Ecology

1.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).Article 

Google Scholar 
2.Awad, A. A., Griffiths, C. L. & Turpie, J. K. Distribution of South African marine benthic invertebrates applied to the selection of priority conservation areas. Divers. Distrib. 8, 129–145 (2002).Article 

Google Scholar 
3.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).4.Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
5.Wallace, A. R. The Geographical Distribution of Animals, with a Study of the Relations of Living and Extinct Faunas as Elucidating the Past Changes of the Earth’s Surface (Macmillan, 1876).6.Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).CAS 
PubMed 
Article 

Google Scholar 
7.Ficetola, G. F., Mazel, F. & Thuiller, W. Global determinants of zoogeographical boundaries. Nat. Ecol. Evol. 1, 89 (2017).PubMed 
Article 

Google Scholar 
8.Kocsis, A. T., Reddin, C. J. & Kiessling, W. The stability of coastal benthic biogeography over the last 10 million years. Glob. Ecol. Biogeogr. 27, 1106–1120 (2018).Article 

Google Scholar 
9.Zaffosa, A., Finnegan, S. & Peters, S. E. Plate tectonic regulation of global marine animal diversity. Proc. Natl Acad. Sci. USA 114, 5653–5658 (2017).Article 
CAS 

Google Scholar 
10.Costello, M. J. et al. Marine biogeographic realms and species endemicity. Nat. Commun. https://doi.org/10.1038/s41467-017-01121-2 (2017).11.Beck, J. et al. What’s on the horizon for macroecology? Ecography 35, 673–683 (2012).Article 

Google Scholar 
12.Sunagawa, S. et al. Ocean plankton: structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 
Article 
CAS 

Google Scholar 
13.Shade, A. et al. Macroecology to unite all life, large and small. Trends Ecol. Evol. 33, 731–744 (2018).PubMed 
Article 

Google Scholar 
14.Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2019).16.Naeem, S., Duffy, J. E. & Zavaleta, E. The functions of biological diversity in an age of extinction. Science 336, 1401–1406 (2012).CAS 
PubMed 
Article 

Google Scholar 
17.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
18.Finderup Nielsen, T., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).PubMed 
Article 

Google Scholar 
19.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).CAS 
PubMed 
Article 

Google Scholar 
20.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
PubMed 
Article 

Google Scholar 
21.Olden, J. D. & Rooney, T. P. On defining and quantifying biotic homogenization. Glob. Ecol. Biogeogr. 15, 113–120 (2006).Article 

Google Scholar 
22.Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).CAS 
PubMed 
Article 

Google Scholar 
23.Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Bernardo-Madrid, R. et al. Human activity is altering the world’s zoogeographical regions. Ecol. Lett. 22, 1297–1305 (2019).PubMed 

Google Scholar 
25.Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).CAS 
PubMed 
Article 

Google Scholar 
26.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 

Google Scholar 
27.Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Emanuel, B. P., Bustamante, R. H., Branch, G. M., Eekhout, S. & Odendaal, F. J. A zoogeographic and functional approach to the selection of marine reserves on the west coast of South Africa. South Afr. J. Mar. Sci. 12, 341–354 (1992).Article 

Google Scholar 
29.Griffiths, C. L., Robinson, T. B., Lange, L. & Mead, A. Marine biodiversity in South Africa: an evaluation of current states of knowledge. PLoS ONE 5, e12008 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Griffiths, C. L. et al. Impacts of human activities on marine animal life in the Benguela: a historical overview. Oceanogr. Mar. Biol. Annu. Rev. 42, 303–392 (2004).
Google Scholar 
31.Kaluza, P., Kolzsch, A., Gastner, M. T. & Blasius, B. The complex network of global cargo ship movements. J. R. Soc. Interface 7, 1093–1103 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Rapacciuolo, G., Beman, J. M., Schiebelhut, L. M. & Dawson, M. N. Microbes and macro-invertebrates show parallel β-diversity but contrasting α-diversity patterns in a marine natural experiment. Proc. R. Soc. B 286, 20190999 (2019).CAS 
PubMed 
Article 

Google Scholar 
33.Astorga, A. et al. Distance decay of similarity in freshwater communities: do macro- and microorganisms follow the same rules? Glob. Ecol. Biogeogr. 21, 365–375 (2012).Article 

Google Scholar 
34.Wang, J. et al. Patterns of elevational beta diversity in micro- and macroorganisms. Glob. Ecol. Biogeogr. 21, 743–750 (2012).CAS 
Article 

Google Scholar 
35.Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–U1107 (2010).CAS 
PubMed 
Article 

Google Scholar 
36.Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Broman, E. et al. Salinity drives meiofaunal community structure dynamics across the Baltic ecosystem. Mol. Ecol. 28, 3813–3829 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Shochat, E., Warren, P. S., Faeth, S. H., McIntyre, N. E. & Hope, D. From patterns to emerging processes in mechanistic urban ecology. Trends Ecol. Evol. 21, 186–191 (2006).PubMed 
Article 

Google Scholar 
39.Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 11609 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Kelly, R. P. et al. Genetic signatures of ecological diversity along an urbanization gradient. PeerJ 4, e2444 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Blouin, D., Pellerin, S. & Poulin, M. Increase in non-native species richness leads to biotic homogenization in vacant lots of a highly urbanized landscape. Urban Ecosyst. 22, 879–892 (2019).Article 

Google Scholar 
42.Holman, L. E. et al. Detection of introduced and resident marine species using environmental DNA metabarcoding of sediment and water. Sci. Rep. 9, 11559 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 1262073 (2015).PubMed 
Article 
CAS 

Google Scholar 
44.Baas-Becking, L. G. M. Geobiologie; of inleiding tot de milieukunde (WP Van Stockum & Zoon NV, 1934).45.Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
46.Farjalla, V. F. et al. Ecological determinism increases with organism size. Ecology 93, 1752–1759 (2012).PubMed 
Article 

Google Scholar 
47.Wu, W. X. et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 12, 485–494 (2018).PubMed 
Article 

Google Scholar 
48.Hellweger, F. L., van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science 345, 1346–1349 (2014).CAS 
PubMed 
Article 

Google Scholar 
49.Balint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).PubMed 
Article 

Google Scholar 
50.He, K. S. et al. Will remote sensing shape the next generation of species distribution models? Remote Sens. Ecol. Conserv. 1, 4–18 (2015).Article 

Google Scholar 
51.Rius, M. et al. Range expansions across ecoregions: interactions of climate change, physiology and genetic diversity. Glob. Ecol. Biogeogr. 23, 76–88 (2014).Article 

Google Scholar 
52.Spens, J. et al. Comparison of capture and storage methods for aqueous macrobial eDNA using an optimized extraction protocol: advantage of enclosed filter. Methods Ecol. Evol. 8, 635–645 (2017).Article 

Google Scholar 
53.Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4, 558–565 (2013).Article 

Google Scholar 
55.Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of Bacteria and Archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
57.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.R Core Team R: A Language and Environment for Statistical Computing v.3.6.1 (R Foundation for Statistical Computing, 2019).59.Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1188 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8, 4226 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
63.Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).CAS 
PubMed 
Article 

Google Scholar 
64.Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. The new tree of eukaryotes. Trends Ecol. Evol. 35, 43–55 (2020).PubMed 
Article 

Google Scholar 
65.GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis (JPL_OurOceanProject, 2010); https://doi.org/10.5067/GHG1S-4FP0166.Zweng, M. M. et al. World Ocean Atlas 2018, Volume 2: Salinity NOAA Atlas NESDIS 82 (ed. Mishinov, A.) (NESDIS/US Department of Commerce, NOAA, 2019).67.Ocean Colour Climate Change Initiative Dataset Version 4.2 (European Space Agency, 2020).68.Anderson, M. J. in Wiley Stats Ref: Statistics Reference Online (eds Balakrishnan, N. et al.) 1–15 (John Wiley & Sons, 2014).69.Oksanen, J. et al. Vegan: Community ecology package. R package version 2.5–6 (2011).70.Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions. J. Biogeogr. 37, 2029–2053 (2010).Article 

Google Scholar 
71.Salazar, G. EcolUtils: Utilities for community ecology analysis. R package version 0.1 (2018).72.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).PubMed 
Article 

Google Scholar 
73.Crabot, J., Clappe, S., Dray, S. & Datry, T. Testing the Mantel statistic with a spatially-constrained permutation procedure. Methods Ecol. Evol. 10, 532–540 (2019).Article 

Google Scholar 
74.McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).Article 

Google Scholar  More

1.Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat. Rev. Genet. 8, 921–931 (2007).CAS 
PubMed 
Article 

Google Scholar 
2.Wagner, G. P. & Altenberg, L. Complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).PubMed 
Article 

Google Scholar 
3.Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66, 2891–2902 (2012).PubMed 
Article 

Google Scholar 
4.Cheverud, J. M. Quantitative genetics and developmental constraints on evolution by selection. J. Theor. Biol. 110, 155–171 (1984).CAS 
PubMed 
Article 

Google Scholar 
5.Phillips, P. C. & Arnold, S. J. Visualizing multivariate selection. Evolution 43, 1209–1266 (1989).PubMed 
Article 

Google Scholar 
6.Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 16, 948–955 (2002).
Google Scholar 
7.Blows, M. W. & Brooks, R. Measuring nonlinear selection. Am. Nat. 162, 815–820 (2003).PubMed 
Article 

Google Scholar 
8.Blows, M. W., Brooks, R. & Kraft, P. G. Exploring complex fitness surfaces: multiple ornamentation and polymorphism in male guppies. Evolution 57, 1622–1630 (2003).PubMed 
Article 

Google Scholar 
9.Jones, A. G., Arnold, S. J. & Bürger, R. Stability of the G-matrix in a population experiencing pleiotropic mutation, stabilizing selection, and genetic drift. Evolution 57, 1747–1760 (2003).PubMed 
Article 

Google Scholar 
10.Jones, A. G., Arnold, S. J. & Bürger, R. Evolution and stability of the G-matrix on a landscape with a moving optimum. Evolution 58, 1639–1654 (2004).PubMed 
Article 

Google Scholar 
11.Jones, A. G., Arnold, S. J. & Bürger, R. The mutation matrix and the evolution of evolvability. Evolution 61, 727–745 (2007).PubMed 
Article 

