Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

Ocean forcingsThe Nucleus for European Modelling of the Ocean (NEMO) ocean framework46, which includes an online coupling with the biogeochemical component PISCES in a 2° latitude × 2° longitude configuration47,48, was used to simulate the historical oceanic environment (hindcast simulation). This historical simulation was forced by the Drakkar Forcing Sets 5.2 (DFS5.2)49 on the basis of a corrected set of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-Interim) over the period 1979−2011. Salinity, temperature and biogeochemical tracer concentrations (nitrate, phosphate, iron, silicate, alkalinity, dissolved oxygen and dissolved organic and inorganic carbon) were initialized from the World Ocean Atlas climatology (WOA09)50 and previous model climatology for iron and dissolved organic carbon51. To minimize any substantial numerical drift in the simulations related to a non-equilibrated initial state, we applied a spin-up of the ocean model and biogeochemical model for 66 years, cycling twice over the DFS5.2 forcing sets48.Overall, the model simulates basin-scale, historical SST and salinity distribution, together with seasonal and interannual (ENSO) variability with good fidelity52. Classical biases are associated with the coarse (2°) resolution, for example, the latitudinal position of the Kuroshio Current. In the tropical Pacific, there is a cold bias of −1 °C in the central equatorial zone (between 170° W and 100° W) and a warm bias of +1 °C in the eastern part of the basin (east of 90° W). Despite some local discrepancy between simulation outputs and satellite-derived chlorophyll concentration around islands and near the American coasts, simulated mean chlorophyll in the equatorial Pacific Ocean is close to observed values51,52.For future ocean projections, we first selected several ESMs from the CMIP5 intercomparison project53 on the basis of the ability of the models to produce accurate ENSO variability in the Pacific54. The four ESMs selected were IPSL-CM5A55, MIROC56, GFDL-ESM2G57 and MPI-MR58. We then extracted atmospheric fields from these models for the period 2011−2100 under RCP 8.5 to simulate ‘business-as-usual’ climate anomalies to build forcing sets for the NEMO–PISCES ocean model.All ESMs display large biases in their representation of Pacific climate, including the important South Pacific Convergence Zone59,60. These atmospheric biases propagated uncertainties associated with future atmospheres into the coupled, dynamical-biogeochemical oceanic framework. For example, they result in prominent distortions in the extension and position of the warm pool61 and can be expected to affect modelling of the open ocean ecosystem up to the higher trophic levels12.To mitigate the mean state model biases in the selected ESMs, we used a ‘pseudo-warming’ anomaly approach to force the ocean model. To do this, we extracted monthly anomalies (relative to 2010) of surface atmospheric temperature, zonal and meridional wind speeds, radiative heat fluxes, relative humidity and precipitation from the ESM models over the 2010–2100 period and applied a 31-year-wide Hanning filter to remove variability on timescales less than 15 years.Each ESM-filtered timeseries was superimposed onto the repeating 30-year historical forcing (that is, repeated three times to span the twenty-first century) to provide the forcing for the NEMO–PISCES projections. This procedure enabled us to retain a realistic climatology and high-frequency variability from observations subject to long-term trends due to climate change based on the ESMs (Supplementary Fig. 7).For consistency, the control simulation of NEMO–PISCES was forced using the same three, repeated, 30-year historical periods to correct any long-term drift generated internally without climate change forcing.It is important to note that use of all ESM acronyms (for example, IPSL) in the following text refers to NEMO–PISCES or SEAPODYM simulations derived from the ESM anomaly forcing, and not to the ESM models themselves.The four NEMO–PISCES simulations of future ocean conditions produced contrasting results in terms of dynamics and biogeochemistry (Supplementary Fig. 8). In particular, there was strong warming in the IPSL and MIROC simulations and weaker warming for GFDL and especially MPI. Spatial patterns in ocean warming produced by the NEMO–PISCES simulations differed mostly in intensity rather than spatial structure.Using NEMO–PISCES outputs to produce SEAPODYM forcingsThe outputs of NEMO–PISCES were used to provide environmental forcing variables for SEAPODYM, the model used to project the responses of the key life stages of skipjack, yellowfin and bigeye tuna to climate change (Supplementary Note 7). The following physical and biochemical forcing variables were used in SEAPODYM applications: three-dimensional (3D) temperature, dissolved oxygen (O2) concentration, zonal/meridional currents and primary production, and 2D euphotic depth. Before running SEAPODYM, these forcing variables were interpolated to a regular 2° Arakawa A grid and placed in the centre of the grid cells. Primary production was then vertically integrated throughout the water column, whereas the other 3D variables were integrated within three pelagic layers, defined according to the euphotic depth to provide the mean 2D fields for each variable per layer. Selected environmental variables from the historical ocean reanalysis and from four climate-driven ocean outputs are shown in Supplementary Fig. 3.These integrated variables were then used to force the SEAPODYM-LMTL (lower and mid-trophic level) sub-model. SEAPODYM-LMTL relies on primary production, temperature and ocean currents to simulate the biomass of six functional groups of micronekton—mid-trophic-level prey organisms of tunas (Supplementary Fig. 4)—residing or migrating through three pelagic layers within the upper 1,000 m of the water column (the epipelagic layer and the upper and lower mesopelagic layers), with depths linked to the depth of euphotic layer Z as 1.5Z, 4.5Z and 10Z (with 10Z limited to 1,000 m). The definition of these pelagic layers is derived from the diurnal vertical distributions of micronekton species62.Optimal parameterization of SEAPODYM during historical periodThe parameterization of SEAPODYM for each tuna species is highly sensitive to ocean forcing; that is, in its average state it is free from systematic biases, and it represents interannual variability and ENSO correctly. This sensitivity enables the model to reproduce observed variability within large, geo-referenced datasets of tuna catches and length distributions reflecting changes in fish abundance12. The environmental forcings in this study were obtained from the historical NEMO–PISCES reference simulations using a realistic atmospheric reanalysis based on a consistent set of atmospheric observations. Historical fishing datasets used to achieve model optimal parameterizations were compiled from the combination of data provided by the Pacific Community for the WCPO and by IATTC for the EPO. The model spatial resolution was 2° × 2°, and the resolution for time and age dimensions was one month. The skipjack tuna reference model was obtained by integrating all available geo-referenced data—catch, length-frequency of catch and tagging release–recapture data—into a likelihood function and obtaining the solution using the maximum likelihood estimation (MLE) approach (Supplementary Note 7). The initial habitat and movement parameters for bigeye and yellowfin tuna were also estimated by integrating tagging data into the model; however, the final parameterizations of the reference models for these two species were based mainly on fisheries data. The methodology and optimal reference solutions obtained for skipjack, yellowfin and bigeye tuna, and model validations with statistical metrics, are described in other publications documenting the use of