Ecology

1.
Bolda, M. P., Goodhue, R. E. & Zalom, F. G. Spotted wing drosophila: Potential economic impact of a newly established pest. Agric. Resour. Econ. Update 13, 5–8 (2010).
Google Scholar 
2.
Calabria, G., Máca, J., Bächli, G., Serra, L. & Pascual, M. First records of the potential pest species Drosophila suzukii (Diptera: Drosophilidae) in Europe. J. Appl. Entomol. 136, 139–147 (2012).
Article  Google Scholar 

3.
Harris, A. & Shaw, B. First record of Drosophila suzukii (Matsumura) (Diptera, Drosophilidae) in Great Britain. Dipterists Digest. 21, 189–192 (2014).
Google Scholar 

4.
Atallah, J., Teixeira, L., Salazar, R., Zaragoza, G. & Kopp, A. The making of a pest: The evolution of a fruit-penetrating ovipositor in Drosophila suzukii and related species. Proc. R. Soc. B Biol. Sci. 281, 20132840. https://doi.org/10.1098/rspb.2013.2840 (2014).
Article  Google Scholar 

5.
Rombaut, A. et al. Invasive Drosophila suzukii facilitates Drosophila melanogaster infestation and sour rot outbreaks in the vineyards. R. Soc. Open Sci. 4, 170117. https://doi.org/10.1098/rsos.170117 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Gress, B. E. & Zalom, F. G. Identification and risk assessment of spinosad resistance in a California population of Drosophila suzukii. Pest Manag. Sci. 75, 1270–1276. https://doi.org/10.1002/ps.5240 (2019).
CAS  Article  PubMed  Google Scholar 

7.
Lee, K. P. et al. Lifespan and reproduction in Drosophila: New insights from nutritional geometry. Proc. Natl. Acad. Sci. 105, 2498–2503 (2008).
ADS  CAS  Article  Google Scholar 

8.
Piper, M. D. W. et al. A holidic medium for Drosophila melanogaster. Nat. Methods 11, 100–105. https://doi.org/10.1038/nmeth.2731 (2014).
CAS  Article  PubMed  Google Scholar 

9.
Grangeteau, C. et al. Yeast quality in juvenile diet affects Drosophila melanogaster adult life traits. Sci. Rep. 8, 13070. https://doi.org/10.1038/s41598-018-31561-9 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Rohlfs, M. & Kürschner, L. Saprophagous insect larvae, Drosophila melanogaster, profit from increased species richness in beneficial microbes. J. Appl. Entomol. 134, 667–671. https://doi.org/10.1111/j.1439-0418.2009.01458.x (2009).
Article  Google Scholar 

11.
Hardin, J. A., Kraus, D. A. & Burrack, H. J. Diet quality mitigates intraspecific larval competition in Drosophila suzukii. Entomol. Exp. Appl. 156, 59–65. https://doi.org/10.1111/eea.12311 (2015).
CAS  Article  Google Scholar 

12.
Lewis, M. T. & Hamby, K. A. Differential impacts of yeasts on feeding behavior and development in larval Drosophila suzukii (Diptera:Drosophilidae). Sci. Rep. 9, 13370. https://doi.org/10.1038/s41598-019-48863-1 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Bellutti, N. et al. Dietary yeast affects preference and performance in Drosophila suzukii. J. Pest. Sci. 91, 651–660 (2018).
Article  Google Scholar 

14.
Buser, C. C., Newcomb, R. D., Gaskett, A. C. & Goddard, M. R. Niche construction initiates the evolution of mutualistic interactions. Ecol. Lett. 17, 1257–1264. https://doi.org/10.1111/ele.12331 (2014).
Article  PubMed  Google Scholar 

15.
Günther, C. S., Knight, S. J., Jones, R. & Goddard, M. R. Are Drosophila preferences for yeasts stable or contextual?. Ecol. Evol. 9, 8075–8086. https://doi.org/10.1002/ece3.5366 (2019).
Article  PubMed  PubMed Central  Google Scholar 

16.
Christiaens, J. F. et al. The fungal aroma gene ATF1 promotes dispersal of yeast cells through insect vectors. Cell Rep. 9, 425–432. https://doi.org/10.1016/j.celrep.2014.09.009 (2014).
CAS  Article  PubMed  Google Scholar 

17.
Palanca, L., Gaskett, A. C., Günther, C. S., Newcomb, R. D. & Goddard, M. R. Quantifying variation in the ability of yeasts to attract Drosophila melanogaster. PLoS ONE 8, e75332. https://doi.org/10.1371/journal.pone.0075332 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

18.
Scheidler, N. H., Liu, C., Hamby, K. A., Zalom, F. G. & Syed, Z. Volatile codes: Correlation of olfactory signals and reception in Drosophila-yeast chemical communication. Sci. Rep. 5, 1–13. https://doi.org/10.1038/srep14059 (2015).
CAS  Article  Google Scholar 

19.
Becher, P. G. et al. Chemical signaling and insect attraction is a conserved trait in yeasts. Ecol. Evol. 8, 2962–2974. https://doi.org/10.1002/ece3.3905 (2018).
Article  PubMed  PubMed Central  Google Scholar 

20.
Chandler, J. A., Eisen, J. A. & Kopp, A. Yeast communities of diverse Drosophila species: Comparison of two symbiont groups in the same hosts. Appl. Environ. Microbiol. 78, 7327–7336. https://doi.org/10.1128/AEM.01741-12 (2012).
CAS  Article  PubMed  Google Scholar 

21.
Lam, S. S. T. H. & Howell, K. S. Drosophila-associated yeast species in vineyard ecosystems. FEMS Microbiol. Lett. 362, 1–7. https://doi.org/10.1093/femsle/fnv170 (2015).
CAS  Article  Google Scholar 

22.
Hamby, K. A., Hernández, A., Boundy-Mills, K. & Zalom, F. G. Associations of yeasts with spotted-wing Drosophila (Drosophila suzukii; Diptera: Drosophilidae) in cherries and raspberries. Appl. Environ. Microbiol. 78, 4869–4873. https://doi.org/10.1128/AEM.00841-12 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Lewis, M. T., Koivunen, E. E., Swett, C. L. & Hamby, K. A. Associations between Drosophila suzukii (Diptera: Drosophilidae) and fungi in raspberries. Environ. Entomol. 27, 383–392. https://doi.org/10.1093/ee/nvy167 (2018).
Article  Google Scholar 

24.
Fountain, M. T. et al. Alimentary microbes of winter-form Drosophila suzukii. Insect Mol. Biol. 27, 383–392. https://doi.org/10.1111/imb.12377 (2018).
CAS  Article  PubMed  Google Scholar 

25.
Vadkertiová, R., Molnárová, J., Vránová, D. & Sláviková, E. Yeasts and yeast-like organisms associated with fruits and blossoms of different fruit trees. Can. J. Microbiol. 58, 1344–1352. https://doi.org/10.1139/cjm-2012-0468 (2012).
CAS  Article  PubMed  Google Scholar 

26.
Barata, A., Malfeito-Ferreira, M. & Loureiro, V. Changes in sour rotten grape berry microbiota during ripening and wine fermentation. Int. J. Food Microbiol. 154, 152–161. https://doi.org/10.1016/J.IJFOODMICRO.2011.12.029 (2012).
CAS  Article  PubMed  Google Scholar 

27.
Lasa, R. et al. Yeast species, strains, and growth media mediate attraction of Drosophila suzukii (Diptera: Drosophilidae). Insects 10, 228–228. https://doi.org/10.3390/insects10080228 (2019).
Article  PubMed Central  Google Scholar 

28.
Noble, R. et al. Improved insecticidal control of spotted wing drosophila (Drosophila suzukii) using yeast and fermented strawberry juice baits. Crop Protect. https://doi.org/10.1016/J.CROPRO.2019.104902 (2019).
Article  Google Scholar 

29.
Hoang, D., Kopp, A. & Chandler, J. A. Interactions between Drosophila and its natural yeast symbionts—Is Saccharomyces cerevisiae a good model for studying the fly-yeast relationship?. PeerJ 3, e1116. https://doi.org/10.7717/peerj.1116 (2015).
Article  PubMed  PubMed Central  Google Scholar 

30.
Taylor, M. W., Tsai, P., Anfang, N., Ross, H. A. & Goddard, M. R. Pyrosequencing reveals regional differences in fruit-associated fungal communities. Environ. Microbiol. 16, 2848–2858. https://doi.org/10.1111/1462-2920.12456 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
Abdelfattah, A., Wisniewski, M., Li Destri Nicosia, M. G., Cacciola, S. O. & Schena, L. Metagenomic analysis of fungal diversity on strawberry plants and the effect of management practices on the fungal community structure of aerial organs. PLoS ONE 11, e0160470. https://doi.org/10.1371/journal.pone.0160470 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Dobzhansky, T., Cooper, D. M., Phaff, H. J., Knapp, E. P. & Carson, H. L. Differential attraction of species of Drosophila to different species of yeasts. Ecology 37, 544–550. https://doi.org/10.2307/1930178 (1956).
Article  Google Scholar 

33.
Günther, C. S. & Goddard, M. R. Do yeasts and Drosophila interact just by chance?. Fungal Ecol. 38, 37–43. https://doi.org/10.1016/J.FUNECO.2018.04.005 (2018).
Article  Google Scholar 

34.
Günther, C. S., Goddard, M. R., Newcomb, R. D. & Buser, C. C. The context of chemical communication driving a mutualism. J. Chem. Ecol. 41, 929–936. https://doi.org/10.1007/s10886-015-0629-z (2015).
CAS  Article  PubMed  Google Scholar 

35.
Schiabor, K. M., Quan, A. S. & Eisen, M. B. Saccharomyces cerevisiae mitochondria are required for optimal attractiveness to Drosophila melanogaster. PLoS ONE 9, e113899. https://doi.org/10.1371/journal.pone.0113899 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Gayevskiy, V. & Goddard, M. R. Geographic delineations of yeast communities and populations associated with vines and wines in New Zealand. ISME J. 6, 1281–1290 (2012).
CAS  Article  Google Scholar 

37.
Bokulich, N. A., Thorngate, J. H., Richardson, P. M. & Mills, D. A. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proc. Natl. Acad. Sci. 111, E139–E148. https://doi.org/10.1073/PNAS.1317377110 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Martins, G. et al. Influence of the farming system on the epiphytic yeasts and yeast-like fungi colonizing grape berries during the ripening process. Int. J. Food Microbiol. 177, 21–28. https://doi.org/10.1016/J.IJFOODMICRO.2014.02.002 (2014).
Article  PubMed  Google Scholar 

39.
Cordero-Bueso, G. et al. Influence of the farming system and vine variety on yeast communities associated with grape berries. Int. J. Food Microbiol. 145, 132–139. https://doi.org/10.1016/J.IJFOODMICRO.2010.11.040 (2011).
Article  PubMed  Google Scholar 

40.
Cha, D. H., Adams, T., Rogg, H. & Landolt, P. J. Identification and field evaluation of fermentation volatiles from wine and vinegar that mediate attraction of spotted wing drosophila, Drosophila suzukii. J. Chem. Ecol. 38, 1419–1431. https://doi.org/10.1007/s10886-012-0196-5 (2012).
CAS  Article  PubMed  Google Scholar 

