Ecology

1.Fagan, W. F. et al. Nitrogen in insects: Implications for trophic complexity and species diversification. Am. Nat. 160, 784–802 (2002).PubMed 
Article 

Google Scholar 
2.Kuhlmann, F. et al. Exploring the nitrogen ingestion of aphids—A new method using electrical penetration graph and (15)N labelling. PLoS ONE 8, e83085. https://doi.org/10.1371/journal.pone.0083085 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Nalepa, C. A. Origin of termite eusociality: Trophallaxis integrates the social, nutritional, and microbial environments. Ecol. Entomol. 40, 323–335 (2015).Article 

Google Scholar 
4.Tong, R. L., Aguilera-Olivares, D., Chouvenc, T. & Su, N. Y. Nitrogen content of the exuviae of Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae). Heliyon 7, e06697. https://doi.org/10.1016/j.heliyon.2021.e06697 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Nalepa, C. A. Altricial development in subsocial cockroach ancestors: Foundation for the evolution of phenotypic plasticity in termites. Evol. Dev. 12, 95–105 (2011).Article 

Google Scholar 
6.Abe, T. Evolution of life types in termites. In Evolution and coadaptation in biotic Communities (eds. Kawano, S., Connell, J. H. & Hidaka, T.) 126–148, (University of Tokyo Press, 1987).7.Bourguignon, T. et al. The evolutionary history of termites as inferred from 66 mitochondrial genomes. Mol. Biol. Evol. 32, 406–421 (2015).CAS 
PubMed 
Article 

Google Scholar 
8.Bucek, A. et al. Evolution of termite symbiosis informed by transcriptome-based phylogenies. Curr. Biol. 29, 3728–3734 (2019).CAS 
PubMed 
Article 

Google Scholar 
9.Breznak, J. A. Ecology of prokaryotic microbes in the guts of wood-and litter-feeding termites. In Termites: Evolution, Sociality, Symbioses, Ecology (eds Abe, T. et al.) 209–231 (Springer, 2000).Chapter 

Google Scholar 
10.Potrikus, C. J. & Breznak, J. A. Gut bacteria recycle uric acid nitrogen in termites: A strategy for nutrient conservation. Proc. Natl. Acad. Sci. USA 78, 4601–4605 (1981).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bao, W., O’Malley, D. M. & Sederoff, R. R. Wood contains a cell-wall structural protein. Proc. Nat. Acad. Sci. USA 89, 6604–6608 (1992).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Ji, R. & Brune, A. Nitrogen mineralization, ammonia accumulation, and emission of gaseous NH3 by soil-feeding termites. Biogeochem. 78, 267–283 (2006).Article 
CAS 

Google Scholar 
13.Ngugi, D. K., Ji, R. & Brune, A. Nitrogen mineralization, denitrification, and nitrate ammonification by soil-feeding termites: A 15 N-based approach. Biogeochem. 103, 355–369 (2011).CAS 
Article 

Google Scholar 
14.Chouvenc, T., Šobotník, J., Engel, M. S. & Bourguignon, T. Termite evolution: mutualistic associations, key innovations, and the rise of Termitidae. Cell. Mol. Life Sci. 78, 2749–2769 (2021).CAS 
PubMed 
Article 

Google Scholar 
15.Engel, M. S., Grimaldi, D. A. & Krishna, K. Termites (Isoptera): Their phylogeny, classification, and rise to ecological dominance. Am. Mus. Nov. 3650, 1–27 (2009).
Google Scholar 
16.Bignell, D. E. The role of symbionts in the evolution of termites and their rise to ecological dominance in the tropics. In The mechanistic benefits of microbial symbionts (ed. Hurst C. J.) 121–172 (Springer, Cham 2016).17.Nalepa, C. A. Body size and termite evolution. Evol. Biol. 38, 243–257 (2011).Article 

Google Scholar 
18.Breznak, J. A., Brill, W. J., Mertins, J. W. & Coppel, H. C. Nitrogen fixation in termites. Nature 244, 577–580 (1973).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Noda, S., Ohkuma, M. & Kudo, T. Nitrogen fixation genes expressed in the symbiotic microbial community in the gut of the termite Coptotermes formosanus. Microbes Environ. 17, 139–143 (2002).Article 

Google Scholar 
20.Benemann, J. R. Nitrogen fixation in termites. Science 181, 164–165 (1973).ADS 
CAS 
PubMed 
Article 

Google Scholar 
21.Waller, D. A., Breitenbeck, G. A. & La Fage, J. P. Variation in acetylene reduction by Coptotermes formosanus (Isoptera: Rhinotermitidae) related to colony source and termite size. Sociobiology 16, 191–196 (1989).
Google Scholar 
22.Pandey, S., Waller, D. A. & Gordon, A. S. Variation in acetylene-reduction (nitrogen-fixation) rates in Reticulitermes spp. (Isoptera: Rhinotermitidae). Virginia J. Sci. 43, 333–338 (1992).23.Curtis, A. D. & Waller, D. A. Changes in nitrogen fixation rates in termites (Isoptera: Rhinotermitidae) maintained in the laboratory. Ann. Entomol. Soc. 88, 764–767 (1995).Article 

Google Scholar 
24.Golichenkov, M. V., Kostina, N. V., Ul’yanova, T. A., Kuznetsova, T. A. & Umarov, M. M. Diazotrophs in the digestive tract of termite Neotermes castaneus. Biol. Bull. 33, 508–512 (2006).25.Dilworth, M. J. Acetylene reduction by nitrogen-fixing preparations from Clostridium pasteurianum. Biochim. Biophys. Acta General Subjects 127, 285–294 (1966).CAS 
Article 

Google Scholar 
26.Bentley, B. L. Nitrogen fixation in termites: Fate of newly fixed nitrogen. J. Insect Physiol. 30, 653–655 (1984).CAS 
Article 

Google Scholar 
27.Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for delta(13)C analysis of diet. Oecologia 57, 32–37 (1983).ADS 
CAS 
PubMed 
Article 

Google Scholar 
28.Dabundo, R. et al. The contamination of commercial 15N2 gas stocks with 15N-labeled nitrate and ammonium and consequences for nitrogen fixation measurements. PLoS One. https://doi.org/10.1371/journal.pone.0110335 (2014).29.Tayasu, I. Use of carbon and nitrogen isotope ratios in termite research. Ecol. Res. 13, 377–387 (1998).Article 

Google Scholar 
30.Bar-Shmuel, N., Behar, A. & Segoli, M. What do we know about biological nitrogen fixation in insects? Evidence and implications for the insect and the ecosystem. Insect Sci. 27, 392–403 (2020).PubMed 
Article 

Google Scholar 
31.Du, H., Chouvenc, T., Osbrink, W. L. A. & Su, N.-Y. Social interactions in the central nest of Coptotermes formosanus juvenile colonies. Insectes Soc. 63, 279–290. https://doi.org/10.1007/s00040-016-0464-4 (2016).Article 

Google Scholar 
32.Josens, G. & Makatia Wango, S. P. Niche differentiation between two sympatric Cubitermes Species (Isoptera, Termitidae, Cubitermitinae) revealed by stable C and N isotopes. Insects 10, 38. https://doi.org/10.3390/insects10020038 (2019).Article 
PubMed Central 

Google Scholar 
33.Burris, R. H. Nitrogenases. J. Biol. Chem. 266, 9339–9342 (1991).CAS 
PubMed 
Article 

Google Scholar 
34.Nutting, W. L. Flight and colony foundation. In Biology of Termites Vol. 1 (eds Krishna, K & Weesner, F.) 233–282 (Academic Press, 1969).35.Chouvenc, T. & Su, N. Y. Colony age-dependent pathway in caste development of Coptotermes formosanus Shiraki. Insectes Soc. 61, 171–182 (2014).Article 

Google Scholar 
36.Su, N. Y., Ban, P. M. & Scheffrahn, R. H. Foraging populations and territories of the eastern subterranean termite (Isoptera: Rhinotermitidae) in Southeastern Florida. Environ. Entomol. 22, 1113–1117 (1993).Article 

Google Scholar 
37.Su, N. Y., Osbrink, W. L. A., Kakkar, G., Mullins, A. & Chouvenc, T. Foraging distance and population size of juvenile colonies of the Formosan subterranean termite (Isoptera: Rhinotermitidae) in laboratory extended arenas. J. Econ. Entomol. 110, 1728–1735 (2017).PubMed 
Article 

Google Scholar 
38.Rust, M. K. & Su, N. Y. Managing social insects of urban importance. Annu. Rev. Entomol. 57, 355–375 (2012).CAS 
PubMed 
Article 

Google Scholar 
39.Krishna, K., Grimaldi, D. A., Krishna, V. & Engel, M. S. Treatise on the Isoptera of the world. Bull. Am. Mus. Nat. Hist. 377, 1–2704 (2013).Article 

Google Scholar 
40.Bourguignon, T. et al. Oceanic dispersal, vicariance and human introduction shaped the modern distribution of the termites Reticulitermes, Heterotermes and Coptotermes. Proc. Roy. Soc. B: Biol. Sci. 283, 20160179. https://doi.org/10.1098/rspb.2016.0179 (2016).CAS 
Article 

Google Scholar 
41.Cleveland, L. R. The ability of termites to live perhaps indefinitely on a diet of pure cellulose. Biol. Bull. 48, 289–293 (1925).CAS 
Article 

Google Scholar 
42.Roessler, E. S. A Preliminary study of the nitrogen needs of growing Termopsis. Univ. Calif. Publ. Zool. 36, 357–368 (1932).CAS 

Google Scholar 
43.Hendee, E. C. The role of fungi in the diet of the common damp-wood termite Zootermopsis angusticolis. Hilgardia 9, 499–524 (1935).CAS 
Article 

Google Scholar 
44.Hungate, R. E. Experiments on the nitrogen economy of termites. Ann. Entomol. Soc. Am. 34, 467–489 (1941).CAS 
Article 

Google Scholar 
45.Mullins, A. J. & Su, N. Y. Parental nitrogen transfer and apparent absence of N2 fixation during colony foundation in Coptotermes formosanus Shiraki. Insects 9, 37. https://doi.org/10.3390/insects9020037 (2018).Article 
PubMed Central 

Google Scholar 
46.Prestwich, G. D., Bentley, B. L. & Carpenter, E. J. Nitrogen sources for neotropical nasute termites: Fixation and selective foraging. Oecologia 46, 397–401 (1980).ADS 
CAS 
PubMed 
Article 

Google Scholar 
47.Waidele, L., Korb, J., Voolstra, C.R., Dedeine, F. & Staubach, F. Ecological specificity of the metagenome in a set of lower termite species supports contribution of the microbiome to adaptation of the host. Anim. Microbio. 1, 13. https://doi.org/10.1186/s42523-019-0014-2 (2019).48.Oster, G. F. & Wilson, E. O. Caste and ecology in the social insects. (Princeton University Press, Princeton, 1978).49.Janzow, M. P. & Judd, T. M. The termite Reticulitermes flavipes (Rhinotermitidae: Isoptera) can acquire micronutrients from soil. Environ. Entomol. 44, 814–820 (2015).CAS 
PubMed 
Article 

Google Scholar 
50.Noda, S., Ohkuma, M. & Kudo, T. Nitrogen fixation genes expressed in the symbiotic microbial community in the gut of the termite Coptotermes formosanus. Microb. Environ. 17, 139–143 (2002).Article 

Google Scholar 
51.Desai, M. S. & Brune, A. Bacteroidales ectosymbionts of gut flagellates shape the nitrogen-fixing community in dry-wood termites. ISME J. 6, 1302–1313 (2012).CAS 
PubMed 
Article 

Google Scholar 
52.Seefeldt, L. C., Hoffman, B. M. & Dean, D. R. Mechanism of Mo-dependent nitrogenase. Annu. Rev. biochem. 78, 701–722 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Yamada, A., Inoue, T., Noda, S., Hongoh, Y. & Ohkuma, M. Evolutionary trend of phylogenetic diversity of nitrogen fixation genes in the gut community of wood-feeding termites. Mol. Ecol. 16, 3768–3777 (2007).CAS 
PubMed 
Article 

Google Scholar 
54.Brune, A. Symbiotic digestion of lignocellulose in termite guts. Nat. Rev. Microbiol. 12, 168–180 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
55.Thanganathan, S. & Hasan, K. Diversity of nitrogen fixing bacteria associated with various termite species. Pertanika J. Tropic. Agri. Sci. 41, 925–940 (2018).
Google Scholar 
56.Mullins, A. J. et al. Dispersal flights of the Formosan subterranean termite (Isoptera: Rhinotermitidae). J. Econ. Entomol. 108, 707–719 (2015).PubMed 
Article 

Google Scholar 
57.Mullins, D. E. & Cochran, D. G. Nitrogen metabolism in the American cockroach—II. An examination of negative nitrogen balance with respect to mobilization of uric acid stores. Comp. Biochem. Physiol. A Physiol. 50, 501–510 (1975).58.Waller, D. A. & La Fage, j. P. Seasonal patterns in foraging groups of Coptotermes formosanus (Rhinotermitidae). Sociobiology 13, 173–181 (1987).59.Waller, D. A. & La Fage, J. P. Size variation in Coptotermes formosanus Shiraki (Rhinotermitidae): Consequences of host use. Am. Midl. Nat. 119, 436–440 (1988).Article 

