Ecology

1.
Smetacek, V. & Nicol, S. Polar ocean ecosystems in a changing world. Nature 437, 362–368. https://doi.org/10.1038/nature04161 (2005).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Browning, T. J. et al. Nutrient regimes control phytoplankton ecophysiology in the South Atlantic. Biogeosciences 11, 463–479. https://doi.org/10.5194/bg-11-463-2014 (2014).
ADS  Article  Google Scholar 

3.
Garibotti, I. A., Vernet, M. & Ferrario, M. E. Annually recurrent phytoplanktonic assemblages during summer in the seasonal ice zone west of the Antarctic Peninsula (Southern Ocean). Deep-Sea Res. Part I Oceanogr. Res. Pap. 52, 1823–1841. https://doi.org/10.1016/j.dsr.2005.05.003 (2005).
ADS  Article  Google Scholar 

4.
Clem, K. R. et al. Record warming at the South Pole during the past three decades. Nat. Clim. Change 10, 762–770. https://doi.org/10.1038/s41558-020-0815-z (2020).
ADS  Article  Google Scholar 

5.
Martinson, D. G., Stammerjohn, S. E., Iannuzzi, R. A., Smith, R. C. & Vernet, M. Western Antarctic Peninsula physical oceanography and spatio-temporal variability. Deep-Sea Res. Part II Top. Stud. Oceanogr. 55, 1964–1987. https://doi.org/10.1016/j.dsr2.2008.04.038 (2008).
ADS  Article  Google Scholar 

6.
Schofield, O. et al. Changes in the upper ocean mixed layer and phytoplankton productivity along the West Antarctic Peninsula. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170173. https://doi.org/10.1098/rsta.2017.0173 (2018).
ADS  CAS  Article  Google Scholar 

7.
Kim, H. et al. Inter-decadal variability of phytoplankton biomass along the coastal West Antarctic Peninsula. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170174. https://doi.org/10.1098/rsta.2017.0174 (2018).
ADS  Article  Google Scholar 

8.
Lange, P. K., Ligowski, R. & Tenenbaum, D. R. Phytoplankton in the embayments of King George Island (Antarctic Peninsula): a review with emphasis on diatoms. Polar Rec. 54, 158–175. https://doi.org/10.1017/S0032247418000232 (2018).
Article  Google Scholar 

9.
Kopczynska, E. Phytoplankton variability in Admiralty Bay, King George Island, South Shetland Islands: six years of monitoring. Pol. Polar Res. 29, 117–139 (2008).
Google Scholar 

10.
Biggs, T. E. et al. Antarctic phytoplankton community composition and size structure: importance of ice type and temperature as regulatory factors. Polar Biol. 42, 1997–2015. https://doi.org/10.1007/s00300-019-02576-3 (2019).
Article  Google Scholar 

11.
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 1–9. https://doi.org/10.1038/srep40850 (2017).
CAS  Article  Google Scholar 

12.
Egas, C. et al. Short timescale dynamics of phytoplankton in Fildes Bay, Antarctica. Antarct. Sci. 29, 217. https://doi.org/10.1017/S0954102016000699 (2017).
ADS  Article  Google Scholar 

13.
Delmont, T. O., Hammar, K. M., Ducklow, H. W., Yager, P. L. & Post, A. F. Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya. Front. Microbiol. 5, 1–13. https://doi.org/10.3389/fmicb.2014.00646 (2014).
Article  Google Scholar 

14.
Arrigo, K. R. et al. Phytoplankton community structure and the drawdown of nutrients and ({{rm CO}}_{2}) in the Southern Ocean. Science 283, 365–367. https://doi.org/10.1126/science.283.5400.365 (1999).
ADS  CAS  PubMed  Google Scholar 

15.
Lin, Y. et al. Specific eukaryotic plankton are good predictors of net community production in the Western Antarctic Peninsula. Sci. Rep. 7, 1–11. https://doi.org/10.1038/s41598-017-14109-1 (2017).
ADS  CAS  Article  Google Scholar 

16.
Alcamán-Arias, M. E., Farías, L., Verdugo, J., Alarcón-Schumacher, T. & Díez, B. Microbial activity during a coastal phytoplankton bloom on the Western Antarctic Peninsula in late summer. FEMS Microbiol. Lett. 365, 1–13. https://doi.org/10.1093/femsle/fny090 (2018).
CAS  Article  Google Scholar 

17.
Moreno-Pino, M. et al. Variation in coastal Antarctic microbial community composition at sub-mesoscale: spatial distance or environmental filtering? FEMS Microbiol. Ecol. 92, fiw088. https://doi.org/10.1093/femsec/fiw088 (2016).
CAS  PubMed  Article  Google Scholar 

18.
Moon-van der Staay, S. Y., De Wachter, R. & Vaulot, D. Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409, 607–610. https://doi.org/10.1038/35054541 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Fuller, N. J. et al. Analysis of photosynthetic picoeukaryote diversity at open ocean sites in the Arabian Sea using a PCR biased towards marine algal plastids. Aquat. Microbial Ecol. 43, 79–93 (2006).
Article  Google Scholar 

20.
Shi, X. L., Lepère, C., Scanlan, D. J. & Vaulot, D. Plastid 16S rRNA gene diversity among eukaryotic picophytoplankton sorted by flow cytometry from the South Pacific Ocean. PLoS ONE 6, e18979 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Sieburth, J. M., Smetacek, V. & Lenz, J. Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnol. Oceanogr. 23, 1256–1263 (1978).
ADS  Article  Google Scholar 

22.
Marie, D., Shi, X. L., Rigaut-Jalabert, F. & Vaulot, D. Use of flow cytometric sorting to better assess the diversity of small photosynthetic eukaryotes in the English Channel. FEMS Microbiol. Ecol. 72, 165–178 (2010).
CAS  PubMed  Article  Google Scholar 

23.
Balzano, S., Marie, D., Gourvil, P. & Vaulot, D. Composition of the summer photosynthetic pico and nanoplankton communities in the Beaufort Sea assessed by T-RFLP and sequences of the 18S rRNA gene from flow cytometry sorted samples. ISME J. 6, 1480–1498. https://doi.org/10.1038/ismej.2011.213 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Jeong, H. J. et al. Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. Ocean Sci. J. 45, 65–91. https://doi.org/10.1007/s12601-010-0007-2 (2010).
ADS  CAS  Article  Google Scholar 

26.
Wilks, J. V. & Armand, L. K. Diversity and taxonomic identification of Shionodiscus spp. in the Australian sector of the Subantarctic Zone. Diatom Res. 32, 295–307. https://doi.org/10.1080/0269249X.2017.1365015 (2017).
Article  Google Scholar 

27.
Moreno, C. M. et al. Examination of gene repertoires and physiological responses to iron and light limitation in Southern Ocean diatoms. Polar Biol. 41, 679–696. https://doi.org/10.1007/s00300-017-2228-7 (2018).
Article  Google Scholar 

28.
Balzano, S. et al. Morphological and genetic diversity of Beaufort Sea diatoms with high contributions from the Chaetoceros neogracilis species complex. J. Phycol. 53, 161–187. https://doi.org/10.1111/jpy.12489 (2017).
CAS  PubMed  Article  Google Scholar 

29.
Worden, A. Z. et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr. Biol. 22, R675–R677 (2012).
CAS  PubMed  Article  Google Scholar 

30.
Balzano, S. et al. Diversity of cultured photosynthetic flagellates in the North East Pacific and Arctic Oceans in summer. Biogeosciences 9, 4553–4571. https://doi.org/10.5194/bg-9-4553-2012 (2012).
ADS  CAS  Article  Google Scholar 

31.
Kuwata, A. et al. Bolidophyceae, a sister picoplanktonic group of diatoms—a review. Front. Mar. Sci. 5, 370. https://doi.org/10.3389/fmars.2018.00370 (2018).
Article  Google Scholar 

32.
Massana, R., del Campo, J., Sieracki, M. E., Audic, S. & Logares, R. Exploring the uncultured microeukaryote majority in the oceans: reevaluation of ribogroups within stramenopiles. ISME J. 8, 854–866 (2014).
PubMed  Article  Google Scholar 

33.
Tragin, M. & Vaulot, D. Novel diversity within marine Mamiellophyceae (Chlorophyta) unveiled by metabarcoding. Sci. Rep. 9, 5190. https://doi.org/10.1038/s41598-019-41680-6 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
van den Hoff, J., Bell, E. & Whittock, L. Dimorphism in the Antarctic cryptophyte Geminigera cryophila (Cryptophyceae). J. Phycol. 56, 1028–1038. https://doi.org/10.1111/jpy.13004 (2020).
CAS  PubMed  Article  Google Scholar 

35.
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005. https://doi.org/10.1038/nmicrobiol.2016.5 (2016).
CAS  PubMed  Article  Google Scholar 

36.
Lin, Y., Gifford, S., Ducklow, H., Schofield, O. & Cassar, N. Towards quantitative microbiome community profiling using internal standards. Appl. Environ. Microbiol. 85, 1–14 (2019).
Google Scholar 

37.
van Leeuwe, M. A. et al. Annual patterns in phytoplankton phenology in Antarctic coastal waters explained by environmental drivers. Limnol. Oceanogr. 65, 1651–1668. https://doi.org/10.1002/lno.11477 (2020).
ADS  Article  Google Scholar 

38.
Wasilowska, A., Kopczynska, E. E. & Rzepecki, M. Temporal and spatial variation of phytoplankton in Admiralty Bay, South Shetlands: the dynamics of summer blooms shown by pigment and light microscopy analysis. Polar Biol. 38, 1249–1265. https://doi.org/10.1007/s00300-015-1691-2 (2015).
Article  Google Scholar 

39.
Rozema, P. D. et al. Summer microbial community composition governed by upper-ocean stratification and nutrient availability in northern Marguerite Bay, Antarctica. Deep Sea Res. Part II Top. Stud. Oceanogr. 139, 151–166. https://doi.org/10.1016/j.dsr2.2016.11.016 (2016).
ADS  CAS  Article  Google Scholar 

40.
Annett, A. L., Carson, D. S., Crosta, X., Clarke, A. & Ganeshram, R. S. Seasonal progression of diatom assemblages in surface waters of Ryder Bay, Antarctica. Polar Biol. 33, 13–29. https://doi.org/10.1007/s00300-009-0681-7 (2010).
Article  Google Scholar 

41.
Garibotti, I. et al. Phytoplankton spatial distribution patterns along the western Antarctic Peninsula (Southern Ocean). Mar. Ecol. Prog. Ser. 261, 21–39. https://doi.org/10.3354/meps261021 (2003).
ADS  Article  Google Scholar 

42.
de Lima, D. T. et al. Abiotic changes driving microphytoplankton functional diversity in Admiralty Bay, King George Island (Antarctica). Front. Mar. Sci. 6, 1–17. https://doi.org/10.3389/fmars.2019.00638 (2019).
ADS  CAS  Article  Google Scholar 

43.
Luria, C. M., Ducklow, H. W. & Amaral-Zettler, L. A. Marine bacterial, archaeal and eukaryotic diversity and community structure on the continental shelf of the western Antarctic Peninsula. Aquat. Microbial Ecol. 73, 107–121. https://doi.org/10.3354/ame01703 (2014).
Article  Google Scholar 

44.
Luo, W. et al. Molecular diversity of microbial eukaryotes in sea water from Fildes Peninsula, King George Island, Antarctica. Polar Biol. 39, 605–616. https://doi.org/10.1007/s00300-015-1815-8 (2016).
ADS  Article  Google Scholar 

45.
Rozema, P. D. et al. Interannual variability in phytoplankton biomass and species composition in northern Marguerite Bay (West Antarctic Peninsula) is governed by both winter sea ice cover and summer stratification. Limnol. Oceanogr. 62, 235–252. https://doi.org/10.1002/lno.10391 (2017).
ADS  Article  Google Scholar 

46.
Lee, S. H. et al. Large contribution of small phytoplankton at Marian Cove, King George Island, Antarctica, based on long-term monitoring from 1996 to 2008. Polar Biol. 38, 207–220. https://doi.org/10.1007/s00300-014-1579-6 (2015).
Article  Google Scholar 

47.
Kang, J. S., Kang, S. H., Kim, D. & Kim, D. Y. Planktonic centric diatom Minidiscus chilensis dominated sediment trap material in eastern Bransfield Strait, Antarctica. Mar. Ecol. Prog. Ser. 255, 93–99 (2003).
ADS  Article  Google Scholar 

48.
Vaulot, D., Eikrem, W., Viprey, M. & Moreau, H. The diversity of small eukaryotic phytoplankton ((le 3 upmu {{rm m}})) in marine ecosystems. FEMS Microbiol. Rev. 32, 795–820. https://doi.org/10.1111/j.1574-6976.2008.00121.x (2008).
CAS  PubMed  Google Scholar 

49.
Andersen, R. A., Saunders, G. W., Paskind, M. P. & Sexton, J. Ultrastructure and 18S rRNA gene sequence for Pelagomonas calceolata gen. and sp. nov. and the description of a new algal class, the Pelagophyceae classis nov. J. Phycol. 29, 701–715 (1993).
CAS  Article  Google Scholar 

50.
Dìez, B., Pedrós-Alió, C. & Massana, R. Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and sequencing. Appl. Environ. Microbiol. 67, 2932–2941. https://doi.org/10.1128/AEM.67.7.2932-2941.2001 (2001).
PubMed  PubMed Central  Article  Google Scholar 

51.
Gérikas Ribeiro, C. et al. Culturable diversity of Arctic phytoplankton during pack ice melting. Elem. Sci. Anthropocene 8, 6. https://doi.org/10.1525/elementa.401 (2020).
Article  Google Scholar 

52.
Sow, L. S. S., Trull, T. W. & Bodrossy, L. Oceanographic fronts shape Phaeocystis assemblages: a high-resolution 18S rRNA gene survey from the ice-edge to the equator of the South Pacific. Front. Microbiol. 11, 1847. https://doi.org/10.3389/fmicb.2020.01847 (2020).
PubMed  PubMed Central  Article  Google Scholar 

53.
Gaebler, S., Hayes, P. K. & Medlin, L. K. Methods used to reveal genetic diversity in the colony-forming prymnesiophytes Phaeocystis antarctica, P. globosa and P. pouchetii—preliminary results. In Phaeocystis Major Link in the Biogeochemical Cycling of Climate-Relevant Elements (eds van Leeuwe, M. et al.) 330 (Springer Netherlands, Houten, 2007). https://doi.org/10.1007/978-1-4020-6214-8.
Google Scholar 

54.
DiTullio, G. R. et al. Rapid and early export of Phaeocystis antarctica blooms in the Ross Sea, Antarctica. Nature 404, 595–598. https://doi.org/10.1038/35007061 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

55.
Arrigo, K. R. et al. Phytoplankton taxonomic variability in nutrient utilization and primary production in the Ross Sea. J. Geophys. Res. Oceans 105, 8827–8846. https://doi.org/10.1029/1998JC000289 (2000).
ADS  CAS  Article  Google Scholar 

