Ecology

1.
Simpson, G. G. History of the fauna of Latin America. Am. Sci. 38, 361–389 (1950).
Google Scholar 
2.
Hershkovitz, P. A geographic classification of Neotropical mammals. Chicago natural history museum. Fieldiana Zool. 36, 581–620 (1958).
Google Scholar 

3.
Feeley, K. J. & Stroud, J. T. Where on Earth are the “tropics”?. Front. Biogeogr. 10(2), e38649. https://doi.org/10.21425/F5101-238649 (2018).
Article  Google Scholar 

4.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11), 933–938 (2011).
Google Scholar 

5.
Udvardy, M.D.F. A classification of the biogeographical provinces of the world. IUCN Occasional Papers n° 18 (1975).

6.
Canale, G. R., Peres, C. A., Guidorizzi, C. E., Gatto, C. A. F. & Kierulff, M. C. M. Pervasive defaunation of forest remnants in a tropical biodiversity hotspot. PLoS ONE 7, e41671 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

7.
Antunes, A. P. et al. Empty forest or empty rivers? A century of commercial hunting in Amazonia. Sci. Adv. 2, e1600936 (2016).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Galetti, M. et al. Defaunation and biomass collapse of mammals in the largest Atlantic forest remnant. Anim. Conserv. https://doi.org/10.1111/acv.12311 (2016).
Article  Google Scholar 

9.
Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. M. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl. Acad. Sci. 113(4), 892–897 (2016).
ADS  CAS  PubMed  Google Scholar 

10.
Püttker, T. et al. Indirect effects of habitat loss via habitat fragmentation: a cross-taxa analysis of forest-dependent species. Biol. Conserv. 241, 108368 (2020).
Google Scholar 

11.
Vilela, T. et al. A better Amazon road network for people and the environment. Proc. Natl. Acad. Sci. 117(13), 7095–7102 (2020).
ADS  CAS  PubMed  Google Scholar 

12.
Laurance, W. F. et al. Impacts of roads and hunting on central African rainforest mammals. Conserv. Biol. 20(4), 1251–1261 (2006).
PubMed  Google Scholar 

13.
Ceddia, M. G. et al. Governance, agricultural intensification, and land sparing in tropical South America. Proc. Natl. Acad. Sci. 111(2), 7242–7247 (2014).
ADS  CAS  PubMed  Google Scholar 

14.
Pastro, L. A., Dickman, C. R. & Letnic, M. Fire type and hemisphere determine the effects of fire on the alpha and beta diversity of vertebrates: a global meta-analysis. Glob. Ecol. Biogeogr. 23, 1146–1156 (2014).
Google Scholar 

15.
Wilkie, D. S., Bennett, E. L., Peres, C. A. & Cunningham, A. A. The empty forest revisited. Ann. N. Y. Acad. Sci. 1223, 120–128 (2011).
ADS  PubMed  Google Scholar 

16.
Brancalion, P. H. S. et al. Análise crítica da Lei de Proteção da Vegetação Nativa (2012), que substituiu o antigo Código Florestal: atualizações e ações em curso. Nat. Conserv. 14, e1–e16 (2016).
Google Scholar 

17.
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, Causes, and consequences of anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016).
Google Scholar 

18.
Redford, K. H. The empty forest. Bioscience 42, 412–422 (1992).
Google Scholar 

19.
Terborgh, J. The big things that run the world: a sequel to E.O. Wilson. Conserv. Biol. 2(4), 402–403 (1988).
Google Scholar 

20.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS  CAS  PubMed  Google Scholar 

21.
Levi, T. & Peres, C. A. Dispersal vaccum in the seedling recruitment of a primate-dispersed Amazonian tree. Biol. Conserv. 163, 99–106 (2013).
Google Scholar 

22.
Bogoni, J. A., da Silva, P. G. & Peres, C. A. Co-declining mammal–dung beetle faunas throughout the Atlantic Forest biome of South America. Ecography 42, 1803–1818 (2019).
Google Scholar 

23.
Lacher, T. E. et al. The functional roles of mammals in ecosystems. J. Mammal. 100(3), 942–964 (2019).
Google Scholar 

24.
Kaufman, D. M. Diversity of new world mammals: universality of the latitudinal gradients of species and bauplans. J. Mammal. 76(2), 322–334 (1995).
MathSciNet  Google Scholar 

25.
Ceballos, G. & Ehrlich, P. R. Global mammal distributions, biodiversity hotspots, and conservation. Proc. Natl. Acad. Sci. 103(51), 19374–19379 (2006).
ADS  CAS  PubMed  Google Scholar 

26.
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. 114, 6089–6096 (2017).
Google Scholar 

27.
Lima, F. et al. ATLANTIC-CAMTRAPS: a dataset of medium and large terrestrial mammal communities in the Atlantic Forest of South America. Ecology 98(11), 2979 (2017).
PubMed  Google Scholar 

28.
Souza, Y. et al. ATLANTIC MAMMALS: a data set of assemblages of medium- and large-sized mammals of the Atlantic Forest of South America. Ecology 100(10), e02785 (2019).
PubMed  Google Scholar 

29.
Janzen, D. H. (ed.) Costa Rican Natural History (The University of Chicago Press, Chicago, 1983).
Google Scholar 

30.
Gentry, A. H. (ed.) Four Neotropical Rainforest (Yale University Press, London, 1993).
Google Scholar 

31.
Nadkarni, N.M. & Wheelwright, N.T. (eds.). Monteverde: Ecology and Conservation of a Tropical Cloud Forest—2014 Updated Chapters. Bowdoin Scholars Bookshelf (2014).

32.
Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).
ADS  CAS  PubMed  Google Scholar 

33.
IUCN Spatial data download: mammals. https://www.iucnredlist.org/technicaldocuments/spatial-data#mammals. Accessed 11 June 2018. (2016).

34.
Rondinini, C. et al. Global habitat suitability models of terrestrial mammals. Philos Trans. R. Soc. Lond. B Biol. Sci. 366, 2633–2641 (2011).
PubMed  PubMed Central  Google Scholar 

35.
González-Maya, J. F., Martínez-Meyer, E., Medellín, R. & Ceballos, G. Distribution of mammal functional diversity in the Neotropical realm: influence of land-use and exticton risk. PLoS ONE 12(4), e0175931 (2017).
PubMed  PubMed Central  Google Scholar 

36.
Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: a call for caution. Glob. Ecol. Biogeogr. 2017, 1–12. https://doi.org/10.1111/geb.12601 (2017).
Article  Google Scholar 

37.
Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90(9), 2648 (2009).
Google Scholar 

38.
Wilman, H. et al. Elton traits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95(7), 2027–2027 (2014).
Google Scholar 

39.
Wildlife Conservation Society (WCS), and Center for International Earth Science Information Network (CIESIN), Columbia University. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). (NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, 2005). https://doi.org/10.7927/H4M61H5F.

40.
Kobayashi, T. et al. Production of global land cover data—GLCNMO2013. J. Geogr. Geol. 9(3), 1–15 (2017).
Google Scholar 

41.
NASA Earth Observatory. Maps created by Jesse Allen and Reto Stockli, NASA Earth Observatory, using data courtesy the MODIS Land Science Team at NASA Goddard Space Flight Center (2020). https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD14A1_M_FIRE&year=2018.

42.
Bogoni, J. A. et al. What would be the diversity patterns of medium- to large-bodied mammals if the fragmented Atlantic Forest was a large metacommunity?. Biol. Conserv. 211, 85–94 (2017).
Google Scholar 

43.
Morrone, J. J. Biogeographical regionalisation of the Neotropical region. Zootaxa 3782(1), 1–110 (2014).
PubMed  Google Scholar 

44.
Google Earth. KML gallery: explore the earth on Google (2020). https://earth.google.com/gallery/index.html.

45.
Bayes, T. An essay toward solving a problem in the doctrine of chances. Philos. Trans. R. Soc. Lond. 53, 370–418 (1764).
MathSciNet  MATH  Google Scholar 

46.
Cressie, N. A. C. Statistics for Spatial Data Revised. (Wiley, Hoboken, 1993).
Google Scholar 

47.
Rabinowitz, D. Seven forms of rarity. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (Wiley, Hoboken, 1981).
Google Scholar 

48.
Yu, J. & Dobson, F. S. Seven forms of rarity in mammals. J. Biogeogr. 27, 131–139 (2000).
Google Scholar 

49.
Tobler, M. W., Carrillo-Percastegui, S. E., Pitman, R. L., Mares, R. & Powell, G. An evaluation of camera traps for inventorying large- and medium-sized terrestrial rainforest mammals. Anim. Conserv. 11(3), 169–178 (2008).
Google Scholar 

50.
Bogoni, J. A., Pires, J. S. R., Graipel, M. E., Peroni, N. & Peres, C. A. Wish you were here: how defaunated is the Atlantic Forest biome of its medium- to large bodied mammal fauna?. PLoS ONE 13(9), e0204515 (2018).
PubMed  PubMed Central  Google Scholar 

51.
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Soft. 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05 (2008).
Article  Google Scholar 

52.
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2020).

53.
Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37(1), 17–23 (1950).
MathSciNet  CAS  PubMed  MATH  Google Scholar 

54.
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, Cambridge, 2006).
Google Scholar 

55.
Kamata, A. & Bauer, D. J. A note on the relation between factor analytic and item response theory models. Struct. Equ. Model. 15(1), 136–153 (2008).
MathSciNet  Google Scholar 

56.
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).
PubMed  Google Scholar 

57.
Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Soft. 48(2), 1–36 (2012).
Google Scholar 

58.
Maxwell, S., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).
ADS  CAS  PubMed  Google Scholar 

59.
Wen, Z. et al. Using completeness and defaunation indices to understand nature reserve’s key attributes in preserving medium- and large-bodied mammals. Biol. Conserv. 241, 108273 (2020).
Google Scholar 

60.
Camargo-Sanabria, A. A., Mendoza, E., Guevara, R., Martinez-Ramos, M. & Dirzo, R. Experimental defaunation of terrestrial mammalian herbivores alters tropical rainforest understorey diversity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 282, 2580 (2015).
Google Scholar 

61.
Osuri, A. M. et al. Contrasting effects of defaunation on aboveground carbon storage across the global tropics. Nat. Commun. 7, 11351 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

62.
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

63.
Dean, W. With Broadax and Firebrand: The Destruction of the Brazilian Atlantic Forest (University of California Press, Berkeley, 1996).
Google Scholar 

64.
Galetti, M. et al. Priority areas for the conservation of Atlantic forest large mammals. Biol. Conserv. 142(6), 1229–1241 (2009).
Google Scholar 

65.
Leal, I. R., Silva, J. M. C., Tabarelli, M. & Lacher, T. Changing the course of biodiversity conservation in the Caatinga of Northeastern Brazil. Conserv. Biol. 19(3), 701–706 (2005).
Google Scholar 

66.
Chesser, T. & Hackett, S. J. Mammalian diversity in South America. Science 256, 1502–1504 (1992).
ADS  Google Scholar 

67.
Ojeda, R. A. Diversity and Conservation of Neotropical Mammals. Encyclopedia of Biodiversity 2nd edn. (Academic Press, Waltham, 2013).
Google Scholar 

68.
Hansen, M. C. et al. High resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
ADS  CAS  PubMed  Google Scholar 

69.
Lapola, D. M. et al. Pervasive transition of the Brazilian land-use system. Nat. Clim. Change 4, 27–35 (2014).
ADS  Google Scholar 

70.
Ceddia, M. G. The super-rich and cropland expansion via direct investments in agriculture. Nat. Sustain. 3(4), 312–318 (2020).
Google Scholar 

71.
Chape, S., Harrison, J., Spalding, M. D. & Lysenko, I. Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 443–455 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Joppa, L. N., Loarle, S. R. & Pimm, S. L. On the protection of ‘“protected areas”’. Proc. Natl. Acad. Sci. 105(18), 6673–6678 (2008).
ADS  CAS  PubMed  Google Scholar 

73.
Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

74.
Oliveira, U. et al. Biodiversity conservation gaps in the Brazilian protected areas. Sci. Rep. 7, 9141 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

75.
Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Conservation performance of different conservation governance regimes in the Peruvian Amazon. Sci. Rep. 7, 11318 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

76.
Begotti, R.A. & Peres, C.A. Rapidly escalating threats to the biodiversity and ethnocultural capital of Brazilian Indigenous Lands. Land Use Policy (2020) (in press).