Google Scholar 
60.Su, N.-Y. & La Fage, J. P. Forager proportion and caste composition of colonies of the Formosan subterranean termite (Isoptera: Rhinotermitidae) restricted to cypress trees in the Calcasieu River, Lake Charles, Louisiana. Sociobiology 33, 185–193 (1999).
Google Scholar 
61.Osbrink, W. L. A., Cornelius, M. L. & Showler, A. T. Bionomics and Formation of “bonsai” colonies with long-term rearing of Coptotermes formosanus (Isoptera: Rhinotermitidae). J. Econ. Entomol. 109, 770–778 (2016).CAS 
PubMed 
Article 

Google Scholar 
62.Hochmair, H. H. & Scheffrahn, R. H. Spatial association of marine dockage with land-borne infestations of invasive termites (Isoptera: Rhinotermitidae: Coptotermes) in urban South Florida. J. Econ. Entomol. 103, 1338–1346 (2010).PubMed 
Article 

Google Scholar 
63.Scheffrahn, R. H. & Crowe, W. Ship-borne termite (Isoptera) border interceptions in Australia and onboard infestations in Florida, 1986–2009. Florida Entomol. 94, 57–63 (2011).Article 

Google Scholar 
64.Evans, T. A., Forschler, B. T. & Grace, J. K. Biology of invasive termites: A worldwide review. Annu. Rev. Entomol. 58, 455–474 (2013).CAS 
PubMed 
Article 

Google Scholar 
65.Blumenfeld, A. J. et al. Bridgehead effect and multiple introductions shape the global invasion history of a termite. Comm. Biol. 4, 196. https://doi.org/10.1038/s42003-021-01725-x (2021).CAS 
Article 

Google Scholar 
66.Evans, T. A. Predicting ecological impacts of invasive termites. Curr. Op. Insect Sci. 46, 88–94 (2021).Article 

Google Scholar 
67.Ayayee, P. A., Jones, S. C. & Sabree, Z. L. Can 13C stable isotope analysis uncover essential amino acid provisioning by termite-associated gut microbes?. PeerJ 3, e1218. https://doi.org/10.7717/peerj.1218 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Moran, N. A. & Sloan, D. B. The hologenome concept: helpful or hollow?. PLoS Biol. 13, e1002311. https://doi.org/10.1371/journal.pbio.1002311 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
69.Bennett, G. M. & Moran, N. A. Heritable symbiosis: The advantages and perils of an evolutionary rabbit hole. Proc. Natl. Acad. Sci. USA 112, 10169–10176 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Sachs, J. L., Skophammer, R. G. & Regus, J. U. Evolutionary transitions in bacterial symbiosis. Proc. Nat. Acad. Sci. USA 108, 10800–10807 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Peterson B. F. & Scharf M. E. Metatranscriptomic techniques for identifying cellulases in termites and their symbionts. In Cellulases. Methods in Molecular Biology, vol 1796 (ed. Lübeck, M.) 85–101 (Humana Press, New York, NY 2018).72.Gaby, J. C. & Buckley, D. H. A comprehensive evaluation of PCR primers to amplify the nifH gene of nitrogenase. PLoS ONE 7, e42149. https://doi.org/10.1371/journal.pone.0042149 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Poly, F., Ranjard, L., Nazaret, S., Gourbiere, F. & Monrozier, L. J. Comparison of nifH gene pools in soils and soil microenvironments with contrasting properties. App. Environ. Microbiol. 67, 2255–2262 (2001).ADS 
CAS 
Article 

Google Scholar 
74.Rocha, D. J., Santos, C. S. & Pacheco, L. G. Bacterial reference genes for gene expression studies by RT-qPCR: Survey and analysis. Antonie Van Leeuwenhoek 108, 685–693 (2015).CAS 
PubMed 
Article 

Google Scholar 
75.Galisa, P. S. et al. Identification and validation of reference genes to study the gene expression in Gluconacetobacter diazotrophicus grown in different carbon sources using RT-qPCR. J. Microbiol. Methods 91, 1–7 (2012).CAS 
PubMed 
Article 

Google Scholar 
76.Mignard, S. & Flandrois, J. P. Identification of Mycobacterium using the EF-Tu encoding (tuf) gene and the tmRNA encoding (ssrA) gene. J. Med. Microbiol. 56, 1033–1041 (2007).CAS 
PubMed 
Article 

Google Scholar 
77.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Stukenbrock, E. H. & McDonald, B. A. The origins of plant pathogens in agro-ecosystems. Annu. Rev. Phytopathol. https://doi.org/10.1146/annurev.phyto.010708.154114 (2008).Article 
PubMed 

Google Scholar 
2.Savary, S., Ficke, A., Aubertot, J. N. & Hollier, C. Crop losses due to diseases and their implications for global food production losses and food security. Food Secur. https://doi.org/10.1007/s12571-012-0200-5 (2012).Article 

Google Scholar 
3.Strange, R. N. & Scott, P. R. Plant disease: a threat to global food security. Annu. Rev. Phytopathol. https://doi.org/10.1146/annurev.phyto.43.113004.133839 (2005).Article 
PubMed 

Google Scholar 
4.Anderson, P. K. et al. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2004.07.021 (2004).Article 
PubMed 

Google Scholar 
5.Scholthof, K. B. G. et al. Top 10 plant viruses in molecular plant pathology. Mol. Plant Pathol. https://doi.org/10.1111/j.1364-3703.2011.00752.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Stukenbrock, E. H. & Bataillon, T. A population genomics perspective on the emergence and adaptation of new plant pathogens in agro-ecosystems. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1002893 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Gilligan, C. A. Sustainable agriculture and plant diseases: an epidemiological perspective. Philos. Trans. R. Soc. B: Biol. Sci. https://doi.org/10.1098/rstb.2007.2181 (2008).Article 

Google Scholar 
8.Li, L. M., Grassly, N. C. & Fraser, C. Genomic analysis of emerging pathogens: methods, application and future trends. Genome Biol.ogy https://doi.org/10.1186/s13059-014-0541-9 (2014).Article 

Google Scholar 
9.Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1000520 (2009).MathSciNet 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lefeuvre, P. et al. The spread of tomato yellow leaf curl virus from the middle east to the world. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1001164 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Monjane, A. L. et al. Reconstructing the history of maize streak virus strain A dispersal tor reveal diversification hot spots and its origin in southern Africa. J. Virol. https://doi.org/10.1128/jvi.00640-11 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Trovao, N. S. et al. Host ecology determines the dispersal patterns of a plant virus. Virus Evol. https://doi.org/10.1093/ve/vev016 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Rakotomalala, M. et al. Comparing patterns and scales of plant virus phylogeography: rice yellow mottle virus in Madagascar and in continental Africa. Virus Evol. https://doi.org/10.1093/ve/vez023 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Gibbs, A. J., Fargette, D., García-Arenal, F. & Gibbs, M. J. Time – The emerging dimension of plant virus studies. J General Virol. https://doi.org/10.1099/vir.0.015925-0 (2010).Article 

Google Scholar 
15.Simmonds, P., Aiewsakun, P. & Katzourakis, A. Prisoners of war: host adaptation and its constraints on virus evolution. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-018-0120-2 (2019).Article 
PubMed 

Google Scholar 
16.Jones, R. A. C., Boonham, N., Adams, I. P. & Fox, A. Historical virus isolate collections: an invaluable resource connecting plant virology’s pre-sequencing and post-sequencing eras. Plant Pathol. 70, 235–248 (2021).Article 

Google Scholar 
17.Smith, O. et al. A complete ancient RNA genome: Identification, reconstruction and evolutionary history of archaeological Barley Stripe Mosaic Virus. Sci. Rep. https://doi.org/10.1038/srep04003 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Malmstrom, C. M., Shu, R., Linton, E. W., Newton, L. A. & Cook, M. A. Barley yellow dwarf viruses (BYDVs) preserved in herbarium specimens illuminate historical disease ecology of invasive and native grasses. J. Ecol. https://doi.org/10.1111/j.1365-2745.2007.01307.x (2007).Article 

Google Scholar 
19.Peyambari, M., Warner, S., Stoler, N., Rainer, D. & Roossinck, M. J. A 1000-Year-old RNA virus. J. Virol. 93, e01188-18 (2019).CAS 
Article 

Google Scholar 
20.Adams, I. P. et al. Next-generation sequencing and metagenomic analysis: a universal diagnostic tool in plant virology. Mol. Plant Pathol. https://doi.org/10.1111/j.1364-3703.2009.00545.x (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Vayssier-Taussat, M. et al. Shifting the paradigm from pathogens to pathobiome new concepts in the light of meta-omics. Front. Cell. Infect. Microbiol. https://doi.org/10.3389/fcimb.2014.00029 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Massart, S., Olmos, A., Jijakli, H. & Candresse, T. Current impact and future directions of high throughput sequencing in plant virus diagnostics. Virus Res. https://doi.org/10.1016/j.virusres.2014.03.029 (2014).Article 
PubMed 

Google Scholar 
23.Roossinck, M. J., Martin, D. P. & Roumagnac, P. Plant virus metagenomics: advances in virus discovery. Phytopathology https://doi.org/10.1094/PHYTO-12-14-0356-RVW (2015).Article 
PubMed 

Google Scholar 
24.Kreuze, J. F. et al. Complete viral genome sequence and discovery of novel viruses by deep sequencing of small RNAs: a generic method for diagnosis, discovery and sequencing of viruses. Virology https://doi.org/10.1016/j.virol.2009.03.024 (2009).Article 
PubMed 

Google Scholar 
25.Pooggin, M. M. Small RNA-omics for plant virus identification, virome reconstruction, and antiviral defense characterization. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.02779 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Hartung, J. S. et al. History and diversity of Citrus Leprosis virus recorded in herbarium specimens. Phytopathology https://doi.org/10.1094/PHYTO-03-15-0064-R (2015).Article 
PubMed 

Google Scholar 
27.Golyaev, V., Candresse, T., Rabenstein, F. & Pooggin, M. M. Plant virome reconstruction and antiviral RNAi characterization by deep sequencing of small RNAs from dried leaves. Sci. Rep. https://doi.org/10.1038/s41598-019-55547-3 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Patil, B. L. & Fauquet, C. M. Cassava mosaic geminiviruses: actual knowledge and perspectives. Mol. Plant Pathol. https://doi.org/10.1111/j.1364-3703.2009.00559.x (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
29.Legg, J. P., Owor, B., Sseruwagi, P. & Ndunguru, J. Cassava mosaic virus disease in east and central Africa: epidemiology and management of a regional pandemic. Adv. Virus Res. https://doi.org/10.1016/S0065-3527(06)67010-3 (2006).Article 
PubMed 

Google Scholar 
30.Wang, H. L. et al. First report of Sri Lankan cassava mosaic virus infecting cassava in Cambodia. Plant Dis. https://doi.org/10.1094/PDIS-10-15-1228-PDN (2016).Article 
PubMed 

Google Scholar 
31.Minato, N. et al. Surveillance for sri lankan cassava mosaic virus (SLCMV) in Cambodia and Vietnam one year after its initial detection in a single plantation in 2015. PLoS One https://doi.org/10.1371/journal.pone.0212780 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Mugerwa, H., Wang, H. L., Sseruwagi, P., Seal, S. & Colvin, J. Whole-genome single nucleotide polymorphism and mating compatibility studies reveal the presence of distinct species in sub-Saharan Africa Bemisia tabaci whiteflies. Insect Sci. https://doi.org/10.1111/1744-7917.12881 (2020).Article 
PubMed 

Google Scholar 
33.Ntawuruhunga, P. et al. Incidence and severity of cassava mosaic disease in the Republic of Congo. African Crop Sci. J. https://doi.org/10.4314/acsj.v15i1.54405 (2010).Article 

Google Scholar 
34.Zinga, I. et al. Epidemiological assessment of cassava mosaic disease in Central African Republic reveals the importance of mixed viral infection and poor health of plant cuttings. Crop Prot. https://doi.org/10.1016/j.cropro.2012.10.010 (2013).Article 

Google Scholar 
35.Jeske, H. Geminiviruses. Curr. Topics Microbiol. Immunol. https://doi.org/10.1007/978-3-540-70972-5_11 (2009).Article 

Google Scholar 
36.Vanitharani, R., Chellappan, P. & Fauquet, C. M. Geminiviruses and RNA silencing. Trends Plant Sci. https://doi.org/10.1016/j.tplants.2005.01.005 (2005).Article 
PubMed 

Google Scholar 
37.Aregger, M. et al. Primary and secondary siRNAs in geminivirus-induced gene silencing. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1002941 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Olsen, K. M. & Schaal, B. A. Evidence on the origin of cassava: Phylogeography of Manihot esculenta. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.96.10.5586 (1999).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Fauquet, C. African cassava mosaic virus: etiology, epidemiology, and control. Plant Dis. https://doi.org/10.1094/pd-74-0404 (1990).Article 

Google Scholar 
40.Legg, J. P. & Fauquet, C. M. Cassava mosaic geminiviruses in Africa. Plant Mol. Biol. https://doi.org/10.1007/s11103-004-1651-7 (2004).Article 
PubMed 