SEAPODYM13,63,64,65.The structures of the populations of the three tuna species in December 2010 (the last time-step of the reanalysis) were used to set the initial conditions for the projections starting in 2011. A second historical simulation was run to remove the effects of fishing mortality (Supplementary Figs. 9 and 10) to establish the initial conditions for the unfished tuna populations (Supplementary Fig. 10). In these latter simulations, the stocks increase and reach an equilibrium state in a time that is defined by the lifespan of the species and the estimated stock–recruitment relationship. We assume that at the end of the 30-year reanalysis (December 2010), stocks of all three tropical tuna species are at their virgin (unfished) state and influenced by environmental variability and demographic processes only.Projections of climate change impacts on tunaPrevious studies on the impact of climate change on tropical tuna species in the Pacific Ocean produced projections based on the full-field NEMO–PISCES output from a single ESM (IPSL) under the IPCC business-as-usual scenario6,10,12,66,67. These projections were subject to biases, resulting in poor coherence between historical and projected environmental forcings and abrupt changes and biases when switching from a historical reanalysis to a projected time series12. To reduce this problem, we used an approach based on the four, bias-corrected, projected climates from NEMO–PISCES outputs (Supplementary Methods).Simulations of the SEAPODYM tuna model were run with parameters from the reference MLE models for the three tuna species, with forcings from the four NEMO–PISCES and mid-trophic simulations, under the RCP 8.5 scenario to project tuna population dynamics until mid-century. We estimated the virgin biomass of each species in the decade 2011−2020 and computed the relative change in biomass by 2050 (2044−2053) as follows:$$updelta _Bleft( {2050} right) = frac{1}{N}mathop {sum}limits_{t = 2011}^{2020} {left( {frac{{Bleft( {t + {Delta}t} right)}}{{B(t)}} – 1} right)}$$
(1)
where Δt is the time interval corresponding to 33 years and N is the number of monthly time steps in the selected time period (120 months between 2011 and 2020). We chose to average over 10 years at 33-year intervals to compare two distant periods with the same atmospheric variability, thus removing the possible effects of interannual variation and allowing better detection of the climate change signal.The relative biomass change δB (2050) was computed for the EEZs of Pacific SIDS and all high-seas areas in the WCPO and EPO (Supplementary Fig. 1).Sensitivity analyses to explore uncertaintyWe analysed the impacts of climate change on skipjack, yellowfin and bigeye tuna with an ensemble of simulations focusing on the greatest sources of uncertainty in the NEMO–PISCES variables and in SEAPODYM (Supplementary Fig. 11 and Supplementary Table 21). The methods used to explore these uncertainties, and the rationale for these analyses, are explained in the Supplementary Methods.Modelling tuna distribution under lower-emissions scenariosThe simulations based on RCP 8.5 project a redistribution of tuna biomass by 2050 as globally averaged surface temperature rises to 2 °C above pre-industrial levels by mid-century. To evaluate possible effects of a lower GHG emission scenario on tuna redistribution, we also estimated the responses of tropical tuna species to conditions similar to RCP 4.5 and RCP 2.6 by 2050.In the absence of ocean forcings and SEAPODYM outputs for RCP 4.5 and RCP 2.6, we used estimates based on the RCP 8.5 simulations using a ‘time-shift’ approach68. This method consists of identifying the time segment in RCP 8.5 in which a key variable (for example, CO2-equivalent (CO2e)) matches the value expected for the selected RCP in 2050. Accordingly, we selected the periods in the RCP 8.5 curve when total CO2e concentrations in the atmosphere reached those projected for RCP 4.5 and RCP 2.6 in 2050 (Supplementary Fig. 12). On the basis of this method, the equivalent of RCP 4.5 in 2050 is reached in 2037 under RCP 8.5, and the equivalent for RCP 2.6 in 2050 is reached in 2026.An important assumption of this method is that the dynamical pattern corresponding to a given change of global temperature is independent of the rate of change. This assumption is expected to be met for key features of the tropical Pacific Ocean because the upper ocean generally responds rapidly to changes in atmospheric forcing. However, this assumption is unlikely to hold for tuna population dynamics because interannual variability of tuna biomass is driven by demographic processes (recruitment and mortality), which are in turn influenced by environmental variability. Furthermore, due to the slow nature of demographic processes, the repercussions of environmental variability on tuna population dynamics are time lagged. For example, there is a time lag of 8 months between the Southern Oscillation Index and the biomass of young skipjack tuna (aged from 3 to 9 months)17, and a time lag of 12 months between the Southern Oscillation Index and total biomass of skipjack tuna (Supplementary Fig. 13). When combined with the effects of stock–recruitment relationships, and different generation times between tuna species, the speed and duration of climate change processes may have a profound effect on tuna biomass. Therefore, due to the rapidly changing ocean conditions in the RCP 8.5 scenario, the population status of a tuna species in the second and third decade cannot be assumed to be equivalent to that under a scenario with lower emissions by mid-century.To address the complications associated with the population dynamics of tuna in a changing environment, we generated synthetic RCP 4.5 and RCP 2.6 2011−2050 time series by recycling the years from RCP 8.5 simulations. Note that recycling the ‘equivalent’ years from RCP 8.5 simulations to imitate those projected for the RCP 4.5 and RCP 2.6 scenarios involves re-using the same years multiple times because of their lower rate of change. To avoid looping the forcings over the same year multiple times, we selected several years around the equivalent RCP 8.5 year while enlarging the temporal window with increasing differences in the rates of GHG change between the two scenarios and ensuring that the mean CO2e within this window was equal to those in the target RCP 4.5 or RCP 2.6 scenario. The inverse mapping of the RCP 8.5 curve from arrays of CO2e values to the equivalent years in the RCP 8.5 simulation (Supplementary Fig. 14) provided the selected range of RCP 8.5 years to imitate the RCP 4.5 and RCP 2.6 scenarios. The NEMO–PISCES model variables from those years were then used to compute monthly climatology for each year of the surrogate RCP 4.5 or RCP 2.6 forcing to provide smoothed time series of forcing variables over the complete time range. The temporal evolution of epipelagic ocean temperature is compared for four climate models and three RCP scenarios in Supplementary Fig. 14.The biomass changes projected for the three tuna species in 2050 under RCP 8.5 and under the lower surrogate emissions scenarios were then computed for all Pacific Island EEZs (Supplementary Fig. 15) following equation (1) (Supplementary Methods). The biomass changes projected under the RCP 4.5 forcing are smaller in magnitude than those for RCP 8.5, demonstrating that the effect of climate change is less pronounced in the simulations under this lower-emissions scenario.The simulations under the surrogate RCP 2.6 forcing did not follow the expected pattern and were deemed to be too unreliable for use in this study (Supplementary Methods).Estimating changes in tuna biomass in EEZs and the high seasFor this analysis, we produced reference biomasses for skipjack, yellowfin and bigeye tuna for the period 1979−2010 from quantitative assessment studies using SEAPODYM, which estimates population dynamics, habitats, movements and fisheries parameters