77.
Levi, T., Shepard, G. H., Ohl-Schacherer, J. & Peres, C. A. Modelling the long-term sustainability of indigenous hunting in Manu National Park, Peru: landscape-scale management implications for Amazonia. J. Appl. Ecol. 46, 804–814 (2009).
Google Scholar 

78.
Benítez-López, A., Santini, L., Schipper, A. M., Busana, M. & Huijbregts, M. A. Patterns of hunting-induced mammal defaunation in the tropics. PLoS Biol. 17(5), e3000247 (2019).
PubMed  PubMed Central  Google Scholar 

79.
Sanderson, E. S. et al. The human footprint and the last of the wild. Bioscience 52(10), 891–904 (2002).
Google Scholar 

80.
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

81.
Belote, R. T. et al. Mammal species composition reveals new insights into Earth’s remaining wilderness. Front. Ecol. Environ. https://doi.org/10.1002/fee.2192 (2020).
Article  Google Scholar 

82.
Rodrigues, A. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643 (2004).
ADS  CAS  PubMed  Google Scholar 

83.
Phillips, H. R. P., Newbold, T. & Purvis, A. Land-use effects on local biodiversity in tropical forests vary between continents. Biodivers. Conserv. 26, 2251–2270 (2017).
PubMed  PubMed Central  Google Scholar 

84.
Abra, F. D. et al. Pay or prevent? Human safety, costs to society and legal perspectives on animal-vehicle collisions in São Paulo state, Brazil. PLoS ONE 14(4), e0215152 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

85.
Magioli, M. M. et al. Human-modified landscapes alter mammal resource and habitat use and trophic structure. Proc. Natl. Acad. Sci. 116(37), 18466–18472 (2019).
CAS  PubMed  Google Scholar 

86.
Barlow, J. & Peres, C. A. Fire-mediated dieback and compositional cascade in an Amazonian forest. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 1787 (2008).
PubMed  PubMed Central  Google Scholar 

87.
Aragão, L. E. O. C. & Shimabukuro, Y. E. The incidence of fire in Amazonian Forests with implications for REDD. Science 328, 1275–1278 (2010).
ADS  PubMed  Google Scholar 

88.
Martin, P. S. Discovery of America. Science 179, 969–974 (1973).
ADS  CAS  PubMed  Google Scholar 

89.
Simpson, G. G. Splendid Isolation: The Curious History of South American Mammals (Yale University Press, New Haven, 1980).
Google Scholar 

90.
Peters, R. H. The Ecological Implications of Body Size (Cambridge University Press, Cambridge, 1986).
Google Scholar 

91.
Brown, J. H. & Sibly, R. M. Life-history evolution under a production constraint. Proc. Natl. Acad. Sci. 103(47), 17595–17599 (2006).
ADS  CAS  PubMed  Google Scholar 

92.
Hone, D. W. & Benton, M. J. The evolution of large size: how does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).
PubMed  Google Scholar 

93.
Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).
ADS  CAS  PubMed  Google Scholar 

94.
Cardillo, M. et al. The predictability of extinction- biological and external correlates of decline in mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 

95.
Beca, G. et al. High mammal species turnover in forest patches immersed in biofuel plantations. Biol. Conserv. 210, 352–359 (2017).
Google Scholar 

96.
Crooks, K. R. et al. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl. Acad. Sci. 114(29), 7635–7640 (2017).
CAS  PubMed  Google Scholar 

97.
Santini, L. et al. One strategy does not fit all: determinants of urban adaptation in mammals. Ecol. Lett. 22, 365–376 (2019).
PubMed  Google Scholar 

98.
Barnosky, A. D. et al. Variable impact of late-Quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl. Acad. Sci. 113(4), 856–861 (2016).
ADS  CAS  PubMed  Google Scholar 

99.
Rees, J. D., Kingsford, R. T. & Letnic, M. In the absence of an apex predator, irruptive herbivores suppress grass seed production: implications for small granivores. Biol. Conserv. 213, 13–18 (2017).
Google Scholar 

100.
Berzaghi, F. et al. Assessing the role of megafauna in tropical forest ecosystems and biogeochemical cycles—the potential of vegetation models. Ecography 41, 1–21 (2018).
Google Scholar 

101.
Bufalo, F. S., Galetti, M. & Culot, L. Seed dispersal by primates and implications for the conservation of a biodiversity hotspot, the Atlantic Forest of South America. Int. J. Primatol. https://doi.org/10.1007/s10764-016-9903-3 (2016).
Article  Google Scholar 

102.
Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3(1), e1600946 (2017).
ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

103.
Almeida-Rocha, J. M., Peres, C. A. & Oliveira, L. C. Primate responses to anthropogenic habitat disturbance: a pantropical meta-analysis. Biol. Conserv. 215, 30–38 (2017).
Google Scholar 

104.
Paviolo, A. et al. A biodiversity hotspot losing its top predator: the challenge of jaguar conservation in the Atlantic Forest of South America. Sci. Rep. 6(1), 1–16 (2016).
Google Scholar 

105.
Ji, Y. et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol. Lett. 16(10), 1245–1257 (2013).
PubMed  Google Scholar 

106.
Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).
Google Scholar 

107.
Janson, C. H. & Emmons, L. Ecological structure of the nonflying mammal community at Cocha Cashu Biological Station, Manu National Park, Peru. In Four Neotropical Rainforests (ed. Gentry, A. H.) 314–338 (Yale University Press, New Haven, 1990).
Google Scholar 

108.
Peres, C. A. Structure of nonvolant mammal communities in different Amazonian Forest types. In Mammals of the Neotropics: The Central Neotropics (eds Eisenberg, J. F. & Redford, K. H.) 564–581 (University of Chicago, Chicago, 1999).
Google Scholar 

109.
Carbone, C., Cowlishaw, G., Isaac, N. J. B. & Rowcliffe, J. M. How far do animals go? Determinants of day range in mammals. Am. Nat. 165, 290–297 (2005).
PubMed  Google Scholar 

110.
Ferreira, A. S., Peres, C. A., Bogoni, J. A. & Cassano, C. G. Use of agroecosystem matrix habitats by mammalian carnivores (Carnivora): a global-scale analysis. Mammal Rev. https://doi.org/10.1111/mam.12137 (2018).
Article  Google Scholar 

111.
Lomolino, M. V. Elevation gradients of species-density: historical and prospective views. Glob. Ecol. Biogeogr. 10, 3–13 (2001).
Google Scholar 

112.
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).
ADS  CAS  PubMed  Google Scholar 

113.
Waide, R. B. et al. The relationship between productivity and species richness. Annu. Rev. Ecol. Evol. Syst. 30, 257–300 (1999).
Google Scholar 

114.
Oliveira, L. E. C. & Begossi, A. Last trip return rate influence patch choice decisions of small-scale shrimp trawlers: optimal foraging in São Francisco, Coastal Brazil. Hum. Ecol. 39, 323–332 (2011).
Google Scholar 

115.
Cardillo, M. The life-history basis of latitudinal diversity gradients: how do species traits vary from the poles to the equator?. J. Anim. Ecol. 71, 79–87 (2002).
Google Scholar  More

Host cultures
All laboratory experiments were conducted with Emiliania huxleyi strain CCMP374 (https://ncma.bigelow.org/ccmp374) and EhV strain 207 (see below). CCMP374 is a naked strain of E. huxleyi isolated from the Gulf of Maine in 1990, and exhibits rapid growth, high-stationary phase densities (~107 cells per milliliter), and high sensitivity to viral infection27,63. Cultures were maintained at 5·105 to 1·106 cells per millilier and grown at 18 °C, with a 14 h:10 h light:dark cycle with a light intensity of 125 µmol photons m−2 s−1. CCMP374 was grown in batch culture conditions with f/2 rich nutrients42 added to 0.2 µm pore-size filtered (GE Healthcare USA, filter 6718-9582) autoclaved seawater in either polystyrene 50 mL flasks or 6-well plates or polypropylene 96-well plates (Greiner Bio-One, USA; items 690160, 657185, and 780270, respectively; Supplementary Table 1). Addition of f/2 nutrients increases macronutrient concentrations (e.g., NaNO3 882 µM; an ~88-fold enrichment over basal seawater with ~10 µM NaNO3; other nutrients see similar enrichments). This provides ideal, replete conditions conducive to virulent dynamics in which to probe for the presence of virulent viral behavior.
Virus cultures
EhV207 has commonly been used to elucidate virulent dynamics, as it induces the rapid decline of host populations and concomitant production of high titers of viral progeny under culture conditions28,36,64. Together with CCMP374, EhV207 comprises a highly virulent host–virus system, strongly predisposing this work towards the execution of virulent activity. Viruses were cultured by adding them to exponentially growing cultures at ~5·105 to 1·106 cells per milliliter in f/2 media at a virus:host ratio of 10:1 MOI. Cultures visibly cleared after approximately three days and viruses were isolated from cellular debris using 0.45 µm pore-size filtration (EMD Millipore, USA; filters SLHV033RS or SVHV01015) and lysates stored in the dark at 4 °C until use within 1 week. This approach yielded viral titers in excess of 108 viruses per milliliter. In experiments where a virus-negative control was required, a heat-killed lysate was produced by incubation at 90 °C for 10–20 min prior to 0.02 µm pore-size filtration (Anotop, Whatman, USA) and cooling to ~18 °C. All infections were conducted in the morning41. For all experiments, virus infectivity was monitored by running parallel cultures with initial host densities of 105 cells per milliliter coincubated with a MOI of 10 (10:1 viruses:host). These visibly cleared in all cases, showing that our viruses were always infectious in these experiments. All flasks were shaken daily and plates mixed by pipetting to preclude settling and ensure equal exposure to infection. In summary, all experiments were conducted in a manner typically conducive to virulent infection and with viable viruses and sensitive hosts.
Laboratory coincubation experiments
E. huxleyi-EhV virulent infection dynamics were first were studied using coincubation of viruses and hosts at an initial ratio of 10:1 viruses:host (MOI = 10) in laboratory conditions. In experiments without preinfection treatments—Experiments II, III, and VII—viral lysates were added to high-density (~1·106 cells per milliliter; quantified using a Coulter Counter Multi-sizer 3, Beckman, USA) cultures to a final ratio of 10:1 virus:host (MOI = 10; viruses were quantified using an Influx Mariner flow cytometer; BD, USA). Cultures were then serially diluted down to experimental densities; all cells were from the same inoculum within each experiment (see Supplementary Table 1 for densities in each experiment and Supplementary Fig. 3 for experimental rationale). Uninfected controls substituted lysates with heat-killed, filtered lysate. In experiments with preincubation treatments (Experiments I, IV, V, and VI), cultures for 10:1 MOI coincubation treatment were drawn from the uninfected control after centrifugation and washing, so that uninfected controls, 10:1 MOI coincubation, and pre-infected treatments were all subjected to similar centrifugation and washing before lysate/heat-killed viral addition. The initial set of experiments (Experiments I, II, III, and VII) were conducted for approximately a week as we expected lysis to occur at all densities in that time (Supplementary Table 1). These experiments were subsequently repeated due to our initial interpretation that the lack of lysis in scarce densities ( More

1.
Virto, L. R., Weber, J.-L. & Jeantil, M. Natural capital accounts and public policy decisions: findings from a survey. Ecol. Ec. 144, 244–259 (2018). This paper shows how institutional and practical obstacles limit uptake of natural capital accounts in real world policy decision making.
Google Scholar 
2.
United Nations. Framework convention on climate change adoption of the Paris Agreement. 21st Conference of the Parties (United Nations, Paris, 2015).

3.
Liénart, C. et al. Dynamics of particulate organic matter composition in coastal systems: forcing of spatio-temporal variability at multi-systems scale. Prog. Oceanogr. 162, 271–289 (2018).
ADS  Google Scholar 

4.
Pedrosa-Pàmies, R. et al. Composition and sources of sedimentary organic matter in the deep eastern Mediterranean Sea. Biogeosciences 12, 7379–7402 (2015).
ADS  Google Scholar 

5.
Van der Voort, T. S. et al. Deconvolving the fate of carbon in coastal sediments. Geophys. Res. Lett. 45, 4134–4142 (2018).
ADS  Google Scholar 

6.
da Silva Copertino, M. Add coastal vegetation to the climate critical list. Nature 473, 255 (2011).

7.
Cooley, S. R. et al. Overlooked ocean strategies to address climate change, Global Environ. Change 59, 101968 (2019).

8.
Avelar, S., van der Voort, T. S. & Eglinton, T. I. Relevance of carbon stocks of marine sediments for national greenhouse gas inventories of maritime nations. Carbon Balance Manag. 12, 10 (2017).

9.
Holt, J. et al. Prospects for improving the representation of coastal and shelf seas in global ocean models. Geosci. Model Dev. 10, 499–523 (2017).
ADS  Google Scholar 

10.
Ostrom, E., Gardner, R. & Walker, J. (eds). Rules, Games, and Common-Pool Resources (Univ. of Michigan Press, Ann Arbor, 1994).

11.
Legge, O. et al. Carbon on the Northwest European Shelf: contemporary budget and future influences. Front. Mar. Sci. 7, 143 (2020). This paper presents a new budget synthesis of carbon cycling in the North Sea.

12.
IPCC. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S. et al.) (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2007).

13.
Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data https://doi.org/10.5194/essd-10-2141-2018 (2018).

14.
IPCC Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge and New York, 2013).

15.
IPCC Summary for Policymakers. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. R. et al.) (Cambridge University Press, Cambridge and New York, 2014).

16.
IPCC. Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (2019).

17.
Burdige, D. J. The preservation of organic matter in marine sediments: controls, mechanisms and an imbalance in sediment organic carbon budgets? Chem. Rev. 107, 467–485 (2007). This is a major review of sediment organic carbon processing.

18.
Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 1 (2020).

19.
Taillardat, P., Friess, D. & Lupascu, M. Mangrove blue carbon strategies for climate change mitigation are most effective at the national scale. Biol. Lett. 14, 20180251 (2018).

20.
Duarte, C. M., Dennison, W. C., Orth, R. J. & Carruthers, T. J. The charisma of coastal ecosystems: addressing the imbalance. Estuaries Coasts 31, 233–238 (2008).
Google Scholar 

21.
Duarte, C. et al. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Change 3, 961–968 (2013).
ADS  CAS  Google Scholar 

22.
Pendleton, L. et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7, e43542 (2015).

23.
Otto, L. et al. Review of the physical oceanography of the North Sea. Neth. J. Sea Res. 26, 161–238 (1990).
Google Scholar 

24.
Simpson, J. H. & Sharples, J. Introduction to the Physical and Biological Oceanography of Shelf Seas, 413pp (Cambridge University Press, 2012).

25.
Soulsby, R. Dynamics of Marine Sands: A Manual for Practical Applications 249pp (Thomas Telford, 1997).

26.
Hill, A. E. et al. Thermohaline circulation of shallow tidal seas. Geophys. Res. Lett. 35, https://doi.org/10.1029/2008GL033459 (2008).

27.
Brown, J. et al. Observation of the physical structure and seasonal jet-like circulation of the Celtic Sea and St. George’s channel of the Irish. Cont. Shelf Res. 33, 353–361 (2003).
Google Scholar 

28.
Fernand, L. et al. The Irish coastal current: a seasonal jet-like circulation, Cont. Shelf Res. 26, 1775–1793 (2006).