41.
Cha, D. H. et al. A four-component synthetic attractant for Drosophila suzukii (Diptera: Drosophilidae) isolated from fermented bait headspace. Pest Manag. Sci. 70, 324–331. https://doi.org/10.1002/ps.3568 (2014).
CAS  Article  PubMed  Google Scholar 

42.
Faucher, C. P., Hilker, M. & de Bruyne, M. Interactions of carbon dioxide and food odours in Drosophila: Olfactory hedonics and sensory neuron properties. PLoS ONE 8, e56361 (2013).
ADS  CAS  Article  Google Scholar 

43.
Liti, G. et al. Population genomics of domestic and wild yeasts. Nature 458, 337–341 (2009).
ADS  CAS  Article  Google Scholar 

44.
Knight, S., Klaere, S., Fedrizzi, B. & Goddard, M. R. Regional microbial signatures positively correlate with differential wine phenotypes: Evidence for a microbial aspect to terroir. Sci. Rep. 5, 1–10. https://doi.org/10.1038/srep14233 (2015).
CAS  Article  Google Scholar 

45.
Albertin, W. et al. Hanseniaspora uvarum from winemaking environments show spatial and temporal genetic clustering. Front. Microbiol. 6, 1569 (2016).
Article  Google Scholar 

46.
Shearer, P. W. et al. Seasonal cues induce phenotypic plasticity of Drosophila suzukii to enhance winter survival. BMC Ecol. 16, 11. https://doi.org/10.1186/s12898-016-0070-3 (2016).
Article  PubMed  PubMed Central  Google Scholar 

47.
Tochen, S. et al. Temperature-related development and population parameters for Drosophila suzukii (Diptera: Drosophilidae) on cherry and blueberry. Environ. Entomol. 43, 501–510. https://doi.org/10.1603/EN13200 (2014).
Article  PubMed  Google Scholar 

48.
Ryan, G. D., Emiljanowicz, L., Wilkinson, F., Kornya, M. & Newman, J. A. Thermal tolerances of the spotted-wing drosophila Drosophila suzukii (Diptera: Drosophilidae). J. Econ. Entomol 109, 746–752. https://doi.org/10.1093/jee/tow006 (2016).
Article  PubMed  Google Scholar 

49.
Plantamp, C., Estragnat, V., Fellous, S., Desouhant, E. & Gibert, P. Where and what to feed? Differential effects on fecundity and longevity in the invasive Drosophila suzukii. Basic Appl. Ecol. 19, 56–66. https://doi.org/10.1016/j.baae.2016.10.005 (2017).
Article  Google Scholar 

50.
Anfang, N., Brajkovich, M. & Goddard, M. R. Co-fermentation with Pichia kluyveri increases varietal thiol concentrations in Sauvignon Blanc. Aust. J. Grape Wine Res. 15, 1–8 (2009).
CAS  Article  Google Scholar 

51.
Fischer, C. et al. Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior. eLife 6, e18855. https://doi.org/10.7554/eLife.18855 (2017).
Article  PubMed  PubMed Central  Google Scholar 

52.
Mori, B. A. et al. Enhanced yeast feeding following mating facilitates control of the invasive fruit pest Drosophila suzukii. J. Appl. Ecol. 54, 170–177. https://doi.org/10.1111/1365-2664.12688 (2017).
Article  Google Scholar 

53.
Shaw, B., Brain, P., Wijnen, H. & Fountain, M. T. Reducing Drosophila suzukii emergence through inter-species competition. Pest Manag. Sci. 74, 149–160. https://doi.org/10.1002/ps.4836 (2018).
CAS  Article  Google Scholar 

54.
Cini, A., Ioriatti, C. & Anfora, G. A review of the invasion of Drosophila suzukii in Europe and a draft research agenda for integrated pest management. Bull. Insectol. 65, 149–160 (2012).
Google Scholar 

55.
Crawley, M. J. The R book (Wiley, New York, 2013).
Google Scholar 

56.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2019).
Google Scholar 

57.
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 

58.
Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Estimated marginal means, aka least-squares means. R package version 1.3. 2. (2019).

59.
Harrell, F. E., et al. Hmisc: Harrell Miscellaneous. R package version 4.3–1. (2020). More

1.
Wilson, E. O. The Insect Societies (Harvard University Press, Cambridge, 1971).
Google Scholar 
2.
Wilson, E. O. Sociobiology: The New Synthesis (Harvard University Press, Cambridge, 1975).
Google Scholar 

3.
Oster, G. F. & Wilson, E. O. Caste and Ecology in the Social Insects (Princeton University Press, Princeton, 1978).
Google Scholar 

4.
Seeley, T. D. Honeybee Ecology: A Study of Adaptation in Social Life (Princeton University Press, Princeton, 1985).
Google Scholar 

5.
Seeley, T. D. Adaptive significance of the age polyethism schedule in honeybee colonies. Behav. Ecol. Sociobiol. 11, 287–293 (1982).
Article  Google Scholar 

6.
Robinson, G. E. Regulation of division of labor in insect societies. Annu. Rev. Entomol. 37, 637–665 (1992).
CAS  PubMed  Article  Google Scholar 

7.
Beshers, S. N. & Fewell, J. H. Models of division of labor in social insects. Annu. Rev. Entomol. 46, 413–440 (2001).
CAS  PubMed  Article  Google Scholar 

8.
Johnson, B. R. Within-nest temporal polyethism in the honey bee. Behav. Ecol. Sociobiol. 62, 777–784 (2008).
Article  Google Scholar 

9.
Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, Cambridge, 1990).
Google Scholar 

10.
Crosland, M. W. J., Lok, C. M., Wong, T. C., Shakarad, M. & Traniello, J. F. A. Division of labour in a lower termite: The majority of tasks are performed by older workers. Anim. Behav. 54, 999–1012 (1997).
CAS  PubMed  Article  Google Scholar 

11.
Hinze, B. & Leuthold, R. H. Age related polyethism and activity rhythms in the nest of the termite Macrotermes bellicosus (Isoptera, Termitidae). Insect. Soc. 46, 392–397 (1999).
Article  Google Scholar 

12.
Cameron, S. A. Temporal patterns of division of labor among workers in the primitively eusocial bumble bee, Bombus griseocoffis (Hymenoptera: Apidae). Ethology 80, 137–151 (1989).
Article  Google Scholar 

13.
Naug, D. & Gadagkar, R. The role of age in temporal polyethism in a primitively eusocial wasp. Behav. Ecol. Sociobiol. 42, 37–47 (1998).
Article  Google Scholar 

14.
Biedermann, P. H. W. & Taborsky, M. Larval helpers and age polyethism in ambrosia beetles. Proc. Natl. Acad. Sci. USA 108, 17064–17069 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

15.
Wakano, J. N., Nakata, K. & Yamamura, N. Dynamic model of optimal age polyethism in social insects under stable and fluctuating environments. J. Theor. Biol. 193, 153–165 (1998).
Article  Google Scholar 

16.
Duarte, A., Weissing, F. J., Pen, I. & Keller, L. An evolutionary perspective on self-organized division of labor in social insects. Annu. Rev. Ecol. Evol. Syst. 42, 91–110 (2011).
Article  Google Scholar 

17.
Stern, D. L. & Foster, W. A. The evolution of soldiers in aphids. Biol. Rev. 71, 27–79 (1996).
CAS  PubMed  Article  Google Scholar 

18.
Aoki, S. & Kurosu, U. A review of the biology of Cerataphidini (Hemiptera, Aphididae, Hormaphidinae), focusing mainly on their life cycles, gall formation, and soldiers. Psyche 2010, 380351 (2010).
Google Scholar 

19.
Abbot, P., Tooker, J. & Lawson, S. P. Chemical ecology and sociality in aphids: Opportunities and directions. J. Chem. Ecol. 44, 770–784 (2018).
CAS  PubMed  Article  Google Scholar 

20.
Aoki, S. Colophina clematis (Homoptera, Pemphigidae), an aphid species with” soldiers”. Kontyu 5, 276–282 (1977).
Google Scholar 

21.
Aoki, S. & Kurosu, U. Gall cleaning by the aphid Hormaphis betulae. J. Ethol. 9, 51–55 (1989).
Google Scholar 

22.
Benton, T. G. & Foster, W. A. Altruistic housekeeping in a social aphid. Proc. R. Soc. B 247, 199–202 (1992).
ADS  Article  Google Scholar 

23.
Aoki, S. & Kurosu, U. Soldiers of Astegopteryx styraci (Homoptera, Aphidoidea) clean their gall. Jpn. J. Entomol. 57, 407–416 (1989).
Google Scholar 

24.
Aoki, S., Kurosu, U. & Stern, D. L. Aphid soldiers discriminate between soldiers and non-soldiers, rather than between kin and non-kin Ceratoglyphina bambusae. Anim. Behav. 42, 865–866 (1991).
Article  Google Scholar 

25.
Kurosu, U., Narukawa, J., Buranapanichpan, S. & Aoki, S. Head-plug defense in a gall aphid. Insect. Soc. 53, 86–91 (2006).
Article  Google Scholar 

26.
Kurosu, U., Aoki, S. & Fukatsu, T. Self-sacrificing gall repair by aphid nymphs. Proc. R. Soc. B 270, S12–S14 (2003).
PubMed  Article  Google Scholar 

27.
Pike, N. & Foster, W. Fortress repair in the social aphid species Pemphigus spyrothecae. Anim. Behav. 67, 909–914 (2004).
Article  Google Scholar 

28.
Kutsukake, M., Shibao, H., Uematsu, K. & Fukatsu, T. Scab formation and wound healing of plant tissue by soldier aphid. Proc. R. Soc. B 276, 1555–1563 (2009).
PubMed  Article  Google Scholar 

29.
Kutsukake, M. et al. Exaggeration and cooption of innate immunity for social defense. Proc. Natl. Acad. Sci. USA 116, 8950–8959 (2019).
CAS  PubMed  Article  Google Scholar 

30.
Aoki, S. Evolution of sterile soldiers in aphids. In Animal Societies: Theories andFacts (eds Ito, Y. et al.) 53–65 (Japan Scientific Societies Press, Tokyo, 1987).
Google Scholar 

31.
Aoki, S. & Kurosu, U. Social aphids. In Encyclopedia of Social Insects (ed. Starr, C. K.) (Springer, New York, 2020). https://doi.org/10.1007/978-3-319-90306-4_107-1.
Google Scholar 

32.
Aoki, S. & Kurosu, U. Biennial galls of the aphid Astegopteryx styraci on a temperate deciduous tree Styrax obassia. Acta Phytopathol. Entomol. Hung. 25, 57–65 (1990).
Google Scholar 

33.
Shibao, H., Kutsukake, M., Lee, J. & Fukatsu, T. Maintenance of soldier-producing aphids on an artificial diet. J. Insect Physiol. 48, 495–505 (2002).
CAS  PubMed  Article  Google Scholar 

34.
Shibao, H., Lee, J. M., Kutsukake, M. & Fukatsu, T. Aphid soldier differentiation: density acts on both embryos and newborn nymphs. Naturwissenschaften 90, 501–504 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

35.
Shibao, H., Kutsukake, M. & Fukatsu, T. Density triggers soldier production in a social aphid. Proc. R. Soc. B 271, S71–S74 (2004).
PubMed  Article  Google Scholar 

36.
Shibao, H., Kutsukake, M. & Fukatsu, T. The proximate cue of density-dependent soldier production in a social aphid. J. Insect Physiol. 50, 143–147 (2004).
CAS  PubMed  Article  Google Scholar 