56.
van Leeuwe, M. A. & Stefels, J. Photosynthetic responses in Phaeocystis antarctica towards varying light and iron conditions. Biogeochemistry 83, 61–70. https://doi.org/10.1007/s10533-007-9083-5 (2007).
CAS  Article  Google Scholar 

57.
Gast, R. J., McKie-Krisberg, Z. M., Fay, S. A., Rose, J. M. & Sanders, R. W. Antarctic mixotrophic protist abundances by microscopy and molecular methods. FEMS Microbiol. Ecol. 89, 388–401. https://doi.org/10.1111/1574-6941.12334 (2014).
CAS  PubMed  Article  Google Scholar 

58.
Sekiguchi, H., Kawachi, M., Nakayama, T. & Inouye, I. A taxonomic re-evaluation of the Pedinellales (Dictyochophyceae), based on morphological, behavioural and molecular data. Phycologia 42, 165–182. https://doi.org/10.2216/i0031-8884-42-2-165.1 (2003).
Article  Google Scholar 

59.
Li, Q., Edwards, K. F., Schvarcz, C. R., Selph, K. E. & Steward, G. F. Plasticity in the grazing ecophysiology of Florenciella (Dichtyochophyceae), a mixotrophic nanoflagellate that consumes Prochlorococcus and other bacteria. Limnol. Oceanogr.. https://doi.org/10.1002/lno.11585 (2020).
CAS  Article  Google Scholar 

60.
Maruyama, S. & Kim, E. A modern descendant of early green algal phagotrophs. Curr. Biol. 23, 1081–1084. https://doi.org/10.1016/j.cub.2013.04.063 (2013).
CAS  PubMed  Article  Google Scholar 

61.
Darling, K. F. et al. Molecular evidence for genetic mixing of Arctic and Antarctic subpolar populations of planktonic foraminifers. Nature 405, 43–47. https://doi.org/10.1038/35011002 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

62.
Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl. Acad. Sci. USA 110, 2342–2347. https://doi.org/10.1073/pnas.1212424110 (2013).
ADS  PubMed  Article  Google Scholar 

63.
Wolf, C., Kilias, E. & Metfies, K. Protists in the polar regions: comparing occurrence in the Arctic and Southern oceans using pyrosequencing. Polar Res. 34, 23225. https://doi.org/10.3402/polar.v34.23225 (2015).
Article  Google Scholar 

64.
Lovejoy, C. & Potvin, M. Microbial eukaryotic distribution in a dynamic Beaufort Sea and the Arctic Ocean. J. Plankton Res. 33, 431–444. https://doi.org/10.1093/plankt/fbq124 (2011).
Article  Google Scholar 

65.
Delmont, T. O., Murat Eren, A., Vineis, J. H. & Post, A. F. Genome reconstructions indicate the partitioning of ecological functions inside a phytoplankton bloom in the Amundsen Sea, Antarctica. Front. Microbiol. 6, 1–19. https://doi.org/10.3389/fmicb.2015.01090 (2015).
Article  Google Scholar 

66.
Simmons, M. P. et al. Intron invasions trace algal speciation and reveal nearly identical arctic and antarctic Micromonas populations. Mol. Biol. Evol. 32, 2219–2235. https://doi.org/10.1093/molbev/msv122 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Joli, N., Monier, A., Logares, R. & Lovejoy, C. Seasonal patterns in Arctic prasinophytes and inferred ecology of Bathycoccus unveiled in an Arctic winter metagenome. ISME J. 6, 1372–1385. https://doi.org/10.1038/ismej.2017.7 (2017).
Article  Google Scholar 

68.
Benner, I., Irwin, A. J. & Finkel, Z. Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming warming ocean. Limnol. Oceanogr. Lett. 5, 221–227 (2019).
Article  Google Scholar 

69.
Li, W. K., McLaughlin, F. A., Lovejoy, C. & Carmack, E. C. Smallest algae thrive as the Arctic Ocean freshens. Science 326, 539. https://doi.org/10.1126/science.1179798 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

70.
Hoppe, C. J. M., Flintrop, C. M. & Rost, B. The arctic picoeukaryote Micromonas pusilla benefits synergistically from warming and ocean acidification. Biogeosciences 15, 4353–4365. https://doi.org/10.5194/bg-15-4353-2018 (2018).
ADS  CAS  Article  Google Scholar 

71.
Vannier, T. et al. Survey of the green picoalga Bathycoccus genomes in the global ocean. Sci. Rep. 6, 37900. https://doi.org/10.1038/srep37900 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Vaulot, D. et al. Metagenomes of the Picoalga Bathycoccus from the Chile coastal upwelling. PLoS ONE 7, e39648. https://doi.org/10.1371/journal.pone.0039648 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Kauko, H. M. et al. Algal colonization of young Arctic sea ice in spring. Front. Mar. Sci. 5, 1–20. https://doi.org/10.3389/fmars.2018.00199 (2018).
Article  Google Scholar 

74.
Schloss, I. R. et al. On the phytoplankton bloom in coastal waters of southern King George Island (Antarctica) in January 2010: an exceptional feature? Limnol. Oceanogr. 59, 195–210. https://doi.org/10.4319/lo.2014.59.1.0195 (2014).
ADS  CAS  Article  Google Scholar 

75.
Świło, M., Majewski, W., Minzoni, R. T. & Anderson, J. B. Diatom assemblages from coastal settings of West Antarctica. Mar. Micropaleontol. 125, 95–109. https://doi.org/10.1016/j.marmicro.2016.04.001 (2016).
ADS  Article  Google Scholar 

76.
Pike, J. et al. Observations on the relationship between the Antarctic coastal diatoms Thalassiosira antarctica Comber and Porosira glacialis (Grunow) Jørgensen and sea ice concentrations during the late Quaternary. Mar. Micropaleontol. 73, 14–25. https://doi.org/10.1016/j.marmicro.2009.06.005 (2009).
ADS  Article  Google Scholar 

77.
Luddington, I. A., Lovejoy, C. & Kaczmarska, I. Species-rich meta-communities of the diatom order Thalassiosirales in the Arctic and northern Atlantic Ocean. J. Plankton Res. 38, 781–797. https://doi.org/10.1093/plankt/fbw030 (2016).
CAS  Article  Google Scholar 

78.
Hoppenrath, M. et al. Thalassiosira species (Bacillariophyceae, Thalassiosirales) in the North Sea at Helgoland (German Bight) and Sylt (North Frisian Wadden Sea) – A first approach to assessing diversity. Eur. J. Phycol. 42, 271–288. https://doi.org/10.1080/09670260701352288 (2007).
Article  Google Scholar 

79.
Schoemann, V., Becquevort, S., Stefels, J., Rousseau, V. & Lancelot, C. Phaeocystis blooms in the global ocean and their controlling mechanisms: a review. J. Sea Res. 53, 43–66. https://doi.org/10.1016/j.seares.2004.01.008 (2005).
ADS  CAS  Article  Google Scholar 

80.
Lange, M., Chen, Y. Q. & Medlin, L. K. Molecular genetic delineation of Phaeocystis species (Prymnesiophyceae) using coding and non-coding regions of nuclear and plastid genomes. Eur. J. Phycol. 37, 77–92. https://doi.org/10.1017/S0967026201003481 (2002).
Article  Google Scholar 

81.
Medlin, L. K., Lange, M. & Baumann, M. E. Genetic differentiation among three colony-forming species of Phaeocystis: further evidence for the phylogeny of the Prymnesiophyta. Phycologia 33, 199–212. https://doi.org/10.2216/i0031-8884-33-3-199.1 (1994).
Article  Google Scholar 

82.
Thompson, D. W. & Solomon, S. Interpretation of recent Southern Hemisphere climate change. Science 296, 895–899. https://doi.org/10.1126/science.1069270 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

83.
Smith, R. C. & Stammerjohn, S. E. Variations of surface air temperature and sea-ice extent in the western Antarctic Peninsula region. Ann. Glaciol. 33, 493–500. https://doi.org/10.3189/172756401781818662 (2001).
ADS  Article  Google Scholar 

84.
Hansen, M. O., Nielsen, T. G., Stedmon, C. A. & Munk, P. Oceanographic regime shift during 1997 in Disko Bay, Western Greenland. Limnol. Oceanogr. 57, 634–644. https://doi.org/10.4319/lo.2012.57.2.0634 (2012).
ADS  Article  Google Scholar 

85.
Holm-Hansen, O., Lorenzen, C. J., Holmes, R. W. & Strickland, J. D. H. Fluorometric determination of chlorophyll. ICES J. Mar. Sci. 30, 3–15. https://doi.org/10.1093/icesjms/30.1.3 (1965).
CAS  Article  Google Scholar 

86.
Marie, D., Rigaut-Jalabert, F. & Vaulot, D. An improved protocol for flow cytometry analysis of phytoplankton cultures and natural samples. Cytometry 85, 962–968. https://doi.org/10.1002/cyto.a.22517 (2014).
CAS  PubMed  Article  Google Scholar 

87.
Gérikas Ribeiro, C., Lopes dos Santos, A., Marie, D., Pereira Brandini, F. & Vaulot, D. Small eukaryotic phytoplankton communities in tropical waters off Brazil are dominated by symbioses between Haptophyta and nitrogen-fixing cyanobacteria. ISME J. 12, 1360–1374. https://doi.org/10.1038/s41396-018-0050-z (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Piredda, R. et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol. Ecol. 93, fiw200. https://doi.org/10.1093/femsec/fiw200 (2017).
CAS  PubMed  Article  Google Scholar 

89.
Lepère, C. et al. Whole Genome Amplification (WGA) of marine photosynthetic eukaryote populations. FEMS Microbiol. Ecol. 76, 516–523 (2011).
Article  Google Scholar 

90.
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12. https://doi.org/10.14806/ej.17.1.200 (2011).
Article  Google Scholar 

91.
R Development Core Team. R: A Language and Environment for Statistical Computing. https://doi.org/10.1007/978-3-540-74686-7 (2013).

92.
Guillou, L. et al. The Protist Ribosomal Reference database (({{rm PR}}^{2})): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604. https://doi.org/10.1093/nar/gks1160 (2013).
CAS  PubMed  Google Scholar 

93.
Decelle, J. et al. PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15, 1435–1445. https://doi.org/10.1111/1755-0998.12401 (2015).
CAS  PubMed  Article  Google Scholar 

94.
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.2. (2020)

95.
Wilkins, D. treemapify: Draw Treemaps in ’ggplot2’. R package version 2.5.3. (2019)

96.
McMurdie, P. J. & Holmes, S. phyloseq: an r package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, 1–11. https://doi.org/10.1371/journal.pone.0061217 (2013).
CAS  Article  Google Scholar 

97.
Dixon, P. Vegan, a package of r functions for community ecology. J. Veg. Sci. 14, 927–930. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x (2003).
Article  Google Scholar 

98.
Gehlenborg, N. UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets. R package version 1.4.0. (2019) More

1.
Strauss, S. Y., Lau, J. A. & Carroll, S. P. Evolutionary responses of natives to introduced species: what do introductions tell us about natural communities? Evolutionary responses of natives to introduced species. Ecol. Lett. 9, 357–374 (2006).
PubMed  Article  Google Scholar 
2.
Smith, D. C. Heritable divergence of Rhagoletis pomonella host races by seasonal asynchrony. Nature 336, 66–67 (1988).
ADS  Article  Google Scholar 

3.
Filchak, K. E., Roethele, J. B. & Feder, J. L. Natural selection and sympatric divergence in the apple maggot Rhagoletis pomonella. Nature 407, 739–742 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Carroll, S. P., Dingle, H., Famula, T. R. & Fox, C. W. Genetic architecture of adaptive differentiation in evolving host races of the soapberry bug, Jadera haematoloma. in Microevolution Rate, Pattern, Process (eds. Hendry, A. P. & Kinnison, M. T.) vol. 8 257–272 (Springer Netherlands, 2001).

5.
Nice, C. C., Fordyce, J. A., Shapiro, A. M. & Ffrench-Constant, R. Lack of evidence for reproductive isolation among ecologically specialised lycaenid butterflies. Ecol. Entomol. 27, 702–712 (2002).
Article  Google Scholar 

6.
Graves, S. D. & Shapiro, A. M. Exotics as host plants of the California butterfly fauna. 110, 413–433 (2003).
Google Scholar 

7.
Thomas, J. A., Simcox, D. J. & Hovestadt, T. Evidence based conservation of butterflies. J. Insect Conserv. 15, 241–258 (2011).
Article  Google Scholar 

8.
Battin, J. When good animals love bad habitats: Ecological traps and the conservation of animal populations. Conserv. Biol. 18, 1482–1491 (2004).
Article  Google Scholar 

9.
Casagrande, R.A. & Dacey, J. E. Monarch butterfly oviposition on swallow-worts (Vincetoxicum spp.). Environ. Entomol. 36, 631–636 (2007).

10.
Davis, S. L. & Cipollini, D. Do mothers always know best? Oviposition mistakes and resulting larval failure of Pieris virginiensis on Alliaria petiolata, a novel, toxic host. Biol. Invasions 16, 1941–1950 (2014).
Article  Google Scholar 

11.
Janzen, D. H. On ecological fitting. Oikos 45, 308 (1985).
Article  Google Scholar 

12.
Singer, M. C. & Parmesan, C. Lethal trap created by adaptive evolutionary response to an exotic resource. Nature 557, 238–241 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

13.
Thomas, C. D. et al. Incorporation of a European weed into the diet of a North American herbivore. Evolution 41, 892–901 (1987).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Bowers, M. D., Stamp, N. E. & Collinge, S. K. Early stage of host range expansion by a specialist herbivore Euphydryas phaeton. Ecology 73, 526–536 (1992).
Article  Google Scholar 

15.
Severns, P. M. & Breed, G. A. Behavioral consequences of exotic host plant adoption and the differing roles of male harassment on female movement in two checkerspot butterflies. Behav. Ecol. Sociobiol. 68, 805–814 (2014).
Article  Google Scholar 

16.
United States Fish and Wildlife Service. Endangered and threatened wildlife and plants; proposed designation of critical habitat for the bay checkerspot butterfly (Euphydryas editha bayensis); proposed rule. (2000).

17.
United States Fish and Wildlife Service. Endangered and threatened wildlife and plants; designation of critical habitat for the Quino checkerspot butterfly (Euphydryas editha quino). (2002).

18.
United States Fish and Wildlife Service. ESA Proposed Listing Taylor’s Checkerspot. Fed. Regist. 77, (2012).

19.
Ehrlich, P. R. & Hanski, I. On the wings of checkerspots: a model system for population biology. Oxford University Press (2004).

