Ecology

1.
Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).
CAS  PubMed  Article  Google Scholar 
2.
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Kellermann, V. et al. Phylogenetic constraints in key functional traits behind species’ climate niches: patterns of desiccation and cold resistance across 95 Drosophila species. Evolution 66, 3377–3389 (2012).
PubMed  Article  Google Scholar 

4.
Baselga, A., Recuero, E., Parra-Olea, G. & García-París, M. Phylogenetic patterns in zopherine beetles are related to ecological niche width and dispersal limitation. Mol. Ecol. 20, 5060–5073 (2011).
PubMed  Article  PubMed Central  Google Scholar 

5.
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).
PubMed  Article  Google Scholar 

6.
Dormann, C. F., Gruber, B., Winter, M. & Herrman, D. Evolution of climate niches in European mammals?. Biol. Lett. 6, 229–232 (2010).
PubMed  Article  PubMed Central  Google Scholar 

7.
Hof, C., Rahbek, C. & Araújo, M. B. Phylogenetic signals in the climatic niches of the world’s amphibians. Ecography 33, 242–250 (2010).
Google Scholar 

8.
Duran, A. & Pie, M. R. Tempo and mode of climate niche evolution in Primates. Evolution 69, 2496–2506 (2015).
PubMed  Article  Google Scholar 

9.
Khaliq, I. et al. Global variation in thermal physiology of birds and mammals: evidence for phylogenetic niche conservatism only in the tropics. J. Biogeogr. 42, 2187–2196 (2015).
Article  Google Scholar 

10.
Pie, M. R. The macroevolution of climatic niches and its role in ant diversification. Ecol. Entomol. 41, 301–307 (2016).
Article  Google Scholar 

11.
Wiens, J. J. & Donoghue, M. J. Historical biogeography, ecology and species richness. Trends Ecol. Evol. 19, 639–644 (2004).
PubMed  Article  Google Scholar 

12.
Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).
Article  Google Scholar 

13.
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Evol. Syst. 33, 475–505 (2002).
Article  Google Scholar 

14.
Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).
CAS  PubMed  Article  Google Scholar 

15.
Fitzpatrick, M. C., Weltzin, J. F., Sanders, N. J. & Dunn, R. R. The biogeography of prediction error: Why does the introduced range of the fire ant over-predict its native range?. Glob. Ecol. Biogeogr. 16, 24–33 (2007).
Article  Google Scholar 

16.
Prinzing, A., Durka, W., Klotz, S. & Brandl, R. The niche of higher plants: evidence for phylogenetic conservatism. Proc. Biol. Sci. 268, 1–7 (2001).
Article  Google Scholar 

17.
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Kozak, K. H. & Wiens, J. J. Does niche conservatism promote speciation? A case study in North American salamanders. Evolution 60, 2604–2621 (2006).
PubMed  Article  Google Scholar 

19.
Rice, N. H., Martinez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: a phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
Article  Google Scholar 

20.
Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. & Moritz, C. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58, 1781–1793 (2004).
PubMed  Article  Google Scholar 

21.
Knouft, J. H., Losos, J. B., Glor, R. E. & Kolbe, J. J. Phylogenetic analysis of the evolution of the niche in lizards of the Anolis sagrei group. Ecology 87, S29–S38 (2006).
PubMed  Article  Google Scholar 

22.
Cooper, N., Freckleton, R. P. & Jetz, W. Phylogenetic conservatism of environmental niches in mammals. Proc. Biol. Sci. 278, 2384–2391 (2011).
PubMed  PubMed Central  Google Scholar 

23.
Kamilar, J. M. & Muldoon, K. M. The climatic niche diversity of Malagasy primates: a phylogenetic approach. PLoS ONE 5, e11073 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Peixoto, F. P., Villalobos, F. & Cianciaruso, M. V. Phylogenetic conservatism of climatic niche in bats. Glob. Ecol. Biogeogr. 26, 1055–1065 (2017).
Article  Google Scholar 

25.
Ricciardi, A., Hoopes, M. F., Marchetti, M. P. & Lockwood, J. L. Progress toward understanding the ecological impacts of nonnative species. Ecol. Monogr. 83, 263–282 (2013).
Article  Google Scholar 

26.
Bellard, C. & Jeschke, J. M. A spatial mismatch between invader impacts and research publications. Conserv. Biol. 30, 230–232 (2016).
CAS  PubMed  Article  Google Scholar 

27.
Arnan, X. et al. Dominance-diversity relationships in ant communities differ with invasion. Glob. Change Biol. 24, 4614–4625 (2018).
ADS  Article  Google Scholar 

28.
Gussow, A. B., Auslander, N., Wolf, Y. I. & Koonin, E. V. Prediction of the incubation period for COVID-19 and future virus disease outbreaks. BMC Biol. 18, 1–12 (2020).
Article  CAS  Google Scholar 

29.
Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).
CAS  Article  Google Scholar 

30.
Chown, S. L. et al. Biological invasions, climate change and genomics. Evol Appl 8, 23–46 (2015).
PubMed  Article  Google Scholar 

31.
Rollins, L. A., Richardson, M. F. & Shine, R. A genetic perspective on rapid evolution in cane toads (Rhiniella marina). Mol. Ecol. 24, 2264–2276 (2015).
PubMed  Article  PubMed Central  Google Scholar 

32.
Estoup, A. et al. Is there a genetic paradox of biological invasion?. Annu. Rev. Ecol. Evol. Syst. 47, 51–72 (2016).
Article  Google Scholar 

33.
Ricciardi, A. et al. Invasion science: a horizon scan of emerging challenges and opportunities. Trends Ecol. Evol. 32, 464–474 (2017).
PubMed  Article  PubMed Central  Google Scholar 

34.
Fenderson, L. E., Kovach, A. I. & Llamas, B. Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time. Mol. Ecol. 29, 218–246 (2020).
PubMed  Article  PubMed Central  Google Scholar 

35.
Violle, C., Nemergut, D. R., Pu, Z. & Jiang, L. Phylogenetic limiting similarity and competitive exclusion. Ecol. Lett. 14, 782–787 (2011).
PubMed  Article  PubMed Central  Google Scholar 

36.
Novak, S. J. The role of evolution in the invasion process. Proc. Natl. Acad. Sci. USA 104, 3671–3672 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Buswell, J. M., Moles, A. T. & Hartley, S. Is rapid evolution common in introduced plant species?. J. Ecol. 99, 214–224 (2011).
Article  Google Scholar 

38.
Saul, W.-C. & Jeschke, J. M. Eco-evolutionary experience in novel species interactions. Ecol. Lett. 18, 236–245 (2015).
PubMed  Article  PubMed Central  Google Scholar 

39.
Hölldobler, B. & Wilson, E. O. The ants (Harvard University Press, Cambridge, 1990).
Google Scholar 

40.
Lowe, S., Browne, M., Boudjelas, S. & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database (Invasive Species Specialist Group, Auckland, 2000).
Google Scholar 

41.
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Evol. Syst. 33, 181–233 (2002).
Article  Google Scholar 

42.
Lessard, J.-P. et al. Strong influence of regional species pools on continent-wide structuring of local communities. Proc. Biol. Sci. 279, 266–274 (2011).
PubMed  PubMed Central  Google Scholar 

43.
Lucky, A., Trautwein, M. D., Guénard, B., Weiser, M. D. & Dunn, R. R. Tracing the rise of ants—out of the ground. PLoS ONE 8, e84012 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Economo, E. P. et al. Global phylogenetic structure of the hyperdiverse ant genus Pheidole reveals the repeated evolution of macroecological patterns. Proc. Biol. Sci. 282, 20141416 (2015).
PubMed  PubMed Central  Google Scholar 

45.

46.
http://www.antwiki.org/.

47.
https://www.gbif.org/.

48.
https://www.antweb.org/.

49.
Lebas, C., Galkowski, C., Blatrix, R. & Wegnez, P. Forumis d’Europe occidentale Delachaux et Niestle (Le Premier guide complet d’Europe, Paris, 2016).
Google Scholar 

50.
Bertelsmeier, C., Ollier, S., Liebhold, A. & Keller, L. Recent human history governs global ant invasion dynamics. Nat. Ecol. Evol. 1, 0184 (2017).
PubMed  PubMed Central  Article  Google Scholar 

51.
Bernard, F. Faune de l’Europe et du Bassin Méditerranéen. 3. Les Fourmis (Hymenoptera Formicidae) d’Europe Occidentale et Septentrionale. Eur. et Bas. Med. 3. Masson éditeurs, Paris (1968)

52.
Seifert, B. The Ants of Central and North Europe (Lutra Verlags-und Vertriebsgesellschaf, Tauer, 2018).
Google Scholar 

53.
http://www.iucngisd.org/gisd/100_worst.php.

54.
Wetterer, J. K. Worldwide spread of Emery’s sneaking ant, Cardiocondyla emeryi (Hymenoptera: Formicidae). Myrmecol. News 17, 13–20 (2012).
Google Scholar 

55.
Heinze, J., Cremer, S., Eckl, N. & Schrempf, A. Stealthy invaders: the biology of Cardiocondyla tramp ants. Insect. Soc. 53, 1–7 (2006).
Article  Google Scholar 

56.
Fournier, A., Penone, C., Pennino, M. G. & Courchamp, F. Predicting future invaders and future invasions. Proc. Natl. Acad. Sci. USA 116, 7905–7910 (2019).
CAS  PubMed  Article  Google Scholar 

57.
Moreau, C. S. & Bell, C. D. Testing the museum versus cradle biological diversity hypothesis: phylogeny, diversification, and ancestral biogeographic range evolution of the ants. Evolution 67, 2240–2257 (2013).
PubMed  Article  Google Scholar 

58.
Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. The evolution of myrmicine ants: phylogeny and biogeography of a hyperdiverse ant clade (Hymenoptera:Formicidae). Syst. Entomol. 40, 61–81 (2015).
Article  Google Scholar 

59.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

60.
https://www.creaf.cat.

61.
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2016).

62.
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Article  Google Scholar 

63.
Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Article  Google Scholar 

64.
Hijmans, R.J. & van Etten, J. raster: Geographic Data Analysis and Modeling. R package version 2.9-5. https://cran.r-project.org/web/packages/raster/index.html (2016).

65.
Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.1–4. https://cran.r-project.org/web/packages/dismo/index.html (2011).

66.
Wiens, J. J. The niche, biogeography and species interactions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2336–2350 (2011).
PubMed  PubMed Central  Article  Google Scholar 

67.
King, J. R. & Tschinkel, W. R. Experimental evidence that human impacts drive fire ant invasions and ecological change. Proc. Natl. Acad. Sci. USA 105, 20339–20343 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

68.
Vonshak, M. & Gordon, D. M. Intermediate disturbance promotes invasive ant abundance. Biol. Conserv. 186, 359–367 (2015).
Article  Google Scholar 

69.
McGlynn, T. P. The worldwide transfer of ants: Geographical distribution and ecological invasions. J. Biogeogr. 26, 535–548 (1999).
Article  Google Scholar 

70.
Kaspari, M. & Vargo, E. Does colony size buffer environmental variation? Bergmann’s rule and social insects. Am. Nat. 145, 610–632 (1995).
Article  Google Scholar 

71.
McGlynn, T. P. Non-native ants are smaller than related native ants. Am. Nat. 154, 690–699 (1999).
PubMed  Article  PubMed Central  Google Scholar 

72.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Article  Google Scholar 

73.
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).
Article  Google Scholar 

74.
Jenkins, C. N. et al. Global diversity in light of climate change: The case of ants. Divers. Distrib. 17, 652–662 (2011).
Article  Google Scholar 

75.
Mooney, H. A. & Cleland, E. E. The evolutionary impact of invasive species. Proc. Natl. Acad. Sci. USA 98, 5446–5451 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

76.
Ness, J. H. & Bronstein, J. L. The effects of invasive ants on prospective ant mutualists. Biol. Invasions 6, 445–461 (2004).
Article  Google Scholar 

77.
Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8, 875–894 (2005).
Article  Google Scholar 

78.
Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).
PubMed  Article  PubMed Central  Google Scholar 

79.
https://www.cbd.int/sp/targets/rationale/target-9. More

1.
Bowman, D. M. et al. Fire in the earth system. Science 324, 481–484. https://doi.org/10.1126/science.1163886 (2009).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Scott, A. C. The pre-quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 281–329. https://doi.org/10.1016/s0031-0182(00)00192-9 (2000).
Article  Google Scholar 

