Ecology

1.Partecke, J., Gwinner, E. & Bensch, S. Is urbanisation of European blackbirds (Turdus merula) associated with genetic differentiation?. J. Ornithol. 147, 549–552 (2006).Article 

Google Scholar 
2.Perrier, C. et al. Great tits and the city: Distribution of genomic diversity and gene–environment associations along an urbanization gradient. Evol. Appl. 11, 593–613 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Chace, J. F. & Walsh, J. J. Urban effects on native avifauna: A review. Landsc. Urban Plan. 74, 46–69 (2006).Article 

Google Scholar 
4.Evans, K. L. et al. Independent colonization of multiple urban centres by a formerly forest specialist bird species. Proc. R. Soc. B 276(1666), 2403–2410 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Björklund, M., Ruiz, I. & Senar, J. C. Genetic differentiation in the urban habitat: The great tits (Parus major) of the parks of Barcelona city. Biol. J. Linn. Soc. 99, 9–19 (2010).Article 

Google Scholar 
6.Crooks, K. R. & Sanjayan, M. A. Connectivity conservation: Maintaining connections for nature. In Connectivity Conservation (eds Crooks, K. R. & Sanjayan, M. A.) 1–20 (Cambridge University Press, Cambridge, 2006).
Google Scholar 
7.Evans, K. L., Chamberlain, D. E., Hatchwell, B. J., Gregory, R. D. & Gaston, K. J. What makes an urban bird?. Glob. Change Biol. 17, 32–44 (2011).ADS 
Article 

Google Scholar 
8.Seress, G. & Liker, A. Habitat urbanization and its effects on birds. Acta Zool. Acad. Sci. Hung. 61(4), 373–408 (2015).Article 

Google Scholar 
9.Miles, L. S., Rivkin, L. R., Johnson, M. T. J., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).PubMed 
Article 

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

Google Scholar 
11.Chamberlain, D. E. et al. Avian productivity in urban landscapes: A review and meta-analysis. Ibis 151, 1–18 (2009).Article 

Google Scholar 
12.Delaney, K. S., Riley, S. P. D. & Fisher, R. N. A rapid, strong, and convergent genetic response to urban habitat fragmentation in four divergent and widespread vertebrates. PLoS ONE 5(9), e12767 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Unfried, T. M., Hauser, L. & Marzluff, J. M. Effects of urbanization on Song Sparrow (Melospiza melodia) population connectivity. Conserv. Genet. 14(1), 41–53 (2013).Article 

Google Scholar 
14.Cureton, J. C. et al. Effects of urbanization on genetic diversity, gene flow, and population structure in the ornate box turtle (Terrapene ornato). Amphib-Reptil. 35, 87–97 (2014).Article 

Google Scholar 
15.Indykiewicz, P., Podlaszczuk, P., Janiszewska, A. & Minias, P. Extensive gene flow along the urban-rural gradient in a migratory colonial bird. J. Avian Biol. 49(6), e01723 (2018).Article 

Google Scholar 
16.Hurtado, G. & Mabry, K. E. Genetic structure of an abundant small mammal is influenced by low intensity urbanization. Conserv. Genet. 20, 705–715 (2019).CAS 
Article 

Google Scholar 
17.Khimoun, A. et al. Urbanization without isolation: The absence of genetic structure among cities and forests in the tiny acorn ant Temnothorax nylanderi. Biol. Lett. 16, 20190741 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Munshi-South, J., Zolnik, C. P. & Harris, S. E. Population genomics of the Anthropocene: Urbanization is negatively associated with genome-wide variation in white-footed mouse populations. Evol. Appl. 9, 546–564 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Brewer, V. N., Lane, S. J., Sewall, K. B. & Mabry, K. E. Effects of low-density urbanization on genetic structure in the Song Sparrow. PLoS ONE 15(6), e0234008 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Slatkin, M. Gene flow in natural populations. Annu. Rev. Ecol. Syst. 16, 393–430 (1985).Article 

Google Scholar 
21.Balloux, F. & Lugon-Moulin, N. The estimation of population differentiation with microsatellite markers. Mol. Ecol. 11, 155–165 (2002).PubMed 
Article 

Google Scholar 
22.Vangestel, C., Mergeay, J., Dawson, D. A., Vandomme, V. & Lens, L. Spatial heterogeneity in genetic relatedness among house sparrows along an urban—rural gradient as revealed by individual-based analysis. Mol. Ecol. 20, 4643–4653 (2011).PubMed 
Article 

Google Scholar 
23.Barnett, J. R., Ruiz-Gutierrez, V., Coulon, A. & Lovette, I. J. Weak genetic structuring indicates ongoing gene flow across White-ruffed Manakin (Corapipo altera) populations in a highly fragmented Costa Rica landscape. Conserv. Genet. 9, 1403–1412 (2008).Article 

Google Scholar 
24.Riegert, J., Fainová, D. & Bystrická, D. Genetic variability, body characteristics and reproductive parameters of neighbouring rural and urban common kestrel (Falco tinnuculus) populations. Popul. Ecol. 52, 73–79 (2009).Article 

Google Scholar 
25.MacDougall-Shackleton, E. A., Clinchy, M., Zanette, L. & Neff, B. D. Songbird genetic diversity is lower in anthropogenically versus naturally fragmented landscapes. Conserv. Genet. 12, 1195–1203 (2011).Article 

Google Scholar 
26.Caizergues, A. E. et al. Testing for parallel genomic and epigenomic footprints of adaptation to urban life in a passerine bird. bioRxiv. https://doi.org/10.1101/2021.02.10.43045227.Schmidt, C., Domaratzki, M., Kinnunen, R. P., Bowman, J. & Garroway, C. J. Continent-wide effects of urbanization on bird and mammal genetic diversity. Proc. R. Soc. B. 287, 20192497 (2020).CAS 
PubMed 
Article 

Google Scholar 
28.Cramp, S. & Perrins, C. M. The Birds of the Western Palearctic Vol. 7 (Oxford University Press, 1993).
Google Scholar 
29.Dauwe, T. et al. Great and Blue tit feathers as biomonitors for heavy metal pollution. Ecol. Indic. 1, 227–234 (2002).CAS 
Article 

Google Scholar 
30.Bańbura, J. & Bańbura, M. Blue tits Cyanistes caeruleus and great tits Parus major as urban habitat breeders. Inter Stud. Sparrows 36, 66–72 (2012).Article 

Google Scholar 
31.Charmantier, A., Doutrelant, C., Dubuc-Messier, G., Fargevieille, A. & Szulkin, M. Mediterranean blue tits as a case study of local adaptation. Evol. Appl. 9, 135–152 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Lemoine, M. et al. Low but contrasting neutral genetic differentiation shaped by winter temperature in European Great Tits. Biol. J. Linn. Soc. 118, 668–685 (2016).Article 

Google Scholar 
33.Porlier, M. Garant, D. Perret, P. and Charmantier, A. Habitat-linked population genetic differentiation in the Blue tit Cyanistes caeruleus. J. Hered. 103, 781–791 (2012).34.Szulkin, M., Gagnaire, P. A., Bierne, N. & Charmantier, A. Population genomic footprints of fine-scale differentiation between habitats in Mediterranean blue tits. Mol. Ecol. 25, 542–558 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Dubuc-Messier, G. et al. Gene flow does not prevent personality and morphological differentiation between two blue tit populations. J. Evol. Biol. 31, 1127–1137 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Postma, E. D., Tex, R.-J., Noordwijk, A. J. & Mateman, A. C. Neutral markers mirror small-scale quantitative genetic differentiation in an avian island population. Biol. J. Linn. Soc. 97, 867–875 (2009).Article 

Google Scholar 
37.Salmón, P. et al. Repeated genomic signature of adaptation to urbanisation in a songbird across Europe. bioRxiv. https://doi.org/10.1101/2020.05.05.078568 (2020).38.Dhondt, A. A. Effects of competition on great and blue tit reproduction: Intensity and importance in relation to habitat quality. J. Anim. Ecol. 79, 257–265 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Nilsson, A. L. K., Lindström, Å., Jonzén, N., Nilsson, S. G. & Karlsson, L. The effect of climate change on partial migration: The blue tit paradox. Glob. Change Biol. 12, 2014–2022. https://doi.org/10.1111/j.1365-2486.2006.01237.x (2006).ADS 
Article 

Google Scholar 
40.Nilsson, A. L. K., Alerstam, T. & Nilsson, J. Å. Diffuse, short and slow migration among Blue Tits. J. Ornithol. 149, 365–373. https://doi.org/10.1007/s10336-008-0280-3 (2008).Article 

Google Scholar 
41.Bańbura, J. et al. Spatial and temporal variation in heterophil-to-lymphocyte ratios of nestling passerine birds: Comparison of blue tits and great tits. PLoS ONE 8(9), e74226 (2013).42.Adamou, A.-E., Bańbura, M. & Bańbura, J. Subtle differences in breeding performance between Great Tits Parus major and Afrocanarian Blue Tits Cyanistes teneriffae in the peripheral zone of the species geographic ranges in NE Algeria. Eur. Zool. J. 87, 263–271 (2020).Article 

Google Scholar 
43.Dhondt, A. A. & Eyckerman, R. Competition between the great tit and the blue tit outside the breeding season in field experiments. Ecology 61, 1291–1296 (1980).Article 

Google Scholar 
44.Ortego, J., Garcia-Navas, V., Ferrer, E. S. & Sanz, J. J. Genetic structure reflects natal dispersal movements at different spatial scales in the blue tit Cyanistes caeruleus. Anim. Behav. 82, 131–137 (2011).Article 

Google Scholar 
45.Langin, K. M. et al. Characterizing range-wide divergence in an alpine-endemic bird: A comparison of genetic and genomic approaches. Conserv. Genet. 19(6), 1471–1485 (2018).CAS 
Article 

Google Scholar 
46.Roques, S., Chancerel, E., Boury, C., Pierre, M. & Acolas, M. L. From microsatellites to single nucleotide polymorphisms for the genetic monitoring of a critically endangered sturgeon. Ecol. Evol. 9(12), 7017–7029 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Zimmerman, S. J., Aldridge, C. L. & Oyler-McCance, S. J. An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genom. 21, 1–16 (2020).Article 
CAS 

Google Scholar 
48.Markowski, M. et al. Effects of experimental lead exposure on physiological indices of nestling great tits Parus major: Haematocrit and heterophile-to-lymphocyte ratio. Conserv. Physiol. 7, coz067 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Bańbura, J. et al. Habitat and year-to-year variation in haemoglobin concentration in nestling blue tits Cyanistes caeruleus. Comp. Biochem. Phys. A 148, 572–577 (2007).Article 
CAS 

Google Scholar 
50.Kiedrzyński, M. The impact of forest management on the flora and vegetation of old oak-stands (an example from The Spała Forests, central Poland). Nat. Conserv. 65, 51–62 (2008).
Google Scholar 
51.Glądalski, M. et al. Effects of human-related disturbance on breeding success of urban and non-urban blue tits (Cyanistes caeruleus). Urban Ecosyst. 19, 1325–1334 (2016).Article 

Google Scholar 
52.Markowski, M. et al. Spatial and temporal variation of lead, cadmium, and zinc in feathers of great tit and blue tit nestlings in Central Poland. Arch. Environ. Contam. Toxicol. 67, 507–518 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Richard, M. & Thorpe, R. S. Highly polymorphic microsatellites in the lacertid Gallotia Gallowi from the western Canary Islands. Mol. Ecol. 9, 1919–1952 (2000).CAS 
PubMed 
Article 

Google Scholar 
54.Saladin, V., Bonfils, D., Binz, T. & Richner, H. Isolation and characterization of 16 microsatellite loci in the Great Tit Parus major. Mol. Ecol. Notes 3, 520–522 (2003).CAS 
Article 

Google Scholar 
55.Dawson, D. A., Hanotte, O., Greig, C., Stewart, I. R. K. & Burke, T. Polymorphic microsatellites in the blue tit Parus caeruleus and their cross-species utility in 20 songbird families. Mol. Ecol. 9, 1941–1944 (2000).CAS 
PubMed 
Article 

Google Scholar 
56.van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Res. 4, 535–538 (2004).
Google Scholar 
57.Guo, S. W. & Thompson, E. A. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48, 361–372 (1992).CAS 
MATH 
Article 

Google Scholar 
58.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Res. 10, 564–567 (2010).Article 

Google Scholar 
59.Goudet, J. FSTAT (version 12): A computer program to calculate F-statistics. J. Hered. 86, 485–486 (1995).Article 

Google Scholar 
60.Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 
Article 

Google Scholar 
61.Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel: Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
62.Peakall, R. & Smouse, P. E. GENALEX 6.5: Genetic analysis in Excel. Population genetic software for teaching and research: An update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Slatkin, M. A measure of subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Hardy, O. J., Charbonnel, N., Fréville, H. & Heuertz, M. Microsatellite allele sizes: A simple test to assess their significance on genetic differentiation. Genetics 163, 1467–1482 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Hardy, O. J. & Vekemans, X. SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).Article 
CAS 

Google Scholar 
66.Hedrick, P. W. A standardized genetic differentiation measure. Evolution 59, 1633–1638 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Meirmans, P. G. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60, 2399–2402 (2006).Article 

Google Scholar 
68.Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Dray, S. & Dufour, A. The ade4 Package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22(4), 1–20. https://doi.org/10.18637/jss.v022.i04 (2007).Article 

Google Scholar 
70.Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24(11), 1403–1405 (2008).CAS 
Article 

Google Scholar 
71.TIBCO Software Inc. Statistica (Data Analysis Software System), Version 13. http://statistica.io. (2017). More

1.Murphy, P. G. & Lugo, A. E. Ecology of tropical dry forest. Ann. Rev. Ecol. Syst. 17, 67–88. https://doi.org/10.1146/annurev.es.17.110186.000435 (1986).Article 

Google Scholar 
2.Hasselquist, N. J., Allen, M. F. & Santiago, L. S. Water relations of evergreen and drought-deciduous trees along a seasonally dry tropical forest chronosequence. Oecologia 164, 881–890. https://doi.org/10.1007/s00442-010-1725-y (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Maass, M. et al. Long-term (33 years) rainfall and runoff dynamics in a tropical dry forest ecosystem in western Mexico: Management implications under extreme hydrometeorological events. For. Ecol. Manage. 426, 7–17. https://doi.org/10.1016/j.foreco.2017.09.040 (2018).Article 

Google Scholar 
4.NOAA. National Weather Service. Climate Prediction Center. Cold and warm episodes by season. http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. (Accessed 19 October 2019).5.Detto, M., Wright, J., Calderón, O. & Muller-Landau, H. C. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation. Nat. Commun. 9, 9–13. https://doi.org/10.1038/s41467-018-03306-9 (2018).CAS 
Article 

Google Scholar 
6.Bretfeld, M., Ewers, B. E. & Hal, J. S. Plant water use responses along secondary forest succession during the 2015–2016 El Niño drought in Panama. New Phytol. 219, 885–899. https://doi.org/10.1111/nph.15071 (2018).Article 
PubMed 

Google Scholar 
7.Meakem, V. et al. Role of tree size in moist tropical forest carbon cycling and water deficit responses. New Phytol. 219, 947–958. https://doi.org/10.1111/nph.14633 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Salmon, Y. et al. Drought impacts on tree phloem: From cell-level responses to ecological significance. Tree Physiol. 39, 173–191. https://doi.org/10.1093/treephys/tpy153 (2019).CAS 
Article 
PubMed 

Google Scholar 
9.Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266. https://doi.org/10.1126/science.aat7631 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Change Biol. 26, 3122–3133. https://doi.org/10.1111/gcb.15037 (2020).ADS 
Article 

Google Scholar 
11.Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603. https://doi.org/10.1126/sciadv.aay4603 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Martinez-Vilalta, J. & Lloret, F. Drought-induced vegetation shifts in terrestrial ecosystems: The key role of regeneration dynamics. Glob. Planet. Change 144, 94–108. https://doi.org/10.1016/j.gloplacha.2016.07.009 (2016).ADS 
Article 

Google Scholar 
13.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

Google Scholar 
14.Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. PNAS 113, 5024–5029. https://doi.org/10.1073/pnas.1525678113 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Greenwood, S. et al. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecol. Lett. 20, 539–553. https://doi.org/10.1111/ele.12748 (2017).Article 
PubMed 

Google Scholar 
16.Sperry, J. S., Meinzer, F. C. & McCulloh, K. A. Safety and efficiency conflicts in hydraulic architecture: Scaling from tissues to trees. Plant Cell Environ. 31, 632–645. https://doi.org/10.1111/j.1365-3040.2007.01765.x (2008).Article 
PubMed 

Google Scholar 
17.Borchert, R. & Pockman, W. T. Water storage capacitance and xylem tension in isolated branches of temperate and tropical trees. Tree Physiol. 25, 457–466. https://doi.org/10.1093/treephys/25.4.457 (2005).Article 
PubMed 

Google Scholar 
18.Valdez-Hernández, M., Andrade, J. L., Jackson, P. C. & Rebolledo-Vieyra, M. Phenology of five tree species of a tropical dry forest in Yucatán, Mexico: Effects of environmental and physiological factors. Plant Soil 329, 155–171. https://doi.org/10.1007/s11104-009-0142-7 (2010).CAS 
Article 

Google Scholar 
19.Santiago, L. S. et al. Coordination and trade-offs among hydraulic safety, efficiency and drought avoidance traits in Amazonian rainforest canopy tree species. New Phytol. 218, 1015–1024. https://doi.org/10.1111/nph.15058 (2018).Article 
PubMed 

Google Scholar 
20.Bussotti, F., Pollastrini, M., Holland, V. & Bruggemann, W. Functional traits and adaptive capacity of European forests to climate change. Environ. Experim. Bot. 111, 91–113. https://doi.org/10.1016/j.envexpbot.2014.11.006 (2015).Article 