29.
Bristow, L. A. et al. Tracing estuarine organic matter sources into the southern North Sea using C and N isotopic signatures. Biogeochemistry 113, 9–22 (2013).
ADS  CAS  Google Scholar 

30.
Huettel, M., Berg, P. & Kostka, J. E. Benthic exchange and biogeochemical cycling in permeable sediments. Annu. Rev. Mar. Sci. 6, 23–51 (2014).
ADS  Google Scholar 

31.
Middelburg, J. J. Reviews and syntheses: to the bottom of carbon processing at the seafloor. Biogeosciences 15, 413–427 (2018).
ADS  CAS  Google Scholar 

32.
Couceiro, F. et al. Impact of resuspension of cohesive sediments at the Oyster Grounds (North Sea) on nutrient exchange across the sediment–water interface. Biogeochemistry 113, 37–52 (2013).
CAS  Google Scholar 

33.
Wilson, R. J., Speirs, D. C., Sabatino, A. & Heath, M. R. A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science. Earth Syst. Sci. Data 10, 109–130 (2018).
ADS  Google Scholar 

34.
Bianchi, T. S., Blair, N., Burdige, D., Eglinton, T. I. & Galy, V. Centers of organic carbon burial at the land-ocean interface. Org. Geochem. 115, 138–155 (2018).
CAS  Google Scholar 

35.
Bauer, J. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).
ADS  CAS  PubMed  Google Scholar 

36.
Sharples, J., Middelburg, J. J., Fennel, K. & Jickells, T. D. What proportion of riverine nutrients reaches the open ocean? Global Biogeochem. Cycles, 31, 39–58 (2017).

37.
Kao, S. J. et al. Preservation of terrestrial organic carbon in marine sediments offshore Taiwan: mountain building and atmospheric carbon dioxide sequestration. Earth Surf. Dyn. 2, 127–139 (2014).
ADS  Google Scholar 

38.
Burdige, D. J. Geochemistry of Marine Sediments 1–609 (Princeton University Press, 2006).

39.
Aller, R. C. Bioturbation and remineralization of sedimentary organic matter: effects of redox oscillation. Chem. Geol. 114, 331–345 (1994).
ADS  CAS  Google Scholar 

40.
Aller, R. C. In The Benthic Boundary Layer: Transport Processes and Biogeochemistry (eds Boudreau, B. & Jørgensen, B. B.) 269–301 (Oxford Press, 2001).

41.
Teal, L. R., Bulling, M. T., Parker, E. R. & Solan, M. Global patterns of bioturbation intensity and mixed depth of marine soft sediments. Aquat. Biol. 2, 207–218 (2008).
Google Scholar 

42.
Arndt, S. et al. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci. Rev. 123, 53–86 (2013).
ADS  CAS  Google Scholar 

43.
Levin, L. A. & Sibuet, M. Understanding continental margin biodiversity: a new imperative. Annu. Rev. Mar. Sci. 4, 79–112 (2012).
ADS  Google Scholar 

44.
Pusceddu, A. et al. Chronic and intensive bottom trawling impairs deep-sea biodiversity and ecosystem functioning. PNAS 111, 8861–8866 (2014).
ADS  CAS  PubMed  Google Scholar 

45.
Paradis, S. et al. Organic matter contents and degradation in a highly trawled area during fresh particle inputs (Gulf of Castellammare, southwestern Mediterranean). Biogeosciences 16, 4307–4320 (2019).
ADS  CAS  Google Scholar 

46.
Maier, K. L. et al. Sediment and organic carbon transport and deposition driven by internal tides along Monterey Canyon, offshore California. Deep Sea Res. Part I: Oceanogr. Res. Pap. 153, 103–108 (2019).

47.
IPCC. IPCC Guidelines for National Greenhouse Gas Inventories—A Primer. Prepared by the National Greenhouse Gas Inventories Programme (eds Eggleston, H. S., Miwa, K., Srivastava, N. & Tanabe, K.) (IGES, Japan, 2008).

48.
IPCC. 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (eds Hiraishi, T. et al.) (IPCC, Switzerland, 2014).

49.
Schuerch, M. et al. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234 (2018).
ADS  CAS  PubMed  Google Scholar 

50.
Griscom, B. W. et al. Natural climate solutions. PNAS 114, 11645–11650 (2017).
ADS  CAS  PubMed  Google Scholar 

51.
Troxler, T., Kennedy, H., Crooks, S. & Sutton-Grier, A. In A Blue Carbon Primer: The State of Coastal Wetlands Carbon Science, Practice and Policy (eds Windham-Myers, L., Crooks, S. & Troxler, T.) (CRC Press, Boca Raton, Fl, 2018).

52.
Crooks, S. et al. Coastal wetland management as a contribution to the US National Greenhouse Gas Inventory. Nat. Clim. Change 8, 1109–1112 (2018).
ADS  CAS  Google Scholar 

53.
Ogle, S. M. et al. Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change. Carbon Bal. Manag. 13, 9 (2018).

54.
IPCC. Current Scientific Understanding Of The Processes Affecting Terrestrial Carbon Stocks And Human Influences Upon Them. IPCC Meeting on Expert Meeting Report (eds David Schimel, D. & Martin Manning, M.), Geneva, Switzerland, 21–23 July (2003).

55.
Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl Acad. Sci. USA 104, 18866–18870 (2007).
ADS  CAS  PubMed  Google Scholar 

56.
Krug, J. H. A. Accounting of GHG emissions and removals from forest management: a long road from Kyoto to Paris. Carbon Bal. Manag. 13, 1 (2018).

57.
Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15, 257–265 (2017).
Google Scholar 

58.
Thorhaug, A., Poulos, H. M., Lopez-Portillo, J., Ku, T. C. W. & Berlyn, G. P. Seagrass blue carbon dynamics in the Gulf of Mexico: stock, losses from anthropogenic disturbance, gains through seagrass restoration. Sci. Total Environ. 15, 626–636 (2017).
ADS  Google Scholar 

59.
United Nations. Technical Recommendations in Support of the System of Environmental – Economic Accounting 2012 – Experimental Ecosystem Accounting, 193 (White Cover Publication, United Nations, 2017).

60.
United Nations, European Commission, Food and Agriculture Organization, International Monetary Fund, Organisation for Economic Co-operation and Development, and the World Bank. System of Environmental-Economic Accounting 2012. Central Framework (2014).

61.
United Nations, European Commission, Food and Agriculture Organization, Organisation for Economic Co-operation and Development, and the World Bank. System of Environmental-Economic Accounting 2012: Experimental Ecosystem Accounting—final, official publication (2014).

62.
Edens, B., Elsasser, P. & Ivanov, E. Discussion paper 6: Defining and valuing carbon related services in the SEEA EEA. Paper submitted to the Expert Meeting on Advancing the Measurement of Ecosystem Services for Ecosystem Accounting, New York, 22–24 January 2019 and subsequently revised. https://seea.un.org/events/expert-meeting-advancing-measurement-ecosystem-services-ecosystem-accounting (2019).

63.
European Environment Agency. Natural Capital Accounting in Support of Policymaking in Europe—A Review Based on EEA Ecosystem Accounting Work. EEA Report No 26/2018 (Publication Office of the European Union, Luxembourg, 2019). Natural capital accounting provides evidence on ecosystem trends in a structured and integrated manner that allows for analysis of environment-economy interactions, with diverse entry points into policymaking processes.

64.
UK National Ecosystem Assessment. The UK National Ecosystem Assessment: Follow-on (UK NEA-FO) (UNEP-WCMC, LWEC, UK, 2014). This report applies the ecosystem services approach and balance sheet decision support system to environmental policy.

65.
Thomas, S. Blue carbon: knowledge gaps, critical issues, and novel approaches. Ecol. Econ. 107, 22–38 (2014).
Google Scholar 

66.
Luisetti, T. et al. Quantifying and valuing carbon flows and stores in coastal and shelf ecosystems in the UK. Ecosyst. Serv. 35, 67–76 (2019). This paper provides an economic valuation of the potential damage to shelf sea sediments.
Google Scholar 

67.
Santos, M. M. et al. The last frontier: coupling technological developments with scientific challenges to improve hazard assessment of deep-sea mining. Sci. Total Environ. 627, 1505–1514 (2018).
ADS  CAS  PubMed  Google Scholar 

68.
Orbach, M. Beyond the freedom of the seas: ocean policy for the third millennium. Oceanography 16, 20 (2003).

69.
International Seabed Authority (ISA) Voluntary Commitments to Support Implementation of SDG14: https://www.isa.org.jm/isa-voluntary-commitments.

70.
Epstein, G., Nenadovic, M. & Boustany, A. Into the deep blue sea: commons theory and international governance of Atlantic Bluefin Tuna. Int. J. Commons 8, 277–303 (2014).

71.
United Nations. Convention of Biological Diversity, https://www.cbd.int/sp/ (1992).

72.
CBD – Subsidiary Body on Implementation. Second meeting Montreal, Canada, 9–13 July 2018. Item 3 of the provisional agenda. Analysis of the Contribution of Targets Established by Parties and Progress Towards the Aichi Biodiversity Targets: Note by the Executive Secretary (Montreal, Canada, 2018).

73.
UNEP. Assessment of post-2010 National Biodiversity Strategies and Action Plans (Nairobi, Kenya, 2018).

74.
UN General Assembly Intergovernmental conference on an international legally binding instrument under the United Nations Convention on the Law of the Sea on the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction. Fourth session New York, 23 March–3 April 2020 – Revised draft text of an agreement under the United Nations Convention on the Law of the Sea on the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction. Note by the President (2020).

75.
Scharin, H. et al. Processes for the sustainable stewardship of marine environments. Ecol. Econ. 128, 55–67 (2016). This paper applies the balance sheet decision support system to the Baltic Sea Clean Up Action Plan.
Google Scholar 

76.
Hilton, R. G. et al. Tropical-cyclone-driven erosion of the terrestrial biosphere from mountains. Nat. Geosci. 1, 759–762 (2008).
ADS  CAS  Google Scholar 

77.
Wakelin, S. L. et al. Modeling the carbon fluxes of the northwest European continental shelf: validation and budgets. J. Geophys. Res. 117, https://doi.org/10.1029/2011JC007402 (2012). This study provides the most complete model-derived carbon fluxes for the northwest European continental shelf to date.

78.
Keil, R. Anthropogenic forcing of carbonate and organic carbon preservation in marine sediments. Annu. Rev. Mar. Sci. 9, 151–172 (2017).
ADS  Google Scholar 

79.
Diesing, M. et al. Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf. Biogeochemistry 135, 183–200 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

80.
OSPAR Assessment Portal. Socioeconomics of the OSPAR Maritime Area—Towards and Assessment Framework. https://oap.ospar.org/en/ospar-assessments/intermediate-assessment-2017/socio-economics/ (2019).

81.
Thornton, A. et al. Initial Natural Capital Accounts for the Uk Marine and Coastal Environment. Final Report. Report prepared for the Department for Environment Food and Rural Affairs (2019).

82.
Ajani, J. Carbon Stock Accounts. Information Paper for the United Nations Statistics Division Technical Expert Meeting on Ecosystem Accounts, London, 5–7 December (2011). This paper examines issues connected with the use of the SEEA for ecosystem carbon accounting, including measurement and valuation of ocean carbon stocks.

83.
United Nation – Statistical Commission – Report on the fifty-first session (3-6 March 2020), Economic and Social Council, Official Records, 2020 – Supplement No. 4: https://unstats.un.org/unsd/statcom/51st-session/documents/2020-37-FinalReport-E.pdf.

84.
Turner, R. K., Badura, T. & Ferrini, S. Natural capital accounting perspectives: a pragmatic way forward. Ecosyst. Health Sustainability 5, 237–241 (2019). This paper summarises the range of approaches to natural capital accounting and the ongoing debate surrounding them.
Google Scholar 

85.
van de Ven, P., Obst, C. & Edens, B. Accounting treatments when integrating ecosystem accounts in the SNA. Paper prepared for SEEA EEA Revision coordinated by the United Nations Statistics Division OSPAR Assessment Portal. Socioeconomics of the OSPAR Maritime Area – Towards and Assessment Framework. https://oap.ospar.org/en/ospar-assessments/intermediate-assessment-2017/socio-economics/ (2019).

86.
Acquaye, A. et al. Measuring the environmental sustainability performance of global supply chains: A multi-regional input-output analysis for carbon, sulphur oxide and water footprints. J. Environ. Manag. 187, 571–585 (2017).
CAS  Google Scholar 

87.
Brizga, J., Feng, K. & Hubacek, K. Household carbon footprints in the Baltic States: a global multi-regional input–output analysis from 1995 to 2011. Appl. Energy 189, 780–788 (2017).
Google Scholar 

88.
Bordt, M. Discourses in ecosystem accounting: a survey of the expert community. Ecol. Econ. 144, 82–99 (2018).
Google Scholar 

89.
Hein, L. et al. Progress in natural capital accounting for ecosystems. Science 367, 514–515 (2020).

90.
Fuso Nerini, F. et al. Connecting climate action with other Sustainable Development Goals. Nat. Sustainability 2, 674–680 (2019).
Google Scholar 

91.
Le Blanc, D., Freire, C. & Vierros, M. Mapping the linkages between oceans and other Sustainable Development Goals: a preliminary exploration. DESA Working Paper 149 (2017).

92.
Hardin, G. The tragedy of the commons. Science 162, 1243–1248 (1968).
ADS  CAS  PubMed  Google Scholar 

93.
Brun, A. Conference diplomacy: the making of the Paris Agreement. Politics Gov. 4, 115–123 (2016).
Google Scholar 

94.
Christoff, P. The promissory note: COP 21 and the Paris Climate Agreement. Environ. Politics 25, 765–787 (2016).
MathSciNet  Google Scholar 

95.
Pauw, W. P. et al. Beyond headline mitigation numbers: we need more transparent and comparable NDCs to achieve the Paris Agreement on climate change. Clim. Change 147, 23–29 (2017).
ADS  Google Scholar 

96.
Gallo, N. D., Victor, D. V. & Levin, L. A. Ocean commitments under the Paris Agreement. Nat. Clim. Change 7, 833–840 (2017).
ADS  Google Scholar 

97.
United Nations Environment Programme – The Regional Seas Programme: https://www.unenvironment.org/explore-topics/oceans-seas/what-we-do/working-regional-seas/why-does-working-regional-seas-matter.