3.
Roebroeks, W. & Villa, P. On the earliest evidence for habitual use of fire in Europe. Proc. Natl. Acad. Sci. 108, 5209–5214. https://doi.org/10.1073/pnas.1018116108 (2011).
ADS  Article  PubMed  Google Scholar 

4.
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildl. Fire 18, 483–507. https://doi.org/10.1071/wf08187 (2009).
Article  Google Scholar 

5.
Hantson, S., Pueyo, S. & Chuvieco, E. Global fire size distribution is driven by human impact and climate: Spatial trends in global fire size distribution. Glob. Ecol. Biogeogr. 24, 77–86. https://doi.org/10.1111/geb.12246 (2015).
Article  Google Scholar 

6.
Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).
CAS  Article  Google Scholar 

7.
Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S. & Kloster, S. Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback. Geophys. Res. Lett. 43, 6324–6331. https://doi.org/10.1002/2016gl069365 (2016).
ADS  Article  Google Scholar 

8.
Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): ANALYSIS OF BURNED AREA. J. Geophys. Res. Biogeosci. 118, 317–328. https://doi.org/10.1002/jgrg.20042 (2013).
Article  Google Scholar 

9.
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017 (2017).
ADS  Article  Google Scholar 

10.
Loehman, R. A., Reinhardt, E. & Riley, K. L. Wildland fire emissions, carbon, and climate: Seeing the forest and the trees-a cross-scale assessment of wildfire and carbon dynamics in fire-prone, forested ecosystems. For. Ecol. Manag. 317, 9–19. https://doi.org/10.1016/j.foreco.2013.04.014 (2014).
Article  Google Scholar 

11.
Landry, J.-S. & Matthews, H. D. Non-deforestation fire vs. fossil fuel combustion: the source of CO(_{{2}}) emissions affects the global carbon cycle and climate responses. Biogeosciences 13, 2137–2149. https://doi.org/10.5194/bg-13-2137-2016 (2016).
ADS  Article  Google Scholar 

12.
Fischer, A. P. et al. Wildfire risk as a socioecological pathology. Front. Ecol. Environ. 14, 276–284. https://doi.org/10.1126/science.11638860 (2016).
Article  Google Scholar 

13.
Langmann, B., Duncan, B., Textor, C., Trentmann, J. & van der Werf, G. R. Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 43, 107–116. https://doi.org/10.1126/science.11638861 (2009).
ADS  CAS  Article  Google Scholar 

14.
Urbanski, S. Wildland fire emissions, carbon, and climate: Emission factors. For. Ecol. Manag. 317, 51–60. https://doi.org/10.1016/j.foreco.2013.05.045 (2014).
Article  Google Scholar 

15.
Veraverbeke, S., Verstraeten, W. W., Lhermitte, S., Van De Kerchove, R. & Goossens, R. Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire – burn severity using multitemporal MODIS imagery. Int. J. Wildl. Fire 21, 243. https://doi.org/10.1071/WF10075 (2012).
Article  Google Scholar 

16.
Bowman, D. M. J. S., Murphy, B. P., Williamson, G. J. & Cochrane, M. A. Pyrogeographic models, feedbacks and the future of global fire regimes: Correspondence. Glob. Ecol. Biogeogr. 23, 821–824. https://doi.org/10.1126/science.11638864 (2014).
Article  Google Scholar 

17.
Harris, R. M. B., Remenyi, T. A., Williamson, G. J., Bindoff, N. L. & Bowman, D. M. J. S. Climate-vegetation-fire interactions and feedbacks: Trivial detail or major barrier to projecting the future of the Earth system?: Climate-vegetation-fire interactions and feedbacks. Wiley Interdiscip. Rev. Clim. Change 7, 910–931. https://doi.org/10.1002/wcc.428 (2016).
Article  Google Scholar 

18.
Bradstock, R. A. A biogeographic model of fire regimes in Australia: Current and future implications: A biogeographic model of fire in Australia. Glob. Ecol. Biogeogr. 19, 145–158. https://doi.org/10.1111/j.1466-8238.2009.00512.x (2010).
Article  Google Scholar 

19.
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362. https://doi.org/10.1126/science.aal4108 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Pechony, O. & Shindell, D. T. Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl. Acad. Sci. 107, 19167–19170. https://doi.org/10.1073/pnas.1003669107 (2010).
ADS  Article  PubMed  Google Scholar 

21.
Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J. & Hayhoe, K. Global pyrogeography: The current and future distribution of wildfire. PLoS One 4, e5102 (2009).
ADS  Article  Google Scholar 

22.
Pausas, J. G. & Keeley, J. E. Abrupt climate-independent fire regime changes. Ecosystems 17, 1109–1120. https://doi.org/10.1007/s10021-014-9773-5 (2014).
CAS  Article  Google Scholar 

23.
Pausas, J. G. & Ribeiro, E. The global fire-productivity relationship: Fire and productivity. Glob. Ecol. Biogeogr. 22, 728–736. https://doi.org/10.1016/s0031-0182(00)00192-90 (2013).
Article  Google Scholar 

24.
Mondal, N. & Sukumar, R. Fires in seasonally dry tropical forest: Testing the varying constraints hypothesis across a regional rainfall gradient. PLoS One 11, e0159691. https://doi.org/10.1371/journal.pone.0159691 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Foley, J. A. et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob. Biogeochem. Cycles 10, 603–628. https://doi.org/10.1029/96gb02692 (1996).
ADS  CAS  Article  Google Scholar 

26.
Thonicke, K., Venevsky, S., Sitch, S. & Cramer, W. The role of fire disturbance for global vegetation dynamics: Coupling fire into a dynamic global vegetation model. Glob. Ecol. Biogeogr. 10, 661–677. https://doi.org/10.1046/j.1466-822x.2001.00175.x (2001).
Article  Google Scholar 

27.
Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991. https://doi.org/10.1016/s0031-0182(00)00192-94 (2010).
ADS  CAS  Article  Google Scholar 

28.
Rabin, S. S. et al. The fire modeling intercomparison project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions. Geosci. Model Dev. 10, 1175–1197. https://doi.org/10.5194/gmd-10-1175-2017 (2017).
ADS  CAS  Article  Google Scholar 

29.
Li, F., Zeng, X. & Levis, S. A process-based fire parameterization of intermediate complexity in a dynamic global vegetation model. Biogeosciences 9, 2761–2780. https://doi.org/10.1016/s0031-0182(00)00192-96 (2012).
ADS  Article  Google Scholar 

30.
Li, F., Levis, S. & Ward, D. Quantifying the role of fire in the earth system-part 1: Improved global fire modeling in the community earth system model (cesm1). Biogeosciences 10, 2293 (2013).
ADS  CAS  Article  Google Scholar 

31.
Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geosci. Model Dev. 13, 3299–3318. https://doi.org/10.5194/gmd-13-3299-2020 (2020).
ADS  Article  Google Scholar 

32.
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537. https://doi.org/10.1038/ncomms8537 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships. Glob. Change Biol. 24, 5164–5175. https://doi.org/10.1016/s0031-0182(00)00192-98 (2018).
ADS  Article  Google Scholar 

34.
Archibald, S., Roy, D. P., van Wilgen, B. W. & Scholes, R. J. What limits fire? An examination of drivers of burnt area in Southern Africa. Glob. Change Biol. 15, 613–630. https://doi.org/10.1111/j.1365-2486.2008.01754.x (2009).
ADS  Article  Google Scholar 

35.
Aldersley, A., Murray, S. J. & Cornell, S. E. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 409, 3472–3481. https://doi.org/10.1016/s0031-0182(00)00192-99 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

36.
Yang, L., Dawson, C. W., Brown, M. R. & Gell, M. Neural network and GA approaches for dwelling fire occurrence prediction. Knowl. Based Syst. 19, 213–219. https://doi.org/10.1016/j.knosys.2005.11.021 (2006).
Article  Google Scholar 

37.
Dutta, R., Aryal, J., Das, A. & Kirkpatrick, J. B. Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data. Sci. Rep. 3, 3188. https://doi.org/10.1038/srep03188 (2013).
ADS  Article  PubMed  PubMed Central  Google Scholar 

38.
Satir, O., Berberoglu, S. & Donmez, C. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomat. Nat. Hazards Risk 7, 1645–1658. https://doi.org/10.1080/19475705.2015.1084541 (2016).
Article  Google Scholar 

39.
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the cru ts3. 10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Article  Google Scholar 

40.
Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 (2003).
ADS  Article  Google Scholar 

41.
Zhao, M., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176. https://doi.org/10.1073/pnas.10181161083 (2005).
ADS  Article  Google Scholar 

42.
Freire, S. & Pesaresi, M. Ghs population grid, derived from gpw4, multitemporal (1975, 1990, 2000, 2015).European Commission Joint Research Centre (JRC) (2015).

43.
Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).
ADS  Article  Google Scholar 

44.
Friedl, M. A. et al. Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182. https://doi.org/10.1073/pnas.10181161084 (2010).
ADS  Article  Google Scholar 

45.
Channan, S., Collins, K. & Emanuel, W. Global Mosaics of the Standard Modis Land Cover Type Data Vol. 30 (University of Maryland and the Pacific Northwest National Laboratory, College Park, 2014).
Google Scholar 

46.
Lay, E. H. et al. Wwll global lightning detection system: Regional validation study in brazil. Geophys. Res. Lett. 31, 20 (2004).
Google Scholar 

47.
Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in north American boreal forests. Nat. Clim. Change 7, 529–534 (2017).
ADS  Article  Google Scholar 

48.
Chen, Y. et al. A pan-tropical cascade of fire driven by el niño/southern oscillation. Nat. Clim. Change 7, 906. https://doi.org/10.1038/s41558-017-0014-8 (2017).
ADS  CAS  Article  Google Scholar 

49.
Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of amazon deforestation carbon emissions. Nat. Commun. 9, 536. https://doi.org/10.1038/s41467-017-02771-y (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Yin, Y. et al. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño: FIRE CARBON EMISSIONS IN EQUATORIAL ASIA. Geophys. Res. Lett. 43, 10472–10479. https://doi.org/10.1073/pnas.10181161087 (2016).
ADS  CAS  Article  Google Scholar 

51.
Verdon, D. C., Kiem, A. S. & Franks, S. W. Multi-decadal variability of forest fire risk-eastern Australia. Int. J. Wildl. Fire 13, 165–171. https://doi.org/10.1071/WF03034 (2004).
Article  Google Scholar 

52.
Mariani, M., Fletcher, M.-S., Holz, A. & Nyman, P. Enso controls interannual fire activity in southeast Australia: Enso and fire activity in SE Australia. Geophys. Res. Lett. 43, 10891–10900. https://doi.org/10.1002/2016GL070572 (2016).
ADS  Article  Google Scholar 

53.
Li, L.-M., Song, W.-G., Ma, J. & Satoh, K. Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. Int. J. Wildl. Fire 18, 640–647. https://doi.org/10.1071/WF07136 (2009).
Article  Google Scholar 

54.
Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. & Matsinos, I. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat. Hazards 50, 125–143. https://doi.org/10.1071/wf081871 (2009).
Article  Google Scholar 

55.
Whitman, E., Parisien, M.-A., Thompson, D. K. & Flannigan, M. D. Short-interval wildfire and drought overwhelm boreal forest resilience. Sci. Rep. 9, 18796. https://doi.org/10.1038/s41598-019-55036-7 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Hawbaker, T. J. et al. Human and biophysical influences on fire occurrence in the united states. Ecol. Appl. 23, 565–582 (2013).
Article  Google Scholar 

57.
Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058. https://doi.org/10.1038/s41559-016-0058 (2017).
Article  Google Scholar 

58.
Andela, N. & van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat. Clim. Change 4, 791–795. https://doi.org/10.1038/nclimate2313 (2014).
ADS  Article  Google Scholar 

59.
Walker, X. J. et al. Fuel availability not fire weather controls boreal wildfire severity and carbon emissions. Nat. Clim. Changehttps://doi.org/10.1038/s41558-020-00920-8 (2020).
Article  Google Scholar 