Google Scholar 
21.Reich, P. B. & Borchert, R. Water stress and tree phenology in a tropical dry forest in the lowlands of Costa Rica. J. Ecol. 72(1), 61–74. https://doi.org/10.2307/2260006 (1984).Article 

Google Scholar 
22.Holbrook, N. M., Whitbeck, J. L. & Mooney, H. A. Drought responses of neotropical dry forest trees. In Seasonally Dry Tropical Forests (eds Bullock, S. H. et al.) (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511753398.010.
Google Scholar 
23.Wolfe, B. T. & Kursar, T. A. Diverse patterns of stored water use among saplings in seasonally dry tropical forests. Oecologia 179, 925–936. https://doi.org/10.1007/s00442-015-3329-z (2015).ADS 
Article 
PubMed 

Google Scholar 
24.Borchert, R., Rivera, G. & Hagnauer, W. Modification of vegetative phenology in a tropical semi-deciduous forest by abnormal drought and rain. Biotropica 34, 27–39. https://doi.org/10.1111/j.1744-7429.2002.tb00239.x (2002).Article 

Google Scholar 
25.Markesteijn, L., Poorter, L., Paz, H., Sack, L. & Bongers, F. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ. 34, 137–148. https://doi.org/10.1111/j.1365-3040.2010.02231.x (2011).Article 
PubMed 

Google Scholar 
26.Aragón-Moreno, A. A., Islebe, G. A., Torrescano-Valle, N. & Arellano-Verdejo, J. Middle and late Holocene mangrove dynamics of the Yucatan Peninsula, Mexico. J. S. Am. Earth Sci. 85, 307–311. https://doi.org/10.1016/j.jsames.2018.05.015 (2018).Article 

Google Scholar 
27.De la Barreda, B., Metcalfe, E. S. & Boyd, D. S. Precipitation regionalization, anomalies and drought occurrence in the Yucatan peninsula, Mexico. Int. J. Climatol. 40(10), 1–15. https://doi.org/10.1002/joc.6474 (2020).Article 

Google Scholar 
28.IPCC. Summary for Policymakers. In: Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (V. Masson-Delmotte, P., Zhai, H. O., Pörtner, D., Roberts, J., Skea, P.R., Shukla, A., Pirani, W., Moufouma-Okia, C., Péan, R., Pidcock, S., Connors, J.B.R., Matthews, Y., Chen, X., Zhou, M. I., Gomis, E., Lonnoy, T., Maycock, M., Tignor, T., Waterfield, eds.). World Meteorological Organization, Geneva, Switzerland. https://www.ipcc.ch/sr15/ (Accessed 15 November 2019).29.Eller, C. B., Rowland, L. & Oliveira, R. S. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Phil. Trans. R. Soc. B 373, 1–12. https://doi.org/10.1098/rstb.2017.0315 (2018).CAS 
Article 

Google Scholar 
30.Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Chang. 3(9), 811–815. https://doi.org/10.1038/nclimate1907 (2013).ADS 
Article 

Google Scholar 
31.Goldstein, G. et al. Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant Cell Environ. 21, 397–406. https://doi.org/10.1046/j.1365-3040.1998.00273.x (1998).Article 

Google Scholar 
32.Landsberg, J. & Waring, R. Water relations in tree physiology: where to from here?. Tree Physiol. 37, 18–32. https://doi.org/10.1093/treephys/tpw102 (2016).Article 

Google Scholar 
33.Kim, J. S. & Kug, J.-S. Increased atmospheric CO2 growth rate during El Niño driven by reduced terrestrial CO2 capture in the CMIP5 ESMs. J. Clim. 29, 8783–8805. https://doi.org/10.1175/JCLI-D-14-00672.1 (2016).ADS 
Article 

Google Scholar 
34.Kim, J. S., Kug, J.-S. & Jeong, S. Intensification of terrestrial carbon cycle related to El Niño-Southern Oscillation under greenhouse warming. Nat. Commun. 8, 1674. https://doi.org/10.1038/s41467-017-01831-7 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Wang, Q., Cai, W., Zeng, L. & Wang, D. Nonlinear meridional moisture advection and the ENSO-southern China rainfall teleconnection. Geophys. Res. Lett. 45(9), 4353–4360. https://doi.org/10.1029/2018GL077446 (2018).ADS 
Article 

Google Scholar 
36.Wang, Q., Wang, Y., Sui, J., Zhou, W. & Li, D. Effects of weak and strong winter currents on the thermal state of the South China Sea. J. Clim. 34(1), 313–325. https://doi.org/10.1175/JCLI-D-19-0790.1 (2021).ADS 
Article 

Google Scholar 
37.Xie, S.-P. et al. Eastern Pacific ITCZ dipole and ENSO diversity. J. Clim. 31, 4449–4462. https://doi.org/10.1175/JCLI-D-17-0905.1 (2018).ADS 
Article 

Google Scholar 
38.Peng, Q., Xie, S.-P., Wang, D., Zheng, X.-T. & Zhang, H. Coupled ocean–atmosphere dynamics of the 2017 extreme coastal El Niño. Nat. Commun. 10, 298. https://doi.org/10.1038/s41467-018-08258-8 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Peng, Q. et al. Eastern Pacific winds in the evolution of El Niño: implications for ENSO diversity. J. Clim. 33, 3197–3212. https://doi.org/10.1175/JCLI-D-19-0435.1 (2020).ADS 
Article 

Google Scholar 
40.Barkhodarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331. https://doi.org/10.1038/s41598-019-51857-8 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Meinzer, F. C., James, S. A., Goldstein, G. & Woodruff, D. Whole-tree water transport scales sapwood capacitance in tropical forest canopy trees. Plant Cell Environ. 26, 1147–1155. https://doi.org/10.1046/j.1365-3040.2003.01039.x (2003).Article 

Google Scholar 
42.Luo, Z. et al. Responses of plant water use to a severe summer drought for two subtropical tree species in the central southern China. J. Hydrol. Reg. Stud. 8, 1–9. https://doi.org/10.1016/j.ejrh.2016.08.001 (2016).CAS 
Article 

Google Scholar 
43.Vinya, R., Malhi, Y., Brown, N. & Fisher, J. Functional coordination between branch hydraulic properties and leaf functional traits in miombo woodlands: Implications for water stress management and species habitat preference. Acta Physiol. Plant 34, 1701–1710. https://doi.org/10.1007/s11738-012-0965-3 (2012).CAS 
Article 

Google Scholar 
44.Choat, B., Ball, M. C., Luly, J. G. & Holtum, J. A. M. Hydraulic architecture of deciduous and evergreen dry rainforest tree species from north-eastern Autralia. Trees 19, 305–311. https://doi.org/10.1007/s00468-004-0392-1 (2005).Article 

Google Scholar 
45.Romero, E., González, E. J., Meave, J. A. & Terrazas, T. Wood anatomy of dominant species with contrasting ecological performance in tropical dry forest succession. Plant Biosyst. 154, 524–534. https://doi.org/10.1080/11263504.2019.1651775 (2019).Article 

Google Scholar 
46.Pineda-García, F., Paz, H. & Meinzer, F. C. Drought resistance in early and late secondary successional species from a tropical dry forest: The interplay between xylem resistance to embolism, sapwood water storage and leaf shedding. Plant Cell Environ. 36, 405–418. https://doi.org/10.1111/j.1365-3040.2012.02582.x (2013).Article 
PubMed 

Google Scholar 
47.Choat, B., Sack, L. & Holbrook, M. Diversity of hydraulic traits in nine Cordia species growing in tropical forests with contrasting precipitation. New Phytol. 175, 686–698. https://doi.org/10.1111/j.1469-8137.2007.02137.x (2007).Article 
PubMed 

Google Scholar 
48.Fallas-Cedeño, L., Holbrook, N. M., Rocha, O. J., Vásquez, N. & Gutiérrez-Soto, M. Phenology, lignotubers, and water relations of Cochlospermum vitifolium, a pioneer tropical dry forest tree in Costa Rica. Biotropica 42, 104–111. https://doi.org/10.1111/j.1744-7429.2009.00539.x (2010).Article 

Google Scholar 
49.Quintanar-Isaías, A., Velasquez-Nuñez, M., Solares-Arenas, F., Pérez-Olvera, C. P. & Torre-Blanco, A. Secondary stem anatomy and uses or four drought-deciduous species of a tropical dry forest in Mexico. Rev. Biol. Trop. 53, 29–48. https://doi.org/10.15517/RBT.V53I1-2.14297 (2005).Article 

Google Scholar 
50.Veneklaas, E. J., Santos-Silva, M. P. & den Ouden, F. Determinants of growth rate in Ficus benjamina L. compared to related faster-growing woody and herbaceous species. Sci. Hortic. 93, 75–84. https://doi.org/10.1016/S0304-4238(01)00315-6 (2002).Article 

Google Scholar 
51.Mediavilla, S., Escudero, A. & Heilmeier, H. Internal leaf anatomy and photosynthetic resource-use efficiency: Interspecific and intraspecific comparisons. Tree Physiol. 21, 251–259. https://doi.org/10.1093/treephys/21.4.251 (2001).CAS 
Article 
PubMed 

Google Scholar 
52.Peguero-Pina, J. J., Sancho-Knapik, D. & Gil-Pelegrin, E. Ancientcell structural traits and photosynthesis in today’s environment. J. Exp. Bot. 68, 1389–1392. https://doi.org/10.1093/jxb/erx081 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
53.Kitajima, K. & Poorter, L. Tissue-level leaf toughness, but not lamina thickness, predicts sapling leaf lifespan and shade tolerance of tropical tree species. New Phytol. 186, 708–721. https://doi.org/10.1111/j.1469-8137.2010.03212.x (2010).Article 
PubMed 

Google Scholar 
54.Schwedenman, L., Pendall, E., Sanchez-Bragado, R., Kunert, N. & Holscher, D. Tree water uptake in a tropical plantation varying in tree diversity: Interspecific differences, seasonal shifts and complementary. Ecohydrology 8, 1–12. https://doi.org/10.1002/eco.1479 (2015).Article 

Google Scholar 
55.Reyes-García, C., Andrade, J. L., Simá, J. L., Us-Santamaría, R. & Jackson, P. C. Sapwood to heartwood ratio affects whole-tree water use in dry forest legume and non-legume trees. Trees 26, 1317–1330. https://doi.org/10.1007/s00468-012-0708-5 (2012).Article 

Google Scholar 
56.Santiago, L. et al. Leaf photosynthetic traits scale with hydraulic conductivity and wood density in Panamanian forest canopy trees. Oecologia 140, 543–550. https://doi.org/10.1007/s00442-004-1624-1 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
57.Li, X. et al. Tree hydraulic traits are coordinated and strongly linked to climate-of-origin across a rainfall gradient. Plant Cell Environ. 41, 646–660. https://doi.org/10.1111/pce.13129 (2018).CAS 
Article 
PubMed 

Google Scholar 
58.Querejeta, J. I., Estrada-Medina, H., Allen, M. F., Jiménez-Osorio, J. J. & Ruenes, R. Utilization of bedrock water by Brosimum alicastrum trees growing on shallow soil atop limestone in a dry tropical climate. Plant Soil 287, 187–197. https://doi.org/10.1007/s11104-006-9065-8 (2006).CAS 
Article 

Google Scholar 
59.Scholz, F. G., Phillips, N. G., Bucci, S. J., Meinzer, F. C. & Goldstein, G. Hydraulic capacitance: Biophysics and functional significance of internal water sources in relation to tree size. In Size- and Age-Related Changes in Tree Structure and Function (eds Meinzer, F. C. et al.) 341–362 (Springer, 2011). https://doi.org/10.1007/978-94-007-1242-3_13.
Google Scholar 
60.Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 139, 1–5. https://doi.org/10.1038/nplants.2015.139 (2015).Article 

Google Scholar 
61.Sobrado, M. A. Embolism vulnerability in drought-deciduous and evergreen species of a tropical dry forest. Acta Oecol. 18, 383–391. https://doi.org/10.1016/S1146-609X(97)80030-6 (1997).ADS 
Article 

Google Scholar 
62.Brodribb, T. J., Holbrook, N. M., Edwards, E. J. & Gutierrez, M. V. Relation between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant Cell Environ. 26, 443–450. https://doi.org/10.1046/j.1365-3040.2003.00975.x (2003).Article 

Google Scholar 
63.Orellana, R., Balam, M. & Bañuelos, I. Balance Ombrotérmico, evaluación climática. In Atlas de procesos territoriales de Yucatán (eds de Fuentes, A. G. et al.) 174–175 (Universidad Autónoma de Yucatán, 1999).
Google Scholar 
64.Instituto Nacional de Estadística Geografía e Informática, 2017. Anuario estadístico y geográfico de Quintana Roo. INEGI, México. https://www.datatur.sectur.gob.mx/ITxEF_Docs/QROO_ANUARIO. (Accessed 12 December 2019).65.Espinoza-Avalos, J., Islebe, G. A. & Hernández-Arana, H. A. El sistema ecológico de la bahía de Chetumal/corozal: Costa occidental del mar caribe (El Colegio de la Frontera Sur, 2009).
Google Scholar 
66.McKee, T.B., Doesken, N.J. & Kelist, J. The relationship of drought frequency and duration to time scale. in American Meteorological Society, Proceedings of the Eighth Conference on Applied Climatology, 17–22 January, Anaheim, California 179–184 (1993).67.Cheval, S. The Standardized Precipitation Index—An overview. Rom. J. Meteorol. 12(1–2), 17–64 (2015).
68.Koide, R. T., Robichaux, R. H., Morse, S. R. & Smith, C. M. Plant water status, hydraulic resistance and capacitance. In Plant Physiological Ecology, Field Methods and Instrumentation (eds Pearcy, R. W. et al.) 161–178 (Chapman and Hall, 1991). https://doi.org/10.1007/978-94-010-9013-1_9.
Google Scholar 
69.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.StatSoft, Inc. STATISTICA (data analysis software system), version 12. www.statsoft.com (2013). More