Google Scholar 
41.De Bruyn, A. et al. Divergent evolutionary and epidemiological dynamics of cassava mosaic geminiviruses in Madagascar. BMC Evol. Biol. https://doi.org/10.1186/s12862-016-0749-2 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Weiß, C. L. et al. Temporal patterns of damage and decay kinetics of dna retrieved from plant herbarium specimens. R. Soc. Open Sci. https://doi.org/10.1098/rsos.160239 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Chellappan, P., Vanitharani, R., Ogbe, F. & Fauquet, C. M. Effect of temperature on geminivirus-induced RNA silencing in plants. Plant Physiol. https://doi.org/10.1104/pp.105.066563 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Smith, O. & Gilbert, M. T. P. Ancient RNA. in (2018). doi:https://doi.org/10.1007/13836_2018_17.45.Filloux, D. et al. The genomes of many yam species contain transcriptionally active endogenous geminiviral sequences that may be functionally expressed. Virus Evol. https://doi.org/10.1093/ve/vev002 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Sharma, V. et al. Large-scale survey reveals pervasiveness and potential function of endogenous geminiviral sequences in plants. Virus Evol. https://doi.org/10.1093/ve/veaa071 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Bredeson, J. V. et al. Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity. Nat. Biotechnol. https://doi.org/10.1038/nbt.3535 (2016).Article 
PubMed 

Google Scholar 
48.Serfraz, S. et al. Insertion of Badnaviral DNA in the Late Blight Resistance Gene (R1a) of Brinjal Eggplant (Solanum melongena). Front. Plant Sci. https://doi.org/10.3389/fpls.2021.683681 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Lefeuvre, P. et al. Evolutionary time-scale of the begomoviruses: evidence from integrated sequences in the Nicotiana genome. PLoS One https://doi.org/10.1371/journal.pone.0019193 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Martin, D. P., Murrell, B., Golden, M., Khoosal, A. & Muhire, B. RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol. https://doi.org/10.1093/ve/vev003 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Murray, G. G. R. et al. The effect of genetic structure on molecular dating and tests for temporal signal. Methods Ecol. Evol. 7, 80–89 (2016).Article 

Google Scholar 
52.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. https://doi.org/10.1186/1471-2148-7-214 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Yoshida, K. et al. Mining herbaria for plant pathogen genomes: back to the future. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1004028 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Dufrénoy, J. & Hédin, L. . La. Mosaïque des feuilles du Manioc au Cameroun. J. d’agriculture Tradit. Bot. appliquée 94, 361–365 (1929).
Google Scholar 
55.Duffy, S. & Holmes, E. C. Validation of high rates of nucleotide substitution in geminiviruses: phylogenetic evidence from East African cassava mosaic viruses. J. Gen. Virol. 90, 1539–1547 (2009).CAS 
Article 

Google Scholar 
56.Worobey, M. et al. Direct evidence of extensive diversity of HIV-1 in Kinshasa by 1960. Nature https://doi.org/10.1038/nature07390 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Mühlemann, B. et al. Ancient hepatitis B viruses from the Bronze Age to the Medieval period. Nature https://doi.org/10.1038/s41586-018-0097-z (2018).Article 
PubMed 

Google Scholar 
58.Toppinen, M. et al. Bones hold the key to DNA virus history and epidemiology. Sci. Rep. https://doi.org/10.1038/srep17226 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Gilbert, M. T. P., Bandelt, H. J., Hofreiter, M. & Barnes, I. Assessing ancient DNA studies. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2005.07.005 (2005).Article 
PubMed 

Google Scholar 
60.Inoue-Nagata, A. K., Albuquerque, L. C., Rocha, W. B. & Nagata, T. A simple method for cloning the complete begomovirus genome using the bacteriophage φ29 DNA polymerase. J. Virol. Methods https://doi.org/10.1016/j.jviromet.2003.11.015 (2004).Article 
PubMed 

Google Scholar 
61.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics https://doi.org/10.1093/bioinformatics/btu170 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Zheng, Y. et al. VirusDetect: An automated pipeline for efficient virus discovery using deep sequencing of small RNAs. Virology https://doi.org/10.1016/j.virol.2016.10.017 (2017).Article 
PubMed 

Google Scholar 
63.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics https://doi.org/10.1093/bioinformatics/btp324 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. https://doi.org/10.1186/gb-2009-10-3-r25 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: Fast approximate Bayesian estimates of ancient DNA damage parameters. in Bioinformatics (2013). doi:https://doi.org/10.1093/bioinformatics/btt193.66.Broad Institute. Picard Tools – By Broad Institute. Github (2009).67.Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics https://doi.org/10.1093/bioinformatics/btq033 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
68.Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. https://doi.org/10.1101/gr.092759.109 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Depristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. https://doi.org/10.1038/ng.806 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. https://doi.org/10.1093/molbev/mst010 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
Article 

Google Scholar 
73.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. JModelTest 2: More models, new heuristics and parallel computing. Nat. Methods https://doi.org/10.1038/nmeth.2109 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Jombart, T. & Dray, S. Adephylo: Exploratory analyses for the phylogenetic comparative method. Bioinformatics (2010).75.Duchêne, S., Duchêne, D., Holmes, E. C. & Ho, S. Y. W. The performance of the date-randomization test in phylogenetic analyses of time-structured virus data. Mol. Biol. Evol. 32, 1895–1906 (2015).Article 

Google Scholar 
76.Rieux, A. & Khatchikian, C. E. Tipdatingbeast: an r package to assist the implementation of phylogenetic tip-dating tests using beast. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.12603 (2017).Article 
PubMed 

Google Scholar 
77.Raftery, A. E. Approximate Bayes factors and accounting for model uncertainty in generalised linear models. Biometrika https://doi.org/10.1093/biomet/83.2.251 (1996).MathSciNet 
Article 
MATH 

Google Scholar 
78.Ho, S. Y. W. & Shapiro, B. Skyline-plot methods for estimating demographic history from nucleotide sequences. Mol. Ecol. Resour. https://doi.org/10.1111/j.1755-0998.2011.02988.x (2011).Article 
PubMed 

Google Scholar 
79.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. (2018) doi:https://doi.org/10.1093/sysbio/syy032. More

A framework to estimate unowned cat abundanceIn the following sections, we describe the application of an IAM, a hierarchical modelling approach, which estimates unowned cat abundance in discrete geographical units from spatially replicated citizen data, in combination with expert data obtained from 162 sites across five urban areas in England. In doing so, we explored key predictors of unowned cat abundance. We then estimated unowned cat abundance across urban areas in England and the UK with respect to the modelling results. We used WinBUGS53 and R54 for all data analysis via the R package R2Winbugs55 and QGIS56 for plotting maps.Data collation and preparationA database of unowned cat count data were compiled from citizen science data and expert data collected throughout a one-year period that began between 2016 and 2018 across five urban areas in the UK. Areas included Beeston, Bradford, Bulwell, Dunstable & Houghton Regis and Everton (Fig. 1). These data were collected as part of Cat Watch, a community partnership project set-up by Cats Protection, a UK feline welfare charity, to control cat numbers39,57. Two distinct forms of citizen science data were collected: (1) the first consisted of an initial cross-sectional random-sample door-to-door survey carried out with approximately 10% of households. At that stage, residents were asked how many cats they know of locally and how many they think were owned in the form of a multiple-choice question with the following options; none, 1–2, 3–4, 5–9, 10 or more, from which the number of unowned cats were derived. When a range was selected the central value was taken; for ten or more we used 15 (the average from reports when 10 or more was specified was 14.7). Location data were available for 3101 survey responses, within which there were estimates of 4411 unowned cats; (2) throughout the project, residents were able to report unowned cats in their area directly via social media or through a mobile application. During the study period, 877 reports were received reporting on the locations of 2790 unowned cats. These data were collected according to the study protocol approved by University of Bristol Faculty of Health Science Research Ethics Committee approval number 38661. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was secured in advance of survey participation. Residents provided report data voluntarily, with no identifying information collected. No experimental protocols were used.Expert data were obtained from an experienced community team (CT) that recorded when and where an unowned cat was found or confirmed the lack of presence of an unowned cat. The CT carried out extensive door-to-door surveillance across both reported hot spot and cold spot areas. These data are considered of higher quality, due to the ability of the CT to correctly identify an unowned cat and with no risk of double counting the same individual. Unowned cats can be either stray or feral. Protocols to accurately identify a stray cat included; scanning for a microchip, attaching a paper collar to notify potential owners, advertising online, door-to-door notifications, local posters and contacting other animal welfare organisations, including veterinary practices. If no owner was found during this process it was identified as unowned. Feral cats were more likely to be identified via behavioural means; as they have not been socialised to humans, they will be more fearful and will not approach humans47. If they have already been neutered they may also have their left ear “tipped”. During the study period, there were 601 records from the CT, reporting on the location of 605 confirmed unowned cats. All three of these data sources provided detailed location data (postcodes and/or addresses) enabling geo-referencing of unowned cat location data.To account for duplicate sightings, the citizen science data required clustering to account for neighbours in close-proximity reporting the same cats. There is limited understanding of urban unowned cats in the UK, however studies of urban unowned cats in other areas indicate home range sizes between 3.7 and 10.4 ha for urban areas58,59. Studies on unowned cats in the UK indicate that home ranges vary between 10 and 15 hectares60. We assume a maximum 20 ha home range, equivalent to a circular area with a diameter of 504 m. Consequently, we apply a 500 m cluster function in R that derives clusters of cat sightings that are within 500 m of each other. The individual records were maintained as replicate counts within each cluster. Clustering of 500 m has also been shown to provide reasonable estimates in an urban area with high expert coverage (91%), where you would not anticipate cat numbers to be significantly inflated above those observed by experts25. In the absence of expert data, the effect of violating this assumption (i.e. reporting them as replicate sightings when they are not) would result in lower estimates of cats. However, where expert data is available, the effect of violating this assumption would result in bias in the observation parameters, not estimates of the cats themselves, which are also inferred from the expert data that do not contain duplicate sightings.Data analysisWe applied an integrated abundance model (IAM) within a Bayesian framework that combines count data across sites from two forms of citizen science data and expert data25. The hierarchical structure of the IAM enables it to borrow strength from the sites with expert data to inform detection biases of citizen science data, including detection probability of an unowned cat and false positives due to misidentification of an owned cat as unowned. The goal of the inference is to estimate the abundance of unowned cats within each site and explore covariates as predictors of population density.Specifically, observed citizen science counts at each site i and during each replicate survey j are linked to true site-specific population sizes (Ni) via a detection probability (p) and the expected number of misidentifications (m). We apply a Poisson distribution to account for additional stochasticity in spatial replicates not accounted for in the systematic biases (m and p). Each type of citizen science data is modelled separately to account for the different biases in collection methods between the survey data (y) and report data (u):$$ {y_{i,j}}sim {text{ Poisson }}({N_i}{p_y} + {m_y}) $$$$ {u_{i,j}}sim {text{ Poisson }}({N_i}{p_u} + {m_u}) $$Expert consensus (wi) was available on the abundance of individuals for 104 sites and linked to true population sizes via a Poisson observation error.$$ {w_i}sim {text{ Poisson }}left( {N_i} right) $$We additionally assume that where expert counts are available they are accurate at the level of presence or absence.$$ {z_i}sim {text{Bernoulli}}left( Omega right) $$$$ {N_i}_= {z_i}{lambda_i} $$whereby zi is a binary measure of occurrence, with each of the i sites occupied or not, that is modelled as a Bernoulli random variable determined by occupancy probability (Ω). True site-specific population sizes (Ni) are therefore a function of whether a site is occupied or not and a site-specific mean λi. When expert data on occurrence can be inferred from expert consensus this was included in zi.We extend the original development of an IAM25 described above to model the log the site specific mean (λi) as a linear function of covariates (x) using the following linear relationship:$$ log{lambda }_{i} = mu +sum_{j=1}^{n}{beta }_{j};{x}_{j,i}+{varepsilon }_{i}$$$$varepsilon sim N(0,{sigma }^{2})$$where xj, I are the values of the jth covariate across sites i, βs are the regression coefficients for each covariate and ɛ is the residual site-specific variation providing estimates of unexplained variance. We also fitted a model without covariate effects to gain an estimate of total site-specific variance. The proportional reduction in the residual site-specific variation component is a measure for the proportion of the site-specific variance in abundance explained by that covariate or covariates.To assess the credibility of covariate effects we calculated the probability that their effects were positive [P(β  > 0)] or negative [P(β  More