60.
Zubkova, M., Boschetti, L., Abatzoglou, J. T. & Giglio, L. Changes in fire activity in Africa from 2002 to 2016 and their potential drivers. Geophys. Res. Lett. 46, 7643–7653. https://doi.org/10.1029/2019GL083469 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

61.
Moritz, M. A. et al. Climate change and disruptions to global fire activity. Ecosphere 3, 1–22 (2012).
Article  Google Scholar 

62.
Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69. https://doi.org/10.1016/j.gloplacha.2016.12.017 (2017).
ADS  Article  Google Scholar 

63.
Van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735. https://doi.org/10.1071/wf081877 (2010).
ADS  CAS  Article  Google Scholar 

64.
Archibald, S. et al. Biological and geophysical feedbacks with fire in the earth system. Environ. Res. Lett. 13, 033003. https://doi.org/10.1071/wf081878 (2018).
ADS  Article  Google Scholar 

65.
Ponomarev, E., Kharuk, V. & Ranson, K. Wildfires dynamics in Siberian larch forests. Forests 7, 125. https://doi.org/10.3390/f7060125 (2016).
Article  Google Scholar 

66.
van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N. & Dolman, A. J. Climate controls on the variability of fires in the tropics and subtropics: Climate controls on fires. Glob. Biogeochem. Cycleshttps://doi.org/10.1029/2007GB003122 (2008).
Article  Google Scholar  More

1.
Guerbois, C., Chapanda, E. & Fritz, H. Combining multi-scale socio-ecological approaches to understand the susceptibility of subsistence farmers to elephant crop raiding on the edge of a protected area. J. Appl. Ecol. 49, 1149–1158 (2012).
Article  Google Scholar 
2.
Felton, A. M. et al. Protein content of diets dictates the daily energy intake of a free-ranging primate. Behav. Ecol. 20, 685–690 (2009).
Article  Google Scholar 

3.
Rode, K. D., Chiyo, P. I., Chapman, C. A. & McDowell, L. R. Nutritional ecology of elephants in Kibale National Park, Uganda, and its relationship with crop-raiding behaviour. J. Trop. Ecol. 22, 441–449 (2006).
Article  Google Scholar 

4.
Duckworth, J. W., Sankar, K., Williams, A. C., Kumar, N. S. & Timmins, R. J. Bos gaurus. The IUCN Red List of Threatened Species 2016, e.T2891A46363646 (2016).

5.
Royal Thai Government Gazette. Wildlife Preservation and Protection Act, B.E.2562 (2019) of Thailand. www.ratchakitcha.soc.go.th (2019, in Thai).

6.
Srikosamatara, S. & Suteethorn, V. Populations of gaur and banteng and their management in Thailand. Nat. His. Bull. Siam Soc. 43(1), 55–83 (1995).
Google Scholar 

7.
Laichanthuek, P., Sukmasuang, R. & Duengkae, P. Population and habitat use of gaur (Bos gaurus) around Khao Phaeng Ma Non-hunting Area, Nakhon Ratchasima Province. J. Wildl. Thailand 24, 83–95 (2017).
Google Scholar 

8.
Chetri, M. Diet analysis of gaur, Bos gaurus gaurus (Smith, 1827) by micro-histological analysis of fecal samples in Parsa Wildlife Reserve, Nepal. Our Nat. 4, 20–28 (2006).
Article  Google Scholar 

9.
Bell, R. H. V. The use of the herb layer by grazing ungulates in Serangeti. In Watson, A. (Ed.). Animal Population in Relation to Their Food Resources. (Blackwell Scientific Publication 1970).

10.
Jarman, P. J. The social organization of antelope in relation to their ecology. Behaviour 48, 215–266 (1974).
Article  Google Scholar 

11.
Bhumpakphan, N. & McShea, W. J. Ecology of gaur and banteng in the seasonally dry forests of Thailand. In: McShea, W. J., Davies, S. J. & Bhumpakphan, N. (Eds.). The Ecology and Conservation of Seasonally Dry Forests in Asia. (Rowman and Littlefield Publishers, Inc. and Smithsonian Institution Scholarly Press 2011).

12.
Steinmetz, R. Gaur (Bos gaurus) and banteng (Bos javanicus) in the lowland forest mosaic of Xe Pian Protection Area, Lao PDR: Abundance, habitat use and conservation. Mammalia 68, 141–157 (2004).
Article  Google Scholar 

13.
Karanth, K. U. & Sunquist, M. E. Population structure, density and biomass of large herbivores in the tropical forests of Nagarahole, India. J. Trop. Ecol. 8, 21–35 (1992).
Article  Google Scholar 

14.
Steinmetz, R. Ecological surveys, monitoring, and the role of local people in protected areas of Lao PDR. (International Institute for Environment and Development 2000).

15.
Choudhury, A. Distribution and conservation of gaur Bos gaurus in the Indian Subcontinent. Mammal. Rev. 12, 199–226 (2002).
Article  Google Scholar 

16.
Gad, S. D. & Shyama, S. K. Diet composition and quality in Indian bison (Bos gaurus) based on fecal analysis. Zool. Sci. 28(4), 264–267 (2011).
Article  Google Scholar 

17.
Velho, N., Srinivasan, U., Singh, P. & Laurance, W. F. Large mammal use of protected and community-managed lands in a biodiversity hotspot. Anim. Conserv. https://doi.org/10.1111/acv.12234 (2015).
Article  Google Scholar 

18.
Bidayabha, T. Ecology and behavior of gaur (Bos gaurus) in a degraded area at Khao Phaeng-Ma, the Northestern Edge of Khao Yai National Park. (Faculty of Biology, Mahidol University 2001).

19.
Panusittikorn, P. & Prato, T. Conservation of protected areas in Thailand: the case of Khao Yai National Park, protected areas in East Asia. George Wright Forum 18(2), 66–76 (2001).
Google Scholar 

20.
Sorensen, A. A., van Beest, F. M. & Brook, R. K. Quantifying overlap in crop selection patterns among three sympatric ungulates in an agricultural landscape. Basic App. Ecol. 16, 601–609 (2015).
Article  Google Scholar 

21.
Todorov, N. A. Cereals, pulses and oilseeds. Livestock Produc. Sci. 19, 47–95 (1988).
Article  Google Scholar 

22.
Curzer, H. J., Wallace, M. C., Perry, G., Muhlberger, P. J. & Perry, D. The ethics of wildlife research: a nine R theory. ILAR J. 54(1), 52–57 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Department of National Parks, Wildlife and Plant Conservation (DNP). Management plan for Dong Phayayen-Khao Yai Forest Complex. (Department of National Parks, Wildlife, and Plant Conservation 2006).

24.
Kao-mim, N. Use of Hyperspectral Imaging System Data from HJ – 1A Satellite for Forest Types Classification in Khao Yai National Park. (MS thesis, Kasetsart University 2018, in Thai).

25.
Ngoprasert, D. & Gale, G. A. Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen – Khao Yai Forest Complex, Thailand. Mamm. Biol. 95, 51–58 (2019).
Article  Google Scholar 

26.
Lashley, M. A., Chitwood, M. C., Street, G. M., Moorman, C. E. & DePerno, C. S. Do indirect bite count surveys accurately represent diet selection of white-tailed deer in a forested environment?. Wildl. Res. 43, 254–260 (2016).
CAS  Article  Google Scholar 

27.
Lashley, M. A., Chitwood, M. C., Harper, C. A., Moorman, C. E. & DePerno, C. S. Collection, handling and analysis of forages for concentrate selectors. Wildl. Biol. Prac. 10(1), 6–15 (2014).
Google Scholar 

28.
Shafer, E. L. Jr. The twig-count method for measuring hardwood deer browse. J. Wildl. Manag. 27(3), 428–437 (1963).
Article  Google Scholar 

29.
Ivlev, V. S. Experimental Ecology of the Feeding of Fishes. (Yale University Press 1961).

30.
Association of Official Analytical Chemists (AOAC). Guidelines for collaborative study procedure to validate characteristics of a method of analysis. J. Assoc. Off. Anal. Chem. 71, 161–171 (1988).

31.
Petterson, D. S., Harris, D. J., Rayner, C. J., Blakeney, A. B. & Choct, M. Methods for the analysis of premium livestock grains. Aus. J Agric. Res. 50, 775–787 (1999).
Article  Google Scholar 

32.
Midkiff, V. A century of analytical excellence. The history of feed analysis, as chronicled in the development of AOAC official methods, 1884–1984. J. Assoc. Off. Anal. Chem. 67, 851–860 (1984).
CAS  PubMed  PubMed Central  Google Scholar 

33.
Brown, R. H. & Mueller-Harvey, I. Evaluation of the novel Soxflo technique for rapid extraction of crude fat in foods and animal feeds. J. AOAC Int. 82, 1369–1374 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Leopold, B. D. & Krausman, P. R. Diurnal activity patterns of desert mule deer in relation to temperature. Texas J. Sci. 39, 49–53 (1987).
Google Scholar 

35.
Holechek, J. L., Vavra, M. & Pieper, R. D. Botanical composition determination of range herbivore diets: a review. J. Range Manag. 35(3), 309–315 (1982).
Article  Google Scholar 

36.
Jayson, E. A. Assessment of Human-Wildlife Conflict and Mitigation Measures in Northern Kerala: Final Report of the Research Project KFRI/653/12. (Kerala Forest Research Institute 2016).

37.
Prayong, N. & Srikosamatara, S. Cutting trees in a secondary forest to increase gaur Bos gaurus numbers in Khao Phaeng Ma Reforestation area, Nakhon Ratchasima Province, Thailand. Conserv. Evid. 14, 5–9 (2017).
Google Scholar 

38.
Cervasio, F., Argenti, G., Genghini, M. & Ponzetta, M. P. Agronomic methods for mountain grassland habitat restoration for faunistic purposes in a protected area of the northern Apennines (Italy). iForest 9, 490–496 (2016).
Article  Google Scholar 

39.
Argenti, G., Racanelli, V., Bartolozzi, S., Staglianò, N. & Guerri, F. S. Evaluation of wild animals browsing preferences in forage resources. Ital. J. Agron. 12, 884 (2017).
Google Scholar 

40.
Freschi, P. et al. Diet composition of the Italian roe deer (Capreolus capreolus italicus) (Mammalia: Cervidae) from two protected areas. Eur. Zool. J. 84, 34–42 (2017).
Article  CAS  Google Scholar 

41.
Robbins, C. T., Spalinger, D. E. & van Hoven, W. Adaptation of ruminants to browse and grass diets: are anatomical-based browser-grazer interpretations valid?. Oecologia 103, 208–213 (1995).
ADS  PubMed  Article  PubMed Central  Google Scholar 

42.
Ahrestani, F. S., Heitkönig, I. M. A., Matsubayashi, H. & Prins, H. H. T. 2016. Grazing and browsing by large herbivores in South and Southeast Asia. In: Ahrestani, F. S. & Sankaran, M. (Eds). The ecology of large herbivores in South and Southeast Asia. Ecol. Stud. 225 (2016).

43.
Krishnan, M. An ecological survey of the large mammals of Peninsular India. J. Bombay Nat. His. Soc. 69, 297–315 (1972).
Google Scholar 

44.
Ahrestani, F. S. et al. Estimating densities of large herbivores in tropical forests: rigorous evaluation of a dung-based method. Ecol. Evol. 8, 7312–7322 (2018).
PubMed  PubMed Central  Article  Google Scholar 

45.
Ahrestani, F. S. Bos gaurus (Artiodactyla: Bovidae). Mamm. Species 50, 34–50 (2018).
Article  Google Scholar 

46.
Nayak, B. K. & Patra, A. K. Food and feeding habits of Indian bison, Bos gaurus (Smith, 1827) in Kuldiha Wildlife Sanctuary, Balasore, Odisha, India and its conservation. Int. Res. J. Biol. Sci. 4(5), 73–79 (2015).
Google Scholar 

47.
Gad, S. D. & Shyama, S. K. Studies on the food and feeding habits of gaur Bos gaurus H Smith (Mammalia: Artiodactyla: Bovidae) in two protected areas of Goa. J. Threat. Taxa 1, 128–130 (2009).
Article  Google Scholar 

48.
Fresehi, P., Riccioli, F., Argenti, G. & Ponzetta, M. P. The sustainability of wildlife in agroforestry land. Agric. Agric. Sci. Proc. 8, 148–157 (2016).
Google Scholar 

49.
Moser, B., Schutz, M. & Hindenlang, K. E. Resource selection by roe deer: Are wind throw gaps attractive feeding places?. For. Ecol. Manag. 255, 1179–1185 (2008).
Article  Google Scholar 

50.
Wilsey, B. J. & Martin, L. M. Top-down control of rare species abundances by native ungulates in a grassland restoration. Restor. Ecol. 23, 465–472 (2015).
Article  Google Scholar 

51.
Kouch, T., Preston, T. R. & Ly, J. Studies on utilization of trees and shrubs as the sole feedstuff by growing goats; foliage preferences and nutrient utilization. Livestock Res. Rural Dev. 15(7), http://www.lrrd.org/irrd15/7/kouc157.htm (2003).