Google Scholar 
12.Jones, A. G., Bürger, R. & Arnold, S. J. Epistasis and natural selection shape the mutational architecture of complex traits. Nat. Commun. 5, 3709 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Lande, R. The genetic covariance between characters maintained by pleiotropic mutations. Genetics 94, 203–215 (1980).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Armbruster, W. S., Pélabon, C., Hansen, T. F. & Mulder, C. P. H. in Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes (eds Pigliucci, M. & Preston, K.) 23–49 (Oxford Univ. Press, 2004).15.Bell, A. M. & Sih, A. Exposure to predation generates personality in threespined sticklebacks (Gasterosteus aculeatus). Ecol. Lett. 10, 828–834 (2007).PubMed 
Article 

Google Scholar 
16.Dingemanse, N. J., Barber, I. & Dochtermann, N. A. Non-consumptive effects of predation: does perceived risk strengthen the genetic integration of behaviour and morphology in stickleback? Ecol. Lett. 23, 107–118 (2020).PubMed 
Article 

Google Scholar 
17.Hansen Wheat, C., Fitzpatrick, J. L., Rogell, B. & Temrin, H. Behavioural correlations of the domestication syndrome are decoupled in modern dog breeds. Nat. Commun. 10, 2422 (2019).18.Hurst, L. D., Pál, C. & Lercher, M. J. The evolutionary dynamics of eukaryotic gene order. Nat. Rev. Genet. 5, 299–310 (2004).PubMed 
Article 
CAS 

Google Scholar 
19.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).PubMed 
Article 

Google Scholar 
20.Schluter, D. & Nychka, D. Exploring fitness surfaces. Am. Nat. 143, 597–616 (1994).Article 

Google Scholar 
21.Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).CAS 
PubMed 
Article 

Google Scholar 
22.Roff, D. A. & Fairbairn, D. J. A test of the hypothesis that correlational selection generates genetic correlations. Evolution 66, 2953–2960 (2012).PubMed 
Article 

Google Scholar 
23.Svensson, E. I., McAdam, A. G. & Sinervo, B. Intralocus sexual conflict over immune defense, gender load, and sex-specific signaling in a natural lizard population. Evolution 63, 3124–3135 (2009).PubMed 
Article 

Google Scholar 
24.McGlothlin, J. W., Parker, P. G., Nolan, V. & Ketterson, E. D. Correlational selection leads to genetic integration of body size and an attractive plumage trait in dark-eyed juncos. Evolution 59, 658–671 (2005).PubMed 
Article 

Google Scholar 
25.Duckworth, R. A. & Kruuk, L. E. B. Evolution of genetic integration between dispersal and colonization ability in a bird. Evolution 63, 968–977 (2009).PubMed 
Article 

Google Scholar 
26.Brodie, E. D. III Correlational selection for color pattern and antipredator behavior in the garter snake Thamnophis ordinoides. Evolution 46, 1284–1298 (1992).PubMed 
Article 

Google Scholar 
27.Wise, M. J. & Rausher, M. D. Costs of resistance and correlational selection in the multiple-herbivore community of Solanum carolinense. Evolution 70, 2411–2420 (2016).PubMed 
Article 

Google Scholar 
28.Fenster, C. B., Reynolds, R. J., Williams, C. W., Makowsky, R. & Dudash, M. R. Quantifying hummingbird preference for floral trait combinations: the role of selection on trait interactions in the evolution of pollination syndromes. Evolution 69, 1113–1127 (2015).PubMed 
Article 

Google Scholar 
29.Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Martin, C. H. & Wainwright, P. C. Multiple fitness peaks on the adaptive landscape drive adaptive radiation in the wild. Science 339, 208–211 (2013).CAS 
PubMed 
Article 

Google Scholar 
31.Phillips, P. C. Epistasis – the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Steppan, S. J., Phillips, P. C. & Houle, D. Comparative quantitative genetics: evolution of the G matrix. Trends Ecol. Evol. 17, 320–327 (2002).Article 

Google Scholar 
33.Blows, M. W. & McGuigan, K. The distribution of genetic variance across phenotypic space and the response to selection. Mol. Ecol. 24, 2056–2072 (2015).PubMed 
Article 

Google Scholar 
34.Schluter, D. Adaptive radiation along genetic lines of least resistance. Evolution 50, 1766–1774 (1996).PubMed 
Article 

Google Scholar 
35.Lande, R. The maintenance of genetic variability by mutation in a polygenic character with linked loci. Genet. Res. 26, 221–235 (1976).Article 

Google Scholar 
36.Lande, R. The genetic correlation between characters maintained by selection, linkage and inbreeding. Genet. Res. 44, 309–320 (1984).CAS 
PubMed 
Article 

Google Scholar 
37.Bulmer, M. G. The effect of selection on genetic variability: a simulation study. Genet. Res. 28, 101–117 (1976).CAS 
PubMed 
Article 

Google Scholar 
38.Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).39.Guillaume, F. & Whitlock, M. C. Effects of migration on the genetic covariance matrix. Evolution 61, 2398–2409 (2007).PubMed 
Article 

Google Scholar 
40.Noble, D. W. A., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
PubMed 
Article 

Google Scholar 
41.Houle, D., Bolstad, G. H., van der Linde, K. & Hansen, T. F. Mutation predicts 40 million years of fly wing evolution. Nature 548, 447–450 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Svensson, E. I. & Berger, D. The role of mutation bias in adaptive evolution. Trends Ecol. Evol. 34, 422–434 (2019).PubMed 
Article 

Google Scholar 
43.Schweizer, G. & Wagner, A. Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis. PLoS Comput. Biol. 16, e1008082 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Delph, L. F., Steven, J. C., Anderson, I. A., Herlihy, C. R. & Brodie, E. D. III Elimination of a genetic correlation between the sexes via artificial correlational selection. Evolution 65, 2872–2880 (2011).PubMed 
Article 

Google Scholar 
45.Conner, J. K. Genetic mechanisms of floral trait correlations in a natural population. Nature 420, 407–410 (2002).CAS 
PubMed 
Article 

Google Scholar 
46.Wagner, G. P. & Zhang, J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12, 204–213 (2011).CAS 
PubMed 
Article 

Google Scholar 
47.Barton, N. H., Etheridge, A. M. & Véber, A. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118, 50–73 (2017).CAS 
PubMed 
Article 

Google Scholar 
48.Orr, H. A. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52, 935–948 (1998).PubMed 
Article 

Google Scholar 
49.Flint, J. & Mackay, T. F. C. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Stinchcombe, J. R., Weinig, C., Heath, K. D., Brock, M. T. & Schmitt, J. Polymorphic genes of major effect: consequences for variation, selection and evolution in Arabidopsis thaliana. Genetics 182, 911–922 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Orr, H. A. The genetics of species differences. Trends Ecol. Evol. 16, 343–350 (2001).Article 

Google Scholar 
52.Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Pitchers, W. et al. A multivariate genome-wide association study of wing shape in Drosophila melanogaster. Genetics 211, 1429–1447 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Mackay, T. F. C. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat. Rev. Genet. 15, 22–33 (2014).CAS 
PubMed 
Article 

Google Scholar 
56.Sailer, Z. R. & Harms, M. J. Detecting high-order epistasis in nonlinear genotype–phenotype maps. Genetics 205, 1079–1088 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Hill, W. G. “Conversion” of epistatic into additive genetic variance in finite populations and possible impact on long-term selection response. J. Anim. Breed. Genet. 134, 196–201 (2017).CAS 
PubMed 
Article 

Google Scholar 
58.Gienapp, P. et al. Genomic quantitative genetics to study evolution in the wild. Trends Ecol. Evol. 32, 897–908 (2017).PubMed 
Article 

Google Scholar 
59.Nosil, P. et al. Ecology shapes epistasis in a genotype–phenotype–fitness map for stick insect colour. Nat. Ecol. Evol. 4, 1673–1684 (2020).60.Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).PubMed 
Article 
CAS 

Google Scholar 
61.Bolnick, D. I., Barrett, R. D. H., Oke, K. B., Rennison, D. J. & Stuart, Y. E. (Non)parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).Article 

Google Scholar 
62.Therkildsen, N. O. et al. Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing. Science 365, 487–490 (2019).CAS 
PubMed 
Article 

Google Scholar 
63.Stern, D. L. & Orgogozo, V. Is genetic evolution predictable? Science 323, 746–751 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Colosimo, P. F. et al. Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science 307, 1928–1933 (2005).CAS 
PubMed 
Article 

Google Scholar 
65.Archambeault, S. L., Bärtschi, L. R., Merminod, A. D. & Peichel, C. L. Adaptation via pleiotropy and linkage: association mapping reveals a complex genetic architecture within the stickleback Eda locus. Evol. Lett. 4, 282–301 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
66.van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).PubMed 
Article 
CAS 

Google Scholar 
67.Stapley, J., Feulner, P. G. D., Johnston, S. E., Santure, A. W. & Smadja, C. M. Variation in recombination frequency and distribution across eukaryotes: patterns and processes. Phil. Trans. R. Soc. B 372, 20160455 (2017).PubMed 
Article 

Google Scholar 
68.Choudhury, R. R., Rogivue, A., Gugerli, F. & Parisod, C. Impact of polymorphic transposable elements on linkage disequilibrium along chromosomes. Mol. Ecol. 28, 1550–1562 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Thompson, M. J. & Jiggins, C. D. Supergenes and their role in evolution. Heredity 113, 1–8 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Yeaman, S. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proc. Natl Acad. Sci. USA 110, E1743–E1751 (2013).CAS 
PubMed 
Article 

Google Scholar 
71.Faria, R., Johannesson, K., Butlin, R. K. & Westram, A. M. Evolving inversions. Trends Ecol. Evol. 34, 239–248 (2019).PubMed 
Article 

Google Scholar 
72.Tuttle, E. M. et al. Divergence and functional degradation of a sex chromosome-like supergene. Curr. Biol. 26, 344–350 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Kupper, C. et al. A supergene determines highly divergent male reproductive morphs in the ruff. Nat. Genet. 48, 79–83 (2016).CAS 
PubMed 
Article 

Google Scholar 
74.Lamichhaney, S. et al. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nat. Genet. 48, 84–88 (2016).CAS 
PubMed 
Article 

Google Scholar 
75.Huu, C. N., Keller, B., Conti, E., Kappel, C. & Lenhard, M. Supergene evolution via stepwise duplications and neofunctionalization of a floral-organ identity gene. Proc. Natl Acad. Sci. USA 117, 23148–23157 (2020).CAS 
PubMed 
Article 

Google Scholar 
76.Merrill, R. M. et al. Genetic dissection of assortative mating behavior. PLoS Biol. 17, e2005902 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Whitlock, M. C., Phillips, P. C., Moore, F. B.-G. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995).Article 