Genome sequencingWe sequenced the genomes of 35 strains of B. pseudocatenulatum (Supplementary Table S1). These strains were isolated at the Yakult Central Institute and the species were identified based on the 16S rRNA gene sequence analysis. These strains have been isolated in the course of various studies over the past few decades, including many studies on infants and adults. B. pseudocatenulatum cultures were anaerobically incubated in modified Gifu anaerobic medium (Nissui Pharmaceutical, Tokyo, Japan) supplemented with lactose and glucose (both 0.5% wt/vol) at 37 °C for 16 h. These culture conditions were applied throughout the study unless stated otherwise. The detailed procedures for genomic DNA extraction, library preparation for MiSeq (Illumina, San Diego, CA, USA), MinION (Oxford Nanopore Technologies, Oxford, UK) and PacBio RS2 (Pacific Biosciences, Menlo Park, CA, USA), and sequencing are described in the Supplementary Methods.Genome assembly, gene prediction and pangenome analysisWe used Unicycler [26] with default parameters for both short-read and hybrid assembly, and Prokka [27] with default parameters for annotating the reconstructed genomes and those downloaded from the RefSeq database. The annotated genomes were then processed with Roary [28] with a default gene identity cut-off parameter of 95% for species level pangenome analysis. A representative sequence from each gene cluster was translated into a protein sequence, and CAZymes were identified using the dbCAN2 server [29]. Proteins were considered CAZymes if they were identified using HMMER, DIAMOND and Hotpep with default parameters. We then built a CAZyme gene distribution matrix (Supplementary Table S2) based on the gene presence-absence table determined using Roary.Carbohydrate utilisation assaysStrains of B. pseudocatenulatum were cultured until they reached the exponential phase, centrifuged, and then, the resulting pellets were suspended to an OD600 of 0.2 in modified peptone yeast extract (PY) medium (100 mM PIPES, pH 6.7, 2 g/L peptone, 2 g/L BBL trypticase peptone, 2 g/L bacto-yeast extract, 8 mg/L CaCl2, 19.2 mg/L MgSO4 ∙ 7H2O, 80 mg/L NaCl, 4.9 mg/L hemin, 0.5 g/L L-cysteine hydrochloride and 100 ng/L vitamin K1). These suspension cultures were inoculated (1% vol/vol) into modified PY medium supplemented with 0.5% (wt/vol) XOS (Xylo-Oligo95P, B Food Science, Aichi, Japan) (PY-XOS), wheat arabinoxylan (Megazyme, Bray, Ireland) (PY-AX) or beechwood xylan (Sigma-Aldrich, Darmstadt, Germany) (PY-XY) and covered with sterile mineral oil (50 μL) to prevent evaporation. Growth was monitored anaerobically by measuring the OD600 using a PowerWave 340 plate reader (BioTek, Winooski, VT, USA) every 30 min in an anaerobic chamber for 48 h. The organic acids produced in PY-XY were analysed using high-pressure liquid chromatography as described [8].Cloning, expression and purification of recombinant BpXyn10AThe GH10 domain of the BpXyn10A gene was amplified by PCR using the primers xynA-GH-F (5’-CATCATCATCATCATGCGGAAGGCGACGCCGTA-3’) and xynA-GH-R (5’-AGCAGAGATTACCTAATCCTTGAATGCGTTCATGC-3’), with the genomic DNA of YIT 11027 as a template. A linearised vector was synthesised by PCR using primers pColdII-F (5’-GTAATCTCTGCTTAAAAGCACAGAATCTA-3’) and pColdII-R (5’-ATGATGATGATGATGATGCACTTTGT-3’), and the pColdII vector (Takara Bio, Otsu, Japan) as a template. These fragments were ligated using In-Fusion HD Cloning Kits (Takara Bio, Otsu, Japan), resulting in pColdII-xynA. Escherichia coli BL21 was transformed with pColdII-xynA and cultured to express recombinant BpXyn10A as described by the manufacturer. Bacterial cells were harvested by centrifugation and lysed with B-PER Bacterial Cell Lysis Reagent (Thermo Fisher Scientific, Waltham, MA, USA) containing lysozyme at 100 µg/mL and 10 U/mL of DNase I. Recombinant BpXyn10A was further purified using Ni-NTA Spin Column (Qiagen, Hilden, Germany) and analysed by SDS-PAGE.Endo-xylanase activity assayB. pseudocatenulatum YIT 11027, YIT 11952 and YIT 4072T cells were grown anaerobically in PY-AX or PY-XOS medium for 16 h. Cultures (1.5 mL) were centrifuged (8000× g for 2 min at room temperature); then, supernatants were sterilised by passage through a 0.22-μm filter. Pelleted cells were washed with modified PY medium and resuspended in 1.5 mL of the same medium. The endo-xylanase activity of the supernatant and the cell fractions were assayed using Xylanase Assay kits (XylX6 method) (Megazyme, Bray, Ireland) as described by the manufacturer. According to the manufacturer, this kit is designed to specifically detect only endo-xylanase activity, and not xylosidase or exo-xylanase enzyme activity.Purified BpXyn10A-added cultureB. pseudocatenulatum YIT 4072T and Ba. ovatus YIT 6161T cells were cultured anaerobically until they reached the exponential phase. Thereafter, cultures (200 μL) were centrifuged (8000× g for 2 min at room temperature), then pelleted cells were resuspended in modified PY medium (500 μL), and inoculated (1% vol/vol) into PY-AX medium supplemented with 0, 10, 100 and 1000 ng/mL purified recombinant BpXyn10A. Growth was monitored anaerobically by measuring the OD600 using the PowerWave 340 plate reader.RNA-seq analysisB. pseudocatenulatum YIT 11952 was cultured in modified PY medium supplemented with 0.5% (wt/vol) lactose, xylose, XOS, beechwood xylan or arabinoxylan and harvested at mid- to late-log phase. The detailed procedures for total RNA extraction, rRNA removal and sequencing using MiSeq are described in the Supplementary Methods. We obtained a total of 23 million paired-end reads. Low-quality bases (average quality More