Triassic herbivore ecomorphological feeding guildsWe use herbivorous tetrapod jaws as an ecomorphological proxy and consider variation in both shape and function. Archosauromorphs and therapsids occupy different areas of shape morphospace with almost no overlap (Fig. 1a). The main discrimination between these two clades is along the major axis of variation, principal component (PC) 1, while PC2 discriminates therapsid subgroups, but not the sauropsids, which remain clustered on PC2. This pattern of greater sauropsid conservatism relative to synapsids appears to remain consistent in morphospaces generated from combinations of the first three PCs (Supplementary Fig. 4). Two clades crosscut this general pattern: the areas of morphospace occupied by rhynchosaurs (Archosauromorpha) and procolophonoids (Parareptilia) overlap with other sauropsids as well as with therapsids (Fig. 1a). This functional-ecological discrimination between the two major tetrapod clades, including the ancestors of modern birds and crocodilians on the one hand (archosauromorphs) and mammals on the other (therapsids) helps explain how both clades survived and neither overwhelmed the other, despite evidence for arms races between both through the Triassic14,16,21.Fig. 1: Shape and functional morphospace occupation of early Mesozoic herbivores.a Shape morphospace based on geometric morphometric data. b–i Contour plot of (interpolated) functional character data mapped onto shape morphospace. Increasing magnitude of functional character values indicated by colour gradient from dark to light (scale varies across characters). j Functional morphospace based on the above functional characters. Misc., Miscellaneous pseudosuchians. MA, Mechanical advantage. Asterisk indicates tooth row length or length of the mandibular functional surface. N = 136 taxa. All silhouettes created by S.S., but some are vectorised from artwork by Felipe Alves Elias (https://www.paleozoobr.com/) and Jeff Martz (United States National Park Service), available for academic use with attribution.Full size imageContour mapping of the functional characters (Supplementary Table 1) helps to reveal how jaw shape reflects function (Fig. 1b–i). The sauropsid-therapsid division along PC1 appears closely linked with anterior (Fig. 1b) and posterior (Fig. 1c) mechanical advantage (MA) and maximum aspect ratio (MAR) (Fig. 1e), reflecting biting efficiency and speed, and jaw robusticity. PC2 reflects a more complex pattern and appears to document the opening MA (Fig. 1d), relative symphyseal length (RSL) (Fig. 1g), and articulation offset (AO) (Fig. 1i), reflecting the speed of jaw opening, anterior robusticity, and efficiency of jaw lever mechanics, respectively. These functional characters were used to generate a separate jaw ‘functional’ morphospace (Fig. 1j) in which PC contribution scores indicate that functional PC1 (fPC1) is equally dependent on posterior MA, anterior MA, and MAR, while fPC2 is dominated by the opening MA and AO (Supplementary Table 2). Taxon distribution is more extended along fPC2, but the functional morphospace shows largely the same patterns as seen in the shape morphospace (Fig. 1j and Supplementary Fig. 5). In the functional morphospace, only the rhynchosaurs overlap with therapsids, and they occupy a space between cynognathian cynodonts and dicynodonts, rather than being associated more closely with dicynodonts as in the shape morphospace (Fig. 1a).Triassic therapsid jaws were highly efficient, granting them relatively high power and speed, as shown by the shape and functional morphospaces (Fig. 1a, j). Therapsids have relatively compressed mandibles (Fig. 1a) that maximise the areas of muscle attachment, increasing MA (Fig. 1b–c). Among therapsids, eutheriodonts developed this characteristic further, diverging from other taxa in terms of the greater compression of their mandibles and the reduced offset between tooth row and jaw joint. This progression continues through the successive positions in morphospace of the bauriid therocephalians, cynognathian cynodonts and tritylodont mammaliamorphs. Relative expansion of the tooth row (Fig. 1f) and development of the jaw musculature supports therapsid optimisation for powerful bites. The more anterior positioning of the adductor musculature in dicynodonts manifests as the highest anterior and posterior MA values of any group with the quadrate-articular jaw joint. Tooth row expansion and low opening MA in eutheriodonts indicates power was directed towards oral processing/mastication, while dicynodont edentulism supports optimisation for a powerful, shearing bite22.Triassic sauropsid jaws were less efficient, but follow similar trends to therapsids in developing comminution ability. Sauropodomorphs and allokotosaurs diverged from these trends, opting for fairly quick but weak bites with relatively large tooth rows to optimise ingestion of vegetation. Aetosaurs, ornithischians and some procolophonoids exhibit morphologies that mechanically improved on the basal morphology of the sauropodomorphs and allokotosaurs, with greater MA and robusticity, although jaw closure was notably slower. This may suggest greater cropping ability and further herbivorous specialisation. Rhynchosaurs show similar trends in developing their jaw musculature, exhibiting MA values (Fig. 1b–d) that converge towards those of therapsids. Leptopleuronine procolophonids are interesting in that their jaws were very stout with slower bite speed and high MA, suggesting they were feeding on very hard/ tough materials. The expansion of the tooth row in aetosaurs, ornithischians and rhynchosaurs suggests they were emulating the eutheriodonts in developing more effective mastication. Consequently, early Mesozoic herbivores can be subdivided broadly by their preference for gut or oral processing23. Different groups of therapsids and sauropsids followed common adaptive pathways as specialised herbivores: as phylogenetic contingency combined with ecology to produce convergent forms. This pattern has already been observed among dinosaurs24 and our results suggest it runs even deeper in the tetrapod tree.Regional mapping on the functional morphospace plot (Fig. 1j) shows qualitative groupings that may reflect different functional feeding groups (FFG) or guilds. To quantitatively identify these FFGs, three separate cluster analyses were run using a distance matrix of the standardised functional data. All methods gave similar results with regards to the separation and stability of the cluster groups but disagree over the precise groups (Supplementary Table 5 and Supplementary Data 5). External validation metrics were used to assess how closely the cluster groups corresponded with broad and higher resolution taxonomic groupings (Supplementary Data 14), which highlighted the relatively strong phylogenetic control on mandibular morpho-function (Supplementary Table 6 and Supplementary Data 14). By removing inconsistent taxa and looking for consensus among the three sets of cluster results, we identified five main FFGs: the ingestion generalists (relatively unspecialised), the prehension specialists (stronger, larger bites), the durophagous specialists (slow, powerful bites), the shearing pulpers (that cut and smash plant food), and the heavy oral processors (using teeth to reduce the food). Many sauropsid taxa were recovered within the ingestion generalist FFG, and so the clustering methodology was repeated with the ingestion generalists in an effort to generate higher resolution functional feeding subgroups (FFsG) for use in analysis of potential competition (Supplementary Data 5 and 6). This allowed identification of three additional FFsG within the ingestion generalist group: the basal generalists, tough generalists and light oral processors.Dissecting the functional properties within each of the FFGs enables us to determine the likely feeding specialisations (Fig. 2 and Supplementary Data 7) and track their prevalence through geological time (Fig. 3 and Supplementary Data 8 and 9). MA is the main discriminant for our FFGs. The FFGs show that therapsid herbivores fall into three FFGs, and archosauromorphs into two groups. However, the identification of the FFsG shows that archosauromorph morpho-functional differences are more subtle than those present in therapsids, illustrating the varying levels of specialisation and phylogenetic constraints within the two clades. We note that only two FFGs include both therapsids and sauropsids, the ‘shearing pulper’ group, including both hyperodapedontine rhynchosaurs and dicynodonts, and the light oral processor subgroup of the ingestion generalists, which included both archosauromorph rhynchosaurs and trilophosaurs and bauriid therocephalians. Sauropsids show much greater FFG variability within clades than therapsids, where feeding mode is largely common to the entire clade (Fig. 2 and Supplementary Data 5 and 6). This may reflect greater ecological diversification within sauropsid clades as a result of being relatively unspecialised compared to contemporaneous therapsid herbivores, which were already quite specialised at the onset of the Mesozoic. This contrast in specialisation granted sauropsids greater freedom to diversify across different guilds, despite therapsids possessing more mechanically efficient jaws (Fig. 2).Fig. 2: Functional feeding groups. Characteristics of the different functional feeding groups with silhouettes of the taxa that exhibit these feeding modes (see Fig. 1 for silhouette key).Preference of each group for gut or oral processing/comminution of food is indicated. The strength of separation between the groups is illustrated by the darkness of the band connecting each FFG description box. Violin plots show taxon density. Box plots showing median value (centre) and upper and lower quartiles representing the minimum and maximum bounds of the boxes, with whisker illustrating standard deviation. DS durophagous specialist, HOP heavy oral processor, IG ingestion generalist, PS prehension specialist, R Relative, SP shearing pulper, SA symphyseal angle. N = 136 taxa. All silhouettes created by S.S., but some are vectorised from artwork by Felipe Alves Elias (https://www.paleozoobr.com/) and Jeff Martz (United States National Park Service), available for academic use with attribution.Full size imageFig. 3: Functional feeding groups of early Mesozoic herbivores through time.a The relative species richness of different clades through time. b The relative richness of different functional feeding groups through time. c Distribution of functional feeding groups across different taxonomic groups and subgroups of herbivores is indicated. Clade and guild changes shown at the midpoints for each stage/substage in panels a and b. Temporal ranges of the groups are based on first and last fossil occurrence dates, highlighting the span of ecological prominence for each group. Environmental changes from arid to humid shown by background colour gradient. Predominant vegetation4,60,61 and characteristic vegetation (relative) height93,94 indicated by tree silhouettes. Geological Events: PTME Permian-Triassic mass extinction, CPE Carnian Pluvial Event, TJE Triassic-Jurassic mass extinction, Timebins: ANS Anisan, CH Changhsingian, H Hettangian, I Induan, L CRN Lower Carnian, L NOR Lower Norian, LAD Ladinian, Lop Lopingian, M. NOR Middle Norian, OLE Olenekian, PLB Pliensbachian, RHT Rhaetian, SIN Sinemurian, TOA Toarcian, U. NOR Upper Norian, W Wuchiapingian, Feeding Functional Groups: BG basal generalist, DS durophagous specialist, HOP heavy oral processor, IG ingestion generalist, LOP light oral processor, PS prehension specialist, SP shearing pulper, TG tough generalist, Larger Clades: Dm Dinosauromorpha, Psd Pseudosuchia, BAm Basal Archosauromorpha, Pr Parareptilia, Th Therapsida, Taxonomic Groups: Parareptilia: OWN Owenettidae, B. PRC Basal Procolophonidae, PRCn Procolophoninae, LEP Leptopleuroninae, Therapsida: DCYN Dicynodontia, BAUR Bauriidae, CYNG Cynognathia, TRTY Tritylodontia, Archosauromorpha: ALLOK Allokotosauria, B. RHYN Basal Rhynchosauria, RHYN Rhynchosauridae, RHYN HYP Hyperodapedontinae, PSD Misc Miscellaneous Pseudosuchia, AETO Aetosauria, SILE Silesauridae, B. SPm Basal Sauropodomorpha, PLT Plateosauridae, MSP (non-sauropodiform) Massopoda, SPf (non-sauropod) Sauropodiformes, SP Sauropoda, B. ORN Basal Ornithischia, B. THY Basal Thyreophora, TRL Trilophosauria, All silhouettes created by S.S., but some are vectorised from artwork by Felipe Alves Elias (https://www.paleozoobr.com/) and Jeff Martz (United States National Park Service), available for academic use with attribution.Full size imageNiche partitioning and competition avoidanceWere different clades of herbivores apparently competing for the same resources and in the same way? It seems not. We find that differences in jaw morphology are highly constrained by phylogeny and our FFGs do closely reflect phylogenetic groupings. Such phylogenetic structuring does not preclude meaningful functional interpretation of our FFGs to study divergent feeding strategies;25,26 this simply reflects that morphology and thus functionality is highly controlled by phylogeny. The distinction between the areas of morphospace occupied by therapsids and archosauromorphs (Fig. 1a) represents their fundamentally different feeding priorities, in which archosauromorphs optimised prehension and therapsids optimised comminution. Therapsids appear to have consistently enhanced biting power, possessing greater MA than most sauropsids, and this may reflect differences in the primary jaw adductor musculature of sauropsids (pterygoideus) and therapsids (adductor mandibularis)27. Sauropsid jaw mechanics are less efficient compared to therapsids, but it is clear that sauropsids, particularly the archosaurs achieved significantly larger body sizes than therapsids16. Therefore, it appears that sauropsids favoured increasing their bite forces through boosting jaw muscle mass and the absolute power involved, rather than improve efficiency. Their separation in morphospace suggests broad-scale niche partitioning between members of these two clades, guided in part by phylogenetic constraint. Nonetheless, our patterns of shape and functional morphospace occupation show how both groups converged from basal amniote (faunivorous) morphologies28 towards a common amniote-specific form of herbivory29.At the level of FFGs, minimal overlap between the various therapsid and archosauromorph clades confirms that these herbivores were not in competition for most of the early Mesozoic, contrary to the competitive model (Fig. 3). When our FFGs are applied at ecosystem level for different localities (Fig. 4; Supplementary Data 11 and Supplementary Table 6), we find that most co-occurring taxa belonged to different FFGs. Examples of coexisting herbivores with the same feeding functionality (Supplementary Table 7), and thus possibly competing, include procolophonids, bauriids and rhynchosaurs in the Early Triassic, hyperodapedontine rhynchosaurs and dicynodonts in the Lower Ischigualasto Formation (Carnian), and within dinosaur-dominated assemblages of the latest Triassic and Early Jurassic (Fig. 3), which is expected as most of these dinosaur groups have been shown to employ similar ‘orthal’ jaw mechanics30. Widespread morphological dissimilarity suggests that high herbivore diversity in the Santa Maria, Ischigualasto, and Lossiemouth formations (Fig. 4) was sustained by niche partitioning, which enables ecologically similar taxa to coexist by diverging from each other in their demands on resources31,32. The subdivision of resources by specialisation towards separate niches minimises resource competition, whilst boosting feeding efficiency, and thus the chances of survival33,34,35.Fig. 4: Relative faunal abundances and potential competitive trophic conflicts within early Mesozoic assemblages through time.a The relative abundance of faunivores and herbivores. b The relative species richness of different therapsids and sauropsid clades. c The number of feeding functional group (FFG) conflicts in each assemblage. AZ Assemblage Zone, L Lower, No Number, Geological Events: CPE Carnian Pluvial Event, TJE Triassic-Jurassic mass extinction, Epochs: EJ Early Jurassic, ET Early Triassic, LT Late Triassic, MT Middle Triassic, Timebins: A Anisian, C Carnian, I Induan, L/C Ladinian/Carnian, N Norian, R Rhaetian, S Sinemurian, S/P Sinemurian/Pliensbachian, Diet: FnV Faunivores, HbV Herbivores, Taxonomic groups: BAm Basal Archosauromorpha, Ds Dinosauria, Pr Parareptilia, Psd Pseudosuchia, Sile Silesauridae, Th Therapsida.Full size imageOur FFGs are broadly defined, so even these examples of possible competition may be exaggerated. The further identification of large subgroups within the ingestion generalist FFG (Fig. 2) highlights this, as use of these subgroups dramatically reduced the occurrences of potential trophic conflict (Supplementary Data 11). Additionally, in the Carnian examples, the kannemeyeriiform dicynodonts were much larger36 and lacked the dental plates of rhynchosaurs37. These two clades may well have specialised on different plant food while coexisting within the same broad feeding guild. Further, among the Late Triassic herbivorous dinosaurs that also coexisted within broad feeding guilds (Fig. 3), niche partitioning has been noted already among sauropodomorph dinosaurs, expressed in their body size38 and postural disparity39. Further evidence of tetrapod niche differentiation may be found in their dentition40, body size41, limb anatomy42, and even spatiotemporal behaviour43. Therefore, other aspects of ecology may support divergent trophic strategies and the avoidance of competition within these groups, although further comparative studies are needed. Competition between Early Triassic diapsids is more convincing as there are greater levels of coexistence, similarities between sizes, and abundances where found together (Supplementary Data 10).Temporal trends: changing of the guildsPatterns of shape and functional disparity through geological time (Fig. 5a) generally show near reciprocal traces for therapsids and archosauromorphs—when values for one clade are trending upwards, those for the other are trending downwards. This is particularly apparent in the lower Carnian and Rhaetian. However, this pattern appears to vanish in the Norian, possibly due to poor sampling of the therapsids. Crossovers occur at the times of the Carnian Pluvial Event, 233 Ma, and in the aftermath of the Triassic-Jurassic mass extinction (TJE), 201 Ma. Both metrics broadly agree, showing rising archosauromorph shape and functional disparity through the Early and Middle Triassic, and then higher values for therapsids through most of the Late Triassic, and equivalent values in the Early Jurassic. Interestingly, this concordance breaks down in the Early Jurassic as a disconnect appears within therapsids (tritylodonts), with high shape disparity producing lower functional disparity.Fig. 5: The shape and functional disparity and morphospace occupation of early Mesozoic herbivores through time.a Shape (Procrustes variance) and functional (sum of variance) disparity of Archosauromorpha, Therapsida, and Parareptilia, with standard error bands. b Shape and functional morphospace time-slices at stage and substage levels. Major extrinsic, environmental events are shown by the dashed red line. Faunal turnovers are highlighted by stars. Misc Miscellaneous pseudosuchians, MPD Mean Pairwise distances, PTME Permo-Triassic mass extinction, CPE Carnian Pluvial Event, TJE Triassic-Jurassic extinction, Timebins: ANS Anisan, CHX Changhsingian, HET Hettangian, IND Induan. L, CRN Lower Carnian, L. NOR Lower Norian, LAD Ladinian, M. NOR Middle Norian, OLE Olenekian, PLB Pliensbachian, RHT Rhaetian, SIN Sinemurian, TOA Toarcian, U. NOR Upper Norian, WUC Wuchiapingian, All silhouettes created by S.S., but some are vectorised from artwork by Felipe Alves Elias (https://www.paleozoobr.com/) and Jeff Martz (United States National Park Service), available for academic use with attribution.Full size imageDividing the shape and functional morphospaces temporally as stacked plots shows more detail of how different herbivorous clades waxed and waned (Fig. 5b). Herbivore guilds in the Early Triassic were dominated by procolophonoids and dicynodonts. During the Middle Triassic, parareptile disparity rose as the Early Triassic disaster fauna was complemented by new groups such as the gomphodont cynognathian cynodonts and archosauromorph allokotosaurs and rhynchosaurs. Archosauromorph disparity also increased as diversity increased with the emergence of new groups with new forms and functions, such as the rhynchosaurs and allokotosaurs. Therapsid disparity remained stable with the diversification of many morphologically similar kannemeyeriform dicynodonts masking the new diversity of cynodonts.Near the beginning of the Late Triassic, the CPE marked a substantial change, as rhynchosaurs and dicynodonts disappeared or reduced to very low diversity and abundance, and archosauromorph herbivores took over11,12,13. These were initially aetosaurs and sauropodomorph dinosaurs and, while expanding in diversity, their disparity declined (Fig. 5a) because new taxa were morphologically conservative, exhibiting limited variance and emerging within the existing morphospace of each respective clade (Fig. 5b). At the same time, all other herbivore clades declined, with remaining (parareptile and dicynodont) taxa shifting towards the extreme edges of their former morphospace occupancy. Cynognathians also dwindled in the early Norian. This transition within the herbivore guilds marks a shift from oral to gut processing among the majority of large terrestrial herbivores23 (Figs. 2, 3, and 5b).During the Rhaetian, herbivore diversity and disparity declined with only dinosaur and mammalian herbivores surviving into the Jurassic. Both groups underwent morphological and taxonomic radiations in the Early Jurassic, with dinosaurs and mammals typically occupying the roles of large and small herbivores, respectively. There was also a brief reappearance of pseudosuchian herbivores. We note that through the course of the early Mesozoic, sauropsid and therapsid morphospace became increasingly distanced from each other, with further comparison of the distances between therapsid and archosauromorph morphospace centroids showing that this separation accelerated at the onset of the Late Triassic (Supplementary Table 12).At epoch scale, NPMANOVA identified significant shifts in morphospace occupation between the Early and Middle Triassic (shape and function: p = 0.02). At stage level, only the Olenekian-Anisian transition shows a significant shift in both shape and functional morphological diversity (shape: p = 0.009, function: p = 0.007) (Supplementary Table S14). These results denote the distinct shift from disaster faunas through the Early Triassic, marked by repeated climate perturbations, to the more stable conditions of the mid-Anisian onwards and faunal recovery from the PTME44,45. The transitions between the lower Carnian-upper Carnian and Sinemurian-Pliensbachian were identified as being significant to shape but not function (p = 0.01 and 0.03) (Supplementary Table 14). These results for the Carnian are tantalising and tentatively highlight the impacts of the CPE as an important macroevolutionary event13. Furthermore, at the p  More

1.Burnham, P. M. & Hendrixson, D. R. Campylobacter jejuni: Collective components promoting a successful enteric lifestyle. Nat. Rev. Microbiol. 16, 551–565. https://doi.org/10.1038/s41579-018-0037-9 (2018).CAS 
Article 
PubMed 

Google Scholar 
2.Humphrey, T., O’Brien, S. & Madsen, M. Campylobacters as zoonotic pathogens: A food production perspective. Int. J. Food Microbiol. 117, 237–257. https://doi.org/10.1016/j.ijfoodmicro.2007.01.006 (2007).Article 
PubMed 

Google Scholar 
3.Hale, C. R. et al. Estimates of enteric illness attributable to contact with animals and their environments in the United States. Clin. Infect. Dis. 54, S472–S479. https://doi.org/10.1093/cid/cis051 (2012).Article 
PubMed 

Google Scholar 
4.Friedman, C. R. et al. Risk factors for sporadic Campylobacter infection in the United States: A case-control study in FoodNet sites. Clin. Infect. Dis. 38(Suppl 3), S285–S296. https://doi.org/10.1086/381598 (2004).Article 
PubMed 