98.
Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).
ADS  CAS  Google Scholar 

99.
Najjar, R. G. et al. Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America. Glob. Biogeochemical Cycles 32, 389–416 (2018).
ADS  CAS  Google Scholar 

100.
Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).
Google Scholar  More

1.
Bradbury, J. & Vehrencamp, S. Principles of Animal Communication (Sinauer Associates, Sunderland, 1998).
Google Scholar 
2.
McGregor, P. K. & Peake, T. M. Communication networks: social environments for receiving and signalling behaviour. Acta Ethologica 2(2), 71–81 (2000).
Google Scholar 

3.
Fichtel, C. & Manser, M. Vocal communication in social groups. In Animal Behaviour: Evolution and Mechanisms. 29–54 (Springer, 2010).

4.
Snijders, L. & Naguib, M. Communication in animal social networks: a missing link? In Advances in the Study of Behavior (eds Naguib, M. et al.) 297–359 (Elsevier Academic Press, Oxford, 2017).
Google Scholar 

5.
McGregor, P. Animal Communication Networks (Cambridge University Press, Cambridge, 2005).
Google Scholar 

6.
Seyfarth, R. M. & Cheney, D. L. Signalers and receivers in animal communication. Annu. Rev. Psychol. 54(1), 145–173 (2003).
PubMed  Google Scholar 

7.
Mitani, M. Voiceprint identification and its application to sociological studies of wild Japanese monkeys (Macaca fuscata yakui). Primates 27(4), 397–412 (1986).
Google Scholar 

8.
Kulahci, I. G., Rubenstein, D. I. & Ghazanfar, A. A. Lemurs groom-at-a-distance through vocal networks. Anim. Behav. 110, 179–186 (2015).
Google Scholar 

9.
Sugiura, H. Temporal and acoustic correlates in vocal exchange of coo calls in Japanese macaques. Behaviour 124(3–4), 207–225 (1993).
Google Scholar 

10.
Stoeger-Horwath, A. S. et al. Call repertoire of infant African elephants: first insights into the early vocal ontogeny. J. Acoust. Soc. Am. 121(6), 3922–3931 (2007).
ADS  PubMed  PubMed Central  Google Scholar 

11.
Soltis, J., Leong, K. & Savage, A. African elephant vocal communication I: antiphonal calling behaviour among affiliated females. Anim. Behav. 70(3), 579–587 (2005).
Google Scholar 

12.
Nakahara, F. & Miyazaki, N. Vocal exchanges of signature whistles in bottlenose dolphins (Tursiops truncatus). J. Ethol. 29(2), 309–320 (2011).
Google Scholar 

13.
Sugiura, H. Vocal exchange of coo calls in Japanese macaques. In Primate Origins of Human Cognition and Behavior (ed. Matsuzawa, T.) 135–154 (Springer, Tokyo, 2008).
Google Scholar 

14.
Greeno, N. C. & Semple, S. Sex differences in vocal communication among adult rhesus macaques. Evol. Hum. Behav. 30(2), 141–145 (2009).
Google Scholar 

15.
Sayigh, L. S. et al. Individual recognition in wild bottlenose dolphins: a field test using playback experiments. Anim. Behav. 57(1), 41–50 (1999).
CAS  PubMed  Google Scholar 

16.
Bauers, K. A. A functional analysis of staccato grunt vocalizations in the stumptailed macaque (Macaca arctoides). Ethology 94(2), 147–161 (1993).
Google Scholar 

17.
De Waal, F. B. & Bauers, K. “ Coo” vocalizations in stumptailed macaques: a controlled functional analysis. Behaviour 119(1–2), 143–160 (1991).
Google Scholar 

18.
Silk, J. B., Kaldor, E. & Boyd, R. Cheap talk when interests conflict. Anim. Behav. 59(2), 423–432 (2000).
CAS  PubMed  Google Scholar 

19.
Cheney, D. L. & Seyfarth, R. M. Reconciliatory grunts by dominant female baboons influence victims’ behaviour. Anim. Behav. 54(2), 409–418 (1997).
CAS  PubMed  Google Scholar 

20.
Faragó, T., Townsend, S. & F. Range. The information content of wolf (and dog) social communication. In Biocommunication of Animals. 41–62 (Springer, 2014).

21.
Bradbury, J. & Vehrencamp, S. Communication networks. In Principles of Animal Communication. 611–651 (2015).

22.
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84(5), 1144–1163 (2015).
PubMed  PubMed Central  Google Scholar 

23.
Darwin, C. The Descent of Man: and Selection in Relation to Sex (John Murray, Albemarle Street, 1888).
Google Scholar 

24.
Andersson, M. Sexual Selection Vol. 72 (Princeton University Press, Princeton, 1994).
Google Scholar 

25.
Bene, J. & Zuberbueler, K. Sex differences in the use of vocalizations in wild olive Colobus monkeys. Eur. J. Sci. Res. 25(2), 266–279 (2009).
Google Scholar 

26.
Lemasson, A. et al. Age-and sex-dependent contact call usage in Japanese macaques. Primates 54(3), 283–291 (2013).
PubMed  Google Scholar 

27.
Wright, S. L. & Brown, R. E. Sex differences in ultrasonic vocalizations and coordinated movement in the California mouse (Peromyscus californicus). Behav. Proc. 65(2), 155–162 (2004).
Google Scholar 

28.
Warren, M. R. et al. Sex differences in vocal communication of freely interacting adult mice depend upon behavioral context. PLoS ONE 13(9), e0204527 (2018).
PubMed  PubMed Central  Google Scholar 

29.
Bouchet, H. et al. Sex differences in the vocal repertoire of adult red-capped mangabeys (Cercocebus torquatus): a multi-level acoustic analysis. Am. J. Primatol. 72(4), 360–375 (2010).
PubMed  Google Scholar 

30.
Bouchet, H., Blois-Heulin, C. & Lemasson, A. Age-and sex-specific patterns of vocal behavior in De Brazza’s monkeys (Cercopithecus neglectus). Am. J. Primatol. 74(1), 12–28 (2012).
PubMed  Google Scholar 

31.
Green, S. M. Sex differences and age gradations in vocalizations of Japanese and lion-tailed monkeys (Macaca fuscata and Macaca silenus). Am. Zool. 21(1), 165–183 (1981).
Google Scholar 

32.
Bowyer, R. T. & Kitchen, D. W. Sex and age-class differences in vocalizations of Roosevelt elk during rut. Am. Midl. Nat. 118(2), 225–235 (1987).
Google Scholar 

33.
Fernandez-Vargas, M. & Johnston, R. E. Ultrasonic vocalizations in golden hamsters (Mesocricetus auratus) reveal modest sex differences and nonlinear signals of sexual motivation. PLoS ONE 10(2), e0116789 (2015).
PubMed  PubMed Central  Google Scholar 

34.
Shimizu, M. Vocalizations of feral cats: sexual differences in the breeding season. Mamm. Study 26(2), 85–92 (2001).
Google Scholar 

35.
Rendall, D. et al. Sex differences in the acoustic structure of vowel-like grunt vocalizations in baboons and their perceptual discrimination by baboon listeners. J. Acoust. Soc. Am. 115(1), 411–421 (2004).
ADS  PubMed  Google Scholar 

36.
Smith, A. S. et al. Production and perception of sex differences in vocalizations of Wied’s black-tufted-ear marmosets (Callithrix kuhlii). Am. J. Primatol. 71(4), 324–332 (2009).
PubMed  PubMed Central  Google Scholar 

37.
Miller, C. T., Scarl, J. & Hauser, M. D. Sensory biases underlie sex differences in tamarin long call structure. Anim. Behav. 68(4), 713–720 (2004).
Google Scholar 

38.
Grilliot, M. E., Burnett, S. C. & Mendonça, M. T. Sexual dimorphism in big brown bat (Eptesicus fuscus) ultrasonic vocalizations is context dependent. J. Mammal. 90(1), 203–209 (2009).
Google Scholar 

39.
Baotic, A. & Stoeger, A. S. Sexual dimorphism in African elephant social rumbles. PLoS ONE 12(5), e0177411 (2017).
PubMed  PubMed Central  Google Scholar 

40.
Marler, P. & Dufty, A. Pickert R (1986) Vocal communication in the domestic chicken: II. Is a sender sensitive to the presence and nature of a receiver?. Anim. Behav. 34, 194–198 (1986).
Google Scholar 

41.
Bell, M. B. Receiver identity modifies begging intensity independent of need in banded mongoose (Mungos mungo) pups. Behav. Ecol. Sociobiol. 19(6), 1087–1094 (2008).
Google Scholar 

42.
Leighty, K. A. et al. Rumble vocalizations mediate interpartner distance in African elephant Loxodonta africana. Anim. Behav. 76(5), 1601–1608 (2008).
Google Scholar 

43.
Fedurek, P. et al. Pant hoot chorusing and social bonds in male chimpanzees. Anim. Behav. 86(1), 189–196 (2013).
Google Scholar 

44.
Giacoma, C. et al. Sex differences in the song of Indri indri. Int. J. Primatol. 31(4), 539–551 (2010).
Google Scholar 

45.
Emslie, R. Ceratotherium simum. The IUCN Red List of Threatened Species 2012 (2012).

46.
Owen-Smith, R. N. The Behavioural Ecology of the White Rhinoceros (University of Wisconsin Madison, Madison, 1973).
Google Scholar 

47.
Estes, R.D. Rhinoceroses—Family Rhinocerotidae. In The Behavior Guide to African Mammals—Including Hoofed Mammals, Carnivores, Primates. 228–235 (University of California Press Berkeley, 1991).

48.
Penny, M. Rhinos: Endangered Species (Christopher Helm Publishers, London, 1987).
Google Scholar 

49.
Pienaar, D. Social organization and behaviour of the white rhinoceros. In Rhinos as Game Ranch Animals. Proceedings of a Symposium. Onderstepoort, Republic of South Africa: South African Veterinary Association. p. (1994).

50.
Owen-Smith, R. N. Territoriality in the white rhinoceros (Ceratotherium simum). Nat. Commun. 231(5301), 294–296 (1971).
CAS  Google Scholar 

51.
Shrader, A. M. & Owen-Smith, R. N. The role of companionship in the dispersal of white rhinoceroses (Ceratotherium simum). Behav. Ecol. Sociobiol. 52(3), 255–261 (2002).
Google Scholar 

52.
Policht, R. et al. The vocal repertoire in northern white rhinoceros Ceratotherium simum cottoni as recorded in the last surviving herd. Bioacoustics 18(1), 69–96 (2008).
Google Scholar 

53.
Linn, S. N., Boeer, M. & Scheumann, M. First insights into the vocal repertoire of infant and juvenile Southern white rhinoceros. PLoS ONE 13(3), e0192166 (2018).
PubMed  PubMed Central  Google Scholar 

54.
Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton University Press, Princeton, 2008).
Google Scholar 

55.
Castles, M. et al. Social networks created with different techniques are not comparable. Anim. Behav. 96, 59–67 (2014).
Google Scholar 

56.
Altmann, J. Observational study of behavior: sampling methods. Behaviour 49(3), 227–266 (1974).
CAS  PubMed  Google Scholar 

57.
Noldus, L. The observer: a software system for collection and analysis of observational data. Behav. Res. Methods Instrum. Comput. 23(3), 415–429 (1991).
Google Scholar 

58.
Landis, J. R. & Koch, G. G. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33(2), 363–374 (1977).
CAS  PubMed  MATH  Google Scholar 

59.
Team, R. RStudio: integrated development for R. RStudio Inc, Boston, MA 42, 14 (2015).
Google Scholar 

60.
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. In Third international AAAI Conference on Weblogs and Social Media (2009).

61.
Wey, T. et al. Social network analysis of animal behaviour: a promising tool for the study of sociality. Anim. Beahv. 75(2), 333–344 (2008).
Google Scholar 

62.
Bradbury, J.W. & Vehrencamp, S.L. Communication networks. In Principles of Animal Communication 612–650 (Sinauer Associates, Inc., 2011).