With S1GBM’s characteristics as a global, PLIA-normalised, high-resolution C-band backscatter dataset, a direct validation experiment is not feasible since we lack matching reference backscatter data collected during airborne or ground based radar campaigns. Other existing global mosaics were generated based on different time-spans, polarisations17, frequencies18, or do not share the novel feature of the PLIA-normalisation20.On these grounds, we prefer to assess the characteristics of the S1GBM layers with respect to different land cover types on a global scale, and to incorporate the gained knowledge into an easy-to-use classification algorithm for permanent water bodies (PWB). This simple mapping experiment acts as an example and should on the one hand demonstrate the integrity and quality of the S1GBM mosaics (and document its limitations), and on the other hand, stimulate more advanced applications and ingestion-models by the remote sensing- and the wider user -communities. Our validation of the obtained PWB-map compares—over a representative and diverse set of eight world regions (see Fig. 1b)—the S1GBM mosaic with a reference water body map, as well as with true-colour imagery from the Sentinel-2 optical sensor. This arrangement should also portray the shape and texture of the S1GBM mosaic and help the audience with the interpretation of the SAR imagery, which as stated at the outset, allows a unique view on the Earth’s surface.In the following, 1) we examine in detail the appearance and spatial features of the S1GBM VV- and VH-mosaics over the region of Bordeaux, also investigating the effect of the PLIA-normalisation. Then, 2) we derive the characteristic C-band backscatter signature for global land classes. Finally, 3) we perform the PWB-experiment in eight world regions a) to evaluate the dataset’s integrity, b) to demonstrate its spatial information and exemplify its use, and c) to comment on the S1GBM’s assets and caveats.Detail example BordeauxFigure 2 gives an example of the land cover signal in the S1GBM VH and VV mosaics over Bordeaux, France. Comparing it with the recent PROBA-V-based Land Cover dataset of the Copernicus Global Land Service (CGLS LC10052), several surface features are apparent in the mosaics, including urban areas with varying density in both VV- and VH-channels. In the VH mosaic, a clear discrimination of forest areas (cf. with LC100’s broadleaf in brighter green, needle leaf in darker green) against crops (brighter yellow) and vineyards (darker yellow) is apparent. The cross-polarised VH-backscatter is more sensitive to vegetation-density, -structure, and -status, as multiple scattering between branches and volume scattering increases the share of backscattered microwaves with changed polarisation. Most prominent, in both VH and VV, is the very large contrast between land surfaces and open waters with significant lower backscatter signatures. This is the basis for our PWB-mapping experiment discussed in detail in the subsequent section.We would also like to draw the attention to the spatial detail carried by the S1GBM mosaics, with various features at deca- and hectometric scale shown for example in Fig. 2. For instance, one can see bridges, highways, railways, and airports in the Bordeaux metropolitan area in the south-west corner of the here displayed T1-tile (100 km extent). Also, in the west, from north to south, the shorelines of the Gironde estuary and its downstream rivers are clearly mapped, resolving small islands and narrow straits. Agricultural plots and forest sections may be differentiated especially in the VH mosaic, e.g. with particular structures in the north-west corner. For further exploration, users may visit the open web-based S1GBM viewer51 offering a pan-and-zoom exploration of the full S1GBM VV- and VH-mosaics.Figure 2b,d allows the comparison of the S1GBM VV backscatter mosaic (which underwent the PLIA-normalisation) against the mean of non-normalised Sentinel-1 VV backscatter from the same observation period (not part of the dataset publication; just for comparison). As discussed above, radar backscatter is strongly dependent to PLIA, and hence Sentinel-1 SAR images are subject to the observation geometry defined by the mission’s relative orbit configuration and the overlapping pattern (cf. global map in Fig. 1b). One can clearly see this impact in Fig. 2d, where data from all local orbits are averaged in their native orbit geometry (i.e. mean of σ0 (θro, t), resulting to characteristic linear artefacts of backscatter discontinuities along the limits of the (repeating) orbit footprints. The mini-map of the Bordeaux-T1-tile in Fig. 2d plots the number of input Sentinel-1 scenes, also reflecting the heterogeneous coverage pattern induced by the different number of overlapping relative orbits (from 2 to 4 in this area), each with a different local PLIA-range, generally. Notably, the triangular zone covered by only 2 orbits (yellow, 194 scenes) is a zone that features a PLIA-spread that is not large enough to reliably estimate the local PLIA-slope β. This zone is part of the pixel domain where we applied the static slope value of −0.13 dB/° to the S1GBM mosaic, with a resulting backscatter image that is free from orbit-related artefacts (Fig. 2b). We note that the sections covered by 3 or 4 orbits in this example are normalised with the regular regression slope, letting us conclude that our approach yields a smooth mosaicking impression in areas of mixed coverage density.Backscatter signature analysisDelving into above concept that SAR backscatter characteristics in the S1GBM are determined by land cover, we analysed the backscatter signature for the global land surface for each major land cover class (LCC). We globally aggregated data from the normalised S1GBM VV and VH mosaics per LCC and formed the backscatter distribution within each LCC, allowing the discrimination of typical SAR backscatter signatures for a specific land cover class.Land cover definitionsAs land cover dataset, we selected the above-mentioned PROBA-V-based CGLS LC100 for its full global coverage and the (for global datasets) relatively high spatial resolution with a pixel spacing of 100 m. To allow a fast pixel-by-pixel comparison, we resampled the CGLS LC100 to the Equi7Grid at 10 m using nearest-neighbour-downsampling. After a first inspection of backscatter signatures, we grouped the 23 LCC of the LC100 to 13 major LCC, accounting for the similarity between certain classes: Respective open and closed forest classes were aggregated to evergreen needle leaf forest, evergreen broad leaf forest, deciduous needle leaf forest, and deciduous broad leaf forest, and herbaceous wetland was grouped with herbaceous vegetation. Table 2 lists the main statistics per land cover and the group aggregations.Table 2 Sentinel-1 backscatter statistics per land cover class (LCC) of the CGLS LC100 dataset, mean and standard deviation, for the S1GBM mosaics in VV and VH polarisation.Full size tableC-band backscatter signaturesThe C-band backscatter signatures of our major 13 LCC are plotted for VV- and VH-polarisation as distribution-density-“heatlines” in the upper part of Fig. 3, illustrating the global average backscatter levels of each surface class, and the variance within. Forest and water-body classes have a very narrow distribution, whereas snow and ice and bare vegetation have a greater spatial backscatter variability. Snow and ice packs often have a heterogeneous structure from its complex genesis involving melting and freezing phases, leading to a mixture of surface- and volume-scattering when observed by radar. Likewise, the LCC bare vegetation comprises very different surfaces dominated by rocky, sandy, or mountainous surfaces, each governed by a distinct backscatter behaviour and hence create the wide spread within this LCC.Fig. 3Results from the S1GBM C-band backscatter signature analysis for major land cover classes, which are provided by the 100 m Land Cover Version 2.0 product of CGLS. The heatlines in (a) and (b) show the S1GBM’s normalised backscatter distribution within the total area of each major land cover class, for VV and VH, respectively. In preparation for the mapping of permanent water bodies (PWB), (c) and (d) show the distributions for the globally combined water- and land- surfaces, with the combined classes indicated by blue and brown bars in (a) and (b) legends. For the PWB-mapping, three land cover classes have been excluded due to the lack of clear separability against the water classes, i.e. due to largely overlapping distributions. The selected thresholds for VV and VH mosaics used in our PWB-mapping algorithm are indicated as red lines.Full size imageThe LCC-heatlines in Fig. 3a,b are approximately ordered by the mean backscatter value. On top, one can find the two water LCCs with a very low backscatter level that is caused by mirror-like-reflection away from the sensor, followed by bare and herbaceous vegetation LCCs that are dominated by dry conditions and hence are generally weak scatterer. The LCCs moss & lichen, shrubs, and agriculture feature medium backscatter and variation thereof. Higher backscatter levels are observed over the forest LCCs, where volume and multiple scattering become more dominant, as well as over the LCC urban & built up, where corner reflections acting as echo cause the strongest radar backscatter.When comparing VV and VH polarisation, the biggest difference is in the overall level of backscatter, with about 7 dB between both polarisations across all LCCs. The order of LCCs as a function of mean backscatter is mostly the same for VV and VH, except for the water and ice classes. Interestingly, the open sea class shows a steeper drop from VV to VH, whereas shrubs show a comparatively small drop. We found that the strongest changes in the backscatter distributions are apparent in the non-forest vegetation classes, e.g. for bare vegetation and agriculture, supporting our initial assumptions on the sensitivity of Sentinel-1 VH backscatter to complex vegetation dynamics and crop varieties.Permanent water body mappingFollowing up to what we have already seen along the rivers in Fig. 2, water bodies (represented by the LCCs open sea and permanent water bodies) show a most distinctive backscatter signature in relation to other land cover classes (cf. 3a-b). Effectively, water surfaces show in radar images a strong contrast with land surfaces. The reason for this are the different microwave scattering mechanism over water- and land-surfaces and the side-looking geometry of SAR systems. A specular reflection of the radar pulses by the water surfaces leads to backscatter intensities received by the sensor that are much lower than for most other land cover types. With the S1GBM VV- and VH-mosaics at hand, we exploited this discriminative feature of water bodies and employed a simple permanent water body mapping method. Unlike the backscatter mosaics of the S1GBM, the obtained PWB map can be validated directly, as we have available matching global water body maps as a reference. Moreover, the experiment should demonstrate the ease of realising a land cover mapping application in short time, exploiting the novel S1GBM data and its high-resolution radar imagery of the Earth’s land surfaces.Based on above insights from the Sentinel-1 backscatter signature analysis, our first step was to spatially merge all water- and all land-LCCs and build the combined backscatter signatures for VV and VH (Fig. 3c,d). The water distribution (all water classes; bright blue) is plotted for both polarisations next to the non-water distribution (all land classes, bright brown), already demonstrating an acceptable feature separation. However, as one can see in the heatlines above, water has still some significant overlap with some land LCCs, e.g. with bare vegetation, herbaceous vegetation, and moss & lichen. Naturally, this translates to a considerable overlap in the merged distributions below, especially in the VH case and for moss & lichen. We concluded that for these LCCs no robust separability against water bodies is given in the S1GBM data and excluded the three classes from further PWB-mapping. Also, we dropped the LCC open sea in further processing as we limit the PWB experiment to inland surfaces (that are also covered by the reference dataset). The backscatter distributions of the PWB LCC and the selected land LCCs are shown in dark blue and brown (permanent water bodies and selected land classes in Fig. 3c,d), with a noticeably improved separability, especially in VH polarisation.As a next step, evoking the theory of Bayesian inference with equal priors for binary classification, we obtained a statistically optimal global threshold for VV and VH, each. In this respect, we identified two thresholds, −15.0 dB for VV and −22.9 dB for VH polarisation, which we applied in a third step as an upper-bound backscatter-value on the complete S1GBM mosaics to map the global PWBs. Note again that the LCCs bare vegetation, herbaceous vegetation, moss & lichen, and open sea are not included in the PWB-mapping and are masked in all later results.Although the VV and VH mosaics are redundant to some degree, the consideration of both channels is most advantageous for the PWB-mapping. First, the classification based on Bayesian inference is more robust when resulting from two discriminations. Second, while the VH mosaic offers a better separability between water and non-water (having less overlap in the distributions and hence less false positive and negative classifications), and the heatline of the PWB-LCC is better defined in VH, the VV mosaic offers in general a higher spatial detail due to its stronger backscatter signal and hence more favourable signal-to-noise ratio.By applying the obtained thresholds to the normalised S1GBM mosaics as simple classification rules$${sigma }_{0}^{{rm{VV}}}(38)le -15.0;{rm{dB}}$$
(2)
$${sigma }_{0}^{{rm{VH}}}(38)le -22.9,{rm{dB}}$$
(3)
and through joining them with logical “AND”, we were able to produce a global PWB map in less than two hours, using 70 parallel cores on the VSC-3 supercomputer.Evaluation of S1GBM mosaics and PWB mapTo evaluate our S1GBM permanent water body (PWB) map, we chose as a reference dataset the Global Surface Water (GWS23) from the European Commission’s Joint Research Centre (JRC-EC). The GSW offers globally at a 30 m native sampling different variables on water bodies, e.g. annual seasonality, occurrence, recurrence, or maximum extent, and is based on 36 years of Landsat data in its newest version (GSW1_2). Although the annual seasonality for 2015 or 2016 was not accessible from version GSW1_2 at the time of writing this manuscript, we found the Seasonality 2015 dataset of the GSW1_0 version suitable as a reference. Pixels valued with seasonality “12” (i.e. all months) are labelled permanent water and constitute our reference PWB map, which we warped by means of bilinear resampling to the Equi7Grid at a 10 m pixel spacing.The evaluation presented in this paper was carried out on a representative and diverse set of eight world regions (see locations in Fig. 1b). For each region, classification results were assessed by a pixel-by-pixel comparison between the PWB map from S1GBM and from the GSW reference. Having such binary maps (water vs. non-water) it was straightforward to generate an “accuracy layer” representing the four elements of the commonly used confusion matrix, i.e. true positives, false positives, false negatives, and true negatives, to discuss the skill of the S1GBM to map PWBs. Areas belonging to the four excluded LCCs were masked in the result plots. Furthermore, to give some visual guidance in the evaluation regions, we acquired from the Copernicus Sentinel-2 Global Mosaic (S2GM) service the RGB-composite for the year 201953 (the mosaic for 2015 was available only over Europe).In the following, we present results for four large-scale regions (500 km × 500 km) in Fig. 4, and for four small-scale regions (120 km × 120 km) in Fig. 5. For each region, the S1GBM VV mosaic is displayed on the left panel (space-saving/omitting the VH mosaic, which contributes likewise to the PWB mapping), the accuracy maps showing the performance against the GSW reference in the centre panel, and the Sentinel-2 RGB-composite to aid visual interpretation on the right panel. The accuracy maps are annotated with the respective User’s Accuracy (UA) and Producer’s Accuracy (PA), as the percentage of the agreement between the two PWB-maps.Fig. 4For four example sites at the large scale (500 km extent), the S1GBM VV mosaic (left) is contrasted with classification results from the S1GBM PWB mapping against the PWB taken from JRC Global Surface Water (GSW) in 2015 (centre), and with the RGB-composite of the Copernicus Sentinel-2 Global Mosaic (S2GM) for the year 2019 (right). Box outlines are shown in global overview in Fig. 1b.Full size imageFig. 5For four detailed example sites (120 km extent), the S1GBM VV mosaic (left) is contrasted with classification results from the S1GBM PWB mapping against the PWB taken from JRC GSW in 2015 (centre), and with the RGB-composite of the Copernicus S2GM for the year 2019 (right). Box outlines are shown in global overview in Fig. 1b.Full size imageLarge-scale examinationsFigure 4a–c shows the southern part of Finland, an area accommodating a multitude of small and large post-glacial lakes. Those are clearly visible in dark colours representing low backscatter values in the S1GBM mosaic, while the other parts of the country (which is dominated by vast forests) shows rather uniform medium backscatter. The optical RGB-composite from Sentinel-2 does not feature the same accentuation of the lakes, troubled by remainders of cloud coverage in the yearly mosaic. The PWB accuracy map shows perfect agreement between S1GBM and GSW, with an UA and PA of 100% each. We identified two reasons for the excellent performance: First, the C-band backscatter signatures of the predominant land covers in Finland, such as forests, cities, agriculture, are well distinguishable against water bodies and hence allow an almost sterile PWB-mapping. Second, northern Europe is well covered by the Sentinel-1 mission and the S1GBM has been built with a high data density, letting us expect the best mosaic quality.Moving to the region of the Lake Superior Basin in Canada and USA presented in Fig. 4d–f, we encounter a very similar, cold-temperate environment, but with a substantial higher share in spacious inland water bodies. Also here, the accuracy map shows a perfect agreement between S1GBM and GSW, which, in our interpretation, is clearly because of good feature separability in the SAR image. Particularly remarkable is that North America is much less covered by Sentinel-1 than Europe and that the imperfect modelling of the PLIA-dependency over water surfaces (as apparent e.g. in the east section of Lake Superior) does not impair the S1GBM PWB-mapping. Generally, imperfect PLIA-normalisation of SAR images is prominent over water bodies, whose specular reflection regime is characterised by a very strong PLIA-gradient (i.e. the slope β). However, we note that also the Sentinel-2 mosaic has striping artefacts bound to orbit footprints, and additionally suffers from cloud cover. The latter is a common problem in optical observation of higher latitudes, but is without effect in SAR imagery.Figure 4g–i depicts the situation for a section of the Albertine Rift Valley in eastern Africa with its lake system. Reflecting to a great deal the region’s diverse flora, which is displayed in many green and brown tones in the RGB-composite, the S1GBM VV mosaic shows a much more heterogeneous pattern than in the above examples. The forested sections in the west show distinct higher backscatter values than the savanna sections in the east, and also other geomorphological features correspond well with the radar and optical mosaic. Concerning the PWB-mapping, we see again perfect agreement, but with one large exception: the eastern end of Lake Albert is entirely labelled in red as false land, suggesting that these water areas are missed in the S1GBM PWB map (what can be confirmed after a quick check with common thematic maps). In this area we see the impact of the relatively poor input data density of about only 50 Sentinel-1 scenes (cf. Figure 1b), and apparently, we overlooked the impact of a few images with outlying backscatter levels during the manual quality curation. Moreover, the three Sentinel-1 relative orbits covering this area create almost identical viewing angles and yield a very small PLIA-range, troubling our backscatter normalisation. As a result, striping artefacts appear not only over water bodies (cf. Canada example) but also over land (in north-west part Fig. 4g), while, however, the Sentinel-2 mosaic is likewise affected by striping issues (cf. Figure 4i), for other reasons, though.The last row in Fig. 4(j–m) is centred at Bangladesh and displays the confluence of the Ganges and Brahmaputra streams, which are joined downstream by the Meghna river and ultimately discharge into the Bay of Bengal. Also in this region, the geomorphological features perceivable in the RGB-composite are reflected well by strong textural patterns in the S1GBM mosaic, promoting its broader use in land cover applications (note also the zoom-in plotted in Fig. 5j–m). The PWB-mapping results are inconclusive, as rivers of all sizes are correctly mapped, but many pixels are labelled in yellow as false waters. We consider this disagreement between S1GBM and GSW to be most likely a result of the different temporal resolutions of the two datasets, as the S1GBM is a two-year data aggregation reduced to single layers, whereas the GSW allows monthly snapshots of water bodies. For example, the Hoar ecosystem—which appears as yellow bulb in the north-east of Fig. 4k—is a large monsoon-fed lagoon system that is labelled by the GSW with seasonality-values ranging from 9 to 12 months. In the S1GBM mosaics, which are built using temporally averaged backscatter, these areas are obviously dominated by the high occurrence of water surfaces and act therefore as “most-of-the-time water bodies”. Some more vindication comes from the Sentinel-2 yearly mosaic, which also draws the Hoar area with a water texture. We conclude on this matter that seasonal water bodies are not properly modelled by our simple approach with Eq. 3, and it would need additional inputs from variance measures like the backscatter standard deviation.Small-scale examinationsFigure 5 depicts the small-scale example regions with respect to the PWB-mapping experiment. The first row in a-c) zooms to the Swiss lakes in central Europe and both, the radar and the optical mosaic, feature a high level of heterogeneity and detail, with many individual forests, cities, valleys, rivers, alpine lakes, and with the airport north of Zurich resolvable (in the centre-left of the box). The results from the PWB-mapping are very good with high UA- and PA-values, but with two anomalies: First, the southern arm of Lake Lucerne (in the south-west) shows some red segments of false land along the mountain flanks reaching into the lake. After inspection of the S1GBM mosaics we can state that this is clearly an artefact from the terrain modelling with the rather coarse, 90 m-sampled VFP SRTM Digital Elevation Model (DEM) during the Sentinel-1 preprocessing. At the time of the project, we selected the VFP DEM35 for its complete global coverage and its manually-checked quality, and accepted the coarse resolution (with respect to the 10 m-sampled Sentinel-1 SAR data). The second small anomaly can be found in the Alps in the south of the image, with the west-end of the Klöntalersee labelled in yellow as false water. The S1GBM is artefact-free at this location, and after checking the GSW’s seasonality, we hypothesise that ice covers this mountain lake during winters and leads to the different interpretation.Figure 5d–f presents the area around the confluence of the Amazon and Tapajós streams in central Pará in Brazil. Here, the rivers ramify into a multitude of lagoons and channels at various sizes, forming a complex system of water bodies. Fortunately, while the Sentinel-2’s RGB mosaic appears impure and rugged from contamination with the frequent cloud coverage in the central tropics, the Sentinel-1 mosaic offers a clear image that fully resolves the capillary structure of the water bodies and its shorelines. We consider this a remarkable feature, also recognising the very low input data density of the S1GBM mosaics in this area (cf. Figure 1b). Concerning the PWB-mapping, we obtained a good agreement with the GSW’s reference, labelling most PWBs correctly and misclassifying only small sections of the lagoons and river-arms. The false-water deviations are bound again to the seasonality of those segments that are most of the time under water, much alike to the situation in Bangladesh discussed around Fig. 4j–m. The red-labelled areas highlight water bodies which are mapped by the GSW but not by the S1GBM, and are of particular interest, as they exemplify that water surfaces seen by optical sensors are not necessarily identical to those seen by radars54. Swamp-like structures and waters with out-growing vegetation show a completely different SAR signature and hence might be distinguishable from open waters within a SAR image.The third small-scale example is the Great Salt Lake in Utah, USA, as displayed in Fig. 5g–i. The S1GBM offers many details of Salt Lake City’s structures in the south-east, and of the mining facilities at the eastern shorelines of the lake, as also visible in the RGB-composite. Obviously, the radar image does not account for the difference in salinity between the north- and south-section of the Great Salt Lake that is visible in the optical image. However, our S1GBM PWB method maps correctly—contrary to the GSW reference—the east-west rail causeway splitting the lake, which one can see as a red line in the accuracy map in Fig. 5h. With its pronounced semi-arid climate, this region shows a different behaviour than above examples. The dry conditions and the sparse vegetation with its weak scattering trouble seriously the S1GBM PWB-mapping, with many false water pixel all around the area. Here, we see the weak performance of the simple threshold approach with Eq. 3 in regions with a general low backscatter from land, and hence small contrast to water bodies.Figure 5j–m zooms into the Sundarbans at the southern shorelines of Bangladesh, with its multifaceted surface and its complex river-deltas. Both, the true-colour image from Sentinel-2 and the VV-mosaic from Sentinel-1 produce a feature-rich image and highlight the mangrove forest in the southern section with strong green colour or high backscatter, respectively. Adjacent to the north, the rice and bean agriculture draws large contrast patterns in the satellite images. For the PWB-mapping, a similar result as from the larger view on this region (cf. Figure 4k) is obtained, with all rivers and channels correctly classified, but with a substantial overestimation of permanent water bodies in areas of high water seasonality. To what extent rice fields and its managed inundations play a role here is left unanswered by the data, though, as managed rice fields typically show significant jumps in seasonal backscatter time series. More