with an MLE approach (Supplementary Note 7). The fit between observations and predictions (for catch and catch size frequencies) was used to validate the optimal solutions of the models within and outside the time window for the model parameter estimates. The fit was analysed spatially by fishery to ensure that there were no regional biases. Once the optimal solution was achieved, a final simulation was made with the same set of parameter estimates but without considering any fishing, to obtain the unfished biomass dynamics during both the historical period and the projection for the twenty-first century. The differences in unfished biomass between the historical period (2001−2010) and projections in 2050 (mean of 2046−2050) for each species were used to compute the weighted mean change in total tuna biomass in the EEZs of the ten Pacific SIDS, the high-seas areas shown in Supplementary Fig. 1 and the EEZs of the other Pacific SIDS listed in Supplementary Table 1 for the RCP 8.5 and RCP 4.5 emission scenarios by 2050.Estimating changes in catch in EEZs and the high seasTo evaluate the impacts of climate change scenarios on purse-seine fisheries, comparisons were restricted to the EEZs of the ten tuna-dependent Pacific SIDS and the high-seas areas, particularly EPO-C (Supplementary Fig. 1).To estimate the effects of projected changes in biomass of skipjack, yellowfin and bigeye tuna due to RCP 8.5 and RCP 4.5 on purse-seine catches in the EEZs of Pacific SIDS and in high-seas areas by 2050, in the absence of management interventions to reallocate catch entitlements to maintain historical access rights for Pacific SIDS, we assumed that there would be a direct relationship between projected changes in biomass and catch. Because purse-seine catches are composed of different proportions of the three tuna species, and because each species is projected to have a different response to climate change (Fig. 2), changes in purse-seine catches by 2050 were estimated using the weighted mean response of the three tuna species to RCP 8.5 and to RCP 4.5. These estimates were derived from the average relative abundance of each species in purse-seine catches in the EEZs of the ten Pacific SIDS (Supplementary Table 3) and in high-seas areas (Supplementary Table 4) and the projected percentage change in biomass of each species under each emission scenario (Supplementary Tables 17 and 18).The weighted average percentage changes in biomass of all tuna species combined were then applied to the 10-year average (2009−2018) purse-seine catches from the EEZs of the ten Pacific SIDS and high-seas areas (Supplementary Tables 3 and 4) to estimate the changes in purse-seine catches for these jurisdictions by 2050 under RCP 8.5 and RCP 4.5. In the case of Kiribati, which has three separate EEZ areas (Fig. 1), we estimated the change in catch for each EEZ area and amalgamated the results to produce the overall estimated change in purse-seine catch for the country.The projected percentage change in total purse-seine catch differs from the percentage change in total tuna biomass due to variation in the relative contributions of the three tuna species to total catch and to total biomass.Estimating the effects of tuna redistribution on economiesTo assess the effects of climate-driven redistribution of tuna on the economies of the 10 Pacific SIDS, we assumed that estimated changes in purse-seine catch within their EEZs due to the redistribution of tuna biomass described above would result in a proportional change in access fees earned from purse-seine fishing and associated operations.To estimate the effects of RCP 8.5 and RCP 4.5 on the capacity of Pacific Island governments to earn access fees from industrial tuna fishing, and the contributions of these access fees to total government revenue excluding grants (‘government revenue’), we used annual averages of government revenue, tuna-fishing access fees earned by the ten Pacific SIDS and the percentage contribution of access fees to government revenue for the period 2015−2018 (Supplementary Table 2) as a baseline. We applied the projected average percentage changes in total purse-seine catch in each EEZ for RCP 8.5 and RCP 4.5 (summarized in Supplementary Tables 17 and 18) to the average annual access fees received in 2015−2018 by each of the Pacific SIDS to estimate the change in value of their access fees by 2050 under each emissions scenario. The change in value of access fees was used to estimate decreases or increases in government revenue in 2050 relative to 2015–2018 under both emissions scenarios in US$ and percentage terms, assuming that the relative contributions of other sources of government revenue remain the same.The estimated percentage changes in government revenue for each Pacific SIDS do not account for (1) management responses; (2) variation in the value of access to particular EEZs and the willingness of fleets to pay for this access due to the effects of changes in tuna biomass on catchability of each species, levels of fishing effort/catch rates, the price of tuna or cost of landing tuna; and (3) the impact of tuna redistribution on the degree of control that Pacific SIDS exert over fisheries targeting tuna. The third factor is expected to be particularly important. For example, substantial movement of tuna from the EEZs of PNA countries into high-seas areas would be expected to limit the effectiveness of the VDS69 by reducing the degree of control over the fishery exerted by PNA members.Overall, it is important to note that the simple approach used to assess the potential effects of tuna redistribution on government revenue is intended only to provide indicative information on the magnitude of these impacts. To obtain robust estimates of climate-driven changes in government revenue, more complex bio-economic analyses will be required, beginning with, for example, a fleet-dynamics analysis to investigate the potential response of purse-seine vessels to redistribution of tuna and the flow-on effects on access fees.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Bai, S.-N. The concept of the sexual reproduction cycle and its evolutionary significance. Front. Plant Sci. 6, 11 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Heitman, J. Evolution of sexual reproduction: A view from the Fungal Kingdom supports an evolutionary epoch with sex before sexes. Fungal Biol. Rev. 29, 108–117 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Goodenough, U. & Heitman, J. Origins of eukaryotic sexual reproduction. Cold Spring Harb. Perspect. Biol. 6, a016154 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Speijer, D., Lukeš, J. & Eliáš, M. Sex is a ubiquitous, ancient, and inherent attribute of eukaryotic life. Proc. Natl. Acad. Sci. USA 112, 8827–8834 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.O’Malley, M. A., Leger, M. M., Wideman, J. G. & Ruiz-Trillo, I. Concepts of the last eukaryotic common ancestor. Nat. Ecol. Evol. 3, 338–344 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Martindale, M. Q., Pang, K. & Finnerty, J. R. Investigating the origins of triploblasty: “Mesodermal” gene expression in a diploblastic animal, the sea anemone Nematostella vectensis (phylum, Cnidaria; class, Anthozoa). Development 131, 2463–2474 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Ball, E. E., Hayward, D. C., Saint, R. & Miller, D. J. A simple plan–cnidarians and the origins of developmental mechanisms. Nat. Rev. Genet. 5, 567–577 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Miller, K. J. & Ayre, D. J. The role of sexual and asexual reproduction in structuring high latitude populations of the reef coral Pocillopora damicornis. Heredity 92, 557–568 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Baird, A. H., Guest, J. R. & Willis, B. L. Systematic and biogeographical patterns in the reproductive biology of scleractinian corals. Annu. Rev. Ecol. Evol. Syst. 40, 551–571 (2009).Article 