Google Scholar 
5.Marder, E. P. et al. Incidence and trends of infections with pathogens transmitted commonly through food and the effect of increasing use of culture-independent diagnostic tests on surveillance—Foodborne diseases active surveillance network, 10 U.S. Sites, 2013–2016. MMWR. Morb. Mortal. Wkly. Rep. 66, 397–403. https://doi.org/10.15585/mmwr.mm6615a1 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Kaakoush, N. O., Castaño-Rodríguez, N., Mitchell, H. M. & Man, S. M. Global epidemiology of Campylobacter infection. Clin. Microbiol. Rev. 28, 687–720. https://doi.org/10.1128/CMR.00006-15 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Didelot, X. & Falush, D. Inference of bacterial microevolution using multilocus sequence data. Genetics 175, 1251–1266. https://doi.org/10.1534/genetics.106.063305 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Sheppard, S. K. et al. Niche segregation and genetic structure of Campylobacter jejuni populations from wild and agricultural host species. Mol. Ecol. 20, 3484–3490. https://doi.org/10.1111/j.1365-294X.2011.05179.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Griekspoor, P. et al. Marked host specificity and lack of phylogeographic population structure of Campylobacter jejuni in wild birds. Mol. Ecol. 22, 1463–1472. https://doi.org/10.1111/mec.12144 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Ogden, I. D. et al. Campylobacter excreted into the environment by animal sources: Prevalence, concentration shed, and host association. Foodborne Pathog. Dis. 6, 1161–1170. https://doi.org/10.1089/fpd.2009.0327 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Dearlove, B. L. et al. Rapid host switching in generalist Campylobacter strains erodes the signal for tracing human infections. ISME J. 10, 721–729. https://doi.org/10.1038/ismej.2015.149 (2016).Article 
PubMed 

Google Scholar 
12.Hermans, D. et al. Colonization factors of Campylobacter jejuni in the chicken gut. Vet. Res. 42, 82. https://doi.org/10.1186/1297-9716-42-82 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Sheppard, S. K. et al. Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl. Acad. Sci. U. S. A. 110, 11923–11927. https://doi.org/10.1073/pnas.1305559110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Yahara, K. et al. Genome-wide association of functional traits linked with Campylobacter jejuni survival from farm to fork. Environ. Microbiol. 19, 361–380. https://doi.org/10.1111/1462-2920.13628 (2017).CAS 
Article 
PubMed 

Google Scholar 
15.Thépault, A. et al. Genome-wide identification of host-segregating epidemiological markers for source attribution in Campylobacter jejuni. Appl. Environ. Microbiol. 83, e03085-e3116. https://doi.org/10.1128/AEM.03085-16 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Buchanan, C. J. et al. A genome-wide association study to identify diagnostic markers for human pathogenic Campylobacter jejuni strains. Front. Microbiol. 8, 1224. https://doi.org/10.3389/fmicb.2017.01224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
17.de Vries, S. P. W. et al. Genome-wide fitness analyses of the foodborne pathogen Campylobacter jejuni in in vitro and in vivo models. Sci. Rep. 7, 1251. https://doi.org/10.1038/s41598-017-01133-4 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Gormley, F. J. et al. Has retail chicken played a role in the decline of human Campylobacteriosis?. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01455-07 (2008).Article 
PubMed 

Google Scholar 
19.Korczak, B. M., Zurfluh, M., Emler, S., Kuhn-Oertli, J. & Kuhnert, P. Multiplex strategy for multilocus sequence typing, fla typing, and genetic determination of antimicrobial resistance of Campylobacter jejuni and Campylobacter coli isolates collected in Switzerland. J. Clin. Microbiol. https://doi.org/10.1128/JCM.00237-09 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Lévesque, S., Frost, E., Arbeit, R. D. & Michaud, S. Multilocus sequence typing of Campylobacter jejuni isolates from humans, chickens, raw milk, and environmental water in Quebec, Canada. J. Clin. Microbiol. https://doi.org/10.1128/JCM.00042-08 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Habib, I., Uyttendaele, M. & De Zutter, L. Survival of poultry-derived Campylobacter jejuni of multilocus sequence type clonal complexes 21 and 45 under freeze, chill, oxidative, acid and heat stresses. Food Microbiol. 27, 829–834. https://doi.org/10.1016/j.fm.2010.04.009 (2010).CAS 
Article 
PubMed 

Google Scholar 
22.Alter, T. & Scherer, K. Stress response of Campylobacter spp. and its role in food processing. J. Vet. Med. Ser. B 53, 351–357. https://doi.org/10.1111/j.1439-0450.2006.00983.x (2006).Article 

Google Scholar 
23.Murphy, C., Carroll, C. & Jordan, K. N. Environmental survival mechanisms of the foodborne pathogen Campylobacter jejuni. J. Appl. Microbiol. 100, 623–632. https://doi.org/10.1111/j.1365-2672.2006.02903.x (2006).CAS 
Article 
PubMed 

Google Scholar 
24.Mourkas, E. et al. Agricultural intensification and the evolution of host specialism in the enteric pathogen Campylobacter jejuni. Proc. Natl. Acad. Sci. 117, 11018–11028. https://doi.org/10.1073/pnas.1917168117 (2020).CAS 
Article 
PubMed 

Google Scholar 
25.Lees, J. A., Galardini, M., Bentley, S. D., Weiser, J. N. & Corander, J. pyseer: A comprehensive tool for microbial pangenome-wide association studies. Bioinformatics 34, 4310–4312. https://doi.org/10.1093/bioinformatics/bty539 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Schröder, G. & Lanka, E. TraG-like proteins of type IV secretion systems: Functional dissection of the multiple activities of TraG (RP4) and TrwB (R388). J. Bacteriol. 185, 4371–4381. https://doi.org/10.1128/JB.185.15.4371-4381.2003 (2003).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Poly, F., Threadgill, D. & Stintzi, A. Genomic diversity in Campylobacter jejuni: Identification of C. jejuni 81–176-specific genes. J. Clin. Microbiol. 43, 2330–2338. https://doi.org/10.1128/JCM.43.5.2330-2338.2005 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Lee, K.-Y. et al. Structure-based functional identification of Helicobacter pylori HP0268 as a nuclease with both DNA nicking and RNase activities. Nucleic Acids Res. 43, 5194–5207. https://doi.org/10.1093/nar/gkv348 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Sheppard, S. K., Guttman, D. S. & Fitzgerald, J. R. Population genomics of bacterial host adaptation. Nat. Rev. Genet. 19, 549–565. https://doi.org/10.1038/s41576-018-0032-z (2018).CAS 
Article 
PubMed 

Google Scholar 
30.Sheppard, S. K. et al. Cryptic ecology among host generalist Campylobacter jejuni in domestic animals. Mol. Ecol. 23, 2442–2451. https://doi.org/10.1111/mec.12742 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Mohan, V. et al. Campylobacter jejuni colonization and population structure in urban populations of ducks and starlings in New Zealand. Microbiologyopen 2, 659–673. https://doi.org/10.1002/mbo3.102 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Dingle, K. E. et al. Multilocus sequence typing system for Campylobacter jejuni. J. Clin. Microbiol. 39, 14–23. https://doi.org/10.1128/JCM.39.1.14-23.2001 (2001).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Hershberg, R. Mutation—The engine of evolution: Studying mutation and its role in the evolution of bacteria: Figure 1. Cold Spring Harb. Perspect. Biol. 7, a018077. https://doi.org/10.1101/cshperspect.a018077 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Falush, D. Bacterial genomics: Microbial GWAS coming of age. Nat. Microbiol. 1, 16059. https://doi.org/10.1038/nmicrobiol.2016.59 (2016).CAS 
Article 
PubMed 

Google Scholar 
35.Power, R. A., Parkhill, J. & de Oliveira, T. Microbial genome-wide association studies: Lessons from human GWAS. Nat. Rev. Genet. 18, 41–50. https://doi.org/10.1038/nrg.2016.132 (2017).CAS 
Article 
PubMed 

Google Scholar 
36.Brandley, M. C., Warren, D. L., Leaché, A. D. & McGuire, J. A. Homoplasy and clade support. Syst. Biol. 58, 184–198. https://doi.org/10.1093/sysbio/syp019 (2009).Article 
PubMed 

Google Scholar 
37.Hassanin, A., Lecointre, G. & Tillier, S. The ‘evolutionary signal’ of homoplasy in proteincoding gene sequences and its consequences for a priori weighting in phylogeny. C. R. l’Acad. Sci. Ser. III Sci. Vie 321, 611–620. https://doi.org/10.1016/S0764-4469(98)80464-2 (1998).CAS 
Article 

Google Scholar 
38.Sheppard, S. K. & Maiden, M. C. J. The evolution of Campylobacter jejuni and Campylobacter coli. Cold Spring Harb. Perspect. Biol. 7, a018119. https://doi.org/10.1101/cshperspect.a018119 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Motiejūnaitė, R., Armalytė, J., Markuckas, A. & Sužiedėlienė, E. Escherichia coli dinJ-yafQ genes act as a toxin-antitoxin module. FEMS Microbiol. Lett. 268, 112–119. https://doi.org/10.1111/j.1574-6968.2006.00563.x (2007).CAS 
Article 
PubMed 

Google Scholar 
40.Buts, L., Lah, J., Dao-Thi, M.-H., Wyns, L. & Loris, R. Toxin–antitoxin modules as bacterial metabolic stress managers. Trends Biochem. Sci. 30, 672–679. https://doi.org/10.1016/j.tibs.2005.10.004 (2005).CAS 
Article 
PubMed 

Google Scholar 
41.Gerdes, K., Christensen, S. K. & Løbner-Olesen, A. Prokaryotic toxin–antitoxin stress response loci. Nat. Rev. Microbiol. 3, 371–382. https://doi.org/10.1038/nrmicro1147 (2005).CAS 
Article 
PubMed 

Google Scholar 
42.Han, Z. et al. Influence of the gut microbiota composition on Campylobacter jejuni colonization in chickens. Infect. Immun. https://doi.org/10.1128/IAI.00380-17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Indikova, I., Humphrey, T. J. & Hilbert, F. Survival with a helping hand: Campylobacter and Microbiota. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01266 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Fijalkowska, I. J., Schaaper, R. M. & Jonczyk, P. DNA replication fidelity in Escherichia coli : A multi-DNA polymerase affair. FEMS Microbiol. Rev. 36, 1105–1121. https://doi.org/10.1111/j.1574-6976.2012.00338.x (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Vandewiele, D., Fernández de Henestrosa, A. R., Timms, A. R., Bridges, B. A. & Woodgate, R. Sequence analysis and phenotypes of five temperature sensitive mutator alleles of dnaE, encoding modified α-catalytic subunits of Escherichia coli DNA polymerase III holoenzyme. Mutat. Res. Mol. Mech. Mutagen. 499, 85–95. https://doi.org/10.1016/S0027-5107(01)00268-8 (2002).CAS 
Article 

Google Scholar 
46.Shan, S., Stroud, R. M. & Walter, P. Mechanism of association and reciprocal activation of two GTPases. PLoS Biol. 2, e320. https://doi.org/10.1371/journal.pbio.0020320 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Yosef, I., Bochkareva, E. S. & Bibi, E. Escherichia coli SRP, its protein subunit Ffh, and the Ffh M domain are able to selectively limit membrane protein expression when overexpressed. MBio https://doi.org/10.1128/mBio.00020-10 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Balaban, M., Joslin, S. N. & Hendrixson, D. R. FlhF and its GTPase activity are required for distinct processes in flagellar gene regulation and biosynthesis in Campylobacter jejuni. J. Bacteriol. 191, 6602–6611. https://doi.org/10.1128/JB.00884-09 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Budroni, S. et al. Neisseria meningitidis is structured in clades associated with restriction modification systems that modulate homologous recombination. Proc. Natl. Acad. Sci. 108, 4494–4499. https://doi.org/10.1073/pnas.1019751108 (2011).ADS 
Article 
PubMed 

Google Scholar 
50.McCarthy, N. D. et al. Host-associated genetic import in Campylobacter jejuni. Emerg. Infect. Dis. 13, 267–272. https://doi.org/10.3201/eid1302.060620 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Asakura, H. et al. Molecular evidence for the thriving of Campylobacter jejuni ST-4526 in Japan. PLoS ONE 7, e48394. https://doi.org/10.1371/journal.pone.0048394 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Morley, L. et al. Gene loss and lineage-specific restriction-modification systems associated with niche differentiation in the Campylobacter jejuni sequence type 403 clonal complex. Appl. Environ. Microbiol. 81, 3641–3647. https://doi.org/10.1128/AEM.00546-15 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.National Research Council. Nutrient Requirements of Swine. Nutrient Requirements of Swine. https://doi.org/10.17226/13298 (National Academies Press, 2012).
Google Scholar 
54.Schröder, G. et al. TraG-like proteins of DNA transfer systems and of the Helicobacter pylori type IV secretion system: Inner membrane gate for exported substrates?. J. Bacteriol. 184, 2767–2779. https://doi.org/10.1128/JB.184.10.2767-2779.2002 (2002).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Kienesberger, S. et al. Interbacterial macromolecular transfer by the Campylobacter fetus subsp. venerealis type IV secretion system. J. Bacteriol. 193, 744–758. https://doi.org/10.1128/JB.00798-10 (2011).CAS 
Article 
PubMed 

Google Scholar 
56.Velayudhan, J. & Kelly, D. J. Analysis of gluconeogenic and anaplerotic enzymes in Campylobacter jejuni: An essential role for phosphoenolpyruvate carboxykinase. Microbiology 148, 685–694. https://doi.org/10.1099/00221287-148-3-685 (2002).CAS 
Article 
PubMed 

Google Scholar 
57.Korczak, B. M. et al. Genetic relatedness within the genus Campylobacter inferred from rpoB sequences. Int. J. Syst. Evol. Microbiol. 56, 937–945. https://doi.org/10.1099/ijs.0.64109-0 (2006).CAS 
Article 
PubMed 

Google Scholar 
58.González-González, A., Hug, S. M., Rodríguez-Verdugo, A., Patel, J. S. & Gaut, B. S. Adaptive mutations in RNA polymerase and the transcriptional terminator rho have similar effects on Escherichia coli gene expression. Mol. Biol. Evol. 34, 2839–2855. https://doi.org/10.1093/molbev/msx216 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Richards, S. A. The significance of changes in the temperature of the skin and body core of the chicken in the regulation of heat loss. J. Physiol. 216, 1–10. https://doi.org/10.1113/jphysiol.1971.sp009505 (1971).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Hottes, A. K. et al. Bacterial adaptation through loss of function. PLoS Genet. 9, e1003617. https://doi.org/10.1371/journal.pgen.1003617 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Iranzo, J., Wolf, Y. I., Koonin, E. V. & Sela, I. Gene gain and loss push prokaryotes beyond the homologous recombination barrier and accelerate genome sequence divergence. Nat. Commun. 10, 5376. https://doi.org/10.1038/s41467-019-13429-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
62.Riedel, C. et al. Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress. Front. Microbiol. 11. https://doi.org/10.3389/fmicb.2020.00523 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Epping, L. et al. Comparison of different technologies for the decipherment of the whole genome sequence of Campylobacter jejuni BfR-CA-14430. Gut Pathog. 11, 59. https://doi.org/10.1186/s13099-019-0340-7 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Roehr, J. T., Dieterich, C. & Reinert, K. Flexbar 3.0—SIMD and multicore parallelization. Bioinformatics 33, 2941–2942. https://doi.org/10.1093/bioinformatics/btx330 (2017).CAS 
Article 
PubMed 

Google Scholar 
65.Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14(Suppl 1), S7. https://doi.org/10.1186/1471-2164-14-S1-S7 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069. https://doi.org/10.1093/bioinformatics/btu153 (2014).CAS 
Article 
PubMed 

Google Scholar 
68.Jolley, K. A. & Maiden, M. C. J. BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinform. 11, 595. https://doi.org/10.1186/1471-2105-11-595 (2010).Article 

Google Scholar 
69.Zhou, Z. et al. GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens. Genome Res. 28, 1395–1404. https://doi.org/10.1101/gr.232397.117 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Page, A. J. et al. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693. https://doi.org/10.1093/bioinformatics/btv421 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. Am. Math. Soc. Lect. Math. Life Sci. 17, 57–86 (1986).MathSciNet 
MATH 

Google Scholar 
73.Didelot, X. & Wilson, D. J. ClonalFrameML: Efficient inference of recombination in whole bacterial genomes. PLOS Comput. Biol. 11, e1004041. https://doi.org/10.1371/journal.pcbi.1004041 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
74.Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. W. & Corander, J. RhierBAPs: An R implementation of the population clustering algorithm hierbaps [version 1; referees: 2 approved]. Wellcome Open Res. 3, 93. https://doi.org/10.12688/wellcomeopenres.14694.1 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
75.van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).MATH 

Google Scholar 
76.Marttinen, P. et al. Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Res. 40, e6. https://doi.org/10.1093/nar/gkr928 (2012).CAS 
Article 
PubMed 

Google Scholar 
77.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293. https://doi.org/10.1093/nar/gkv1248 (2016).CAS 
Article 