52.
Kaitho, R. J. et al. Palatability of multipurpose tree species: effect of species and length of study on intake and relative palatability by sheep. Agrofor. Syst. 33, 249–261 (1996).
Article  Google Scholar 

53.
Kaitho, R. J. et al. Palatability of wilted and dried multipurpose tree species fed to sheep and goats. Anim. Feed Sci. Technol. 65, 151–163 (1997).
Article  Google Scholar 

54.
Provenza, M. P., Cervasio, F., Crocetti, C., Messeri, A. & Argenti, G. Habitat improvements with wildlife purposes in a grazed area on the Apennine mountains. Ital. J. Agron. 5, 233–238 (2003).
Google Scholar 

55.
Lucas, J. R. Role of foraging time constraints and variable prey encounter in optimal diet choice. Am. Nat. 122(2), 191–209 (1983).
Article  Google Scholar 

56.
Poapongsakorn, N., Ruhs, M. & Tangjitwisuth, S. Problems and outlook of agriculture in Thailand. TDRI Quar. Rev. 13(2), 3–14 (1998).
Google Scholar 

57.
Retamosa, M. I., Humberg, L. A., Beasley, J. C. & Rhodes, O. E. Jr. Modeling wildlife damage to crops in northern Indiana. Human-Wild. Conf. 2(2), 225–239 (2008).
Google Scholar 

58.
Su, K., Ren, J., Yang, J., Hou, Y. & Wen, Y. Human-Elephant conflicts and villagers’ attitudes and knowledge in the Xishuangbanna Nature Reserve, China. Inter. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph17238910 (2020).
Article  Google Scholar  More

Wintertime outbreaks in the northern hemisphere
In Fig. 1a we use case data (see “Methods”) to estimate the effective reproductive number of infection for New York City from the start of 2020 to the present (July 2020)14. Estimated values of Reffective peak early in the outbreak and then settle close to 1 in the summer months as NPIs act to lower transmission. We assume the Reffective values approximate R0 and compare them to the predicted seasonal R0, derived from our climate-driven SIRS model. The model assumes the climate sensitivity of betacoronavirus HKU1 and that seasonal variations in transmission are driven by specific humidity. Current rates (average over second and third weeks of July) of Reffective in New York city are found to be approximately 35% below the R0 levels predicted by our climate-driven model. We assume this 35% decline is due to the efficacy of NPIs. To project future scenarios we assume that R0 remains at either the current levels (constant) or a relative 35% decrease in our climate-driven R0, which means R0 oscillates with specific humidity (Fig. 1a, top plot).
Fig. 1: Wintertime outbreaks in New York City.

Estimated and projected R0 values (top plot) assuming a 35% and b 15% reduction in R0 due to NPIs. Corresponding time series show the simulated outbreaks in the climate (blue) or constant (black/dashed) scenarios, with middle row plots assuming a 10% reporting rate and bottom row plots assuming a 3% reporting rate. Corresponding susceptible time series are shown in orange (susceptibles = S/population = N). Case data from New York City are shown in gray. Surface plots (top) show the peak wintertime proportion infected (infected = I/population = N) in the scenarios with c the constant R0 and d the climate-driven R0. e shows the difference between the climate and constant R0 scenario. The timing of peak incidence in years from July is shown for the f constant and g climate scenarios. The difference between climate and constant scenario is shown in h. Points in c–h show the scenarios is a, b. Dashed line shows estimated susceptibility in New York based on ref. 24.

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In Fig. 1a (lower plots) we show the proportion infected over time using the climate-driven and constant R0 values. We also vary the reporting rate of observed cases relative to modeled cases; while this accounts for under-reporting it also allows us to vary the proportion susceptible over a feasible range (see “Methods”). In the middle figure, the reporting rate is 10% (estimates for US reporting rates are  1 for both the climate and constant scenario and case numbers begin to grow exponentially. With a 10% reporting rate a large secondary outbreak is observed in both the constant and climate scenarios (Fig. 1b, middle plot). With a 3% reporting rate, meaning a larger depletion of susceptibles, the secondary outbreak appears much larger in the climate scenario: this supports the hypothesis that the disease will become more sensitive to climate as the susceptible proportion declines, much like the seasonal endemic diseases.
In Fig. 1c–h we simulate model outcomes across a broad range of parameter space varying the proportion susceptible (in July) and the reduction in R0 due to NPIs. The proportion susceptible is varied by initializing the epidemic with different sizes of the infected population (initializing with a large number results in a relatively larger outbreak and initializing with a small number results in a smaller outbreak). We vary this starting number over a feasible range given the case data, i.e., such that observed cases never exceed modeled cases or that the reporting rate never drops below 1%. Over this range, the model plausibly tracks the observed case data.
Figure 1e shows the change in winter peak size (max proportion infected between September–March) due to climate. Peak size results for the constant and climate scenarios are shown in Fig. 1c and d, respectively. When the susceptible proportion is high and the effect of NPIs are minimal (relative R0 given NPI = 1), large outbreaks are possible in both the climate and constant R0 scenarios meaning the relative effect of climate on peak size and timing is close to 0 (top right Fig. 1e). As the proportion susceptible declines (moving left along the x-axis of Fig. 1e), case trajectories become more sensitive to the wintertime weather resulting in larger peaks in the climate scenario. However, sufficiently strong NPIs, in combination with low susceptibility, reduce incidence to zero in both the climate and control scenarios (bottom left Fig. 1e). NPIs are not as effective at reducing cases when susceptibility is higher (bottom right Fig. 1e).
We also consider the effect of climate on secondary peak timing. Figure 1f, g shows the peak timing in years (relative to July 2020) in the constant and climate scenarios, respectively. In the climate scenario, peak timing for New York is clustered in the winter months (Fig. 1a, b). In the constant R0 scenario, secondary peaks can occur at a wide range of times over the next 1.5 years. As in the peak size results, high susceptibility and limited NPIs reduce the effect of climate and peak timing is matched for both the climate and control scenarios (top right Fig. 1h). Gray areas represent regions where there is no secondary peak in either the climate or control scenario.
Climate effects on global risk
We next consider the relative effect of climate on peak size for nine global locations (Fig. 2b). In this case, as opposed to using estimated Reffective values (given case data are not available for several of the global cities), we simulate the epidemic from July 2020 using a fixed number of infecteds and vary the starting proportion of susceptibles (example results from select global locations, using estimated Reffective, are shown in Supplementary Figs. 1–3). Results from the New York surface in Fig. 2b qualitatively match our tailored simulation in Fig. 1. Locations in the southern hemisphere are expected to be close to their maximum wintertime R0 values in mid-2020 (Fig. 2a), meaning that secondary peaks in the climate scenario are lower than the constant R0 scenario for these locations (Fig. 2b). Tropical locations experience minimal difference in the climate versus constant R0 scenario given the relatively mild seasonal variations in specific humidity in the tropics. Broadly, the results across hemisphere track the earlier results from New York: high susceptibility and a lack of NPIs lead to a limited role of climate, but an increase in NPI efficacy or a reduction in susceptibility may increase climate effects. This result is more striking in regions with a large seasonality in specific humidity (e.g. New York, Delhi and Johannesburg).
Fig. 2: Climate sensitivity of outbreaks across global locations.

a The climate effect on R0 assuming a 35% reduction due to NPIs shown for August and December. b The effect of climate, changing susceptibility, and NPIs on peak proportion infected (infected = I/population = N), post July 2020, for nine global locations.

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Drivers of variability in secondary outbreak size
Our results suggest that climate may play an increasing role in determining the future course of the SARS-CoV-2 pandemic, depending on levels of susceptibility and NPIs. We next evaluate the extent to which interannual variability in specific humidity could influence peak size. We simulate separate New York pandemic trajectories using 11 years (2008–2018) of specific humidity data. Figure 3a shows the variability in R0 and secondary peak size based on these runs (with 35% reduction in R0 due to NPIs and 10% reporting rate—the same as Fig. 1a). While a relatively large peak occurs in all years, the largest peak (0.038 proportion infected) is almost double the smallest peak year (0.020 proportion infected). In Fig. 3b we calculate the coefficient of variation of the peak size for different susceptible proportions and NPI intensities. These results qualitatively track Fig. 1e. Sensitivity to interannual variation appears most important when the susceptible population has been reduced by at least 20% and minimal controls are in place.
Fig. 3: Climate variability and wintertime cases in New York.

a Climate-driven R0 and corresponding infected time series (infected = I/population = N) based on the last 10 years of specific humidity data for New York, assuming a 35% reduction due to NPIs. b The effect of changing susceptibility and NPIs on the coefficient of variation of peak incidence for simulations using specific humidity data from 2008 to 2018. Dashed line shows estimated susceptibility in New York based on ref. 24.

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Many factors, including weather variability, determine the size of a possible secondary outbreak. Another factor that may play an important role is the length of immunity to the disease. While the length of immunity may not affect the dynamics in the early stage of the pandemic, it could have complex and uncertain outcomes for future trajectories16. In our main results, we assume a length of immunity equal to betacoronvirus HKU1, based on prior estimates1. We also assume a climate sensitivity based on estimates for HKU1. However parameters for SARS-CoV-2, such as immunity length and climate sensitivity, are still fundamentally uncertain.
We consider the possible contribution of uncertainty in parameters to the variance in the wintertime peak size following the method developed by Yip et al.17 (see “Methods”). We run our simulation for New York while varying parameter values for the efficacy of NPIs, the length of immunity to the disease, the reporting rate of prior cases (which defines susceptibility in July), the climate sensitivity of the pathogen (in terms of the strength of the relationship with specific humidity), and the weather variability (interannual variability determined by historic weather observations from a particular year, 2009–2018). We then perform an analysis of variance (ANOVA) on the determinants of wintertime peak size.
Figure 4 shows contribution to variance in wintertime peak size of these five parameters: NPIs efficacy, immunity length, reporting rate, climate sensitivity of the virus, and interannual weather variability. We find that climate sensitivity is an important factor but secondary to the efficacy of NPIs and immunity length in determining peak transmission. Uncertainty in immunity length and reporting together influence susceptibility and collectively account for the second largest portion of total uncertainty. Uncertainty in interannual variability, i.e. weather, has a smaller impact on peak size. NPIs contribute the largest proportion to total variance in peak size. It is important to note that while other parameters are external features of either the virus, climate, or disease trajectories to date, the efficacy of NPIs is determined directly by policy interventions and therefore the size of future outbreaks is largely under human control.
Fig. 4: Contribution to uncertainty in New York wintertime 20/21 peak size.

The relative importance of NPI efficacy [0–35%], immunity length (10–60 weeks), reporting (1–100%), climate sensitivity of the virus [−32.5 to −227.5], and interannual weather variability [10 years] in determining wintertime peak size. Immunity length and reporting rate collectively determine susceptibility, S.

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1.
Lehmann, L. & Keller, L. The evolution of cooperation and altruism—a general framework and a classification of models. J. Evol. Biol. 19, 1365–1376 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Taborsky, M., Frommen, J. G. & Riehl, C. Correlated pay-offs are key to cooperation. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150084 (2016).
Article  Google Scholar 

3.
Trivers, R. L. The evolution of altruism. Q. Rev. Biol. 46, 35–57 (1971).
Article  Google Scholar 

4.
Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science (80-. ) 211, 1390–1396 (1981).
ADS  MathSciNet  CAS  MATH  Article  Google Scholar 

5.
Pfeiffer, T., Rutte, C., Killingback, T., Taborsky, M. & Bonhoeffer, S. Evolution of cooperation by generalized reciprocity. Proc. R. Soc. B Biol. Sci. 272, 1115–1120 (2005).
Article  Google Scholar 

6.
Rankin, D. J. & Taborsky, M. Assortment and the evolution of generalized reciprocity. Evolution (N. Y). 63, 1913–1922 (2009).