Google Scholar 
10.Hagman, D. K., Gittings, S. R. & Vize, P. D. Fertilization in broadcast-spawning corals of the Flower Garden Banks National Marine Sanctuary. Gulf Mex. Sci. 16, 180–187 (1998).
Google Scholar 
11.Harrison, P. L. et al. Mass spawning in tropical reef corals. Science 223, 1186–1189 (1984).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Dunlap, J. C. Molecular bases for circadian clocks. Cell 96, 271–290 (1999).CAS 
Article 

Google Scholar 
13.Pittendrigh, C. S. Temporal organization: Reflections of a Darwinian clock-watcher. Annu. Rev. Physiol. 55, 16–54 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Shearman, L. P. et al. Interacting molecular loops in the mammalian circadian clock. Science 288, 1013–1019 (2000).ADS 
CAS 
Article 

Google Scholar 
15.Babcock, R. C. et al. Synchronous spawnings of 105 scleractinian coral species on the Great Barrier Reef. Mar. Biol. 90, 379–394 (1986).Article 

Google Scholar 
16.Babcock, R. C., Wills, B. L. & Simpson, C. J. Mass spawning of corals on a high latitude coral reef. Coral Reefs 13, 161–169 (1994).ADS 
Article 

Google Scholar 
17.Kaniewska, P., Alon, S., Karako-Lampert, S., Hoegh-Guldberg, O. & Levy, O. Signaling cascades and the importance of moonlight in coral broadcast mass spawning. eLife 4, e09991 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Levy, O. et al. Light-responsive cryptochromes from a simple multicellular animal, the coral Acropora millepora. Science 318, 467–470 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Rosenberg, Y., Doniger, T., Harii, S., Sinniger, F. & Levy, O. Canonical and cellular pathways timing gamete release in Acropora digitifera, Okinawa, Japan. Mol. Ecol. 26, 2698–2710 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Reitzel, A. M., Tarrant, A. M. & Levy, O. Circadian clocks in the cnidaria: Environmental entrainment, molecular regulation, and organismal outputs. Integr. Comp. Biol. 53, 118–130 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Shoguchi, E., Tanaka, M., Shinzato, C., Kawashima, T. & Satoh, N. A genome-wide survey of photoreceptor and circadian genes in the coral, Acropora digitifera. Gene 515, 426–431 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Vize, P. D. Transcriptome analysis of the circadian regulatory network in the coral Acropora millepora. Biol Bull 216, 131–137 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Layden, M. J., Rentzsch, F. & Röttinger, E. The rise of the starlet sea anemone Nematostella vectensis as a model system to investigate development and regeneration. Wiley Interdiscip. Rev. Dev. Biol. 5, 408–428 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Rentzsch, F. & Technau, U. Genomics and development of Nematostella vectensis and other anthozoans. Curr. Opin. Genet. Dev. 39, 63–70 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Putnam, N. H. et al. Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science 317, 86–94 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Fritzenwanker, J. H. & Technau, U. Induction of gametogenesis in the basal cnidarian Nematostella vectensis (Anthozoa). Dev. Genes Evol. 212, 99–103 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Stefanik, D. J., Friedman, L. E. & Finnerty, J. R. Collecting, rearing, spawning and inducing regeneration of the starlet sea anemone, Nematostella vectensis. Nat. Protoc. 8, 916–923 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
28.Hand, C. & Uhlinger, K. R. The culture, sexual and asexual reproduction, and growth of the sea anemone Nematostella vectensis. Biol. Bull 182, 169–176 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Darling, J. A. et al. Rising starlet: The starlet sea anemone, Nematostella vectensis. BioEssays 27, 211–221 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Genikhovich, G. & Technau, U. Induction of spawning in the starlet sea anemone Nematostella vectensis, in vitro fertilization of gametes, and dejellying of zygotes. Cold Spring Harb. Protoc. 2009, pdb.prot5281 (2009).PubMed 
PubMed Central 

Google Scholar 
31.Levitan, S. et al. The making of an embryo in a basal metazoan: Proteomic analysis in the sea anemone Nematostella vectensis. Proteomics 15, 4096–4104 (2015).CAS 
PubMed 
Article 

Google Scholar 
32.Eckelbarger, K. J., Hand, C. & Uhlinger, K. R. Ultrastructural features of the trophonema and oogenesis in the starlet sea anemone, Nematostella vectensis (Edwardsiidae). Invertebr. Biol. 127, 381–395 (2008).Article 

Google Scholar 
33.Moiseeva, E., Rabinowitz, C., Paz, G. & Rinkevich, B. Histological study on maturation, fertilization and the state of gonadal region following spawning in the model sea anemone, Nematostella vectensis. PLoS ONE 12, e0182677 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Lamb, T. D., Collin, S. P. & Pugh, E. N. Evolution of the vertebrate eye: Opsins, photoreceptors, retina and eye cup. Nat. Rev. Neurosci. 8, 960–976 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Quiroga Artigas, G. et al. A G protein-coupled receptor mediates neuropeptide-induced oocyte maturation in the jellyfish Clytia. PLoS Biol. 18, e3000614 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Quiroga Artigas, G. et al. A gonad-expressed opsin mediates light-induced spawning in the jellyfish Clytia. eLife 7, e29555 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Lau, C. G. & Zukin, R. S. NMDA receptor trafficking in synaptic plasticity and neuropsychiatric disorders. Nat. Rev. Neurosci. 8, 413–426 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Paoletti, P., Bellone, C. & Zhou, Q. NMDA receptor subunit diversity: Impact on receptor properties, synaptic plasticity and disease. Nat. Rev. Neurosci. 14, 383–400 (2013).CAS 
PubMed 
Article 