Google Scholar 
79.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122. https://doi.org/10.1093/molbev/msx148 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Generation of Resp-and bab-recombinant linesMale informative backcross (BC) families using O. nubilalis Slovenia and Hungary strains15 were generated that exhibited fixed recombination between the flanking genes of the Resp region, trol and not. ZE and EZ hybrid males were backcrossed to a Z-strain female to generate backcross 1 (BC1) (Supplementary Fig. 1a, b). Recombinants between trol and not were identified via polymerase chain reaction (PCR) (Supplementary Methods, Supplementary Table 1), and crossed to Z-strain individuals to obtain BC2 (Supplementary Fig. 1c). BC2 individuals were genotyped to detect recombinants, then mated with each other to generate inbred 1 (IB1) crosses (Supplementary Fig. 1d). IB1 adults with the desired genotype were mass reared to obtain IB2 (Supplementary Fig. 1e). IB2 families that originated from a BC1 male cross were fixed homozygote recombinants, whereas BC1 female cross descendants were genotyped and inbred again to obtain fixed recombinant homozygotes (Supplementary Fig. 1f, g). Nine Resp-recombinant lines had one recombination point between homozygous trol and not genes (L165, L173, L185, L190, L195, L205, L215, L220, L237). bab-recombinant lines exhibited fixed recombination between bab’s flanking genes, ago and not, and were generated using the two homozygote recombinant lines L165 with Z-strain phenotype and L205 with E-strain phenotype. Single pair matings between L165 females and L205 males were set up to obtain hybrid males, which were backcrossed to L165 females. The BC individuals were screened with PCR (Supplementary Methods) to select recombinant adults that were used for inbred mass rearing. The PCR selection process continued until two fixed homozygote populations were established, i.e. line L44-Z and line L44-E (Fig. 2a).Genomic sequencing of Resp-recombinant linesA pool of 10 male pupae of lines L165 and L205 were homogenized in liquid nitrogen using mortar and pestle and DNA extractions were performed with QIAGEN Genomic-tip 100/G and the Genomic DNA Buffer Set (Qiagen, Hilden, Germany) according to the manufacturers’ instructions, but extending incubation times with buffer G2 containing proteinase K and RNase A to 12 h. HMW genomic DNA was sent to GATC Biotech for sequencing. Sequencing was done using an Illumina HiSeq2500 instrument, obtaining ~200 Mio paired end (2 × 150 bp) sequences per Resp-recombinant line. Shotgun genome assemblies were generated using the CLC Genomics Workbench v10.1. For PacBio sequencing, HMW genomic DNA was isolated from individual pupae of lines L165 and L205 by the Max Planck-Genome Centre Cologne (MPGCC) using the Qiagen MagAttract HMW DNA Kit. Sequencing of the size-selected HMW genomic DNA of each strain further purified with AMPure beads was performed at the MPGCC on a PacBio Sequel instrument. PacBio reads for both recombinant lines were assembled separately using the HGAP4 assembly pipeline implemented in the SMRT analysis software with standard settings. After genome sequencing of lines L165 and L205, primers were designed which amplified line-specific size polymorphisms and used to narrow down the breakpoint within all Resp-recombinant lines (Supplementary Methods, Supplementary Table 1).Phenotyping with wind tunnel assaysWind tunnel experiments were conducted with 0–5-day-old unmated males in a 2.5 × 1 × 1 m wind tunnel at 20–25 °C, 70% humidity, 30 cm/s airflow, and 26% red light. Synthetic lures (Z-strain lure: 97% Z11-14:OAc + 3% E11-14:OAc; E-strain lure: 99% E11-14:OAc + 1% Z11-14:OAc) diluted Z11-14:OAc and E11-14:OAc (purity of ≥99%, Pherobank, Wijk bij Duurstede, Netherlands) with hexane to 30 µg per lure. Blend quality and quantity was confirmed with gas chromatography. Pheromones were applied to rubber septa (Thomas Scientific, Swedesboro, NJ, USA) and stored at −20 °C. Individual males were placed in a small cylinder (10 cm, 3.2 cm diameter) covered with netted cloth at both ends permitting flow of odorized air. After placing the cylinder at the downwind end of the wind tunnel, male behavior, i.e. (1) resting (=no response), (2) wing fanning (=medium response), and (3) hair-pencil extrusion (=highest response), was recorded using setup adapted from Koutroumpa et al. 15, Supplementary Fig. 11). Each male was exposed to one blend for 60 s, kept for 30–60 min in the tunnel without any odor, and then the opposite blend was tested. Blends testing order was switched between experimental days. Statistical analysis was performed with R version 3.6.144 using Fisher’s Exact or Chi-squared test. To complement behavioral phenotypes, electrophysiological phenotypes (electroantennogram (EAG) and single sensillum recordings (SSR)) of bab-recombinant and CRISPR lines (described below) were recorded (Supplementary Methods).RNA isoform identificationDe novo transcriptomes of US laboratory populations45 were constructed using Trinity46 separately for E- and Z-strain individuals following methods in Levy et al. 47 to identify all splice variants of candidate genes. RNA was isolated from larval heads45, adult female heads47, or from whole pupae newly reported here. Briefly, RNA was extracted from samples using RNeasy kits (Qiagen, Hilden, Germany), then quantified with a Nanodrop (Thermo Scientific, Wilmington, DE, USA) and Qubit Broad Range RNA assays (Life Technologies, Carlsbad, CA, USA). cDNA libraries were prepared from mRNA using the TruSeq Sample Prep Kit v2 Set A (Illumina Inc., San Diego, CA, USA) using 1 mg total RNA, and prepared libraries were quantified using the Qubit High Sensitivity DNA assay. Libraries were quantified a second time on an Agilent Bioanalyzer (Santa Clara, CA, USA). Libraries were run on an Illumina HiSeq 2500, located at the Tufts University Core Facility for Genomics (Boston, MA, USA) to generate 100 bp single-end reads. Single-end reads were assessed for quality using the FastQC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Sequences were then trimmed using Trimmomatic version 0.35 to remove adapter sequences, bases with low sequence quality, and any reads that were shorter than 36 base pairs. FastQC reports were generated for each file again to confirm post-trimming quality. Mitochondrial DNA and ribosomal RNA sequences were removed using Bowtie248 by aligning against known mtDNA sequences and identical reads were collapsed prior to assembly (but counts retained) using the FastX Toolkit version 0.013 (http://hannonlab.cshl.edu/fastx_toolkit). The transcriptome was assembled de novo using Trinity46 and a k-mer length of 25. The longest transcript for each component were retained using custom scripts.Reverse-transcription quantitative PCR (RT-qPCR)Six genes in the Resp region between kon and not (Bap18, LIM, Bgi-A, Bgi-B, ago, bab), plus Orco and OnubOR6 were analyzed for their expression ratio in different tissues of E-strain and Z-strain individuals of European laboratory populations. Stages and tissues include: 5th instar larvae (antennae, head without antennae, thorax, abdomen), prepupal instar (head, thorax, abdomen), 2- and 4-day-old male and female pupae (antennae), 2-day-old male and female adults (antennae, brain, 1st pair of legs, 2nd plus 3rd pair of legs, abdomen). Expression ratios of bab were additionally evaluated for 7-day-old male and female pupal antennae as well as for 7-day-old male and female antennae and brains. Due to the large number of samples needing to be tested for expression simultaneously, a first qPCR was run comparing all tissues within each strain (Fig. 1a). At a next step only most expressed and most related tissues to the scientific question (i.e., antennae and brain) were included and comparisons were made simultaneously for the two strains (Supplementary Fig. 3). Three biological replicates of each of 27 sample types were collected during the second hour of scotophase from each strain. Total RNAs were extracted from each tissue using a Trizol/Chloroform approach followed by RNeasy Micro Kit purification (QIAGEN). Single-stranded cDNA synthesis was performed from 1 μg total RNA with iScript Reverse Transcription Supermix for RT-qPCR from BioRad (Hercules, CA, USA). Three control genes, (GAPDH, 18S rRNA, rpL8) were tested for stability between samples, and rpL849 was chosen for final comparisons. Gene-specific primers designed using “Primer 3”50 amplified 100–200 bp fragments (Supplementary Table 2). qPCR reactions were performed using Sso Advanced Universal SYBR Green Supermix (BioRad) in a total volume of 12 μl with 3 μl cDNA (or water as negative control or RNA for controlling the absence of genomic DNA) and 0.25 mM of each primer. cDNA amplifications were performed in a BioRad CFX96 Real-Time System using a gradient of annealing temperatures for each gene of interest. Three gradient temperatures were tested per gene on a 4-fold dilution series, 1/4–1/128 of a sample representative cDNA pool [E = 10 (−1/slope)] for relative quantification of the same gene in all other cDNA samples. Two replicates of each dilution were tested. A melting curve ramp (65–95 °C: Increment 0.5 °C/5 s) was generated to confirm that reactions did not produce nonspecific amplification. The final protocol included a denaturation step at 95 °C for 3 min followed by 40 cycles of amplification and quantification (denaturation at 95 °C for 10 s and annealing for 30 s at temperatures given in Supplementary Table 2 for each primer pair). Reactions were performed in two technical replicates. After confirming similar amplification efficiencies of target and control gene, expression levels were calculated relative to rpL8 expression and expressed as the ratio = E(−Cq Resp candidate)/E(−Cq rpL8)51. Statistical comparisons between strains, sexes, and tissues for each gene were assessed using one-way analysis of variance (ANOVA), followed by honest-significant difference (HSD) tests (post hoc Tukey’s test). A Benjamini–Hochberg multiple-test correction was applied over the genes tested.Targeted mutagenesis of bab exon 1.5Nine RNA guides were designed against intron 1A, exon 1.5, and intron 1B of bab (Supplementary Table 3) using the CRISPOR gRNA design tool cripsor.tefor.net and the O. nubilalis bab genomic DNA sequence as target. Guide sequences were subcloned in DR274 (http://www.addgene.org/42250) derived vector. Plasmids were digested by DraI, purified, and transcribed using HiScribe T7 high yield RNA synthesis kit (New England Biolabs). Reactions were purified using EZNA microelute RNA clean-up kit (OMEGA Biotek). Streptococcus pyogenes Cas9 protein, bearing three nuclear localization sequences, was provided by TacGene (Paris-France)52. Nine different guide RNAs were designed; three targeting exon 1.5, three in the preceding intron, and three in the following intron. Aliquots of sgRNA were denatured at 80 °C for 2 min and then left on ice for 2 min before mixing them with the equivalent amount of Cas9 for a sgRNA:Cas9 complex ratio of 1.5:1. Concentrations of the sgRNA are given in Supplementary Table 3 and the Cas9 was 30 µM (Sp-Cas9-NLS-GFP-NLS). The complex was formed at room temperature (RT) for 10 min. sgRNA:Cas9 complexes were formed separately for each sgRNA to ensure that Cas9 would bind equally to each sgRNA. These were combined as desired and placed on ice. Eggs of either strain from the European populations were injected (using an Eppendorf FemtoJet 4i injector) within 0.5 h after oviposition to target the one cell embryo stage. We injected three combinations of sgRNA (Supplementary Table 3) in order to create a deletion 5′ of exon 1.5 (KO1), a deletion 3′ of exon 1.5 (KO2), or a complete deletion of exon 1.5 (DEL). Injected eggs were reared to adulthood and genotyped. DNA of adult legs was extracted51 and amplified with Terra™ PCR Direct Polymerase Mix (Takara Bio Europe) using primer Bab-Z/E-i01-F9 (GTGCATTTCCTGCTTATGA) on intron 1, Bab-E-i01-R10 (AATTTGCCCCTAAGTGTACC) on intron 1.5, and the following program: 98 °C for 2 min, 35×(10 s at 98 °C, 15 s at 60 °C, 30 s at 68 °C). Size polymorphism were detected with agarose gel analysis and confirmed by Sanger sequencing (Macrogen, Amsterdam). Sequences were aligned using SEQUENCHER™ 4.7 (Gene Codes Corporation, Inc.). Heterozygote G0 adults with mutations were crossed to adults from the wild type rearing. G1 heterozygote males and females carrying the same mutation were crossed to obtain homozygote G2 mutants. Four G2 CRISPR lines were established: lines L46 (KO1), L72α (KO2), L72β (KO2), and L73 (KO2). Males of all CRISPR lines were phenotyped using EAG (Supplementary Methods) and wind tunnel assays.Whole mount in situ hybridizationMale O. nubilalis whole antennae were mounted and in situ hybridized with two RNA probes simultaneously. bab digoxigenin-labeled antisense riboprobe, was generated using a Sp6/T7 RNA transcription system (Roche) and linearized recombinant pCRII-TOPO plasmids (TOPO TA cloning kit Invitrogen) following manufacturer’s protocols. Orco, OR4, OR6, and OR7 probes are the same preparations that were used in ref. 21. Two color double in situ hybridization with two different antisense RNA probes (digoxigenin-labeled or biotin-labeled probes), as well as visualization of hybridization were performed as reported previously21,53 and described below. Antennae of 1–2-day-old Z-strain and E-strain male moths from the European laboratory populations were dissected by first cutting off the tips. The remaining antennal stem was further cut into smaller pieces of 5–15 antennal segments. The same procedure was done for 4-day-old pupal antennae that were extracted underneath the pupal cuticle, which was broken and lifted at antennal base so that the antenna could be pulled out with forceps.DIG-labeled probes were detected by an anti-DIG AP-conjugated antibody in combination with HNPP/Fast Red (Fluorescent detection Set; Roche); for biotin-labeled probes the TSA kit (Perkin Elmer, Boston, MA, USA), including an antibiotin–streptavidin–horseradish peroxidase conjugate and FITC tyramides as substrate was used. All incubations and washes were made in a volume of 0.3 mL (unless otherwise stated) in 0.5 mL tubes with slow rotation on a small table rotor at RT or in a hybridization oven (Bambino, Dutcher) when heating was needed. Antennal fragments were fixed in 4% paraformaldehyde in 0.1 M NaCO3, pH 9.5 for 24 h at 4 °C (PF1) followed by washes at RT for 1 min in phosphate-buffered saline (PBS: 0.85% NaCl, 1.4 mM KH2PO4, 8 mM Na2HPO4, pH 7.1), 10 min in 0.2 M HCl and 2 min in PBS with 1% Triton X-100. Antennal fragments were then incubated for 3 h in whole mount hybridization solution (50% formamide, 1% Tween 20, 0.1% CHAPS, 50 µg/mL yeast tRNA, 5× SSC, 1× Denhart’s reagent and 5 mM EDTA, pH 8.0) at 55 °C. Hybridization, using one DIG-labeled and one biotin-labeled probe, took place at 55 °C. Prior to hybridization, probes were diluted to adequate ratios (final volume 200 µL) in hybridization buffer (50% formamide, 10% dextran sulfate, 2× SSC, 0.2 µg/µL yeast tRNA, 0.2 µg/µL herring sperm DNA) and heated for 10 min at 65 °C. After heating, the probes were kept on ice for at least 5 min before use. Post-hybridization antennal fragments were washed four times for 15 min in 200 µL of 0.1× SSC (1× SSC = 0.15 M NaCl, 0.015 M Na-citrate, pH 7.0) at 60 °C then treated for 16 h in 5 mL of blocking solution (10 g blocking reagent from Roche in up to 100 mL maleic acid solution: 0.1 mol/L maleic acid and 0.15 mol/L NaCl) in 45 mL TBS and 150 µL Triton X-100 at 4 °C. The next step was to incubate fragments for 48 h with an anti-dioxigenin alkaline phosphatase-conjugated antibody (Roche) diluted 1:500 and with a streptavidine horse radish peroxidase-conjugate diluted 5:500 in blocking solution in TBS prepared as previously. After washing five times for 10 min in TBS, 0.05% Tween, antennal fragments were rinsed in DAP-buffer (100 mM Tris, pH 9.5, 100 mM NaCl, 50 mM MgCl2), after which hybridization signals were visualized using HNPP (Roche; 1:100 in DAP-buffer, pH 8.0) incubations for 15 h at 4 °C. After washing five times for 10 min in TBS, 0.05% Tween, antennal fragments were incubated for 18 h with the TSA kit substrates (Perkin Elmer, MA, USA): 2% Tyramide in amplification diluent. After a last set of washes, five times for 10 min in TBS, 0.05% Tween, antennal fragments were mounted in 1/3 PBS/glycerol and specific antennal cell stainings were observed with a Zeiss (Oberkochen, Germany) LSM 700 confocal laser scanning microscope (MIMA2 Platform, INRA, France, https://doi.org/10.15454/1.5572348210007727E12). Images were arranged in Powerpoint (Microsoft) and Adobe Illustrator (Adobesystems, San Jose, CA, USA) and were not altered except adjusting brightness or contrast for uniform tone within a figure.Phenotyping pheromone preference in naturePheromone trapping in North America was used to collect wild E-pheromone and Z-pheromone preferring males using Scentry Heliothis traps baited with synthetic E (“New York”) and Z (“Iowa”) lures (Scentry Biologicals, Billings, MO, USA). Traps were placed directly next to sweet corn fields and males were collected from each trap every 1–2 weeks and stored at −20 °C. Lures were replaced every 2 weeks. Trapping of >20 males from each E and Z trap was done at three sympatric sites between 2010 and 2012 (Supplementary Table 4). Tissues were moved from −20 °C within 3 months of collection to at −80 °C for long-term storage. DNA was isolated from both Pennsylvania sites by grinding frozen tissues and using the Qiagen DNeasy tissue protocol (Qiagen, Germantown, MD, USA) without vortexing preserve high molecular weight DNA. DNA isolation of samples from Bellona, NY was conducted with Qiagen genomic tips (20 G). All samples were treated with Qiagen RNase. DNA concentrations were quantified using Qubit prior to sequencing.Individual genome resequencing of field mothsIndividual resequencing data were collected for 31 E-trapped and 31 Z-trapped individuals from two sites (Rockspring, PA, USA (n = 15 per trap), and Landisville, PA, USA (n = 16 per trap); Supplementary Table 5). Landisville, PA, Z-trap data were originally described by Kozak et al. 54; all other data are new. Libraries were prepared using Illumina TruSeq (Illumina Inc.) and sequenced on an Illumina NextSeq using 150 bp paired-end sequencing at Cornell University. Trimmed genomic data were analyzed using the GATK best practices pipeline55,56,57 with data aligned to the repeat-masked genome reference (GenBank BioProject: PRJNA534504; Accession SWFO0000000054) using bwa58, sorted and filtered using Picard and samtools to remove duplicates and reads with a mapping quality score below 20. SNPs and small indels were called using GATK Haplotype caller (joint genotyping mode) after realigning around indels and filtered using recommended GATK filters57. Large structural variants (SV) were called from aligned bam files using information from split paired end reads using split reads and anomalies in pair orientation and insert size in Delly259 (https://github.com/dellytools/delly); these structural variants included indels ( >300 bp), translocations, and inversions. Delly2 was run on all individual files, these were merged to a consensus SV file and genotypes were reassessed.BayPASS 2.160 was used to identify SNPs associated with pheromone trap while controlling for population demography in the individual resequencing data using allele frequencies for our four populations to test the association with pheromone trap (Z = 1, E = −1) using the STD model. As described in Kozak et al. 54, significantly associated polymorphisms had XtX above the 0.001% quantile of pseudo-observed data of simulated “neutral” loci, BF  > 20 dB61, and eBPis  > 2 (equivalent to P value  More