Google Scholar 
78.Dudley, S. A. The response to selection on plant physiological traits: evidence for local adaptation. Evolution 50, 103–110 (1996).PubMed 
Article 

Google Scholar 
79.Kirkpatrick, M. & Ravigné, V. Speciation by natural and sexual selection: models and experiments. Am. Nat. 159, S22–S35 (2002).PubMed 
Article 

Google Scholar 
80.Hohenlohe, P. A., Bassham, S., Currey, M. & Cresko, W. A. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Phil. Trans. R. Soc. B 367, 395–408 (2012).81.Hench, K., Vargas, M., Höppner, M. P., McMillan, W. O. & Puebla, O. Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence. Nat. Ecol. Evol. 3, 657–667 (2019).PubMed 
Article 

Google Scholar 
82.Gienapp, P., Calus, M. P. L., Laine, V. N. & Visser, M. E. Genomic selection on breeding time in a wild bird population. Evol. Lett. 3, 142–151 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
83.McGuigan, K., Collet, J. M., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Pleiotropic mutations are subject to strong stabilizing selection. Genetics 197, 1051–105 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
84.McGuigan, K. et al. The nature and extent of mutational pleiotropy in gene expression of male Drosophila serrata. Genetics 196, 911–921 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Hine, E., Runcie, D. E., McGuigan, K. & Blows, M. W. Uneven distribution of mutational variance across the transcriptome of Drosophila serrata revealed by high-dimensional analysis of gene expression. Genetics https://doi.org/10.1534/genetics.118.300757 (2018).86.Estes, S., Ajie, B. C., Lynch, M. & Phillips, P. C. Spontaneous mutational correlations for life-history, morphological and behavioral characters in Caenorhabditis elegans. Genetics 170, 645–653 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
87.Houle, D. & Fierst, J. Properties of spontaneous mutational variance and covariance for wing size and shape in Drosophila melanogaster. Evolution 67, 1116–1130 (2013).PubMed 
Article 

Google Scholar 
88.Ovaskainen, O., Karhunen, M., Zheng, C., Arias, J. M. C. & Merilä, J. A new method to uncover signatures of divergent and stabilizing selection in quantitative traits. Genetics 189, 621–632 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Csilléry, K. et al. Adaptation to local climate in multi-trait space: evidence from silver fir (Abies alba Mill.) populations across a heterogeneous environment. Heredity 124, 77–92 (2020).PubMed 
Article 

Google Scholar 
90.Berg, J. J. & Coop, G. A population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.Orr, H. A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000).CAS 
PubMed 
Article 

Google Scholar 
92.Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, 1930).93.Pavlicev, M. & Hansen, T. F. Genotype–phenotype maps maximizing evolvability: modularity revisited. Evol. Biol. 38, 371–389 (2011).Article 

Google Scholar 
94.Hine, E., McGuigan, K. & Blows, M. W. Evolutionary constraints in high-dimensional trait sets. Am. Nat. 184, 119–131 (2014).PubMed 
Article 

Google Scholar 
95.Melo, D. & Marroig, G. Directional selection can drive the evolution of modularity in complex traits. Proc. Natl Acad. Sci. USA 112, 470–475 (2015).CAS 
PubMed 
Article 

Google Scholar 
96.Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl Acad. Sci. USA 102, 13773–13778 (2005).CAS 
PubMed 
Article 

Google Scholar 
97.Espinosa-Soto, C. & Wagner, A. Specialization can drive the evolution of modularity. PLoS Comput. Biol. 6, e1000719 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
98.Ancel, L. W. & Fontana, W. in Modularity: Understanding the Development and Evolution of Natural Complex Systems (eds Callebaut, W. & Rasskin-Gutman, D.) 129–141 (MIT Press, 2009).99.Wagner, G. P. & Mezey, J. G. in Modularity in Development and Evolution (eds Schlosser, G. & Wagner, G. P.) 338–358 (Univ. Chicago Press, 2004).100.Fokkens, L. & Snel, B. Cohesive versus flexible evolution of functional modules in eukaryotes. PLoS Comput. Biol. 5, e1000276 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
101.Huang, W. et al. Genetic basis of transcriptome diversity in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, E6010–E6019 (2015).CAS 
PubMed 
Article 

Google Scholar 
102.Schweizer, R. M. et al. Physiological and genomic evidence that selection on the transcription factor Epas1 has altered cardiovascular function in high-altitude deer mice. PLoS Genet. 15, e1008420 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
103.Hämälä, T. et al. Gene expression modularity reveals footprints of polygenic adaptation in Theobroma cacao. Mol. Biol. Evol. 37, 110–123 (2020).PubMed 
Article 
CAS 

Google Scholar 
104.Collet, J. M., McGuigan, K., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Mutational pleiotropy and the strength of stabilizing selection within and between functional modules of gene expression. Genetics 208, 1601–1616 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
105.Jiménez, A., Cotterell, J., Munteanu, A. & Sharpe, J. A spectrum of modularity in multi‐functional gene circuits. Mol. Syst. Biol. 13, 925 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
106.Verd, B., Monk, N. A. & Jaeger, J. Modularity, criticality, and evolvability of a developmental gene regulatory network. eLife 8, e42832 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
107.Pallares, L. F. et al. Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination. PLoS Genet. 11, e1005607 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
108.Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112–113, 9–32 (2001).PubMed 
Article 

Google Scholar 
109.Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).PubMed 
Article 

Google Scholar 
110.Shikov, A. E., Skitchenko, R. K., Predeus, A. V. & Barbitoff, Y. A. Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits. Sci. Rep. 10, 1037 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Sella, G. & Barton, N. H. Thinking about the evolution of complex traits in the era of genome-wide association studies. Annu. Rev. Genom. Hum. Genet. 20, 461–493 (2019).CAS 
Article 

Google Scholar 
112.Walsh, B. & Blows, M. W. Abundant genetic variation plus strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu. Rev. Ecol. Evol. Syst. 40, 41–59 (2009).Article 

Google Scholar 
113.Teplitsky, C. et al. Assessing multivariate constraints to evolution across ten long-term avian studies. PLoS ONE 9, e90444 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
114.Pavlicev, M. & Cheverud, J. M. Constraints evolve: context dependency of gene effects allows evolution of pleiotropy. Annu. Rev. Ecol. Evol. Syst. 46, 413–434 (2015).115.Wei, X. & Zhang, J. Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth. PLoS Biol. 17, e3000121 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
116.Parter, M., Kashtan, N. & Alon, U. Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput. Biol. 4, e1000206 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
117.Lande, R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33, 402–416 (1979).PubMed 
Article 

Google Scholar 
118.Bolstad, G. H. et al. Complex constraints on allometry revealed by artificial selection on the wing of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, 13284–13289 (2015).CAS 
PubMed 
Article 

Google Scholar 
119.Tsuboi, M. et al. Breakdown of brain–body allometry and the encephalization of birds and mammals. Nat. Ecol. Evol. 2, 1492–1500 (2018).PubMed 
Article 

Google Scholar 
120.White, C. R. et al. The origin and maintenance of metabolic allometry in animals. Nat. Ecol. Evol. 3, 598–603 (2019).PubMed 
Article 

Google Scholar 
121.Mullon, C., Keller, L. & Lehmann, L. Social polymorphism is favoured by the co-evolution of dispersal with social behaviour. Nat. Ecol. Evol. 2, 132–140 (2018).PubMed 
Article 

Google Scholar 
122.Schweizer, R. M. et al. Natural selection and origin of a melanistic allele in North American gray wolves. Mol. Biol. Evol. 35, 1190–1209 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
123.Hämälä, T., Gorton, A. J., Moeller, D. A. & Tiffin, P. Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia). PLoS Genet. 16, e1008707 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
124.Roda, F., Walter, G. M., Nipper, R. & Ortiz-Barrientos, D. Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower. Mol. Ecol. 26, 3687–3699 (2017).CAS 
PubMed 
Article 

Google Scholar 
125.Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).CAS 
Article 

Google Scholar 
126.Hughes, K. A., Houde, A. E., Price, A. C. & Rodd, F. H. Mating advantage for rare males in wild guppy populations. Nature 503, 108–110 (2013).CAS 
PubMed 
Article 

Google Scholar 
127.Marques, D. A., Jones, F. C., Di Palma, F., Kingsley, D. M. & Reimchen, T. E. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nat. Ecol. Evol. 2, 1128–1138 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
128.Brodie, E. D. III Genetic correlations between morphology and antipredator behaviour in natural populations of the garter snake Thamnophis ordinoides. Nature 342, 542–543 (1989).PubMed 
Article 

Google Scholar 
129.Auinger, H.-J. et al. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor. Appl. Genet. 129, 2043–2053 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
130.Xie, K. T. et al. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science 363, 81–84 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
131.Slate, J. Quantitative trait locus mapping in natural populations: progress, caveats and future directions. Mol. Ecol. 14, 363–379 (2005).CAS 
PubMed 
Article 

Google Scholar 
132.Brieuc, M. S. O., Waters, C. D., Drinan, D. P. & Naish, K. A. A practical introduction to random forest for genetic association studies in ecology and evolution. Mol. Ecol. Resour. 18, 755–766 (2018).PubMed 
Article 

Google Scholar 
133.Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218 (2005).CAS 
PubMed 
Article 

Google Scholar 
134.Barghi, N., Hermisson, J. & Schlötterer, C. Polygenic adaptation: a unifying framework to understand positive selection. Nat. Rev. Genet. 21, 769–781 (2020).135.Lemos, B., Araripe, L. O. & Hartl, D. L. Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. Science 319, 91–93 (2008).CAS 
PubMed 
Article 

Google Scholar 
136.Haddad, R., Meter, B. & Ross, J. A. The genetic architecture of intra-species hybrid mito-nuclear epistasis. Front. Genet. 9, 481 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
137.Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
138.Bollback, J. P., York, T. L. & Nielsen, R. Estimation of 2Nes from temporal allele frequency data. Genetics 179, 497–502 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
139.Svensson, E. I. & Calsbeek, R. The Adaptive Landscape in Evolutionary Biology (Oxford Univ. Press, 2012). More