37.
Shibao, H., Kutsukake, M. & Fukatsu, T. Density-dependent induction and suppression of soldier differentiation in an aphid social system. J. Insect Physiol. 50, 995–1000 (2004).
CAS  PubMed  Article  Google Scholar 

38.
Shibao, H., Kutsukake, M., Matsuyama, S., Fukatsu, T. & Shimada, M. Mechanisms regulating caste differentiation in an aphid social system. Commun. Integr. Biol. 3, 1–5 (2010).
PubMed  PubMed Central  Article  Google Scholar 

39.
Kutsukake, M. et al. Venomous protease of aphid soldier for colony defense. Proc. Natl. Acad. Sci. USA 101, 11338–11343 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

40.
Stern, D. L., Aoki, S. & Kurosu, U. A test of geometric hypotheses for soldier investment patterns in the gall producing tropical aphid Cerataphis fransseni (Homoptera, Hormaphididae). Insect. Soc. 41, 457–460 (1994).
Article  Google Scholar 

41.
Pike, N., Braendle, C. & Foster, W. A. Seasonal extension of the soldier instar as a route to increased defence investment in the social aphid Pemphigus spyrothecae. Ecol. Entomol. 29, 89–95 (2004).
Article  Google Scholar 

42.
Pike, N. Specialised placement of morphs within the gall of the social aphid Pemphigus spyrothecae. BMC Evol. Biol. 7, 18 (2007).
PubMed  PubMed Central  Article  Google Scholar 

43.
Uematsu, K., Kutsukake, M., Fukatsu, T., Shimada, M. & Shibao, H. Altruistic colony defense by menopausal female insects. Curr. Biol. 20, 1182–1186 (2010).
CAS  PubMed  Article  Google Scholar 

44.
Uematsu, K., Shimada, M. & Shibao, H. Juveniles and the elderly defend, the middle-aged escape: division of labour in a social aphid. Biol. Let. 9, 20121053 (2013).
Article  Google Scholar 

45.
Abe, T., Bignell, D. E., Higashi, M. & Abe, Y. Termites: Evolution, Sociality, Symbioses, Ecology (Springer, Berlin, 2000).
Google Scholar 

46.
Shibao, H. Lack of kin discrimination in the eusocial aphid Pseudoregma bambucicola (Homoptera: Aphididae). J. Ethol. 17, 17–24 (1999).
Article  Google Scholar 

47.
Abbot, P., Withgott, J. H. & Moran, N. A. Genetic conflict and conditional altruism in social aphid colonies. Proc. Natl. Acad. Sci. USA 98, 12068–12071 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

48.
Abbot, P. & Chhatre, V. Kin structure provides no explanation for intruders in social aphids. Mol. Ecol. 16, 3659–3670 (2007).
CAS  PubMed  Article  Google Scholar 

49.
Kutsukake, M. et al. An insect-induced novel plant phenotype for sustaining social life in a closed system. Nat. Commun. 3, 1187 (2012).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Kutsukake, M. et al. Evolution of soldier-specific venomous protease in social aphids. Mol. Biol. Evol. 25, 2627–2641 (2008).
CAS  PubMed  Article  Google Scholar  More

Study areas
We collected survey and salivary hormone data from people at the Camel’s Back—Hulls Gulch Reserve of the Ridge to Rivers Management Area in Boise, Idaho, USA (Fig. 1S). The Lower Hulls Gulch area is largely characterized by sagebrush steppe habitat as well as riparian areas that act as habitat to a variety of avian, mammal, and herpetological species. Common wildlife spotted by recreationists include birds, amphibians, reptiles, and small mammals. We collected data between March 24—May 18, 2017 on both the weekend and weekdays. Weather data, consisting of average wind speed and average air temperature were taken from the Crestline Trail Idaho (Boise, ID).
Participants
We recruited participants at a prominent trail head associated with the recreational area, and the purpose and protocols of the study were explained. Upon agreeing to participate, participants were asked to read an additional written statement of the project and sign a informed consent form. Each recruited individual was minimally required to participate in either the saliva collection or survey, although participation in all parts of the study was highly encouraged. Any recreationists who had been recreating for more than 10 min were not recruited. We defined hikers as recreationists who were intent on walking in the recreational area, and excluded users who were performing exertive activities such as running or biking. All participants were given an anonymous identifier which was used throughout the study. This research project was reviewed and approved by the IRB at Boise State University, Idaho, USA under #006-SB17-061.
Salivary cortisol and testosterone collection
We restricted all sampling until late morning (often 10:00am) to control for diel patterns in hormone concentrations after awakening25,26,27. To further account for any variations due to time of day, the time of collection was recorded for each saliva sample and factored into each analysis. We found that the time of day each cortisol sample was taken did not account for variation, and that the diurnal cycle of cortisol did not explain the change in cortisol concentration that we observed in hikers.
Saliva samples were collected from recreationists using a pre-post paired design. Participants were asked to give at least 0.25 mL of saliva using a saliva collection aid (2 mL cyrovial, Salimetrics PA, USA) via the passive drool method25,28. The passive drool method allows for the collection of large samples for multiple assays, reduces the risk of contamination by collection substances, and allows samples to be frozen without interfering with assay protocols28. Saliva was immediately stored in a portable cooler with ice until frozen at -10 °C. Cortisol and testosterone concentrations were assessed using a Salimetrics Cortisol Enzyme Immunoassay following the manufacturer’s protocols and design (Salimetrics, PA, USA). All assay plates were read using the Gen5 software and Biotek EL800 Plate Reader. Hormone concentrations were then calculated from the optical densities using a standard curve and the online elisaanalysis interface.
Survey collection
After recreating, all participants were asked to take a survey. Survey questions included the following: variables affecting psychological stress levels, observation of wildlife, motivations for recreation (ranging from social, personal challenge, wildlife, and solitude), perceived ecological impact that outdoor recreational activity has on wildlife and habitats, and basic demographics including age and gender. In addition, participants were asked to rate how psychologically stressed they felt after their recreational activity. Participants also had the opportunity to note any negative experiences they had while recreating.
GPS track collection
Participants were encouraged to carry a handheld GPS device to track their route while hiking. We used GPS tracks to connect hiker routes with land cover types such as % vegetation cover, urban cover, water, and riparian cover. Using a 100 m buffer as a standard measurement for visibility, we calculated the proportion of land cover types each hiker traveled through during their trip. We also used GPS tracks to compare line density calculations to estimate areas and trails with higher recreational traffic.
Photograph collection
We used volunteer employed photography and asked participants to take photographs of landscapes they found beautiful. At the end of the trip, participants were asked to select one photograph that captured an area of high aesthetic value to them. Photographs were spatially linked to the landscape using participant GPS tracks, and analyzed using kernel density estimates (KDE) to compare what areas and land cover types were photographed more frequently. In addition, the subject and photographic elements of each photograph were tallied and collected to compare what components of the landscape participants were more likely to take pictures of.
Statistical analyses
To assess the change in salivary cortisol to landscape aesthetics, we used a backward stepwise approach to create a linear model with the following predictor variables: perceived aesthetics (using principal component PC1 scores), land cover metrics, start time, duration, total wildlife observance, plant identification skills (as a measure of ecological familiarity), and all aesthetic interaction terms. . We checked for collinearity among variables and considered pairwise r  >|0.70| to indicate a correlation. Due to high correlation between start time and duration, duration was removed from all models. A total of 6 saliva samples (all hikers) were not used due to unusually high ( > 2 SD) cortisol concentrations.
Non-significant interaction variables were removed first based on lowest SS values, followed by main effect variables with the lowest SS value until only statistically significant variables remained. Cortisol was evaluated as the change in concentration from recreational activity (Post-collection—Pre-collection). Negative changes correspond to a decrease and positive changes to an increase in hormone concentration after recreating. To meet normality assumptions, the change in both cortisol was transformed using a square root function taken at absolute value. After data transformation, all original signs were returned to maintain the negative–positive spectrum of hormone change. The removal of any outliers did not change the interpretation of the results and were kept across analyses.
We used a backward stepwise approach to explain aesthetic perception with the following predictor variables: weekend/weekday (as a metric of visitor use), total wildlife observance, plant identification skills (as a metric of ecological familiarity), gender, age, duration, and all interaction terms. We used a backward stepwise approach and a generalized linear model to evaluate whether land cover, recreational use, and ecological familiarity explained the number of wildlife observations.No variables were correlated with each other, and all interaction terms were included. To investigate the relationship between perceived ecological impact metrics and recreational motivation, we used spearman correlations. More

1.
Adams, D. C. & Nistri, A. Ontogenetic convergence and evolution of foot morphology in european cave salamanders (Family: Plethodontidae). BMC Evol. Biol. 10, 216 (2010).
PubMed  PubMed Central  Article  Google Scholar 
2.
Baken, E. K., Mellenthin, L. E. & Adams, D. C. Macroevolution of desiccation-related morphology in plethodontid salamanders as inferred from a novel surface area to volume ratio estimation approach. Evolution 74, 476–486 (2020).
PubMed  Article  Google Scholar 

3.
Martinez, P. A. et al. The contribution of neutral evolution and adaptive processes in driving phenotypic divergence in a model mammalian species, the andean fox Lycalopex culpaeus. J. Biogeogr. 45, 1114–1125 (2018).
Article  Google Scholar 

4.
Vidal-García, M., Byrne, P. G., Roberts, J. D. & Keogh, J. S. The role of phylogeny and ecology in shaping morphology in 21 genera and 127 species of australo-papuan myobatrachid frogs. J. Evol. Biol. 27, 181–192 (2014).
PubMed  Article  Google Scholar 

5.
Adams, D. C. Parallel evolution of character displacement driven by competitive selection in terrestrial salamanders. BMC Evol. Biol. 10, 72 (2010).
PubMed  PubMed Central  Article  Google Scholar 

6.
Losos, J. B. Ecological character displacement and the study of adaptation. Proc. Natl. Acad. Sci. 97, 5693–5695 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Moen, D. S., Irschick, D. J. & Wiens, J. J. Evolutionary conservatism and convergence both lead to striking similarity in ecology, morphology and performance across continents in frogs. Proc. R. Soc. B. 280, 20132156 (2013).
PubMed  Article  Google Scholar 

8.
Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in new world anurans: energy and water in a balance. Ecography 42, 456–466 (2019).
Article  Google Scholar 

9.
Gouveia, S. F. et al. Biophysical modeling of water economy can explain geographic gradient of body size in anurans. Am. Nat. 193, 51–58 (2019).
PubMed  Article  PubMed Central  Google Scholar 

10.
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: water deficit and anuran body size in the brazilian cerrado. Ecography 32, 581–590 (2009).
Article  Google Scholar 

11.
Cooney, C. R. et al. Ecology and allometry predict the evolution of avian developmental durations. Nat. Commun. 11, 1–9 (2020).
Article  CAS  Google Scholar 

12.
Kriegman, S., Cheney, N. & Bongard, J. How morphological development can guide evolution. Sci. Rep. 8, 1–10 (2018).
Article  CAS  Google Scholar 

13.
Moczek, A. P. Re-evaluating the environment in developmental evolution. Front. Ecol. Evol. 3, 1–8 (2015).
Article  Google Scholar 

14.
Richter-Boix, A., Tejedo, M. & Rezende, E. L. Evolution and plasticity of anuran larval development in response to desiccation. a comparative analysis. Ecol. Evol. 1, 15–25 (2011).
PubMed  PubMed Central  Article  Google Scholar 