Google Scholar 
39.Ikeda, M. et al. Circadian dynamics of cytosolic and nuclear Ca2+ in single suprachiasmatic nucleus neurons. Neuron 38, 253–263 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Ikeda, M. Calcium dynamics and circadian rhythms in suprachiasmatic nucleus neurons. Neuroscientist 10, 315–324 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Oren, M. et al. Profiling molecular and behavioral circadian rhythms in the non-symbiotic sea anemone Nematostella vectensis. Sci. Rep. 5, 11418 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Reitzel, A. M., Behrendt, L. & Tarrant, A. M. Light entrained rhythmic gene expression in the sea anemone Nematostella vectensis: The evolution of the animal circadian clock. PLoS ONE 5, e12805 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Hendricks, W. D., Byrum, C. A. & Meyer-Bernstein, E. L. Characterization of circadian behavior in the starlet sea anemone, Nematostella vectensis. PLoS ONE 7, e46843 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Fonjallaz, P., Ossipow, V., Wanner, G. & Schibler, U. The two PAR leucine zipper proteins, TEF and DBP, display similar circadian and tissue-specific expression, but have different target promoter preferences. EMBO J. 15, 351–362 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Gavriouchkina, D. et al. Thyrotroph embryonic factor regulates light-induced transcription of repair genes in zebrafish embryonic cells. PLoS ONE 5, e12542 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Beaver, L. M. et al. Loss of circadian clock function decreases reproductive fitness in males of Drosophila melanogaster. Proc. Natl. Acad. Sci. USA 99, 2134–2139 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Boden, M. J., Varcoe, T. J. & Kennaway, D. J. Circadian regulation of reproduction: From gamete to offspring. Prog. Biophys. Mol. Biol. 113, 387–397 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Giebultowicz, J. M., Riemann, J. G., Raina, A. K. & Ridgway, R. L. Circadian system controlling release of sperm in the insect testes. Science 245, 1098–1100 (1989).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Naylor, E. Chronobiology of Marine Organisms (Cambridge University Press, 2010). https://doi.org/10.1017/CBO9780511803567.Book 

Google Scholar 
50.Leach, W. B. & Reitzel, A. M. Transcriptional remodelling upon light removal in a model cnidarian: Losses and gains in gene expression. Mol. Ecol. 28, 3413–3426 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Oldach, M. J., Workentine, M., Matz, M. V., Fan, T.-Y. & Vize, P. D. Transcriptome dynamics over a lunar month in a broadcast spawning acroporid coral. Mol. Ecol. 26, 2514–2526 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Nobes, C. D. & Hall, A. Rho, rac, and cdc42 GTPases regulate the assembly of multimolecular focal complexes associated with actin stress fibers, lamellipodia, and filopodia. Cell 81, 53–62 (1995).CAS 
Article 

Google Scholar 
53.Etienne-Manneville, S. Actin and microtubules in cell motility: Which one is in control?. Traffic 5, 470–477 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Parri, M. & Chiarugi, P. Rac and Rho GTPases in cancer cell motility control. Cell Commun. Signal. 8, 23 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Piekny, A., Werner, M. & Glotzer, M. Cytokinesis: Welcome to the Rho zone. Trends Cell Biol. 15, 651–658 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Guilluy, C., Garcia-Mata, R. & Burridge, K. Rho protein crosstalk: Another social network?. Trends Cell Biol. 21, 718–726 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Chauhan, B. K., Lou, M., Zheng, Y. & Lang, R. A. Balanced Rac1 and RhoA activities regulate cell shape and drive invagination morphogenesis in epithelia. Proc. Natl. Acad. Sci. USA 108, 18289–18294 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Friedl, P. & Wolf, K. Tumour-cell invasion and migration: Diversity and escape mechanisms. Nat. Rev. Cancer 3, 362–374 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Damsky, C. H. & Werb, Z. Signal transduction by integrin receptors for extracellular matrix: Cooperative processing of extracellular information. Curr. Opin. Cell Biol. 4, 772–781 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Hynes, R. O. Integrins: Bidirectional, allosteric signaling machines. Cell 110, 673–687 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Bonnans, C., Chou, J. & Werb, Z. Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Karsenty, G. & Park, R. W. Regulation of type I collagen genes expression. Int. Rev. Immunol. 12, 177–185 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Lotan, T. et al. Evolutionary conservation of the mature oocyte proteome. EuPA Open Proteom. 3, 27–36 (2014).CAS 
Article 

Google Scholar 
64.Blanchard, G., Druart, X. & Kestemont, P. Lipid content and fatty acid composition of target tissues in wild Perca fluviatilis females in relation to hepatic status and gonad maturation. J. Fish Biol. 66, 73–85 (2005).CAS 
Article 

Google Scholar 
65.Huynh, M. D., Kitts, D. D., Hu, C. & Trites, A. W. Comparison of fatty acid profiles of spawning and non-spawning Pacific herring, Clupea harengus pallasi. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 146, 504–511 (2007).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
66.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: Accounting for selection bias. Genome Biol. 11, R14 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Sergushichev, A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. BioRxiv https://doi.org/10.1101/060012 (2016).Article 

Google Scholar 
71.Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Bryant, D. M. et al. A tissue-mapped axolotl de novo transcriptome enables identification of limb regeneration factors. Cell Rep. 18, 762–776 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Elran, R. et al. Early and late response of Nematostella vectensis transcriptome to heavy metals. Mol. Ecol. 23, 4722–4736 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative C(T) method. Nat. Protoc. 3, 1101–1108 (2008).CAS 
PubMed 
Article 