63.
Cinková, I. & Policht, R. Contact calls of the northern and southern white rhinoceros allow for individual and species identification. PLoS ONE 9(6), e98475 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

64.
Cinková, I. & Policht, R. Sex and species recognition by wild male southern white rhinoceros using contact pant calls. Anim. Cognit. 19(2), 375–386 (2016).
Google Scholar 

65.
Neumann, C. et al. Loud calls in male crested macaques, Macaca nigra: a signal of dominance in a tolerant species. Anim. Behav. 79(1), 187–193 (2010).
Google Scholar 

66.
Herrera, E. A. & Macdonald, D. W. Aggression, dominance, and mating success among capybara males (Hydrochaeris hydrochaeris). Behav. Ecol. Sociobiol. 4(2), 114–119 (1993).
Google Scholar 

67.
East, M. L. & Hofer, H. Loud calling in a female-dominated mammalian society: II. Behavioural contexts and functions of whooping of spotted hyaenas Crocuta crocuta. Anim. Behav. 42(4), 651–669 (1991).
Google Scholar 

68.
Kitchen, D. M. et al. Loud calls as indicators of dominance in male baboons (Papio cynocephalus ursinus). Behav. Ecol. Sociobiol. 53(6), 374–384 (2003).
Google Scholar 

69.
Hasiniaina, A. F. et al. High frequency/ultrasonic communication in a critically endangered nocturnal primate, Claire’s mouse lemur (Microcebus mamiratra). Am. J. Primatol. 80(6), e22866 (2018).
PubMed  Google Scholar 

70.
Pasch, B. et al. Androgen-dependent male vocal performance influences female preference in Neotropical singing mice. Anim. Behav. 82(2), 177–183 (2011).
Google Scholar 

71.
Zimmermann, E. Castration affects the emission of an ultrasonic vocalization in a nocturnal primate, the grey mouse lemur (Microcebus murinus). Physiol. Behav. 60(3), 693–697 (1996).
CAS  PubMed  Google Scholar 

72.
Leong, K. et al. The use of low-frequency vocalizations in African elephant (Loxodonta africana) reproductive strategies. Horm. Behav. 43(4), 433–443 (2003).
CAS  PubMed  Google Scholar 

73.
Lindburg, D., Czekala, N. & Swaisgood, R. R. Hormonal and behavioral relationships during estrus in the giant panda. Zoo Biol. 20(6), 537–543 (2001).
CAS  Google Scholar 

74.
Wielebnowski, N. & Brown, J. L. Behavioral correlates of physiological estrus in cheetahs. Zoo Biol. 17(3), 193–209 (1998).
Google Scholar 

75.
Cinková, I. & Bičík, V. Social and reproductive behaviour of critically endangered northern white rhinoceros in a zoological garden. Mamm. Biol. 78(1), 50–54 (2013).
Google Scholar 

76.
Patton, F., Campbell, P. & Genade, A. The development of White Rhino social organisation at Ziwa Rhino Sanctuary. Pachyderm 57, 112–113 (2018).
Google Scholar 

77.
Owen-Smith, R. N. The social ethology of the white rhinoceros ceratotberium simum (Burchell 1817). Ethology 38(4), 337–384 (1975).
Google Scholar  More

1.
Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R. & Kushnir, Y. Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proc. Natl Acad. Sci. USA 112, 3241–3246 (2015).
CAS  Google Scholar 
2.
Gleick, P. H. Water, drought, climate change, and conflict in Syria. Weather Clim. Soc. 6, 331–340 (2014).
Google Scholar 

3.
De Châtel, F. The role of drought and climate change in the Syrian uprising: untangling the triggers of the revolution. Middle East. Stud. 50, 521–535 (2014).
Google Scholar 

4.
Selby, J., Dahi, O. S., Fröhlich, C. & Hulme, M. Climate change and the Syrian civil war revisited. Polit. Geogr. 60, 232–244 (2017).
Google Scholar 

5.
Myers, N. Environmental refugees: a growing phenomenon of the 21st century. Phil. Trans. R. Soc. Lond. B 357, 609–613 (2002).
Google Scholar 

6.
Renaud, F., Bogardi, J. J., Dun, O. & Warner, K. Control, Adapt or Flee: How to Face Environmental Migration? InterSecTions, Publication Series of United Nations University, ENS Vol. 5 (United Nations University Institute for Environment and Human Security, 2007).

7.
Stern, N. The Economics of Climate Change: The Stern Review (Cambridge Univ. Press, 2006).

8.
Biermann, F. & Boas, L. Preparing for a warmer world: towards a global governance system to protect climate refugees. Glob. Environ. Polit. https://doi.org/10.1162/glep.2010.10.1.60 (2010).

9.
Cattaneo, C. et al. Human migration in the era of climate change. Rev. Environ. Econ. Policy 13, 189–206 (2019).
Google Scholar 

10.
Berlemann, M. & Steinhardt, M. F. Climate change, natural disasters, and migration—a survey of the empirical evidence. CESifo Econ. Stud. 63, 353–385 (2017).
Google Scholar 

11.
Hunter, L. M., Luna, J. K. & Norton, R. M. Environmental dimensions of migration. Annu. Rev. Sociol. 41, 377–397 (2015).
Google Scholar 

12.
Piguet, E. Linking climate change, environmental degradation, and migration: a methodological overview. Wiley Interdiscip. Rev. Clim. Change 1, 517–524 (2010).
Google Scholar 

13.
Borderon, M. et al. Migration influenced by environmental change in Africa: a systematic review of empirical evidence. Demogr. Res. 41, 491–544 (2019).
Google Scholar 

14.
Black, R., Stephen, R., Bennett, G., Thomas, S. M. & Beddington, J. R. Migration as adaptation. Nature 478, 447–449 (2011).
CAS  Google Scholar 

15.
Barrios, S., Bertinelli, L. & Strobl, E. Climatic change and rural-urban migration: the case of sub-Saharan Africa. J. Urban Econ. 60, 357–371 (2006).
Google Scholar 

16.
Naudé, W. Natural disasters and international migration from sub-Saharan Africa. Migrat. Lett. 6, 165–176 (2009).
Google Scholar 

17.
Ruyssen, I. & Rayp, G. Determinants of intraregional migration in sub-Saharan Africa 1980-2000. J. Dev. Stud. 50, 426–443 (2014).
Google Scholar 

18.
Backhaus, A., Martinez-Zarzoso, I. & Muris, C. Do climate variations explain bilateral migration? A gravity model analysis. IZA J. Migrat. 4, 3 (2015).
Google Scholar 

19.
Beine, M. & Parsons, C. Climatic factors as determinants of international migration. Scand. J. Econ. 117, 723–767 (2015).
Google Scholar 

20.
Coniglio, N. D. & Pesce, G. Climate variability and international migration: an empirical analysis. Environ. Dev. Econ. 20, 434–468 (2015).
Google Scholar 

21.
Ghimire, R., Ferreira, S. & Dorfman, J. H. Flood-induced displacement and civil conflict. World Dev. 66, 614–628 (2015).
Google Scholar 

22.
Cai, R., Feng, S., Oppenheimer, M. & Pytlikova, M. Climate variability and international migration: the importance of the agricultural linkage. J. Environ. Econ. Manage. 79, 135–151 (2016).
Google Scholar 

23.
Cattaneo, C. & Peri, G. The migration response to increasing temperatures. J. Dev. Econ. 122, 127–146 (2016).
Google Scholar 

24.
Maurel, M. & Tuccio, M. Climate instability, urbanisation and international migration. J. Dev. Stud. 52, 735–752 (2016).
Google Scholar 

25.
Beine, M. & Parsons, C. R. Climatic factors as determinants of international migration: Redux. CESifo Econ. Stud. 63, 386–402 (2017).
Google Scholar 

26.
Cattaneo, C. & Bosetti, V. Climate-induced international migration and conflicts. CESifo Econ. Stud. 63, 500–528 (2017).
Google Scholar 

27.
Reuveny, R. & Moore, W. H. Does environmental degradation influence migration? Emigration to developed countries in the late 1980s and 1990s. Soc. Sci. Q. 90, 461–479 (2009).
Google Scholar 

28.
Damette, O. & Gittard, M. Changement climatique et migrations: les transferts de fonds des migrants comme amortisseurs? Mondes Dev. 179, 85 (2017).
Google Scholar 

29.
Gröschl, J. & Steinwachs, T. Do natural hazards cause international migration? CESifo Econ. Stud. 63, 445–480 (2017).
Google Scholar 

30.
Henderson, J. V., Storeygard, A. & Deichmann, U. Has climate change driven urbanization in Africa? J. Dev. Econ. 124, 60–82 (2017).
Google Scholar 

31.
Mahajan, P. & Yang, D. Taken by Storm: Hurricanes, Migrant Networks, and U.S. Immigration Working Paper 23756 (NBER, 2017); https://doi.org/10.3386/w23756

32.
Marchiori, L., Maystadt, J.-F. & Schumacher, I. Is environmentally induced income variability a driver of human migration? Migr. Dev. 6, 33–59 (2017).
Google Scholar 

33.
Missirian, A. & Schlenker, W. Asylum applications respond to temperature fluctuations. Science 358, 1610–1614 (2017).
CAS  Google Scholar 

34.
Spencer, N. & Urquhart, M.-A. Hurricane strikes and migration: evidence from storms in central America and the caribbean. Weather Clim. Soc. 10, 569–577 (2018).
Google Scholar 

35.
Peri, G. & Sasahara, A. The Impact of Global Warming in Rural-Urban Migrations: Evidence from Global Big Data Working Paper 25728 (NBER, 2019).

36.
Wesselbaum, D. & Aburn, A. Gone with the wind: international migration. Glob. Planet. Change 178, 96–109 (2019).
Google Scholar 

37.
Naudé, W. The determinants of migration from sub-Saharan African countries. J. Afr. Econ. 19, 330–356 (2010).
Google Scholar 

38.
Alexeev, A., Good, D. H. & Reuveny, R. Weather-Related Disasters and International Migration (Indiana Univ., 2011); http://www.umdcipe.org/conferences/Maastricht/conf_papers/Papers/Effects_of_Natural_Disasters.pdf

39.
Bettin, G. & Nicolli, F. Does Climate Change Foster Emigration from Less Developed Countries? Evidence from Bilateral Data Working Paper 10 (Univ. degli Stud. di Ferrara, 2012).

40.
Brückner, M. Economic growth, size of the agricultural sector, and urbanization in Africa. J. Urban Econ. 71, 26–36 (2012).
Google Scholar 

41.
Gröschl, J. Climate change and the relocation of population. In Beiträge zur Jahrestagung des Vereins für Soc. 2012 Neue Wege und Herausforderungen für den Arbeitsmarkt des 21. Jahrhunderts – Sess. Migr. II, No. D03-V1, ZBW (Verein für Socialpolitik (German Economic Association), 2012).

42.
Hanson, G. H. & McIntosh, C. Birth rates and border crossings: Latin American migration to the US, Canada, Spain and the UK. Econ. J. 122, 707–726 (2012).
Google Scholar 

43.
Marchiori, L., Maystadt, J. F. & Schumacher, I. The impact of weather anomalies on migration in sub-Saharan Africa. J. Environ. Econ. Manage. 63, 355–374 (2012).
Google Scholar 

44.
Drabo, A. & Mbaye, L. M. Natural disasters, migration and education: an empirical analysis in developing countries. Environ. Dev. Econ. 20, 767–796 (2015).
Google Scholar 

45.
Black, R. et al. The effect of environmental change on human migration. Glob. Environ. Change 21, 3–11 (2011).
Google Scholar 

46.
Boas, I. et al. Climate migration myths. Nat. Clim. Change 9, 901–903 (2019).
Google Scholar 

47.
Black, R. et al. Foresight: Migration and Global Environmental Change. Future Challenges and Opportunities (The Government Office for Science, 2011).

48.
Veroniki, A. A. et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res. Synth. Methods 7, 55–79 (2016).
Google Scholar 

49.
Crespo Cuaresma, J., Fidrmuc, J. & Hake, M. Demand and supply drivers of foreign currency loans in CEECs: a meta-analysis. Econ. Syst. 38, 26–42 (2014).
Google Scholar 

50.
Hsiang, S. M., Burke, M. & Miguel, E. Quantifying the influence of climate on human conflict. Science 341, 1235367 (2013).
Google Scholar 

51.
Nawrotzki, R. J. & Bakhtsiyarava, M. International climate migration: evidence for the climate inhibitor mechanism and the agricultural pathway. Popul. Space Place 23, 1–16 (2016).
Google Scholar 

52.
Beine, M. & Jeusette, L. A Meta-Analysis of the Literature on Climate Change and Migration CREA Discussion Paper Series (Center for Research in Economic Analysis, Univ. Luxembourg, 2018).

53.
Auffhammer, M., Hsiangy, S. M., Schlenker, W. & Sobelz, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7, 181–198 (2013).
Google Scholar 

54.
Hsiang, S. Climate econometrics. Annu. Rev. Resour. Econ. 8, 43–75 (2016).
Google Scholar 

55.
Hugo, G. Future demographic change and its interactions with migration and climate change. Glob. Environ. Change 21(Suppl.), S21–S33 (2011).

56.
Martin, P. L. & Taylor, J. E. in Development Strategy, Employment and Migration: Insights from Models 43–62 (OECD, 1996).

57.
Feng, S., Krueger, A. B. & Oppenheimer, M. Linkages among climate change, crop yields and Mexico–US cross-border migration. Proc. Natl Acad. Sci. USA 107, 14257–14262 (2010).
CAS  Google Scholar 

58.
Mendelsohn, R. & Dinar, A. Climate change, agriculture, and developing countries: does adaptation matter? World Bank Res. Obs. 14, 277–293 (1999).
Google Scholar 

59.
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).
CAS  Google Scholar 

60.
Schlenker, W. & Lobell, D. B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).
Google Scholar 

61.
Abel, G. J., Brottrager, M., Crespo Cuaresma, J. & Muttarak, R. Climate, conflict and forced migration. Glob. Environ. Change 52, 239–249 (2019).
Google Scholar 

62.
Burke, M., Hsiang, S. M. & Miguel, E. Climate and conflict. Annu. Rev. Econ. 7, 577–617 (2015).
Google Scholar 

63.
Barnett, J. & Adger, W. N. Climate change, human security and violent conflict. Polit. Geogr. 26, 639–655 (2007).
Google Scholar 

64.
Schlenker, W., Hanemann, W. M. & Fisher, A. C. The impact of global warming on U.S. agriculture: An econometric analysis of optimal growing conditions. Rev. Econ. Stat. https://doi.org/10.1162/003465306775565684 (2006).

65.
Dimitrova, A. & Bora, J. K. Monsoon weather and early childhood health in India. PLoS ONE 15, e0231479 (2020).
CAS  Google Scholar 

66.
Muttarak, R. & Dimitrova, A. Climate change and seasonal floods: potential long-term nutritional consequences for children in Kerala, India. BMJ Glob. Health 4, e001215 (2019).
Google Scholar 

67.
Deschênes, O. & Greenstone, M. Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).
Google Scholar 

68.
Deschenes, O. Temperature, human health, and adaptation: a review of the empirical literature. Energ. Econ. https://doi.org/10.1016/j.eneco.2013.10.013 (2014).

69.
Burgess, R., Deschênes, O., Donaldson, D. & Greenstone, M. Weather, Climate Change and Death in India Working Paper (LSE, 2017).

