Ecology

Up to 1 December 2020, 156 countries had exhibited at least one peak and then decay of cases/capita (of which 36 had experienced a second peak and decay), 151 countries had exhibited at least one peak and then decay of deaths/capita (of which 32 had experienced a second peak and decay), and 93 countries had sufficient testing data to determine at least one peak and then decay of cases/tests (of which 23 had experienced a second peak and decay). Time-series for all countries and the three metrics are shown in Supplementary Fig. 1. For resilience, having filtered cases of reasonably exponential decay for further analysis (r2 ≥ 0.8) and included multiple instances of well-fitted recovery occurring in one country in the dataset, we obtain n = 177 decays for cases/capita, n = 159 for deaths/capita, n = 105 for cases/tests. In a few countries a minimum had not yet been reached by 1 December 2020, so the reduction dataset is smaller (cases/capita n = 165, deaths/capita n = 150, cases/tests n = 101).Comparable resilience and reduction of cases and deathsThe relative measures of resilience (rate of decay) and (proportional) reduction of cases should be more reliably estimated than absolute case numbers but could still be biased by variations in testing intensity across time and space. Encouragingly, we find across countries and waves, resilience of cases/capita and cases/tests are strongly positively rank correlated (n = 100, (rho) =0.86, p  More

1.Fischer MS, Glass NL. Communicate and fuse: How filamentous fungi establish and maintain an interconnected mycelial network. Front Microbiol. 2019;10:619.2.Fricker MD, Heaton LLM, Jones NS, Boddy L. The mycelium as a network. Microbiol Spectr. 2017;5:335–67.3.Heaton LLM, Jones NS, Fricker MD. A mechanistic explanation of the transition to simple multicellularity in fungi. Nat Commun. 2020;11:2594.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Kiss E, Hegedus B, Viragh M, Varga T, Merenyi Z, Koszo T, et al. Comparative genomics reveals the origin of fungal hyphae and multicellularity. Nat Commun. 2019;10:4080.PubMed 
PubMed Central 

Google Scholar 
5.Nagy LG, Varga T, Csernetics Á, Virágh M. Fungi took a unique evolutionary route to multicellularity: Seven key challenges for fungal multicellular life. Fungal Biol Rev. 2020;34:151–69.
Google Scholar 
6.Naranjo-Ortiz MA, Gabaldon T. Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev Camb Philos Soc. 2019;94:1443–76.PubMed 
PubMed Central 

Google Scholar 
7.Stajich JE, Berbee ML, Blackwell M, Hibbett DS, James TY, Spatafora JW, et al. The fungi. Curr Biol. 2009;19:R840–845.CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Treseder KK, Lennon JT. Fungal traits that drive ecosystem dynamics on land. Microbiol Mol Biol Rev. 2015;79:243–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Adler PB, Salguero-Gómez R, Compagnoni A, Hsu JS, Ray-Mukherjee J, Mbeau-Ache C, et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad Sci USA. 2014;111:740–5.CAS 
PubMed 

Google Scholar 
10.Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, et al. New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot. 2013;61:167.
Google Scholar 
11.Dawson SK, Boddy L, Halbwachs H, Bässler C, Andrew C, Crowther TW, et al. Handbook for the measurement of macrofungal functional traits: A start with basidiomycete wood fungi. Funct Ecol. 2018;33:372–87.
Google Scholar 
12.Aguilar-Trigueros CA, Hempel S, Powell JR, Anderson IC, Antonovics J, Bergmann J, et al. Branching out: Towards a trait-based understanding of fungal ecology. Fungal Biol Rev. 2015;29:34–41.
Google Scholar 
13.Pringle A, Taylor JW. The fitness of filamentous fungi. Trends Microbiol. 2002;10:474–81.CAS 
PubMed 

Google Scholar 
14.Zanne AE, Abarenkov K, Afkhami ME, Aguilar-Trigueros CA, Bates S, Bhatnagar JM, et al. Fungal functional ecology: Bringing a trait-based approach to plant-associated fungi. Biol Rev. 2020;95:409–33.PubMed 

Google Scholar 
15.Boddy L. Saprotrophic cord-forming fungi: Meeting the challenge of heterogeneous environments. Mycologia. 1999;91:13–32.
Google Scholar 
16.Boddy L, Donnelly DP. Fractal geometry and microorganisms in the environment. Biophys Chem Fractal Struct Processes Environ Syst. 2008;11:239–72.17.Lehmann A, Zheng W, Soutschek K, Roy J, Yurkov AM, Rillig MC. Tradeoffs in hyphal traits determine mycelium architecture in saprobic fungi. Sci Rep. 2019;9:14152.PubMed 
PubMed Central 

