved model of biogeochemical cycling over Phanerozoic time. Earth Sci. Rev. 178, 1–28 (2018).
Google Scholar 
31.McElwain, J. C., Wade-Murphy, J. & Hesselbo, S. P. Changes in carbon dioxide during an oceanic anoxic event linked to intrusion into Gondwana coals. Nature 435, 479–482 (2005).
Google Scholar 
32.Ruebsam, W., Reolid, M. & Schwark, L. δ13C of terrestrial vegetation records Toarcian CO2 and climate gradients. Sci. Rep. 10, 117 (2020).
Google Scholar 
33.Huang, C. & Hesselbo, S. P. Pacing of the Toarcian oceanic anoxic event (Early Jurassic) from astronomical correlation of marine sections. Gondwana Res. 25, 1348–1356 (2014).
Google Scholar 
34.Fung, M. K., Katz, M. E., Miller, K. G., Browning, J. V. & Rosenthal, Y. Sequence stratigraphy, micropaleontology, and foraminiferal geochemistry, Bass River, New Jersey paleoshelf, USA: implications for Eocene ice-volume changes. Geosphere 15, 502–532 (2019).
Google Scholar 
35.Oerlemans, J. A model of the Antarctic ice sheet. Nature 297, 550–553 (1982).
Google Scholar 
36.Esch, M. B. & Herterich, K. A two-dimensional coupled atmosphere–ice sheet–continent model designed for paleoclimatic simulations. Ann. Glaciol. 14, 55–57 (1990).
Google Scholar 
37.Foster, G. L. & Rohling, E. J. Relationship between sea level and climate forcing by CO2 on geological timescales. Proc. Natl Acad. Sci. USA 110, 1209–1214 (2013).
Google Scholar 
38.Gasson, E., DeConto, R. M., Pollard, D. & Levy, R. H. Dynamic Antarctic ice sheet during the early to mid-Miocene. Proc. Natl Acad. Sci. USA 113, 3459–3464 (2016).
Google Scholar 
39.Booth, B. B. et al. Narrowing the range of future climate projections using historical observations of atmospheric CO2. J. Clim. 30, 3039–3053 (2017).
Google Scholar 
40.Hesselbo, S. P. & Pienkowski, G. Stepwise atmospheric carbon-isotope excursion during the Toarcian oceanic anoxic event (Early Jurassic, Polish Basin). Earth Planet. Sci. Lett. 301, 365–372 (2011).
Google Scholar 
41.Storm, M. S. et al. Orbital pacing and secular evolution of the Early Jurassic carbon cycle. Proc. Natl Acad. Sci. USA 117, 3974–3982 (2020).
Google Scholar 
42.Jenkyns, H. C. & Clayton, C. J. Lower Jurassic epicontinental carbonates and mudstones from England and Wales: chemostratigraphic signals and the early Toarcian anoxic event. Sedimentology 44, 687–706 (1997).
Google Scholar 
43.Hermoso, M., Minoletti, F. & Pellenard, P. Black shale deposition during Toarcian super-greenhouse driven by sea level. Clim. Past 9, 2703–2712 (2013).
Google Scholar 
44.Schouten, S., van Kaam-Peters, M. E., Rijpstra, W. I. C., Schoell, M. & Damste, J. S. S. Effects of an oceanic anoxic event on the stable carbonate isotopic composition of early Toarcian carbon. Am. J. Sci. 300, 1–22 (2000).
Google Scholar 
45.Sabatino, N. et al. Carbon-isotope records of the Early Jurassic (Toarcian) oceanic anoxic event from the Valdobia (Umbria-Marche Apennines) and Monte Mangart (Julian Alps) sections: palaeooceaogrpahic and stratigraphic implications. Sedimentology 56, 1307–1328 (2009).
Google Scholar 
46.Garbe, J., Albrecht, T., Levermann, A., Donges, J. & Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. Nature 585, 538–544 (2020).
Google Scholar 
47.Suan, G., Mattioli, E., Pittet, B., Mailliot, S. & Lécuyer, C. Evidence for major environmental perturbation prior to and during the Toarcian (Early Jurassic) oceanic anoxic event form the Lusitanian Basin, Portugal. Paleoceanography 23, PA1202 (2008).