7.
Alexander, R. D. The Biology of Moral Systems (Aldine Gruyter, New York, 1987).
Google Scholar 

8.
Nowak, M. A. & Sigmund, K. Evolution of indirect reciprocity by image scoring. Nature 393, 573 (1998).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Winkler, I., Jonas, K. & Rudolph, U. On the usefulness of memory skills in social interactions: Modifying the iterated Prisoner’s Dilemma. J. Conflict Resolut. 52, 375–384 (2008).
Article  Google Scholar 

10.
Stevens, J. R., Volstorf, J., Schooler, L. J. & Rieskamp, J. Forgetting constrains the emergence of cooperative decision strategies. Front. Psychol. 1, 1–12 (2011).
Article  Google Scholar 

11.
Milinski, M. & Wedekind, C. Working memory constrains human cooperation in the Prisoner’s Dilemma. Proc. Natl. Acad. Sci. 95, 13755–13758 (1998).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Volstorf, J., Rieskamp, J. & Stevens, J. R. The good, the bad, and the rare: Memory for partners in social interactions. PLoS One 6, e18945 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Barta, Z. et al. Optimal moult strategies in migratory birds. Philos. Trans. R. Soc. B Biol. Sci. 363, 211–229 (2008).
Article  Google Scholar 

14.
Houston, A. I. & McNamara, J. M. Models of Adaptive Behaviour: An Approach Based on State (Cambridge University Press, Cambridge, 1999).
Google Scholar 

15.
Tinbergen, N. The Study of Instinct (Clarendon Press, Oxford, 1951).
Google Scholar 

16.
McNamara, J. M. & Houston, A. I. Integrating function and mechanism. Trends Ecol. Evol. 24, 670–675 (2009).
PubMed  Article  PubMed Central  Google Scholar 

17.
Gigerenzer, G., Todd, P. M. & ABC Research Group. Simple heuristics that make us smart. (Oxford University Press, 1999).

18.
Hurley, S. Social heuristics that make us smarter. Philos. Psychol. 18, 585–612 (2005).
Article  Google Scholar 

19.
Isen, A. M. Positive affect, cognitive processes, and social behavior. Adv. Exp. Soc. Psychol. 20, 203–253 (1987).
Google Scholar 

20.
Bartlett, M. Y. & DeSteno, D. Gratitude and prosocial behavior: Helping when it costs you. Psychol. Sci. 17, 319–325 (2006).
PubMed  Article  PubMed Central  Google Scholar 

21.
Stanca, L. Measuring indirect reciprocity: Whose back do we scratch? J. Econ. Psychol. 30, 190–202 (2009).
Article  Google Scholar 

22.
Leimgruber, K. L. et al. Give what you get: Capuchin monkeys (Cebus apella) and 4-year-old children pay forward positive and negative outcomes to conspecifics. PLoS One 9, e87035 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Claidière, N. et al. Selective and contagious prosocial resource donation in capuchin monkeys, chimpanzees and humans. Sci. Rep. 5, 7631 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Gfrerer, N. & Taborsky, M. Working dogs cooperate among one another by generalised reciprocity. Sci. Rep. 7, 43867 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

25.
Rutte, C. & Taborsky, M. Generalized reciprocity in rats. PLoS Biol. 5, e196 (2007).
CAS  Article  Google Scholar 

26.
Rutte, C. & Taborsky, M. The influence of social experience on cooperative behaviour of rats (Rattus norvegicus): Direct vs generalised reciprocity. Behav. Ecol. Sociobiol. 62, 499–505 (2008).
Article  Google Scholar 

27.
Schneeberger, K., Dietz, M. & Taborsky, M. Reciprocal cooperation between unrelated rats depends on cost to donor and benefit to recipient. BMC Evol. Biol. 12, 41 (2012).
PubMed  PubMed Central  Article  Google Scholar 

28.
Schweinfurth, M. K., Aeschbacher, J., Santi, M. & Taborsky, M. Male Norway rats cooperate according to direct but not generalized reciprocity rules. Anim. Behav. 152, 93–101 (2019).
Article  Google Scholar 

29.
Dolivo, V. & Taborsky, M. Cooperation among Norway rats: The importance of visual cues for reciprocal cooperation, and the role of coercion. Ethology 121, 1071–1080 (2015).
Article  Google Scholar 

30.
Dolivo, V. & Taborsky, M. Norway rats reciprocate help according to the quality of help they received. Biol. Lett. 11, 20140959 (2015).
PubMed  PubMed Central  Article  Google Scholar 

31.
Wood, R. I., Kim, J. Y. & Li, G. R. Cooperation in rats playing the iterated Prisoner’s Dilemma game. Anim. Behav. 114, 27–35 (2016).
PubMed  PubMed Central  Article  Google Scholar 

32.
Schweinfurth, M. K. & Taborsky, M. The transfer of alternative tasks in reciprocal cooperation. Anim. Behav. 131, 35–41 (2017).
Article  Google Scholar 

33.
Schweinfurth, M. K. & Taborsky, M. Reciprocal trading of different commodities in Norway rats. Curr. Biol. 28, 594–599 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Stieger, B., Schweinfurth, M. K. & Taborsky, M. Reciprocal allogrooming among unrelated Norway rats (Rattus norvegicus) is affected by previously received cooperative, affiliative and aggressive behaviours. Behav. Ecol. Sociobiol. 71, 182 (2017).
Article  Google Scholar 

35.
Delmas, G. E., Lew, S. E. & Zanutto, S. B. High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix. PLoS ONE 14, 1–14 (2019).
Article  CAS  Google Scholar 

36.
Barnett, S. A. & Spencer, M. M. Feeding social behaviour and interspecific competition in wild rats. Behaviour 3, 229–242 (1951).
Google Scholar 

37.
Norton, S., Culver, B. & Mullenix, P. Development of nocturnal behavior in albino rats. Behav. Biol. 15, 317–331 (1975).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Schweinfurth, M. K. & Taborsky, M. Rats play tit-for-tat instead of integrating cooperative experiences over multiple interactions. Proc. R. Soc. 287, 20192423 (2020).
Google Scholar 

39.
Engqvist, L. The mistreatment of covariate interaction terms in linear model analyses of behavioural and evolutionary ecology studies. Anim. Behav. 70, 967–971 (2005).
Article  Google Scholar 

40.
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: The arrive guidelines for reporting animal research. PLoS Biol. 8, 6–10 (2010).
Article  CAS  Google Scholar 

41.
Schweinfurth, M. K. et al. Do female Norway rats form social bonds? Behav. Ecol. Sociobiol. 71, 98 (2017).
Article  Google Scholar 

42.
Davis, D. E. The characteristics of rat populations. Q. Rev. Biol. 28, 373–401 (1953).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
McGuire, B., Pizzuto, T., Bemis, W. E. & Getz, L. L. General ecology of a rural population of Norway rats (Rattus norvegicus) based on intensive live trapping. Am. Midl. Nat. 155, 221–236 (2006).
Article  Google Scholar 

44.
Dunbar, R. I. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 469–493 (1992).
Article  Google Scholar 

45.
Panoz-Brown, D. et al. Replay of episodic memories in the rat. Curr. Biol. 28, 1628–1634 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Crystal, J. D. Prospective memory. Curr. Biol. 23, 750–751 (2013).
Article  CAS  Google Scholar 

47.
Telle, H. Beitrag zur Erkenntnis der Verhaltensweise von Ratten, vergleichend dargestellt bei Rattus norvegicus und Rattus rattus. Zeitschrift für Angew. Zool. 53, 129–196 (1966).
Google Scholar 

48.
Calhoun, J. B. The ecology and sociobiology of the Norway rat. (U.S. Dept. of Health, Education, and Welfare, Public Health Service, 1979).

49.
Mogil, J. S. Mice are people too: Increasing evidence for cognitive, emotional and social capabilities in laboratory rodents. Can. Psychol. 60, 14–20 (2019).
Article  Google Scholar 

50.
Dolivo, V., Rutte, C. & Taborsky, M. Ultimate and proximate mechanisms of reciprocal altruism in rats. Learn. Behav. 44, 223–226 (2016).
PubMed  Article  PubMed Central  Google Scholar 

51.
Müller, J. J. A., Massen, J. J. M., Bugnyar, T. & Osvath, M. Ravens remember the nature of a single reciprocal interaction sequence over 2 days and even after a month. Anim. Behav. 128, 69–78 (2017).
Article  Google Scholar 

52.
Stevens, J. R. & Hauser, M. D. Why be nice? Psychological constraints on the evolution of cooperation. Trends Cogn. Sci. 8, 60–65 (2004).
PubMed  Article  PubMed Central  Google Scholar  More

1.
Steffan-Dewenter, I., Potts, S. G. & Packer, L. Pollinator diversity and crop pollination services are at risk. Trends Ecol. Evol. 20, 651–652 (2005).
PubMed  Article  PubMed Central  Google Scholar 
2.
Aizen, M. A., Garibaldi, L. A., Cunningham, S. A. & Klein, A. M. Long-term global trends in crop yield and production reveal no current pollination shortage but increasing pollinator dependency. Curr. Biol. 18, 1572–1575 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Garibaldi, L. A., Aizen, M. A., Klein, A. M., Cunningham, S. A. & Harder, L. D. Global growth and stability of agricultural yield decrease with pollinator dependence. Proc. Natl. Acad. Sci. 108, 5909–5914 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Goulson, D. Effects of introduced bees on native ecosystems. Annu. Rev. Ecol. Evol. Syst. 34, 1–26 (2003).
Article  Google Scholar 

5.
Paini, D. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Aust. Ecol. 29, 399–407 (2004).
Article  Google Scholar 

6.
Aslan, C. E., Liang, C. T., Galindo, B., Kimberly, H. & Topete, W. The role of honey bees as pollinators in natural areas. Nat. Areas J. 36, 478–489 (2016).
Article  Google Scholar 

7.
Mallinger, R. E., Gaines-Day, H. R. & Gratton, C. Do managed bees have negative effects on wild bees? A systematic review of the literature. PLoS ONE 12, e0189268 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

8.
Wignall, V. R. et al. Seasonal variation in exploitative competition between honeybees and bumblebees. Oecologia 192, 351–361 (2020).
ADS  PubMed  Article  PubMed Central  Google Scholar 

9.
Thomson, D. M. Detecting the effects of introduced species: A case study of competition between Apis and Bombus. Oikos 114, 407–418 (2006).
Article  Google Scholar 

10.
Franco, E. L., Aguiar, C. M. & Ferreiraz, V. S. Plant use and niche overlap between the introduced honey bee (Apis mellifera) and the native bumblebee (Bombus atratus) (Hymenoptera: Apidae) in an area of tropical mountain vegetation in northeastern Brazil. Sociobiology 53, 141–150 (2009).
Google Scholar 

11.
Herbertsson, L., Lindström, S. A., Rundlöf, M., Bommarco, R. & Smith, H. G. Competition between managed honeybees and wild bumblebees depends on landscape context. Basic Appl. Ecol. 17, 609–616 (2016).
Article  Google Scholar 

12.
Thomson, D. M. Local bumble bee decline linked to recovery of honey bees, drought effects on floral resources. Ecol. Lett. 19, 1247–1255 (2016).
PubMed  Article  PubMed Central  Google Scholar 

13.
Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. Proc. Natl. Acad. Sci. 103, 13890–13895 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Badano, E. I. & Vergara, C. H. Potential negative effects of exotic honey bees on the diversity of native pollinators and yield of highland coffee plantations. Agric. For. Entomol. 13, 365–372 (2011).
Article  Google Scholar 

15.
Brittain, C., Williams, N., Kremen, C. & Klein, A.-M. Synergistic effects of non-Apis bees and honey bees for pollination services. Proc. R. Soc. B Biol. Sci. 280, 20122767 (2013).
Article  Google Scholar 

16.
Müller, H. T. Interaction Between Bombus terrestris and Honeybees in Red Clover Fields Reduces Abundance of Other Bumblebees and Red Clover Yield and Honeybees in Red Clover Fields Reduces Abundance of Other Bumblebees and Red Clover Yield (Norwegian University of Life Sciences, Ås, 2016).
Google Scholar 

17.
Grass, I. et al. Pollination limitation despite managed honeybees in South African macadamia orchards. Agric. Ecosyst. Environ. 260, 11–18 (2018).
Article  Google Scholar 

18.
hUallacháin, D. Ó. (United Nations Convention to Combat Desertification, Bonn, Germany, 2017).