1.Rosa, I. M. D., Smith, M. J., Wearn, O. R., Purves, D. & Ewers, R. M. The environmental legacy of modern tropical deforestation. Curr. Biol. 26, 2161–2166 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.FAO. Global Forest Resources Assessment 2020—Key findings. https://doi.org/10.4060/ca8753en. (Accessed July 20, 2020).3.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fetene, A., Yeshitela, K. & Gebremariam, E. The effects of anthropogenic landscape change on the abundance and habitat use of terrestrial large mammals of Nech Sar National Park. Environ. Syst. Res. 8, 19 (2019).Article 

Google Scholar 
5.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Peres, C. A. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian forest vertebrates. Conserv. Biol. 15, 1490–1505 (2001).Article 

Google Scholar 
7.Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: A review. Am. J. Primatol. 74, 696–711 (2012).PubMed 
Article 

Google Scholar 
8.Azhar, B. et al. Contribution of illegal hunting, culling of pest species, road accidents and feral dogs to biodiversity loss in established oil-palm landscapes. Wildl. Res. 40, 1–9 (2012).Article 

Google Scholar 
9.IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org/en. (Accessed July 19, 2020).10.Van Buskirk, J. Behavioural plasticity and environmental change. In Behavioural Responses to a Changing World: Mechanisms and Consequences (eds. Candolin, U. & Wong, B. B. M.) 145–158 (Oxford University Press, 2012).11.McLennan, M. R., Spagnoletti, N. & Hockings, K. J. The implications of primate behavioral flexibility for sustainable human-primate coexistence in anthropogenic habitats. Int. J. Primatol. 38, 105–121 (2017).Article 

Google Scholar 
12.Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals. (Springer International Publishing, 2016).13.Schülke, O. & Ostner, J. Ecological and social influences on sociality. In The Evolution of Primate Societies (eds. Mitani, J. C. et al.) 193–219 (University of Chicago Press, 2012).14.Young, C., Majolo, B., Heistermann, M., Schülke, O. & Ostner, J. Responses to social and environmental stress are attenuated by strong male bonds in wild macaques. PNAS 111, 18195–18200 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
15.McFarland, R. & Majolo, B. Coping with the cold: Predictors of survival in wild Barbary macaques, Macaca sylvanus. Biol. Lett. 9, 20130428 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schülke, O., Bhagavatula, J., Vigilant, L. & Ostner, J. Social bonds enhance reproductive success in male macaques. Curr. Biol. 20, 2207–2210 (2010).PubMed 
Article 
CAS 

Google Scholar 
17.Kulik, L., Muniz, L., Mundry, R. & Widdig, A. Patterns of interventions and the effect of coalitions and sociality on male fitness. Mol. Ecol. 21, 699–714 (2012).PubMed 
Article 

Google Scholar 
18.Silk, J. B. et al. Strong and consistent social bonds enhance the longevity of female baboons. Curr. Biol. 20, 1359–1361 (2010).CAS 
PubMed 
Article 

Google Scholar 
19.Silk, J. B. et al. The benefits of social capital: Close social bonds among female baboons enhance offspring survival. Proc. R. Soc. B 276, 3099–3104 (2009).PubMed 
Article 

Google Scholar 
20.Brent, L. J. N., Lehmann, J. & Ramos-Fernández, G. Social network analysis in the study of nonhuman primates: A historical perspective. Am. J. Primatol. 73, 720–730 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
21.Henzi, S. P. & Barrett, L. The value of grooming to female primates. Primates 40, 47–59 (1999).Article 

Google Scholar 
22.Sueur, C., Jacobs, A., Amblard, F., Petit, O. & King, A. J. How can social network analysis improve the study of primate behavior?. Am. J. Primatol. 73, 703–719 (2011).PubMed 
Article 

Google Scholar 
23.Palagi, E. Not just for fun! Social play as a springboard for adult social competence in human and non-human primates. Behav. Ecol. Sociobiol. 72, 90 (2018).Article 

Google Scholar 
24.Amici, F., Kulik, L., Langos, D. & Widdig, A. Growing into adulthood—A review on sex differences in the development of sociality across macaques. Behav. Ecol. Sociobiol. 73, 18 (2019).Article 

Google Scholar 
25.Vandeleest, J. J. et al. Decoupling social status and status certainty effects on health in macaques: A network approach. PeerJ 4, e2394 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Lehmann, J., Majolo, B. & McFarland, R. The effects of social network position on the survival of wild Barbary macaques, Macaca sylvanus. Behav. Ecol. 27, 20–28 (2016).Article 

Google Scholar 
27.Maestripieri, D. Maternal influences on primate social development. Behav. Ecol. Sociobiol. 72, 130 (2018).Article 

Google Scholar 
28.Maestripieri, D. Social and demographic influences on mothering style in pigtail macaques. Ethology 104, 379–385 (1998).Article 

Google Scholar 
29.Fairbanks, L. A. Individual differences in maternal style: Causes and consequences for mothers and offspring. Adv. Stud. Behav. 25, 579–611 (1996).Article 

Google Scholar 
30.Kulik, L., Langos, D. & Widdig, A. Mothers make a difference: Mothers develop weaker bonds with immature sons than daughters. PLoS ONE 11, e0154845 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Thierry, B. Social epigenesis. In Macaque Societies. A Model for the Study of Social Organization (eds. Thierry, B. et al.) 267–289 (Cambridge University Press, 2004).32.Kaufman, I. C. & Rosenblum, L. A. The waning of the mother–infant bond in two species of macaque. In Determinants of Infant Behavior (ed. Foss, B. M.) vol. IV 41–59 (Metheun, 1969).33.Balasubramaniam, K. N. et al. Impact of individual demographic and social factors on human-wildlife interactions: A comparative study of three macaque species. Sci. Rep. 10, 21991 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.el Alami, A., Lavieren, E. V., Rachida, A. & Chait, A. Differences in activity budgets and diet between semiprovisioned and wild-feeding groups of the endangered Barbary macaque (Macaca sylvanus) in the Central High Atlas Mountains, Morocco. Am. J. Primatol. 74, 210–216 (2012).PubMed 
Article 

Google Scholar 
35.Koirala, S. et al. Diet and activity of Macaca assamensis in wild and semi-provisioned groups in Shivapuri Nagarjun National Park, Nepal. Folia Primatol. 88, 57–74 (2017).Article 

Google Scholar 
36.Balasubramaniam, K. N. et al. Impact of anthropogenic factors on affiliative behaviors among bonnet macaques. Am. J. Phys. Anthropol. 171, 704–717 (2020).PubMed 
Article 

Google Scholar 
37.Kaburu, S. S. K. et al. Interactions with humans impose time constraints on urban-dwelling rhesus macaques (Macaca mulatta). Behaviour 156, 1255–1282 (2019).Article 

Google Scholar 
38.Marty, P. R. et al. Time constraints imposed by anthropogenic environments alter social behaviour in long-tailed macaques. Anim. Behav. 150, 157–165 (2019).Article 

Google Scholar 
39.Meijaard, E. et al. Oil Palm and Biodiversity: A Situation Analysis by the IUCN Oil Palm Task Force (IUCN, 2018).Book 

Google Scholar 
40.Ruppert, N., Holzner, A., See, K. W., Gisbrecht, A. & Beck, A. Activity budgets and habitat use of wild southern pig-tailed macaques (Macaca nemestrina) in oil palm plantation and forest. Int. J. Primatol. 39, 237–251 (2018).Article 

Google Scholar 
41.Holzner, A. et al. Macaques can contribute to greener practices in oil palm plantations when used as biological pest control. Curr. Biol. 29, R1066–R1067 (2019).CAS 
PubMed 
Article 

Google Scholar 
42.Bernstein, I. S. A field study of the pigtail monkey (Macaca nemestrina). Primates 8, 217–228 (1967).Article 

Google Scholar 
43.Barrett, L., Gaynor, D. & Henzi, S. P. A dynamic interaction between aggression and grooming reciprocity among female chacma baboons. Anim. Behav. 63, 1047–1053 (2002).Article 

Google Scholar 
44.Balasubramaniam, K. N., Berman, C. M., Ogawa, H. & Li, J. Using biological markets principles to examine patterns of grooming exchange in Macaca thibetana. Am. J. Primatol. 73, 1269–1279 (2011).CAS 
PubMed 
Article 

Google Scholar 
45.Caldecott, J. O. An Ecological and Behavioural Study of the Pig-Tailed Macaque. (S. Karger, 1986).46.Ciani, A. C. Intertroop agonistic behavior of a feral rhesus macaque troop ranging in town and forest areas in India. Aggress. Behav. 12, 433–439 (1986).Article 

Google Scholar 
47.Williams, S. M. & Lindell, C. A. The influence of a single species on the space use of mixed-species flocks in Amazonian Peru. Mov. Ecol. 7, 37 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Martínez, A. E., Gomez, J. P., Ponciano, J. M. & Robinson, S. K. Functional traits, flocking propensity, and perceived predation risk in an Amazonian understory bird community. Am. Nat. 187, 607–619 (2016).PubMed 
Article 

Google Scholar 
49.Southwick, C. H., Siddioi, M. F., Farooqui, M. Y. & Pal, B. C. Effects of artificial feeding on aggressive behaviour of rhesus monkeys in India. Anim. Behav. 24, 11–15 (1976).CAS 
PubMed 
Article 

Google Scholar 
50.Bonnell, T. R., Vilette, C., Young, C., Henzi, S. P. & Barrett, L. Formidable females redux: male social integration into female networks and the value of dynamic multilayer networks. Curr. Zool. 67, 49–57 (2020).51.Brent, L. J. N. Friends of friends: Are indirect connections in social networks important to animal behaviour?. Anim. Behav. 103, 211–222 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Balasubramaniam, K., Beisner, B., Vandeleest, J., Atwill, E. & McCowan, B. Social buffering and contact transmission: Network connections have beneficial and detrimental effects on Shigella infection risk among captive rhesus macaques. PeerJ 4, 2630 (2016).Article 

Google Scholar 
53.Morrow, K. S., Glanz, H., Ngakan, P. O. & Riley, E. P. Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura). Sci. Rep. 9, 20162 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Snijders, L., Blumstein, D. T., Stanley, C. R. & Franks, D. W. Animal social network theory can help wildlife conservation. Trends Ecol. Evol. 32, 567–577 (2017).PubMed 
Article 

Google Scholar 
55.Johnson, R. L. Mother–infant contact and maternal maintenance activities among free-ranging rhesus monkeys. Primates 27, 191–203 (1986).Article 

Google Scholar 
56.Karssemeijer, G. J., Vos, D. R. & van Hooff, J. A. R. A. M. The effect of some non-social factors on mother–infant contact in long-tailed macaques (Macaca fascicularis). Behaviour 113, 273–291 (1990).Article 

Google Scholar 
57.Gumert, M. D. Grooming and infant handling interchange in Macaca fascicularis: The relationship between infant supply and grooming payment. Int. J. Primatol. 28, 1059–1074 (2007).Article 

Google Scholar 
58.Maestripieri, D. Mother–infant relationships in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides). I. Development of the mother–infant relationship in the first three months. Behaviour 131, 75–96 (1994).Article 

Google Scholar 
59.Gazagne, E. et al. Northern pigtailed macaques rely on old growth plantations to offset low fruit availability in a degraded forest fragment. Am. J. Primatol. 82, e23117 (2020).PubMed 
Article 

Google Scholar 
60.Behie, A. M., Pavelka, M. S. M. & Chapman, C. A. Sources of variation in fecal cortisol levels in howler monkeys in Belize. Am. J. Primatol. 72, 600–606 (2010).PubMed 

Google Scholar 
61.Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M. L. & Brent, L. J. N. Deconstructing sociality: The types of social connections that predict longevity in a group-living primate. Proc. R. Soc. B 286, 20191991 (2019).PubMed 
Article 

Google Scholar 
62.Shutt, K., MacLarnon, A., Heistermann, M. & Semple, S. Grooming in Barbary macaques: Better to give than to receive?. Biol. Lett. 3, 231–233 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Belton, L. E., Cameron, E. Z. & Dalerum, F. Social networks of spotted hyaenas in areas of contrasting human activity and infrastructure. Anim. Behav. 135, 13–23 (2018).Article 

Google Scholar 
64.Testard, C. et al. Rhesus macaques build new social connections after a natural disaster. Curr. Biol. https://doi.org/10.1016/j.cub.2021.03.029 (2021).Article 
PubMed 

Google Scholar 
65.Schino, G. Grooming, competition and social rank among female primates: A meta-analysis. Anim. Behav. 62, 265–271 (2001).Article 

Google Scholar 
66.Wooddell, L. J., Kaburu, S. S. K. & Dettmer, A. M. Dominance rank predicts social network position across developmental stages in rhesus monkeys. Am. J. Primatol. 82, 23024 (2019).
Google Scholar 
67.Cords, M. Post-conflict reunions and reconciliation in long-tailed macaques. Anim. Behav. 44, 57–61 (1992).Article 

Google Scholar 
68.Sosa, S. The influence of gender, age, matriline and hierarchical rank on individual social position, role and interactional patterns in Macaca sylvanus at ‘La Forêt des Singes’: A multilevel social network approach. Front. Psychol. 7, 529 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Dunayer, E. S. & Berman, C. M. Infant handling among primates. Int. J. Comp. Psychol. 31, 1–32 (2018).Article 

Google Scholar 
70.Prescott, M. J., Nixon, M. E., Farningham, D. A. H., Naiken, S. & Griffiths, M.-A. Laboratory macaques: When to wean?. Appl. Anim. Behav. Sci. 137, 194–207 (2012).Article 

Google Scholar 
71.Lancaster, J. B. Play-mothering: The relations between juvenile females and young infants among free-ranging vervet monkeys (Cercopithecus aethiops). Folia Primatol. 15, 163–182 (1971).CAS 
Article 

Google Scholar 
72.Maestripieri, D. Social structure, infant handling, and mothering styles in group-living old world monkeys. Int. J. Primatol. 15, 531–553 (1994).Article 

Google Scholar 
73.Engelhardt, A. & Perwitasari-Farajallah, D. Reproductive biology of Sulawesi crested black macaques (Macaca nigra). Folia Primatol. 79, 326 (2008).
Google Scholar 
74.Takahata, Y. et al. Does troop size of wild Japanese macaques influence birth rate and infant mortality in the absence of predators?. Primates 39, 245–251 (1998).Article 

Google Scholar 
75.Krishna, B. A., Singh, M. & Singh, M. Population dynamics of a group of lion-tailed macaques (Macaca silenus) inhabiting a rainforest fragment in the Western Ghats, India. Folia Primatol. 77, 377–386 (2006).CAS 
Article 

Google Scholar 
76.Okamoto, K., Matsumura, S. & Watanabe, K. Life history and demography of wild moor macaques (Macaca maurus): Summary of ten years of observations. Am. J. Primatol. 52, 1–11 (2000).CAS 
PubMed 
Article 

Google Scholar 
77.Santosa, Y. Determination of long-tailed macaque’s (Macaca fascicularis) harvesting quotas based on demographic parameters. Biodiversitas 13, 79–85 (2012).Article 

Google Scholar 
78.Fürtbauer, I., Schülke, O., Heistermann, M. & Ostner, J. Reproductive and life history parameters of wild female Macaca assamensis. Int. J. Primatol. 31, 501–517 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Marshall, A. J. & Wich, S. A. Why conserve primates? In An Introduction to Primate Conservation (eds. Wich, S. A. & Marshall, A. J.) 13–30 (Oxford University Press, 2016).80.Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

Google Scholar 
81.Kark, S. Effects of ecotones on biodiversity. In Encyclopedia of Biodiversity (ed. Levin, S. A.) (Elsevier, 2013).82.Altmann, J. Observational study of behavior: Sampling methods. Behaviour 49, 227–267 (1974).CAS 
PubMed 
Article 

Google Scholar 
83.Thierry, B. et al. The social repertoire of Sulawesi macaques. Primate Res. 16, 203–226 (2000).Article 

Google Scholar 
84.Widdig, A., Nürnberg, P., Krawczak, M., Streich, W. J. & Bercovitch, F. B. Affiliation and aggression among adult female rhesus macaques: A genetic analysis of paternal cohorts. Behaviour 139, 371–391 (2002).Article 

Google Scholar 
85.Silverman, B. W. Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. vol. 26 (Chapman and Hall, 1988).86.Steiniger, S. & Hunter, A. J. S. OpenJUMP HoRAE – A free GIS and toolbox for home-range analysis. Wildl. Soc. Bull. 36, 600–608 (2012).87.The JUMP Pilot Project. OpenJUMP GIS—The free, java-based open source GIS. http://www.openjump.org. (Accessed February 26, 2021).88.QGIS Development Team. QGIS Geographic Information System. (Open Source Geospatial Foundation, 2020).89.David, H. A. Ranking from unbalanced paired-comparison data. Biometrika 74, 432–436 (1987).MathSciNet 
MATH 
Article 

Google Scholar 
90.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).91.Neumann, C. et al. Assessing dominance hierarchies: Validation and advantages of progressive evaluation with Elo-rating. Anim. Behav. 82, 911–921 (2011).Article 

Google Scholar 
92.de Vries, H., Stevens, J. M. G. & Vervaecke, H. Measuring and testing the steepness of dominance hierarchies. Anim. Behav. 71, 585–592 (2006).Article 