Google Scholar 
18.Serghi EU, Kokkoris V, Cornell C, Dettman J, Stefani F, Corradi N. Homo- and dikaryons of the arbuscular mycorrhizal fungus rhizophagus irregularis differ in life history strategy. Front Plant Sci. 2021;12:1544.
Google Scholar 
19.Held M, Edwards C, Nicolau DV. Probing the growth dynamics of Neurospora crassa with microfluidic structures. Fungal Biol. 2011;115:493–505.PubMed 

Google Scholar 
20.Aleklett K, Ohlsson P, Bengtsson M, Hammer EC. Fungal foraging behaviour and hyphal space exploration in micro-structured Soil Chips. ISME J. 2021;15:1782–1793.21.De Ligne L, Vidal-Diez de Ulzurrun G, Baetens JM, Van den Bulcke J, Van Acker J, De Baets B. Analysis of spatio-temporal fungal growth dynamics under different environmental conditions. IMA Fungus. 2019;10:7.PubMed 
PubMed Central 

Google Scholar 
22.Dikec J, Olivier A, Bobee C, D’Angelo Y, Catellier R, David P, et al. Hyphal network whole field imaging allows for accurate estimation of anastomosis rates and branching dynamics of the filamentous fungus Podospora anserina. Sci Rep. 2020;10:3131.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Du H, Lv P, Ayouz M, Besserer A, Perré P. Morphological characterization and quantification of the mycelial growth of the Brown-Rot fungus Postia placenta for modeling purposes. PLoS One. 2016;11:e0162469.PubMed 
PubMed Central 

Google Scholar 
24.Vidal-Diez de Ulzurrun G, Baetens JM, Van den Bulcke J, Lopez-Molina C, De Windt I, De Baets B. Automated image-based analysis of spatio-temporal fungal dynamics. Fungal Genet Biol. 2015;84:12–25.CAS 
PubMed 

Google Scholar 
25.Boddy L, Wood J, Redman E, Hynes J, Fricker MD. Fungal network responses to grazing. Fungal Genet Biol. 2010;47:522–30.PubMed 

Google Scholar 
26.Rotheray TD, Jones TH, Fricker MD, Boddy L. Grazing alters network architecture during interspecific mycelial interactions. Fungal Ecol. 2008;1:124–32.
Google Scholar 
27.Bebber DP, Hynes J, Darrah PR, Boddy L, Fricker MD. Biological solutions to transport network design. Proc Biol Sci/R Soc. 2007;274:2307–15.
Google Scholar 
28.Fricker MD, Akita D, Heaton LLM, Jones N, Obara B, Nakagaki T. Automated analysis of Physarumnetwork structure and dynamics. J Phys D: Appl Phys. 2017;50:254005.
Google Scholar 
29.Lee SH, Fricker MD, Porter MA. Mesoscale analyses of fungal networks as an approach for quantifying phenotypic traits. J Complex Netw. 2017;5:145–59.
Google Scholar 
30.Obara B, Grau V, Fricker MD. A bioimage informatics approach to automatically extract complex fungal networks. Bioinformatics. 2012;28:2374–81.CAS 
PubMed 

Google Scholar 
31.Bebber DP, Tlalka M, Hynes J, Darrah PR, Ashford A, Watkinson SC et al. Fungi and the environment. Cambridge: Cambridge University Press; 2007. p. 1−21.32.Fricker MD, Lee JA, Bebber DP, Tlalka M, Hynes J, Darrah PR, et al. Imaging complex nutrient dynamics in mycelial networks. J. Microsc. 2008;231:317–31.CAS 
PubMed 

Google Scholar 
33.Vidal-Diez de Ulzurrun G, Huang T-Y, Chang C-W, Lin H-C, Hsueh Y-P. Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi. PLoS Comp Biol. 2019;15:e1007428.CAS 

Google Scholar 
34.Heaton LLM, López E, Maini PK, Fricker MD, Jones NS. Growth-induced mass flows in fungal networks. Proc R Soc B: Biol Sci. 2010;277:3265–74.
Google Scholar 
35.Heaton LLM, López E, Maini PK, Fricker MD, Jones NS. Advection, diffusion, and delivery over a network. Phys Rev E. 2012;86:021905.
Google Scholar 
36.Fricker MD, Boddy L, Nakagaki T, Bebber DP (2009). Adaptive biological networks. In: Gross T, Sayama H, editors. Adaptive networks: theory, models, and applications. Berlin: Springer; 2009. p. 51−70.37.Boddy L, Jones TH. Mycelial responses in heterogeneous environments: parallels with macroorganisms. Fungi Environ. 2007;25:112–58.
Google Scholar 
38.Crowther TW, Boddy L, Hefin Jones T. Functional and ecological consequences of saprotrophic fungus–grazer interactions. ISME J. 2012;6:1992–2001.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Crowther TW, Jones TH, Boddy L. Interactions between saprotrophic basidiomycete mycelia and mycophagous soil fauna. Mycology. 2012;3:77–86.
Google Scholar 
40.Tordoff GM, Boddy L, Jones TH. Grazing by Folsomia candida (Collembola) differentially affects mycelial morphology of the cord-forming basidiomycetes Hypholoma fasciculare, Phanerochaete uelutina, and Resinicium bicolor. Mycol Res. 2006;110:335–45.PubMed 