19.
Vaughan, M. & Skinner, M. Using 2014 farm bill programs for pollinator conservation. USDA Biol. Tech. Note 78, 2nd Ed. (2015).

20.
Vaughan, M. & Skinner, M. Using Farm Bill programs for pollinator conservation. USDA-NRCS National Plant Data Center, USDA Biol. Tech. Note 78 (2008).

21.
FSA. CP42 pollinator habitat: Establishing and supporting diverse pollinator-friendly habitat. (Farm Service Agency, U.S. Department of Agriculture, Washington, D.C., 2013).

22.
Venturini, E. M., Drummond, F. A., Hoshide, A. K., Dibble, A. C. & Stack, L. B. Pollination reservoirs for wild bee habitat enhancement in cropping systems: a review. Agroecol. Sustain. Food Syst. 41, 101–142 (2017).
Article  Google Scholar 

23.
Wood, T. J., Holland, J. M., Hughes, W. O. & Goulson, D. Targeted agri-environment schemes significantly improve the population size of common farmland bumblebee species. Mol. Ecol. 24, 1668–1680 (2015).
PubMed  Article  PubMed Central  Google Scholar 

24.
Haaland, C. & Gyllin, M. Butterflies and bumblebees in greenways and sown wildflower strips in southern Sweden. J. Insect Conserv. 14, 125–132 (2010).
Article  Google Scholar 

25.
Ponisio, L. C., M’Gonigle, L. K. & Kremen, C. On-farm habitat restoration counters biotic homogenization in intensively managed agriculture. Glob. Change Biol. 22, 704–715 (2016).
ADS  Article  Google Scholar 

26.
Dolezal, A. G., Clair, A. L. S., Zhang, G., Toth, A. L. & O’Neal, M. E. Native habitat mitigates feast–famine conditions faced by honey bees in an agricultural landscape. Proc. Natl. Acad. Sci. 116, 25147–25155 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Venturini, E., Drummond, F., Hoshide, A., Dibble, A. & Stack, L. B. Pollination reservoirs in lowbush blueberry (Ericales: Ericaceae). J. Econ. Entomol. 110, 333–346 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

28.
Morandin, L. A. & Kremen, C. Hedgerow restoration promotes pollinator populations and exports native bees to adjacent fields. Ecol. Appl. 23, 829–839 (2013).
PubMed  Article  PubMed Central  Google Scholar 

29.
Blaauw, B. R. & Isaacs, R. Flower plantings increase wild bee abundance and the pollination services provided to a pollination-dependent crop. J. Appl. Ecol. 51, 890–898 (2014).
Article  Google Scholar 

30.
Feltham, H., Park, K., Minderman, J. & Goulson, D. Experimental evidence of the benefit of wild flower strips to crop pollination. Ecol. Evolut. 5, 3523–3530 (2015).
Article  Google Scholar 

31.
Gross, C. & Mackay, D. Honeybees reduce fitness in the pioneer shrub Melastoma affine (Melastomataceae). Biol. Cons. 86, 169–178 (1998).
Article  Google Scholar 

32.
do Carmo, R. M., Franceschinelli, E. V. & da Silveira, F. A. Introduced honeybees (Apis mellifera) reduce pollination success without affecting the floral resource taken by native pollinators. Biotropica 36, 371–376 (2004).
Google Scholar 

33.
Bruckman, D. & Campbell, D. R. Floral neighborhood influences pollinator assemblages and effective pollination in a native plant. Oecologia 176, 465–476 (2014).
ADS  PubMed  Article  PubMed Central  Google Scholar 

34.
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Carvalheiro, L. G. et al. Natural and within-farmland biodiversity enhances crop productivity. Ecol. Lett. 14, 251–259 (2011).
PubMed  Article  PubMed Central  Google Scholar 

36.
Jönsson, A. M. et al. Modelling the potential impact of global warming on Ips typographus voltinism and reproductive diapause. Clim. Change 109, 695–718 (2011).
ADS  Article  Google Scholar 

37.
Scheper, J. et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 52, 1165–1175 (2015).
Article  Google Scholar 

38.
Krimmer, E., Martin, E. A., Krauss, J., Holzschuh, A. & Steffan-Dewenter, I. Size, age and surrounding semi-natural habitats modulate the effectiveness of flower-rich agri-environment schemes to promote pollinator visitation in crop fields. Agric. Ecosyst. Environ. 284, 106590 (2019).
Article  Google Scholar 

39.
Klein, A. M. et al. Wild pollination services to California almond rely on semi-natural habitat. J. Appl. Ecol. 49, 723–732 (2012).
Google Scholar 

40.
Grab, H., Poveda, K., Danforth, B. & Loeb, G. Landscape context shifts the balance of costs and benefits from wildflower borders on multiple ecosystem services. Proc. R. Soc. B Biol. Sci. 285, 20181102 (2018).
Article  Google Scholar 

41.
Prendergast, K. S., Menz, M. H., Dixon, K. W. & Bateman, P. W. The relative performance of sampling methods for native bees: An empirical test and review of the literature. Ecosphere 11, e03076 (2020).
Article  Google Scholar 

42.
Cane, J. H., Minckley, R. L. & Kervin, L. J. Sampling bees (Hymenoptera: Apiformes) for pollinator community studies: pitfalls of pan-trapping. J. Kansas Entomol. Soc. 73, 225–231 (2000).
Google Scholar 

43.
O’Connor, R. S. et al. Monitoring insect pollinators and flower visitation: The effectiveness and feasibility of different survey methods. Methods Ecol. Evol. 10, 2129–2140. https://doi.org/10.1111/2041-210x.13292 (2019).
Article  Google Scholar 

44.
Graystock, P., Blane, E. J., McFrederick, Q. S., Goulson, D. & Hughes, W. O. Do managed bees drive parasite spread and emergence in wild bees?. Int. J. Parasitol. Parasites Wildlife 5, 64–75 (2016).
Article  Google Scholar 

45.
Alger, S. A., Burnham, P. A., Boncristiani, H. F. & Brody, A. K. RNA virus spillover from managed honeybees (Apis mellifera) to wild bumblebees (Bombus spp.). PloS One 14, e0217822 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Schaffer, W. M. et al. Competition, foraging energetics, and the cost of sociality in three species of bees. Ecology 60, 976–987 (1979).
Article  Google Scholar 

47.
Pleasants, J. M. Bumblebee response to variation in nectar availability. Ecology 62, 1648–1661 (1981).
Article  Google Scholar 

48.
Ginsberg, H. S. Foraging ecology of bees in an old field. Ecology 64, 165–175 (1983).
Article  Google Scholar 

49.
Schaffer, W. M. et al. Competition for nectar between introduced honey bees and native North American bees and ants. Ecology 64, 564–577 (1983).
Article  Google Scholar 

50.
Gross, C. L. The effect of introduced honeybees on native bee visitation and fruit-set in Dillwynia juniperina (Fabaceae) in a fragmented ecosystem. Biol. Cons. 102, 89–95 (2001).
Article  Google Scholar 

51.
Hudewenz, A. & Klein, A.-M. Competition between honey bees and wild bees and the role of nesting resources in a nature reserve. J. Insect Conserv. 17, 1275–1283 (2013).
Article  Google Scholar 

52.
Johnson, L. K. & Hubbell, S. P. Aggression and competition among stingless bees: Field studies. Ecology 55, 120–127 (1974).
Article  Google Scholar 

53.
Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).
PubMed  Article  PubMed Central  Google Scholar 

54.
Woodcock, B. A. et al. Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield. Nat. Commun. 10, 1–10 (2019).
ADS  CAS  Article  Google Scholar 

55.
Garibaldi, L. A. et al. From research to action: enhancing crop yield through wild pollinators. Front. Ecol. Environ. 12, 439–447 (2014).
Article  Google Scholar 

56.
Connelly, H., Poveda, K. & Loeb, G. Landscape simplification decreases wild bee pollination services to strawberry. Agric. Ecosyst. Environ. 211, 51–56 (2015).
Article  Google Scholar 

57.
MacInnis, G. & Forrest, J. R. K. Pollination by wild bees yields larger strawberries than pollination by honey bees. J. Appl. Ecol. 56, 824–832. https://doi.org/10.1111/1365-2664.13344 (2019).
Article  Google Scholar 

58.
Seeley, T. D. Social foraging by honeybees: how colonies allocate foragers among patches of flowers. Behav. Ecol. Sociobiol. 19, 343–354 (1986).
Article  Google Scholar 

59.
Bänsch, S., Tscharntke, T., Gabriel, D. & Westphal, C. Crop pollination services: complementary resource use by social vs solitary bees facing crops with contrasting flower supply. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13777 (2020).

60.
Nye, W. P. & Anderson, J. L. Insect pollinators frequenting strawberry blossoms and the effect of honey bees on yield and fruit quality. J. Am. Soc. Horticult. Sci. 99, 40 (1974).
Google Scholar 

61.
De Oliveira, D., Savoie, L. & Vincent, C. in VI International Symposium on Pollination 288, 420–424 (1990).

62.
Chagnon, M., Gingras, J. & DeOliveira, D. Complementary aspects of strawberry pollination by honey and indigenous bees (Hymenoptera). J. Econ. Entomol. 86, 416–420 (1993).
Article  Google Scholar 

63.
Horth, L. & Campbell, L. A. Supplementing small farms with native mason bees increases strawberry size and growth rate. J. Appl. Ecol. 55, 591–599 (2018).
Article  Google Scholar 

64.
Pfister, S. C. et al. Dominance of cropland reduces the pollen deposition from bumble bees. Sci. Rep. 8, 13873 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Artz, D. R. & Nault, B. A. Performance of Apis mellifera, Bombus impatiens, and Peponapis pruinosa (Hymenoptera: Apidae) as pollinators of pumpkin. J. Econ. Entomol. 104, 1153–1161 (2011).
PubMed  Article  PubMed Central  Google Scholar 

66.
Petersen, J., Huseth, A. & Nault, B. Evaluating pollination deficits in pumpkin production in New York. Environ. Entomol. 43, 1247–1253 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
McGrady, C., Troyer, R. & Fleischer, S. Wild bee visitation rates exceed pollination thresholds in commercial cucurbita agroecosystems. J. Econ. Entomol. 113, 562–574 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Geslin, B. et al. Advances in Ecological Research Vol. 57, 147–199 (Elsevier, San Diego, 2017).
Google Scholar 

69.
Steffan-Dewenter, I. & Tscharntke, T. Resource overlap and possible competition between honey bees and wild bees in central Europe. Oecologia 122, 288–296 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Torné-Noguera, A., Rodrigo, A., Osorio, S. & Bosch, J. Collateral effects of beekeeping: Impacts on pollen-nectar resources and wild bee communities. Basic Appl. Ecol. 17, 199–209 (2016).
Article  Google Scholar 

71.
Free, J. B. Insect Pollination of Crops (Academic Press, London, 1970).
Google Scholar 

72.
Delaplane, K. S., Mayer, D. R. & Mayer, D. F. Crop pollination by bees. (CABI, 2000).

73.
Phillips, B. Current honey bee and bumble bee stocking information. Michigan State University, MSU Extension: Pollination (2019). https://www.canr.msu.edu/news/current_honey_bee_stocking_information_and_an_introduction_to_commercial_bu.

74.
Angelella, G. M. & O’Rourke, M. E. Pollinator habitat establishment after organic and no-till seedbed preparation methods. HortScience 52, 1349–1355 (2017).
CAS  Article  Google Scholar 

75.
Blaauw, B. R. & Isaacs, R. Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wildflowers. Basic Appl. Ecol. 15, 701–711 (2014).
Article  Google Scholar 

76.
Klatt, B. K. et al. Bee pollination improves crop quality, shelf life and commercial value. Proc. R. Soc. B Biol. Sci. 281, 20132440 (2014).
Article  Google Scholar 

77.
King, S. R., Davis, A. R. & Wehner, T. C. Classical genetics and traditional breeding. In Genetics, Genomics, and Breeding of Cucurbits (eds. Wang, Y.-H. et al.) 61–92 (CRC Press, 2012).