1.Evers, D. The effects of methylmercury on wildlife: A comprehensive review and approach for interpretation. In Encyclopedia of the Anthropocene (eds Dellasala, D. A. & Goldstein, M. I.) 181–194 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-809665-9.09985-7.Chapter 

Google Scholar 
2.Morel, F. M. M., Kraepiel, A. M. L. & Amyot, M. The chemical cycle and bioaccumulation of mercury. Ann. Rev. Ecol. Syst. 29, 543–566 (1998).Article 

Google Scholar 
3.Pushie, M. J., Pickering, I. J., Korbas, M., Hackett, M. J. & George, G. N. Elemental and chemically specific X-ray fluorescence imaging of biological systems. Chem. Rev. 114, 8499–8541 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.WHO. Exposure to Mercury: a Major Public Health Concern. (2007).5.Berlin, M., Zalups, R. K. & Fowler, B. A. Chapter 46—Mercury. In Handbook on the Toxicology of Metals (Fourth Edition) (eds Nordberg, G. F. et al.) 1013–1075 (Academic Press, Cambridge, 2015). https://doi.org/10.1016/B978-0-444-59453-2.00046-9.Chapter 

Google Scholar 
6.Clarkson, T. W. The Toxicology of mercury. Crit. Rev. Clin. Lab. Sci. 34, 369–403 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Abass, K. et al. Quantitative estimation of mercury intake by toxicokinetic modelling based on total mercury levels in humans. Environ. Int. 114, 1–11 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Liu, G., Cai, Y., O’Driscoll, N., Feng, X. & Jiang, G. Overview of mercury in the environment. In Environmental Chemistry and Toxicology of Mercury (eds Liu, G. et al.) 1–12 (Wiley, 2011). https://doi.org/10.1002/9781118146644.ch1.Chapter 

Google Scholar 
9.García, F., Ortega, A., Domingo, J. L. & Corbella, J. Accumulation of metals in autopsy tissues of subjects living in Tarragona county, Spain. J. Environ. Sci. Health Part A 36, 1767–1786 (2001).Article 

Google Scholar 
10.Clarkson, T. W. & Magos, L. The toxicology of mercury and its chemical compounds. Crit. Rev. Toxicol. 36, 609–662 (2006).CAS 
PubMed 
Article 

Google Scholar 
11.Holmes, P., James, K. A. F. & Levy, L. S. Is low-level environmental mercury exposure of concern to human health?. Sci. Total Environ. 408, 171–182 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Pasetto, R., Martin-Olmedo, P., Martuzzi, M. & Iavarone, I. Exploring available options in characterising the health impact of industrially contaminated sites. Ann. Ist Super Sanita 52, 476–482 (2016).PubMed 

Google Scholar 
13.Álvarez-Fernández, N., Martínez Cortizas, A. & López-Costas, O. Atmospheric mercury pollution deciphered through archaeological bones. J. Archaeol. Sci. 119, 105159 (2020).Article 
CAS 

Google Scholar 
14.Cooke, C. A., Martínez-Cortizas, A., Bindler, R. & Sexauer Gustin, M. Environmental archives of atmospheric Hg deposition—A review. Sci. Total Environ. 709, 134800 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
15.Leblanc, M., Morales, J. A., Borrego, J. & Elbaz-Poulichet, F. 4,500-year-old mining pollution in southwestern Spain: Long-term implications for modern mining pollution. Econ. Geol. 95, 655–662 (2000).CAS 

Google Scholar 
16.Cooke, C. A., Balcom, P. H., Biester, H. & Wolfe, A. P. Over three millennia of mercury pollution in the Peruvian Andes. PNAS 106, 8830–8834 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Hunt Ortiz, M. A., Consuegra, S., Díaz del Río, P., Hurtado Pérez, V. & Montero Ruiz, I. Neolithic and Chalcolithic –VI to III millennia BC– use of cinnabar (HgS) in the Iberian Peninsula: analytical identification and lead isotope data for an early mineral exploitation of the Almadén (Ciudad Real, Spain) mining district. (2011).18.Martı́nez-Cortizas, A., Pontevedra-Pombal, X., Garcı́a-Rodeja, E., Nóvoa-Muñoz, J. C. & Shotyk, W. Mercury in a Spanish peat bog: Archive of climate change and atmospheric metal deposition. Science 284, 939–942 (1999).19.Martínez Cortizas, A., Peiteado Varela, E., Bindler, R., Biester, H. & Cheburkin, A. Reconstructing historical Pb and Hg pollution in NW Spain using multiple cores from the Chao de Lamoso bog (Xistral Mountains). Geochimica et Cosmochimica Acta 82, 68–78 (2012).20.López-Costas, O. et al. Human bones tell the story of atmospheric mercury and lead exposure at the edge of Roman World. Sci. Total Environ. 710, 136319 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
21.Hedges, R. E. M. Bone diagenesis: an overview of processes. Archaeometry 44, 319–328 (2002).CAS 
Article 

Google Scholar 
22.Yamada, M. et al. Accumulation of mercury in excavated bones of two natives in Japan. Sci. Total Environ. 162, 253–256 (1995).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Emslie, S. D. et al. Chronic mercury exposure in Late Neolithic/Chalcolithic populations in Portugal from the cultural use of cinnabar. Sci. Rep. 5, 14679 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Alexandrovskaya, E. & Alexandrovskiy, A. Radiocarbon data and anthropochemistry of ancient Moscow. Geochronometria 24, 87–95 (2005).
Google Scholar 
25.Ávila, A., Mansilla, J., Bosch, P. & Pijoan, C. Cinnabar in mesoamerica: poisoning or mortuary ritual?. J. Archaeol. Sci. 49, 48–56 (2014).Article 
CAS 

Google Scholar 
26.Bocca, B. et al. Metals in bones of the middle-aged inhabitants of Sardinia island (Italy) to assess nutrition and environmental exposure. Environ. Sci. Pollut. Res. 25, 8404–8414 (2018).CAS 
Article 

Google Scholar 
27.Cervini-Silva, J., Muñoz, M. de L., Palacios, E., Ufer, K. & Kaufhold, S. Natural incorporation of mercury in bone. J. Trace Elements Med. Biol. 67, 126797 (2021).28.Cervini-Silva, J., Muñoz, M. de L., Palacios, E., Jimenez-Lopez, J. C. & Romano-Pacheco, A. Ageing and preservation of HgS-enriched ancient human remains deposited in confinement. J. Archaeol. Sci.: Rep. 18, 562–567 (2018).29.Cervini-Silva, J. et al. Cinnabar-preserved bone structures from primary osteogenesis and fungal signatures in ancient human remains. Geomicrobiol. J. 30, 566–577 (2013).CAS 
Article 

Google Scholar 
30.Emslie, S. D. et al. Mercury in archaeological human bone: biogenic or diagenetic?. J. Archaeol. Sci. 108, 104969 (2019).CAS 
Article 

Google Scholar 
31.Kepa, M. et al. Analysis of mercury levels in historical bone material from syphilitic subjects–pilot studies (short report). Anthropol. Anz. 69, 367–377 (2012).PubMed 
Article 

Google Scholar 
32.Ochoa-Lugo, M. et al. The effect of depositional conditions on mineral transformation, chemical composition, and preservation of organic material in archaeological Hg-enriched bone remains. J. Archaeol. Sci.: Rep. 15, 213–218 (2017).
Google Scholar 
33.Panova, T. D., Dmitriev, AYu., Borzakov, S. B. & Hramco, C. Analysis of arsenic and mercury content in human remains of the 16th and 17th centuries from Moscow Kremlin necropolises by neutron activation analysis at the IREN facility and the IBR-2 reactor FLNP JINR. Phys. Part. Nuclei Lett. 15, 127–134 (2018).ADS 
CAS 
Article 

Google Scholar 
34.Rasmussen, K. L. et al. Investigations of the relics and altar materials relating to the apostles St James and St Philip at the Basilica dei Santi XII Apostoli in Rome. Herit. Sci. 9, 14 (2021).CAS 
Article 

Google Scholar 
35.Rasmussen, K. L. et al. Comparison of trace element chemistry in human bones interred in two private chapels attached to Franciscan friaries in Italy and Denmark: An investigation of social stratification in two medieval and post-medieval societies. Heritage Sci. 8, 65 (2020).CAS 
Article 

Google Scholar 
36.Rasmussen, K. L. et al. On the distribution of trace element concentrations in multiple bone elements in 10 Danish medieval and post-medieval individuals. Am. J. Phys. Anthropol. 162, 90–102 (2017).Article 

Google Scholar 
37.Rasmussen, K. L., Skytte, L., Jensen, A. J. & Boldsen, J. L. Comparison of mercury and lead levels in the bones of rural and urban populations in Southern Denmark and Northern Germany during the Middle Ages. J. Archaeol. Sci.: Rep. 3, 358–370 (2015).
Google Scholar 
38.Rasmussen, K. L. et al. Was he murdered or was he not?—Part I: Analyses of mercury in the remains of Tycho Brahe. Archaeometry 55, 1187–1195 (2013).CAS 
Article 

Google Scholar 
39.Rasmussen, K. L. et al. The distribution of mercury and other trace elements in the bones of two human individuals from medieval Denmark—The chemical life history hypothesis. Herit. Sci. 1, 10 (2013).Article 
CAS 

Google Scholar 
40.Torino, M. et al. Convento di San Francesco a Folloni: The function of a Medieval Franciscan Friary seen through the burials. Herit. Sci. 3, 27 (2015).Article 
CAS 

Google Scholar 
41.Walser, J. W., Kristjánsdóttir, S., Gowland, R. & Desnica, N. Volcanoes, medicine, and monasticism: Investigating mercury exposure in medieval Iceland. Int. J. Osteoarchaeol. 29, 48–61 (2019).Article 

Google Scholar 
42.Rasmussen, K. L. et al. Mercury levels in Danish Medieval human bones. J. Archaeol. Sci. 35, 2295–2306 (2008).Article 

Google Scholar 
43.Armesto, A. G. et al. Total mercury distribution among soil aggregate size fractions in a temperate forest podzol. Span. J. Soil Sci. 8(1), 57–73 (2018).
Google Scholar 
44.do Valle, C. M., Santana, G. P., Augusti, R., Egreja Filho, F. B. & Windmöller, C. C. Speciation and quantification of mercury in Oxisol, Ultisol, and Spodosol from Amazon (Manaus, Brazil). Chemosphere 58, 779–792 (2005).45.Fiorentino, J. C., Enzweiler, J. & Angélica, R. S. Geochemistry of mercury along a soil profile compared to other elements and to the parental rock: Evidence of external input. Water Air Soil Pollut. 221, 63–75 (2011).ADS 
CAS 
Article 

Google Scholar 
46.Roulet, M. et al. The geochemistry of mercury in central Amazonian soils developed on the Alter-do-Chão formation of the lower Tapajós River Valley, Pará state, Brazil1The present investigation is part of an ongoing study, the CARUSO project (IDRC-UFPa-UQAM), initiated to determine the sources, fate, and health effects of MeHg in the Lower Tapajós area.1. Sci. Total Environ. 223, 1–24 (1998).47.Qin, F. et al. Evaluation of trace elements and identification of pollution sources in particle size fractions of soil from iron ore areas along the Chao River. J. Geochem. Expl. 138, 33–49 (2014).CAS 
Article 

Google Scholar 
48.Acosta, J. A., Martínez-Martínez, S., Faz, A. & Arocena, J. Accumulations of major and trace elements in particle size fractions of soils on eight different parent materials. Geoderma 161, 30–42 (2011).ADS 
CAS 
Article 

Google Scholar 
49.Janaway, R. C., Percival, S. L. & Wilson, A. S. Decomposition of Human Remains. In Microbiology and Aging: Clinical Manifestations (ed. Percival, S. L.) 313–334 (Humana Press, London, 2009). https://doi.org/10.1007/978-1-59745-327-1_14.Chapter 