20.
Singer, M. C., Ng, D. & Thomas, C. D. Heritability of oviposition preference and its relationship to offspring performance within a single insect population. Evolution 42, 977–985 (1988).
CAS  PubMed  Article  Google Scholar 

21.
Singer, M. C. & McBride, C. S. Multitrait, host-associated divergence among sets of butterfly populations: implications for reproductive isolation and ecological speciation. Evol. Int. J. Org. Evol. 64, 921–933 (2009).
Article  Google Scholar 

22.
Peñuelas, J., Sardans, J., Stefanescu, C., Parella, T. & Filella, I. Lonicera implexa leaves bearing naturally laid eggs of the specialist herbivore Euphydryas aurinia have dramatically greater concentrations of iridoid glycosides than other leaves. J. Chem. Ecol. 32, 1925–1933 (2006).
PubMed  Article  CAS  Google Scholar 

23.
Nieminen, M., Suomi, J., Nouhuys, S. V., Sauri, P. & Riekkola, M.-L. Effect of iridoid glycoside content on oviposition host plant choice and parasitism in a specialist herbivore. J. Chem. Ecol. 22 (2003).

24.
Bowers, M. D. Unpalatability as a defense strategy of Euphydryas phaeton (Lepidoptera: Nymphalidae). Evolution 34, 586–600 (1980).
PubMed  Article  Google Scholar 

25.
Bowers, M. D. Unpalatability as a defense strategy of western checkerspot butterflies (Euphydryas Scudder, Nymphalidae). Evolution 35, 367–375 (1981).
PubMed  Article  Google Scholar 

26.
Dobler, S., Petschenka, G. & Pankoke, H. Coping with toxic plant compounds–the insect’s perspective on iridoid glycosides and cardenolides. Phytochemistry 72, 1593–1604 (2011).
CAS  PubMed  Article  Google Scholar 

27.
Bowers, M. D. & Stamp, N. E. Effects of plant age, genotype and herbivory on Plantago performance and chemistry. Ecology 74, 1778–1791 (1993).
Article  Google Scholar 

28.
Dyer, L. A. & Deane Bowers, M. The importance of sequestered iridoid glycosides as a defense against an ant predator. J. Chem. Ecol. 22, 1527–1539 (1996).

29.
Dunwiddie, P. W. et al. Intertwined fates: Opportunities and challenges in the linked recovery of two rare species. Nat. Areas J. 36, 207–215 (2016).
Article  Google Scholar 

30.
Stinson, D. Washington State Status Report for the Mazama Pocket Gopher, Streaked Horned Lark, and Taylor’s Checkerspot. Washington Department of Fish and Wildlife (2005).

31.
Cavers, P. B., Bassett, I. J. & Crompton, C. W. The biology of Canadian weeds 47. Plantago lanceolata L. Can. J. Plant Sci. 60, 1269–1282 (1980).

32.
Haan, N. L., Bakker, J. D., Dunwiddie, P. W. & Linders, M. J. Instar-specific effects of host plants on survival of endangered butterfly larvae. Ecol. Entomol. 43, 742–753 (2018).
Article  Google Scholar 

33.
Danby, W. H. Food plant of Melitaea taylori Edw. Can. Entomol. 22, 121–122 (1890).
Article  Google Scholar 

34.
Buckingham, D. A., Linders, M., Landa, C., Mullen, L. & LeRoy, C. Oviposition preference of endangered Taylor’s checkerspot butterflies (Euphydryas editha taylori) using native and non-native hosts. Northwest Sci. 90, 491–497 (2016).
Article  Google Scholar 

35.
Mead, E. W. & Stermitz, F. R. Content of iridoid glycosides in different parts of Castilleja. Phytochemistry 32, 1155–1158 (1993).
CAS  Article  Google Scholar 

36.
Barclay, E., Arnold, M., Anderson, M. J. & Shepherdson, D. Husbandry manual: Taylor’s checkerspot (Euphydryas editha taylori)) (Oregon Zoo, Portland OR, 2009).
Google Scholar 

37.
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2020).

38.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).

39.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Lenth, R. V. Least-Squares Means: The R package lsmeans. J. Stat. Softw. 69, (2016).

41.
Bowers, M. D. & Stamp, N. E. Effect of hostplant genotype and predators on iridoid glycoside content of pupae of a specialist insect herbivore, Junonia coenia (Nymphalidae). Biochem. Syst. 25, 571–580 (1997).
CAS  Article  Google Scholar 

42.
Bowers, M. D. Hostplant suitability and defensive chemistry of the Catalpa sphinx Ceratomia catalpae. J. Chem. Ecol. 29, 2359–2367 (2003).
CAS  PubMed  Article  Google Scholar 

43.
Oksanen, J. et al. Package ‘vegan’. Community Ecol. Package Version 2, 1–295 (2013).
Google Scholar 

44.
Yoon, S. & Read, Q. Consequences of exotic host use: Impacts on Lepidoptera and a test of the ecological trap hypothesis. Oecologia 181, 985–996 (2016).
ADS  PubMed  Article  Google Scholar 

45.
Cogni, R. Resistance to plant invasion? A native specialist herbivore shows preference for and higher fitness on an introduced host. Biotropica 42, 188–193 (2010).
Article  Google Scholar 

46.
Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).
PubMed  Article  Google Scholar 

47.
Bowers, M. D., Boockvar, K. & Collinge, S. K. Iridoid glycosides of Chelone glabra (Scrophulariaceae) and their sequestration by larvae of a Sawfly, Tenthredo grandis (Tenthredinidae). J. Chem. Ecol. 19, 815–815 (1993).
CAS  PubMed  Article  Google Scholar 

48.
Singer, M. C. Quantification of host preference by manipulation of oviposition behavior in the butterfly Euphydryas editha. Oecologia 52, 224–229 (1982).
ADS  PubMed  Article  Google Scholar 

49.
Parmesan, C., Singer, M. C. & Harris, I. A. N. Absence of adaptive learning from the oviposition foraging behaviour of a checkerspot butterfly. Anim. Behav. 50, 161–175 (1995).
Article  Google Scholar 

50.
Quintero, C., Lampert, E. C. & Bowers, M. D. Time is of the essence: direct and indirect effects of plant ontogenetic trajectories on higher trophic levels. Ecology 95, 2589–2602 (2014).
Article  Google Scholar 

51.
Gardner, D. R. & Stermitz, F. R. Host plant utilization and iridoid glycoside sequestration by Euphdryas anicia (Lepidoptera: Nymphalidae). J. Chem. Ecol. 14, 2147–2168 (1988).
CAS  PubMed  Article  Google Scholar 

52.
Haan, N. L., Bakker, J. D. & Bowers, M. D. Hemiparasites can transmit indirect effects from their host plants to herbivores. Ecology 99, 399–410 (2018).
PubMed  Article  Google Scholar 

53.
Haan, N. L. Ecological interactions between Euphydryas editha larvae and their host plants (University of Washington, Seattle, 2017).
Google Scholar 

54.
Bowers, M. D. Aposematic caterpillars: life-styles of the warningly colored and unpalatable, in Caterpillars: ecological and evolutionary constraints on foraging (eds. Stamp, N.S., and Casey, T.M.). Chapman & Hall (1993).

55.
Theodoratus, D. H. & Bowers, M. D. Effects of sequestered iridoid glycosides on prey choice of the prairie wolf spider Lycosa carolinensis. J. Chem. Ecol. 25, 283–295 (1999).
CAS  Article  Google Scholar 

56.
Cirak, C. et al. Phenological changes in the chemical content of wild and greenhouse-grown Hypericum pruinatum: hypericins, hyperforins and phenolic acids. Res Rev J Bot. 4, 37–47 (2015).
ADS  Google Scholar 

57.
Richards, L. A. et al. Synergistic effects of iridoid glycosides on the survival, development and immune response of a specialist caterpillar, Junonia coenia (Nymphalidae). J. Chem. Ecol. 38, 1276–1284 (2012).
CAS  PubMed  Article  Google Scholar 

58.
Smilanich, A. M., Dyer, L. A., Chambers, J. Q. & Bowers, M. D. Immunological cost of chemical defence and the evolution of herbivore diet breadth. Ecol. Lett. 12, 612–621 (2009).
PubMed  Article  PubMed Central  Google Scholar 

59.
Hamilton, N.E. & Ferry, M. ggtern: Ternary diagrams using ggplot2. J. Stat. Softw., Code Snippets, 87, 1–17 (2018). More

1.
Lenzner, B. et al. A framework for global twenty-first century scenarios and models of biological invasions. Bioscience 69, 697–710. https://doi.org/10.1093/biosci/biz070 (2019).
Article  PubMed  PubMed Central  Google Scholar 
2.
Sun, Y. et al. Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Clim. Change 4, 1082–1085. https://doi.org/10.1038/nclimate2410 (2014).
ADS  Article  Google Scholar 

3.
van der Geest, K. et al. in Loss and Damage from Climate Change Climate Risk Management, Policy and Governance (eds Mechler R. et al.) 221–236 (2018).

4.
Sage, R. F. Global change biodiversity: A primer. Glob. Change Biol. 26, 3–30. https://doi.org/10.1111/gcb.14893 (2020).
ADS  Article  Google Scholar 

5.
Nunez-Mir, G. C., Guo, Q., Rejmanek, M., Iannone, B. V. III. & Fei, S. Predicting invasiveness of exotic woody species using a traits-absed framework. Ecol. Lett. 100, e02797. https://doi.org/10.1002/ecy.2797 (2019).
Article  Google Scholar 

6.
Zeng, J. et al. Global warming modifies long-distance migration of an agricultural insect pest. J. Pest. Sci. 93, 569–581. https://doi.org/10.1007/s10340-019-01187-5 (2020).
Article  Google Scholar 

7.
Gippet, J. M. W., Liebhold, A. M., Fenn-Moltu, G. & Bertelsmeier, C. Human-mediated dispersal in insects. Curr. Opin. Insect Sci. 35, 96–102. https://doi.org/10.1016/j.cois.2019.07.005 (2019).
Article  PubMed  Google Scholar 

8.
Cassey, P., Delean, S., Lockwood, J. L., Sadowski, J. S. & Blackburn, T. M. Dissecting the null model for biological invasions: A meta-analysis of the propagule pressure effect. PLoS Biol 16, e2005987. https://doi.org/10.1371/journal.pbio.2005987 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Reaser, J. K. et al. The early detection of and rapid response (EDRR) to invasive species: A conceptual framework and federal capacities assessment. Biol. Invas. 22, 1–19. https://doi.org/10.1007/s10530-019-02156-w (2019).
Article  Google Scholar 

10.
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models (Cambridge University Press, Cambridge, 2017).
Google Scholar 

11.
Soberon, J. & Townsend Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiv. Inform. 2, 1–10. https://doi.org/10.17161/bi.v2i0.4 (2005).
Article  Google Scholar 

12.
Porfirio, L. L. et al. Improving the use of species distribution models in conservation planning and management under climate change. PLoS One 9, e113749. https://doi.org/10.1371/journal.pone.0113749 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
IPCC. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 151 (IPCC, Geneva, 2014).
Google Scholar 

14.
Beaumont, L. J., Hughes, L. & Pitman, A. J. Why is the choice of future climate scenarios for species distribution modelling important?. Ecol. Lett. 11, 1135–1146. https://doi.org/10.1111/j.1461-0248.2008.01231.x (2008).
Article  PubMed  Google Scholar 

15.
Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157. https://doi.org/10.1111/j.1365-2486.2009.02000.x (2010).
ADS  Article  Google Scholar 

16.
Faccoli, M. et al. A first worldwide multispecies survey of invasive Mediterranean pine bark beetles (Coleoptera: Curculionidae, Scolytinae). Biol. Invas. 22, 1785–1799. https://doi.org/10.1007/s10530-020-02219-3 (2020).
Article  Google Scholar 

17.
Kirkendall, L. R. & Faccoli, M. Bark beetles and pinhole borers (Curculionidae, Scolytinae, Platypodinae) alien to Europe. Zookeys 56, 227–251. https://doi.org/10.3897/zookeys.56.529 (2010).
Article  Google Scholar 

18.
Raffa, K. F., Grégoire, J.-C. & StaffanLindgren, B. Economics and politics of bark beetles. In Bark Beetles. Biology and Ecology of Native and Invasive Species (eds Fernando, E. V. & Richard, W. H.) 585–614 (Elsevier, New York, 2015).
Google Scholar 

19.
Ngoan, N. D., Wilkinson, R. C., Short, D. E., Moses, C. S. & Mangold, J. R. Biology of an introduced ambrosia beetle, Xylosandrus compactus, in Florida. Ann. Entomol. Soc. Am. 69, 872–876. https://doi.org/10.1093/aesa/69.5.872 (1976).
Article  Google Scholar 

20.
Hara, A. H. & Beardsley, J. W. Jr. The biology of the black twig borer, Xylosandrus compactus (Eichhoff), in Hawaii. Proc. Hawaiian Entomol. Soc. 13, 55–70 (1979).
Google Scholar 

21.
Wood, S. L. New american bark beetles (Coleoptera: Scolytidae) with two recently introduced species. Great Basin Nat. 40, 353–358 (1980).
Article  Google Scholar 

22.
Samuelson, G. A. A synopsis of Hawaiian Xyleborini (Coleoptera: Scolytidae). Pac. Insects 23, 50–92 (1981).
Google Scholar 

23.
Anderson, D. M. First record of Xyleborus semiopacus in the continental United States (Coleoptera, Scolytidae). Cooper. Econ. Insect Rep. 24, 863–864 (1974).
Google Scholar 

24.
Kirkendall, L. Invasive bark beetles (Coleoptera, Curculionidae, Scolytinae) in Chile and Argentina, including two species new for South America, and the correct identity of the Orthotomicus species in Chile and Argentina. Diversity https://doi.org/10.3390/d10020040 (2018).
Article  Google Scholar 

25.
Pennachio, F., Roversi, P. F., Francardi, V. & Gatti, E. Xylosandrus crassiusculus (Motschulsky) a bark beetle new to Europe (Coleoptera Scolytidae). Redia 86, 77–80 (2003).
Google Scholar 

26.
Garonna, A. P., Dole, S. A., Saracino, A., Mazzoleni, S. & Cristinzio, G. First record of the black twig borer Xylosandrus compactus (Eichhoff) (Coleoptera: Curculionidae, Scolytinae) from Europe. Zootaxa 3251, 64–68. https://doi.org/10.11646/zootaxa.3251.1.5 (2012).
Article  Google Scholar 

27.
Roques, A. et al. Les scolytes exotiques: Une menace pour le maquis. Phytoma 727, 16–20 (2019).
Google Scholar 

28.
Gallego, D., Lencina, J. L., Mas, H., Cevero, J. & Faccoli, M. First record of the granulate ambrosia beetle, Xylosandrus crassiusculus (Coleoptera: Curculionidae, Scolytinae), in the Iberian Peninsula. Zootaxa 4273, 431–434. https://doi.org/10.11646/zootaxa.4273.3.7 (2017).
Article  PubMed  Google Scholar 

29.
Kavčič, A. First record of the Asian ambrosia beetle, Xylosandrus crassiusculus (Motschulsky) (Coleoptera: Curculionidae, Scolytinae), Slovenia. Zootaxa 4483, 191–193. https://doi.org/10.11646/zootaxa.4483.1.9 (2018).
Article  PubMed  Google Scholar 

30.
Spanou, K. et al. in 18th Panhellenic Entomological Congress (Komotini, 2019).

31.
Leza, M. A. R. et al. First record of the black twig borer, Xylosandrus compactus (Coleoptera: Curculionidae, Scolytinae) in Spain. Zootaxa 4767, 345–350. https://doi.org/10.11646/zootaxa.4767.2.9 (2020).
Article  Google Scholar 

32.
Greco, E. B. & Wright, M. G. Ecology, biology, and management of Xylosandrus compactus (Coleoptera: Curculionidae: Scolytinae) with emphasis on coffee in Hawaii. J. Integrat. Pest Manag. 6, 1–8. https://doi.org/10.1093/jipm/pmv007 (2015).
CAS  Article  Google Scholar 

33.
Kirkendall, L. R., Dal Cortivo, M. & Gatti, E. First record of the ambrosia beetle, Monarthrum mali (Curculionidae, Scolytinae) in Europe. J. Pest. Sci. 81, 175–178. https://doi.org/10.1007/s10340-008-0196-y (2008).
Article  Google Scholar 

34.
Jordal, B. H., Beaver, R. A. & Kirkendall, L. R. Breaking taboos in the tropics: Incest promotes colonization by wood-boring beetles. Glob. Ecol. Biogeogr. 10, 345–357. https://doi.org/10.1046/j.1466-822X.2001.00242.x (2001).
Article  Google Scholar 

35.
Douglas, H. et al. New Curculionoidea (Coleoptera) records for Canada. Zookeys 309, 13–48. https://doi.org/10.3897/zookeys.309.4667 (2013).
Article  Google Scholar 

36.
EPPO. EPPO Technical Document No. 1081, Study on the risk of bark and ambrosia beetles associated with imported non-coniferous wood. (2020).