15.
Castro, K. M. S. A., do Santos, M. P., Brito, M. F. G., Bidau, C. J. & Martinez, P. A. Ontogenetic allometry conservatism across five teleost orders. J. Fish Biol. 93, 745–749 (2018).
Article  Google Scholar 

16.
Skúlason, S. et al. A way forward with eco evo devo: an extended theory of resource polymorphism with postglacial fishes as model systems. Biol. Rev. 94, 1786–1808 (2019).
PubMed  Article  Google Scholar 

17.
Porter, W. P. & Gates, D. M. Thermodynamic equilibria of animals with environment. Ecol. Monogr. 39, 227–244 (1969).
Article  Google Scholar 

18.
Thompson, D. A. On Growth and Form (Cambridge University Press, Cambridge, 1917).
Google Scholar 

19.
Schmidt-Nielsen, K. Scaling: Why is Animal Size so Important? (Cambridge University Press, Cambridge, 1984).
Google Scholar 

20.
Amado, T. F., Pinto, M. G. M. & Olalla-Tárraga, M. Á. Anuran 3d models reveal the relationship between surface area-to-volume ratio and climate. J. Biogeogr. 46, 1429–1437 (2019).
Google Scholar 

21.
Ashton, K. G. Do amphibians follow bergmann’s rule?. Can. J. Zool. 80, 708–716 (2002).
Article  Google Scholar 

22.
Glazier, D. Effects of contingency versus constraints on the body-mass scaling of metabolic rate. Challenges 9, 4 (2018).
Article  Google Scholar 

23.
Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: water conservation hypothesis revisited. J. Biogeogr. 43, 2075–2084 (2016).
Article  Google Scholar 

24.
Lindsey, C. C. Body sizes of poikilotherm vertebrates at different latitudes. Evolution 20, 456–465 (1966).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Bergmannn, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Göttinger Stud. 1, 595–708 (1847).
Google Scholar 

26.
Nevo, E. Adaptive variation in size of cricket frogs. Ecology 54, 1271–1281 (1973).
Article  Google Scholar 

27.
Tracy, C. R., Christian, K. A. & Tracy, C. R. Not just small, wet, and cold : effects of body size and skin resistance on thermoregulation and arboreality of frogs. Ecology 91, 1477–1484 (2010).
PubMed  Article  PubMed Central  Google Scholar 

28.
Wells, K. D. The Ecology and Behavior of Amphibians (The University of Chicago Press, Chicago, 2007).
Google Scholar 

29.
Perez, D., Sheehy, C. M. & Lillywhite, H. B. Variation of organ position in snakes. J. Morphol. 280, 1798–1807 (2019).
PubMed  Article  PubMed Central  Google Scholar 

30.
Amiel, J. J., Chua, B., Wassersug, R. J. & Jones, D. R. Temperature-dependent regulation of blood distribution in snakes. J. Exp. Biol. 214, 1458–1462 (2011).
PubMed  Article  PubMed Central  Google Scholar 

31.
Canals, M. Thermal ecology of small animals. Biol Res 31, 367–374 (1998).
Google Scholar 

32.
Tracy, C. R. A model of the dynamic exchanges of water and energy between a terrestrial amphibian and its environment. Ecol. Monogr. 46, 293–326 (1976).
Article  Google Scholar 

33.
Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41, 587–640 (1966).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Klingenberg, C. P. Heterochrony and allometry: the analysis of evolutionary change in ontogeny. Biol. Rev. 73, 79–123 (1998).
CAS  PubMed  Article  Google Scholar 

35.
Klingenberg, C. P. Size, shape, and form: concepts of allometry in geometric morphometrics. Dev. Genes Evol. 226, 113–137 (2016).
PubMed  PubMed Central  Article  Google Scholar 

36.
Voje, K. L., Hansen, T. F., Egset, C. K., Bolstad, G. H. & Pélabon, C. Allometric constraints and the evolution of allometry. Evolution 68, 866–885 (2014).
PubMed  Article  Google Scholar 

37.
Pélabon, C. et al. Evolution of morphological allometry. Ann. N. Y. Acad. Sci. 1320, 58–75 (2014).
ADS  PubMed  Article  Google Scholar 

38.
Duellman, W. E., Marion, A. B. & Hedges, S. B. Phylogenetics, classification, and biogeography of the treefrogs (amphibia: anura: arboranae). Zootaxa 4104, 001–109 (2016).
Article  Google Scholar 

39.
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Phil. Trans. R. Soc. B. 368, 20120341 (2013).
PubMed  Article  Google Scholar 

40.
Landis, M. J. & Schraiber, J. G. Pulsed evolution shaped modern vertebrate body sizes. Proc. Natl. Acad. Sci. U. S. A. 114, 13224–13229 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8, a019166 (2015).
PubMed  Article  PubMed Central  Google Scholar 

42.
Boutilier, R. G., Stiffler, D. F. & Toews, D. Exchange of respiratory gases, ions, and water in amphibious and aquatic amphibians. In Environmental Physiology of Amphibians (eds Feder, M. E. & Burggren, W. W.) 81–124 (University of Chicago Press, Chicago, 1992).
Google Scholar 

43.
Spotila, J. R., O’connor, M. P. & Bakken, G. S. Biophysics of heat and mass transfer. In Environmental Physiology of the Amphibians (eds Feder, M. E. & Burggren, W. W.) 59–80 (University of Chicago Press, Chicago, 1992).
Google Scholar 

44.
Sanger, T. J. et al. Convergent evolution of sexual dimorphism in skull shape using distinct developmental strategies. Evolution 67, 2180–2193 (2013).
PubMed  Article  PubMed Central  Google Scholar 

45.
Navas, C. A., Antoniazzi, M. M. & Jared, C. A preliminary assessment of anuran physiological and morphological adaptation to the caatinga, a brazilian semi-arid environment. Int. Congr. Ser. 1275, 298–305 (2004).
Article  Google Scholar 

46.
Wiley, D. F. et al. Evolutionary Morphing Minneapolis, MN, USA Minneapolis, MN, USA (IEEE Computer Society, Minneapolis, 2005).
Google Scholar 

47.
Klingenberg, C. P. & Gidaszewski, N. A. Testing and quantifying phylogenetic signals and homoplasy in morphometric data. Syst. Biol. 59, 245–261 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Dryden, I. L. & Mardia, K. V. Statistical Shape Analysis (Wiley, Hoboken, 1998).
Google Scholar 

49.
Adams, D. C. A generalized k statistic for estimating phylogenetic signal from shape and other high-dimenstional multivariate data. Syst. Biol. 63(5), 685–697 (2014).
PubMed  Article  PubMed Central  Google Scholar 

50.
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).
PubMed  Article  Google Scholar 

51.
Adams, D. C. & Otárola-Castillo, E. Geomorph: an r package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).
Article  Google Scholar 

52.
Fox, J. & Hong, J. Effect displays in r for multinomial and proportional-odds logit models: extensions to the effects package. J. Stat. Softw. 32, 1–24 (2009).
Article  Google Scholar 

53.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2016). More

1.
Barber, J. R., Crooks, K. R. & Fristrup, K. M. The costs of chronic noise exposure for terrestrial organisms. Trends Ecol. Evol. 25, 180–189 (2010).
PubMed  Article  Google Scholar 
2.
Buxton, R. T. et al. Noise pollution is pervasive in US protected areas. Science (80-) 356, 531–533 (2017).
ADS  CAS  Article  Google Scholar 

3.
Manci, K. M., Gladwin, D. N., Villella, R. & Cavendish, M. G. Effects of aircraft noise and sonic booms on domestic animals and wildlife: a literature synthesis (Engineering and Services Center U. S. Air Force, 1988).

4.
Pott-Pollenske, M. et al. Airframe noise characteristics from flyover measurements and prediction. In 12th AIAA/CEAS Aeroacoustics Conference (27th AIAA Aeroacoustics Conference) 2567 (2006).

5.
Khardi, S. Reduction of commercial aircraft noise emission around airports. A new environmental challenge. Eur. Transp. Res. Rev. 1, 175–184 (2009).
Article  Google Scholar 

6.
Dooling, R. J. & Popper, A. N. The effects of highway noise on birds (The California Department of Transportation Division of Environmental Analysis, 2007).

7.
Etzel, R. A. & Balk, S. J. Pediatric environmental health (American Academy of Pediatrics, Itasca, 2011).
Google Scholar 

8.
Schomer, P. D. Growth function for human response to large-amplitude impulse noise. J. Acoust. Soc. Am. 64, 1627–1632 (1978).
ADS  CAS  PubMed  Article  Google Scholar 

9.
Kunc, H. P. & Schmidt, R. The effects of anthropogenic noise on animals: a meta-analysis. Biol. Lett. 15, 20190649 (2019).
PubMed  PubMed Central  Article  Google Scholar 

10.
Shannon, G. et al. A synthesis of two decades of research documenting the effects of noise on wildlife. Biol. Rev. 91, 982–1005 (2016).
PubMed  Article  Google Scholar 

11.
Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).
PubMed  Article  Google Scholar 

12.
Brown, A. L. Measuring the effect of aircraft noise on sea birds. Environ. Int. 16, 587–592 (1990).
Article  Google Scholar 

13.
McLaughlin, K. E. & Kunc, H. P. Experimentally increased noise levels change spatial and singing behaviour. Biol. Lett. 9, 20120771 (2013).
PubMed  PubMed Central  Article  Google Scholar 

14.
Injaian, A. S., Poon, L. Y. & Patricelli, G. L. Effects of experimental anthropogenic noise on avian settlement patterns and reproductive success. Behav. Ecol. 29, 1181–1189 (2018).
Article  Google Scholar 

15.
McClure, C. J. W., Ware, H. E., Carlisle, J., Kaltenecker, G. & Barber, J. R. An experimental investigation into the effects of traffic noise on distributions of birds: avoiding the phantom road. Proc. R. Soc. London B Biol. Sci. 280, 20132290 (2013).
Google Scholar 

16.
Kruger, D. J. D. & Du Preez, L. H. The effect of airplane noise on frogs: a case study on the Critically Endangered Pickersgill’s reed frog (Hyperolius pickersgilli). Ecol. Res. 31, 393–405 (2016).
Article  Google Scholar 

17.
Melcon, M. L. et al. Blue whales respond to anthropogenic noise. PLoS ONE 7, e32681 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Sierro, J., Schloesing, E., Pavón, I. & Gil, D. European blackbirds exposed to aircraft noise advance their chorus, modify their song and spend more time singing. Front. Ecol. Evol. 5, 68 (2017).
Article  Google Scholar 

19.
McCarthy, E. et al. Changes in spatial and temporal distribution and vocal behavior of Blainville’s beaked whales (Mesoplodon densirostris) during multiship exercises with mid-frequency sonar. Mar. Mammal Sci. 27, E206–E226 (2011).
Article  Google Scholar 

20.
Dominoni, D. M., Greif, S., Nemeth, E. & Brumm, H. Airport noise predicts song timing of European birds. Ecol. Evol. 6, 6151–6159 (2016).
PubMed  PubMed Central  Article  Google Scholar 

21.
Gil, D., Honarmand, M., Pascual, J., Pérez-Mena, E. & Macías, G. C. Birds living near airports advance their dawn chorus and reduce overlap with aircraft noise. Behav. Ecol. 26, 435–443 (2014).
Article  Google Scholar 

22.
Habib, L., Bayne, E. M. & Boutin, S. Chronic industrial noise affects pairing success and age structure of ovenbirds Seiurus aurocapilla. J. Appl. Ecol. 44, 176–184 (2007).
Article  Google Scholar 

23.
Halfwerk, W., Holleman, L. J. M., Lessells, C. K. & Slabbekoorn, H. Negative impact of traffic noise on avian reproductive success. J. Appl. Ecol. 48, 210–219 (2011).
Article  Google Scholar 

24.
Wolfenden, A. D., Slabbekoorn, H., Kluk, K. & de Kort, S. R. Aircraft sound exposure leads to song frequency decline and elevated aggression in wild chiffchaffs. J. Anim. Ecol. 88, 1720–1731 (2019).
PubMed  Article  Google Scholar 

25.
Halfwerk, W. et al. Low-frequency songs lose their potency in noisy urban conditions. Proc. Natl. Acad. Sci. 108, 14549–14554 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

26.
Blickley, J. L., Blackwood, D. & Patricelli, G. L. Experimental evidence for the effects of chronic anthropogenic noise on abundance of Greater Sage-Grouse at leks. Conserv. Biol. 26, 461–471 (2012).
PubMed  Article  Google Scholar 

27.
Pepper, C. B., Nascarella, M. A. & Kendall, R. J. A review of the effects of aircraft noise on wildlife and humans, current control mechanisms, and the need for further study. Environ. Manag. 32, 418–432 (2003).
Article  Google Scholar 

28.
Staicer, C. A., Spector, D. A. & Horn, A. G. The dawn chorus and other diel patterns in acoustic signaling. In Ecology and evolution of acoustic communication in birds, 426–453 (1996).