70.
Zivin, J. G. & Neidell, M. Environment, health, and human capital. J. Econ. Lit. 51, 689–730 (2013).
Google Scholar 

71.
Gemenne, F. Why the numbers don’t add up: a review of estimates and predictions of people displaced by environmental changes. Glob. Environ. Change 21, 41–49 (2011).
Google Scholar 

72.
Findley, S. E. Does drought increase migration? A study of migration from rural Mali during the 1983–1985 drought. Int. Migr. Rev. 28, 539 (1994).
CAS  Google Scholar 

73.
Black, R., Arnell, N. W., Adger, W. N., Thomas, D. & Geddes, A. Migration, immobility and displacement outcomes following extreme events. Environ. Sci. Policy 27, S32–S43 (2013).
Google Scholar 

74.
Bohra-Mishra, P., Oppenheimer, M., Cai, R., Feng, S. & Licker, R. Climate variability and migration in the Philippines. Popul. Environ. 38, 286–308 (2017).
Google Scholar 

75.
Bardsley, D. K. & Hugo, G. J. Migration and climate change: examining thresholds of change to guide effective adaptation decision-making. Popul. Environ. 32, 238–262 (2010).
Google Scholar 

76.
Mertz, O., Mbow, C., Reenberg, A. & Diouf, A. Farmers’ perceptions of climate change and agricultural adaptation strategies in rural Sahel. Environ. Manage. 43, 804–816 (2009).
Google Scholar 

77.
Garcia, A. J., Pindolia, D. K., Lopiano, K. K. & Tatem, A. J. Modeling internal migration flows in sub-Saharan Africa using census microdata. Migr. Stud. 3, 89–110 (2015).
Google Scholar 

78.
Naudé, W. Conflict, Disasters and No Jobs: Reasons for International Migration from sub-Saharan Africa. WIDER Research Paper 85 (United Nations Univ., 2008).

79.
Dell, M., Jones, B. F. & Olken, B. A. What do we learn from the weather? The new climate-economy literature. J. Econ. Lit. 52, 740–798 (2014).
Google Scholar 

80.
Gemenne, F. & Blocher, J. How can migration serve adaptation to climate change? Challenges to fleshing out a policy ideal. Geogr. J. 183, 336–347 (2017).
Google Scholar 

81.
Zickgraf, C. Keeping people in place: political factors of (im)mobility and climate change. Soc. Sci. 8, 1–17 (2019).
Google Scholar 

82.
Ayeb-Karlsson, S. et al. I will not go, I cannot go: cultural and social limitations of disaster preparedness in Asia, Africa, and Oceania. Disasters 43, 752–770 (2019).
Google Scholar 

83.
Oakes, R. Culture, climate change and mobility decisions in pacific small island developing states. Popul. Environ. 40, 480–503 (2019).
Google Scholar 

84.
Gharad, B., Chowdhury, S. & Mobarak, A. M. Underinvestment in a profitable technology: the case of seasonal migration in Bangladesh. Econometrica 82, 1671–1748 (2014).
Google Scholar 

85.
Kniveton, D., Black, R. & Schmidt-Verkerk, K. Migration and climate change: towards an integrated assessment of sensitivity. Environ. Plan. A 43, 431–450 (2011).
Google Scholar 

86.
Hornbeck, R. The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. Am. Econ. Rev. 102, 1477–1507 (2012).
Google Scholar 

87.
Libecap, G. D. & Steckel, R. H. in The Economics of Climate Change: Adaptations Past and Present (eds Libecap, G. D. & Steckel, R. H.) 1–22 (Univ. Chicago Press, 2011).

88.
Hsiang, S. M. & Narita, D. Adaptation to cyclone risk: evidence from the global cross-section. Clim. Change Econ. 03, 1250011 (2012).
Google Scholar 

89.
Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).
Google Scholar 

90.
Hedges, Larry V. & Olkin, I. Statistical Method for Meta-Analysis (Academic Press, 1998).

91.
Lipsey, M. W. & Wilson, D. B. Practical meta-analysis (SAGE Publications, 2001).

92.
Hedges, L. V. & Olkin, I. Vote-counting methods in research synthesis. Psychol. Bull. 88, 359–369 (1980).
Google Scholar 

93.
Combs, J. G., Ketchen, D. J., Crook, T. R. & Roth, P. L. Assessing cumulative evidence within ‘macro’ research: why meta-analysis should be preferred over vote counting. J. Manage. Stud. 48, 178–197 (2011).
Google Scholar 

94.
Beine, M., Bertoli, S. & Fernández-Huertas Moraga, J. A practitioners’ guide to gravity models of international migration. World Econ. 39, 496–512 (2016).
Google Scholar 

95.
Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics (Princeton Univ. Press, 2009).

96.
Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. https://doi.org/10.1257/mac.4.3.66 (2012).

97.
Bohra-Mishra, P., Oppenheimer, M. & Hsiang, S. M. Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proc. Natl Acad. Sci. USA 111, 9780–9785 (2014).
CAS  Google Scholar 

98.
World Development Indicators (The World Bank, 2019).

99.
Marshall, M. G. Major Episodes of Political Violence (MEPV) and Conflict Regions, 1946–2018 (Center for Systemic Peace, 2019).

100.
Sundberg, R. & Melander, E. Introducing the UCDP georeferenced event dataset. J. Peace Res. 50, 523–532 (2013).
Google Scholar 

101.
Högbladh, S. UCDP GED Codebook Version 19.1 (Department of Peace and Conflict Research, Uppsala Univ., 2019).

102.
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 

103.
EM-DAT: The Emergency Events Database (Centre for Research on the Epidemiology of Disasters, 2020); www.emdat.be

104.
Bell, M. et al. Internal migration data around the world: assessing contemporary practice. Popul. Space Place 21, 1–17 (2015).
Google Scholar 

105.
Bell, M. & Charles-Edwards, E. Cross-National Comparisons of Internal Migration: An Update of Global Patterns and Trends Technical Paper 2013/1 (United Nations, 2013).

106.
Chindarkar, N. Gender and climate change-induced migration: proposing a framework for analysis. Environ. Res. Lett. 7, 025601 (2012).
Google Scholar 

107.
Hoffmann, R., Dimitrova, A., Muttarak, R., Crespo Cuaresma, J. & Peisker, J. A meta-analysis of country level studies on environmental change and migration | replication data and code. Harvard Dataverse, V1 https://doi.org/10.7910/DVN/HYRXVV (2020).

108.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.r-project.org/ More

1.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
CAS  Google Scholar 
2.
Grooten, M. & Almond, R. E. A. (eds) Living Planet Report ‒ 2018: Aiming Higher (WWF, 2018).

3.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed  PubMed Central  Google Scholar 

4.
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
CAS  PubMed  Google Scholar 

5.
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
CAS  PubMed  Google Scholar 

6.
Newbold, T. et al. Climate and land-use change homogenise terrestrial biodiversity, with consequences for ecosystem functioning and human well-being. Emerg. Top. Life Sci. 3, 207–219 (2019).
Google Scholar 

7.
Nicholson, E. et al. Scenarios and models to support global conservation targets. Trends Ecol. Evol. 34, 57–68 (2019).
PubMed  Google Scholar 

8.
Ferrier, S. et al. (eds) The Methodological Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2016).

9.
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).
Google Scholar 

10.
Marshall, L. et al. The interplay of climate and land use change affects the distribution of EU bumblebees. Glob. Change Biol. 24, 101–116 (2018).
Google Scholar 

11.
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
PubMed  PubMed Central  Google Scholar 

12.
Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).
Google Scholar 

13.
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
Google Scholar 

14.
Araújo, M. B., Alagador, D., Cabeza, M., Nogués-Bravo, D. & Thuiller, W. Climate change threatens European conservation areas. Ecol. Lett. 14, 484–492 (2011).
PubMed  PubMed Central  Google Scholar 

15.
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
CAS  PubMed  Google Scholar 

16.
Alkemade, R. et al. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390 (2009).
Google Scholar 

17.
Martins, I. S. & Pereira, H. M. Improving extinction projections across scales and habitats using the countryside species-area relationship. Sci. Rep. 7, 12899 (2017).
PubMed  PubMed Central  Google Scholar 

18.
Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).
PubMed  PubMed Central  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  Google Scholar 

20.
Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).
Google Scholar 

21.
Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).
CAS  PubMed  Google Scholar 

22.
Stevens, G. C. The latitudinal gradient in geographic range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).
Google Scholar 

23.
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).
Google Scholar 

24.
Forister, M. L. et al. The global distribution of diet breadth in insect herbivores. Proc. Natl Acad. Sci. USA 112, 442–447 (2015).
CAS  PubMed  Google Scholar 

25.
Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. Lond. B Biol. Sci. 280, 20122131 (2013).
Google Scholar 

26.
Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).
Google Scholar 

27.
Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).
Google Scholar 

28.
Wiersma, P., Munoz-Garcia, A., Walker, A. & Williams, J. B. Tropical birds have a slow pace of life. Proc. Natl Acad. Sci. USA 104, 9340–9345 (2007).
CAS  PubMed  Google Scholar 

29.
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
CAS  PubMed  Google Scholar 

30.
Orme, C. D. L. et al. Distance to range edge determines sensitivity to deforestation. Nat. Ecol. Evol. 3, 886–891 (2019).
PubMed  Google Scholar 

31.
Frishkoff, L. O., Hadly, E. A. & Daily, G. C. Thermal niche predicts tolerance to habitat conversion in tropical amphibians and reptiles. Glob. Change Biol. 21, 3901–3916 (2015).
Google Scholar 

32.
Frishkoff, L. O. et al. Climate change and habitat conversion favour the same species. Ecol. Lett. 19, 1081–1090 (2016).
PubMed  Google Scholar 

33.
Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).
Google Scholar 

34.
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).
PubMed  Google Scholar 

35.
Williams, J. J., Bates, A. E. & Newbold, T. Human‐dominated land uses favour species affiliated with more extreme climates, especially in the tropics. Ecography 43, 391–405 (2020).
Google Scholar 

36.
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 

37.
Srinivasan, U., Elsen, P. R. & Wilcove, D. S. Annual temperature variation influences the vulnerability of montane bird communities to land‐use change. Ecography 42, 2084–2094 (2019).
Google Scholar 

38.
Newbold, T. et al. Global patterns of terrestrial assemblage turnover within and among land uses. Ecography 39, 1151–1163 (2016).
Google Scholar 

39.
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
Google Scholar 

40.
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
Google Scholar 

41.
Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
CAS  Google Scholar 

42.
Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A. Pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).
PubMed  PubMed Central  Google Scholar 

43.
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Google Scholar 

44.
Fu, B., Wang, J., Chen, L. & Qiu, Y. The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau, China. Catena 54, 197–213 (2003).
Google Scholar 

45.
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).
Google Scholar 

46.
Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).
CAS  PubMed  Google Scholar 

47.
García-Vega, D. & Newbold, T. Assessing the effects of land use on biodiversity in the world’s drylands and Mediterranean environments. Biodivers. Conserv. 29, 393–408 (2020).
Google Scholar 

48.
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).
CAS  PubMed  Google Scholar 

49.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
CAS  PubMed  Google Scholar 

50.
Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).
PubMed  PubMed Central  Google Scholar 

51.
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).
Google Scholar 

52.
Newbold, T., Sanchez-Ortiz, K., De Palma, A., Hill, S. L. L. & Purvis, A. Reply to ‘The biodiversity intactness index may underestimate losses’. Nat. Ecol. Evol. 3, 864–865 (2019).
PubMed  Google Scholar 

53.
Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744 (2017).
CAS  PubMed  Google Scholar 

54.
The IUCN Red List of Threatened Species Version 2013.7 (IUCN, 2013); http://www.iucnredlist.org/

55.
Bird Species Distribution Maps of the World Version 2.0 (BirdLife International & NatureServe, 2012); http://www.birdlife.org/datazone/info/spcdownload

56.
Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).
PubMed  PubMed Central  Google Scholar 

57.
Zero Draft of the Post-2020 Global Biodiversity Framework Resolution CBD/WG2020/2/3 (Convention on Biological Diversity, 2020).