Google Scholar 
93.Bernstein, I. S. Dominance, aggression and reproduction in primate societies. J. Theor. Biol. 60, 459–472 (1976).CAS 
PubMed 
Article 

Google Scholar 
94.Kaburu, S. S. K. et al. Rates of human-macaque interactions affect grooming behavior among urban-dwelling rhesus macaques (Macaca mulatta). Am. J. Phys. Anthropol. 168, 92–103 (2019).PubMed 
Article 

Google Scholar 
95.Holekamp, K. E. & Smale, L. Dominance acquisition during mammalian social development: the “inheritance” of maternal rank. Am. Zool. 31, 306–317 (1991).Article 

Google Scholar 
96.Csardi, G. & Nepusz, T. The igraph software package for complex network research. Complex Syst. 1695, 1–9 (2006).
Google Scholar 
97.Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. (Cambridge University Press, 2008).98.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
99.Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
100.Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

Google Scholar 
101.Kulik, L., Amici, F., Langos, D. & Widdig, A. Sex differences in the development of aggressive behavior in rhesus macaques (Macaca mulatta). Int. J. Primatol. 36, 764–789 (2015).Article 

Google Scholar 
102.Schielzeth, H. & Forstmeier, W. Conclusions beyond support: Overconfident estimates in mixed models. Behav. Ecol. 20, 416–420 (2009).PubMed 
Article 

Google Scholar 
103.McCullagh, P. & Nelder, J. A. Generalized Linear Models. (Chapman and Hall, 1989).104.Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
105.Farine, D. R. & Carter, G. G. Permutation tests for hypothesis testing with animal social data: problems and potential solutions. Preprint at https://doi.org/10.1101/2020.08.02.232710 (2021).106.Weiss, M. N. et al. Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models. Methods Ecol. Evol. 00, 1–11 (2020).
Google Scholar 
107.Fox, J. & Weisberg, S. An R Companion to Applied Regression. (Sage Publications, 2011).108.Field, A. Discovering Statistics Using IBM SPSS Statistics. (Sage Publications, 2013).109.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists. (Cambridge University Press, 2002). More

1.Purseglove, J. W. Mangoes west of India. Acta Hortic. 24, 107–174 (1972).
Google Scholar 
2.Mukherjee, S. K. Origin, distribution and phylogenetic affinities of the species of Mangifera indica L. Bot. J. Linn. Soc. 55, 65–83 (1953).Article 

Google Scholar 
3.Kostermans, A. J. G. H. & Bompard, J. M. The Mangoes: Their Botany, Nomenclature (Horticulture and Utilization. IBPGR Academic Press, 1993).
Google Scholar 
4.Ravishankar, K. V., Lalitha, A., Anand, L. & Dinesh, M. R. Assessment of genetic relatedness among mango cultivars of India using RAPD markers. J. Hortic. Sci. Biotech. 75, 198–201 (2000).CAS 
Article 

Google Scholar 
5.Karihaloo, J. L., Dwivedi, Y. K., Archak, S. & Gaikwad, A. B. Analysis of genetic diversity of Indian mango cultivars using RAPD markers. J. Hortic. Sci. Biotech. 78, 285–289 (2003).CAS 
Article 

Google Scholar 
6.APEDA, The Agricultural and Processed Food Products Export Development Authority http://apeda.gov.in/apedawebsite/sixheadproduct/FFV.htm (2017).7.National Horticultural Board, Ministry of Agriculture and Farmers Welfare Government of India 85, Institutional Area, Sector-18, Gurugram 122015 (Haryana), India http://www.nhb.gov.in (2016-17).8.Jena, R.C. DNA fingerprinting of some promising Indian genotypes and hybrids of mango (Mangifera indica L.). PhD Thesis (pp 1–422). Utkal University, India (2019).9.Yadav, I. S. & Rajan, S. Genetic resources of mango. Adv. Hortic. 1, 77–93 (1993).
Google Scholar 
10.Zhang, J. et al. Potential of start codon targeted (SCoT) markers to estimate genetic diversity and relationships among Chinese Elymus sibiricus accessions. Molecules 20, 5987–6001 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Harisaranraj, R., Prasitha, R., Saravana Babu, S. & Suresh, K. Analysis of inter-species relationships of Ocimum species using RAPD markers. Ethnobotanical Leaflets. 12, 609–613 (2008).
Google Scholar 
12.Liu, H. et al. Genetic diversity and population structure of the endangered plant Salix taishanensis based on CDDP markers. Glob Ecol. Conserv. 24, (2020).13.Mahar, K. S. et al. Estimation of genetic variability and population structure in Sapindus trifoliatus L., using DNA fingerprinting methods. Trees 27, 85–96 (2013).ADS 
CAS 
Article 

Google Scholar 
14.Kalpana, D. et al. Assessment of genetic diversity among varieties of mulberry using RAPD and ISSR fingerprinting. Sci. Hortic. 134, 79–87 (2012).CAS 
Article 

Google Scholar 
15.Medhi, K. et al. High gene flow and genetic diversity in three economically important Zanthoxylum Spp. of Upper Brahmaputra Valley Zone of NE India using molecular markers. Meta Gene. 2, 706–721 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Wunsch, A. & Hormaza, J. I. Cultivar identification and genetic fingerprinting of temperate fruit tree species using DNA markers. Euphytica 125, 59–67 (2002).Article 

Google Scholar 
17.Flint-Garcia, S. A. et al. Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 44, 1054–1064 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Breton, C., Pinatel, C., Medail, F., Bonhomme, F. & Berville, A. Comparison between classical and Bayesian methods to investigate the history of olive cultivars using SSR-polymorphisms. Plant Sci. 175, 524–532 (2008).CAS 
Article 

Google Scholar 
19.Pillon, Y., Qamaruz-Zaman, F., Fay, M. F., Hendoux, F. & Piquot, Y. Genetic diversity and ecological differentiation in the endangered fen orchid (Liparis loeselii). Conserv. Genet. 8, 177–184 (2007).Article 

Google Scholar 
20.Mahar, K. S., Rana, T. S., Ranade, S. A. & Meena, B. Genetic variability and population structure in Sapindus emarginatus Vahl from India. Gene 485, 32–39 (2011).CAS 
PubMed 
Article 

Google Scholar 
21.Izawa, T., Kawahara, T. & Takahashi, H. Genetic diversity of an endangered plant, Cypripedium macranthosvar. rebunense (Orchidaceae): Background genetic research for future conservation. Conserv. Genet. 8, 1369–1376 (2007).Article 

Google Scholar 
22.Neel, M. C. & Ellstrand, N. C. Conservation of genetic diversity in the endangered plant Eriogonum ovalifolium var. vineum (Polygonaceae). Conserv. Genet. 4, 337–352 (2003).CAS 
Article 

Google Scholar 
23.George, S., Sharma, J. & Yadon, V. L. Genetic diversity of the endangered and narrow endemic Piperia yadonii (Orchidaceae) assessed with ISSR polymorphisms. Am. J. Bot. 96, 2022–2030 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Marsjan, P. & Oldenbroek, J.K. Molecular markers, a tool for exploring genetic diversity. The State of the World’s Animal Genetic Resources for Food and Agriculture, (pp. 359–379). FAO Research report, Rome (2007).25.Kumar, P., Gupta, V. K., Misra, A. K., Modi, D. R. & Pandey, B. K. Potential of molecular markers in plant biotechnology. Plant Omics. 2, 141–162 (2009).CAS 

Google Scholar 
26.Agarwal, M., Shrivastava, N. & Padh, H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 27, 617–631 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Li, M., Zhao, Z. & Miao, X. J. Genetic variability of wild apricot (Prunus Armeniaca L.) populations in the Ili Valley as revealed by ISSR markers. Genet. Resour. Crop Evol. 60, 2293–2302 (2013).CAS 
Article 

Google Scholar 
28.Abdin, M. Z. et al. Population structure and genetic diversity in bottle gourd [Lagenaria siceraria (Mol.) Standl.] germplasm from India assessed by ISSR marker. Plant Syst. Evol. 300, 767–773 (2014).Article 

Google Scholar 
29.Fazeli, S., Sheidai, M., Farahani, F. & Noormohammadi, Z. Looking for genetic diversity in Iranian apple cultivars (Malus × domestica Borkh.). J Sci. 27, 205–221 (2016).
Google Scholar 
30.Qian, X., Wang, C. & Tian, M. Genetic diversity and population differentiation of Calanthe tsoongiana, a rare and endemic orchid in China. Int J Mol Sci. 14, 20399–20413 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Singh, N. et al. Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PLoS ONE 8(12), e84136. https://doi.org/10.1371/journal.pone.0084136 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Jena, R. C., Agarwal, K., Ghosh, T. S. & Chand, P. K. Population structuring of selected mungbean landraces of the Odisha State of India via DNA marker-based genetic diversity analysis. Agric. Gene. 3, 67–86 (2017).Article 

Google Scholar 
33.Dias, A. et al. Portuguese Pinus nigra JF Arnold populations: genetic diversity, structure and relationships inferred by SSR markers. Ann. For. Sci. 77, 1–15 (2020).
Google Scholar 
34.Wu, Q., Zang, F., Ma, Y., Zheng, Y. & Zang, D. Analysis of genetic diversity and population structure in endangered Populus wulianensis based on 18 newly developed EST-SSR markers. Glob. Ecol. Conserv. 24, e01329 (2020).Article 

Google Scholar 
35.Surapaneni, M. et al. Population structure and genetic analysis of different utility types of mango (Mangifera indica L.) germplasm of Andhra Pradesh state of India using microsatellite markers. Plant Syst. Evol. 299, 1215–1229 (2013).CAS 
Article 

Google Scholar 
36.Yilmaz, K. U., Paydas-Kargi, S., Dogan, Y. & Kafkas, S. Genetic diversity analysis based on ISSR, RAPD and SSR among Turkish apricot germplasms in Iran Caucasian eco-geographical group. Sci. Hortic. 138, 138–143 (2012).CAS 
Article 

Google Scholar 
37.Patel, H. K., Fougat, R. S., Kumar, S., Mistry, J. G. & Kumar, M. Detection of genetic variation in Ocimum species using RAPD and ISSR markers. 3. Biotech 5, 697–707 (2015).
Google Scholar 
38.Desai, P. et al. Comparative assessment of genetic diversity among Indian bamboo genotypes using RAPD and ISSR markers. Mol. Biol. Rep. 42, 1265–1273 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Luo, C. et al. Genetic diversity of mango cultivars estimated using SCoT and ISSR markers. Biochem. Syst. Ecol. 39, 676–684 (2011).CAS 
Article 

Google Scholar 
40.Gajera, H. P., Tomar, R. S., Patel, S. V., Viradia, R. R. & Golakiya, B. A. Comparison of RAPD and ISSR markers for genetic diversity analysis among different endangered Mangifera indica genotypes of Indian Gir forest region. J. Plant Biochem. Biotech. 20, 217–223 (2011).Article 

Google Scholar 
41.Hamrick, J. L. & Godt, M. J. W. Conservation genetics of endemic plant species. In Avise, J. C., & J. L. Hamrick (Eds.), Conservation genetics: case histories from nature. (pp. 281–30). Chapman and Hall, New York (1996).42.Wang Z, Kang M, Liu H, Gao J, Zhang Z, Li Y, Wu R, Pang X (2014). High-level genetic diversity and complex population structure of Siberian apricot (Prunus sibirica L.) in China as revealed by nuclear SSR markers. PLOS ONE 9:e87381PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Xie, W. G., Zhang, X. Q., Ma, X., Huang, L. K. & Zeng, B. Genetic variation of Dactylis glomerata germplasm from Southwest China detected by SSR markers. Acta Pratacult. 18, 138–146 (2009).
Google Scholar 
44.Yan, X. B., Guo, Y. X., Zhou, H. & Wang, K. Analysis of geographical conditions affected on genetic variation and relationship among populations of Elymus. J. Plant Res. Environ. 15, 17–24 (2006).
Google Scholar 
45.Hamrick, J. L., Godt, M. J. W. & Sherman-Broyles, S. L. Factors influencing levels of genetic diversity in plant species. New For. 6, 95–124 (1992).Article 

Google Scholar 
46.Li, M., Zhao, Z. & Miao, X. Genetic diversity and relationships of apricot cultivars in North China revealed by ISSR and SRAP markers. Sci. Hortic. 173, 20–28 (2014).Article 

Google Scholar 
47.Kubik, C., Honig, J., Meyer, W. A. & Stacy, A. B. Genetic diversity of creeping bent-grass cultivars using SSR markers. Int. Turfgrass Soc. Res. J. 11, 533–547 (2009).
Google Scholar 
48.Gupta, P. K. & Roy, J. K. Molecular markers in crop improvement: Present status and future needs in India. Plant Cell Tiss. Org. 70, 229–234 (2002).Article 

Google Scholar 
49.Sivaprakash, K. R., Prasanth, S. R., Mohanty, B. P. & Parida, A. Genetic diversity of black gram landraces as evaluated by AFLP markers. Curr. Sci. 86, 1411–1415 (2004).
Google Scholar 
50.Noormohammadi, Z. et al. Genetic Variation among Iranian Pomegranates (Punica granatum L.) using RAPD, ISSR and SSR Markers. Aust. J. Crop Sci. 6, 268–275 (2012).CAS 

Google Scholar 
51.Schaal, B. A., Hayworth, D. A., Olsen, K. M., Rauscher, J. T. & Smith, W. A. Phylogeographic studies in plants: problems and prospects. Mol. Ecol. 7, 464–474 (1998).Article 

Google Scholar 
52.Zong, M. et al. Genetic diversity in geographic differentiation in the threatened species Dysosma pleiantha in China as revealed by ISSR analysis. Biochem. Genet. 46, 180–196 (2008).MathSciNet 
CAS 
PubMed 
Article 

Google Scholar 
53.Wright, S. Evolution and the Genetics of Population (University of Chicago Press, 1978).
Google Scholar 
54.Slatin, M. Gene flow and geographic structure of natural populations. Science 236, 787–792 (1987).ADS 
Article 

Google Scholar 
55.Kumar, A., Mishra, P., Singh, S. C. & Sundaresan, V. Efficiency of ISSR and RAPD markers in genetic divergence analysis and conservation management of Justicia adhatoda L., a medicinal plant. Plant Syst. Evol. 300, 1409–1420 (2014).Article 

Google Scholar 
56.Slatkin, M. & Barton, N. H. A comparison of three indirect methods for estimating the average level of gene flow. Evolution 43, 1349–1368 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Kouam, E. B., Pasquet, R. S., Elteraifi, I. & Muluvi, G. M. Genetic diversity and population structure of Vigna unguiculata ssp. unguiculata var. spontanea in Sudan. J. Res. Biol. 8, 643–652 (2011).
Google Scholar 
58.Xing, C., Tian, Y. & Meng, F. Evaluation of genetic diversity in Amygdalus mira (Koehne) Ricker using SSR and ISSR markers. Plant Syst. Evol. 301, 1055–1064 (2015).Article 

Google Scholar 
59.Ikegami, H., Nogata, H., Hirashima, K., Awamura, M. & Nakahara, T. Analysis of genetic diversity among European and Asian fig varieties (Ficus carica L.) using ISSR, RAPD, and SSR markers. Genet. Resour. Crop Evol. 56, 201–209 (2009).CAS 
Article 

Google Scholar 
60.Takrouni, M. M. & Boussaid, M. Genetic diversity and population’s structure in Tunisian strawberry tree (Arbutus undo L.). Sci. Hortic. 126, 330–337 (2010).Article 

Google Scholar 
61.Arya, L., Narayanan, R. K., Verma, M., Singh, A. K. & Gupta, V. Genetic diversity and population structure analyses of Morinda tomentosa Heyne, with neutral and gene based markers. Genet. Resour. Crop Evol. 61, 1469–1479 (2014).CAS 
Article 

Google Scholar 
62.Hamrick, J. L., Godt, M. J. W., Murawski, D. A., & Loveless, M. D. Correlations between species traits and allozyme diversity: Implications for conservation biology. In Falk, D.A.S., & K. E. Holsinger (Eds.), Genetics and conservation of rare plants. (pp. 75–86), Oxford University Press, Oxford (1991).63.Loveless, M. D. & Hamrick, J. L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Evol. Syst. 15, 65–96 (1984).Article 

Google Scholar 
64.Schoen, D. J. & Brown, A. H. D. Intraspecific variation in population gene diversity and effective population size correlates with mating systems in plants. Proc. Natl. Acad. Sci. USA 88, 4494–4497 (1991).ADS 
CAS 
PubMed 
Article 

Google Scholar 
65.Yan, J. J., Bai, S. Q., Zhang, X. Q. & Chang, D. Genetic diversity of native Elymus sibiricus populations in the Southeastern Margin of Qinghai-Tibetan Plateau as detected by SRAP and SSR marker. Acta Pratacult. Sin. 19, 122–134 (2010).
Google Scholar 
66.Aros, D., Meneses, C. & Infante, R. Genetic diversity of wild species and cultivated varieties of alstroemeria estimated through morphological descriptors and RAPD markers. Sci. Hortic. 108, 86–90 (2006).CAS 
Article 

Google Scholar 
67.Souframanien, J. & Gopalakrishna, T. A comparative analysis of genetic diversity in black gram genotypes using RAPD and ISSR markers. Theor. Appl. Genet. 109, 1687–1693 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Gorji, A. M., Poczai, P., Polgar, Z. & Taller, J. Efficiency of arbitrarily amplified dominant markers (SCoT, ISSR and RAPD) for diagnostic fingerprinting in tetraploid potato. Am. J. Potato Res. 88, 226–237 (2011).Article 

Google Scholar 
69.Saxena, S. et al. Analysis of genetic diversity among papaya cultivars using single primer amplification reaction (SPAR) methods. J. Hortic. Sci. Biotech. 80, 291–296 (2005).CAS 
Article 

Google Scholar 
70.Murty, S. G. et al. Comparison of RAPD, ISSR and DAMD markers for genetic diversity assessment between accessions of Jatropha curcas L., and its related species. J. Agric. Sci Tech. 15, 1007–1022 (2013).CAS 