78.
Kronenberg, H. G. Poor fruit setting in strawberries. I. Euphytica 8, 47–57 (1959).
Article  Google Scholar 

79.
Kronenberg, H. G., Braak, J. & Zeilinga, A. Poor fruit setting in strawberries. II. Euphytica 8, 245–251 (1959).
Article  Google Scholar 

80.
Robinson, R. W. & Decker-Walters, D. S. Cucurbits (CAB Intl., New York, 1997).
Google Scholar 

81.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).

82.
Magnusson, A. et al. Package ‘glmmTMB’. R Package Version 0.2. 0 (2017).

83.
Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 package. R Package Version 2, 74 (2007).
Google Scholar 

84.
Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: Estimated marginal means, aka least-squares means. R Package Version 1, 3 (2018).
Google Scholar 

85.
Wien, H., Stapleton, S., Maynard, D., McClurg, C. & Riggs, D. Flowering, sex expression, and fruiting of pumpkin (Cucurbita sp.) cultivars under various temperatures in greenhouse and distant field trials. HortScience 39, 239–242 (2004).
Article  Google Scholar  More

Samples
Cores were collected during the RV Investigator voyage IN2018_T02 (19 and 20 May 2018, respectively, Fig. 2) to Tasmania, from sites in the Mercury Passage and Maria Island (Fig. 2). We collected one KC Denmark Multi-Core (MCS3, inner core diameter 10 cm, 36 cm long, estimated to cover the last ~ 145 years based on 210Pb dating at the Australian Nuclear Science and Technology Organisation (ANSTO, Lucas Heights, Sydney) in the Mercury Passage (MP, 42.550 S, 148.014 E; 68 m water depth), and one gravity core (GC2; inner core diameter 10 cm, 3 m long) offshore from Maria Island (42.845 S, 148.240 E; 104 m) composed of 2 sections; GC2A (bottom) and GC2B (top) estimated to cover the last ~ 8950 years based on 210Pb and 14C dating, ANSTO). The untreated cores were immediately sealed with plastic caps and sealed with duct-tape, stored initially on-board at 10 °C, followed by transport to and storage at 4 °C at ANSTO. To minimise contamination during core splitting and subsampling (October, 2018, ANSTO), we wiped working benches, sampling and cutting tools with bleach and 80% EtOH, changed gloves immediately when contaminated with sediment, and wore appropriate PPE at all times (gloves, facemask, hairnet, disposable lab gown). We removed the outer ~ 1 cm of the working core-half (working from bottom to the top of the core), then collected plunge samples by pressing sterile 15 mL centrifuge tubes (Falcon) ~ 2 cm deep into the sediment core centre at 5 cm depth intervals. All sedaDNA samples were immediately frozen at − 20 °C and transported to the Australian Centre for Ancient DNA (ACAD), Adelaide. For this study, a total of 30 samples were selected from both cores, representing ~ 2 cm depth intervals within the upper 36 cm of MCS3 and GC2, and ~ 20 cm depth intervals in GC2 downcore from 36 cm below seafloor (cmbsf).
Figure 2

Map of coring sites, inshore (MCS3) and offshore (GC2) of Maria Island, Tasmania, South-East Australian Coast. Map created in ODV (Schlitzer, R., Ocean Data View, https://odv.awi.de, 2018).

Full size image

SedaDNA extractions
We prepared sedaDNA extracts and sequencing libraries at ACAD’s ultra-clean ancient (GC2) and forensic (MCS3) facilities following ancient DNA decontamination standards24. All sample tubes were wiped with bleach on the outside prior to entering the laboratory for subsampling. Our extraction method followed the optimised (“combined”) approach outlined in detail previously7, with a minor modification in that we stored the final purified DNA in TLE buffer (50 μL Tris HCL (1 M), 10 μL EDTA (0.5 M), 5 mL nuclease-free water) instead of customary Elution Buffer (Qiagen) (see Supplementary Material Methods). To monitor laboratory contamination, we used extraction blank controls (EBCs) by processing 1–2 (depending on the extraction-batch size) empty bead-tubes through the extraction protocol. A total of 30 extracts were generated from sediment samples and 7 extracts from EBCs.
RNA-baits design
We designed two RNA hybridisation bait-sets, one targeting phyto- and zooplankton for a more detailed overview of plankton diversity (hereafter ‘Planktonbaits1’), and one targeting specific plankton organisms and their predators to enable detailed investigation of HABs, especially those caused by dinoflagellates, in coastal marine ecosystems (hereafter, ‘HABbaits1’). Planktonbaits1 was based on 18S-V9 and 16S-V4 sequences of major phyto- and zooplankton groups, whereas we designed HABbaits1 from a collection of LSU, SSU, D1-D2-LSU, COI, rbcL and ITS sequences for specific marine target organisms often associated with HABs in our study region (Table 1).
Table 1 Planktonbaits1 and HABbaits1.
Full size table

Planktonbaits1
To design Planktonbaits1 we downloaded the W2_V9_PR2 database25 (containing 18S-V9 rDNA and rRNA sequences of marine protists and their predators, downloaded on 30 July 2018), deduplicated using Geneious software (Geneious NZ), and filtered the remaining sequences to keep only those from major phyto- and zooplankton groups (Table 1). In collaboration with Arbor Biosciences, USA, we designed RNA baits based on these 15,035 target sequences by masking any repeating Ns (i.e., any consecutive Ns that were  83% overlap, and  > 95% identity). We added five 16S-V4 rRNA sequences (the prokaryotic equivalent of the small subunit ribosomal rRNA gene) of common marine cyanobacteria (one Trichodesmium erythraeum sequence, and two Prochlorococcus marinus and Synechococcus sp. sequences each), acquired from the SILVA database26; Table 1). To check and ensure target-taxon specificity, these five cyanobacterial sequences were mapped against a non-target sequence (Escherichia coli 16S RefSeq sequence NR_114042.1), then reverse-transcribed to DNA, and BLASTed to the same NCBI RefSeq database described above. BLAST hits of  More

1.
Abbott, R. D. et al. Hybridization and speciation. J. Evol. Bio. 26, 229–246 (2013).
CAS  Article  Google Scholar 
2.
de Lafontaine, G. & Bousquet, J. Asymmetry matters: a genomic assessment of directional biases in gene flow between hybridizing spruces. Ecol. Evol. 7, 3883–3893 (2017).
PubMed  PubMed Central  Article  Google Scholar 

3.
Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Anderson, E. & Stebbins, G. L. Hybridization as an evolutionary stimulus. Evolution 8, 378–388 (1954).
Article  Google Scholar 

5.
De La Torre, A. R., Li, Z., Van de Peer, Y. & Ingvarsson, P. K. Contrasting rates of molecular evolution and patterns of selection among gymnosperms and flowering plants. Mol. Bio. Evol. 34, 1363–1377 (2017).
Article  CAS  Google Scholar 

6.
Critchfield, W. B. Hybridization and classification of the white pines (Pinus section Strobus). Taxon 35, 647–656 (1986).
Article  Google Scholar 

7.
Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Bouille, M. & Bousquet, J. Trans-species shared polymorphisms at orthologous nuclear gene loci among distant species in the conifer Picea (Pinaceae): implications for long term maintenance of genetic diversity in trees. Am. J. Bot. 92, 63–73 (2005).
PubMed  Article  PubMed Central  Google Scholar 

9.
Hamilton, J. A., Lexer, C. & Aitken, S. N. Genomic and phenotypic architecture of a spruce hybrid zone (Picea sitchensis × P. glauca). Mol. Ecol. 22, 827–841 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Hamilton, J. & Miller, J. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).
PubMed  Article  PubMed Central  Google Scholar 

11.
Jagoda, E. et al. Disentangling immediate adaptive introgression from selection on standing introgressed variation in humans. Mol. Biol. Evol. 35, 623–630 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Bresadola, L. et al. Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits. N. Phytol. 223, 2076–2089 (2019).
CAS  Article  Google Scholar 

13.
Suarez-Gonzalez, A. et al. Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood). Mol. Ecol. 25, 2427–2442 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Suarez-Gonzalez, A., Hefer, C. A., Lexer, C., Cronk, Q. C. & Douglas, C. J. Scale and direction of adaptive introgression between black cottonwood (Populus trichocarpa) and balsam poplar (P. balsamifera). Mol. Ecol. 27, 1667–1680 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Leroy, T. et al. Adaptive introgression as a driver of local adaptation to climate in European white oaks. New. Phytol. 226, 1171–1182 (2019).
PubMed  PubMed Central  Article  Google Scholar 

16.
Hufford, M.B. et al. Genomic signature of crop-wild introgression in Maize. PLoS Genet. 9, e100347 (2013).
Article  Google Scholar 

17.
Ma, Y. et al. Ancient introgression drives adaptation to cooler and drier mountain habitats in a cypress species complex. Commun. Biol. 18, 210–213 (2019).
Google Scholar 

18.
Pyhäjärvi, T., Hufford, M. B., Mezmouk, S. & Ross-Ibarra, J. Complex patterns of local adaptation in Teosinte. Genome Biol. Evol. 5, 1594–1609 (2013).
PubMed  PubMed Central  Article  Google Scholar 

19.
Mei, W., Stetter, M. G. & Stitzer, M. C. Adaptation in plant genomes: bigger is different. Am. J. Bot. 105, 16–19 (2019).
Article  Google Scholar 

20.
Syring, J. et al. Widespread genealogical non-monophyly in species of the Pinus subgenus. Strobus. Syst. Biol. 56, 163–181 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Menon, M. et al. The role of hybridization during ecological divergence of southwestern white pine (Pinus strobiformis) and limber pine (P. flexilis). Mol. Ecol. 27, 1245–1260 (2018).
PubMed  Article  PubMed Central  Google Scholar 

22.
Looney, C. E. & Waring, K. M. Pinus strobiformis (southwestern white pine) stand dynamics, regeneration, and disturbance ecology: a review. For. Ecol. Manag. 287, 90–102 (2013).
Article  Google Scholar 

23.
Schoettle, A. W. & Rochelle, S. G. Morphological variation of Pinus flexilis (Pinaceae), a bird-dispersed pine, across a range of elevations. Am. J. Bot. 87, 1797–1806 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Frankis, M. P. The high altitude white pines (Pinus L. subgenus Strobus Lemmon, Pinaceae) of Mexico and the adjacent SW USA. Int. Dendrol. Soc. Yearb. 2008, 64–72 (2009).
Google Scholar 

25.
Tomback, D. F. et al. Seed dispersal in limber and southwestern white pine: comparing core and peripheral populations. In The Future of High Elevation, Five-Needle White Pines in Western North America: Proceedings of the High Five Symposium. Proceedings RMRS-P- 63 69–71 (US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, 2011).

26.
Moreno-Letelier, A., Ortíz-Medrano, A. & Piñero, D. Niche divergence versus neutral processes: combined environmental and genetic analyses identify contrasting patterns of differentiation in recently diverged pine species. PLoS ONE 8, e78228 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Moreno-Letelier, A. & Barraclough, T. G. Mosaic genetic differentiation along environmental and geographic gradients indicate divergent selection in a white pine species complex. Evol. Ecol. 29, 733–748 (2015).
Article  Google Scholar 

28.
Little, E. L. Jr. Atlas of United States Trees. Vol. 5, 22 (Florida. Misc. Publ. 1361, U.S. Department of Agriculture, Forest Service, 1978).