58.
Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).
CAS  PubMed  Google Scholar 

59.
Kissling, W. D., Sekercioglu, C. H. & Jetz, W. Bird dietary guild richness across latitudes, environments and biogeographic regions. Glob. Ecol. Biogeogr. 21, 328–340 (2012).
Google Scholar 

60.
Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).
PubMed  Google Scholar 

61.
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
PubMed  PubMed Central  Google Scholar 

62.
Terrestrial Ecoregions of the World (The Nature Conservancy, 2009); http://maps.tnc.org/gis_data.html

63.
Hudson, L. N. et al. Dataset: The 2016 Release of the PREDICTS Database (Natural History Museum Data Portal, 2016); https://doi.org/10.5519/0066354

64.
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).
Google Scholar 

65.
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2008).
Google Scholar 

66.
Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).
Google Scholar 

67.
Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: a call for caution. Glob. Ecol. Biogeogr. 26, 930–941 (2017).
Google Scholar 

68.
Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
Google Scholar 

69.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 

70.
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).
Google Scholar 

71.
Bivand, R. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
Google Scholar  More

1.
Barrett, R. D. H. & Hoekstra, H. E. Molecular spandrels: tests of adaptation at the genetic level. Nat. Rev. Genet. 12, 767–780 (2011).
CAS  PubMed  Google Scholar 
2.
Martin, A. & Orgogozo, V. The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation. Evolution 67, 1235–1250 (2013).
CAS  PubMed  Google Scholar 

3.
Barrett, R. D. H., Rogers, S. M. & Schluter, D. Natural selection on a major armor gene in threespine stickleback. Science 322, 255–257 (2008).
CAS  PubMed  Google Scholar 

4.
Barrett, R. D. H. et al. Linking a mutation to survival in wild mice. Science 363, 499–504 (2019).
CAS  PubMed  Google Scholar 

5.
Gratten, J. et al. A localized negative genetic correlation constrains microevolution of coat color in wild sheep. Science 319, 318–320 (2008).
CAS  PubMed  Google Scholar 

6.
Lamichhaney, S. et al. A beak size locus in Darwin’s finches facilitated character displacement during a drought. Science 352, 470–474 (2016).
CAS  PubMed  Google Scholar 

7.
Coberly, L. C. & Rausher, M. D. Pleiotropic effects of an allele producing white flowers in Ipomoea purpurea. Evolution 62, 1076–1085 (2008).
PubMed  Google Scholar 

8.
Korves, T. M., others. Fitness effects associated with the major flowering time gene FRIGIDA in Arabidopsis thaliana in the field. Am. Nat. 169, 141–157 (2007).
Google Scholar 

9.
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  Google Scholar 

10.
de Visser, J. C. F. T. & Elena, S. F. The causes of epistasis. Proc. R. Soc. B 278, 3617–3624 (2011).
PubMed  Google Scholar 

11.
Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Storz, J. F. Causes of molecular convergence and parallelism in protein evolution. Nat. Rev. Genet. 17, 239–250 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Kryazhimskiy, S., Rice, D. P., Jerison, E. R. & Desai, M. M. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

14.
Marques, D. A. et al. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nat. Ecol. Evol. 2, 1128–1138 (2018).
PubMed  PubMed Central  Google Scholar 

15.
Natarajan, C. et al. Epistasis among adaptive mutations in deer mouse hemoglobin. Science 340, 1324–1327 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Dettman, J. R., Sirjusingh, C., Kohn, L. M. & Anderson, J. B. Incipient speciation by divergent adaptation and antagonistic epistasis in yeast. Nature 447, 585–588 (2007).
CAS  PubMed  Google Scholar 

17.
Orr, H. A. The population genetics of speciation— the evolution of hybrid incompatibilities. Genetics 139, 1805–1813 (1995).
CAS  PubMed  PubMed Central  Google Scholar 

18.
Gavrilets, S. Evolution and speciation on holey adaptive landscapes. Trends Ecol. Evol. 12, 307–312 (1997).
CAS  PubMed  Google Scholar 

19.
Schwander, T., Libbrecht, R. & Keller, L. Supergenes and complex phenotypes. Curr. Biol. 24, R288–R294 (2014).
CAS  PubMed  Google Scholar 

20.
Wilfert, L. & Schmid-Hempel, P. The genetic architecture of susceptibility to parasites. BMC Evol. Biol. 8, 187 (2008).
PubMed  PubMed Central  Google Scholar 

21.
Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).
CAS  PubMed  Google Scholar 

22.
Gavrilets, S. Fitness Landscapes and the Origin of Species (Princeton Univ. Press, 2004); https://doi.org/10.2307/j.ctv39x541

23.
Wright, S. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proc. Sixth Int. Congr. Genet. 1, 356–366 (1932).
Google Scholar 

24.
Lehner, B. Molecular mechanisms of epistasis within and between genes. Trends Genet. 27, 323–331 (2011).
CAS  PubMed  Google Scholar 

25.
Whitlock, M. C., Phillips, P. C., Moore, F. B. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995).
Google Scholar 

26.
Whitlock, M. C. Founder effects and peak shifts without genetic drift: adaptive peak shifts occur easily when environments fluctuate slightly. Evolution 51, 1044–1048 (1997).
PubMed  Google Scholar 

27.
Kingsolver, J. G. et al. The strength of phenotypic selection in natural populations. Am. Nat. 157, 245–261 (2001).
CAS  PubMed  Google Scholar 

28.
Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 89, 329–338 (2002).
CAS  PubMed  Google Scholar 

29.
Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).
CAS  PubMed  Google Scholar 

30.
Plucain, J. et al. Epistasis and allele specificity in the emergence of a stable polymorphism in Escherichia coli. Science 343, 1366–1369 (2014).
CAS  PubMed  Google Scholar 

31.
Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).
PubMed  PubMed Central  Google Scholar 

32.
Sandoval, C. P. Differential visual predation on morphs of Timema cristinae (Phasmatodeae:Timemidae) and its consequences for host range. Biol. J. Linn. Soc. 52, 341–356 (1994).
Google Scholar 

33.
Sandoval, C. P. The effects of the relative geographic scales of gene flow and selection on morph frequencies in the walking‐stick Timema cristinae. Evolution 48, 1866–1879 (1994).
PubMed  Google Scholar 

34.
Sandoval, C. P. & Nosil, P. Counteracting selective regimes and host preference evolution in ecotypes of two species of walking-sticks. Evolution 59, 2405–2413 (2005).
CAS  PubMed  Google Scholar 

35.
Comeault, A. A. et al. Selection on a genetic polymorphism counteracts ecological speciation in a stick insect. Curr. Biol. 25, 1975–1981 (2015).
CAS  PubMed  Google Scholar 

36.
Nosil, P. et al. Natural selection and the predictability of evolution in Timema stick insects. Science 359, 765–770 (2018).
CAS  PubMed  Google Scholar 

37.
Villoutreix, R. et al. Large-scale mutation in the evolution of a gene complex for cryptic coloration. Science 369, 460–466 (2020).
CAS  PubMed  Google Scholar 

38.
Lindtke, D. et al. Long-term balancing selection on chromosomal variants associated with crypsis in a stick insect. Mol. Ecol. 26, 6189–6205 (2017).
CAS  PubMed  Google Scholar 

39.
Endler, J. A. A framework for analysing colour pattern geometry: adjacent colours. Biol. J. Linn. Soc. 107, 233–253 (2012).
Google Scholar 

40.
Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. 41, 315–352 (1990).
Google Scholar 

41.
Hurvich, L. M. Color Vision (Sinauer Associates, 1981).

42.
Gompert, Z. et al. Experimental evidence for ecological selection on genome variation in the wild. Ecol. Lett. 17, 369–379 (2014).
PubMed  Google Scholar 

43.
Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

44.
Crawford, L., Zeng, P., Mukherjee, S. & Zhou, X. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genet. 13, e1006869 (2017).
PubMed  PubMed Central  Google Scholar 

45.
Comeault, A. A., Ferreira, C., Dennis, S., Soria-Carrasco, V. & Nosil, P. Color phenotypes are under similar genetic control in two distantly related species of Timema stick insect. Evolution 70, 1283–1296 (2016).
CAS  PubMed  Google Scholar 

46.
Nosil, P. & Crespi, B. J. Experimental evidence that predation promotes divergence in adaptive radiation. Proc. Natl Acad. Sci. USA 103, 9090–9095 (2006).
CAS  PubMed  Google Scholar 

47.
Rennison, D. J., Heilbron, K., Barrett, R. D. H. & Schluter, D. Discriminating selection on lateral plate phenotype and its underlying gene, ectodysplasin, in threespine stickleback. Am. Nat. 185, 150–156 (2015).
PubMed  Google Scholar 

48.
Wright, S. The shifting balance theory and macroevolution. Annu. Rev. Genet. 16, 1–19 (1982).
CAS  PubMed  Google Scholar 

49.
Coyne, J. A., Barton, N. H. & Turelli, M. Perspective: a critique of Sewall Wright’s shifting balance theory of evolution. Evolution 51, 643–671 (1997).
PubMed  Google Scholar 

50.
Wade, M. J. & Goodnight, C. J. Perspective: the theories of Fisher and Wright in the context of metapopulations: when nature does many small experiments. Evolution 52, 1537–1553 (1998).
PubMed  Google Scholar 

51.
Reimchen, T. E. Predator-induced cyclical changes in lateral plate frequencies of Gasterosteus. Behaviour 132, 1079–1094 (1995).
Google Scholar 

52.
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).

53.
Sackman, A. M. & Rokyta, D. R. Additive phenotypes underlie epistasis of fitness effects. Genetics 208, 339–348 (2018).
CAS  PubMed  Google Scholar 

54.
Knief, U. et al. Epistatic mutations under divergent selection govern phenotypic variation in the crow hybrid zone. Nat. Ecol. Evol. 3, 570–576 (2019).
PubMed  PubMed Central  Google Scholar 

55.
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  Google Scholar 

56.
Lewontin, R. C. The Genetic Basis of Evolutionary Change (Columbia Univ. Press, 1974).

57.
Scheffer, M. Critical Transitions in Nature and Society (Princeton Univ. Press, 2009).

58.
Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).
CAS  PubMed  Google Scholar 

59.
Parchman, T. L. et al. Genome-wide association genetics of an adaptive trait in lodgepole pine. Mol. Ecol. 21, 2991–3005 (2012).
CAS  PubMed  Google Scholar 

60.
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

61.
Soria-Carrasco, V. et al. Stick insect genomes reveal natural selection’s role in parallel speciation. Science 344, 738–742 (2014).
CAS  PubMed  Google Scholar 

62.
Guan, Y. & Stephens, M. Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Ann. Appl. Stat. 5, 1780–1815 (2011).
Google Scholar 

63.
Nosil, P. Reproductive isolation caused by visual predation on migrants between divergent environments. Proc. R. Soc. B 271, 1521–1528 (2004).
PubMed  Google Scholar 

64.
Nosil, P. et al. Genomic consequences of multiple speciation processes in a stick insect. Proc. R. Soc. B 279, 5058–5065 (2012).
PubMed  Google Scholar 

65.
Sandoval, C. P. Persistence of a walking-stick population (Phasmatoptera: Timematodea) after a wildfire. Southwest. Nat. 45, 123–127 (2000).
Google Scholar 

66.
Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4-8 (2018).

67.
Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).
PubMed  Google Scholar 

68.
Janzen, F. J. & Stern, H. S. Logistic regression for empirical studies of multivariate selection. Evolution 52, 1564–1571 (1998).
PubMed  Google Scholar 

69.
Zeugner, S. & Feldkircher, M. Bayesian model averaging employing fixed and flexible priors: the BMS package for R. J. Stat. Softw. 68, 1–37 (2015).
Google Scholar 

70.
Zellner, A. in Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti (eds Goel, P. & Zellner, A.) 233–243 (1986).

71.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).
Google Scholar 

72.
Weinberger, E. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol. Cybern. 63, 325–336 (1990).
Google Scholar 

73.
Vassilev, V. K., Fogarty, T. C. & Miller, J. F. Information characteristics and the structure of landscapes. Evol. Comput. 8, 31–60 (2000).
CAS  PubMed  Google Scholar 

74.
Kouyos, R. D. et al. Exploring the complexity of the HIV-1 fitness landscape. PLoS Genet. 8, e1002551–e1002551 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

75.
Malan, K. M. & Engelbrecht, A. P. A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf. Sci. 241, 148–163 (2013).
Google Scholar 

76.
Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet. 31, 24–33 (2015).
CAS  PubMed  Google Scholar 

77.
Poursoltan, S. & Neumann, F. in Evolutionary Constrained Optimization (eds Datta, R. & Deb, K.) 29–50 (Springer, 2015); https://doi.org/10.1007/978-81-322-2184-5_2

78.
Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

79.
Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–1342 (2013).
CAS  PubMed  Google Scholar 

80.
Endler, J. A. & Mielke, P. W. Comparing entire colour patterns as birds see them. Biol. J. Linn. Soc. 86, 405–431 (2005).
Google Scholar  More

1.
Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat Commun. 2018;9:870.
PubMed  PubMed Central  Google Scholar 
2.
Pulina G, Francesconi AHD, Stefanon B, Sevi A, Calamari L, Lacetera N, et al. Sustainable ruminant production to help feed the planet. Ital J Anim Sci. 2017;16:140–71.
Google Scholar 

3.
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8.
CAS  PubMed  Google Scholar 

4.
Haitjema CH, Gilmore SP, Henske JK, Solomon KV, de Groot R, Kuo A, et al. A parts list for fungal cellulosomes revealed by comparative genomics. Nat Microbiol. 2017;2:1–8.
Google Scholar 

5.
Solden LM, Naas AE, Roux S, Daly RA, Collins WB, Nicora CD et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat Microbiol. 2018;3. https://doi.org/10.1038/s41564-018-0225-4.

6.
Seshadri R, Leahy SC, Attwood GT, Teh KH, Lambie SC, Cookson AL et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat Biotechnol. 2018;36. https://doi.org/10.1038/nbt.4110.

7.
Solomon KV, Haitjema CH, Henske JK, Gilmore SP, Borges-Rivera D, Lipzen A, et al. Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes. Science. 2016;351:1192–5.
CAS  PubMed  PubMed Central  Google Scholar 

8.
Hanafy RA, Lanjekar VB, Dhakephalkar PK, Callaghan TM, Dagar SS, Griffith GW et al. Seven new Neocallimastigomycota genera from wild, zoo-housed, and domesticated herbivores greatly expand the taxonomic diversity of the phylum. Mycologia 2020:1–28. https://doi.org/10.1080/00275514.2019.1696619.

9.
Wilken SE, Swift CL, Podolsky IA, Lankiewicz TS, Seppälä S, O’Malley MA. Linking ‘omics’ to function unlocks the biotech potential of non-model fungi. Curr Opin Syst Biol. 2019;14:9–17.
Google Scholar 

10.
Seppälä S, Wilken SE, Knop D, Solomon KV, O’Malley MA. The importance of sourcing enzymes from non-conventional fungi for metabolic engineering and biomass breakdown. Metab Eng. 2017;44:45–59.
PubMed  Google Scholar 

11.
Podolsky IA, Seppälä S, Lankiewicz TS, Brown JL, Swift CL, O’Malley MA. Harnessing nature’s anaerobes for biotechnology and bioprocessing. Annu Rev Chem Biomol Eng. 2019;10:105–28.
CAS  PubMed  Google Scholar 

12.
Kumar S, Indugu N, Vecchiarelli B, Pitta DW. Associative patterns among anaerobic fungi, methanogenic archaea, and bacterial communities in response to changes in diet and age in the rumen of dairy cows. Front Microbiol. 2015;6:781.
PubMed  PubMed Central  Google Scholar 

13.
Nagaraja TG. Microbiology of the Rumen. In: Rumenology. Cham: Springer International Publishing; 2016. p. 39–61.

14.
Edwards JE, Forster RJ, Callaghan TM, Dollhofer V, Dagar SS, Cheng Y, et al. PCR and omics based techniques to study the diversity, ecology and biology of anaerobic fungi: insights, challenges and opportunities. Front Microbiol. 2017;8:1657.
PubMed  PubMed Central  Google Scholar 

15.
Paul SS, Bu D, Xu J, Hyde KD, Yu Z. A phylogenetic census of global diversity of gut anaerobic fungi and a new taxonomic framework. Fungal Divers. 2018;89:253–66.
Google Scholar 

16.
Hanafy RA, Elshahed MS, Liggenstoffer AS, Griffith GW, Youssef NH. Pecoramyces ruminantium, gen. nov., sp. nov., an anaerobic gut fungus from the feces of cattle and sheep. Mycologia. 2017;109:231–43.
PubMed  Google Scholar 

17.
Youssef NH, Couger MB, Struchtemeyer CG, Liggenstoffer AS, Prade RA, Najar FZ, et al. The genome of the anaerobic fungus Orpinomyces sp. strain C1A reveals the unique evolutionary history of a remarkable plant biomass degrader. Appl Environ Microbiol. 2013;79:4620–34.
CAS  PubMed  PubMed Central  Google Scholar 

18.
John Wallace R, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv 2019;5. https://doi.org/10.1126/sciadv.aav8391.