Distribution models were produced for Cuvier’s beaked whale, sperm whale, and Risso’s dolphin based on the availability of both visually and acoustically distinctive features for species detection such as size, body markings and shape, and on temporal-spectral characteristics of their echolocation clicks22,23,24.Visual surveysVisual survey data were collected during five cruises conducted by the National Oceanographic and Atmospheric Administration Southeast Fisheries Science Center (NOAA SEFSC) aboard the R/V Gordon Gunter in 2003, 2004, 2009, 2012, and 2014 (Fig. 1, Supplementary Table 1)25. These cruises were designed to survey the oceanic GOM; therefore, the survey area was delimited by the 200 m bathymetric contour to the north, west, and east, and by the limit of the US exclusive economic zone (EEZ) to the south. Cruises conducted in 2012 and 2014 were limited to the eastern GOM. For this study, cruise data from 2009 was used only for model testing, while other years were used for training. The 2009 data were selected for testing because the entire area was surveyed in that year, allowing model predictions to be evaluated across the full region of interest. Pre-2003 visual survey data were not used due to limited availability of environmental covariate measurements for earlier years.Figure 1Map of GOM visual survey effort for five NOAA cruises between 2003 and 2014 (lines) and passive acoustic monitoring locations (orange triangles). The 2009 cruise effort (red lines) was used for model testing. Track lines for all other years, used for model training, are shown in black. The gray outline shows the extent of the modeled region, a pelagic area encompassing depths greater than 200 m within the US EEZ. Bathymetric contours (blue lines) are shown for the 200 m, 1000 m and 2000 m contours (Map created using ArcGIS software by ESRI29).Full size imageVisual survey effort was conducted along transect lines with the vessel traveling at or above 18.5 km/h (10 kn). To mitigate spatial autocorrelation between successive sightings, transect lines were divided into equal length segments of 10 km or less with transect segments each representing approximately 0.5 h of survey effort. The visual survey dataset consisted of 1,956 training segments and 449 test segments. Observations were used as point estimates. Implications of this approach are considered in the discussion.On all visual surveys, observation data were collected by one team of trained visual observers on the vessel’s flying bridge using 150 × 25 Bigeye binoculars to search for, identify, and estimate group sizes of cetaceans. All surveys operated in closing mode with the vessel departing from the track line for closer approaches to identify to the lowest possible taxonomic level and to obtain group size estimates.Raw count data were converted into densities for each 10 km transect segment for each of the three species of interest. All sightings for each of the species of interest are shown in Supplementary Figs. 1, 3, and 5. To obtain densities using distance sampling methodologies, the best model to fit the distribution of sighting distances was selected from a range of options (half normal, hazard-rate, hazard-rate with a second order polynomial adjustment, or uniform) using AIC implemented in the R software package mrds26. The species-specific sighting probability (Pvis) along a transect segment was given by the fitted detection function. Species-specific estimated truncation distances (or effective strip half width; w) were computed as the distance from the transect line within which 95% of the sightings of each species occurred (Supplementary Figs. 2, 4, and 6).For each transect segment and species, the total area monitored visually (({A}_{Vis})) was computed as$${A}_{Vis} = 2wL$$
(1)
where L is the transect segment length. Animal density was calculated for all transect segments as the number of animals detected per 1000 km2.Density (({widehat{D}}_{t}^{V})) along each visual survey transect segment t was calculated as.$$hat{D}_{t}^{V} = hat{G}_{tot} /({text{A}}_{vis} cdot , gleft( 0 right) , cdothat{P}_{vis} )$$
(2)
where (widehat{G}) tot is the sum of the best estimate group sizes from all sightings of the species of interest along the transect segment, and g(0) is the probability of observing the species directly on the transect line27 (Supplementary Table 2). Estimation of g(0) typically requires survey effort using independent (double-blind) observer teams and estimates were not available for the GOM surveys; therefore g(0) was estimated for each species from western Atlantic surveys aboard a similarly-sized vessel (R/V Endeavor), as the average of g(0) estimates from upper and lower observation teams28. The upper and lower observation platforms of the R/V Endeavor were 17.6 and 10.2 m high respectively and a cruise speed of 10 knots. The R/V Gordon Gunter has a primary observation deck height of 13.9 m, and a survey speed of 10 knots.Passive acoustic monitoringPAM data were collected from five sites in the GOM (Fig. 1) between 2011 and 2013 (Supplementary Table 2) using High-frequency Acoustic Recording Packages (HARPs)30. Recordings from three deep ( > 1000 m bottom depth) monitoring sites were used for this study’s deepest-diving species, sperm whales and Cuvier’s beaked whales. Recordings from two additional continental shelf monitoring sites ( More

1.Krause, J. & Ruxton, G. D. Living in Groups (Oxford University Press, Oxford, 2002).
Google Scholar 
2.Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals (Springer, Berlin, 2016).
Google Scholar 
3.Costa, J. T. & Ross, K. G. Fitness effects of group merging in a social insect. Proc. R. Soc. B 270, 1697–1702 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Nicieza, A. G. Interacting effects of predation risk and food availability on larval anuran behaviour and development. Oecologia 123, 497–505 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Dugatkin, L. A. Animal cooperation among unrelated individuals. Naturwissenschaften 89, 533–541 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Clutton-Brock, T. Breeding together: Kin selection and mutualism in cooperative vertebrates. Science 69, 69–72 (2002).ADS 
Article 

Google Scholar 
7.Haney, B. R. & Fewell, J. H. Ecological drivers and reproductive consequences of non-kin cooperation by ant queens. Oecologia 187, 643–655 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Tschinkel, W. R. Brood raiding and the population dynamics of founding and incipient colonies of the fire ant, Solenopsis invicta. Ecol. Entomol. 17, 179–188 (1992).Article 

Google Scholar 
9.Clark, R. M. & Fewell, J. H. Transitioning from unstable to stable colony growth in the desert leafcutter ant Acromyrmex versicolor. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-013-1632-4 (2013).Article 

Google Scholar 
10.Cole, B. The ecological setting of social evolution: the demography of ant populations. In Organization of Insect Societies: From Genome to Sociocomplexity (eds Gadau, J. & Fewell, J.) 74–104 (Harvard University Press, Cambridge, 2009).
Google Scholar 
11.Kang, Y., Clark, R., Makiyama, M. & Fewell, J. Mathematical modeling on obligate mutualism: Interactions between leaf-cutter ants and their fungus garden. J. Theor. Biol. 289, 116–127 (2011).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
12.Karsai, I. & Wenzel, J. Productivity, individual-level and colony-level flexibility, and organization of work as consequences of colony size. J. Theor. Biol. 289, 116–127 (1998).
Google Scholar 
13.Tibbetts, E. A. & Reeve, H. K. Benefits of foundress associations in the paper wasp Polistes dominulus : increased productivity and survival, but no assurance of fitness returns. Behav. Ecol. 14, 510–514 (2003).Article 

Google Scholar 
14.Cahan, S. & Julian, G. E. Fitness consequences of cooperative colony founding in the desert leaf-cutter ant Acromyrmex versicolor. Behav. Ecol. 10, 585–591 (1999).Article 

Google Scholar 
15.Tschinkel, W. R. Colony growth and the ontogeny of worker polymorphism in the fire ant, Solenopsis invicta. Behav. Ecol. Sociobiol. 22, 103–115 (1988).Article 

Google Scholar 
16.Choe, J. & Perlman, D. Social conflict and cooperation among founding queens in ants (Hymenoptera: Formicidae). In Social Behavior in Insects and Arachnids 392–406 (Cambridge University Press, Cambridge, 1997).
Google Scholar 
17.Bernasconi, G. & Strassmann, J. E. Cooperation among unrelated individuals: The ant foundress case. Trends Ecol. Evol. 14, 477–482 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hartke, T. R. & Rosengaus, R. B. Costs of pleometrosis in a polygamous termite. Proc. R. Soc. B 280, 20122563 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gamboa, G. J. Intraspecific defense: Advantage of social cooperation among paper wasp foundresses. Science 199, 1463–1466 (1978).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Kolmer, K. & Heinze, J. Rank orders and division of labour among unrelated cofounding ant queens. Proc. R. Soc. B 267, 1729–1734 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Clark, R. M. & Fewell, J. H. Social dynamics drive selection in cooperative associations of ant queens. Behav. Ecol. 25, 117–123 (2014).Article 

Google Scholar 
22.Johnson, R. A. Colony founding by pleometrosis in the semiclaustral seed-harvester ant Pogonomyrmex californicus (Hymenoptera: Formicidae ). Anim. Behav. 68, 1189–1200 (2004).Article 

Google Scholar 
23.Tschinkel, W. R. & Howard, D. F. Colony founding by pleometrosis in the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol. 12, 103–113 (1983).Article 

Google Scholar 
24.Rissing, S. W. & Pollock, G. B. Queen aggression, pleometrotic advantage and brood raiding in the ant Veromessor pergandei (Hymenoptera: Formicidae). Anim. Behav. 35, 975–981 (1987).Article 

Google Scholar 
25.Deslippe, R. J. & Savolainen, R. Colony Foundation and Polygyny in the Ant Formica podzolic. Behav. Ecol. Sociobiol. 37, 1–6 (1995).Article 

Google Scholar 
26.Bourke, A. F. G. & Franks, N. R. Social Evolution in Ants (Princeton University Press, Princeton, 1995).
Google Scholar 
27.Hölldobler, B. & Wilson, E. The Ants (Harvard University Press, Cambridge, 1990).
Google Scholar 
28.Mintzer, A. Primary polygyny in the ant Atta texana: number and weight of females nad colony foundation success in the laboratory. Insect Soc 34, 108–117 (1987).Article 

Google Scholar 
29.Heinze, J. & Hölldobler, B. Ants in the cold. Memorab. Zool. 48, 99–108 (1994).
Google Scholar 
30.Helms Cahan, S. Cooperation and conflict in ant foundress associations: Insights from geographical variation. Anim. Behav. 61, 819–825 (2001).Article 

Google Scholar 
31.Heinze, J. & Rüppel, O. The frequency of multi-queen colonies increases in a Nearctic ant. Ecol Entomol 39, 527–529 (2014).Article 

Google Scholar 
32.Brown, M. Semi-claustral founding and worker behaviour in gynes of Messor andrei. Insect Soc. 46, 194–195 (1999).Article 

Google Scholar 
33.Oster, G. & Wilson, E. Caste and Ecology in the Social Insects (Princeton University Press, Princeton, 1978).
Google Scholar 
34.Hölldobler, B. & Wilson, E. O. The Superorganism: The Beauty, Elegance, and Strangeness of Insect Societies (W.W. Norton & Company, New York, 2009).
Google Scholar 
35.Holbrook, C. T., Eriksson, T. H., Overson, R. P., Gadau, J. & Fewell, J. H. Colony-size effects on task organization in the harvester ant Pogonomyrmex californicus. Insectes Soc. 60, 191–201 (2013).Article 