29.
Bisbee, J. Cone morphology of the Pinus ayacahuite-flexilis complex of the southwestern United States and Mexico. Bull. Cupressus Conserv. Proj. 3, 3–33 (2014).
Google Scholar 

30.
Borgman, E. M., Schoettle, A. W. & Angert, A. L. Assessing the potential for maladaptation during active management of limber pine populations: a common garden study detects genetic differentiation in response to soil moisture in the Southern Rocky Mountains. Can. J. For. Res. 45, 496–505 (2015).
CAS  Article  Google Scholar 

31.
Neale, D. B. & Kremer, A. Forest tree genomics: growing resources and applications. Nat. Rev. Genet. 12, 111–122 (2011).
CAS  PubMed  Article  Google Scholar 

32.
Mitton, J., Kreiser, B. R. & Latta, R. G. Glacial refugia of limber pine (Pinus flexilis James) inferred from the population structure of mitochondrial DNA. Mol. Ecol. 9, 91–97 (2000).
CAS  PubMed  Article  Google Scholar 

33.
Jorgensen, S., Hamrick, J. L. & Wells, P. V. Regional patterns of genetic diversity in Pinus flexilis (Pinaceae) reveal complex species history. Am. J. Bot. 89, 792–800 (2002).
PubMed  Article  Google Scholar 

34.
Goodrich, B. A., Waring, K. M. & Kolb, T. E. Genetic variation in Pinus strobiformis growth and drought tolerance from southwestern US populations. Tree Physiol. 36, 1219–1235 (2016).
CAS  PubMed  Article  Google Scholar 

35.
DaBell, J. Pinus Strobiformis Response to an Elevational Gradient and Correlation with Source Climate. Master’s thesis, Northern Arizona University (2017).

36.
Francis, J. A. & Vavrus, S. J. Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett. 10, 014005 (2015).
Article  Google Scholar 

37.
Rellstab, C. et al. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).
PubMed  Article  PubMed Central  Google Scholar 

38.
Coop, G., Witonsky, D., Di Rienzo, A. & Pritchard, J. K. Using environmental correlations to identify loci underlying local adaptation. Genetics 185, 1411–1423 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Levitt J. Responses of Plants to Environmental Stress. Chilling, Freezing, and High Temperature Stresses 2nd edn (Academic Press, 1980).

40.
Bierne, N., Welch, J., Loire, E., Bonhomme, F. & David, P. The coupling hypothesis: why genome scans may fail to map local adaptation genes. Mol. Ecol. 20, 2044–2072 (2011).
PubMed  Article  PubMed Central  Google Scholar 

41.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. 1, 3–14 (2010).
Article  Google Scholar 

42.
Harrison, K. A. et al. Signatures of polygenic adaptation associated with climate across the range of a threatened fish species with high genetic connectivity. Mol. Ecol. 26, 6253–6269 (2017).
Article  Google Scholar 

43.
Lind, B. M. et al. Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Mol. Ecol. 26, 3168–3185 (2017).
PubMed  Article  PubMed Central  Google Scholar 

44.
Csillery, K. et al. Detecting short spatial scale local adaptation and epistatic selection in climate‐related candidate genes in European beech (Fagus sylvatica) populations. Mol. Ecol. 23, 4696–4708 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Schumer, M. & Brandvain, Y. Determining epistatic selection in admixed populations. Mol. Ecol. 25, 2577–2591 (2016).
PubMed  Article  PubMed Central  Google Scholar 

46.
Menon, M. et al. Tracing the footprints of a moving hybrid zone under a demographic history of speciation with gene flow. Evol. Appl. 13, 195–209 (2019).
PubMed  PubMed Central  Article  Google Scholar 

47.
Whitney, K. D. et al. Quantitative trait locus mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower. Mol. Ecol. 24, 2194–2211 (2015).
PubMed  PubMed Central  Article  Google Scholar 

48.
Chhatre, V. E., Evan, L. M., DiFazio, S. P. & Keller, S. R. Adaptive introgression and maintenance of a trispecies hybrid complex in range‐edge populations of Populus. Mol. Ecol. 27, 4820–4838 (2018).
PubMed  Article  PubMed Central  Google Scholar 

49.
Aitken, S. A. et al. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).
PubMed  PubMed Central  Article  Google Scholar 

50.
Alberto, F. J. et al. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Chn. Bio. 19, 1645–1661 (2013).
Article  Google Scholar 

51.
Kirkpatrick, M. & Barton, N. H. Evolution of a species’ range. Am. Nat. 150, 1–23 (1997).
CAS  PubMed  Article  Google Scholar 

52.
Stebbins, G. L. The role of hybridization in evolution. Proc. Am. Philos. Soc. 103, 231–251 (1959).
Google Scholar 

53.
Petit, R. J. & Excoffier, L. Gene flow and species delimitation. Trends Ecol. Evol. 24, 386–393 (2009).
PubMed  Article  PubMed Central  Google Scholar 

54.
Barton, N. H. & Hewitt, G. M. Analysis of hybrid zones. Annu. Rev. Ecol. Evol. S 16, 113–148 (1985).
Article  Google Scholar 

55.
Mimura, M., Mishima, M., Lascoux, M. & Yahara, T. Range shift and introgression of the rear and leading populations in two ecologically distinct Rubus species. BMC Evol. Biol. 2014, 209 (2014).
PubMed  Article  PubMed Central  Google Scholar 

56.
De La Torre, A. R., Wang, T., Jaquish, B. & Aitken, S. N. Adaptation and exogenous selection in a Picea glauca × Picea engelmannii hybrid zone: implications for forest management under climate change. N. Phytol. 201, 687–699 (2014).
Article  CAS  Google Scholar 

57.
Hamilton, J. R., De La Torre, A. R. & Aitken, S. N. Fine-scale environmental variation contributes to introgression in a three-species spruce hybrid complex. Tree Genet. Genomes 11, 1–14 (2015).
Article  Google Scholar 

58.
Fraïsse, C. K., Belkhir, J., Welch, J. & Bierne, N. Local interspecies introgression is the main cause of extreme levels of intraspecific differentiation in mussels. Mol. Ecol. 25, 269–770 (2016).
PubMed  Article  PubMed Central  Google Scholar 

59.
Wu, D. D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).
PubMed  Article  PubMed Central  Google Scholar 

60.
Kremer, A. & Le Corre, V. Decoupling of differentiation between traits and their underlying genes in response to divergent selection. Heredity 10, 375–385 (2012).
Article  Google Scholar 

61.
Eckert, A. J. et al. Local adaptation at fine spatial scales: an example from sugar pine (Pinus lambertiana, Pinaceae). Tree Genet. Genomes 11, 42 (2015).
Article  Google Scholar 

62.
Hornoy, B., Pavy, N., Gérardi, S., Beaulieu, J. & Bousquet, J. Genetic adaptation to climate in white spruce involves small to moderate allele frequency shifts in functionally diverse genes. Genome Biol. Evol. 7, 3269–3285 (2015).
PubMed  PubMed Central  Article  Google Scholar 

63.
Rieseberg, L. H. et al. Hybridization and the colonization of novel habitats by annual sunflowers. Genetica 129, 149–165 (2007).
PubMed  Article  PubMed Central  Google Scholar 

64.
Lewontin, R. C. & Birch, L. C. Hybridization as a source of variation for adaptation to new environments. Evolution 20, 315–336 (1966).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Pavy, N. et al. The heterogeneous levels of linkage disequilibrium in white spruce genes and comparative analysis with other conifers. Heredity 108, 273–284 (2011).
PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
Kim, B. Y., Huber, C. D. & Lohmueller, K. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 14, e1007741 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Eyre-Walker, A., Woolfit, M. & Phelps, T. The distribution of fitness effects of new deleterious amino acid mutations in humans. Genetics 173, 891–900 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Christe, C. et al. Adaptive evolution and segregating load contribute to the genomic landscape of divergence in two tree species connected by episodic gene flow. Mol. Ecol. 26, 59–76 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Lu, M., Hodgins, K. A., Degner, J. C. & Yeaman, S. Purifying selection does not drive signatures of convergent local adaptation of lodgepole pine and interior spruce. BMC Evol. Biol. 19, 110 (2019).
PubMed  PubMed Central  Article  Google Scholar 

70.
Whitlock, M. C. Temporal fluctuations in demographic parameters and the genetic variance among populations. Evolution 46, 608–615 (1992).
PubMed  Article  Google Scholar 

71.
Lexer, C. et al. Genomic admixture analysis in European Populus spp. reveals unexpected patterns of reproductive isolation and mating. Genetics 186, 699–712 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Lowry, D. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).
CAS  PubMed  Article  Google Scholar 

73.
Parchman, T. L. et al. RADseq approaches and applications for forest tree genetics. Tree Genet. Genomes 14, 39 (2018).
Article  Google Scholar 

74.
Gossmann, T. I., Keightley, P. D. & Eyre-Walker, A. The effect of variation in the effective population size on the rate of adaptive molecular evolution in eukaryotes. Genome Biol. Evol. 4, 658–667 (2012).
PubMed  PubMed Central  Article  Google Scholar 

75.
Lexer, C. & Widmer, A. The genic view of plant speciation: recent progress and emerging questions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3023–3036 (2008).
PubMed  PubMed Central  Article  Google Scholar 

76.
Bucholz, E. Early Growth, Water Relations and Growth: Common Garden Studies of Pinus Strobiformis under Climate Change. PhD dissertation, Northern Arizona University (2020).

77.
Lotterhos, K. & Whitlock, M. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).
PubMed  Article  Google Scholar 

78.
Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Goudet, J. hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Article  Google Scholar 

80.
R Core Team. R v.3.3.2: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

81.
Parchman, T. L. et al. Genome -wide association genetics of an adaptive trait in lodgepole pine: association mapping of serotiny. Mol. Ecol. 21, 2991–3005 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant -calling pipeline designed for population genomics of non -model organisms. PeerJ 2, e431 (2014).
PubMed  PubMed Central  Article  Google Scholar 

83.
Wang, T., Hamann, A., Spittlehouse, D. L. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

84.
Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).
PubMed  PubMed Central  Article  Google Scholar 

85.
Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).
PubMed  PubMed Central  Article  Google Scholar 

86.
Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).
Google Scholar 

87.
Camacho et al. BLAST+: architecture and applications. BMC Bioinfo. 10, 421 (2009).
Article  CAS  Google Scholar 

88.
Warnes, G., Gorjanc, G., Leisch, F. & Man, M. genetics: Population Genetics. R package version 1.3.8.1 (2013).

89.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2 (2013).

90.
Legendre, P. & Legendre, L. Numerical Ecology 2nd English edn (Elsevier, 1998).

91.
Montgomery, D. C. & Peck, E. A. Introduction to Linear Regression Analysis 2nd edn (John Wiley & Sons, 1992).

92.
Liu, Q. Variation partitioning by partial redundancy analysis (RDA). Environmetrics 8, 75–85 (1997).
CAS  Article  Google Scholar 

93.
Kemppainen, P. et al. Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure. Mol. Ecol. Resour. 15, 1031–1045 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
Ohta, T. Linkage disequilibrium with the island model. Genetics 101, 139–155 (1982).
CAS  PubMed  PubMed Central  Article  Google Scholar 

95.
Beissinger, T. M. et al. Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens. Heredity 116, 58–166 (2015).
Google Scholar 

96.
Hijmans, R. J. geosphere: Spherical trigonometry. R package version 1.5‐7 (2017).

97.
Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).
Article  Google Scholar 

98.
Gompert, Z. & Buerkle, A. C. introgress: methods for analyzing introgression between divergent lineages. R package version 1.2.3 (2012).

99.
Gompert, Z. & Buerkle, C. A. A powerful regression-based method for admixture mapping of isolation across the genome of hybrids. Mol. Ecol. 18, 1207–1224 (2009).
PubMed  Article  PubMed Central  Google Scholar 

100.
Janoušek, V. et al. Genome‐wide architecture of reproductive isolation in a naturally occurring hybrid zone between Mus musculus musculus and M. m. domesticus. Mol. Ecol. 21, 3032–3047 (2012).
PubMed  Article  PubMed Central  Google Scholar 

101.
Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome. Science 334, 83–86 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

102.
Menon, M. et al. Data from: adaptive evolution in a confier hybrid zone is driven by a mosaic of recently introgressed and background genetic variants. Figshare, Dataset https://doi.org/10.6084/m9.figshare.c.5130104 (2020).

103.
Shirk, A. J. et al. Southwestern white pine (Pinus strobiformis) species distribution models predict large range shift and contraction due to climate change. For. Ecol. Manag. 411, 176–186 (2018).
Article  Google Scholar 

104.
Little, E. L., Jr. Atlas of United States Trees, Vol. 1., Conifers and important hardwoods. Misc. Publ. 1146, 320 (U.S. Department of Agriculture, Forest Service, 1971).

105.
Menon, M. et al. Code from: adaptive evolution in a confier hybrid zone is driven by a mosaic of recently introgressed and background genetic variants. Zenodo, Dataset https://doi.org/10.5281/zenodo.4054085 (2020). More