37.
Guiot, J. & Cramer, W. Climate change: The 2015 Paris agreement thresholds and Mediterranean basin ecosystems. Science 354, 465–468. https://doi.org/10.1126/science.aah5015 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).
Article  Google Scholar 

39.
Radosavljevic, A., Anderson, R. P. & Araújo, M. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643. https://doi.org/10.1111/jbi.12227 (2014).
Article  Google Scholar 

40.
Beaumont, L. J. et al. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?. Ecol. Model. 342, 135–146. https://doi.org/10.1016/j.ecolmodel.2016.10.004 (2016).
Article  Google Scholar 

41.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).
Article  Google Scholar 

42.
Godefroid, M., Meurisse, N., Groenen, F., Kerdelhué, C. & Rossi, J. P. Current and future distribution of the invasive oak processionary moth. Biol. Invas. 22, 523–534. https://doi.org/10.1007/s10530-019-02108-4 (2019).
Article  Google Scholar 

43.
Qiao, H., Soberón, J., Peterson, A. T. & Kriticos, D. No silver bullets in correlative ecological niche modelling: Insights from testing among many potential algorithms for niche estimation. Methods Ecol. Evol. 6, 1126–1136. https://doi.org/10.1111/2041-210x.12397 (2015).
Article  Google Scholar 

44.
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods Ecol. Evol. 5, 1198–1205. https://doi.org/10.1111/2041-210X.12261 (2014).
Article  Google Scholar 

45.
Gettelman, A. & Rood, R. B. A Users Guide to Earth System Models Earth Systems Data and Models 282 (Springer, Berlin, 2016).
Google Scholar 

46.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

47.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Dismo: Species Distribution Modeling. R package version 1.1-4. (2017).

48.
Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble platform for species distribution modeling. R package version 3.3-7.1. (2019).

49.
Wilke, C. O. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9.4. (2019).

50.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).
Google Scholar 

51.
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. (2017).

52.
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. (2019).

53.
Department for Environment Food and Rural Affairs. Rapid pest risk analysis for Xylosandrus crassisuculus. 30 (2015).

54.
ANSES. Évaluation du risque simplifiée sur Xylosandrus compactus (Eichhoff) identifié en France métropolitaine. (2017).

55.
Kavčič, A. & de Groot, M. Pest risk analysis for the Asian Ambrosia Beetle (Xylosandrus crassiusculus (Motschulsky, 1866)). (Slovenian Forestry Institute, 2017).

56.
Storer, C., Payton, A., McDaniel, S., Jordal, B. & Hulcr, J. Cryptic genetic variation in an inbreeding and cosmopolitan pest, Xylosandrus crassiusculus, revealed using ddRADseq. Ecol. Evol. 7, 10974–10986. https://doi.org/10.1002/ece3.3625 (2017).
Article  PubMed  PubMed Central  Google Scholar 

57.
Ito, M. & Kajimura, H. Phylogeography of an ambrosia beetle, Xylosandrus crassiusculus (Motschulsky) (Coleoptera: Curculionidae: Scolytinae), Japan. Appl. Entomol. Zool. 44, 549–559. https://doi.org/10.1303/aez.2009.549 (2009).
CAS  Article  Google Scholar 

58.
Godefroid, M., Rasplus, J.-Y. & Rossi, J.-P. Is phylogeography helpful for invasive species risk assessment? The case study of the bark beetle genus Dendroctonus. Ecography 39, 1197–1209. https://doi.org/10.1111/ecog.01474 (2016).
Article  Google Scholar 

59.
Godefroid, M., Cruaud, A., Rossi, J. P. & Rasplus, J. Y. Assessing the risk of invasion by Tephritid fruit flies: Intraspecific divergence matters. PLoS One 10, e0135209. https://doi.org/10.1371/journal.pone.0135209 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E. & Thuiller, W. Invasive species distribution models—how violating the equilibrium assumption can create new insights. Glob. Ecol. Biogeogr. 21, 1126–1136. https://doi.org/10.1111/j.1466-8238.2012.00768.x (2012).
Article  Google Scholar 

61.
Rey, O. et al. Where do adaptive shifts occur during invasion? A multidisciplinary approach to unravelling cold adaptation in a tropical ant species invading the Mediterranean area. Ecol. Lett. 15, 1266–1275. https://doi.org/10.1111/j.1461-0248.2012.01849.x (2012).
Article  PubMed  Google Scholar 

62.
Ma, Z. & Yang, Q. (2017) Global patterns of aridity trends and time regimes in transition. In Aridity Trend in Northern China (eds Congbin, F. & Huiting, M.) 67–90 (World Scientific Publishing, Singapore, 2017).
Google Scholar 

63.
Gugliuzzo, A. et al. Seasonal changes in population structure of the ambrosia beetle Xylosandrus compactus and its associated fungi in a southern Mediterranean environment. PLoS One 15, e0239011. https://doi.org/10.1371/journal.pone.0239011 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Formby, J. P. et al. Cold tolerance and invasive potential of the redbay ambrosia beetle (Xyleborus glabratus) in the eastern United States. Biol. Invas. 20, 995–1007. https://doi.org/10.1007/s10530-017-1606-y (2017).
Article  Google Scholar 

65.
Reding, M. E., Ranger, C. M., Oliver, J. B. & Schultz, P. B. Monitoring attack and flight activity of Xylosandrus spp. (Coleoptera: Curculionidae: Scolytinae): The influence of temperature on activity. Hortic. Entomol. 106, 1780–1787. https://doi.org/10.1603/ec13134 (2013).
Article  Google Scholar 

66.
Gugliuzzo, A., Criscione, G., Siscaro, G., Russo, A. & Tropea Garzia, G. First data on the flight activity and distribution of the ambrosia beetle Xylosandrus compactus (Eichhoff) on carob trees in Sicily. EPPO Bull. 49, 340–351. https://doi.org/10.1111/epp.12564 (2019).
Article  Google Scholar 

67.
Williams, J. E. & Blois, J. L. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts?. J. Biogeogr. 45, 2175–2189. https://doi.org/10.1111/jbi.13395 (2018).
Article  Google Scholar 

68.
Hlásny, T. et al. Living with Bark Beetles: Impacts, Outlook and Management Options 52 (European Forest Institute, Joensuu, 2019).
Google Scholar 

69.
Ranger, C. M., Schultz, P. B., Frank, S. D., Chong, J. H. & Reding, M. E. Non-native ambrosia beetles as opportunistic exploiters of living but weakened trees. PLoS One 10, e0131496. https://doi.org/10.1371/journal.pone.0131496 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Ranger, C. M., Reding, M. E., Schultz, P. B. & Oliver, J. B. Influence of flood-stress on ambrosia beetle host-selection and implications for their management in a changing climate. Agric. For. Entomol. 15, 56–64. https://doi.org/10.1111/j.1461-9563.2012.00591.x (2013).
Article  Google Scholar 

71.
LaBonte, J. R. in USDA Research Forum on Invasive Species. 41–43.

72.
Castrillo, L. A., Griggs, M. H., Ranger, C. M., Reding, M. E. & Vandenberg, J. D. Virulence of commercial strains of Beauveria bassiana and Metarhizium brunneum (Ascomycota: Hypocreales) against adult Xylosandrus germanus (Coleoptera: Curculionidae) and impact on brood. Biol. Control 58, 121–126. https://doi.org/10.1016/j.biocontrol.2011.04.010 (2011).
Article  Google Scholar 

73.
Horn, S. & Horn, G. N. New host record for the asian ambrosia beetle, Xylosandrus crassiusculus (Motschulsky) (Coleoptera: Curculionidae). J. Entomol. Sci. 41, 90–91. https://doi.org/10.18474/0749-8004-41.1.90 (2006).
Article  Google Scholar 

74.
Wu, T. et al. An overview of BCC climate system model development and application for climate change studies. J. Meteorol. Res. 28, 34–56. https://doi.org/10.1007/s13351-014-3041-7 (2013).
Article  Google Scholar 

75.
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991. https://doi.org/10.1175/2011jcli4083.1 (2011).
ADS  Article  Google Scholar 

76.
Schmidt, G. A. et al. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst. 6, 141–184. https://doi.org/10.1002/2013ms000265 (2014).
ADS  Article  Google Scholar 

77.
Collins, W. J. et al. Development and evaluation of an Earth-System model—HadGEM2. Geosci. Model Dev. 4, 1051–1075. https://doi.org/10.5194/gmd-4-1051-2011 (2011).
ADS  Article  Google Scholar 

78.
Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165. https://doi.org/10.1007/s00382-012-1636-1 (2013).
Article  Google Scholar 

79.
Watanabe, M. et al. Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335. https://doi.org/10.1175/2010jcli3679.1 (2010).
ADS  Article  Google Scholar 

80.
Yukimoto, S. et al. A new global climate model of the Meteorological Research Institute: MRI-CGCM3. J. Meteorol. Soc. Jpn 90A, 23–64. https://doi.org/10.2151/jmsj.2012-A02 (2012).
Article  Google Scholar 

81.
Watanabe, S. et al. MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872. https://doi.org/10.5194/gmd-4-845-2011 (2011).
ADS  Article  Google Scholar 

82.
Bentsen, M. et al. The Norwegian earth system model, NorESM1-M—Part 1: Description and basic evaluation of the physical climate. Geosci. Model Dev. 6, 687–720. https://doi.org/10.5194/gmd-6-687-2013 (2013).
ADS  Article  Google Scholar  More

1.
Zenner, E. Does old-growth condition imply high live-tree structural complexity?. For. Ecol. Manag. 195, 243–258 (2004).
Article  Google Scholar 
2.
Forest Ecosystem Management Assessment Team (FEMAT). Draft Supplemental Environmental Impact Statement on Management of Habitat for Late Successional and Oldgrowth Forest Related Species within the Range of the Northern Spotted Owl (US Government Printing Office, Washington, DC, 1993).
Google Scholar 

3.
Wan, P. et al. Impacts of different forest management methods on the stand spatial structure of a natural Quercus aliena var. acuteserrata forest in Xiaolongshan, China. Ecol. Inform. 50, 86–94 (2019).
Article  Google Scholar 

4.
Carrer, M., Castagneri, D., Popa, I., Pividori, M. & Lingua, E. Tree spatial patterns and stand attributes in temperate forests: The importance of plot size, sampling design, and null model. For. Ecol. Manag. 407, 125–134 (2018).
Article  Google Scholar 

5.
Bauhus, J., Puettmann, K. & Messier, C. Silviculture for old-growth attributes. For. Ecol. Manag. 258, 525–537 (2009).
Article  Google Scholar 

6.
Messier, C., Puettmann, K. J. & Coates, D. K. Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change (Routledge, Abingdon, 2013).
Google Scholar 

7.
McElhinny, C., Gibbons, P., Brack, C. & Bauhus, J. Forest and woodland stand structural complexity: Its definition and measurement. For. Ecol. Manage. 218, 1–24 (2005).
Article  Google Scholar 

8.
Di Filippo, A., Biondi, F., Piovesan, G. & Ziaco, E. Tree ring-based metrics for assessing old-growth forest naturalness. J. Appl. Ecol. 54, 737–749 (2017).
Article  Google Scholar 

9.
Parrotta, J. A., Turnbull, J. W. & Jones, N. Catalyzing native forest regeneration on degraded tropical lands. For. Ecol. Manag. 99, 1–7 (1997).
Article  Google Scholar 

10.
Neumann, M. & Starlinger, F. The significance of different indices for stand structure and diversity in forests. For. Ecol. Manag. 145, 91–106 (2001).
Article  Google Scholar 

11.
McCleary, K. & Mowat, G. Using forest structural diversity to inventory habitat diversity of forest-dwelling wildlife in the West Kootenay region of British Columbia 2 1–13 (2002).

12.
Ishii, H. T., Tanabe, S.-I. & Hiura, T. Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. For. Sci. 50, 342–355 (2004).
Google Scholar 

13.
Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J. Biogeogr. 31, 79–92 (2004).
Article  Google Scholar 

14.
Long, J. N. & Shaw, J. D. The influence of compositional and structural diversity on forest productivity. Forestry 83, 121–128 (2010).
Article  Google Scholar 

15.
Dănescu, A., Albrecht, A. & Bauhus, J. Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany. Oecologia 182, 319–333 (2016).
ADS  PubMed  Article  Google Scholar 

16.
Ehbrecht, M., Schall, P., Ammer, C. & Seidel, D. Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate. Agric. For. Meteorol. 242, 1–9 (2017).
ADS  Article  Google Scholar 

17.
Zenner, E. K. Do residual trees increase structural complexity in pacific northwest?. Ecol. Appl. 10, 800–810 (2000).
Article  Google Scholar 

18.
Hardiman, B. S., Bohrer, G., Gough, C. M., Vogel, C. S. & Curtisi, P. S. The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. Ecology 92, 1818–1827 (2011).
PubMed  Article  Google Scholar 

19.
Puettmann, K. J., Coates, K. D. & Messier, C. C. A Critique of Silviculture: Managing for Complexity (Island Press, Washington, D.C., 2012).
Google Scholar 

20.
Robertson, G. P. & Tiedje, J. Spatial variability in a successional plant community: patterns of nitrogen availability. Ecology 69, 0–1524 (1988).

21.
Palmer, M. W. Spatial scale and patterns of species-environment relationships in hardwood forest of the North Carolina piedmont. Coenoses, 79–87 (1990).

22.
Lechowicz, M. & Bell, G. The ecology and genetics of fitness in forest plants. II. Microspatial heterogeneity of the edaphic environment. J. Ecol. 79, 687 (1991).