Google Scholar 
36.Thomas, M. L. & Elgar, M. A. Colony size affects division of labour in the ponerine ant Rhytidoponera metallica. Naturwissenschaften 90, 88–92 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Jeanson, R., Fewell, J. H., Gorelick, R. & Bertram, S. M. Emergence of increased division of labor as a function of group size. Behav. Ecol. Sociobiol. 62, 289–298 (2007).Article 

Google Scholar 
38.Holbrook, C. T., Barden, P. M. & Fewell, J. H. Division of labor increases with colony size in the harvester ant Pogonomyrmex californicus. Behav. Ecol. 22, 960–966 (2011).Article 

Google Scholar 
39.Dornhaus, A., Powell, S. & Bengston, S. Group size and its effects on collective organization. Ann. Rev. Entomol. 57, 123–141 (2012).CAS 
Article 

Google Scholar 
40.Wilson, E. Colony ontogeny of Atta cephalotes. Behav. Ecol. Sociobiol. 7, 143–156 (1983).Article 

Google Scholar 
41.Jeanne, R. Social complexity in the Hymenoptera, with special attention to the wasps. In Genes, Behaviors, and Evolution of Social Insects (eds Kikuchi, T. et al.) 81–130 (Hokkaido University Press, Sapporo, 2003).
Google Scholar 
42.Gordon, D. M. The organization of work in social insect colonies. Nature 380, 121–124 (1996).ADS 
CAS 
Article 

Google Scholar 
43.Mailleux, A., Deneubourg, J. & Detrain, C. How does colony growth influence communication in ants?. Insectes Soc. 50, 24–31 (2003).Article 

Google Scholar 
44.Overson, R., Fewell, J. & Gadau, J. Distribution and origin of intraspecific social variation in the California harvester ant Pogonomyrmex californicus. Insectes Soc. 63, 531–541 (2016).Article 

Google Scholar 
45.Haney, B. R. et al. Ecological Drivers and Reproductive Consequences of Queen Cooperation in the California Harvester Ant Pogonomyrmex Californicus (Arizona State University, Tempe, 2017).
Google Scholar 
46.Bhatkar, A. & Whitcomb, W. H. Artificial diet for rearing various species of ants. Fla. Entomol. 53, 229–232 (1970).Article 

Google Scholar 
47.Cahan, S. H. & Fewell, J. H. Division of labor and the evolution of task sharing in queen associations of the harvester ant Pogonomyrmex californicus. Behav. Ecol. Sociobiol. 56, 9–17 (2004).Article 

Google Scholar 
48.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 
49.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
50.Therneau, T. A Package for Survival Analysis in R. R Package Version 3.2-7. (2020).51.Therneau, T. coxme: Mixed Effects Cox Models. R Package Version 2.2-16.52.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
53.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. (2020).54.Riehl, C. & Riehl, C. Evolutionary routes to non-kin cooperative breeding in birds. Proc. R. Soc. B 278, 20132242 (2013).
Google Scholar 
55.Clutton-Brock, T. Cooperation between non-kin in animal societies. Nature 461, 51–57 (2009).ADS 
Article 
CAS 

Google Scholar 
56.Emlen, S. T. The evolution of helping. I. An Ecological Constraints Model. Am. Nat. 119, 29–39 (1982).Article 

Google Scholar 
57.Heg, D., Bachar, Z., Brouwer, L. & Taborsky, M. Predation risk is an ecological constraint for helper dispersal in a cooperatively breeding cichlid. Proc. R. Soc. B 271, 2367–2374 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Kennedy, P., Higginson, A. D., Radford, A. N. & Sumner, S. Altruism in a volatile world. Nature 555, 359–362 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Gasperin, O. D., Blacher, P., Grasso, G. & Chapuisat, M. Winter is coming: Harsh environments limit independent reproduction of cooperative-breeding queens in a socially polymorphic ant. Biol. Lett. 16, 20190730 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Lukas, D. & Clutton-Brock, T. Climate and the distribution of cooperative breeding in mammals. R. Soc. Open Sci. 4, 160897 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Jetz, W. & Rubenstein, D. R. Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72–78 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 1–10 (2017).Article 

Google Scholar 
63.Heinze, J. Queen-queen interactions in polygynous ants. In Queen Number and Sociality in Insects (ed. Keller, L.) 262–293 (Oxford University Press, Oxford, 1993).
Google Scholar 
64.Schmid-Hempel, P. & Crozier, R. H. Polyandry versus polygyny versus parasites. Phil. Trans. R. Soc. B 354, 507–515 (1999).Article 

Google Scholar 
65.Hughes, W. O. H. & Boomsma, J. J. Genetic diversity and disease resistance in leaf-cutting ant societies. Evolution 58, 1251–1260 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Mattila, H. R. & Seeley, T. D. Genetic diversity in honey productivity and fitness. Science 317, 362–365 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Seeley, T. D. & Tarpy, D. R. Queen promiscuity lowers disease within honeybee colonies. Proc. R. Soc. B 274, 67–72 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Whitehorn, P. R., Tinsley, M. C., Brown, M. J. F., Darvill, B. & Goulson, D. Genetic diversity, parasite prevalence and immunity in wild bumblebees. Proc. R. Soc. B 278, 1195–1202. https://doi.org/10.1098/rspb.2010.1550 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Johnson, R. A. Water loss in desert ants: Caste variation and the effect of cuticle abrasion. Physiol. Entomol. 25, 48–53 (2000).Article 

Google Scholar 
70.Reber, A., Purcell, J., Buechel, S. D., Buri, P. & Chapuisat, M. The expression and impact of antifungal grooming in ants. J. Evol. Biol. 24, 954–964 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Wilson, S. N. et al. How emergent social patterns in allogrooming combat parasitic infections. Front. Ecol. Evol. 8, 54 (2020).Article 

Google Scholar 
72.Hutchins, M. & Barash, D. Grooming in primates: Implications for its utilitarian function. Primates 17, 145–150 (1976).Article 

Google Scholar 
73.Lobo, J., Bettencourt, L. M. A., Strumsky, D. & West, G. B. Urban scaling and the production function for cities. PLoS ONE 8, e58407–e58407 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C. & West, G. Growth, innovation, scaling and the pace of life in cities. PNAS 104, 7301–7306 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Bondi, A. Characteristics of scalability and their impact on performance. 195–203 (2000).76.Duboc, L., Rosenblum, D. & Wicks, T. A framework for characterization and analysis of software system scalability. Proceedings of the European Software Engineering Conference 375–384 (2007).77.Johnson, R. A. Semi-claustral colony founding in the seed-harvester ant Pogonomyrmex californicus: A comparative analysis of colony founding strategies. Oecologia 132, 60–67 (2002).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Wilson, E. The Insect Societies (Harvard University Press, Cambridge, 1971).
Google Scholar 
79.Seid, M. A. & Traniello, J. F. A. Age-related repertoire expansion and division of labor in Pheidole dentata (Hymenoptera: Formicidae): A new perspective on temporal polyethism and behavioral plasticity in ants. Behav. Ecol. Sociobiol. 60, 631–644 (2006).Article 

Google Scholar 
80.Seeley, T. Adaptive significance of the age polyethism schedule in honey bee colonies. Behav. Ecol. Sociobiol. 11, 287–293 (1982).Article 