Google Scholar 
71.Ferrao, L. F. V. et al. Comparative study of different molecular markers for classifying and establishing genetic relationships in Coffea canephora. Plant. Syst. Evol. 299, 225–238 (2013).CAS 
Article 

Google Scholar 
72.Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).
Google Scholar 
73.Doyle, J. J. & Doyle, J. L. Isolation of plant DNA from fresh tissue. Focus 12, 13–15 (1990).
Google Scholar 
74.Sambrook, J., Fritsch, E. F. & Maniatis, T., Agarose gel electrophoresis of DNA and pulse field gel electrophoresis. In: Molecular Cloning: a Laboratory Manual, 3rd Edn. Cold Springer Harbor Laboratory Press, (pp. 5.1–5.86). New York, USA (1989).75.Zhou, Z., Bebeli, P. J., Somers, D. J. & Gustafson, J. P. Direct amplification of minisatellite-region DNA with VNTR core sequences in the genus Oryza. Theor. Appl. Genet. 95, 942–949 (1997).CAS 
Article 

Google Scholar 
76.Winberg, B. C., Shori, Z., Dallas, J. F., Mclntyre, C. L. & Gustafson, J. P. Characterization of minisatellite sequences from Oryza sativa. Genome 36, 978–983 (1993).CAS 
PubMed 
Article 

Google Scholar 
77.Kang, H. W., Park, D. S., Go, S. J. & Eun, M. Y. Fingerprinting of diverse genomes using PCR with universal rice primers generated from repetitive sequence of Korean weedy rice. Mol. Cell. 13, 281–287 (2002).CAS 

Google Scholar 
78.Jeffreys, A. J., Wilson, V. & Thein, S. L. Hypervariable minisatellite regions in human DNA. Nature 314, 67–72 (1985).ADS 
CAS 
PubMed 
Article 

Google Scholar 
79.Nakamura, Y. et al. Variable number of tandem repeats (VNTR) markers for human gene mapping. Science 235, 1616–1622 (1987).ADS 
CAS 
PubMed 
Article 

Google Scholar 
80.Anderson, T. H. & Nilsson-Tillgren, T. A fungal minisatellite. Nature 386, 771 (1997).ADS 
Article 

Google Scholar 
81.Collard, B. C. Y. & Mackill, D. J. Start Codon Targeted (SCoT) polymorphism: a simple novel DNA marker technique for generating gene-targeted markers in plants. Plant. Mol. Biol. Rep. 27, 86–93 (2009).CAS 
Article 

Google Scholar 
82.Luo, C., He, X. H., Chen, H., Ou, S. J. & Gao, M. P. Analysis of diversity and relationships among mango cultivars using start codon targeted (SCoT) markers. Biochem. Syst. Ecol. 38, 1176–1184 (2010).CAS 
Article 

Google Scholar 
83.Singh, A. K. et al. CAAT box-derived polymorphism (CBDP): A novel promoter-targeted molecular marker for plants. J. Plant Biochem. Biotech. 23, 175–183 (2013).Article 
CAS 

Google Scholar 
84.Schnell, R. J., Olano, C. T., Quintanilla, W. E. & Meerow, A. W. Isolation and characterization of 15 microsatellite loci from mango (Mangifera indica L.) and cross-species amplification in closely related taxa. Mol. Ecol. Notes. 5, 625–627 (2005).CAS 
Article 

Google Scholar 
85.Viruel, M. A., Escribano, P., Barbieri, M., Ferri, M. & Hormaza, J. I. Fingerprint, embryo type, and geographic differentiation in mango (Mangifera indica L., Anacardiaceae) with microsatellites. Mol. Breed. 15, 383–393 (2005).CAS 
Article 

Google Scholar 
86.Ukoskit, K. Development of microsatellite markers in mango (Mangifera indica L.) using 5’ anchored PCR. Thammasat. Int. J. Sci. Tech. 12, 1–7 (2007).
Google Scholar 
87.Ravishankar, K. V., Mani, B. H. R., Anand, L. & Dinesh, M. R. Development of new microsatellite markers from mango (Mangifera indica) and cross-species amplification. Am. J. Bot. 98, 96–99 (2011).Article 

Google Scholar 
88.Yeh, F.C., Yang, R.C. & Boyle, T., POPGENE Version 1.32: Microsoft Window-Based Freeware for Population Genetics Analysis, (p. 12). University of Alberta, Edmonton (1999).89.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Jaccard, P. Nouvellesrecherchessur la distribution florale. Bull. Soc. vaudoise sci. nat. 44, 223–270 (1908).
Google Scholar 
91.Rohlf, F.J. NTSYS pc numerical taxonomy and multivariate system, Version 2.1.Exeter Publ Ltd, Setauket, New York (1993).92.Sneath, P. H. A. & Sokal, R. R. Numerical taxonomy (Freeman Press, 1973).MATH 

Google Scholar 
93.Nei, M. Genetic distance between populations. Am. Nat. 106, 283–392 (1972).Article 

Google Scholar 
94.Yap, V., Nelson, R. J. WinBoot: A program for performing bootstrap analysis of binary data to determine the confidence limits of UPGMA-based dendrograms. IRRI, Philippines (1996). More

1.Selig, E. R., Casey, K. S. & Bruno, J. F. Temperature-driven coral decline: the role of marine protected areas. Glob. Chang. Biol. 18, 1561–1570 (2012).ADS 
Article 

Google Scholar 
2.Mumby, P. J., Wolff, N. H., Bozec, Y. M., Chollett, I. & Halloran, P. Operationalizing the resilience of coral reefs in an era of climate change. Conserv. Lett. 7, 176–187 (2014).Article 

Google Scholar 
3.Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Hughes, T. P. et al. Phase shifts, herbivory, and the resilience of coral reefs to climate change. Curr. Biol. 17, 360–365 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Burkepile, D. E. et al. Species-specific patterns in corallivory and spongivory among Caribbean parrotfishes. Coral Reefs 38, 417–423 (2019).ADS 
Article 

Google Scholar 
6.Bonaldo, R., Hoey, A. & Bellwood, D. The Ecosystem Roles of Parrotfishes on Tropical Reefs. In Oceanography and Marine Biology: An Annual Review 81–132 (2014). https://doi.org/10.1201/b17143-3.7.Mendes, T. C., Ferreira, C. E. L. & Clements, K. D. Discordance between diet analysis and dietary macronutrient content in four nominally herbivorous fishes from the Southwestern Atlantic. Mar. Biol. 165, 180 (2018).Article 
CAS 

Google Scholar 
8.Vergés, A., Doropoulos, C., Malcolm, H. A., Skye, M. & Garcia-pizá, M. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. PNAS https://doi.org/10.1073/pnas.1610725113 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Chang. 8, 499–503 (2018).ADS 
Article 

Google Scholar 
10.Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Ann. Rev. Mar. Sci. https://doi.org/10.1146/annurev-marine-010419-010916 (2020).Article 
PubMed 
PubMed Central 

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

Google Scholar 
12.Munday, P. L., Jones, G. P., Pratchett, M. S. & Williams, A. J. Climate change and the future for coral reef fishes. Fish Fish. 9, 261–285 (2008).Article 

Google Scholar 
13.Veilleux, H. D. & Donelson, J. M. Reproductive gene expression in a coral reef fish exposed to increasing temperature across generations. Conserv. Physiol. 6, 1–12 (2018).Article 
CAS 

Google Scholar 
14.Iftikar, F. I., MacDonald, J. R., Baker, D. W., Renshaw, G. M. C. & Hickey, A. J. R. Could thermal sensitivity of mitochondria determine species distribution in a changing climate?. J. Exp. Biol. 217, 2348–2357 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Chang. 2, 30–32 (2012).ADS 
Article 

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

Google Scholar 
17.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hausfather, Z. & Peters, G. P. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Freer, J. J., Partridge, J. C., Tarling, G. A., Collins, M. A. & Genner, M. J. Predicting ecological responses in a changing ocean: the effects of future climate uncertainty. Mar. Biol. 165, 7 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Floeter, S. R. et al. Atlantic reef fish biogeography and evolution. J. Biogeogr. 35, 22–47 (2008).
Google Scholar 
21.Diversity, C. B. Target 11: Protected areas increased and improved. Quick Guides for the Aichi Biodiversity Targets (2011).22.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
23.McLeod, I. M. et al. Latitudinal variation in larval development of coral reef fishes: implications of a warming ocean. Mar. Ecol. Prog. Ser. 521, 129–141 (2015).ADS 
Article 

Google Scholar 
24.Sponaugle, S., Grorud-Colvert, K. & Pinkard, D. Temperature-mediated variation in early life history traits and recruitment success of the coral reef fish Thalassoma bifasciatum in the Florida Keys. Mar. Ecol. Prog. Ser. 308, 1–15 (2006).ADS 
Article 

Google Scholar 
25.Sponaugle, S., Boulay, J. & Rankin, T. Growth- and size-selective mortality in pelagic ­larvae of a common reef fish. Aquat. Biol. 13, 263–273 (2011).Article 

Google Scholar 
26.Barneche, D. R., Jahn, M. & Seebacher, F. Warming increases the cost of growth in a model vertebrate. Funct. Ecol. https://doi.org/10.1111/1365-2435.13348 (2019).Article 

Google Scholar 
27.Munday, P. L., Kingsford, M. J., O’Callaghan, M. & Donelson, J. M. Elevated temperature restricts growth potential of the coral reef fish Acanthochromis polyacanthus. Coral Reefs 27, 927–931 (2008).ADS 
Article 

Google Scholar 
28.Munday, P. L. et al. Climate change and coral reef connectivity. Coral Reefs 28, 379–395 (2009).ADS 
Article 

Google Scholar 
29.Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Chang. 6, 83–88 (2016).ADS 
Article 

Google Scholar 
30.Twiname, S. et al. A cross-scale framework to support a mechanistic understanding and modelling of marine climate-driven species redistribution, from individuals to communities. Ecography (Cop). https://doi.org/10.1111/ecog.04996 (2020).Article 

Google Scholar 
31.Donelson, J. M., McCormick, M. I., Booth, D. J. & Munday, P. L. Reproductive acclimation to increased water temperature in a tropical reef fish. PLoS ONE 9, e97223 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2, 686–690 (2012).ADS 
Article 

Google Scholar 
33.Habary, A., Johansen, J. L., Nay, T. J., Steffensen, J. F. & Rummer, J. L. Adapt, move or die—how will tropical coral reef fishes cope with ocean warming?. Glob. Chang. Biol. 23, 566–577 (2017).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Chang. 10, 576–581 (2020).ADS 
CAS 
Article 

Google Scholar 
35.Gerber, L. R., Mancha-Cisneros, M. D. M., O’Connor, M. I. & Selig, E. R. Climate change impacts on connectivity in the ocean: Implications for conservation. Ecosphere 5, art33 (2014).Article 

Google Scholar 
36.Shchepetkin, A. F. & McWilliams, J. C. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9, 347–404 (2005).ADS 
Article 

Google Scholar 
37.Berenshtein, I. et al. Biophysical simulations support schooling behavior of fish larvae throughout ontogeny. Front. Mar. Sci. 5, 1–12 (2018).Article 

Google Scholar 
38.IOCCG. Remote sensing in fisheries and aquaculture. and Aquaculture. , IOCCG,. http://www.ioccg.org/reports/report8.pdf (2009).39.Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).ADS 
Article 

Google Scholar 
40.Donlon, C. J. et al. The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens. Environ. 116, 140–158 (2012).ADS 
Article 

Google Scholar 
41.Carton, J. A., Chepurin, G. A. & Chen, L. SODA3: a new ocean climate reanalysis. J. Clim. 31, 6967–6983 (2018).ADS 
Article 

Google Scholar 
42.Franco, B. C. et al. Climate change impacts on the atmospheric circulation, ocean, and fisheries in the southwest South Atlantic Ocean: a review. Clim. Change https://doi.org/10.1007/s10584-020-02783-6 (2020).Article 

Google Scholar 
43.Yang, H. et al. Intensification and poleward shift of subtropical western boundary currents in a warming climate. J. Geophys. Res. Ocean 121, 4928–4945 (2016).ADS 
Article 

Google Scholar 
44.Endo, C. A. K., Gherardi, D. F. M., Pezzi, L. P. & Lima, L. N. Low connectivity compromises the conservation of reef fishes by marine protected areas in the tropical South Atlantic. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
45.Pontes, G. M. et al. Projected changes to South Atlantic boundary currents and confluence region in the CMIP5 models: the role of wind and deep ocean changes. Environ. Res. Lett. 11, 094013 (2016).ADS 
Article 

Google Scholar 
46.Donelson, J. M., Munday, P. L., Mccormick, M. I. & Nilsson, G. E. Acclimation to predicted ocean warming through developmental plasticity in a tropical reef fish. Glob. Chang. Biol. 17, 1712–1719 (2011).ADS 
Article 

Google Scholar 
47.Lett, C. et al. A Lagrangian tool for modelling ichthyoplankton dynamics. Environ. Model. Softw. 23, 1210–1214 (2008).Article 

Google Scholar 
48.Pinheiro, H. T. et al. South-western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean. Divers. Distrib. 24, 951–965 (2018).Article 

Google Scholar 
49.Giglio, V. J. et al. Large and remote marine protected areas in the South Atlantic Ocean are flawed and raise concerns: Comments on Soares and Lucas (2018). Mar. Policy 96, 13–17 (2018).Article 

Google Scholar 
50.Garciá Molinos, J., Burrows, M. T. & Poloczanska, E. S. Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep. 7, 1–9 (2017).Article 
CAS 

Google Scholar 
51.Inagaki, K. Y., Pennino, M. G., Floeter, S. R., Hay, M. E. & Longo, G. O. Trophic interactions will expand geographically but be less intense as oceans warm. Glob. Chang. Biol. 26, 6805–6812 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Brierley, C. & Wainer, I. Inter-annual variability in the tropical Atlantic from the Last Glacial Maximum into future climate projections simulated by CMIP5/PMIP3. Clim. Past 14, 1377–1390 (2018).Article 

Google Scholar 
53.Andrello, M., Mouillot, D., Somot, S., Thuiller, W. & Manel, S. Additive effects of climate change on connectivity between marine protected areas and larval supply to fished areas. Divers. Distrib. 21, 139–150 (2015).Article 

Google Scholar 
54.Carr, M. H. et al. The central importance of ecological spatial connectivity to effective coastal marine protected areas and to meeting the challenges of climate change in the marine environment. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 6–29 (2017).Article 

Google Scholar 
55.Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Morais, R. A., Ferreira, C. E. L. & Floeter, S. R. Spatial patterns of fish standing biomass across Brazilian reefs. J. Fish Biol. 91, 1642–1667 (2017).57.Aued, A. W. et al. Large-scale patterns of benthic marine communities in the Brazilian province. PLoS ONE 13, 1–15 (2018).58.Toste, R., Assad, L. P. D. F. & Landau, L. Downscaling of the global HadGEM2-ES results to model the future and present-day ocean conditions of the southeastern Brazilian continental shelf. Clim. Dyn. 0, 1–17 (2017).59.Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century : projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
60.Fairall, C. W. et al. Cool-skin and warm-layer effects on sea surface temperature. J. Geophys. Res. Ocean 101, 1295–1308 (1996).ADS 
Article 

Google Scholar 
61.Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A. & Edson, J. B. Bulk parameterization of air-sea fluxes: updates and verification for the COARE algorithm. J. Clim. 16, 571–591 (2003).ADS 
Article 

Google Scholar 
62.Lima, L. N., Pezzi, L. P., Penny, S. G. & Tanajura, C. A. S. An investigation of ocean model uncertainties through ensemble forecast experiments in the Southwest Atlantic Ocean. J. Geophys. Res. Ocean. 124, 432–452 (2019).ADS 
Article 

Google Scholar 
63.Marchesiello, P., McWilliams, J. C. & Shchepetkin, A. Open boundary conditions for long-term integration of regional oceanic models. Ocean Model 3, 1–20 (2001).ADS 
Article 

Google Scholar 
64.Large, W. G., McWilliams, J. C. & Doney, S. C. Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32, 363 (1994).ADS 
Article 

Google Scholar 
65.Foltz, G. R., Schmid, C. & Lumpkin, R. An enhanced PIRATA dataset for tropical Atlantic Ocean-atmosphere research. J. Clim. 31, 1499–1524 (2018).ADS 
Article 

Google Scholar 
66.Booth, D. J., Beretta, G. A., Brown, L. & Figueira, W. F. Predicting success of range-expanding coral reef fish in temperate habitats using temperature-abundance relationships. Front. Mar. Sci. 5, (2018).67.Molina-ureña, H. Towards an Ecosystem Approach for Non-Target Reef Fishes: Habitat Uses and Population Dynamics of South Florida Parrotfishes (Perciformes: Scaridae). (Open Access Dissertations 237, 2009).68.Robertson, D. R. Egg size in relation to fertilization dynamics in free-spawning tropical reef fishes. Oecologia 108, 95–104 (1996).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Robertson, D. R. et al. Mechanisms of speciation and faunal enrichment in Atlantic parrotfishes. Mol. Phylogenet. Evol. 40, 795–807 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Islam, M. A. A comparative study on numerical solutions of initial value problems (IVP) for ordinary differential equations (ODE) with Euler and Runge Kutta methods. Am. J. Comput. Math. 05, 393–404 (2015).Article 

Google Scholar 
71.D’Agostini, A., Gherardi, D. F. M. & Pezzi, L. P. Connectivity of marine protected areas and its relation with total kinetic energy. PLoS ONE 10, 1–19 (2015).Article 

Google Scholar 
72.Mitarai, S., Siegel, D. A., Watson, J. R., Dong, C. & McWilliams, J. C. Quantifying connectivity in the coastal ocean with application to the Southern California Bight. J. Geophys. Res. 114, C10026 (2009).ADS 
Article 

Google Scholar  More