Google Scholar 
50.Obrist, D., Johnson, D. W. & Lindberg, S. E. Mercury concentrations and pools in four Sierra Nevada forest sites, and relationships to organic carbon and nitrogen. Biogeosciences 6, 765–777 (2009).ADS 
CAS 
Article 

Google Scholar 
51.Schuster, E. The behavior of mercury in the soil with special emphasis on complexation and adsorption processes—A review of the literature. Water Air Soil Pollut. 56, 667–680 (1991).ADS 
CAS 
Article 

Google Scholar 
52.Taboada, T., Cortizas, A. M., García, C. & García-Rodeja, E. Particle-size fractionation of titanium and zirconium during weathering and pedogenesis of granitic rocks in NW Spain. Geoderma 131, 218–236 (2006).ADS 
CAS 
Article 

Google Scholar 
53.Babuśka-Roczniak, M. et al. Occurrence of mercury in the knee joint tissues. Pol. Ann. Med. 28, 39–44 (2021).
Google Scholar 
54.Domingo, J. L., García, F., Nadal, M. & Schuhmacher, M. Autopsy tissues as biological monitors of human exposure to environmental pollutants. A case study: Concentrations of metals and PCDD/Fs in subjects living near a hazardous waste incinerator. Environ. Res. 154, 269–274 (2017).CAS 
PubMed 
Article 

Google Scholar 
55.López-Costas, O., Lantes-Suárez, Ó. & Martínez Cortizas, A. Chemical compositional changes in archaeological human bones due to diagenesis: Type of bone vs soil environment. J. Archaeol. Sci. 67, 43–51 (2016).56.Taboada, T., Martínez Cortizas, A., García, C. & García-Rodeja, E. Uranium and thorium in weathering and pedogenetic profiles developed on granitic rocks from NW Spain. Sci. Total Environ. 356, 192–206 (2006).57.Windmöller, C. C., Durão, W. A., de Oliveira, A. & do Valle, C. M. The redox processes in Hg-contaminated soils from Descoberto (Minas Gerais, Brazil): Implications for the mercury cycle. Ecotoxicol. Environ. Saf. 112, 201–211 (2015).58.Blanco Freijeiro, A., Fusté Ara, M. & García Alén, A. La necrópolis galaico-romana de La Lanzada (Noalla, Pontevedra) II. Cuadernos de estudios gallegos 22, 5–23 (1967).59.Blanco Freijeiro, A., Fusté Ara, M. & García Alén, A. La necrópolis galaico-romana de La Lanzada (Noalla, Pontevedra). Cuadernos de estudios gallegos 16, 141–158 (1961).60.Kaal, J., López-Costas, O. & Martínez Cortizas, A. Diagenetic effects on pyrolysis fingerprints of extracted collagen in archaeological human bones from NW Spain, as determined by pyrolysis-GC-MS. J. Archaeol. Sci. 65, 1–10 (2016).61.López Costas, O. Antropología de los restos óseos humanos de Galicia: estudio de la población romana y medieval gallega. (Universidad de Granada, 2012).62.López-Costas, O. Taphonomy and burial context of the Roman/post-Roman funerary areas (2nd to 6th centuries AD) of A Lanzada, NW Spain. Estudos do Quaternário/Quaternary Studies 55–67 (2015) https://doi.org/10.30893/eq.v0i12.111.63.López-Costas, O. & Müldner, G. Fringes of the empire: Diet and cultural change at the Roman to post-Roman transition in NW Iberia. Am. J. Phys. Anthropol. 161, 141–154 (2016).PubMed 
Article 

Google Scholar 
64.García López, Z., López Costas, O. & Martínez Cortizas, A. Análisis de sedimentos asociados a restos humanos de la Necrópolis de A Lanzada y Adro Vello (Pontevedra). (2019).65.Rodríguez Martínez, R. M. Informe valorativo da intervención arqueolóxica para a recuperación patrimonial do xacemento de A Lanzada (Sanxenxo, Pontevedra). Fase II. (2017).66.Brickley, M. & McKinley, J. I. Determination of sex from archaeological skeletal material and assessment of parturition. in Guidelines to the Standards for Recording Human Remains. 23–25 (BABAO, Dept. of Archaeology, University of Southampton. Institute of Field Archaeologist, University of Reading, 2004).67.López Costas, O. et al. Informe final: Estudio de esqueletos humanos y de secuencias edafo-sedimentárias del yacimiento de A Lanzada. En: Rodríguez Martínez, R.M., 2017. Informe valorativo da intervención arqueolóxica para a recuperación patrimonial do xacemento de A Lanzada (Sanxenxo, Pontevedra). Fase II. (2017).68.Cheburkin, A. K. & Shotyk, W. Determination of trace elements in aqueous solutions using the EMMA miniprobe XRF analyzer. X-Ray Spectrom. 28, 379–383 (1999).ADS 
CAS 
Article 

Google Scholar 
69.Cheburkin, A. K. & Shotyk, W. High-sensitivity XRF analyzer (OLIVIA) using a multi-crystal pyrographite assembly to reduce the continuous background. X-Ray Spectrom. 28, 145–148 (1999).ADS 
CAS 
Article 

Google Scholar 
70.Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometrics Intell. Lab. Syst. 58, 109–130 (2001).CAS 
Article 

Google Scholar 
71.Martín-Fernández, J. A., Hron, K., Templ, M., Filzmoser, P. & Palarea-Albaladejo, J. Model-based replacement of rounded zeros in compositional data: Classical and robust approaches. Comput. Stat. Data Anal. 56, 2688–2704 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
72.Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. Isometric logratio transformations for compositional data analysis. Mathe. Geol. 35, 279–300 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
73.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).74.Filzmoser, P., Hron, K. & Templ, M. Applied Compositional Data Analysis. With Worked Examples (Springer, 2018).MATH 
Book 

Google Scholar 
75.Garrett, R. G. rgr: Applied Geochemistry EDA. (2018).76.Bertrand, F. & Maumy-Bertrand, M. Partial Least Squares Regression for Generalized Linear Models. (2019).77.Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2020).78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
79.Punta A Lanzada, O Grove (Galicia, Spain) 42°25′44.61″N 8°52′29.31″W elev 16 m eye alt 585m. Google Earth. Jully 18, 2020. March 20, 2021. https://bit.ly/3FwpZrE.80.A Lanzada site (Galicia, Spain) 42°25′44.64″N 8°52″29.42″W elev 16m eye alt 549m. Google Earth. Jully 18, 2020. October 12, 2021. https://bit.ly/3BBqxKy. More

1.Pachauri, R. K. et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I. II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2014).2.International Geosphere Biosphere Programme (IGBP). Ocean acidification summary for policymakers (2013).3.Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 
Article 

Google Scholar 
4.Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 1–7. https://doi.org/10.1038/s41559-017-0084 (2017).CAS 
Article 

Google Scholar 
5.Dupont, S., Hall, E., Calosi, P. & Lundve, B. First evidence of altered sensory quality in a shellfish exposed to decreased pH relevant to ocean acidification. J. Shellfish Res. 33, 857–861 (2014).Article 

Google Scholar 
6.Lemasson, A. J. et al. Sensory qualities of oysters unaltered by a short exposure to combined elevated pCO2 and temperature. Front. Mar. Sci. 4, 352. https://doi.org/10.3389/fmars.2017.00352 (2017).Article 

Google Scholar 
7.San Martin, V. A. et al. Linking social preferences and ocean acidification impacts in mussel aquaculture. Sci. Rep. 9, 1–9 (2019).ADS 

Google Scholar 
8.Shahidi, F. & Cadwallader, K. R. Flavor and lipid chemistry of seafoods: an overview (1997).9.Nelson, G. et al. An amino acid taste receptor. Nature 416, 199–202 (2002).ADS 
CAS 
Article 

Google Scholar 
10.Guillen, J. et al. Global seafood consumption footprint. Ambio 48(2), 111–122 (2019).Article 

Google Scholar 
11.FAO. The state of world fisheries and aquaculture. Contributing to food security and nutrition for all. FAO, Rome (2016).12.FAO. The state of world fisheries and aquaculture—sustainability in action (2020).13.Gerland, P. et al. World population stabilization unlikely this century. Science 346(6206), 234–237 (2014).ADS 
CAS 
Article 

Google Scholar 
14.Minh, N. P., Nhi, T. T. Y., Hiep, P. T. H., Nhan, D. T. & Anh, S. T. Quality characteristics of dried salted black tiger shrimp (Penaeus monodon) affected by different pre-treatment and drying variables. J. Pharm. Sci. Res. 11, 1377–1381 (2019).CAS 

Google Scholar 
15.FAO. The state of food and agriculture (1980).16.Solms, J. Taste of amino acids, peptides, and proteins. J. Agric. Food Chem. 17(4), 686–688 (1969).CAS 
Article 

Google Scholar 
17.Jiro, K., Akira, S. & Akimitsu, K. The contribution of peptides and amino acids to the taste of foodstuffs. J. Agric. Food Chem. 17(4), 689–695 (1969).Article 

Google Scholar 
18.Schiffman, S. S., Sennewald, K. & Gagnon, J. Comparison of taste qualities and thresholds of D-and L-amino acids. Physiol. Behav. 27(1), 51–59 (1981).CAS 
Article 

Google Scholar 
19.Kawai, M., Sekine-Hayakawa, Y., Okiyama, A. & Ninomiya, Y. Gustatory sensation of L- and D-amino acids in humans. Amino Acids 43, 2349–2358 (2012).CAS 
Article 

Google Scholar 
20.Dissanayake, A., Clough, R., Spicer, J. I. & Jones, M. B. Effects of hypercapnia on acid–base balance and osmo-/iono-regulation in prawns (Decapoda: Palaemonidae). Aquat. Biol. 11, 27–36 (2010).Article 

Google Scholar 
21.Ries, J., Choen, A. L. & McCorkle, D. C. Marine calcifiers exhibit mixed responses to CO2-induced ocean acidification. Geology 37, 1131–1134 (2009).ADS 
CAS 
Article 

Google Scholar 
22.Liu, Y. W., Sutton, J. N., Ries, J. B. & Eagle, R. A. Regulation of calcification site pH is a polyphyletic but not always governing response to ocean acidification. Sci. Adv. 6, eaax1314 (2020).ADS 
CAS 
Article 

Google Scholar 
23.Corteel, M. et al. Moult cycle of laboratory-raised Penaeus (Litopenaeus) vannamei and P. monodon. Aquac. Int. 20, 13–18 (2011).Article 

Google Scholar 
24.Taylor, J. R., Gilleard, J. M., Allen, M. C. & Deheyn, D. D. Effects of CO2-induced pH reduction on the exoskeleton structure and biophotonic properties of the shrimp Lysmata californica. Sci. Rep. 5, 10608 (2015).ADS 
CAS 
Article 

Google Scholar 
25.McLean, E. L., Katenka, N. V. & Seibel, B. A. Decreased growth and increased shell disease in early benthic phase Homarus americanus in response to elevated CO2. Mar. Ecol. Prog. Ser. 596, 113–126 (2018).ADS 
CAS 
Article 

Google Scholar 
26.Chen, S. M. & Chen, J. C. Effect of low pH on the acid-base balance, osmolality and ion concentrations of giant freshwater prawn Macrobrachium rosenbergii. J. Fish. Soc. Taiwan 30, 227–239 (2003).
Google Scholar 
27.Kurihara, H., Matsui, M., Furukawa, H., Hayashi, M. & Ishimatsu, A. Long-term effects of predicted future seawater CO2 conditions on the survival and growth of the marine shrimp Palaemon pacificus. J. Exp. Mar. Biol. Ecol. 367, 41–46 (2008).CAS 
Article 

Google Scholar 
28.Findlay, H. S., Kendall, M. A., Spicer, J. I. & Widdicombe, S. Future high CO2 in the intertidal may compromise adult barnacle Semibalanus balanoides survival and embryonic development rate. Mar. Ecol. Prog. Ser. 389, 193–202 (2009).ADS 
Article 

Google Scholar 
29.Cameron, J. N. & Iwama, G. K. Compensation of progressive hypercapnia in channel catfish and blue crabs. J. Exp. Biol. 133, 183–197 (1987).Article 

Google Scholar 
30.Pane, E. F. & Barry, J. P. Extracellular acid-base regulation during short-term hypercapnia is effective in a shallow-water crab, but ineffective in a deep-sea crab. Mar. Ecol. Prog. Ser. 334, 1–9 (2007).ADS 
CAS 
Article 

Google Scholar 
31.Lowder, K. B., Allen, M. C., Day, J. M. D., Deheyn, D. D. & Taylor, J. R. A. Assessment of ocean acidification and warming on the growth, calcification, and biophotonics of a California grass shrimp. ICES J. Mar. Sci. 74, 1150–1158 (2017).Article 

Google Scholar 
32.Pörtner, H. O., Langenbunh, M. & Reipschläger, A. Biological impact of elevated ocean CO2 concentrations: Lessons from animal physiology and earth history. J. Oceanogr. 60, 705–718 (2004).Article 

Google Scholar 
33.Dissanayake, A. & Ishimatsu, A. Synergistic effects of elevated CO2 and temperature on the metabolic scope and activity in a shallow-water coastal decapod (Metapenaeus joyneri; Crustacea: Penaeide). ICES J. Mar. Sci. 68, 1147–1154 (2011).Article 