Google Scholar  More

Based on the composition of the dated samples, two calibration procedures must be undertaken to transform the radiocarbon (14C) dates into accurate calendar dates. The 14C dates of the wood, newspaper and textile fragments were calibrated using the IntCal20 atmospheric calibration curve22 (Table 2, Fig. 4). All results are statistically consistent and give calibrated dates between 1646 and 1950 AD. The combination of the three dates provides the interval 1667–1950 AD. The elongated distribution is due to the flat shape of the calibration curve for this period23. Nevertheless, the results show that all the wood, newspaper and textile samples found inside the statue definitively date after 1650.Figure 4Calibrated 14C dates for wood, textile and paper samples taken from the Flora bust (in grey). The statistical combination of the three dates in green gives the interval of 1667–1950 AD. The χ2 test value of T = 4.1 (5% 6.0) shows their consistency.Full size imageTo calibrate the 14C dates obtained from the wax samples, the composition of the material has to be carefully considered. The Flora bust and “Leda and the swan” relief waxes are principally composed of spermaceti from a sperm whale that lives in the ocean, mixed with minor amounts of beeswax and other organic compounds extracted from terrestrial animals. The wax is thus primarily composed of marine material with some of terrestrial origin. The 14C source of terrestrial animals is in equilibrium with the atmosphere whereas that of whales 14C source is subject to the Marine Reservoir Effect (MRE)24. The MRE affects 14C dates since carbon consumed by organisms in the ocean is older than that consumed on land. Because the wax used for the sculptures is composed of carbon from different sources, other than just atmospheric carbon, the 14C measurements produce apparent old uncalibrated radiocarbon ages from 340 to 420 BP (Table 3) and a correction is needed to compensate this effect in calibration calculations.The mixture of marine and terrestrial sources in the wax requires the use of a combination of two calibration curves: IntCal20 atmospheric22 and Marine20 marine25, both weighted by the proportion of terrestrial and marine materials. In the case of the Flora bust, the determination of the exact ratio of spermaceti wax and terrestrial wax was not feasible because only a few samples of wax were available for analysis.To further complicate the procedure, the location of the marine source must be known to accurately calibrate marine material. Whales travel long distances, integrating the reservoir ages of the different water masses along their paths making that the determination of the marine reservoir age (MRA) for whale material 14C dates difficult. The global-average (MRA) of surface waters is c. 500 years25 but values range from about 400 years in subtropical oceans to over 1000 years in the poles. According to our knowledge no MRA has been reported for sperm whale (Physeter Macrocephalus L.) bone or for spermaceti except the estimation of 300 ± 200 years made by Freundlich5. Various values can be found for other cetacean materials in literature. One of the more complete studies, which is based on the analysis of 21 whales caught in Norway during the 19th c., proposed an average marine reservoir age (MRA) of 370 ± 30 years for various whales from the North Atlantic26. Previous publications recommended to use a c. 200 years marine reservoir correction for bowhead whales from Canadian Artic27, or determined a mean value correction of 320 ± 35 years for marine mammals, including whales, living near Sweden28 or c. 350 years correction for a 17th c. Finnback whale bone collected in Spitsbergen29. Additionally, based on an exhaustive compilation of published marine mammal radiocarbon dates, both live-harvested materials and subfossils, from the Canadian Arctic Archipelago, Furze et al.30 provided reservoir offset values for beluga (D. leucas) and bowhead (B. mysticetus) corresponding to a MRA of 570 ± 95 years for the latter.Calibration of the 14C dates of the 19th c. wax objects made by Richard Cockle LucasSince the spermaceti MRA value and the spermaceti wax content cannot be determined precisely, another approach was developed to calibrate the 14C dates of the Flora bust. This approach is based on the well-dated wax relief, “Leda and the Swan”. This relief was created by R. C. Lucas in 1850 and the chemical analysis has shown that its composition is similar to that of the Flora bust (Figs. 2, 3). The “Leda and the Swan” relief was used as reference to determine the appropriate combination of the IntCal20 and Marine20 calibration curves to be applied to the Flora wax material. The percentage of each curve was established by adjusting the calibrated date distribution of the Leda relief on both sides of the year 1850. To obtain this result, a combination of 15% atmospheric/85% marine curves was selected with an uncertainty of 10% to reflect material variability. The resulting distribution of dates is from 1704 to 1950 AD (Table 3, lower part of Fig. 5) which is not very precise, but this method has the advantage to take into account uncertainties on spermaceti MRA and on the spermaceti/beeswax content ratio. Figure 5 also shows that the results calibrated with the IntCal20 atmospheric curve are inconsistent with the known date of creation of the “Leda and the Swan” relief, which confirms the presence of marine material in the wax.Figure 5Calibrated 14C dates for the wax samples of the Leda and the Swan relief using atmospheric curve only, in light grey and light green, give dates out of range of the known date of creation of this artwork made by Lucas in 1850. A calibration of the same samples with a combination of 15% atmospheric/85% marine (± 10%) calibration curves, in dark grey, gives dates in the time frame of the relief’s creation in 1850. The statistical combination of the three dates, in blue, gives the interval of 1704–1950 AD.Full size imageCalibration of the 14C dates of the Flora bustThe same combination of atmospheric and marine calibration curves was applied to calibrate the 14C dates obtained for wax samples taken from six different locations at the surface and inside of the Flora bust because the composition of the Flora is similar to that of the Lucas wax objects. The results are presented in Fig. 6 and Table 3. All the dates are after 1704 AD, with a statistical combination on the six dates of 1712–1950. Uncertainty on the calibration curves lead to a broad interval for the dates of the Flora wax with about two centuries precision. Calibrated dates obtained on the wax samples, when the MRE is taken into account, agree with those of the wood, paper and textile samples, which confirms the strength and validity of our approach. All of the analysed constituents of the Flora bust are dated after 1700 AD, precluding the bust from being created in the Renaissance period.Figure 6Calibrated 14C dates for the wax samples of the Flora bust using a combination of 15% atmospheric/85% marine (± 10%) calibration curves (in dark grey). The statistical combination of the three dates in blue gives the interval of 1707–1950 AD.Full size imageChemical analyses and absolute dating were performed on different materials and several wax samples taken from the surface and inner parts from the Flora bust as well as on two dated wax reliefs made by the British 19th c. sculptor Richard Cockle Lucas, who some claim is the author of the Flora bust. The Lucas object “Leda and the swan” dated at 1850 could only be accurately dated using 14C measurements when a mixed terrestrial and marine calibration was taken into consideration because the wax is primarily made from spermaceti with minor amount of beeswax. Because the spermaceti was extracted from sperm whales living in deep and shallow seawaters, 14C dating must to consider the MRE. The Flora bust was shown to have an extremely similar composition to the Lucas object. Thus the same calibration correction procedure was applied to the uncalibrated 14C dates of the Flora bust. This new procedure involved calibrating of the 14C dates by considering a combination of 85% marine/15% atmospheric curves. The result dates the Flora materials to the 18-19th c., which proves that the bust was not produced during the Renaissance, and therefore cannot be attributed to Leonardo. This study also illustrates that 14C dating must take into account the heterogeneity and diversity of art objects, some of which may contain uncommon materials such as spermaceti wax.While it is somewhat disappointing to learn that the bust cannot be attributed to Leonardo, this information does provide useful insight into history. The sperm whale population suffered a serious decline in the 1740s when sperm whaling started on an industrial scale. The use of spermaceti in art objects shows how widespread the use of sperm whale products was and highlights the whaling industry’s importance during the industrial revolution. Other culturally significant objects may also be composed of materials that show the importance of certain industries or materials. There is clearly a need for art historical research to integrate natural science investigations in order to provide information allowing an improved attribution of art works and allowing to give another dimension to the historical value of such objects. More

1.Biard, C., Monceau, K., Motreuil, S. & Moreau, J. Interpreting immunological indices: the importance of taking parasite community into account. An example in blackbirds Turdus merula. Methods Ecol. Evol. 6, 960–972. https://doi.org/10.1111/2041-210x.12371 (2015).Article 

Google Scholar 
2.Boughton, R. K., Joop, G. & Armitage, S. A. O. Outdoor immunology: methodological considerations for ecologists. Funct. Ecol. 25, 81–100. https://doi.org/10.1111/j.1365-2435.2010.01817.x (2011).Article 

Google Scholar 
3.Maizels, R. M. & Nussey, D. H. Into the wild: digging at immunology’s evolutionary roots. Nat. Immunol. 14, 879–883. https://doi.org/10.1038/ni.2643 (2013).CAS 
Article 
PubMed 

Google Scholar 
4.Martin, L. B., Weil, Z. M. & Nelson, R. J. Refining approaches and diversifying directions in ecoimmunology. Integr. Comp. Biol. 46, 1030–1039. https://doi.org/10.1093/icb/icl039 (2006).CAS 
Article 
PubMed 

Google Scholar 
5.Johnson, W. et al. Pathogenic and humoral immune responses to porcine reproductive and respiratory syndrome virus (PRRSV) are related to viral load in acute infection. Vet. Immunol. Immunopathol. 102, 233–247. https://doi.org/10.1016/j.vetimm.2004.09.010 (2004).CAS 
Article 
PubMed 

Google Scholar 
6.Ortiz, R. H. et al. Differences in virulence and immune response induced in a murine model by isolates of Mycobacterium ulcerans from different geographic areas. Clin. Exp. Immunol. 157, 271–281. https://doi.org/10.1111/j.1365-2249.2009.03941.x (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Sela, U., Euler, C. W., da Rosa, J. C. & Fischetti, V. A. Strains of bacterial species induce a greatly varied acute adaptive immune response: the contribution of the accessory genome. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1006726 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Skjesol, A. et al. IPNV with high and low virulence: host immune responses and viral mutations during infection. Virol. J. https://doi.org/10.1186/1743-422x-8-396 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Hornef, M. W., Wick, M. J., Rhen, M. & Normark, S. Bacterial strategies for overcoming host innate and adaptive immune responses. Nat. Immunol. 3, 1033–1040. https://doi.org/10.1038/ni1102-1033 (2002).CAS 
Article 
PubMed 

Google Scholar 
10.Fassbinder-Orth, C. A. et al. Immunoglobulin detection inwild birds: effectiveness of three secondary anti-avian IgY antibodies in direct ELISAs in 41 avian species. Methods Ecol. Evol. 7, 1174–1181. https://doi.org/10.1111/2041-210x.12583 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Janeway, C. Immunobiology: The Immune System in Health and Disease (Garland Science, 2005).
Google Scholar 
12.Coltman, D. W., Pilkington, J., Kruuk, L. E. B., Wilson, K. & Pemberton, J. M. Positive genetic correlation between parasite resistance and body size in a free-living ungulate population. Evolution 55, 2116–2125 (2001).CAS 
Article 

Google Scholar 
13.Hayward, A. D. et al. Natural selection on individual variation in tolerance of gastrointestinal nematode infection. PLoS. Biol. https://doi.org/10.1371/journal.pbio.1001917 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Johnson, J. S. et al. Antibodies to Pseudogymnoascus destructans are not sufficient for protection against white-nose syndrome. Ecol. Evol. 5, 2203–2214. https://doi.org/10.1002/ece3.1502 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Fischer, J. R., Stallknecht, D. E., Luttrell, M. P., Dhondt, A. A. & Converse, K. A. Mycoplasmal conjunctivitis in wild songbirds: the spread of a new contagious disease in a mobile host population. Emerg. Infect. Dis. 3, 69–72. https://doi.org/10.3201/eid0301.970110 (1997).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Luttrell, M. P., Fischer, J. R., Stallknecht, D. E. & Kleven, S. H. Field investigation of Mycoplasma gallisepticum infections in house finches (Carpodacus mexicanus) from Maryland and Georgia. Avian Dis. 40, 335–341 (1996).CAS 
Article 

Google Scholar 
17.Delaney, N. F. et al. Ultrafast evolution and loss of CRISPRs following a host shift in a novel wildlife pathogen, Mycoplasma gallisepticum. Plos Genet. https://doi.org/10.1371/journal.pgen.1002511 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Dhondt, A. A., Tessaglia, D. L. & Slothower, R. L. Epidemic mycoplasmal conjunctivitis in house finches from Eastern North America. J. Wildl. Dis. 34, 265–280 (1998).CAS 
Article 

Google Scholar 
19.Nolan, P. M., Hill, G. E. & Stoehr, A. M. Sex, size, and plumage redness predict house finch survival in an epidemic. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 961–965 (1998).Article 

Google Scholar 
20.Bonneaud, C. et al. Rapid evolution of disease resistance is accompanied by functional changes in gene expression in a wild bird. Proc. Natl. Acad. Sci. U.S.A. 108, 7866–7871 (2011).ADS 
CAS 
Article 

Google Scholar 
21.Bonneaud, C. et al. Rapid antagonistic coevolution in an emerging pathogen and its vertebrate host. Curr. Biol. 28, 2978–2983 (2018).CAS 
Article 

Google Scholar 
22.Staley, M., Bonneaud, C., McGraw, K. J., Vleck, C. M. & Hill, G. E. Detection of Mycoplasma gallisepticum in House Finches (Haemorhous mexicanus) from Arizona. Avian Dis. 62, 14–17 (2018).Article 

Google Scholar 
23.Tardy, L., Giraudeau, M., Hill, G. E., McGraw, K. J. & Bonneaud, C. Contrasting evolution of virulence and replication rate in an emerging bacterial pathogen. Proc. Natl. Acad. Sci. U.S.A. 116, 16927–16932. https://doi.org/10.1073/pnas.1901556116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Grodio, J. L., Buckles, E. L. & Schat, K. A. Production of house finch (Carpodacus mexicanus) IgA specific anti-sera and its application in immunohistochemistry and in ELISA for detection of Mycoplasma gallisepticum-specific IgA. Vet. Immunol. Immunopathol. 132, 288–294 (2009).CAS 
Article 