Google Scholar 
48.Müller, T. et al. New multiproxy record of the Jenkyns Event (also known as the Toarcian anoxic event) from the Mecsek Mountains (Hungary): differences, duration and drivers. Sedimentology 64, 66–86 (2017).
Google Scholar 
49.Ogg, J. G. & Hinnov, A. L. in The Geologic Time Scale 2012 (eds Gradstein, F. M. et al.) Ch. 26 (Elsevier, 2012).50.McCarroll, D. & Loader, N. J. Stable isotopes in tree rings. Quat. Sci. Rev. 23, 771–801 (2004).
Google Scholar 
51.Voelker, S. L. et al. A dynamic leaf gas-exchange strategy is conserved in woody plants under changing ambient CO2: evidence from carbon isotope discrimination in paleo and CO2 enrichment studies. Glob. Change Biol. 22, 889–902 (2016).
Google Scholar 
52.Tholen, D. & Zhu, X. G. The mechanistic basis of internal conductance: a theoretical analysis of mesophyll cell photosynthesis and CO2 diffusion. Plant Physiol. 156, 90–105 (2011).
Google Scholar 
53.Cui, Y. & Schubert, B. A. Quantifying the uncertainty of past pCO2 determined from changes in C3 plant carbon isotope fractionation. Geochim. Cosmochim. Acta 172, 127–138 (2016).
Google Scholar 
54.Schubert, B. A. & Jahren, A. H. The effect of atmospheric CO2 concentration on carbon isotope fractionation in C3 land plants. Geochim. Cosmochim. Acta 96, 29–43 (2012).
Google Scholar 
55.Philippe, M. et al. The palaeolatitudinal distribution of fossil wood genera as a proxy for European Jurassic terrestrial climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 466, 373–381 (2017).
Google Scholar 
56.Zhou, Z. A heterophyllous conifer from the Cretaceous of East China. Palaeontology 26, 789–811 (1983).
Google Scholar 
57.Farjon A. A. Natural History of Conifers (Timber Press, 2008).58.Diefendorf, A. F., Mueller, K. E., Wing, S. L., Koch, P. L. & Freeman, K. H. Global patterns in leaf 13C discrimination and implications for studies of past and future climate. Proc. Natl Acad. Sci. USA 107, 5738–5743 (2010).
Google Scholar 
59.Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo)ecology and (paleo)climate. Proc. Natl Acad. Sci. USA 107, 19691–19695 (2010).
Google Scholar 
60.Schlesser, G. H., Helle, G., Lücke, A. & Vos, H. Isotope signals as climate proxies: the role of transfer functions in the study of terrestrial archives. Quat. Sci. Rev. 18, 927–943 (1999).
Google Scholar 
61.Silva, R. L., Duarte, L. V. & Filho, J. G. M. Optical and geochemical characterization of upper Sinemurian (Lower Jurassic) fossil wood from the Lusitanian Basin (Portugal). Geochem. J. 47, 489–498 (2013).
Google Scholar 
62.Lukens, W. E., Eze, P. & Schubert, B. A. The effect of diagenesis on carbon isotope values of fossil wood. Geology 47, 987–991 (2019).
Google Scholar 
63.Armendáriz, M. et al. An approach to estimate Lower Jurassic seawater oxygen isotope composition using δ18O and Mg/Ca ratios of belemnite calcites (early Pliensbachian, northern Spain). Terra Nova 25, 439–445 (2013).
Google Scholar 
64.Lear, C. H., Elderfield, H. & Wilson, P. A. Cenozoic deep-sea temperatures and global ice volumes from Mg/Ca in benthic foraminiferal calcite. Science 287, 269–272 (2000).
Google Scholar 
65.Hollis, C. J. et al. The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database. Geosci. Model Dev. 12, 3149–3206 (2019).
Google Scholar 
66.Rosales, I. et al. Isotope records (C–O–Sr) of late Pliensbachian–early Toarcian environmental perturbations in the westernmost Tethys (Majorca Island, Spain). Palaeogeogr. Palaeoclimatol. Palaeoecol. 497, 168–185 (2018).
Google Scholar 
67.Val, J., Bádenas, B., Aurell, M. & Rosales, I. Cyclostratigraphy and chemostratigraphy of a bioclastic storm-dominated carbonate ramp (late Pliensbachian, Iberian Basin). Sediment. Geol. 355, 93–113 (2017).