29.
Gil, D. & Llusia, D. The bird dawn chorus revisited. In Coding strategies in vertebrate acoustic communication 45–90 (Springer, Berlin, 2020).

30.
Warren, P. S., Katti, M., Ermann, M. & Brazel, A. Urban bioacoustics: It’s not just noise. Anim. Behav. 71, 491–502 (2006).
Article  Google Scholar 

31.
Dooling, R. Avian hearing and the avoidance of wind turbines (University of Maryland, College Park, 2002).
Google Scholar 

32.
Díaz, M., Parra, A. & Gallardo, C. Serins respond to anthropogenic noise by increasing vocal activity. Behav. Ecol. 22, 332–336 (2011).
Article  Google Scholar 

33.
Gentry, K. E. & Luther, D. A. Spatiotemporal patterns of avian vocal activity in relation to urban and rural background noise. J. Ecoacoust. https://doi.org/10.22261/jea.z9tqh (2017).
Article  Google Scholar 

34.
Cunnington, G. M. & Fahrig, L. Plasticity in the vocalizations of anurans in response to traffic noise. Acta Oecologica 36, 463–470 (2010).
ADS  Article  Google Scholar 

35.
Kaiser, K. & Hammers, J. The effect of anthropogenic noise on male advertisement call rate in the neotropical treefrog, Dendropsophus triangulum. Behaviour 146, 1053–1069 (2009).
Article  Google Scholar 

36.
Brumm, H. & Slater, P. J. B. Ambient noise, motor fatigue, and serial redundancy in chaffinch song. Behav. Ecol. Sociobiol. 60, 475–481 (2006).
Article  Google Scholar 

37.
Meh, F. et al. Humpback whales Megaptera novaeangliae alter calling behavior in response to natural sounds and vessel noise. Mar. Ecol. Prog. Ser. 607, 251–268 (2018).
Article  Google Scholar 

38.
Slabbekoorn, H. & Peet, M. Ecology: birds sing at a higher pitch in urban noise. Nature 424, 267 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Ríos-Chelén, A. A., Lee, G. C. & Patricelli, G. L. Anthropogenic noise is associated with changes in acoustic but not visual signals in red-winged blackbirds. Behav. Ecol. Sociobiol. 69, 1139–1151 (2015).
Article  Google Scholar 

40.
Gross, K., Pasinelli, G. & Kunc, H. P. Behavioral plasticity allows short-term adjustment to a novel environment. Am. Nat. 176, 456–464 (2010).
PubMed  Article  Google Scholar 

41.
Gentry, K. E., McKenna, M. F. & Luther, D. A. Evidence of suboscine song plasticity in response to traffic noise fluctuations and temporary road closures. Bioacoustics 27, 165–181 (2018).
Article  Google Scholar 

42.
Conomy, J. T., Dubovsky, J. A., Collazo, J. A. & Fleming, W. J. Do black ducks and wood ducks habituate to aircraft disturbance?. J. Wildl. Manag. 62, 1135–1142 (1998).
Article  Google Scholar 

43.
Neo, Y. Y., Hubert, J., Bolle, L. J., Winter, H. V. & Slabbekoorn, H. European seabass respond more strongly to noise exposure at night and habituate over repeated trials of sound exposure. Environ. Pollut. 239, 367–374 (2018).
CAS  PubMed  Article  Google Scholar 

44.
Halfwerk, W., Both, C. & Slabbekoorn, H. Noise affects nest-box choice of 2 competing songbird species, but not their reproduction. Behav. Ecol. 27, 1592–1600 (2016).
Article  Google Scholar 

45.
Ware, H. E., McClure, C. J. W., Carlisle, J. D. & Barber, J. R. A phantom road experiment reveals traffic noise is an invisible source of habitat degradation. Proc. Natl. Acad. Sci. 112, 12105–12109 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

46.
Williams, R., Erbe, C., Ashe, E., Beerman, A. & Smith, J. Severity of killer whale behavioral responses to ship noise: A dose–response study. Mar. Pollut. Bull. 79, 254–260 (2014).
CAS  PubMed  Article  Google Scholar 

47.
Cynx, J., Lewis, R., Tavel, B. & Tse, H. Amplitude regulation of vocalizations in noise by a songbird Taeniopygia guttata. Anim. Behav. 56, 107–113 (1998).
CAS  PubMed  Article  Google Scholar 

48.
Rushing, C. S., Ryder, T. B. & Marra, P. P. Quantifying drivers of population dynamics for a migratory bird throughout the annual cycle. Proc. R. Soc. B Biol. Sci. 283, 20152846 (2016).
Article  CAS  Google Scholar 

49.
Stanley, C. Q. et al. Connectivity of wood thrush breeding, wintering, and migration sites based on range-wide tracking. Conserv. Biol. 29, 164–174 (2015).
PubMed  Article  Google Scholar 

50.
Kleist, N. J., Guralnick, R. P., Cruz, A. & Francis, C. D. Anthropogenic noise weakens territorial response to intruder’s songs. Ecosphere 7, e01259 (2016).
Article  Google Scholar 

51.
Ward, S., Speakman, J. R. & Slater, P. J. B. The energy cost of song in the canary, Serinus canaria. Anim. Behav. 66, 893–902 (2003).
Article  Google Scholar 

52.
Nemeth, E. & Brumm, H. Birds and anthropogenic noise: are urban songs adaptive?. Am. Nat. 176, 465–475 (2010).
PubMed  Article  Google Scholar 

53.
Oberweger, K. & Goller, F. The metabolic cost of birdsong production. J. Exp. Biol. 204, 3379–3388 (2001).
CAS  PubMed  Google Scholar 

54.
Ophir, A. G., Schrader, S. B. & Gilooly, J. F. Energetic cost of calling: general constraints and species-specific differences. J. Evol. Biol. 23, 1564–1569 (2010).
CAS  PubMed  Article  Google Scholar 

55.
Thomas, R. et al. The trade-off between singing and mass gain in a daytime-singing bird, the European robin. Behaviour 140, 387–404 (2003).
Article  Google Scholar 

56.
Sheikh, P. A. & Uhl, C. Airplane noise: A pervasive disturbance in Pennsylvania Parks, USA. J. Sound Vib. https://doi.org/10.1016/j.jsv.2003.09.014 (2004).
Article  Google Scholar 

57.
Burnham, K. P. & Anderson, D. R. Multimodel inference understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).
MathSciNet  Article  Google Scholar 

58.
Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).
MathSciNet  MATH  Article  Google Scholar  More

Study site
The study site was the Kamigamo Experimental Station of Kyoto University, located in a suburban area of Kyoto, Japan (Supplementary Figure S1). This area is located in a warm and humid climate zone, with an elevation of 109–225 m above sea level. The mean annual precipitation and temperature are 1582 mm and 14.6 °C, respectively. The overall area is 46.8 ha. 65% of the area is naturally generated forest, primarily consisting of Japanese cypress (Chamaecyparis obtuse) and some broad-leaved trees such as oak (Quercus serrata or Quercus glauca). Within this area, 28% is planted forest, mainly consisting of foreign coniferous species. 7% consists of sample gardens, nurseries, or buildings.
In this work, we focused on the northern part (an area of 11 ha) of the Kamigamo Experimental Station, containing a naturally regenerated forest of Japanese cypress, and a managed forest of Metasequoia (Metasequoia glyptostroboides), strobe pine (Pinus strobus), slash pine (Pinus elliottii), and taeda pine (Pinus taeda).
Remote sensing data
Flight campaigns were conducted around noon in two seasons: on October 2, 2016, which is the end of the leaf season, and November 20, 2016, the peak of the fall leaf offset season. We used UAV DJI Phantom 4 (DJI, Shenzhen China). The UAV had an onboard camera with a 1/2.3 CMOS sensor that can capture RGB spectral information. The UAV was operated automatically using the DroneDeploy v2.66 application (https://ww.dronedeploy.com, Infatics Inc., San Francisco, United States). On October 2, we set flight parameters as follows: both the overlap and sidelap were set to 75%, and the flight height was set to 80 m from the take-off ground level. However, we failed to align some parts of the images; thus, we changed the overlap and height parameters to 80% and 100 m on November 20. We used 10 ground-control points (GCPs) for reducing the error of the GPS with the images. From the images taken by the UAV, we produced an orthomosaic photo and a digital surface model (DSM) using the Agisoft PhotoScan Professional v1.3.4 software (https://www.agisoft.com, Agisoft LLC, St. Petersburg, Russia). An orthomosaic photo is an image that is composed of multiple overhead images corrected for perspective and scale. The parameter settings used in generating the orthomosaic photo are shown in Supplementary Table S1. These parameters are for November 20. The parameters for October 2 differ only in that the ground sampling distance (GSD) were approximately one centimetre. GSD of the orthomosaic photo and DSM was approximately 5 cm and 10 cm, respectively.
Segmentation and preparation of supervised data
The technological workflow of the individual tree image segmentation and extraction method we used is summarised in Fig. 1. First, we segmented each tree crown using UAV image (orthomosaic photo), a DSM, and a slope model. Second, we visually constructed the ground truth map. Third, we extracted each tree image with a ground truth label. Further details are discussed in sections from “Object-based tree crown segmentation” to “Tree image extraction with ground truth label”.
Figure 1

Workflow for supervised images extraction.