23.
Song, B. et al. Modeling canopy structure and heterogeneity across scales: from crowns to canopy. For. Ecol. Manage. 96, 217–229 (1997).
Article  Google Scholar 

24.
Zenner, E. & Peck, J. Characterizing structural conditions in mature managed red pine: spatial dependency of metrics and adequacy of plot size. For. Ecol. Manag. 257, 311–320 (2009).
Article  Google Scholar 

25.
Pommerening, A. & Uria-Diez, J. Do large forest trees tend towards high species mingling? Ecol. Inform. 42 (2017).

26.
Wang, H., Peng, H., Hui, G., Hu, Y. & Zhao, Z. Large trees are surrounded by more heterospecific neighboring trees in Korean pine broad-leaved natural forests. Sci. Rep. 8, 9149 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Hubbell, S. P., Ahumada, J. A., Condit, R. & Foster, R. B. Local neighborhood effects on long-term survival of individual trees in a neotropical forest. Ecol. Res. 16, 859–875 (2001).
Article  Google Scholar 

28.
Stoll, P. & Newbery, D. M. Evidence of species-specific neighborhood effects in the dipterocarpaceae of a bornean rain forest. Ecology 86, 3048–3062 (2005).
Article  Google Scholar 

29.
Pillay, T. & Ward, D. Spatial pattern analysis and competition between Acacia karroo trees in humid savannas. Plant Ecol. 213 (2012).

30.
Fueldner, K., Sattler, S., Zucchini, W. & Gadow, K. V. Modelling person-specific tree selection probabilities in a thinning. Allgemeine Forst Und Jagdzeitung (1996).

31.
Zenner, E. & Hibbs, D. A new method for modeling the heterogeneity of forest structure. For. Ecol. Manag. 129 (2000).

32.
Pommerening, A. Approaches to quantifying forest structures. Forestry 75(3), 305–324 (2002).
Article  Google Scholar 

33.
Beckschäfer, P. et al. Enhanced structural complexity index: an improved index for describing forest structural complexity. Open J. For. 3, 23–29 (2013).
Google Scholar 

34.
Kint, V., van Meirvenne, M., Nachtergale, L., Geudens, G. & Lust, N. Spatial methods for quantifying forest stand structure development: a comparison between nearest-neighbor indices and variogram analysis. For. Sci. 49, 36–49 (2003).
Google Scholar 

35.
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).
Article  Google Scholar 

36.
Ripley, B. D. Spatial Statistics (Wiley, New York, 1981).

37.
Ripley, B. D. Modelling spatial patterns. J. R. Stat. Soc. 39(2), 172–212 (1977).
MathSciNet  Google Scholar 

38.
Pommerening, A. & Grabarnik, P. Individual-Based Methods in Forest Ecology and Management (Springer, Berlin, 2019).

39.
Gadow, K., Albert, M. & Hui, G. Das Winkelmaß – ein Strukturparameter zur beschreibung der Individualverteilung in Waldbeständen. Centralblatt für das gesamte Forstwesen 115(1), 1–10 (1998).
Google Scholar 

40.
Aguirre, O., Hui, G., Gadow, K. v. & Jiménez, J. An analysis of spatial forest structure using neighbourhood-based variables. For. Ecol. Manag. 183, 137–145 (2003).

41.
Hui, G. & Gadow, K. Das Winkelmass – Theoretische Überlegungen zum optimalen Standardwinkel. Allgemeine Forst u. Jagdzeitung 173(9), 173–177 (2002).
Google Scholar 

42.
Pommerening, A. Evaluating structural indices by reversing forest structural analysis. For. Ecol. Manage. 224, 266–277 (2006).
Article  Google Scholar 

43.
Li, Y., Hui, G., Zhao, Z., Hu, Y. & Adler, P. The bivariate distribution characteristics of spatial structure in natural Korean pine broad-leaved forest. Journal of Vegetation Science 23 (2012).

44.
Graz, F. P. Spatial diversity of dry savanna woodlands. Assessing the spatial diversity of a dry savanna woodland stand in northern Namibia using neighbourhood-based measures. Biodivers. Conserv. 00, 1–16 (2004).

45.
Pastorella, F. & Paletto, A. Stand structure indices as tools to support forest management: an application in Trentino forests (Italy). J. For. Sci. 59, 159–168 (2013).
Article  Google Scholar 

46.
Zhao, Z. et al. Testing the significance of different tree spatial distribution patterns based on the Uniform Angle Index. Can. J. For. Res. 44(11), 1417–1425 (2014).
Article  Google Scholar 

47.
Zhang, G. et al. Composition of basal area in natural forests based on the uniform angle index. Ecol. Inform. 45, 1–8 (2018).
Article  Google Scholar 

48.
Stiell, W. How uniformity of tree distribution affects stand growth. For. Chron. 54, 156–158 (1978).
Article  Google Scholar 

49.
Jay, A., Nichols, J. & Vanclay, J. Social and ecological issues for private native forestry in north-eastern New South Wales Australia. Small Scale For. 6, 115–126 (2007).
Article  Google Scholar 

50.
Zhang, G. et al. Designing near-natural planting patterns for plantation forests in China. For. Ecosyst. 6, 137 (2019).
Article  Google Scholar 

51.
Moeur, M. Characterising spatial patterns of trees using stem-mapped data. For. Sci. 39, 756–775 (1993).
ADS  Google Scholar 

52.
Stohlgren, T. Spatial patterns of giant sequoia (Sequoiadendrongiganteum) in two sequoia groves in Sequoia National Park California. Can. J. For. Res. 23, 120–132 (2011).
Article  Google Scholar 

53.
Pommerening, A. & Grabarnik, P. Individual-based Methods in Forest Ecology and Management (2019).

54.
Clark, P. & Evans, F. Distance to nearest neighbor as a measure of spatial relations. Ecology 35, 445–453 (1954).
Article  Google Scholar 

55.
Assunçáo, R. Testing spatial randomness by means of angle. Biometrics 50, 531–537 (1994).
MATH  Article  Google Scholar 

56.
Corral-Rivas JJ. PhD thesis. University of Göttingen (2006).

57.
Hui, G., Zhang, G., Zhao, Z. & Yang, A. Methods of forest structure research: a review. Curr. For. Rep. 5(3), 142–154. https://doi.org/10.1007/S40725-019-00090-7 (2019).
Article  Google Scholar 

58.
Gadow, K., Hui, G. & Albert, M. Das Winkelmaß – Ein Strukturparameter zur Beschreibung der Individualverteilung in Waldbeständen. Centralblatt für das Gesamte Forstwesen 115, 1–10 (1998).
Google Scholar 

59.
Wang, H. et al. The influence of sampling unit size and spatial arrangement patterns on neighborhood-based spatial structure analyses of forest stands. For. Syst. 25, e056 (2016).
Google Scholar 

60.
Kraft, G. Beiträge zur Lehre von den Durchforstungen, Schlagstellungen und Lichtungshieben, Vol. 154 (Klindworth’s Verlag, Hanover, 1884).

61.
Röhrig, E. & Gussone, H. A. Waldbau auf Ökologischer Grundlage: Zweiter Band (Hamburg, Paul Parey, 1982).
Google Scholar 

62.
Hawley, R. C. & Smith, M. D. The practice of silviculture. Ecology 17(1), 172 (1936).
Article  Google Scholar 

63.
Larsen, J. B. & Nielsen, A. B. Nature-based forest management—Where are we going?. For. Ecol. Manag. 238, 107–117 (2007).
Article  Google Scholar 

64.
Ajani, J. The Forest Wars (Melbourne University, Melbourne, 2007).
Google Scholar 

65.
Nichols, J. D., Bristow, M. & Vanclay, J. K. Mixed-species plantations: prospects and challenges. For. Ecol. Manag. 233, 383–390 (2006).
Article  Google Scholar 

66.
Carnus, J.-M. et al. Planted forests and biodiversity. J. For. 104, 65–77 (2006).
Google Scholar 

67.
Gadow, K. V. & Hui, G. Y. Characterizing forest spatial structure and diversity Institute of Forest Management, Georg-August-University Göttingen, Büsgenweg 5, D-37077 Göttingen, Germany Published in: Sustainable Forestry in Temperate Regions; Proc. of an international workshop organized at the University of Lund, Sweden: 20–30. More

1.
Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, Princeton, 1991).
Google Scholar 
2.
Trivers, R. L. Parental investment and sexual selection. In Sexual Selection and the Descent of Man (ed. Campbell, B.) 136–179 (John Murray, Aldine, 1972).
Google Scholar 

3.
Alonzo, S. H. & Klug, H. Maternity, paternity and parental care. In The Evolution of Parental Care (eds Royle, N. J. et al.) 189–203 (Oxford University Press, Oxford, 2012).
Google Scholar 

4.
Møller, A. P. & Cuervo, J. J. The evolution of paternity and paternal care in birds. Behav. Ecol. 11, 472–485 (2000).
Article  Google Scholar 

5.
Neff, B. D. Paternity and condition affect cannibalistic behavior in nest-tending bluegill sunfish. Behav. Ecol. Sociobiol. 54, 377–384 (2003).
Article  Google Scholar 

6.
Benowitz, K. M., Head, M. L., Williams, C. A., Moore, A. J. & , Royle, N.J. ,. Male age mediates reproductive investment and response to paternity assurance. Proc. R. Soc. B 280, 20131124 (2013).
PubMed  Article  Google Scholar 

7.
Wisenden, B. D. Alloparental care in fishes. Rev. Fish Biol. Fish. 9, 45–70 (1999).
Article  Google Scholar 

8.
Griffin, A. S., Alonzo, S. H. & Cornwallis, C. K. Why do cuckolded males provide paternal care? PLoS ONE 11, e1001520 (2013).
CAS  Article  Google Scholar 

9.
Stevens, M. Bird brood parasitism. Curr. Biol. 23, R909–R913 (2013).
CAS  PubMed  Article  Google Scholar 

10.
Cohen, M. S., Hawkins, M. B., Stock, D. W. & Cruz, A. Early life-history features associated with brood parasitism in the cuckoo catfish, Synodontis multipunctatus (Siluriformes: Mochokidae). Philos. Trans. R. Soc. B 374, 20180205 (2019).
Article  Google Scholar 

11.
Taborsky, M. Sneakers, satellites, and helpers: Parasitic and cooperative behavior in fish reproduction. Adv. Stud. Behav. 23, 1–100 (1994).
Article  Google Scholar 

12.
Zahavi, A. Mate selection: A selection for handicap. J. Theor. Biol. 53, 205–214 (1975).
CAS  PubMed  Article  Google Scholar 

13.
Price, T., Schluter, D. & Heckman, N. E. Sexual selection when the female directly benefits. Biol. J. Linn. Soc. 48, 187–211 (1993).
Article  Google Scholar 

14.
Arnold, S. J. & Duvall, D. Animal mating systems: A synthesis based on selection theory. Am. Nat. 143, 317–348 (1994).
Article  Google Scholar 

15.
Klug, H., Alonzo, S. H. & Bonsall, M. B. Theoretical foundations of parental care. In The Evolution of Parental Care (eds Royle, N. J. et al.) 21–39 (Oxford University Press, Oxford, 2012).
Google Scholar 

16.
Nazareth, T. M. & Machado, G. Mating system and exclusive postzygotic paternal care in a Neotropical harvestman (Arachnida: Opiliones). Anim. Behav. 79, 547–554 (2010).
Article  Google Scholar 

17.
Matsumoto, Y., Tawa, A. & Takegaki, T. Female mate choice in a paternal brooding blenny: the process and benefits of mating with males tending young eggs. Ethology 117, 227–235 (2011).
Article  Google Scholar 

18.
Rohwer, S. Selection for adoption versus infanticide by replacement “mates” in birds. In Current Ornithology (ed. Johnston, R. F.) 353–395 (Plenum Press, New York, 1986).
Google Scholar 

19.
Valencia-Aguilar, A., Zamudio, K. R., Haddad, C. F. B., Bogdanowicz, S. M. & Prado, C. P. A. Show me you care: Female mate choice based on egg attendance rather than male or territorial traits. Behav. Ecol. 31, 1054–1064 (2020).
Article  Google Scholar 

20.
Schulte, L. M., Ringler, E., Rojas, B. & Stynoski, J. L. Developments in amphibian parental care research: History, present advances, and future perspectives. Herpetol. Monogr. 34, 71–97 (2020).
Article  Google Scholar 

21.
Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Parental care and the evolution of terrestriality in frogs. Proc. R. Soc. B 286, 20182737 (2019).
PubMed  Article  Google Scholar 

22.
Guayasamin, J. M., Cisneros-Heredia, D. F., McDiarmid, R. W., Peña, P. & Hutter, C. R. Glassfrogs of ecuador: Diversity, evolution, and conservation. Diversity 12, 222 (2020).
CAS  Article  Google Scholar 

23.
Stynoski, J. L. Discrimination of offspring by indirect recognition in an egg-feeding dendrobatid frog, Oophaga pumilio. Anim. Behav. 78, 1351–1356 (2009).
Article  Google Scholar 

24.
Ringler, E., Beck, K. B., Weinlein, S., Huber, L. & Ringler, M. Adopt, ignore, or kill? Male poison frogs adjust parental decisions according to their territorial status. Sci. Rep. 7, 43544 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

25.
Waldman, B. Mechanisms of kin recognition. J. Theor. Biol. 128, 159–185 (1987).
Article  Google Scholar 

26.
Penn, D. & Frommen, J. Kin recognition: An overview of conceptual issues, mechanisms and evolutionary theory. In Animal Behaviour: Evolution and Mechanisms (ed. Kappeler, P.) 55–86 (Springer, Heidelberg, 2010).
Google Scholar 

27.
Delia, J. R., Bravo-Valencia, L. & Warkentin, K. The evolution of extended parental care in glassfrogs: Do egg-clutch phenotypes mediate coevolution between the sexes? Ecol. Monogr. 90, e01411 (2020).
Article  Google Scholar 

28.
Pašukonis, A. et al. Induced parental care in a poison frog: A tadpole cross-fostering experiment. J. Exp. Biol. 220, 3949–3954 (2017).
PubMed  PubMed Central  Article  Google Scholar 

29.
Townsend, D. & Moger, W. H. Plasma androgen levels during male parental care in a tropical frog (Eleutherodactylus). Horm. Behav. 21, 93–99 (1987).
CAS  PubMed  Article  Google Scholar 

30.
Knapp, R., Wingfield, J. C. & Bass, A. H. Steroid hormones and paternal care in the plainfin midshipman fish (Porichthys notatus). Horm. Behav. 35, 81–89 (1999).
CAS  PubMed  Article  Google Scholar 

31.
Pikus, A. E., Guindre-Parker, S. & Rubenstein, D. R. Testosterone, social status and parental care in a cooperatively breeding bird. Horm. Behav. 97, 85–93 (2018).
CAS  PubMed  Article  Google Scholar 

32.
Fischer, E. K. & O’Connell, L. A. Hormonal and neural correlates of care in active versus observing poison frog parents. BioRxiv 27, 765503 (2019).
Google Scholar 