Google Scholar  More

Keller Science Action Center, Field Museum of Natural History, Chicago, IL, USANigel C. A. Pitman, Tomomi Suwa, Juliana Philipp, Corine F. Vriesendorp, Abigail Derby Lewis & Sinem PerkMissouri Botanical Garden, St Louis, MO, USACarmen Ulloa Ulloa, James Miller & James SolomonAMAP, Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, FrancePierre BonnetINRIA Sophia-Antipolis, ZENITH team, LIRMM – UMR 5506, Montpellier, FranceAlexis JolyInstitute for Conservation Research, San Diego Zoo Global, Escondido, CA, USAMathias W. ToblerBotanical Research Institute of Texas, Fort Worth, TX, USAJason H. BestFacultad de Ciencias Forestales, Universidad Nacional Agraria La Molina, La Molina, PeruJohn P. JanovecLiberty Hyde Bailey Hortorium, Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USAKevin C. NixonNew York Botanical Garden, Bronx, NY, USABarbara M. Thiers & Melissa TuligSchool of Life Sciences, Arizona State University, Tempe, AZ, USAEdward E. GilbertJardim Botânico do Rio de Janeiro, Rio de Janeiro, BrazilRafaela Campostrini Forzza & Fabiana Luiza Ranzato FilardiUniversidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilGeraldo ZimbrãoRoyal Botanic Gardens, Kew, Richmond, United KingdomRobert TurnerInstituto de Botánica Darwinion, San Isidro, Buenos Aires, ArgentinaFernando O. Zuloaga & Manuel J. BelgranoInstituto de Limnología ‘Dr. Raúl A. Ringuelet’ (CONICET), Buenos Aires, ArgentinaChristian A. ZanottiHerbaria Basel, Department of Environmental Sciences, University of Basel, Basel, SwitzerlandJurriaan M. de VosDepartamento de Ecologia e Zoologia, Universidade Federal de Santa Catarina, Florianópolis, BrazilEduardo L. Hettwer GiehlEnvironmental and Rural Science, University of New England, Armidale, New South Wales, AustraliaC. E. Timothy PaineDepartamento de Sistemática e Ecologia, Universidade Federal da Paraíba, João Pessoa, PB, BrazilRubens Texeira de QueirozEscuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, EcuadorKatya RomolerouxUniversidade Federal do Recôncavo da Bahia, Bahia, BrazilEverton Hilo de SouzaN.C.A.P. and T.S. conceived the study. N.C.A.P., T.S., C.U.U., J.M., J.S., J.P., C.F.V., A.D.L., P.B., A.J., M.W.T., J.H.B., J.P.J., K.C.N., B.M.T., M.T., E.E.G., R.C.F., G.Z., F.L.R.F., R.T., F.O.Z., M.J.B., C.A.Z., J.M.d.V., E.L.H.G., C.E.T.P., R.T.d.Q. and K.R. contributed photographic databases. S.P. queried the Flickr database. C.U.U. provided the taxonomic backbone for the analyses. T.S. assembled the master photographic database, standardized the taxonomy of contributed databases and carried out statistical analyses. T.S. supervised the four Field Museum volunteers who assisted in data collection. N.C.A.P. and T.S. wrote the paper. T.S. prepared Fig. 1a. N.C.A.P. prepared Fig. 1b. T.S. prepared the Supplementary Tables. All authors discussed the results and implications of the analyses, commented on the manuscript at all stages and contributed revisions. More

School of Environmental Studies, University of Victoria, Victoria, British Columbia, CanadaNancy ShackelfordEcology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USANancy Shackelford, Nichole Barger, Julie E. Larson & Katharine L. SudingDepartamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal, BrazilGustavo B. PaternoDepartment of Ecology and Ecosystem Management, Restoration Ecology Research Group, Technical University of Munich, Freising, GermanyGustavo B. PaternoUS Geological Survey, Southwest Biological Science Center, Moab, UT, USADaniel E. Winkler & Stephen E. FickSchool of Biological Sciences, The University of Western Australia, Crawley, Western Australia, AustraliaTodd E. EricksonKings Park Science, Department of Biodiversity Conservation and Attractions, Kings Park, Western Australia, AustraliaTodd E. Erickson & Peter J. GolosDepartment of Biology, University of Nevada, Reno, Reno, NV, USAElizabeth A. LegerUSDA Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR, USALauren N. Svejcar, Chad S. Boyd & Kirk W. DaviesCollege of Science and Engineering, Flinders University, Bedford Park, South Australia, AustraliaMartin F. BreedDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM, USAAkasha M. FaistSchool of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, AustraliaPeter A. HarrisonProgram in Ecology, University of Wyoming, Laramie, WY, USAMichael F. CurranUSDA FS – Southern Research Station, Research Triangle Park, NC, USAQinfeng GuoDepartment of Nature Conservation and Landscape Planning, Anhalt University of Applied Sciences, Bernburg, GermanyAnita Kirmer & Sandra DullauSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USADarin J. LawDepartment of Agricultural Sciences, South Eastern Kenya University, Kitui, KenyaKevin Z. MgangaUS Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USASeth M. Munson & Hannah L. FarrellUS Department of Agriculture – Agricultural Research Service Rangeland Resources and Systems Research Unit, Fort Collins, CO, USALauren M. PorenskyInstituto Nacional de Tecnología Agropecuaria, Estación Experimental Agropecuaria Catamarca, Catamarca, ArgentinaR. Emiliano QuirogaCátedra de Manejo de Pastizales Naturales, Facultad de Ciencias Agrarias, Universidad Nacional de Catamarca, Catamarca, ArgentinaR. Emiliano QuirogaMTA-DE Lendület Functional and Restoration Ecology Research Group, Debrecen, HungaryPéter TörökTennessee Department of Environment and Conservation, Division of Water Resources, Nashville, TN, USAClaire E. WainwrightHirola Conservation Programme, Nairobi, KenyaAli AbdullahiUSDA Natural Resources Conservation Service, Merced Field Office, Merced, CA, USAMatt A. BahmNational Park Service, Southeast Utah Group, Moab, UT, USAElizabeth A. BallengerThe Nature Conservancy of Oregon, Burns, OR, USAOwen W. BaughmanPlant Conservation Unit, Biological Sciences, University of Cape Town, Rondebosch, South AfricaCarina BeckerUniversity of Castilla-La Mancha, Campus Universitario, Albacete, SpainManuel Esteban Lucas-BorjaUniversity of Northern British Columbia, 3333 University Way, Prince George, British Columbia, CanadaCarla M. Burton & Philip J. BurtonInstitute of Applied Sciences, Malta College for Arts, Sciences and Technology, Fgura, MaltaEman Calleja & Alex CaruanaPlant Conservation Unit, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaPeter J. CarrickUSDA, Agricultural Research Service, Great Basin Rangelands Research Unit, Reno, NV, USACharlie D. ClementsLendület Seed Ecology Research Group, Institute of Ecology and Botany, Centre for Ecological Research, Debrecen, HungaryBalázs Deák, Réka Kiss & Orsolya ValkóMurrang Earth Sciences, Ngunnawal Country, Canberra, Australian Capital Territory, AustraliaJessica DrakeGreat Ecology, Denver, CO, USAJoshua EldridgeUSDA-ARS Pest Management Research Unit, Northern Plains Agricultural Research Laboratory, Sidney, MT, USAErin EspelandGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, GermanyMagda GarbowskiDepartment of Ecology, Brandenburg University of Technology, Cottbus, GermanyEnrique G. de la RivaBiodiversity Management Branch, Environmental Resource Management Department, Cape Town, South AfricaPenelope A. GreyGreening Australia, Melbourne, Victoria, AustraliaBarry HeydenrychDepartment of Conservation Ecology & Entomology, Stellenbosch University, Stellenbosch Central, Stellenbosch, South AfricaPatricia M. HolmesNatural Resource Management and Environmental Sciences, Cal Poly State University, San Luis Obispo, CA, USAJeremy J. JamesDepartment of Biology, University of Nebraska-Kearney, Kearney, NE, USAJayne Jonas-BrattenNegaunee Institute for Plant Conservation Science and Action, Chicago Botanic Garden, Glencoe, IL, USAAndrea T. KramerDepartment of Botany, University of Granada, Granada, SpainJuan LoriteInteruniversity Institute for Earth System Research, University of Granada, Granada, SpainJuan LoriteNew Zealand Department of Conservation, Christchurch, New ZealandC. Ellery MayenceDepartamento de Biología y Geología, Física y Química inorgánica, ESCET, Universidad Rey Juan Carlos, Madrid, SpainLuis Merino-MartínÖMKi – Research Institute of Organic Agriculture, Budapest, HungaryTamás MigléczHadison Park, Kimberley, South AfricaSuanne Jane MiltonWolwekraal Conservation and Research Organisation (WCRO), Prince Albert, South AfricaSuanne Jane MiltonUS Department of Agriculture, Agricultural Research Service, Forage and Range Research Laboratory, Utah State University, Logan, UT, USAThomas A. MonacoUniversity of California, Riverside, Riverside, CA, USAArlee M. MontalvoDepartment of Environment and Agronomy, National Institute for Agricultural and Food Research and Technology (INIA-CSIC), Madrid, SpainJose A. Navarro-CanoForest and Rangeland Stewardship Department, Colorado State University, Fort Collins, CO, USAMark W. PaschkeInstituto Nacional de Tecnología Agropecuaria (INTA), Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Cruz, ArgentinaPablo Luis PeriUSDA – NRCS, Bozeman, MT, USAMonica L. PokornyUSDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT, USAMatthew J. RinellaPlant Science, Western Cape Department of Agriculture, Elsenburg, South AfricaNelmarie SaaymanRed Rock Resources LLC, Miles City, MT, USAMerilynn C. SchantzBush Heritage Australia, Eurardy, Western Australia, AustraliaTina ParkhurstDeptartment of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, MN, USAEric W. SeabloomHolden Arboretum, Kirtland, OH, USAKatharine L. StubleDepartment of Natural Resources and Environmental Science, University of Nevada, Reno, NV, USAShauna M. UselmanDepartment of Wildland Resources & Ecology Center, Utah State University, Logan, UT, USAKari VeblenDepartment of Biology, University of Regina, Regina, Saskatchewan, CanadaScott WilsonCentre of eResearch and Digital Innovation, Federation University Australia, Ballarat, Victoria, AustraliaMegan WongSchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaZhiwei XuInstitute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USAKatharine L. Suding More