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Object-based tree crown segmentation
At the segmentation stage, we segmented at the tree level. First, we constructed a slope model by calculating the slope from the DSM using the ArcGIS Desktop v10.4 software (https://www.esri.com, Environmental Systems Research Institute, Inc., Redlands, United States). The slope model showed the maximum rate of elevation change between each cell and its neighbours, such that the borders of trees were emphasised. From the orthomosaic photo, the DSM, and the slope model, tree crown segmentation was performed in the eCognition Developer v9.0.0 software (https://www.trimble.com, Trimble, Inc., Sunnyvale, United States) using the ‘Multiresolution Segmentation’ algorithm36. The parameter values were adjusted by trial and error. The tree crown map made by this segmentation process is shown in Fig. 2 with enlarged images for visual confirmation of the result, and the best parameters are presented in Supplementary Table S2.
Figure 2

Whole area and representative enlarged tree crown map. The blue line show grid-line of segmented polygons. The white rectangle shows the location of enlarged area, and light blue polygons are used for evaluating the accuracy of tree segmentation. This map was constructed via multiresolution segmentation using colour, DSM, and Slope model. This figure was created using ArcGIS Desktop v10.6 software (https://www.esri.com, Environmental Systems Research Institute, Inc., Redlands, United States).

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Herein, we evaluated the accuracy of the segmentation. The segmented crowns were placed into the following five categories according to their spatial relationships with the visually confirmed reference crown. The five categories, set based on a previous study 37, and illustrated in Supplementary Figure S2, are as follows.
(a) Matched: If the overlap of the segmented polygon and the reference crown was more than 80%, the segmented polygon was categorized as “Matched”.
(b) Nearly matched: If the overlap of the segmented polygon and the reference crown was 60–80%, the segmented polygon was categorized as “Nearly matched”.
(c) Split: If the overlap of the segmented polygon and the reference crown was 20–60%, the segmented polygon was categorized as “Split”.
(d) Merged: If multiple reference crowns covered by the segmented polygon, and even one overlap was more than 20%, the segmented polygon was categorized as “Merged”. If the segmented polygon had only one class reference crowns, the polygon was categorized as “one class merged”. If the segmented polygon had multiple class reference crowns, the polygon was categorized as “multiple class merged”.
(e) Fragmented: If one or multiple reference crowns covered by the segmented polygon, and their respective overlaps were less than 20%, the segmented polygon was considered as a “fragmented polygon”.
We calculated the segmentation accuracy of trees at four areas: Areas 1–4. Area 1 was a deciduous coniferous forest and Area 2 was a strobe pine forest, for which we calculated the entire area. Area 3 was a slash pine and taeda pine forest, for which we calculated part of the areas. Area 4 was a naturally regenerated forest, for which we calculated 1 ha in area. As a result, some segmented images had multiple tree crowns, but this method almost succeeded in separating each tree class (Table 1).
Table 1 Accuracy statistics of the tree crown maps. (Area 1: deciduous coniferous tree; Area 2: strobe pine forest; Area 3: slash pine and taeda pine forest; Area 4: naturally regenerated forest).
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Ground truth label attachment to tree crown map
After segmentation, we classified segmented images into the following seven classes: deciduous broad-leaved tree, deciduous coniferous tree, evergreen broad-leaved tree, Chamaecyparis obtuse, Pinus elliottii or Pinus taeda, Pinus strobus, and non-forest. The ‘non-forest’ class included understory vegetation and bare land, as well as artificial structures. For deciding these classes, we conducted field research. We set three rectangular plots sized 30 m × 30 m and checked the tree species, regarding the classes we decided could be identified from the November 20 drone images. The Pinus elliottii or Pinus taeda class consisted of two Pinus species, because these two species are difficult to identify from drone images. At the ground truth map-making phase, we visually attached the class label to each tree crown, using nearest neighbour classification in the eCognition software to improve operational efficiency, which was then used for forest mapping38 (Fig. 3). More specifically, we chose some image objects as training samples and applied that algorithm to the overall tree crowns. In subsequent steps, by adding wrongly classified objects to correct classes of the training samples, we improved the accuracy of the ground truth map.
Figure 3

Segmentation and ground truth map-making result. The tree classes found in the image on the left are represented by the colours explained in the legend in the figure on the right. This figure was created using ArcGIS Desktop v10.6 software (https://www.esri.com, Environmental Systems Research Institute, Inc., Redlands, United States).

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Tree image extraction with ground truth label
From the orthomosaic photos of the two season and the ground truth map, we extracted each tree image with a class label using the ‘Extract by Mask’ function in ArcGIS. There were some inappropriate images, such as fragments of trees, those difficult to be interpreted or classified visually, and those including multiple classes; thus, we manually deleted inappropriate images and placed wrongly classified images into the correct class by group consensus. Representative images of the tree classes are shown in Figs. 4 and 5. The number of extracted images and that of arranged images are shown in Supplementary Table S3. After arrangement, the number of each class ranged from 37 to 416. The images had a wide range of sizes, but the length of one side of the largest image was approximately 400 pixels.
Figure 4

Representative extracted images from each class in the November 20 images. These images were segmented well at each tree crown level. However, the image of Pinus strobus includes several tree images. The image of the non-forest class shows the roof of a house.

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Figure 5

Representative extracted images from each class in the October 2 images. These images were extracted from the same tree crown map polygon as the November 20 images.

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After extraction, we resized the images from October 2 to the size of images from November 20 in order to align the two season conditions. Thus, all images were adjusted to the size of images taken from a height of approximately 100 m.
Machine learning
To construct a model for object identification, we used the publicly available package PyTorch v0.4.139 as a deep learning framework and four standard neural network models—specifically, AlexNet23, VGG1640, Resnet18, and Resnet15241—for fine-tuning. Fine-tuning is an effective method to improve the learning performance, especially when the amount of data is insufficient for training42. We used each neural network model, which had been learned with the ImageNet dataset43, and trained all neural network layers using our data. At the CNN training phase, we augmented the training images eight times by flipping and rotating them. Further augmentation did not improve accuracy. For the input to the CNN, we applied ‘random resized crop’ at a scale of 224 × 224 pixel size for training, which crops the given image to a random size and aspect ratio. For validation and training, we resized the images into 256 × 256 pixel sizes and used ‘centre crop’ at a scale of 224 × 224 pixel size. These cropping algorithms extracted only one resized image (patch) from each cropped image. The ranges of the other learning settings are outlined in Supplementary Table S4.
To evaluate the performance of the CNN, we used SVM as a machine learning platform. We used the average and standard deviation of each band and GLCM texture values as features. GLCM is a spatial co-occurrence matrix that computes the relationships of pixel values, and uses these relationships to compute the texture statistics44. For calculating GLCM, images with a large number of data bits result in huge computational complexity. In this case, the images that were converted to grey scale were 8-bit data. It is known that reduction of bit size causes only minor decrease in classification accuracy; hence, we rescaled from 8-bit to 5-bit45,46. After calculation of GLCM, we extracted five GLCM texture features (angular second moment (ASM), contrast, dissimilarity, entropy, and homogeneity). Their algorithms are defined in Eqs. (1)–(5):

$$begin{array}{c}ASM=sum_{i,j}^{N}{(P}_{i,j}{)}^{2} end{array}$$
(1)

$$begin{array}{c}Contrast=sum_{i,j}^{N}{P}_{i,j}{left(i-jright)}^{2}end{array}$$
(2)

$$begin{array}{c}Dissimilarity=sum_{i,j}^{N}{P}_{i,j}left|i-jright|end{array}$$
(3)

$$begin{array}{c}Entropy=sum_{i,j}^{N}{P}_{i,j}mathrm{log}left({P}_{i,j}right)end{array}$$
(4)

$$begin{array}{c}Homogeneity=sum_{i,j}^{N}{P}_{i,j}/(1+{left(i-jright)}^{2})end{array}$$
(5)

where ({P}_{i,j}) is the GLCM at the pixel which is located in row number i and column number j. We obtained these GLCM texture features at each pixel, excluding pixels close to the image margin, and then calculated their mean and standard deviation for each image. Another important parameter that affects classification performance is the kernel size47,48. To determine the most suitable kernel size for GLCM operation, we calculated GLCM texture features with various kernel sizes of 3, 11, 19, 27, 35, 43, 51, and 59. For SVM validation, we used radial basis function (rbf) kernel and conducted a parameter grid search in the range of gamma from ({10}^{-1}) to ({10}^{-5}) and cost from 1 to ({10}^{5}). As a result of the grid search, we obtained the best validation accuracy and the best parameters at each GLCM kernel size (Supplementary Figure S3). The validation accuracy slightly increased along with the increase in kernel size, and the accuracy stopped increasing at the 51 × 51 kernel size. Considering this result, we adopted the 51 × 51 kernel size and the best parameters as follows: gamma and cost were ({10}^{-2}) and ({10}^{3}) in the fall peak season, and ({10}^{-3}) and ({10}^{4}) in the green leaf season, respectively. We then used these parameters for SVM learning and the comparative evaluation.
For machine learning, we divided the data into training, validation, and testing sets. The validation dataset was used for hyperparameters tuning such as learning rate, batch size for deep learning, and kernel size, cost, and gamma values for SVM. In the testing phase, we used the data which had not been used for training and parameter tuning. Validation accuracy is not suitable for comparing performance as a final result because validation accuracy can be higher than testing accuracy; we tuned the hyperparameters to get higher accuracy using the validation data. Using testing data, we can exclude the bias of parameter tuning. We also used a kind of cross-validation because we had a limited amount of data and decreased the contingency of accuracy. In this case, we randomly divided all the images evenly into four datasets and used two of them for training, one for validation, and one for testing. Subsequently, we interchanged successively the datasets used for training, validation, and testing. This process was repeated four times. For the accuracy evaluation and confusion matrix, we used total accuracy and all the images.
For this calculation, we used a built to order (BTO) desktop computer with a Xeon E5-2640 CPU, 32 GB RAM, and a Geforce GTX 1080 graphics card; the OS was Ubuntu 16.04.
Evaluation
For evaluation, we used the overall accuracy, Cohen’s Kappa coefficient49, and the macro average F1 score. F1 score is the harmonic mean of Recall and Precision. In this study, the number of images acquired for each class varied significantly. The overall accuracy, which is typically utilised for evaluating the machine learning performance, is subject to the difference in the amount of data available to each class. Therefore, we used the Kappa and F1 score, which is suitable for evaluating imbalanced dataset accuracy, as well as overall accuracy to obtain an objective evaluation. Additionally, for evaluating the per-class accuracy, we used the F1 score of each class. More

1.
Kuwae, T. et al. Biofilm grazing in a higher vertebrate: The Western Sandpiper, Calidris mauri. Ecology 89, 599–606 (2008).
PubMed  Article  PubMed Central  Google Scholar 
2.
Góngora, E., Braune, B. M. & Elliott, K. H. Nitrogen and sulfur isotopes predict variation in mercury levels in Arctic seabird prey. Mar. Pollut. Bull. 135, 907–914 (2018).
PubMed  Article  CAS  Google Scholar 

3.
Ben-Yosef, M., Aharon, Y., Jurkevitch, E. & Yuval, B. Give us the tools and we will do the job: Symbiotic bacteria affect olive fly fitness in a diet-dependent fashion. Proc. R. Soc. B Biol. Sci. 277, 1545–1552 (2010).
CAS  Article  Google Scholar 

4.
Alberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. & Gilbert, M. T. P. Do vertebrate gut metagenomes confer rapid ecological adaptation?. Trends Ecol. Evol. 31, 689–699 (2016).
PubMed  Article  PubMed Central  Google Scholar 