33.
Goymann, W. & Dávila, P. F. Acute peaks of testosterone suppress paternal care: evidence from individual hormonal reaction norms. Proc. R. Soc. B 284, 20170632 (2017).
PubMed  Article  CAS  Google Scholar 

34.
Butin, J. D. Parental behavior and hormones in non-mammalian vertebrates. In Encyclopedia of Animal Behavior (eds Breed, M. & Moore, J.) 664–671 (Elsevier, Amsterdam, 2010).
Google Scholar 

35.
Townsend, D. S., Palmer, B. & Guillette, L. G. The lack of influence of exogenous testosterone on male parental behavior in a neotropical frog (Eleutherodactylus): A field experiment. Horm. Behav. 25, 313–322 (1991).
CAS  PubMed  Article  Google Scholar 

36.
Magee, S. E., Neff, B. D. & Knapp, R. Plasma levels of androgens and cortisol in relation to breeding behavior in parental male bluegill sunfish, Lepomis macrochirus. Horm. Behav. 49, 598–609 (2006).
CAS  PubMed  Article  Google Scholar 

37.
Ouyang, J. Q., Sharp, P. J., Dawson, A., Quetting, M. & Hau, M. Hormone levels predict individual differences in reproductive success in a passerine bird. Proc. R. Soc. B 278, 2537–2545 (2011).
CAS  PubMed  Article  Google Scholar 

38.
Mota, M. T. S., Franci, C. R. & Sousa, M. B. C. Hormonal changes related to paternal and alloparental care in common marmosets (Callithrix jacchus). Horm. Behav. 49, 293–302 (2006).
CAS  Article  Google Scholar 

39.
Romero, L. M. Seasonal changes in plasma glucocorticoid concentrations in free-living vertebrates. Gen. Comp. Endocrinol. 128, 1–24 (2002).
CAS  Article  Google Scholar 

40.
Consolmagno, R. C., Requena, G. S., Machado, G. & Brasileiro, C. A. Costs and benefits of temporary egg desertion in a rocky shore frog with male-only care. Behav. Ecol. Sociobiol. 70, 785–795 (2016).
Article  Google Scholar 

41.
Kelly, N. B. & Alonzo, S. H. Will male advertisement be a reliable indicator of paternal care, if offspring survival depends on male care? Proc. R. Soc. B 276, 3175–3183 (2009).
PubMed  Article  Google Scholar 

42.
Stiver, K. A. & Alonzo, S. H. Alloparental care increases mating success. Behav. Ecol. 22, 206–211 (2011).
Article  Google Scholar 

43.
Roldán, M. & Soler, M. Parental-care parasitism: How do unrelated offspring attain acceptance by foster parents? Behav. Ecol. 22, 679–691 (2011).
Article  Google Scholar 

44.
Maynard-Smith, J. Parental investment: A prospective analysis. Anim. Behav. 25, 1–9 (1977).
Article  Google Scholar 

45.
Valencia-Aguilar, A., Rodrigues, D. & Prado, C. P. A. Male care status influences the risk-taking decisions in a glassfrog. Behav. Ecol. Sociobiol. 74, 1–11 (2020).
Article  Google Scholar 

46.
Delia, J., Bravo-Valencia, L. & Warkentin, K. M. Patterns of parental care in Neotropical glassfrogs: Fieldwork alters hypotheses of sex-role evolution. J. Evol. Biol. 30, 898–914 (2017).
CAS  PubMed  Article  Google Scholar 

47.
Noronha, J. C. & Rodrigues, D. J. Reproductive behaviour of the glass frog Hyalinobatrachium cappellei (Anura: Centrolenidae) in the Southern Amazon. J. Nat. Hist. 52, 207–224 (2018).
Article  Google Scholar 

48.
Drake, D. L. & Ranvestel, A. W. Hyalinobatrachium colymbihpyllum (glass frog). Egg mass defense. Herpetol. Rev. 36, 434 (2005).
Google Scholar 

49.
Vockenhuber, E. A., Hödl, W. & Amézquita, A. Glassy fathers do matter: Egg attendance enhances embryonic survivorship in the glass frog Hyalinobatrachium valerioi. J. Herpetol. 43, 340–344 (2009).
Article  Google Scholar 

50.
Salgado, A. L. & Guayasamin, J. M. Parental care and reproductive behavior of the minute dappled glassfrog (Centrolenidae: Centrolene peristictum). S. Am. J. Herpetol. 13, 211–219 (2018).
Article  Google Scholar 

51.
Foster, W. A. & Treherne, J. E. Evidence for the dilution effect in the selfish herd from fish predation on a marine insect. Nature 293, 466–467 (1981).
ADS  Article  Google Scholar 

52.
Lehtonen, J. & Jaatinen, K. Safety in numbers: The dilution effect and other drivers of group life in the face of danger. Behav. Ecol. Sociobiol. 70, 449–458 (2016).
Article  Google Scholar 

53.
Gloag, R., Fiorini, V. D., Reboreda, J. C. & Kacelnik, A. Brood parasite eggs enhance egg survivorship in a multiply parasitized host. Proc. Biol. Sci. 279, 1831–1839 (2012).
PubMed  Google Scholar 

54.
Schulte, L. M. et al. The smell of success: Choice of larval rearing sites by means of chemical cues in a Peruvian poison frog. Anim. Behav. 81, 1147–1154 (2011).
Article  Google Scholar 

55.
Kam, Y. C. & Yang, H. W. Female–offspring communication in a Taiwanese tree frog, Chirixalus eiffingeri (Anura: Rhacophoridae). Anim. Behav. 64, 881–886 (2002).
Article  Google Scholar 

56.
Riedman, M. The evolution of alloparental care and adoption in mammals and birds. Q. Rev. Biol. 57, 405–435 (1982).
Article  Google Scholar 

57.
Briga, M., Pen, I. & Wright, J. Care for kin: Within-group relatedness and allomaternal care are positively correlated and conserved throughout the mammalian phylogeny. Biol. Lett. 8, 533–536 (2012).
PubMed  PubMed Central  Article  Google Scholar 

58.
Phillips, E., DeAngelis, R., Gogola, J. V. & Rhodes, J. S. Spontaneous alloparental care of unrelated offspring by non-breeding Amphiprion ocellaris in absence of the biological parents. Sci. Rep. 10, 4610 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Lee, H. J., Heim, V. & Meyer, A. Genetic evidence for prevalence of alloparental care in a socially monogamous biparental cichlid fish, Perissodus microlepis, from Lake Tanganyika supports the “selfish shepherd effect” hypothesis. Ecol. Evol. 6, 2843–2853 (2016).
PubMed  PubMed Central  Article  Google Scholar 

60.
Gosner, K. L. A simplified table for staging anuran embryos an larvae with notes on identification. Herpetologica 16, 183–190 (1960).
Google Scholar  More

2017 honey sampling
Beekeepers were invited to provide honey for analysis via a nationwide campaign publicised on the gardening programme, BBC Gardener’s World (broadcast July 2017). Participating beekeepers were asked to supply ~30 ml of honey from any date in 2017, reporting the date of sample collection and the location of the apiary, using a grid reference or postcode. In total 441 honey samples were processed from beekeepers.
Honey DNA extraction
Any wax was removed using sterile forceps and DNA was extracted from 10 g of honey using a modified version of the DNeasy Plant Mini extraction kit (Qiagen). Firstly, the 10 g of honey was made up to 30 ml with molecular grade water and incubated in a water bath at 65 °C for 30 min. Samples were then centrifuged (Sorvall RC-5B) for 30 min at 15,000 rpm, the supernatant was discarded, and the pellet resuspended in 400 μL of a buffer made from a mix of 400 μL AP1 from the DNeasy Plant Mini Kit (Qiagen), 80 μL proteinase K (1 mg/ml) (Sigma) and 1 μL RNase A (Qiagen). This was incubated again for 60 min at 65 °C in a water bath and then disrupted using a TissueLyser II (Qiagen) for 4 min at 30 Hz with 3 mm tungsten carbide beads. The remaining steps were carried out according to the manufacturer’s protocol, excluding the use of the QIAshredder and the second wash stage. The extracted DNA was purified using the OneStep PCR Inhibitor Removal Kit (Zymo Research) and diluted 1 in 10.
PCR and library preparation
Illumina MiSeq paired-end indexed amplicon libraries were created via a two-step PCR protocol. Two libraries were prepared for the DNA barcode regions, rbcL and ITS2. Initial amplification used the template specific primers rbcLaf and rbcLr50637, and ITS2F and ITS3R, with universal tails designed to attach custom indices in the second-round PCR. To improve clustering on the Illumina MiSeq, a 6N sequence was also added between the forward template specific primer and the universal tail.
Forward universal tail, 6N sequence and rbcLaf: [ACACTCTTTCCCTACACGACGCTCTTCCGATCT]NNNNNN[ATGTCACCACAAACAGAGACTAAAGC]
Reverse universal tail and rbcLr506: [GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT][AGGGGACGACCATACTTGTTCA]
Forward universal tail, 6N sequence and ITS2F: [ACACTCTTTCCCTACACGACGCTCTTCCGATCT]NNNNNN[ATGCGATACTTGGTGTGAAT]
Reverse universal tail and ITS3R: [GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT][GACGCTTCTCCAGACTACAAT]
This first PCR used a final volume of 20 μl: 2 μl template DNA, 10 μl of 2× Phusion Hot Start II High-Fidelity Mastermix (New England Biolabs UK), 0.4 μl (2.5 µM) forward and reverse primers, and 7.2 μl of PCR grade water. Thermal cycling conditions for rbcL were: 98 °C for 30 s, 95 °C for 2 min; 95 °C for 30 s, 50 °C for 30 s, 72 °C for 40 s (40 cycles); 72 °C for 5 min, 30 °C for 10 s. Thermal cycling conditions for the first ITS2 PCR were: 98 °C for 30 s 94 °C for 5 min; 94 °C for 30 s, 56 °C for 30 s, 72 °C for 40 s (40 cycles); 72 °C for 10 min, 30 °C for 1 min. The initial PCR was carried out three times and pooled.
The pooled products from the first PCR were purified following Illumina’s 16S Metagenomic Sequencing Library Preparation protocol using Agencourt AMPure XP beads (Beckman Coulter). The purified PCR product from round one was followed by a second round of amplification to anneal custom unique and identical i5 and i7 indices to each sample (Ultramer, Integrated DNA Technologies).
This index PCR stage used a final volume of 25 μl reaction (12.5 μl of 2× Phusion Hot Start II High-Fidelity Mastermix, 1 μl of i7 Index Primer and i5 Index Primer, 6.5 μl of PCR grade water, and 5 μl of purified first-round PCR product). Thermal cycling conditions were: 98 °C for 30 s; 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s (8 cycles); 72 °C for 5 min, 4 °C for 10 min. Following the index PCR, a 1% gel was run to verify its success. The index PCR product was then purified following the PCR clean-up two sections of the Illumina protocol. The purified products of the index PCR were quantified using a Qubit 3.0 fluorescence spectrophotometer (Thermo Fisher Scientific) and pooled at equal concentrations to produce the final library. Positive and negative controls were amplified and sequenced alongside honey samples. The positive control was made from a mixture of five tropical tree species that were not present in the survey site. The species Baccaurea stipulata, Colona serratifolia., Dillenia excelsa, Kleinhovia hospita, and Pterospermum macrocarpum were used, taking 5 μl from each separate DNA extraction and mixing, before following the protocol as with the honey samples. All five species were detected within the sequencing results.
Bioinformatic analysis
Sequence data were processed using a modified data analysis pipeline14,38. Raw reads were trimmed to remove low-quality regions (Trimmomatic v. 0.33), paired, and then merged (FLASH v. 1.2.11), with merged reads shorter than 450 bp discarded. Identical reads were dereplicated within samples and then clustered at 100% identity across all samples (vsearch v. 2.3.2), with singletons (sequence reads that occurred only once across all samples) discarded.
The Barcode Wales and Barcode UK projects provide 98% coverage for the native flowering plants and conifers of the UK37. This reference library was supplemented with a curated library of the non-native and horticultural species, downloaded from GenBank. This UK species list was generated using the list of native species of the UK from Stace (2010)39, 505 naturalised alien species (BSBI), and horticultural species from the IRIS BG database at the National Botanic Garden of Wales.
The sequence data from the honey samples were compared against the reference database using blastn, using the script vsearch-pipe.py. The top BLAST hits were then summarised using the script vsearch_blast_summary.py. Sequences with bit scores below the 1st percentile were excluded. If the top bit scores of a sequence matched to a single species, then the sequence was identified to that species. If the top bit scores matched to different species within the same genus, then the result was attributed to the genus level. If the top bit score belonged to multiple genera within the same family then a family level designation was made. Sequences that returned families from different clades were excluded. These automated identifications were then checked manually for botanical veracity. To check identified plant species against their availability across the UK, species records from the BSBI (Botanical Society of Britain and Ireland) were used for native species, while commercial availability for horticultural species was verified with the RHS Plant Finder40. Within each sample, the number of sequences returned from rbcL and ITS2 for each plant taxon was summed to combine the results of each marker.
The proportion of sequences was used in the analysis, which has been shown to be an appropriate method to control for differences in read number41. Alternatively, the sequencing data can be rarefied, but this has been criticised as a statistical technique, due to requiring the removal of valid data41. To investigate the impact of rarefying on the conclusions drawn from the data, all analyses were rerun with rarefied data (Supplementary Results).
1952 Honey sampling
In 1952, 855 honey samples were characterised from 66 counties across the UK and Ireland using melissopalynology15,16. The methods reported for the research conducted in 1952 are described here fully for comparison. Samples were obtained via a general appeal and were all collected during the honey season of 1952. For each honey sample, ~200 pollen grains were identified using the morphology of the pollen under the microscope, following a standardised protocol42. To extract the pollen, 10 g of honey was dissolved in 20 ml of distilled water, from which 10 ml was taken and centrifuged at ~2000 rpm for one minute. The supernatant was discarded, and the sediment retained, and then the process was repeated for the remaining liquid. From the sediment, a drop was transferred to a glass slide and spread out over an area of 1 cm2, before being stained with fuchsin and dried. Euparal vert was used as a final mounting medium. Pollen was identified by comparison with a reference library of pollen preparations and available pollen morphological data43,44. Each plant taxon found in the sampled honey was reported according to the proportion of pollen grains found and classed into predominant ( >45% of pollen grains), secondary (15–45% of pollen grains) and important minor (1–15% of pollen grains). The location data for the honey samples were restricted to the county level, and summary data tables were presented for each UK county that returned honey.
Comparing the 1952 and 2017 honey samples
The plants detected using DNA metabarcoding and melissopalynology have been compared in previous studies with concordance found between the two methods45,46,47,48. Both methods detect the same major taxa, but rarer species in a sample are less likely to be found consistently, both when comparing methods and also during replicates of the same method45,46,47. DNA metabarcoding is often able to detect more taxa when compared to melissopalynology, by identifying rarer species in the sample and by achieving higher taxonomic resolution in certain cases. While melissopalynology uses counts of pollen grains to provide a starting point for quantitative analysis, DNA metabarcoding as a process is semi-quantitative, with biases associated with the process of DNA extraction, PCR and sequencing33,45. To allow for these considerations we placed the proportion of DNA sequence reads and pollen counts into four broad abundance classes matching the classifications used in melissopalynology (predominant, secondary, important minor and minor) and focus our analyses and conclusions on changes in the frequency of occurrence of the major taxa, classed as predominant and secondary. Both methods capture information on both nectar and pollen plants within the honey, however, certain species can be over or under represented in pollen analysis compared to their relative nectar contribution49. Both pollen and nectar plants are required to meet the foraging requirements of pollinators.
Statistics and reproducibility
Statistical analysis of DNA metabarcoding data
To understand how the plant taxa composition within the honey sample was structured in space and time, the effect of time (measured as the calendar month number in 2017), latitude and longitude of sampling location were included in a single, two-tailed generalized linear model using the ‘manyglm’ function in the package ‘mvabund’50. Honey samples with missing metadata were excluded, giving a sample size of 428. An abundance table of taxa (number of sequence reads) found in each sample was set as the multivariate response variable and a common set of predictor variables (month, latitude and longitude) were fit using a negative binomial distribution. The number of sequence reads per sample was included as an “offset” in the model in order to control for differences in the number of sequence reads between samples. Monte Carlo resampling was used to test for significant community-level responses to our predictors. The strong mean-variance relationship in the data (Supplementary Fig. 6) and the distribution of the count data (Supplementary Figs. 7, 8) support the use of a negative binomial distribution in the model. The appropriateness of the models was checked by visual inspection of the residuals against predicted values from the models (Supplementary Figs. 9–11).
We completed a spatial eigenfunction analysis using distance-based Moran’s eigenvectors. Moran’s Eigenvector Maps were computed using the ‘mem’ function from the adespatial package. Moran’s I was computed for each taxa using the ‘moran.randtest’, with Bonferroni correction for multiple testing. The direction of autocorrelation (positive and negative) was tested using the ‘moranNP.randtest’ function, using the adespatial package in R.
Statistical analysis of the 1952 and 2017 honey samples
Abundance classes were assigned based on the percentage of reads returned for the two DNA regions rbcL and ITS2, matching the classifications used in melissopalynology. Plant taxa represented by over 45% of reads were designated predominant for that sample; between 15 and 45% were secondary; between 1 and 15% were important minor taxa, and More