Tick paralysis is a common tick-borne illness in humans and animals throughout the world, caused by neurotoxins produced in the salivary glands of ticks and secreted into a host during the course of feeding by females and immature stages19. Fifty-nine ixodid and fourteen argasid ticks are currently believed to be involved in the transmission of tick paralysis worldwide19, 20. In Australia, I. holocyclus is considered to be the leading tick species implicated in the transmission of tick paralysis primarily in dogs, but also other species, viz. cats, sheep, cattle, goats, swine and horses. Humans are also occasionally affected, and the disease can be fatal2, 21. A second tick species, I. cornuatus has also been implicated in the transmission of tick paralysis in Australia; however, it is also considered a minor player in this disease22. Given the differences in their biology, distribution, and natural history of these two species, we focused on estimating the spatial distribution of I. holocyclus in the present study. We recognize, however, that it is important to consider the distributions of both species for proper epidemiological planning and management of tick paralysis in Australia.Ecological niche modeling is a well-tested approach for estimating species distributions based on abiotic factors13, 23. Several new recommendations have been made in recent years for proper construction of niche models; such as the appropriate thinning of occurrence data24, consideration of an accessible area for a species being studied (M)25, thorough exploration of model complexity26, 27, and use of multiple statistical criteria for model selection28, 29. We carefully considered all these recommendations to produce a robust spatial distribution model for I. holocyclus. The resulting replicated models were fairly consistent in predicting suitability for I. holocyclus, as indicated by moderate range estimates (Fig. 2B). Further, the MOP analysis indicated satisfactory performance of the present-day model with extrapolation only in small areas outside the predicted suitable areas. These qualities, along with the model’s very low omission rate (0.044%) gives high confidence in the predicted suitable area for this species in Australia. It will be essential, however, to confirm the actual presence of I. holocyclus outside the traditionally known areas through acarological surveys to assess our findings.The present-day spatial distribution predicted in this study (Fig. 2A) indicates that the geographic areas suitable for I. holocyclus match the currently known distribution of this species along the eastern seaboard, but the suitability also extends through most of the coastal areas in the south, and up to the Kimbolton Peninsula in Western Australia in the north. Highly suitable areas are present around and south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania are also highly suitable for this species. The current distribution in the Eastern Seaboard may be wider than the traditionally known extents in some areas compared to Roberts30. It is likely that I. holocyclus will succeed in establishing permanent populations if introduced into areas that are currently free of them along the southern and northern coasts, and along the southwestern coast of Western Australia and Tasmania. Appropriate prevention of tick movement including pet inspections and quarantine will be necessary to avoid introductions.Future potential distribution of I. holocyclus in year 2050 based on both low- and high-emissions scenarios indicate moderate increases in climatic suitability from the present-day prediction (Fig. 4A,B); but noticeably also moderate to low loss of climatically suitable areas in 2050. This loss could be at least partly attributed to potential future temperature and precipitation conditions exceeding suitable ranges for these ticks in these areas, limiting their ability to survive. Predicted loss of suitable areas in future can also be observed to be irregular, and in some areas, particularly along northern Queensland and in Northern Territory, enveloped between stretches of suitable areas. Our use of relatively coarse resolution data (1 km2) limits our ability to thoroughly interpret such phenomenon, but this is likely due to variations in the geography in these areas that respond differently to future climate, as well as the potential increase in ocean temperature and subsequent influences on areas along the coast that may render them unsuitable for this species. Despite the noticeable loss in climatically suitable areas, likely no net loss in area will accrue for this species by 2050.Teo et al.31 assessed present and future potential distribution for I. holocyclus using both CLIMEX32, 33 and a novel, as-yet unpublished “climatic-range” approach to determine the suitability on monthly intervals. CLIMEX allows users to specify different upper and lower thresholds for climatic parameters, some of which were derived for their study from laboratory evaluations of I. holocyclus34. The present-day distribution reported in that study resembles our results in identification of a relatively narrow area along the East Coast as suitable; however, much of the northern and northeastern areas along the coast, the coasts of South Australia and southwestern Australia, and Tasmania are reported unsuitable. Their future predictions (2050) of the species’ potential distribution were based on two GCMs (CSIRO MK3 and MIROC-H) climate models, were also markedly different from our predictions, anticipating rather dramatic distributional loss for the species. Such model transfers are challenging, with many factors potentially producing inconsistencies35. However, the two studies reflect two fundamentally different classes of ecological niche models; CLIMEX is deterministic, whose predictions are largely constrained by user supplied threshold values for model inputs of physiological tolerance limits of a species33, whereas Maxent is a machine-learning correlative approach, in which known occurrences of a species is used in conjunction with environmental layers to determine conditions that meet a species’ environmental requirements, and therefore the suitability of geographic spaces. Although the former (CLIMEX) approach is appealing conceptually, scaling environmental dimensions between the micro-scales of physiological measurements and the macro-scales of geography is well-known to present practical and conceptual challenges36.Different ixodid ticks employ different life-history strategies in response to adverse environmental conditions, including behavioral adaptations, active uptake of atmospheric moisture, restriction of water-loss, and tolerance towards extreme temperatures37. Precisely which of these mechanisms I. holocyclus utilizes, if any at all, for its survival during diverse temperature and humidity conditions is not clearly known, but it is likely to involve multiple mechanisms. In this sense, the threshold values used by Teo et al.31, based purely on laboratory observations may have been overly restrictive, leading to a conservative distributional estimate for this species. Further, because relationships between abiotic variables and species’ occurrences are fairly complex and highly dimensional, a physiological thresholding approach wherein values are set independently for different abiotic parameters may not capture species’ relationships with environments adequately. The correlative approaches employed in the present study are data-driven, and as such may capture more of this complexity, with fewer problems of scaling across orders of magnitude of space and time.In conclusion, ticks are poikilothermic ectoparasites, whose survival, reproduction and other biological functions are regulated by ambient climatic conditions. Although ixodid ticks are known to regulate their body temperatures by moving about their habitat (vegetation), attempts to model their spatial distribution has resulted in models largely based on climate variables. Nevertheless, other factors such as host availability play a significant role in tick distribution, which unfortunately cannot be readily included in correlative ecological niche models largely because such data are rarely available. These suitability predictions, in addition to being entirely based on large-scale climate, also do not reveal the highly likely heterogeneity in abundance or density in different geographic areas within the realized climatically suitable areas. For these reasons, the distribution maps produced in this study must be used with some caution, and perhaps as a guide to target sampling and not as a substitute for thorough acarological surveys. More