Google Scholar 
25.Warr, G. W., Magor, K. E. & Higgins, D. A. IgY—clues to the origins of modern antibodies. Immunol. Today 16, 392–398. https://doi.org/10.1016/0167-5699(95)80008-5 (1995).CAS 
Article 
PubMed 

Google Scholar 
26.Diebolder, C. A. et al. Complement is activated by IgG hexamers assembled at the cell surface. Science 343, 1260–1263. https://doi.org/10.1126/science.1248943 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Bonneaud, C. et al. Evolution of both host resistance and tolerance to an emerging bacterial pathogen. Evol. Lett. 3, 544–554. https://doi.org/10.1002/evl3.133 (2019).Article 

Google Scholar 
28.Staley, M., Hill, G. E., Josefson, C. C., Armbruster, J. W. & Bonneaud, C. Bacterial pathogen emergence requires more than direct contact with a novel passerine host. Infect. Immun. 86, 9. https://doi.org/10.1128/iai.00863-17 (2018).CAS 
Article 

Google Scholar 
29.Grodio, J. L. et al. Pathogenicity and immunogenicity of three Mycoplasma gallisepticum isolates in house finches (Carpodacus mexicanus). Vet. Microbiol. 155, 53–61. https://doi.org/10.1016/j.vetmic.2011.08.003 (2012).CAS 
Article 
PubMed 

Google Scholar 
30.Javed, M. A. et al. Correlates of immune protection in chickens vaccinated with Mycoplasma gallisepticum strain GT5 following challenge with pathogenic M-gallisepticum strain R-low. Infect. Immun. 73, 5410–5419 (2005).CAS 
Article 

Google Scholar 
31.Dumke, R. & Jacobs, E. Antibody response to Mycoplasma pneumoniae: Protection of host and influence on outbreaks?. Front. Microbiol. 7, 7. https://doi.org/10.3389/fmicb.2016.00039 (2016).Article 

Google Scholar 
32.Avakian, A. P. & Ley, D. H. Protective immune-response to Mycoplasma-gallisepticum demonstrated in respiratory-tract washings from M-gallisepticum-infected chickens. Avian Dis. 37, 697–705. https://doi.org/10.2307/1592017 (1993).CAS 
Article 
PubMed 

Google Scholar 
33.Yagihashi, T. & Tajima, M. Antibody-responses in sera and respiratory secretions from chickens infected with Mycoplasma gallisepticum. Avian Dis. 30, 543–550. https://doi.org/10.2307/1590419 (1986).CAS 
Article 
PubMed 

Google Scholar 
34.Glatman-Freedman, A. & Casadevall, A. Serum therapy for tuberculosis revisited: reappraisal of the role of antibody-mediated immunity against Mycobacterium tuberculosis. Clin. Microbiol. Rev. 11, 514. https://doi.org/10.1128/cmr.11.3.514 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Vogl, G. et al. Mycoplasma gallisepticum invades chicken erythrocytes during infection. Infect. Immun. 76, 71–77. https://doi.org/10.1128/iai.00871-07 (2008).CAS 
Article 
PubMed 

Google Scholar 
36.Dowling, A. J., Hill, G. E. & Bonneaud, C. Multiple differences in pathogen-host cell interactions following a bacterial host shift. Sci. Rep. 10, 6779 (2020).ADS 
CAS 
Article 

Google Scholar 
37.Arfi, Y. et al. MIB-MIP is a mycoplasma system that captures and cleaves immunoglobulin G. Proc. Natl. Acad. Sci. U.S.A. 113, 5406–5411. https://doi.org/10.1073/pnas.1600546113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Staley, M., Bonneaud, C., McGraw, K. J., Vleck, C. M. & Hill, G. E. Detection of Mycoplasma gallisepticum in House Finches (Haemorhous mexicanus) from Arizona. Avian Dis. https://doi.org/10.1637/11610-021317-RegR (2018).Article 
PubMed 

Google Scholar 
39.Roberts, S. R., Nolan, P. M., Lauerman, L. H., Li, L. Q. & Hill, G. E. Characterization of the mycoplasmal conjunctivitis epizootic in a house finch population in the southeastern USA. J. Wildl. Dis. 37, 82–88 (2001).CAS 
Article 

Google Scholar 
40.Papazisi, L. et al. GapA and CrmA coexpression is essential for Mycoplasma gallisepticum cytadherence and virulence. Infect. Immun. 70, 6839–6845 (2002).CAS 
Article 

Google Scholar 
41.Ruijter, J. M. et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 37, 12. https://doi.org/10.1093/nar/gkp045 (2009).CAS 
Article 

Google Scholar 
42.Tuomi, J. M., Voorbraak, F., Jones, D. L. & Ruijter, J. M. Bias in the C-q value observed with hydrolysis probe based quantitative PCR can be corrected with the estimated PCR efficiency value. Methods 50, 313–322. https://doi.org/10.1016/j.ymeth.2010.02.003 (2010).CAS 
Article 
PubMed 

Google Scholar 
43.Ruijter, J., Villalba, A., Hellemans, J., Untergasser, A. & van den Hoff, M. Removal of between-run variation in a multi-plate qPCR experiment. Biomol. Detect. Quantif. 5, 10–14 (2015).CAS 
Article 

Google Scholar 
44.Grodio, J. L., Dhondt, K. V., O’Connell, P. H. & Schat, K. A. Detection and quantification of Mycoplasma gallisepticum genome load in conjunctival samples of experimentally infected house finches (Carpodacus mexicanus) using real-time polymerase chain reaction. Avian Pathol. 37, 385–391. https://doi.org/10.1080/03079450802216629 (2008).CAS 
Article 
PubMed 

Google Scholar 
45.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2016).46.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
47.ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).48.Repeatability Estimation for Gaussian and Non-Gaussian Data v. 0.9.21 (2018). More

Fossils, such as this skeleton of a T. rex on display in the Netherlands, may be even rarer than scientists realised. Credit: Marten van Dijl/AFP via Getty

Ever wondered how many Tyrannosaurus rex ever roamed the Earth? The answer is 2.5 billion over the two million or so years for which the species existed, according to a calculation published today in Science1. The figure has allowed researchers to estimate just how exceedingly rare it is for animals to fossilize.Palaeontologists led by Charles Marshall at the University of California, Berkeley, used a method employed by ecologists studying contemporary creatures to estimate the population density of T. rex during the late Cretaceous period. “You hold a fossil in your hand and you know it’s rare. The question is, how rare?” says Marshall. “To know that, you need to know how many of them existed.”To do that, he and his co-authors turned to a method used to estimate the population density of living animals from their body mass and the geographic ranges that they occupy. Damuth’s Law stipulates that the average population density of a species decreases in a predictable way as body mass increases; for example, there are fewer elephants than mice in a given area.Chances of being fossilized vanishingly smallThe team used their estimates of the total range of T. rex across modern North America, combined with their estimates of the dinosaur’s body mass, to calculate that, at any one time, around 20,000 T. rex would have been alive on the planet. That translates to around 3,800 T. rex in an area the size of California, or just two T. rex patrolling Washington DC. Calculating that T. rex survived for about 127,000 generations before becoming extinct, the researchers came up with a figure of 2.5 billion individuals over the species’ entire existence. Only 32 adult T. rex have been discovered as fossils, so the fossil record accounts for just 1 in about every 80 million T. rex. This means that the chances of being fossilized — even for one of the largest-ever carnivores — were vanishingly small.These numbers suggest that fossils in general are exceedingly rare, and hint that many species that were much less widespread than T. rex were probably never preserved, says Marshall, who adds: “The fossil record is our only direct knowledge of these completely unimaginable past histories of our planet.”Thomas Holtz, a vertebrate palaeontologist at the University of Maryland in College Park, calls the calculation an “interesting speculation”, adding that “we always knew that the chance of any individual becoming a fossil was exceedingly rare, but we lacked the calculation to figure out how rare”.But he says it would be good “to see someone ground-truth these kinds of estimations against living species to get a better sense of accuracy”. He’d also like to see comparable studies made on extinct species with more abundant fossils, such as woolly mammoths, Neanderthals and dire wolves, which might allow us to better understand historic ecosystems. More

1.Carslaw, K. S. et al. Atmospheric aerosols in the earth system: a review of interactions and feedbacks. Atmos. Chem. Phys. Discuss. 9, 11087–11183 (2009).ADS 

Google Scholar 
2.Müller, A., Miyazaki, Y., Tachibana, E., Kawamura, K. & Hiura, T. Evidence of a reduction in cloud condensation nuclei activity of submicron water-soluble aerosols caused by biogenic emissions in a cool-temperate forest. Sci. Rep. https://doi.org/10.1038/s41598-017-08112-9 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).ADS 
CAS 
Article 

Google Scholar 
4.Mentel, T. F. et al. Secondary aerosol formation from stress-induced biogenic emissions and possible climate feedbacks. Atmos. Chem. Phys. 13, 8755–8770 (2013).ADS 
Article 
CAS 

Google Scholar 
5.Celedon, J. M. & Bohlmann, J. Oleoresin defenses in conifers: chemical diversity, terpene synthases and limitations of oleoresin defense under climate change. New Phytol. 224, 1444–1463 (2019).CAS 
PubMed 
Article 

Google Scholar 
6.Hammerbacher, A., Coutinho, T. A. & Gershenzon, J. Roles of plant volitiles in defence aganst microbial pathogens and microbial explotation of volatiles. Plant Cell Environ. 42, 2827–2843 (2019).CAS 
PubMed 
Article 

Google Scholar 
7.Ninkovic, V., Markovic, D. & Rensing, M. Plant volatiles as cues and signals in plant communication. Plant Cell Environ. https://doi.org/10.1111/pce.13910 (2020).Article 
PubMed 

Google Scholar 
8.Sharifi, R. & Ryu, C. M. Social networking in crop plants: wired and wireless cross-plant communications. Plant Cell Environ. https://doi.org/10.1111/pce.13966 (2020).Article 
PubMed 

Google Scholar 
9.Garbeva, P. & Weisskopf, L. Airborne medicine: bacterial volatiles and their influence on plant health. New Phytol. 226, 32–43 (2019).PubMed 
Article 