1.
Roddy, K. M. & Arita-Tsutsumi, L. A history of honey bees in the Hawaiian islands. J. Hawaiian Pac. Agric. 8, 59–70 (1997).
Google Scholar 
2.
Danka, R. G., Hellmich, R. L., Rinderer, T. E. & Collins, A. M. Diet-selection ecology of tropically and temperately adapted honey-bees. Anim. Behav. 35, 1858–1863 (1987).
Article  Google Scholar 

3.
Roberts, J. M. K., Anderson, D. L. & Durr, P. A. Absence of deformed wing virus and Varroa destructor in Australia provides unique perspectives on honeybee viral landscapes and colony losses. Sci. Rep. https://doi.org/10.1038/s41598-017-07290-w (2017).
PubMed  PubMed Central  Article  Google Scholar 

4.
de Guzman, L. I., Rinderer, T. E. & Stelzer, J. A. DNA evidence of the origin of Varroa jacobsoni Oudemans in the Americas. Biochem. Genet. 35, 327–335. https://doi.org/10.1023/a:1021821821728 (1997).
PubMed  Article  Google Scholar 

5.
Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. USA. 116, 1792–1801. https://doi.org/10.1073/pnas.1818371116 (2019).
CAS  PubMed  Article  Google Scholar 

6.
Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).
PubMed  Article  Google Scholar 

7.
Sammataro, D., Gerson, U. & Needham, G. Parasitic mites of honey bees: Life history, implications, and impact. Annu. Rev. Entomol. 45, 519–548 (2000).
CAS  PubMed  Article  Google Scholar 

8.
Amdam, G. V., Hartfelder, K., Norberg, K., Hagen, A. & Omholt, S. W. Altered physiology in worker honey bees (Hymenoptera: Apidae) infested with the mite Varroa destructor (Acari: Varroidae): A factor in colony loss during overwintering?. J. Econ. Entomol. 97, 741–747 (2004).
PubMed  Article  Google Scholar 

9.
Dejong, D., Dejong, P. H. & Goncalves, L. S. Weight-loss and other damage to developing worker honeybees from infestation with Varroa jacobsoni. J. Apic. Res. 21, 165–167. https://doi.org/10.1080/00218839.1982.11100535 (1982).
Article  Google Scholar 

10.
Ramadan, M. M., Reimer, N. J., Oishi, D. E., Young, C. L. & Heu, R. A. Varroa Mite Varroa destructor Anderson and Trueman (Acari: Varroidae) (Springer, New York, 2019).
Google Scholar 

11.
Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306. https://doi.org/10.1126/science.1220941 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

12.
Seeley, T. D. Honey bees of the Arnot Forest: A population of feral colonies persisting with Varroa destructor in the northeastern United States. Apidologie 38, 19–29 (2007).
Article  Google Scholar 

13.
Brettell, L. E. & Martin, S. J. Oldest Varroa tolerant honey bee population provides insight into the origins of the global decline of honey bees. Sci. Rep. https://doi.org/10.1038/srep45953 (2017).
PubMed  PubMed Central  Article  Google Scholar 

14.
Nielsen, D. I. Genetic structure of feral honey bee (Apis mellifera L.) populations in California Ph.D. thesis, University of California, Davis (2000).

15.
Doebler, S. A. The rise and fall of the honeybee: Mite infestations challenge the bee and the beekeeping industry. Bioscience 50, 738–742. https://doi.org/10.1641/0006-3568(2000)050[0738:Trafot]2.0.Co;2 (2000).
Article  Google Scholar 

16.
Fuchs, S. Preference for drone brood cells by Varroa jacobsoni oud in colonies of Apis-Mellifera-Carnica. Apidologie 21, 193–199 (1990).
Article  Google Scholar 

17.
Boot, W. J., Calis, J. N. M. & Beetsma, J. Differential periods of varroa mite invasion into worker and drone cells of honey-bees. Exp. Appl. Acarol. 16, 295–301 (1992).
Article  Google Scholar 

18.
Estoup, A., Solignac, M. & Cornuet, J. Precise assessment of the number of patrilines and of genetic relatedness in honeybee colonies. Proc. R. Soc. Lond. B 258, 1–7 (1994).
ADS  CAS  Article  Google Scholar 

19.
Tarpy, D. R., Nielsen, R. & Nielsen, D. I. A scientific note on the revised estimates of effective paternity frequency in Apis. Insectes Soc. 51, 203–204 (2004).
Article  Google Scholar 

20.
Akyol, E., Yeninar, H. & Kaftanoglu, O. Live weight of queen honey bees (Apis mellifera L.) predicts reproductive characteristics. J. Kansas Entomol. Soc. 81, 92–100. https://doi.org/10.2317/jkes-705.13.1 (2008).
Article  Google Scholar 

21.
Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects. https://doi.org/10.3390/insects8020048 (2017).
PubMed  PubMed Central  Article  Google Scholar 

22.
Tarpy, D. R., Keller, J. J., Caren, J. R. & Delaney, D. A. Experimentally induced variation in the physical reproductive potential and mating success in honey bee queens. Insectes Soc. 58, 569–574. https://doi.org/10.1007/s00040-011-0180-z (2011).
Article  Google Scholar 

23.
Hatjina, F. et al. A review of methods used in some European countries for assessing the quality of honey bee queens through their physical characters and the performance of their colonies. J. Apic. Res. 53, 337–363. https://doi.org/10.3896/ibra.1.53.3.02 (2014).
Article  Google Scholar 

24.
De Souza, D. A. et al. Morphometric identification of queens, workers and intermediates in in vitro reared honey bees (Apis mellifera). PLoS ONE. https://doi.org/10.1371/journal.pone.0123663 (2015).
PubMed  PubMed Central  Article  Google Scholar 

25.
Woyke, J. Correlations between the age at which honeybee brood was grafted, characteristics of the resultant queens, and results of insemination. J. Apic. Res. 10, 45–55 (1971).
Article  Google Scholar 

26.
Dedej, S., Hartfelder, K., Aumeier, P., Rosenkranz, P. & Engels, W. Caste determination is a sequential process: Effect of larval age at grafting on ovariole number, hind leg size and cephalic volatiles in the honey bee (Apis mellifera carnica). J. Apic. Res. 37, 183–190 (1998).
Article  Google Scholar 

27.
Al-Lawati, H., Kamp, G. & Bienefeld, K. Characteristics of the spermathecal contents of old and young honeybee queens. J. Insect Physiol. 55, 116–121 (2009).
CAS  PubMed  Article  Google Scholar 

28.
Tarpy, D. R., Keller, J. J., Caren, J. R. & Delaney, D. A. Assessing the mating “health” of commercial honey bee queens. J. Econ. Entomol. 105, 20–25 (2012).
PubMed  Article  Google Scholar 

29.
Pettis, J. S., Rice, N., Joselow, K., vanEngelsdorp, D. & Chaimanee, V. Colony failure linked to low sperm viability in honey bee (Apis mellifera) queens and an exploration of potential causative factors. PLoS ONE. https://doi.org/10.1371/journal.pone.0147220 (2016).
PubMed  PubMed Central  Article  Google Scholar 

30.
Woyke, J. Natural and artificial insemination of queen honeybees. Bee World 43, 21–25 (1962).
Article  Google Scholar 

31.
Delaney, D. A., Keller, J. J., Caren, J. R. & Tarpy, D. R. The physical, insemination, and reproductive quality of honey bee queens (Apis mellifera). Apidologie 42, 1–13. https://doi.org/10.1051/apido/2010027 (2011).
Article  Google Scholar 

32.
McAfee, A. et al. Vulnerability of honey bee queens to heat-induced loss of fertility. Nat. Sustain. https://doi.org/10.1038/s41893-020-0493-x (2020).
Article  Google Scholar 

33.
Chaimanee, V., Evans, J. D., Chen, Y., Jackson, C. & Pettis, J. S. Sperm viability and gene expression in honey bee queens (Apis mellifera) following exposure to the neonicotinoid insecticide imidacloprid and the organophosphate acaricide coumaphos. J. Insect Physiol. 89, 1–8 (2016).
CAS  PubMed  Article  Google Scholar 

34.
Williams, G. R. et al. Neonicotinoid pesticides severely affect honey bee queens. Sci. Rep. https://doi.org/10.1038/srep14621 (2015).
PubMed  PubMed Central  Article  Google Scholar 

35.
Rangel, J. & Tarpy, D. R. The combined effects of miticides on the mating health of honey bee (Apis mellifera L.) queens. J. Apicult. Res. 54, 275–283. https://doi.org/10.1080/00218839.2016.1147218 (2015).
Article  Google Scholar 

36.
Buechler, R. et al. Standard methods for rearing and selection of Apis mellifera queens. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.1.07 (2013).
Article  Google Scholar 

37.
Lee, K. V., Goblirsch, M., McDermott, E., Tarpy, D. R. & Spivak, M. Is the brood pattern within a honey bee colony a reliable indicator of queen quality?. Insects. https://doi.org/10.3390/insects10010012 (2019).
PubMed  PubMed Central  Article  Google Scholar 

38.
de Miranda, J. R. et al. Standard methods for virus research in Apis mellifera. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.4.22 (2013).
Article  Google Scholar 

39.
Kevill, J. L. et al. The pathogen profiles of queen honey bees does not reflect those of their colonies workers. Insects 11, 382. https://doi.org/10.3390/insects11060382 (2020).
PubMed Central  Article  Google Scholar 

40.
Evans, J. D. et al. Standard methods for molecular research in Apis mellifera. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.4.11 (2013).
Article  Google Scholar 

41.
Jones, O. R. & Wang, J. COLONY: A program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x (2010).
PubMed  Article  Google Scholar 

42.
Nielsen, R., Tarpy, D. R. & Reeve, H. K. Estimating effective paternity number in social insects and the effective number of alleles in a population. Mol. Ecol. 12, 3157–3164 (2003).
PubMed  Article  Google Scholar 

43.
Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49, 1–6 (2010).
Article  Google Scholar 

44.
van Engelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invertebr. Pathol. 103, S80–S95 (2010).
Article  Google Scholar 

45.
Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and Colony Collapse Disorder in the United States. J. Apic. Res. 49, 134–136. https://doi.org/10.3896/ibra.1.49.1.30 (2010).
Article  Google Scholar 

46.
Lee, K. V. et al. A national survey of managed honey bee 2013–2014 annual colony losses in the USA: Results from the Bee Informed Partnership. Apidologie. https://doi.org/10.1007/s13592-015-0356-z (2015).
Article  Google Scholar 

47.
Steinhauer, N. et al. Drivers of colony losses. Curr. Opin. Insect Sci. 26, 142–148. https://doi.org/10.1016/j.cois.2018.02.004 (2018).
PubMed  Article  Google Scholar 

48.
Tarpy, D. R., Delaney, D. A. & Seeley, T. D. Mating frequencies of honey bee queens (Apis mellifera L.) in a population of feral colonies in the northeastern United States. PLoS ONE. https://doi.org/10.1371/journal.pone.0118734 (2015).
PubMed  PubMed Central  Article  Google Scholar 

49.
Lensky, Y. & Demter, M. Mating flights of the queen honeybee (Apis mellifera) in a subtropical climate. Comp. Biochem. Physiol. 81, 229–241 (1985).
Article  Google Scholar 

50.
USDA-NASS. (ed National Agricultural Statistics Service) (2018).

51.
DeGrandi-Hoffman, G. et al. Comparisons of pollen substitute diets for honey bees: Consumption rates by colonies and effects on brood and adult populations. J. Apic. Res. 47, 265–270. https://doi.org/10.3896/ibra.1.47.4.06 (2008).
Article  Google Scholar 

52.
Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS ONE. https://doi.org/10.1371/journal.pone.0150362 (2016).
PubMed  PubMed Central  Article  Google Scholar 

53.
Le Conte, Y. et al. Honey bee colonies that have survived Varroa destructor. Apidologie 38, 566–572 (2007).
Article  Google Scholar 

54.
Chen, Y. P., Evans, J. & Feldlaufer, M. Horizontal and vertical transmission of viruses in the honeybee, Apis mellifera. J. Invertebr. Pathol. 92, 152–159 (2006).
PubMed  Article  Google Scholar 

55.
de Miranda, J. R. & Fries, I. Venereal and vertical transmission of deformed wing virus in honeybees (Apis mellifera L.). J. Invertebr. Pathol. 98, 184–189 (2008).
PubMed  Article  Google Scholar 

56.
Yue, C., Schroder, M., Bienefeld, K. & Genersch, E. Detection of viral sequences in semen of honeybees (Apis mellifera): Evidence for vertical transmission of viruses through drones. J. Invertebr. Pathol. 92, 105–108 (2006).
CAS  PubMed  Article  Google Scholar 