Google Scholar 
41.Heaton L, Obara B, Grau V, Jones N, Nakagaki T, Boddy L, et al. Analysis of fungal networks. Fungal Biol Rev. 2012;26:12–29.
Google Scholar 
42.Barthelemy M. Morphogenesis of spatial networks. Cham, Switzerland: Springer International Publishing; 2018.43.Fricker MD, Bebber D, Boddy L. Chapter 1 Mycelial networks: structure and dynamics. In: Boddy L, Frankland JC, van West P, editors. British mycological society symposia series. London, UK, Academic Press; 2008. p. 3−18.44.Fricker M, Boddy L, Bebber D. Biology of the fungal cell. Berlin Heidelberg: Springer Verlag; 2007. p. 309−30.45.Fricker MD, Lee JA, Boddy L, Bebber DP. The Interplay between structure and function in fungal networks. Topologica 2008;1:004.46.Moore D, Robson GD, Trinci AP. 21st century guidebook to fungi. Cambridge, UK: Cambridge University Press; 2011.47.Bielčik M, Aguilar-Trigueros CA, Lakovic M, Jeltsch F, Rillig MC. The role of active movement in fungal ecology and community assembly. Movement Ecol. 2019;7:36.
Google Scholar 
48.Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, et al. The worldwide leaf economics spectrum. Nature. 2004;428:821–7.CAS 

Google Scholar 
49.Hart Y, Sheftel H, Hausser J, Szekely P, Ben-Moshe NB, Korem Y, et al. Inferring biological tasks using Pareto analysis of high-dimensional data. Nat Methods. 2015;12:233–5.CAS 
PubMed 

Google Scholar 
50.Shoval O, Sheftel H, Shinar G, Hart Y, Ramote O, Mayo A, et al. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science. 2012;336:1157–60.CAS 
PubMed 

Google Scholar 
51.Andrade-Linares DR, Veresoglou SD, Rillig MC. Temperature priming and memory in soil filamentous fungi. Fungal Ecol. 2016;21:10–15.
Google Scholar 
52.A’Bear AD, Boddy L, Hefin Jones T. Impacts of elevated temperature on the growth and functioning of decomposer fungi are influenced by grazing collembola. Global Change Biol. 2012;18:1823–32.
Google Scholar 
53.Boddy L, Wells JM, Culshaw C, Donnelly DP. Fractal analysis in studies of mycelium in soil. Geoderma. 1999;88:301–28.
Google Scholar 
54.Pain C, Kriechbaumer V, Kittelmann M, Hawes C, Fricker M. Quantitative analysis of plant ER architecture and dynamics. Nat Commun. 2019;10:984.PubMed 
PubMed Central 

Google Scholar 
55.Xu H, Blonder B, Jodra M, Malhi Y, Fricker M. Automated and accurate segmentation of leaf venation networks via deep learning. New Phytol. 2021;229:631–48.PubMed 

Google Scholar 
56.Wickham H, Bryan J. Read Excel Files. R package version 1.3.1. 2019. https://CRAN.R-project.org/package=readxl.57.Csardi G, Nepusz T. The igraph software package for complex network research. InterJ, Complex Syst. 2006;1695:1–9.
Google Scholar 
58.R Development Core Team. A language and environment for statistical computing. Vienna, Austria: R Foundation for statistical computing; 2017.59.Oksanen J, Guillaume Blanchet F, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. vegan: Community ecology package. 2012. https://CRAN.R-project.org/package=vegan.60.A’Bear AD, Jones TH, Boddy L. Size matters: What have we learnt from microcosm studies of decomposer fungus–invertebrate interactions? Soil Biol Biochem. 2014;78:274–83.
Google Scholar 
61.Trinci APJ. A kinetic study of the growth of Aspergillus nidulans and other fungi. Microbiology. 1969;57:11–24.CAS 

Google Scholar 
62.Morin-Sardin S, Nodet P, Coton E, Jany J-L. Mucor: A Janus-faced fungal genus with human health impact and industrial applications. Fungal Biol Rev. 2017;31:12–32.
Google Scholar 
63.Grigoriev IV, Nikitin R, Haridas S, Kuo A, Ohm R, Otillar R, et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 2014;42:D699–704.CAS 
PubMed 