Data constructionWe construct an annual panel dataset from 2000 to 2012 of 2549 coastal communities within 102 countries. Population counts from 2000 to 2012 for each community were calculated from the Landscan population database27 and coastal communities were defined as the lowest level administration units with an ocean coastline of each country using the Global Administrative Areas Database v2.7. Using the National Oceanic and Atmospheric Administration’s (NOAA) global nighttime lights data, we examine trends in economic activity before and after a cyclone event. The growth rate in average annual luminosity from nighttime lights trends with economic growth and has been used as an effective proxy for local economic activity22,24,28,29,30,31,32.However, trends in nighttime luminosity should not be interpreted as a measure of economic growth. Instead, we focus on tracking the dynamic impacts of nighttime luminosity (e.g. deviations from trends) that indicates whether an exposed community’s economic activity recovers or suffers permanent damage. The average elevation of each coastal community was calculated using a void-filled Shuttle Radar Topography Mission (SRTM) data at 3 arc-seconds, or approximately 90 m2 at the equator33. The SRTM has the potential to result in an overestimation of elevation in heavily built environment areas or areas of dense high forest canopies compared against locations without such trees. However, during the timeframe of our analysis, the SRTM product was the most appropriate and available product.The mangrove coverage dataset was adapted from the Continuous Global Mangrove Forest Cover for the 21st Century (CGMFC-21) database for the years 2000 to 201212. The coastline length of each community, based on Global Self-Consistent, Hierarchical, High-Resolution Shoreline Database v2.3.5 full resolution data34, was used to normalize the area of mangroves offshore of each coastal community creating a measurement for the “width” of mangroves per meter of coastline.Tropical storm locations for all years were recreated from the International Best Track Archive for Climate Stewardship (IBTrACS) Annual Tropical Cyclone Best Track Database35. Precise measurements of exposure, combined with high-resolution luminosity data, allows to distinguish the heterogeneous impacts of cyclones on exposed communities and the capacity for mangroves to shelter coastal economic activity at different elevations and for different mangrove widths. The intensity of exposure is measured by the distance of the cyclone’s “eye” from the exposed village’s nearest boundary.Tropical cyclone wind profile36, villages passing within 100 km of the cyclone’s eye were likely to experience maximum wind velocity and surface level pressure whereas those villages passing within more distant bands—i.e., 100–200 km and 200–300 km, were likely to experience similar surface level pressure but a non-linear reduction in wind velocity. Binning wind velocities in this way recognizes the highly non-linear relationship between wind velocity and on-the-ground damages from cyclone events37. We therefore expect the capacity for mangroves and elevation to shelter economic activity also to depend on this intensity of exposure.Our full sample encompasses nearly 400 million individuals in 102 countries and 2549 mangrove-holding communities (Table 1). Based on 2019 fiscal year World Bank categorizations, most of our sample resides in developing countries (85.1%) with 46.7% in lower-middle income (gross national income/per capita between $996 and $3895) and 35.3% in upper-middle income countries (gross national income/ per capita between $3896 and $12,056). We also find that most mangrove coverage in our sample exists within developing countries (88.7%) and overwhelmingly in upper-middle income countries (56.0%) in the Latin America and Caribbean (LAC) and East Asian and Pacific (EAP) developing regions. While only 14.9% of our sample’s global population resides in LAC countries, these countries account for 39.8% of mangrove holdings in our sample whereas the 45.5% of the population residing in EAP countries only account for 30.3% of mangrove coverage.Empirical strategyWe use a distributed-lag autoregressive model to measure the initial and permanent effect of cyclone exposure on economic activity in coastal communities. The growth in economic activity for each coastal community is proxied by the difference in logs between years, (growth={ln}left(luminosit{y}_{t}right)-{ln}left(luminosit{y}_{t-1}right)). Our estimating equation is$$growt{h}_{i,j,t}=sumlimits_{L=0}^{n}{[beta }_{L} x {C}_{i,j,t-L}]+{gamma }_{j}+{delta }_{t}+eta {X}_{i,j,t}+{epsilon }_{i,j,t}$$
(1)

where the (beta) coefficients capture the marginal effects, across three bins of cyclone exposure, on the growth rate of luminosity for the (j{^{prime}}th) administrative unit, within country (i), and in time (t-L) where (t) is the observed year and L is the number of lags ranging from (0 ; to ;n). Here, ({C}_{i,j,t}) is a vector of cyclone exposures binned by the distance from the cyclone’s “eye” to the nearest boundary of the exposed community ( More