5.
Lapanje, A., Zrimec, A., Drobne, D. & Rupnik, M. Long-term Hg pollution-induced structural shifts of bacterial community in the terrestrial isopod (Porcellio scaber) gut. Environ. Pollut. 158, 3186–3193 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Lewis, W. B., Moore, F. R. & Wang, S. Characterization of the gut microbiota of migratory passerines during stopover along the northern coast of the Gulf of Mexico. J. Avian Biol. 47, 659–668 (2016).
Article  Google Scholar 

7.
Bolnick, D. I. et al. Individuals’ diet diversity influences gut microbial diversity in two freshwater fish (threespine stickleback and Eurasian perch). Ecol. Lett. 17, 979–987 (2014).
PubMed  PubMed Central  Article  Google Scholar 

8.
Bolnick, D. I. et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat. Commun. 5, 4500 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Bolnick, D. I., Yang, L. H., Fordyce, J. A., Davis, J. M. & Svanbäck, R. Measuring individual-level resource specialization. Ecology 83, 2936–2941 (2002).
Article  Google Scholar 

10.
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).
MathSciNet  PubMed  Article  PubMed Central  Google Scholar 

11.
Apajalahti, J. H. A., Kettunen, A., Bedford, M. R. & Holben, W. E. Percent G + C profiling accurately reveals diet-related differences in the gastrointestinal microbial community of broiler chickens. Appl. Environ. Microbiol. 67, 5656–5667 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Apajalahti, J. & Kettunen, A. Microbes of the chicken gastrointestinal tract. In Avian Gut Function in Health and Disease (ed. Perry, G. C.) 124–137 (CAB International, Wallingford, 2006).
Google Scholar 

13.
Oakley, B. B. et al. The chicken gastrointestinal microbiome. FEMS Microbiol. Lett. 360, 100–112 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Bangert, R. L., Ward, A. C. S., Stauber, E. H., Cho, B. R. & Widders, P. R. A survey of the aerobic bacteria in the feces of captive raptors. Avian Dis. 32, 53–62 (1988).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Soucek, Z. & Mushin, R. Gastrointestinal bacteria of certain Antarctic birds and mammals. Appl. Microbiol. 20, 561–566 (1970).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Mead, G. C., Griffiths, N. M., Impey, C. S. & Coplestone, J. C. Influence of diet on the intestinal microflora and meat flavour of intensively-reared broiler chickens. Br. Poult. Sci. 24, 261–272 (1983).
Article  Google Scholar 

17.
Waldenström, J. et al. Prevalence of Campylobacter jejuni, Campylobacter lari, and Campylobacter coli in different ecological guilds and taxa of migrating birds. Appl. Environ. Microbiol. 68, 5911–5917 (2002).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Waite, D. W. & Taylor, M. W. Exploring the avian gut microbiota: Current trends and future directions. Front. Microbiol. 6, 1–12 (2015).
Article  Google Scholar 

19.
Maul, J. D., Gandhi, J. P. & Farris, J. L. Community-level physiological profiles of cloacal microbes in songbirds (order: Passeriformes): Variation due to host species, host diet, and habitat. Microb. Ecol. 50, 19–28 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Risely, A., Waite, D. W., Ujvari, B., Hoye, B. J. & Klaassen, M. Active migration is associated with specific and consistent changes to gut microbiota in Calidris shorebirds. J. Anim. Ecol. 87, 428–437 (2018).
PubMed  Article  PubMed Central  Google Scholar 

21.
Dewar, M. L. et al. Interspecific variations in the gastrointestinal microbiota in penguins. Microbiologyopen 2, 195–204 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Waite, D. W. & Taylor, M. W. Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front. Microbiol. 5, 1–12 (2014).
Article  Google Scholar 

23.
Teyssier, A. et al. Inside the guts of the city: Urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. 612, 1276–1286 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).
PubMed  Article  PubMed Central  Google Scholar 

25.
Capunitan, D. C., Johnson, O., Terrill, R. S. & Hird, S. M. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea. Mol. Ecol. 29, 829–847 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Michel, A. J. et al. The gut of the finch: Uniqueness of the gut microbiome of the Galápagos vampire finch. Microbiome 6, 167 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Elliott, K. H., Woo, K. J. & Gaston, A. J. Specialization in murres: The story of eight specialists. Waterbirds 32, 491–506 (2009).
Article  Google Scholar 

28.
Woo, K. J., Elliott, K. H., Davidson, M., Gaston, A. J. & Davoren, G. K. Individual specialization in diet by a generalist marine predator reflects specialization in foraging behaviour. J. Anim. Ecol. 77, 1082–1091 (2008).
PubMed  Article  PubMed Central  Google Scholar 

29.
Elliott, K. H., Gaston, A. J. & Crump, D. Sex-specific behavior by a monomorphic seabird represents risk partitioning. Behav. Ecol. 21, 1024–1032 (2010).
Article  Google Scholar 

30.
Paredes, R., Jones, I. & Boness, D. Parental roles of male and female thick-billed murres and razorbills at the Gannet Islands, Labrador. Behaviour 143, 451–481 (2006).
Article  Google Scholar 

31.
Atwell, L., Hobson, K. A. & Welch, H. E. Biomagnification and bioaccumulation of mercury in an arctic marine food web: Insights from stable nitrogen isotope analysis. Can. J. Fish. Aquat. Sci. 55, 1114–1121 (1998).
CAS  Article  Google Scholar 

32.
Carr, M. K. et al. Stable sulfur isotopes identify habitat-specific foraging and mercury exposure in a highly mobile fish community. Sci. Total Environ. 586, 338–346 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320 (1987).
Article  Google Scholar 

34.
Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, e00021-e116 (2016).
PubMed  PubMed Central  Article  Google Scholar 

35.
Grond, K., Sandercock, B. K., Jumpponen, A. & Zeglin, L. H. The avian gut microbiota: Community, physiology and function in wild birds. J. Avian Biol. 49, e01788 (2018).
Article  Google Scholar 

36.
Lawson, P. A., Collins, M. D., Falsen, E. & Foster, G. Catellicoccus marimammalium gen. nov., sp. nov., a novel Gram-positive, catalase-negative, coccus-shaped bacterium from porpoise and grey seal. Int. J. Syst. Evol. Microbiol. 56, 429–432 (2006).
CAS  PubMed  Article  Google Scholar 

37.
Sinigalliano, C. D. et al. Multi-laboratory evaluations of the performance of Catellicoccus marimammalium PCR assays developed to target gull fecal sources. Water Res. 47, 6883–6896 (2013).
CAS  PubMed  Article  Google Scholar 

38.
Ryu, H. et al. Comparison of gull feces-specific assays targeting the 16S rRNA genes of Catellicoccus marimammalium and Streptococcus spp. Appl. Environ. Microbiol. 78, 1909–1916 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Koskey, A. M., Fisher, J. C., Traudt, M. F., Newton, R. J. & McLellan, S. L. Analysis of the gull fecal microbial community reveals the dominance of Catellicoccus marimammalium in relation to culturable enterococci. Appl. Environ. Microbiol. 80, 757–765 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Lu, J., Santo Domingo, J. W., Lamendella, R., Edge, T. & Hill, S. Phylogenetic diversity and molecular detection of bacteria in gull feces. Appl. Environ. Microbiol. 74, 3969–3976 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Benskin, C. M. H., Rhodes, G., Pickup, R. W., Wilson, K. & Hartley, I. R. Diversity and temporal stability of bacterial communities in a model passerine bird, the zebra finch. Mol. Ecol. 19, 5531–5544 (2010).
PubMed  Article  PubMed Central  Google Scholar 

42.
Kreisinger, J. et al. Temporal stability and the effect of transgenerational transfer on fecal microbiota structure in a long distance migratory bird. Front. Microbiol. 8, 1–19 (2017).
Article  Google Scholar 

43.
Grond, K., Ryu, H., Baker, A. J., Santo Domingo, J. W. & Buehler, D. M. Gastro-intestinal microbiota of two migratory shorebird species during spring migration staging in Delaware Bay, USA. J. Ornithol. 155, 969–977 (2014).
Article  Google Scholar 

44.
Santos, S. S. et al. Diversity of cloacal microbial community in migratory shorebirds that use the Tagus estuary as stopover habitat and their potential to harbor and disperse pathogenic microorganisms. FEMS Microbiol. Ecol. 82, 63–74 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Laviad-Shitrit, S., Izhaki, I., Lalzar, M. & Halpern, M. Comparative analysis of intestine microbiota of four wild waterbird species. Front. Microbiol. 10, 1–13 (2019).
Article  Google Scholar 

46.
Weigand, M. R., Ryu, H., Bozcek, L., Konstantinidis, K. T. & Santo Domingo, J. W. Draft genome sequence of Catellicoccus marimammalium, a novel species commonly found in gull feces. Genome Announc. 1, 12–13 (2013).
Article  Google Scholar 

47.
Dewar, M. L. et al. Influence of fasting during moult on the faecal microbiota of penguins. PLoS ONE 9, e99996 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Dewar, M. L., Arnould, J. P. Y., Krause, L., Dann, P. & Smith, S. C. Interspecific variations in the faecal microbiota of Procellariiform seabirds. FEMS Microbiol. Ecol. 89, 47–55 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Roggenbuck, M. et al. The microbiome of New World vultures. Nat. Commun. 5, 5498 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Potrykus, J., White, R. L. & Bearne, S. L. Proteomic investigation of amino acid catabolism in the indigenous gut anaerobe Fusobacterium varium. Proteomics 8, 2691–2703 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Tsuchiya, C., Sakata, T. & Sugita, H. Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish. Lett. Appl. Microbiol. 46, 071018031740002–000 (2007).
Article  CAS  Google Scholar 

52.
Tegtmeier, D., Riese, C., Geissinger, O., Radek, R. & Brune, A. Breznakia blatticola gen. nov. sp. nov. and Breznakia pachnodae sp. nov., two fermenting bacteria isolated from insect guts, and emended description of the family Erysipelotrichaceae. Syst. Appl. Microbiol. 39, 319–329 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Vandamme, P. et al. Ornithobacterium rhinotracheale gen. nov., sp. nov. isolated from the avian respiratory tract. Int. J. Syst. Bacteriol. 44, 24–37 (1994).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Cerdà-Cuéllar, M. et al. Do humans spread zoonotic enteric bacteria in Antarctica?. Sci. Total Environ. 654, 190–196 (2019).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

55.
Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).
Article  Google Scholar 

56.
Lott, C. A., Meehan, T. D. & Heath, J. A. Estimating the latitudinal origins of migratory birds using hydrogen and sulfur stable isotopes in feathers: Influence of marine prey base. Oecologia 134, 505–510 (2003).
ADS  PubMed  Article  PubMed Central  Google Scholar 

57.
Góngora, E., Elliott, K. & Whyte, L. Dataset from Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia). Mendeley Data v4, (2020).