Google Scholar 
64.Naranjo-Ortiz MA, Gabaldon T. Fungal evolution: diversity, taxonomy, and phylogeny of the Fungi. Biol Rev Camb Philos Soc. 2019;94:2101–37.PubMed 
PubMed Central 

Google Scholar 
65.Domsch K, Gams W, Anderson T-H. Compendium of soil fungi. 2nd ed. Eching: IHW-Verlag; 2007.66.Thomma BP. Alternaria spp.: from general saprophyte to specific parasite. Mol Plant Pathol. 2003;4:225–36.CAS 
PubMed 

Google Scholar 
67.Bacon C, Yates I. Endophytic root colonization by fusarium species: histology, plant interactions, and toxicity. In: Schulz BE, Boyle CC, Sieber T, editors. Microbial root endophytes. Berlin: Springer; 2006. p. 133−52.68.Nguyen TA, Le S, Lee M, Fan J-S, Yang D, Yan J, et al. Fungal wound healing through instantaneous protoplasmic gelation. Curr Biol. 2021;31:271–82. e275CAS 
PubMed 

Google Scholar 
69.Scheu S, Simmerling F. Growth and reproduction of fungal feeding Collembola as affected by fungal species, melanin, and mixed diets. Oecologia. 2004;139:347–53.PubMed 

Google Scholar 
70.Rayner ADM, Boddy L. Fungal decomposition of wood. Its biology and ecology. Chichester, Sussex: John Wiley & Sons Ltd.; 1988.71.Connolly JH, Shortle WC, Jellison J. Translocation and incorporation of strontium carbonate derived strontium into calcium oxalate crystals by the wood decay fungus Resinicium bicolor. Can J Botany. 1999;77:179–87.CAS 

Google Scholar 
72.A’Bear AD, Jones TH, Boddy L. Potential impacts of climate change on interactions among saprotrophic cord-forming fungal mycelia and grazing soil invertebrates. Fungal Ecol. 2014;10:34–43.
Google Scholar 
73.Fukasawa Y, Savoury M, Boddy L. Ecological memory and relocation decisions in fungal mycelial networks: responses to quantity and location of new resources. ISME J. 2020;14:380–8.PubMed 

Google Scholar 
74.Crowther TW, Maynard DS, Crowther TR, Peccia J, Smith JR, Bradford MA. Untangling the fungal niche: the trait-based approach. Front Microbiol. 2014;5:579. More

Divergent CuMMOs identified in MAGs recovered from soil and sediment ecosystemsIn previous work we identified putative divergent amoA/pmoA homologues in 7 Thermoplasmatota genomes recovered from Mediterranean grassland soil [25]. This was intriguing, given that amo/pmo homologues had not been previously observed in archaea outside of the Nitrososphaerales. Here we searched for additional genomes encoding related (divergent) amo/pmo’s using a series of readily available, and custom built, hidden markov models (HMMs) across all archaeal genomes in the Genome Taxonomy Database (GTDB), and in all archaeal MAGs in our unpublished datasets from ongoing studies (Supplementary Fig. 1 and Supplementary Data 1). We found additional amoA/pmoA genes in genomes recovered from soils at the South Meadow and Rivendell sites of the Angelo Coast Range Reserve (CA) [25, 26], the nearby Sagehorn site [26], a hillslope of the East River watershed (CO) [27], and in sediments from the Rifle aquifer (CO) [28] and the deep ocean [29]. In total we identified 201 archaeal MAGs taxonomically placed using phylogenetically informative single copy marker genes outside of Nitrososphaerales containing divergent amo/pmo proteins (Supplementary Table 1 and Supplementary Data 1). Genome de-replication resulted in 34 species-level genome clusters, 20 of which encoded an amo/pmo homologue (Supplementary Table 2). Of these genomes, 11 are species not previously available in public databases. In all cases where assembled sequences were of sufficient length, the amoA/pmoA, B, and C protein coding genes were found co-located with each other and with a hypothetical protein here called amoX/pmoX in the order C-A-X-B (Fig. 1A, Supplementary Table 2, and Supplementary Fig. 2). The mean sequence identity of the novel amoA/pmoA, B, and C proteins to known bacterial sequences were 16.7, 8.0, and 14.2% and 13.8, 9.5, and 20.8% to known archaeal sequences. This level of divergent amino acid identity is typical for CuMMOs, as known bacterial and known archaeal amoA/pmoA, B, and C proteins share mean identities of 16.1, 9.7, and 16.5% respectively. As might be expected considering the large sequence divergence between the recovered sequences and known amo/pmo proteins, we found that no pair of typical primers used for bacterial and archaeal amoA/pmoA environmental surveys [30] matched any novel amoA/pmoA gene with More

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