19.
Gordon GLR, Phillips MW. Removal of anaerobic fungi from the rumen of sheep by chemical treatment and the effect on feed consumption and in vivo fibre digestion. Lett Appl Microbiol. 1993;17:220–3.
Google Scholar 

20.
Söllinger A, Tveit T, Poulsen M, Noel J, Bengtsson M, Bernhardt J, et al. Holistic assessment of rumen microbiome dynamics through quantitative metatranscriptomics reveals multifunctional redundancy during key steps of anaerobic feed degradation. mSystems. 2018;3:1–19.
Google Scholar 

21.
Dai X, Tian Y, Li J, Luo Y, Liu D, Zheng H, et al. Metatranscriptomic analyses of plant cell wall polysaccharide degradation by microorganisms in the cow rumen. Appl Environ Microbiol. 2015;81:1375–86.
PubMed  PubMed Central  Google Scholar 

22.
Comtet-Marre S, Parisot N, Lepercq P, Chaucheyras-Durand F, Mosoni P, Peyretaillade E et al. Metatranscriptomics reveals the active bacterial and eukaryotic fibrolytic communities in the rumen of dairy cow fed a mixed diet. Front Microbiol. 2017;8. https://doi.org/10.3389/fmicb.2017.00067.

23.
Gruninger RJ, Nguyen TTM, Reid ID, Yanke JL, Wang P, Abbott DW, et al. Application of transcriptomics to compare the carbohydrate active enzymes that are expressed by diverse genera of anaerobic fungi to degrade plant cell wall carbohydrates. Front Microbiol. 2018;9:1581.
PubMed  PubMed Central  Google Scholar 

24.
Henske JK, Wilken SE, Solomon KV, Smallwood CR, Shutthanandan V, Evans JE, et al. Metabolic characterization of anaerobic fungi provides a path forward for bioprocessing of crude lignocellulose. Biotechnol Bioeng. 2018;115:874–84.
CAS  PubMed  Google Scholar 

25.
Morrison JM, Elshahed MS, Youssef NH. Defined enzyme cocktail from the anaerobic fungus Orpinomyces sp. Strain C1A effectively releases sugars from pretreated corn stover and switchgrass. Sci Rep. 2016;6:1–12.
Google Scholar 

26.
O’Malley MA, Theodorou MK, Kaiser CA. Evaluating expression and catalytic activity of anaerobic fungal fibrolytic enzymes native topiromyces sp E2 inSaccharomyces cerevisiae. Environ Prog Sustain Energy. 2012;31:37–46.
Google Scholar 

27.
Hess M, Sczyrba A, Egan R, Kim T-W, Chokhawala H, Schroth G, et al. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science. 2011;463:463–7.
Google Scholar 

28.
Piao H, Lachman M, Malfatti S, Sczyrba A, Knierim B, Auer M, et al. Temporal dynamics of fibrolytic and methanogenic rumen microorganisms during in situ incubation of switchgrass determined by 16S rRNA gene profiling. Front Microbiol. 2014;5:307.
PubMed  PubMed Central  Google Scholar 

29.
Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–805.
CAS  PubMed  Google Scholar 

30.
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.
CAS  PubMed  Google Scholar 

31.
Kunath BJ, Minniti G, Skaugen M, Hagen LH, Vaaje-Kolstad G, Eijsink VGH et al. Metaproteomics: sample preparation and methodological considerations. In: Capelo-Martínez JL. et al. editors. Emerging Sample Treatments in Proteomics. Advances in Experimental Medicine and Biology. Vol. 1073. Springer, Cham; 2019. p. 187–215.

32.
Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017; 2. https://doi.org/10.1038/s41564-017-0012-7.

33.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Google Scholar 

34.
Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;38:e132–e132.
PubMed  PubMed Central  Google Scholar 

35.
Deusch S, Camarinha-Silva A, Conrad J, Beifuss U, Rodehutscord M, Seifert J. A structural and functional elucidation of the rumen microbiome influenced by various diets and microenvironments. Front Microbiol. 2017;8:1605.
PubMed  PubMed Central  Google Scholar 

36.
Li F, Hitch TCA, Chen Y, Creevey CJ, Guan LL. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle 06 Biological Sciences 0604 Genetics 06 Biological Sciences 0605 Microbiology. Microbiome. 2019;7:6.
PubMed  PubMed Central  Google Scholar 

37.
Suen G, Weimer PJ, Stevenson DM, Aylward FO, Boyum J, Deneke J, et al. The complete genome sequence of fibrobacter succinogenes S85 reveals a cellulolytic and metabolic specialist. PLoS One. 2011;6:e18814.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Leahy SC, Kelly WJ, Altermann E, Ronimus RS, Yeoman CJ, Pacheco DM, et al. The genome sequence of the rumen methanogen methanobrevibacter ruminantium reveals new possibilities for controlling ruminant methane emissions. PLoS ONE. 2010;5:e8926.
PubMed  PubMed Central  Google Scholar 

39.
Mondo SJ, Dannebaum RO, Kuo RC, Louie KB, Bewick AJ, LaButti K, et al. Widespread adenine N6-methylation of active genes in fungi. Nat Genet. 2017;49:964–8.
CAS  PubMed  Google Scholar 

40.
Grigoriev IV, Nikitin R, Haridas S, Kuo A, Ohm R, Otillar R, et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 2014;42:D699–D704.
CAS  PubMed  Google Scholar 

41.
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
CAS  PubMed  PubMed Central  Google Scholar 

42.
Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56:564–77.
CAS  PubMed  Google Scholar 

43.
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
CAS  PubMed  PubMed Central  Google Scholar 

44.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Google Scholar 

45.
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–W245.
CAS  PubMed  PubMed Central  Google Scholar 

46.
Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, Termed MaxLFQ. Mol Cell Proteom. 2014;13:2513–26.
CAS  Google Scholar 

47.
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13:731–40.
CAS  PubMed  Google Scholar 

48.
Piao H, Meng Markillie L, Culley DE, Mackie RI, Hess M. Improved method for isolation of microbial RNA from biofuel feedstock for metatranscriptomics. Adv Microbiol. 2013;3:101–7.
CAS  Google Scholar 

49.
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
CAS  PubMed  Google Scholar 

50.
Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26:589–95.
PubMed  PubMed Central  Google Scholar 

51.
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.
CAS  PubMed  PubMed Central  Google Scholar 

52.
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–D495.
CAS  PubMed  Google Scholar 

53.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018. Available online at https://www.R-project.org/.

54.
Hagen LH, Frank JA, Zamanzadeh M, Eijsink VGH, Pope PB, Horn SJ et al. Quantitative metaproteomics highlight the metabolic contributions of uncultured phylotypes in a thermophilic anaerobic digester. Appl Environ Microbiol. 2017; 83. https://doi.org/10.1128/AEM.01955-16.

55.
Murphy CL, Youssef NH, Hanafy RA, Couger MB, Stajich JE, Wang Y et al. Horizontal gene transfer as an indispensable driver for evolution of neocallimastigomycota into a distinct gut-dwelling fungal lineage. Appl Environ Microbiol. 2019; 85. https://doi.org/10.1128/AEM.00988-19.

56.
Wang Y, Youssef NH, Couger MB, Hanafy RA, Elshahed MS, Stajich JE. Molecular dating of the emergence of anaerobic rumen fungi and the impact of laterally acquired genes. mSystems 2019; 4. https://doi.org/10.1128/msystems.00247-19.

57.
Shinkai T, Mitsumori M, Sofyan A, Kanamori H, Sasaki H, Katayose Y, et al. Comprehensive detection of bacterial carbohydrate-active enzyme coding genes expressed in cow rumen. Anim Sci J. 2016;87:1363–70.
CAS  PubMed  Google Scholar 

58.
Naas AE, Solden LM, Norbeck AD, Brewer H, Hagen LH, Heggenes IM, et al. ‘Candidatus Paraporphyromonas polyenzymogenes’ encodes multi-modular cellulases linked to the type IX secretion system. Microbiome. 2018;6:1–13.
Google Scholar 

59.
Parsiegla G, Reverbel C, Tardif C, Driguez H, Haser R. Structures of mutants of cellulase Cel48F of clostridium cellulolyticum in complex with long hemithiocellooligosaccharides give rise to a new view of the substrate pathway during processive action. J Mol Biol. 2008;375:499–510.
CAS  PubMed  Google Scholar 

60.
Steenbakkers PJM, Freelove A, Van Cranenbroek B, Sweegers BMC, Harhangi HR, Vogels GD, et al. The major component of the cellulosomes of anaerobic fungi from the genus Piromyces is a family 48 glycoside hydrolase. Mitochondrial DNA. 2002;13:313–20.
CAS  Google Scholar 

61.
Guimarães BG, Souchon H, Lytle BL, David Wu JH, Alzari PM. The crystal structure and catalytic mechanism of cellobiohydrolase celS, the major enzymatic component of the Clostridium thermocellum cellulosome. J Mol Biol. 2002;320:587–96.
PubMed  Google Scholar 

62.
Pope PB, Denman SE, Jones M, Tringe SG, Barry K, Malfatti SA, et al. Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores. Proc Natl Acad Sci. 2010;107:14793–8.
CAS  PubMed  Google Scholar 

63.
Qi M, Wang P, O’Toole N, Barboza PS, Ungerfeld E, Leigh MB, et al. Snapshot of the eukaryotic gene expression in muskoxen rumen—a metatranscriptomic approach. PLoS ONE. 2011;6:e20521.
CAS  PubMed  PubMed Central  Google Scholar 

64.
Benoit I, Coutinho PM, Schols HA, Gerlach JP, Henrissat B, de Vries RP. Degradation of different pectins by fungi: correlations and contrasts between the pectinolytic enzyme sets identified in genomes and the growth on pectins of different origin. BMC Genom. 2012;13:321.
CAS  Google Scholar 

65.
Shi H, Ding H, Huang Y, Wang L, Zhang Y, Li X, et al. Expression and characterization of a GH43 endo-arabinanase from Thermotoga thermarum. BMC Biotechnol. 2014;14:35.
PubMed  PubMed Central  Google Scholar 

66.
Israeli-Ruimy V, Bule P, Jindou S, Dassa B, Moraïs S, Borovok I, et al. Complexity of the Ruminococcus flavefaciens FD-1 cellulosome reflects an expansion of family-related protein-protein interactions. Sci Rep. 2017;7:42355.
CAS  PubMed  PubMed Central  Google Scholar 

67.
Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6:121–31.
CAS  PubMed  Google Scholar 

68.
Arntzen M, Várnai A, Mackie RI, Eijsink VGH, Pope PB. Outer membrane vesicles from S85 contain an array of carbohydrate-active enzymes with versatile polysaccharide-degrading capacity. Environ Microbiol. 2017;19:2701–14.
CAS  PubMed  Google Scholar 

69.
Devillard E, Goodheart DB, Karnati SKR, Bayer EA, Lamed R, Miron J, et al. Ruminococcus albus 8 mutants defective in cellulose degradation are deficient in two processive endocellulases, Cel48A and Cel9B, both of which possess a novel modular architecture. J Bacteriol. 2004;186:136–45.
CAS  PubMed  PubMed Central  Google Scholar 

70.
Vodovnik M, Duncan SH, Reid MD, Cantlay L, Turner K, Parkhill J, et al. Expression of Cellulosome Components and Type IV Pili within the Extracellular Proteome of Ruminococcus flavefaciens 007. PLoS ONE. 2013;8:e65333.
CAS  PubMed  PubMed Central  Google Scholar 

71.
Henske JK, Gilmore SP, Haitjema CH, Solomon KV, O’Malley MA. Biomass-degrading enzymes are catabolite repressed in anaerobic gut fungi. AIChE J. 2018;64:4263–70.
CAS  Google Scholar 

72.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
CAS  Google Scholar 

73.
Garcia-Vallvé S, Romeu A, Palau J. Horizontal gene transfer of glycosyl hydrolases of the rumen fungi. Mol Biol Evol. 2000;17:352–61.
PubMed  Google Scholar 

74.
Murphy CL, Youssef NH, Hanafy RA, Couger MB, Stajich JE, Wang Y et al. Horizontal gene transfer as an indispensable driver for evolution of Neocallimastigomycota into a distinct gutdwelling fungal lineage. Appl Environ Microbiol. 2019;85. https://doi.org/10.1128/AEM.00988-19.

75.
Hart EH, Creevey CJ, Hitch T, Kingston-Smith AH. Meta-proteomics of rumen microbiota indicates niche compartmentalisation and functional dominance in a limited number of metabolic pathways between abundant bacteria. Sci Rep. 2018;8. https://doi.org/10.1038/s41598-018-28827-7.

76.
Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–50.
CAS  PubMed  Google Scholar  More