Google Scholar 
34.Pan, L. Q., Zhang, L. J. & Liu, H. Y. Effects of salinity and pH on ion-transport enzyme activities, survival and growth of Litopenaeus vannamei postlarvae. Aquaculture 273, 711–720 (2007).CAS 
Article 

Google Scholar 
35.Rathburn, C. K. et al. Transcriptomic responses of juvenile Pacific whiteleg shrimp, Litopenaeus vannamei, to hypoxia and hypercapnic hypoxia. Physiol. Genomics 45, 794–807 (2013).CAS 
Article 

Google Scholar 
36.Yu, Q. R. et al. Growth and health responses to a long-term pH stress in Pacific white shrimp Litopenaeus vannamei. Aquacul. Rep. 16, 100280 (2020).Article 

Google Scholar 
37.Chen, J. C., Chen, C. T. & Cheng, S. Y. Nitrogen excretion and changes of hemocyanin, protein and free amino acid levels in the hemolymph of Penaeus monodon exposed to different concentrations of ambient ammonia-N at different salinity levels. Mar. Ecol. Prog. Ser. 110, 85–94 (1994).ADS 
CAS 
Article 

Google Scholar 
38.Dayal, J. S., Ambasankar, K., Rajendran, R., Rajaram, V. & Muralidhar, M. Effect of abiotic salinity stress on haemolymph metabolic profiles in cultured tiger shrimp Penaeus monodon. Int. J. Bio-resour. Stress Manag. 4, 339–343 (2013).
Google Scholar 
39.Ardo, Y. Flavour formation by amino acid catabolism. Biotechnol. Adv. 24, 238–242 (2006).CAS 
Article 

Google Scholar 
40.Engström-Öst, J. et al. Eco-physiological responses of copepods and pteropods to ocean warming and acidification. Sci. Rep. 9, 4748 (2019).ADS 
Article 

Google Scholar 
41.Liao, H. et al. Impact of ocean acidification on the energy metabolism and antioxidant responses of the Yesso scallop (Patinopecten yessoensis). Front. Physiol. 27, 1967 (2019).Article 

Google Scholar 
42.Richard, L. et al. The effect of choline and cystine on the utilisation of methionine for protein accretion, remethylation and trans-sulfuration in juvenile shrimp Penaeus monodon. Br. J. Nutr. 28, 825–835 (2011).Article 

Google Scholar 
43.Peng, B., Huang, R. & Zhou, X. oxidation resistance of the sulfur amino acids: methionine and cysteine. Biomed. Res. Int. 2017, 9584932 (2017).
Google Scholar 
44.DeVries, M. S. et al. Stress physiology and weapon integrity of intertidal mantis shrimp under future ocean conditions. Sci. Rep. 6, 38637 (2016).ADS 
CAS 
Article 

Google Scholar 
45.Dupont, S. & Thorndyke, M. C. Impact of CO2-driven ocean acidification on invertebrates early life-history—What we know, what we need to know and what we can do. Biogeosci. Discuss. 6, 3109–3131 (2009).ADS 
Article 

Google Scholar 
46.Weerathunga, V. V. et al. Impacts of pH on the fitness and immune system of pacific white shrimp. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.748837 (2021).Article 

Google Scholar 
47.Fuller, P. L. et al. Invasion of Asian tiger shrimp, Penaeus monodon Fabricius, 1798, in the western north Atlantic and Gulf of Mexico. Aquat. Invasions 9, 59–70 (2014).Article 

Google Scholar 
48.Lewis, E. & Wallace, D. Program developed for CO2 system calculations (Environmental System Science Data Infrastructure for a Virtual Ecosystem, 1998).49.Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep Sea Res. Part A Oceanogr. Res. Pap. 34, 1733–1743 (1987).ADS 
CAS 
Article 

Google Scholar 
50.AOAC. Method 991.42 & 993.19. Official methods of analysis (16th ed.). Washington, DC: Association of Official Analytical Chemists (1995).51.Motoh, H. Biology and ecology of Penaeus monodon. Iloilo City, Philippines. Aquaculture Department, Southeast Asian Fisheries Development Center (1985).52.Mayor, D. J., Matthews, C., Cook, K., Zuur, A. F. & Hay, S. CO2-induced acidification affects hatching success in Calanus finmarchicus. Mar. Ecol. Prog. Ser. 350, 91–97 (2007). More

1.Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).PubMed 
Article 

Google Scholar 
2.Aronson, M. F. J. et al. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B 281, 20133330 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Nielsen, A. B., Van Den Bosch, M., Maruthaveeran, S. & Van Den Bosch, C. K. Species richness in urban parks and its drivers: A review of empirical evidence. Urban Ecosyst. 17, 305–327 (2014).Article 

Google Scholar 
4.Ives, C. D. et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 25, 117–126 (2016).Article 

Google Scholar 
5.Luck, G. W., Davidson, P., Boxall, D. & Smallbone, L. Relations between urban bird and plant communities and human well-being and connection to nature. Conserv. Biol. 25, 816–826 (2011).PubMed 
Article 

Google Scholar 
6.Soga, M. & Gaston, K. J. Extinction of experience: the loss of human–nature interactions. Front. Ecol. Environ. 14, 94–101 (2016).Article 

Google Scholar 
7.Dean, J., van Dooren, K. & Weinstein, P. Does biodiversity improve mental health in urban settings?. Med. Hypotheses 76, 877–880 (2011).PubMed 
Article 

Google Scholar 
8.Knight, A. T. et al. Knowing but not doing: Selecting priority conservation areas and the research-implementation gap. Conserv. Biol. 22, 610–617 (2008).PubMed 
Article 

Google Scholar 
9.Waldron, A. et al. Reductions in global biodiversity loss predicted from conservation spending. Nature 551, 364–367 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
10.Caro, T. M. Conservation by Proxy: Indicator, Umbrella, Keystone, Flagship and Other Surrogate Species (Island Press, 2010).
Google Scholar 
11.Sergio, F., Newton, I. & Marchesi, L. Top predators and biodiversity. Nature 236, 192 (2005).ADS 
Article 
CAS 

Google Scholar 
12.Burgas, D., Byholm, P. & Parkkima, T. Raptors as surrogates of biodiversity along a landscape gradient. J. Appl. Ecol. 51, 786–794 (2014).Article 

Google Scholar 
13.Sergio, F., Newton, I., Marchesi, L. & Pedrini, P. Ecologically justified charisma: Preservation of top predators delivers biodiversity conservation. J. Appl. Ecol. 43, 1049–1055 (2006).Article 

Google Scholar 
14.Sergio, F. et al. Top predators as conservation tools: Ecological rationale, assumptions, and efficacy. Annu. Rev. Ecol. Evol. Syst. 39, 1–19 (2008).Article 

Google Scholar 
15.Sergio, F. Raptor monitoring: Challenges and benefits. Bird Study 65, S3–S3 (2018).Article 

Google Scholar 
16.Millsap, B. A., Cooper, M. E. & Holroyd, G. Legal considerations. In Raptor Research and Management Techniques (eds Bird, D. M. & Bildstein, K. L.) 365–382 (Hancock House Publishers, 2007).
Google Scholar 
17.Maciorowski, G., Jankowiak, Ł, Sparks, T. H., Polakowski, M. & Tryjanowski, P. Biodiversity hotspots at a small scale: The importance of eagles’ nests to many other animals. Ecology 102, e03220 (2021).PubMed 
Article 

Google Scholar 
18.Natsukawa, H. Raptor breeding sites as a surrogate for conserving high avian taxonomic richness and functional diversity in urban ecosystems. Ecol. Indic. 119, 106874 (2020).Article 

Google Scholar 
19.Natsukawa, H. Raptor breeding sites indicate high taxonomic and functional diversities of wintering birds in urban ecosystems. Urban For. Urban Green. 60, 127066 (2021).Article 

Google Scholar 
20.Sergio, F., Newton, I. & Marchesi, L. Top predators and biodiversity: Much debate, few data. J. Appl. Ecol. 45, 992–999 (2008).Article 

Google Scholar 
21.Estrada, C. G. & Rodríguez-Estrella, R. In the search of good biodiversity surrogates: Are raptors poor indicators in the Baja California Peninsula desert?. Anim. Conserv. 19, 360–368 (2016).Article 

Google Scholar 
22.Kenward, R. E. The Goshawk (T&A D Poyser, 2006).
Google Scholar 
23.Manning, A. D., Fischer, J. & Lindenmayer, D. B. Scattered trees are keystone structures–implications for conservation. Biol. Conserv. 132, 311–321 (2006).Article 

Google Scholar 
24.Ozanne, C. M. P. et al. Biodiversity meets the atmosphere: A global review of forest canopies. Science 301, 183–186 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
25.Yan, Z. et al. Impervious surface area is a key predictor for urban plant diversity in a city undergone rapid urbanization. Sci. Total Environ. 650, 335–342 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Atauri, J. A., De Pablo, C. L., De Agar, P. M., Schmitz, M. F. & Pineda, F. D. Effects of management on understory diversity in the forest ecosystems of Northern Spain. Environ. Manag. 34, 819–828 (2004).Article 

Google Scholar 
27.Martín-Queller, E., Gil-Tena, A. & Saura, S. Species richness of woody plants in the landscapes of Central Spain: The role of management disturbances, environment and non-stationarity. J. Veg. Sci. 22, 238–250 (2011).Article 

Google Scholar 
28.Rodriguez, S. A., Kennedy, P. L. & Parker, T. H. Timber harvest and tree size near nests explains variation in nest site occupancy but not productivity in northern goshawks (Accipiter gentilis). For. Ecol. Manage. 374, 220–229 (2016).Article 

Google Scholar 
29.Rosich, J. et al. Northern Goshawk breeding sites indicate the presence of mature forest in Mediterranean pinewoods. For. Ecol. Manag. 479, 118602 (2021).Article 

Google Scholar 
30.Natsukawa, H., Ichinose, T. & Higuchi, H. Factors affecting breeding-site selection of Northern Goshawks at two spatial scales in urbanized areas. J. Raptor Res. 51, 417–428 (2017).Article 

Google Scholar 
31.Natsukawa, H. et al. Forest cover and open land drive the distribution and dynamics of the breeding sites for urban-dwelling Northern Goshawks. Urban For. Urban Green. 53, 126732 (2020).Article 

Google Scholar 
32.Boal, C. W. & Dykstra, C. R. Urban Raptors: Ecology and Conservation of Birds of Prey in Cities (Island Press, 2018).Book 

Google Scholar 
33.Burgas, D., Ovaskainen, O., Blanchet, F. G. & Byholm, P. The ghost of the hawk: Top predator shaping bird communities in space and time. Front. Ecol. Evol. 9, 638039 (2021).Article 

Google Scholar 
34.Byholm, P., Gunko, R., Burgas, D. & Karell, P. Losing your home: Temporal changes in forest landscape structure due to timber harvest accelerate Northern goshawk (Accipiter gentilis) nest stand losses. Ornis Fenn. 97, 1–11 (2020).
Google Scholar 
35.Ozaki, K. et al. A mechanistic approach to evaluation of umbrella species as conservation surrogates. Conserv. Biol. 20, 1507–1515 (2006).PubMed 
Article 

Google Scholar 
36.Santangeli, A. et al. Voluntary non-monetary approaches for implementing conservation. Biol. Conserv. 197, 209–214 (2016).Article 

Google Scholar 
37.Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manage. 58, 576–597 (2015).Article 

Google Scholar 
38.Iwai, Y. Forestry and the Forest Industry in Japan (UBC Press, 2002).
Google Scholar 
39.Sirakaya, A., Cliquet, A. & Harris, J. Ecosystem services in cities: Towards the international legal protection of ecosystem services in urban environments. Ecosyst. Serv. 29, 205–212 (2018).Article 

Google Scholar 
40.Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).Article 

Google Scholar 
41.Kumar, N., Jhala, Y. V., Qureshi, Q., Gosler, A. G. & Sergio, F. Human-attacks by an urban raptor are tied to human subsidies and religious practices. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
42.Mak, B., Francis, R.A. & Chadwick, M.A. Living in the concrete jungle: A review and socio-ecological perspective of urban raptor habitat quality in Europe. Urban Ecosyst. 21 (2021).43.Demographia. Demographia World Urban Areas, 16th annual edition. Available: http://www.demographia.com/db-worldua.pdf. Date of access February 20, 2021 (2020).44.Yang, J., Yan, P., He, R. & Song, X. Exploring land-use legacy effects on taxonomic and functional diversity of woody plants in a rapidly urbanizing landscape. Landsc. Urban Plan. 162, 92–103 (2017).Article 

Google Scholar 
45.Spellerberg, I. F. & Fedor, P. J. A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’Index. Glob. Ecol. Biogeog. 12, 177–179 (2003).Article 

Google Scholar 
46.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).Article 

Google Scholar 
47.R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).48.Oksanen, J. et al. Vegan: Community ecology package. R package version 2, 5–5 (2019).
Google Scholar 
49.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).50.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
51.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
52.Betts, M. G., Diamond, A. W., Forbes, G. J., Villard, M. A. & Gunn, J. S. The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence. Ecol. Model. 191, 197–224 (2006).Article 

Google Scholar 
53.Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
54.Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography 30, 609–628 (2007).Article 

Google Scholar 
55.Harrell, F. E. rms: Regression Modeling Strategies. R package version 6.0–1 (2020).56.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, 1–36 (2015).
Google Scholar  More