58.
Eriksson, P., Mourkas, E., González-Acuna, D., Olsen, B. & Ellström, P. Evaluation and optimization of microbial DNA extraction from fecal samples of wild Antarctic bird species. Infect. Ecol. Epidemiol. 7, 1386536 (2017).
PubMed  PubMed Central  Google Scholar 

59.
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
PubMed  PubMed Central  Article  Google Scholar 

64.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Braune, B. M., Gaston, A. J., Hobson, K. A., Gilchrist, H. G. & Mallory, M. L. Changes in food web structure alter trends of mercury uptake at two seabird colonies in the Canadian arctic. Environ. Sci. Technol. 48, 13246–13252 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor workflow for microbiome data analysis: From raw reads to community analyses. F1000Research 5, 1492 (2016).
PubMed  PubMed Central  Article  Google Scholar 

67.
Bokulich, N. A. et al. q2-longitudinal: Longitudinal and paired-sample analyses of microbiome data. mSystems 3, 1–9 (2018).
Article  Google Scholar 

68.
Wilcoxon, F. Individual comparisons by Ranking methods. Biometrics Bull. 1, 80 (1945).
Article  Google Scholar 

69.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 

70.
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Article  Google Scholar 

72.
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, Champaign, 1949).
Google Scholar 

73.
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).
MATH  Article  Google Scholar 

74.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  PubMed Central  MATH  Article  Google Scholar 

75.
Legendre, P. & Legendre, L. Ordination in reduced space. In Numerical Ecology Vol. 24 (eds Legendre, P. & Legendre, L.) 425–520 (Elsevier, Amsterdam, 2012).
Google Scholar 

76.
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: A tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
PubMed  PubMed Central  Article  Google Scholar 

77.
Vázquez-Baeza, Y. et al. Bringing the dynamic microbiome to life with animations. Cell Host Microbe 21, 7–10 (2017).
PubMed  Article  CAS  Google Scholar 

78.
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

80.
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).
MathSciNet  MATH  Article  Google Scholar 

82.
Bartoń, K. MuMIn: Multi-Model Inference. (2019). More

Samples included in this dataset were taken from olive trees sampled from November 2013 until April 2018 by the Apulian Regional Phytosanitary Service. From April 2016 to April 2018, sampling was done only in the buffer zone and containment zone (Fig. 1) and was structured in quadrats of one hectares (ha) area, with at least one sample collected in each quadrat. Within each quadrat, priority was given to sample symptomatic trees and if within the quadrat several trees showed disease symptoms, these were also sampled and individually tested. Samples consisted of mature olive twigs (at least 8 twigs/tree), collected close to symptomatic branches, or from the 4 cardinal points of the canopy when sampling asymptomatic trees. The samples were first tested for X. fastidiosa by using Enzyme-linked immunosorbent assay (ELISA)21. All ELISA-positive samples, and those yielding doubtful ELISA results, plus 3% of the negative samples, were subsequently tested using quantitative PCR.
The total data set comprises 409,515 records and 7 columns. The columns are the ID number of the measurement, longitude, latitude, result (0 for negative on X. fastidiosa presence, 1 for positive), day, year, and month. The number of rows was reduced to 298,230 rows after removing NA (not available) values for the result column or missing coordinates for the longitude and latitude columns. We initially tried to work with the point data as observed, but found that these data were extremely difficult to analyse, presumably because of large variability in the data leading to very flat likelihood surfaces that did not support convergence of the optimization algorithms tested for fitting spatial expansion models (Simplex, Simulated annealing, etc.). We therefore grouped the observation data in 1-km wide distance classes from the port of Gallipoli, the likely origin of the disease invasion (latitude: 40.055851, longitude: 17.992615)22 and calculated the proportion of infected trees in each class. We thus obtained a reduced data set with approximately 200 distance classes comprising an inner circle of 1 km radius, and concentric rings of 1 km width each, with for each class the number of sampled trees and the number of infected trees. We then analysed the relationship between the proportion of infected trees and the distance from Gallipoli (Fig. 4). This relationship was first identified separately for each year, and subsequently by assuming a constant rate of displacement over time (i.e. the rate of spread) of a disease front with a fixed shape.
Figure 4

Relationship between proportion of positive samples per each km ring (Y-axis) and distance to Gallipoli (X-axis; km). Points with different colour represents different years.

Full size image

We expected a high proportion of positive samples at short distance from Gallipoli, with the proportion declining with increasing distance. Therefore, we chose for the shape of the disease front the following deterministic functions (1) a negative exponential function, (2) a decreasing logistic function, and (3) a constrained negative exponential function (CNE; constrained to have a maximum proportion diseased trees (p = 1.0)) (Table 1). The shape of the tail of the invasion front is in many instances exponential18,23,24,25,26, but the proportion of disease cannot exceed one, hence the CNE was used as a modification of an exponential relationship. The sampled data is binary count data (number of positive samples out of the total number of samples at a given distance) and the distance is transformed to discrete distance circles. Because the data are based on a known number of samples in each distance class with a stochastic number of positive outcomes, we chose the binomial distribution and the beta-binomial distribution as candidate stochastic models for fitting the model to the data (Table 1). The binomial model is a model for count data with a defined maximum (N), assuming a fixed probability of “success” (infection). The beta-binomial takes overdispersion into account by drawing the probability of success from a beta distribution around the mean probability of success. The probability of success, i.e. the proportion of positive samples, depends on the distance from Gallipoli and the time since first detection. In our model for the invasion front, the mean probability of disease presence at a distance (x) from Gallipoli is described by the deterministic part of the model (e.g. logistic), while the beta-binomial variability in the detection result is described by an overdispersion parameter (theta) which increases in value as the variance tends towards the variance of the binomial distribution (Bolker, 2008). Mathematically, the parameter θ equals the sum of the parameters (a + b), where (a) and (b) are the shape parameters of the beta distribution27. Given a same mean, the beta-binomial distribution has a larger variance than the binomial distribution (Table 1). The beta-binomial distribution tends to the binomial distribution as (theta) gets large. For all model fits, we calculated the AIC (Akaike information criterion):

$${text{AIC}} = 2k – 2 log left( L right)$$
(1)

Table 1 Deterministic and stochastic models used for fitting all combinations of deterministic and stochastic models.
Full size table

where (k) is the number of estimated parameters, log is the natural logarithm, and L is the likelihood27. The model with the lowest AIC was selected as the most supported model. Models with a difference in AIC from the minimum AIC model of two or less are considered equivalent. In that case, we selected the simplest model.
Next, we used the two best fitting models (see “Results” section), the logistic function with beta-binomial distribution and the CNE function with beta-binomial distribution, to analyse the speed with which X. fastidiosa spreads through Puglia. To keep the models in a simplified form, it can be assumed that the dispersal front retains its shape over time and space and moves in space at a constant rate28,29. Therefore, for this analysis the deterministic functions from Table 1 are modified to include a yearly spread rate c (km per year) and time variable t (year):

$${text{Logistic}};{text{function:}};p_{l} = frac{1}{{1 + {text{exp}}left( {rleft( {x – (x_{50} + ct} right)} right))}}$$
(2)

$${text{CNE}};{text{function:}};p_{c} = left{ {begin{array}{ll} 1 & { mid; x < x_{100} + ct,} \ {exp left( { - rleft( {x - left( {x_{100} + ct} right)} right)} right) } & {mid; x ge x_{100} + ct.} \ end{array} } right.$$ (3) where (p_{l}) and (p_{c}) are the proportion of positive measurements of the logistic and CNE functions respectively, (r) is the relative growth rate of the disease in the tail in km-1, (x) is the distance in km from the disease origin, Gallipoli, (x_{50}) is the (negative) x-value (distance from Gallipoli) of the half-maximum of the curve at (t = 0) in km, (x_{100}) is the (negative) (x)-value where the CNE function curve reaches a value of 1.0 at (t = 0) in km, (t) is the time since 2013 in years, and the parameter c is the rate of spread in km per year. With these equations, one curve for every (t) (year) is displayed. 95% confidence limits (CLs) were calculated with the likelihood ratio test method27. To test the adequacy of the methodology for estimating the shape of the invasion front and the rate of spread, we did stochastic simulations in which we generated data on an expanding disease, collected samples in the same spatially heterogeneous manner from the simulated data as we did for the actual data sets, and re-estimated the rate of spread from the data. The estimated parameter values were then compared to the known parameter input values. The simulations were done using the logistic function and CNE function for the shape of the disease front and a beta-binomial distribution to describe variability. Data was randomly generated using a beta-binomial distribution for every distance circle according to the expected proportion of disease ((p)) calculated from the deterministically moving front, while the number of samples (N) within each distance circle was the same as in the empirical data. Again, a constant shape and rate of spread of the dispersal front is assumed29. Because of the uncertainty regarding the location of the front when sampling started (2013) and the rate of spread, the parameters that describe these aspects of the model, (x_{50}) (logistic) or (x_{100}) (CNE) and (c) respectively, were also varied in the stochastic simulations. For the logistic function, the parameters (r) (the relative growth rate of the disease in the tail) and (theta) (overdispersion) were fixed at 0.08 km−1 and 1 respectively, while parameter (x_{50}) was varied from − 40 to − 5 km from Gallipoli with steps of 5 km, and the parameter (c) was varied from 5 to 16 km per year with steps of 1 km per year. For the CNE function, the parameters (r) and (theta) were again fixed at 0.08 km−1 and 1 respectively, while parameter (x_{100}) was varied from − 45 to − 10 km with steps of 5 km, and parameter (c) was varied from 5 to 16 km per year with steps of 1 km per year. Data generation and estimation of parameters was done 10 times for each combination of parameters. For every combination of the location parameter, (x_{50}) or (x_{100}), and the rate of range expansion, c, the mean difference between the set rate of spread and the estimated rate of spread was calculated ((X_{i}); where i is the index for a parameter combination). Using the generated set of differences Xi, we calculated the mean bias ((overline{X})): $$overline{X} = frac{{mathop sum nolimits_{i}^{n} X_{i} }}{n}$$ (4) where (n) is the total number of parameter combinations. We also calculated the root-mean-squared error (RMSE): $${text{RMSE}} = sqrt {frac{{mathop sum nolimits_{i}^{n} X_{i}^{2} }}{n}}$$ (5) We estimated the width of the invasion front using a logistic shape of the invasion front. Width was calculated as the distance between the 1st and 99th percentile of the front or between the 5th and 95th percentile. For this, a curve at any point in time can be used since the curves have the same shape, and the width is the same in every year (Fig. 6). For the logistic function and the calculation of the 1st and 99th percentile the following applies: $$frac{1}{{1 + {text{exp}}left( {rleft( {x_{99} - left( {x_{50} + ct} right)} right)} right)}} = 0.99$$ (6) $$frac{1}{{1 + {text{exp}}left( {rleft( {x_{1} - left( {x_{50} + ct} right)} right)} right)}} = 0.01$$ (7) This is solved to find: $$x_{1} - x_{99} = frac{{2{text{log}}left( {99} right)}}{r}$$ (8) where log is the natural logarithm. Using Eq. (7), we also estimate the supposed starting time of the logistic growth of the disease by calculating (t) for (x_{1} = 0). To assess the sensitivity of our analysis to the point of origin, for which we chose Gallipoli in accordance with the best available evidence, we repeated our analyses of the shape of the front and the rate of spread when assuming different points of origin. For this we use three fictitious origin locations (Fig. 1): Santa Maria di Leuca, Otranto, and Maglie. We choose Santa Maria di Leuca and Otranto because these are also cities in Puglia with ports. We choose Maglie because it lies approximately in between the other three locations. These locations are not chosen because we think they are plausible points where Xylella could have been introduced for the first time, but only because they are suitable locations for a sensitivity analysis. To further asses the sensitivity of choosing Gallipoli as the point of origin, we repeat our simulations when generating data with Santa Maria di Leuca, Otranto, or Maglie as the point of origin, but analyse this data assuming Gallipoli as the point of origin. All calculations and model fitting were done in R 3.6.030. The complete dataset and details on the data analysis are available in the R script online at https://github.com/DBKottelenberg/OQDS_Xf_Puglia. More