57.
Amiri, E., Meixner, M. D. & Kryger, P. Deformed wing virus can be transmitted during natural mating in honey bees and infect the queens. Sci. Rep. https://doi.org/10.1038/srep33065 (2016).
PubMed  PubMed Central  Article  Google Scholar 

58.
Szalanski, A. L., Tripodi, A. D., Trammel, C. E. & Downey, D. Mitochondrial DNA genetic diversity of honey bees, Apis mellifera in Hawaii. Apidologie 47, 679–687. https://doi.org/10.1007/s13592-015-0416-4 (2016).
CAS  Article  Google Scholar 

59.
Danka, R. G., Harris, J. W., Villalobos, E. & Glenn, T. Varroa destructor resistance of honey bees in Hawaii, USA, with different genetic proportions of Varroa Sensitive Hygiene (VSH). J. Apic. Res. 51, 288–290. https://doi.org/10.3896/ibra.1.51.3.13 (2012).
Article  Google Scholar 

60.
Metz, B. N. & Tarpy, D. R. Reproductive senescence in drones of the honey bee (Apis mellifera). Insects. https://doi.org/10.3390/insects10010011 (2019).
PubMed  PubMed Central  Article  Google Scholar 

61.
Sturup, M., Baer-Imhoof, B., Nash, D. R., Boomsma, J. J. & Baer, B. When every sperm counts: Factors affecting male fertility in the honeybee Apis mellifera. Behav. Ecol. 24, 1192–1198. https://doi.org/10.1093/beheco/art049 (2013).
Article  Google Scholar  More

1.
FAO Food and agriculture data [Internet]. www.fao.org/faostat/en/#home. Accessed 17 July 2019 (2018).
2.
Badu-Apraku, B. & Fakorede, M. Maize in Sub-Saharan Africa: Importance and Production Constraints. Advances in Genetic Enhancement of Early and Extra-Early Maize for Sub-Saharan Africa 3–10 (Springer, Cham, 2017).
Google Scholar 

3.
Jin, Z. et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sens. Environ. 228, 115–128 (2019).
ADS  Article  Google Scholar 

4.
De Groote, H. et al. Spread and impact of fall armyworm (Spodoptera frugiperda J.E. Smith) in maize production areas of Kenya. Agric. Ecosyst. Environ. 292, 106804 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Mumo, L., Yu, J. & Fang, K. Assessing impacts of seasonal climate variability on maize yield in Kenya. Int. J. Plant Prod. 12, 297–307 (2018).
Article  Google Scholar 

6.
Mwalusepo, S. et al. Predicting the impact of temperature change on the future distribution of maize stem borers and their natural enemies along East African mountain gradients using phenology models. PLoS One 10, e0130427 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
GuoFa, Z., Overholt, W. A. & Mochiah, M. B. Changes in the distribution of lepidopteran maize stemborers in Kenya from the 1950s to 1990s. Int. J. Trop. Insect Sc. 21, 395–402 (2001).
Article  Google Scholar 

8.
Tounou, A. K., Agboka, K., Agbodzavu, K. M. & Wegbe, K. Maize stemborers distribution, their natural enemies and farmers’ perception on climate change and stemborers in southern Togo. J. Appl. Biosci. 64, 4773–4786 (2013).
Article  Google Scholar 

9.
Kfir, R., Overholt, W. A., Khan, Z. R. & Polaszek, A. Biology and management of economicaly important lepidopteran cereal stem borers in Africa. Annu. Rev. Entomol. 47, 701–731 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Nwilene, F. E., Nwanze, K. F. & Youdeowei, A. Impact of integrated pest management on food and horticultural crops in Africa. Entomol. Exp. Appl. 128, 355–363 (2008).
Article  Google Scholar 

11.
Krüger, W., Van den Berg, J. & Van Hamburg, H. The relative abundance of maize stem borers and their parasitoids at the Tshiombo irrigation scheme in Venda, South Africa. S. Afr. J. Plant Soil 25, 144–151 (2008).
Article  Google Scholar 

12.
Ongamo, G. et al. Distribution, pest status and agro-climatic preferences of lepidopteran stem borers of maize in Kenya. Ann. Soc. Entomol. Fr. 42, 171–177 (2006).
Article  Google Scholar 

13.
Kfir, R. Competitive displacement of Busseola fusca (Lepidoptera: Noctuidae) by Chilo partellus (Lepidoptera: Pyralidae). Ann. Entomol. Soc. Am. 90, 619–624 (1997).
Article  Google Scholar 

14.
Ofomata, V. C., Overholt, W. A., Lux, S. A., Van Huis, A. & Egwuatu, A. R. I. Comparative studies on the fecundity, egg survival, larval feeding, and development of Chilo partellus and Chilo orichalcociliellus (Lepidoptera: Crambidae) on five grasses. Ann. Entomol. Soc. Am. 93, 492–499 (2000).
Article  Google Scholar 

15.
Ntiri, E. S., Calatayud, P.-A., Van den Berg, J., Schulthess, F. & Le Ru, B. P. Influence of temperature on intra- and interspecific resource utilization within a community of lepidopteran maize stemborers. PLoS One 11, e148735 (2016).
Google Scholar 

16.
Fotso-Kuate, A. et al. Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) in Cameroon: Case study on its distribution, damage, pesticide use, genetic differentiation and host plants. PLoS One 14, e0215749 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS One 11, e0165632 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Sokame, B. M. et al. Influence of temperature on the interaction for resource utilization between Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), and a community of lepidopteran maize stemborers larvae. Insects 11, 73 (2020).
PubMed Central  Article  PubMed  Google Scholar 

19.
Sokame, B. M. et al. Impact of the exotic fall armyworm on larval parasitoids associated with the lepidopteran maize stemborers in Kenya. Biocontrol https://doi.org/10.1007/s10526-020-10059-2 (2020).
Article  Google Scholar 

20.
Chabaane, Y., Laplanche, D., Turlings, T. C. & Desurmont, G. A. Impact of exotic insect herbivores on native tritrophic interactions: A case study of the African cotton leafworm, Spodoptera littoralis and insects associated with the field mustard Brassica rapa. J. Ecol. 103, 109–117 (2015).
Article  Google Scholar 

21.
Forrester, J. W. Industrial Dynamics (The MIT Press, Cambridge, 1961).
Google Scholar 

22.
Sapiri, H., Zulkepli, J., Abidin, N. Z., Ahmad, N. & Hawari, N. N. Introduction to System Dynamics Modelling and Vensim Software 173 (Universiti Utara Malaysia, Malaysia, 2016).
Google Scholar 

23.
Maani, K. E. & Cavana, R. Y. System Thinking and Modelling: Understanding Change and Complexity (Prentice Hall, Auckland, 2000).
Google Scholar 

24.
Mwalusepo, S., Tonnang, H. E. Z., Massawe, E. S., Johansson, T. & Le Ru, B. P. Stability analysis of competing insect species for a single resource. J. Appl. Math. 20, 2014 (2014).
MathSciNet  MATH  Google Scholar 

25.
Neill, W. E. The community matrix and interdependence of the competition coefficients. Am. Nat. 108, 399–408 (1974).
Article  Google Scholar 

26.
Calatayud, P.-A. et al. Can climate-driven change influence silicon assimilation by cereals and hence the distribution of lepidopteran stem borers in East Africa?. Agric. Ecosyst. Environ. 224, 95–103 (2016).
CAS  Article  Google Scholar 

27.
Ntiri, E. S., Calatayud, P.-A., Van den Berg, J. & Le Ru, B. P. Spatio-temporal interactions between maize lepidopteran stemborer communities and possible implications from the recent invasion of Spodoptera frugiperda (Lepidoptera : Noctuidae) in sub-Saharan Africa. Environ. Entomol. 48, 573–582 (2019).
PubMed  Article  PubMed Central  Google Scholar 

28.
Sisay, B. et al. First report of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), natural enemies from Africa. J. Appl. Entomol. 142, 800–804 (2018).
Article  Google Scholar 

29.
Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Calatayud, P.-A. & Dupas, S. Species diversity of lepidopteran stem borer parasitoids in cultivated and natural habitats in Kenya. J. Appl. Entomol. 133, 416–429 (2009).
Article  Google Scholar 

30.
Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Calatayud, P.-A. & Dupas, S. Geographic distribution, host range and perennation of Cotesia sesamiae and Cotesia flavipes Cameron in cultivated and natural habitats in Kenya. Biol. Control 54, 1–8 (2010).
Article  Google Scholar 

31.
Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Dupas, S. & Calatayud, P.-A. Parasitism of lepidopterous stemborers in cultivated and natural habitats. J. Insect Sci. 11, 1–19 (2011).
Article  Google Scholar 

32.
Sisay, B. et al. Fall armyworm, Spodoptera frugiperda infestations in East Africa: Assessment of damage and parasitism. Insects 10, 195 (2019).
PubMed Central  Article  PubMed  Google Scholar 

33.
Pitre, H. N., Mulrooney, J. E. & Hogg, D. B. Fall armyworm (Lepidoptera: Noctuidae) oviposition: Crop preferences and egg distribution on plants. J. Econ. Entomol. 76, 463–466 (1983).
Article  Google Scholar 

34.
Polaszek, A. African Cereal Stem Borers: Economic Importance, Taxonomy, Natural Enemies and Control 530 (CAB International, Wallingford, 1998).
Google Scholar 

35.
Sokame, B. M., Subramanian, S., Kilalo, D. C., Juma, G. & Calatayud, P.-A. Larval dispersal of the invasive fall armyworm, Spodoptera frugiperda, the exotic stemborer, Chilo partellus, and the indigenous maize stemborers in Africa. Entomol. Exp. Appl. 168, 322–331 (2020).
CAS  Article  Google Scholar 

36.
Morrill, W. L. & Greene, G. L. Distribution of fall Armyworm larvae. 1. Regions of field corn plants infested by larvae. Environ. Entomol. 2, 195–198 (1973).
Article  Google Scholar 

37.
Van den Berg, J. Economy of Stem Borer Control in Sorghum. ARC-Crop Protection Series no 2 4 (South Africa, Potchefstroom, 1997).
Google Scholar 

38.
CAB International. How to Identify Fall Armyworm. Poster. Plantwise, http://www.plantwise.org/FullTextPDF/2017/20177800461.pdf. Accessed 23 Nov 2018 (2017).

39.
Bischof, R. & Zedrosser, A. The educated prey: Consequences for exploitation and control. Behav. Ecol. 20, 1228–1235 (2009).
Article  Google Scholar 

40.
Boukal, D. & Kivan, V. Lyapunov functions for Lotka–Volterra predator-prey models with optimal foraging behavior. J. Math. Biol. 39, 493–517 (1999).
MathSciNet  MATH  Article  Google Scholar 

41.
Sterman, J. Business Dynamics: Systems Thinking and Modeling for a Complex World (Irwin/McGraw-Hill, Boston, 2000).
Google Scholar 

42.
Din, Q. & Donchev, T. Global character of a host-parasite model. Chaos Soliton Fract. 54, 1–7 (2013).
ADS  MathSciNet  MATH  Article  Google Scholar 

43.
Sarmento, R. D. A. et al. Biology review, occurrence and control of Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) in corn in Brazil. Biosci. J. 18, 41–48 (2002).
Google Scholar 

44.
Chapman, J. W., Williams, T., Martínez, A. M. & Cisneros, J. Does cannibalism in Spodoptera frugiperda (Lepidoptera: Noctuidae) reduce the risk of predation?. Behav. Ecol. Sociobiol. 48, 321–327 (2000).
Article  Google Scholar 

45.
Zhou, S. Z., Chen, Z.-P. & Xu, Z.-F. Niches and interspecific competitive relationships of the parasitoids, Microplitis prodeniae and Campoletis chlorldeae, of the oriental leafworm moth, Spodoptera litura, in tobacco. J. Insect Sci. 10, 10 (2010).
PubMed  PubMed Central  Google Scholar 

46.
Bentivenha, J. P. F., Baldin, E. L. L., Hunt, T. E., Paula-Moraes, S. V. & Blankenship, E. E. Intraguild competition of three noctuid maize pests. Environ. Entomol. 45, 999–1008 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—the role of mutalists. Biol. Rev. 75, 65–93 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Sujay, Y. H., Sattagi, H. N. & Patil, R. K. Invasive alien insects and their impact on agroecosystem. Karnatka J. Agric. Sci. 23, 26–34 (2010).
Google Scholar 

49.
Reitz, S. & Trumble, J. Competitive displacement among insects and arachnids. Annu. Rev. Entomol. 47, 435–465 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
McClure, M. S. Biology, population trends, and damage of Pineus boerneri and P. coloradiensis (Homoptera: Adelgidae) on red pine. Environ. Entomol. 18, 1066–1073 (1989).
Article  Google Scholar 

51.
Ekesi, S., Billah, M. K., Nderitu, P. W., Lux, S. A. & Rwomushana, I. Evidence for competitive displacement of Ceratitis cosyra by the invasive fruit fly Bactrocera invadens (Diptera: Tephritidae) on mango and mechanisms contributing to the displacement. J. Econ. Entomol. 102, 981–991 (2009).
PubMed  Article  Google Scholar 

52.
Rwomushana, I., Ekesi, S., Ogol, C. K. P. O. & Gordon, I. Mechanisms contributing to the competitive success of the invasive fruit fly Bactrocera invadens over the indigenous mango fruit fly, Ceratitis cosyra: The role of temperature and resource pre-emption. Entomol. Exp. Appl. 133, 27–37 (2009).
Article  Google Scholar 

53.
Fabre, J. P., Auger-Rozenberg, M. A., Chalon, A., Boivin, S. & Roques, A. Competition between exotic and native insects for seed resources in trees of a Mediterranean forest ecosystem. Biol. Invas. 6, 11–22 (2004).
Article  Google Scholar 

54.
Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
Article  Google Scholar 

55.
Ventana. Ventana Systems Incl. Vensim software PLE 8.0.9. https://vensim.com/download/ (2019).

56.
Sokame, B. M. Functioning of a community of lepidopteran maize stemborers and associated parasitoids following the fall armyworm invasion in Kenya 276 (PhD thesis, University of Nairobi, Kenya, 2020).

57.
Tonnang, H. E. Z., Nedorezov, L. V., Ochanda, H., Owino, J. & Löhr, B. Assessing the impact of biological control of Plutella xylostella through the application of Lotka–Volterra model. Ecol. Model. 220, 60–70 (2009).
Article  Google Scholar 

58.
Kroschel, J., Mujica, N., Carhuapoma, P. & Sporleder, M. Pest Distribution and Risk Atlas for Africa-Potential Global and Regional Distribution and Abundance of Agricultural and Horticultural Pests and Associated Biocontrol Agents Under Current and Future Climates (International Potato Center (CIP), Lima, 2016).
Google Scholar 

59.
Prasanna, B. M., Huesing, J. E., Eddy, R. & Peschke, V. M. Fall Armyworm in Africa: A Guide for Integrated Pest Management. First Edition, Mexico (CDMX: IMMYT, Mexico, 2018).
Google Scholar 

60.
Sokame, B. M. et al. Carry-over niches for lepidopteran maize stemborers and associated parasitoids during non-cropping season. Insect 10, 191 (2019).
Article  Google Scholar  More