Google Scholar 
68.Grossman, E. in The Geologic Time Scale 2012 (eds Gradstein, F. M. et al.) Ch. 10 (Elsevier, 2012).69.Ruebsam, W., Munzberger, P. & Schwark, L. Chronology of the early Toarcian environmental crisis in the Lorraine Sus-Basin (NE Paris Basin). Earth Planet. Sci. Lett. 404, 273–282 (2014).
Google Scholar 
70.Pittet, B., Suan, G., Fabien, L., Duarte, L. V. & Mattioli, E. Carbon isotope evidence for sedimentary discontinuities in the lower Toarcian of the Lusitanian Basin (Portugal): sea level change at the onset of the oceanic anoxic event. Sediment. Geol. 303, 1–14.71.Royer, D. L., Pagani, M. & Beerling, D. J. Geobiological constraints on Earth system sensitivity of CO2 during the Cretaceous and Cenozoic. Geobiology 10, 298–310 (2012).
Google Scholar 
72.Metodiev, L. & Koleva-Rekalova, E. Stable isotope records (δ18O and δ13C) of Lower–Middle Jurassic belemnites from the Western Balkan mountains (Bulgaria): palaeoenvironmental application. Appl. Geochem. 23, 2845–2856 (2008).
Google Scholar 
73.McArthur, J. M., Donovan, D. T., Thirlwall, M. F., Fouke, B. W. & Mattey, D. Strontium isotope profile of the early Toarcian (Jurassic) oceanic anoxic event, the duration of ammonite biozones, and belemnite palaeotemperatures. Earth Planet. Sci. Lett. 179, 269–285 (2000).
Google Scholar 
74.Jenkyns, H. G., Jones, C. E., Gröcke, D., Hesselbo, S. P. & Parkinson, D. N. Chemostratigraphy of the Jurassic System: applications, limitations and implications for palaeoceanography. J. Geol. Soc. Lond. 159, 351–378 (2002).
Google Scholar 
75.Ullmann, C. V., Thibault, N., Ruhl, M., Hesselbo, S. P. & Korte, C. Effect of a Jurassic oceanic anoxic event on belemnite ecology and evolution. Proc. Natl Acad. Sci. USA 111, 10073–10076 (2014).
Google Scholar 
76.Harazim, D. et al. Spatial variability of watermass conditions within the European Epicontinental Seaway during the Early Jurassic (Pliensbachian–Toarcian). Sedimentology 60, 359–390 (2010).
Google Scholar 
77.Dera, G. et al. Water mass exchange and variations in seawater temperature in the NW Tethys during the Early Jurassic: evidence from neodymium and oxygen isotopes of fish teeth and belemnites. Earth Planet. Sci. Lett. 286, 198–207 (2009).
Google Scholar 
78.Bailey, T. R., Rosenthal, Y., McArthur, J. M., van de Schootbrugge, B. & Thirlwall, M. F. Paleoceanographic changes of the late Pliensbachian–early Toarcian interval: a possible link to the genesis of an oceanic anoxic event. Earth Planet. Sci. Lett. 212, 307–320 (2003).
Google Scholar 
79.Montañez, I. P. & Poulsen, C. J. The late Paleozoic ice age: an evolving paradigm. Annu. Rev. Earth Planet. Sci. 41, 629–656 (2013).
Google Scholar 
80.Ahokas, J. M., Nystuen, J. P. & Martinius, A. W. Stratigraphic signatures of punctuated rise in relative sea-level in an estuary-dominated heterolithic succession: incised valley fills of the Toarcian Ostrealv Formation, Neill Klinter Group (Jameson Land, East Greenland). Mar. Pet. Geol. 50, 103–129 (2014).
Google Scholar 
81.Krencker, F.-C., Kindstrom, S. & Bodin, S. A major sea-level drop briefly precedes the Toarcian oceanic anoxic event: implication for Early Jurassic climate and carbon cycle. Sci. Rep. 9, 12518 (2014).
Google Scholar 
82.Marjanac, T. & Steel, R. J. Dunlin Group sequence stratigraphy in the northern North Sea: a model for Cook Sandstone deposition. Am. Assoc. Pet. Geol. Bull. 81, 276–292 (1997).
Google Scholar  More