Google Scholar 
10.Thompson, J. N. The Geographic Mosaic of Coevolution (Univ of Chicago Press, 2005).
Google Scholar 
11.Hiura, T. & Nakamura, M. Different mechanisms explain feeding type-specific patterns of latitudinal variation in herbivore damage among diverse feeding types of herbivorous insects. Basic Appl. Ecol. 14, 480–488 (2013).Article 

Google Scholar 
12.Okuzaki, Y. & Sota, T. Predator size divergence depends on community context. Ecol. Lett. 21, 1097–1107 (2018).PubMed 
Article 

Google Scholar 
13.Karban, R., Wetzel, W. C., Shiojiri, K., Pezzola, E. & Blande, J. D. Geographic dialects in volatile communication between sagebrush individuals. Ecology 97, 2917–2924 (2016).PubMed 
Article 

Google Scholar 
14.Friberg, M., Schwind, C., Guimaraes, P. R. Jr., Raguso, R. A. & Thompson, J. N. Extreme diversification of floral volatiles within and among species of Lithophragma (Saxifragaceae). Proc. Natl. Acad. Sci. USA 116, 4406–4415 (2019).CAS 
PubMed 
Article 

Google Scholar 
15.Heilmann-Clausen, J. et al. Communities of wood-inhabiting bryophytes and fungi on dead beech logs in Europe—reflecting substrate quality or shaped by climate and forest conditions?. J. Biogeogr. 41, 2269–2282 (2014).Article 

Google Scholar 
16.Fukasawa, Y. & Matsuoka, S. Communities of wood-inhabiting fungi in dead pine logs along geographical gradient in Japan. Fung. Ecol. 18, 75–82 (2015).Article 

Google Scholar 
17.Kubart, A., Vasaitis, R., Stenlid, J. & Dahlberg, A. Fungal communities in Norway spruce stumps along a latitudinal gradient in Sweden. For. Ecol. Manag. 371, 50–58 (2016).Article 

Google Scholar 
18.Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the earth mycobiome. Nat. Rev. Microbiol. 14, 434–447 (2016).CAS 
PubMed 
Article 

Google Scholar 
19.Manninnen, A. M., Tarhanen, S., Vuorinen, M. & Kainulainen, P. Comparing the variation of needle and wood terpenoids in Scots pine provenances. J. Chem. Ecol. 28, 211–228 (2002).Article 

Google Scholar 
20.Wallis, C. M., Reich, R. W., Lewis, K. J. & Huber, D. P. W. Lodgepole pine provenances differ in chemical defense capacities against foliage and stem diseases. Can. J. For. Res. 40, 2333–2344 (2010).Article 

Google Scholar 
21.López-Goldar, X. et al. Genetic variation in the constitutive defensive metabolome and its inducibility are geographically structured and largely determined by demographic processes in maritime pine. J. Ecol. 107, 2464–2477 (2019).Article 
CAS 

Google Scholar 
22.Fukuda, M., Iehara, T. & Matsumoto, M. Carbon stock estimates for sugi and hinoki forests in Japan. For. Ecol. Manag. 184, 1–16 (2003).Article 

Google Scholar 
23.Forestry Agency of Japan. 2011 Forestry Census (Forestry Agency, 2011).
Google Scholar 
24.Memari, H. R., Pazouski, L. & Niinemets, U. The biochemistry and molecular biology of volatile messengers in trees. In Biology, Controls and Models of Tree Volatile Organic Compound Emissions (eds Niinemets, U. & Monson, R. K.) 47–93 (Springer, 2013).
Google Scholar 
25.Kimura, M. K. et al. Evidence for cryptic northern refugia in the last glacial period in Cryptomeria japonica. Ann. Bot. 114, 1687–1700 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Nishizono, T., Kitahara, F., Iehara, T. & Mitsuda, Y. Geographical variation in age-height relationships for dominant trees in Japanese cedar (Cryptomeria japonica D. Don) forests in Japan. J. For. Res. 19, 305–316 (2014).Article 

Google Scholar 
27.Ohta, T., Niwa, S. & Hiura, T. Geographical variation in Japanese cedar shapes soil nutrient dynamics and invertebrate community. Plant Soil 437, 355–373 (2019).CAS 
Article 

Google Scholar 
28.Moreira, X. et al. Trade-offs between constitutive and induced defences drive geographical and climatic clines in pine chemical defences. Ecol. Lett. 17, 537–546 (2014).PubMed 
Article 

Google Scholar 
29.Suzuki, K., Aihara, H. & Yamada, T. Diseases of Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) predisposed by weather conditions. Bull. Univ. Tokyo For. 77, 39–48 (1987).
Google Scholar 
30.Cheng, S. S., Lin, H. Y. & Chang, S. T. Chemical composition and antifungal activity of essential oils from different tissuee of Japanese Cedar (Cryptomeria japonica).. J. Agr. Food Chem. 53, 614–619 (2005).CAS 
Article 

Google Scholar 
31.Hirooka, Y., Masuya, H., Akiba, M. & Kubono, T. Sydowia japonica, a new name for Leptosphaerulina japonica based on morphological and molecular data. Mycol. Prog. 12, 173–183 (2013).Article 

Google Scholar 
32.Kobayashi, T. & Katsumoto, K. Illustrated Genera of Plant Pathogenic Fungi in Japan (Zenkoku-Noson-Kyoiku Kyokai Publishing, 1992).
Google Scholar 
33.Rizzo, D. M., Rentmeester, R. M. & Burdsall, H. H. Jr. Sexuality and somatic incompatibility in Phellinus gilvus. Mycologia 87, 805–820 (1995).Article 

Google Scholar 
34.Homma, H. et al. Lignin-degrading activitu of edible mushroom Strobilurus ohshimae that forms fruiting bodies on buries sugi (Cryptomeria japonica) twigs. J. Wood Sci. 53, 80–84 (2007).ADS 
Article 

Google Scholar 
35.Ota, Y. et al. Taxonomy and phylogenetic position of Fomitiporia torreyae, a causal agent of trunk rot on Sanbu-sugi, a cultivar of Japanese cedar (Cryptomeria japonica) in Japan. Mycologia 106, 66–76 (2014).PubMed 
Article 

Google Scholar 
36.Fukui, Y., Miyamoto, T., Tamai, Y., Koizumi, A. & Yajima, T. Use of DNA sequence data to identify wood-decay fungi likely associated with stem failure caused by windthrow in urban trees during a typhoon. Trees 32, 1147–1156 (2018).CAS 
Article 

Google Scholar 
37.Kusumoto, N. & Shibutani, S. Evaporation of volatiles from essential oils of Japanese conifers enhances antifungal activity. J. Essential Oil Res. 27, 380–394 (2015).CAS 
Article 

Google Scholar 
38.Yamamoto, H., Noguchi, Y. & Suzuki, J. Synthesis of antibacterial terpenes by photooxidation of terpenes obtained from Cryptomeria japonica D. Don. Bull. Edu. Ibaraki Univ. 46, 53–62 (1997).
Google Scholar 
39.Mukai, A., Takahashi, K., Kofujita, H. & Ashitani, T. Antitermite and antifungal activities of thujopsene natural autoxidation products. Eur. J. Wood Prod. 77, 311–317 (2018).Article 
CAS 

Google Scholar 
40.Lee, G. W. et al. Direct suppression of a rice bacterial blight (Xanthomonas oryzae pv. oryzae) by monoterpene (S)-limonene. Protoplasma 253, 683–690 (2016).CAS 
PubMed 
Article 

Google Scholar 
41.Rodriguez, A. et al. Engineering D-limonen synthase down regulation in orange fruit induces resistance against the fungus Phyllosticta citricarpa through enhanced accumulation of monoterpene alcohols and activation of defence. Mol. Plant Pathol. 19, 2077–2093 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Matsunaga, S. N. et al. Determination and potential importance of diterpene (kaur-16-ene) emitted from dominant coniferous trees in Japan. Chemosphere 87, 886–893 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
43.Niinemets, Ü., Fares, S., Harley, P. & Jardine, K. J. Bidirectional exchange of biogenic volatiles with vegetation: emission sources, reactions, breakdown and deposition. Plant Cell Environ. 37, 1790–1809 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Yáñez-Serrano, A. M. et al. Volatile diterpene emission by two Mediterranean Cistaceae shrubs. Sci. Rep. https://doi.org/10.1038/s41598-018-25056-w (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Miyama, T. et al. Seasonal changes in interclone variation following ozone exposure on three major gene pools: an analysis of Cryptomeria japonica clones. Atmosphere 10, 643. https://doi.org/10.3390/atmos10110643 (2019).ADS 
CAS 
Article 

Google Scholar 
46.Ponzio, C., Gols, R., Pieterse, C. M. J. & Dicke, M. Ecological and phytohormonal aspects of plant volatile emission in response to single and dual infeststions with herbivores and phytopathogens. Fuct. Ecol. 27, 587–598 (2013).Article 

Google Scholar 
47.Salazar, D. et al. Origin and maintenance of chemical diversity in a species-rich tropical tree lineage. Nat. Ecol. Evol. 2, 983–990 (2018).PubMed 
Article 

Google Scholar 
48.Japan Meteorological Agency. Mesh Climate Data of Japan (Japan Meteorological Agency, 2014).
Google Scholar 
49.Kimura, M. K. et al. Effects of genetic and environmental factors on clonal reproduction in old-growth natural populations of Cryptomeria japonica. For. Ecol. Manag. 304, 10–19 (2013).Article 

Google Scholar 
50.Yasue, M. et al. Geographical differentiation of natural Cryptomeria stands analyzed by diterpene hydrocarbon constituents of individual trees. J. Jpn. For. Soc. 69, 152–156 (1987).
Google Scholar 
51.Tsumura, Y. et al. Genetic differentiation and evolutionary adaptation in Cryptomeria japonica. G3 4, 2389–2402 (2014).PubMed 
Article 

Google Scholar 
52.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).53.Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. Isometric logratio transformations for compositional data analysis. Math. Geol. 35, 279–300 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
54.van den Boogaart, K. G. & Tolosana-Delgado, R. Analyzing Compositional Data in R (Springer, 2013).
Google Scholar 
55.Kobayashi, T. Index of fungi inhabiting woody plants in Japan–Host, Distribution and Literature (Zenkoku-Noson-Kyoiku Kyokai Publishing, 2007).
Google Scholar 
56.Oksanen, J. et al. Vegan: Community Ecology Package, Version 2.5-6. http://CRAN.R-project.org/package=vegan (2019). More